Add Moodle data source integration (#11325)

### What problem does this PR solve?

This PR adds a native Moodle connector to sync content (courses,
resources, forums, assignments, pages, books) into RAGFlow.

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

---------

Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>

.gitattributes

@@ -1 +1,2 @@
-*.sh text eol=lf
+*.sh text eol=lf
+docker/entrypoint.sh text eol=lf executable

.github/ISSUE_TEMPLATE/agent_scenario_request.yml

@@ -0,0 +1,46 @@
+name: "❤️‍🔥ᴬᴳᴱᴺᵀ Agent scenario request"
+description: Propose a agent scenario request for RAGFlow.
+title: "[Agent Scenario Request]: "
+labels: ["❤️‍🔥ᴬᴳᴱᴺᵀ agent scenario"]
+body:
+  - type: checkboxes
+    attributes:
+      label: Self Checks
+      description: "Please check the following in order to be responded in time :)"
+      options:
+        - label: I have searched for existing issues [search for existing issues](https://github.com/infiniflow/ragflow/issues), including closed ones.
+          required: true
+        - label: I confirm that I am using English to submit this report ([Language Policy](https://github.com/infiniflow/ragflow/issues/5910)).
+          required: true
+        - label: Non-english title submitions will be closed directly ( 非英文标题的提交将会被直接关闭 ) ([Language Policy](https://github.com/infiniflow/ragflow/issues/5910)).
+          required: true
+        - label: "Please do not modify this template :) and fill in all the required fields."
+          required: true
+  - type: textarea
+    attributes:
+      label: Is your feature request related to a scenario?
+      description: |
+        A clear and concise description of what the scenario is. Ex. I'm always frustrated when [...]
+      render: Markdown
+    validations:
+      required: false
+  - type: textarea
+    attributes:
+      label: Describe the feature you'd like
+      description: A clear and concise description of what you want to happen.
+    validations:
+      required: true
+  - type: textarea
+    attributes:
+      label: Documentation, adoption, use case
+      description: If you can, explain some scenarios how users might use this, situations it would be helpful in. Any API designs, mockups, or diagrams are also helpful.
+      render: Markdown
+    validations:
+      required: false
+  - type: textarea
+    attributes:
+      label: Additional information
+      description: |
+        Add any other context or screenshots about the feature request here.
+    validations:
+      required: false

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -1,30 +1,36 @@
-name: Bug Report
+name: "🐞 Bug Report"
 description: Create a bug issue for RAGFlow
 title: "[Bug]: "
-labels: [bug]
+labels: ["🐞 bug"]
 body:
 - type: checkboxes
   attributes:
-    label: Is there an existing issue for the same bug?
-    description: Please check if an issue already exists for the bug you encountered.
+    label: Self Checks
+    description: "Please check the following in order to be responded in time :)"
     options:
-    - label: I have checked the existing issues.
-      required: true
+      - label: I have searched for existing issues [search for existing issues](https://github.com/infiniflow/ragflow/issues), including closed ones.
+        required: true
+      - label: I confirm that I am using English to submit this report ([Language Policy](https://github.com/infiniflow/ragflow/issues/5910)).
+        required: true
+      - label: Non-english title submitions will be closed directly ( 非英文标题的提交将会被直接关闭 ) ([Language Policy](https://github.com/infiniflow/ragflow/issues/5910)).
+        required: true
+      - label: "Please do not modify this template :) and fill in all the required fields."
+        required: true
 - type: markdown
   attributes:
     value: "Please provide the following information to help us understand the issue."
 - type: input
   attributes:
-    label: Branch name
-    description: Enter the name of the branch where you encountered the issue.
-    placeholder: e.g., main
+    label: RAGFlow workspace code commit ID
+    description: Enter the commit ID associated with the issue.
+    placeholder: e.g., 26d3480e
   validations:
     required: true
 - type: input
   attributes:
-    label: Commit ID
-    description: Enter the commit ID associated with the issue.
-    placeholder: e.g., c3b2a1
+    label: RAGFlow image version
+    description: Enter the image version(shown in RAGFlow UI, `System` page) associated with the issue.
+    placeholder: e.g., 26d3480e(v0.13.0~174)
   validations:
     required: true
 - type: textarea

.github/ISSUE_TEMPLATE/feature_request.md

@@ -1,10 +0,0 @@
----
-name: Feature request
-title: '[Feature Request]: '
-about: Suggest an idea for RAGFlow
-labels: ''
----
-
-**Summary**
-
-Description for this feature.

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -1,14 +1,20 @@
-name: Feature request
+name: "💞 Feature request"
 description: Propose a feature request for RAGFlow.
 title: "[Feature Request]: "
-labels: [feature request]
+labels: ["💞 feature"]
 body:
   - type: checkboxes
     attributes:
-      label: Is there an existing issue for the same feature request?
-      description: Please check if an issue already exists for the feature you request.
+      label: Self Checks
+      description: "Please check the following in order to be responded in time :)"
       options:
-        - label: I have checked the existing issues.
+        - label: I have searched for existing issues [search for existing issues](https://github.com/infiniflow/ragflow/issues), including closed ones.
+          required: true
+        - label: I confirm that I am using English to submit this report ([Language Policy](https://github.com/infiniflow/ragflow/issues/5910)).
+          required: true
+        - label: Non-english title submitions will be closed directly ( 非英文标题的提交将会被直接关闭 ) ([Language Policy](https://github.com/infiniflow/ragflow/issues/5910)).
+          required: true
+        - label: "Please do not modify this template :) and fill in all the required fields."
           required: true
   - type: textarea
     attributes:

.github/ISSUE_TEMPLATE/question.yml

@@ -1,8 +1,21 @@
-name: Question
+name: "🙋‍♀️ Question"
 description: Ask questions on RAGFlow
 title: "[Question]: "
-labels: [question]
+labels: ["🙋‍♀️ question"]
 body:
+- type: checkboxes
+  attributes:
+    label: Self Checks
+    description: "Please check the following in order to be responded in time :)"
+    options:
+      - label: I have searched for existing issues [search for existing issues](https://github.com/infiniflow/ragflow/issues), including closed ones.
+        required: true
+      - label: I confirm that I am using English to submit this report ([Language Policy](https://github.com/infiniflow/ragflow/issues/5910)).
+        required: true
+      - label: Non-english title submitions will be closed directly ( 非英文标题的提交将会被直接关闭 ) ([Language Policy](https://github.com/infiniflow/ragflow/issues/5910)).
+        required: true
+      - label: "Please do not modify this template :) and fill in all the required fields."
+        required: true
 - type: markdown
   attributes:
     value: |

.github/workflows/release.yml

@@ -0,0 +1,94 @@
+name: release
+
+on:
+  schedule:
+    - cron: '0 13 * * *'  # This schedule runs every 13:00:00Z(21:00:00+08:00)
+  # The "create tags" trigger is specifically focused on the creation of new tags, while the "push tags" trigger is activated when tags are pushed, including both new tag creations and updates to existing tags.
+  create:
+    tags:
+      - "v*.*.*"                  # normal release
+      - "nightly"                 # the only one mutable tag
+
+# https://docs.github.com/en/actions/using-jobs/using-concurrency
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  release:
+    runs-on: [ "self-hosted", "ragflow-test" ]
+    steps:
+      - name: Ensure workspace ownership
+        run: echo "chown -R ${USER} ${GITHUB_WORKSPACE}" && sudo chown -R ${USER} ${GITHUB_WORKSPACE}
+
+      # https://github.com/actions/checkout/blob/v3/README.md
+      - name: Check out code
+        uses: actions/checkout@v4
+        with:
+          token: ${{ secrets.GITHUB_TOKEN }}  # Use the secret as an environment variable
+          fetch-depth: 0
+          fetch-tags: true
+
+      - name: Prepare release body
+        run: |
+          if [[ ${GITHUB_EVENT_NAME} == "create" ]]; then
+            RELEASE_TAG=${GITHUB_REF#refs/tags/}
+            if [[ ${RELEASE_TAG} == "nightly" ]]; then
+              PRERELEASE=true
+            else
+              PRERELEASE=false
+            fi
+            echo "Workflow triggered by create tag: ${RELEASE_TAG}"
+          else
+            RELEASE_TAG=nightly
+            PRERELEASE=true
+            echo "Workflow triggered by schedule"
+          fi
+          echo "RELEASE_TAG=${RELEASE_TAG}" >> ${GITHUB_ENV}
+          echo "PRERELEASE=${PRERELEASE}" >> ${GITHUB_ENV}
+          RELEASE_DATETIME=$(date --rfc-3339=seconds)
+          echo Release ${RELEASE_TAG} created from ${GITHUB_SHA} at ${RELEASE_DATETIME} > release_body.md
+
+      - name: Move the existing mutable tag
+        # https://github.com/softprops/action-gh-release/issues/171
+        run: |
+          git fetch --tags
+          if [[ ${GITHUB_EVENT_NAME} == "schedule" ]]; then
+            # Determine if a given tag exists and matches a specific Git commit.
+            # actions/checkout@v4 fetch-tags doesn't work when triggered by schedule
+            if [ "$(git rev-parse -q --verify "refs/tags/${RELEASE_TAG}")" = "${GITHUB_SHA}" ]; then
+              echo "mutable tag ${RELEASE_TAG} exists and matches ${GITHUB_SHA}"
+            else
+              git tag -f ${RELEASE_TAG} ${GITHUB_SHA}
+              git push -f origin ${RELEASE_TAG}:refs/tags/${RELEASE_TAG}
+              echo "created/moved mutable tag ${RELEASE_TAG} to ${GITHUB_SHA}"
+            fi
+          fi
+
+      - name: Create or overwrite a release
+        # https://github.com/actions/upload-release-asset has been replaced by https://github.com/softprops/action-gh-release
+        uses: softprops/action-gh-release@v2
+        with:
+          token: ${{ secrets.GITHUB_TOKEN }}  # Use the secret as an environment variable
+          prerelease: ${{ env.PRERELEASE }}
+          tag_name: ${{ env.RELEASE_TAG }}
+          # The body field does not support environment variable substitution directly.
+          body_path: release_body.md
+
+      - name: Build and push ragflow-sdk
+        if: startsWith(github.ref, 'refs/tags/v')
+        run: |
+          cd sdk/python && uv build && uv publish --token ${{ secrets.PYPI_API_TOKEN }}
+
+      - name: Build and push ragflow-cli
+        if: startsWith(github.ref, 'refs/tags/v')
+        run: |
+          cd admin/client && uv build && uv publish --token ${{ secrets.PYPI_API_TOKEN }}
+
+      - name: Build and push image
+        run: |
+          sudo docker login --username infiniflow --password-stdin <<< ${{ secrets.DOCKERHUB_TOKEN }}
+          sudo docker build --build-arg NEED_MIRROR=1 -t infiniflow/ragflow:${RELEASE_TAG} -f Dockerfile .
+          sudo docker tag infiniflow/ragflow:${RELEASE_TAG} infiniflow/ragflow:latest
+          sudo docker push infiniflow/ragflow:${RELEASE_TAG}
+          sudo docker push infiniflow/ragflow:latest

.github/workflows/tests.yml

@@ -0,0 +1,271 @@
+name: tests
+
+on:
+  push:
+    branches:
+      - 'main'
+      - '*.*.*'
+    paths-ignore:
+      - 'docs/**'
+      - '*.md'
+      - '*.mdx'
+  # The only difference between pull_request and pull_request_target is the context in which the workflow runs:
+  # — pull_request_target workflows use the workflow files from the default branch, and secrets are available.
+  # — pull_request workflows use the workflow files from the pull request branch, and secrets are unavailable.
+  pull_request_target:
+    types: [ synchronize, ready_for_review ]
+    paths-ignore:
+      - 'docs/**'
+      - '*.md'
+      - '*.mdx'
+  schedule:
+    - cron: '0 16 * * *'  # This schedule runs every 16:00:00Z(00:00:00+08:00)
+
+# https://docs.github.com/en/actions/using-jobs/using-concurrency
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  ragflow_tests:
+    name: ragflow_tests
+    # https://docs.github.com/en/actions/using-jobs/using-conditions-to-control-job-execution
+    # https://github.com/orgs/community/discussions/26261
+    if: ${{ github.event_name != 'pull_request_target' || contains(github.event.pull_request.labels.*.name, 'ci') }}
+    runs-on: [ "self-hosted", "ragflow-test" ]
+    steps:
+      # https://github.com/hmarr/debug-action
+      #- uses: hmarr/debug-action@v2
+
+      - name: Ensure workspace ownership
+        run: |
+          echo "Workflow triggered by ${{ github.event_name }}"
+          echo "chown -R ${USER} ${GITHUB_WORKSPACE}" && sudo chown -R ${USER} ${GITHUB_WORKSPACE}
+
+      # https://github.com/actions/checkout/issues/1781
+      - name: Check out code
+        uses: actions/checkout@v4
+        with:
+          ref: ${{ (github.event_name == 'pull_request' || github.event_name == 'pull_request_target') && format('refs/pull/{0}/merge', github.event.pull_request.number) || github.sha }}
+          fetch-depth: 0
+          fetch-tags: true
+
+      - name: Check workflow duplication
+        if: ${{ !cancelled() && !failure() }}
+        run: |
+          if [[ ${GITHUB_EVENT_NAME} != "pull_request_target" && ${GITHUB_EVENT_NAME} != "schedule" ]]; then
+            HEAD=$(git rev-parse HEAD)
+            # Find a PR that introduced a given commit
+            gh auth login --with-token <<< "${{ secrets.GITHUB_TOKEN }}"
+            PR_NUMBER=$(gh pr list --search ${HEAD} --state merged --json number --jq .[0].number)
+            echo "HEAD=${HEAD}"
+            echo "PR_NUMBER=${PR_NUMBER}"
+            if [[ -n "${PR_NUMBER}" ]]; then
+              PR_SHA_FP=${RUNNER_WORKSPACE_PREFIX}/artifacts/${GITHUB_REPOSITORY}/PR_${PR_NUMBER}
+              if [[ -f "${PR_SHA_FP}" ]]; then
+                read -r PR_SHA PR_RUN_ID < "${PR_SHA_FP}"
+                # Calculate the hash of the current workspace content
+                HEAD_SHA=$(git rev-parse HEAD^{tree})
+                if [[ "${HEAD_SHA}" == "${PR_SHA}" ]]; then
+                  echo "Cancel myself since the workspace content hash is the same with PR #${PR_NUMBER} merged. See ${GITHUB_SERVER_URL}/${GITHUB_REPOSITORY}/actions/runs/${PR_RUN_ID} for details."
+                  gh run cancel ${GITHUB_RUN_ID}
+                  while true; do
+                    status=$(gh run view ${GITHUB_RUN_ID} --json status -q .status)
+                    [ "${status}" = "completed" ] && break
+                    sleep 5
+                  done
+                  exit 1
+                fi
+              fi
+            fi
+          elif [[ ${GITHUB_EVENT_NAME} == "pull_request_target" ]]; then
+            PR_NUMBER=${{ github.event.pull_request.number }}
+            PR_SHA_FP=${RUNNER_WORKSPACE_PREFIX}/artifacts/${GITHUB_REPOSITORY}/PR_${PR_NUMBER}
+            # Calculate the hash of the current workspace content
+            PR_SHA=$(git rev-parse HEAD^{tree})
+            echo "PR #${PR_NUMBER} workspace content hash: ${PR_SHA}"
+            mkdir -p ${RUNNER_WORKSPACE_PREFIX}/artifacts/${GITHUB_REPOSITORY}
+            echo "${PR_SHA} ${GITHUB_RUN_ID}" > ${PR_SHA_FP}
+          fi
+
+      # https://github.com/astral-sh/ruff-action
+      - name: Static check with Ruff
+        uses: astral-sh/ruff-action@v3
+        with:
+          version: ">=0.11.x"
+          args: "check"
+
+      - name: Check comments of changed Python files
+        if: ${{ false }}
+        run: |
+          if [[ ${{ github.event_name }} == 'pull_request_target' ]]; then
+            CHANGED_FILES=$(git diff --name-only ${{ github.event.pull_request.base.sha }}...${{ github.event.pull_request.head.sha }} \
+              | grep -E '\.(py)$' || true)
+
+            if [ -n "$CHANGED_FILES" ]; then
+              echo "Check comments of changed Python files with check_comment_ascii.py"
+
+              readarray -t files <<< "$CHANGED_FILES"
+              HAS_ERROR=0
+
+              for file in "${files[@]}"; do
+                if [ -f "$file" ]; then
+                  if python3 check_comment_ascii.py "$file"; then
+                    echo "✅ $file"
+                  else
+                    echo "❌ $file"
+                    HAS_ERROR=1
+                  fi
+                fi
+              done
+
+              if [ $HAS_ERROR -ne 0 ]; then
+                exit 1
+              fi
+            else
+              echo "No Python files changed"
+            fi
+          fi
+
+      - name: Build ragflow:nightly
+        run: |
+          RUNNER_WORKSPACE_PREFIX=${RUNNER_WORKSPACE_PREFIX:-${HOME}}
+          RAGFLOW_IMAGE=infiniflow/ragflow:${GITHUB_RUN_ID}
+          echo "RAGFLOW_IMAGE=${RAGFLOW_IMAGE}" >> ${GITHUB_ENV}
+          sudo docker pull ubuntu:22.04
+          sudo DOCKER_BUILDKIT=1 docker build --build-arg NEED_MIRROR=1 -f Dockerfile -t ${RAGFLOW_IMAGE} .
+          if [[ ${GITHUB_EVENT_NAME} == "schedule" ]]; then
+            export HTTP_API_TEST_LEVEL=p3
+          else
+            export HTTP_API_TEST_LEVEL=p2
+          fi
+          echo "HTTP_API_TEST_LEVEL=${HTTP_API_TEST_LEVEL}" >> ${GITHUB_ENV}
+          echo "RAGFLOW_CONTAINER=${GITHUB_RUN_ID}-ragflow-cpu-1" >> ${GITHUB_ENV}
+
+      - name: Start ragflow:nightly
+        run: |
+          # Determine runner number (default to 1 if not found)
+          RUNNER_NUM=$(sudo docker inspect $(hostname) --format '{{index .Config.Labels "com.docker.compose.container-number"}}' 2>/dev/null || true)
+          RUNNER_NUM=${RUNNER_NUM:-1}
+
+          # Compute port numbers using bash arithmetic
+          ES_PORT=$((1200 + RUNNER_NUM * 10))
+          OS_PORT=$((1201 + RUNNER_NUM * 10))
+          INFINITY_THRIFT_PORT=$((23817 + RUNNER_NUM * 10))
+          INFINITY_HTTP_PORT=$((23820 + RUNNER_NUM * 10))
+          INFINITY_PSQL_PORT=$((5432 + RUNNER_NUM * 10))
+          MYSQL_PORT=$((5455 + RUNNER_NUM * 10))
+          MINIO_PORT=$((9000 + RUNNER_NUM * 10))
+          MINIO_CONSOLE_PORT=$((9001 + RUNNER_NUM * 10))
+          REDIS_PORT=$((6379 + RUNNER_NUM * 10))
+          TEI_PORT=$((6380 + RUNNER_NUM * 10))
+          KIBANA_PORT=$((6601 + RUNNER_NUM * 10))
+          SVR_HTTP_PORT=$((9380 + RUNNER_NUM * 10))
+          ADMIN_SVR_HTTP_PORT=$((9381 + RUNNER_NUM * 10))
+          SVR_MCP_PORT=$((9382 + RUNNER_NUM * 10))
+          SANDBOX_EXECUTOR_MANAGER_PORT=$((9385 + RUNNER_NUM * 10))
+          SVR_WEB_HTTP_PORT=$((80 + RUNNER_NUM * 10))
+          SVR_WEB_HTTPS_PORT=$((443 + RUNNER_NUM * 10))
+
+          # Persist computed ports into docker/.env so docker-compose uses the correct host bindings
+          echo "" >> docker/.env
+          echo -e "ES_PORT=${ES_PORT}" >> docker/.env
+          echo -e "OS_PORT=${OS_PORT}" >> docker/.env
+          echo -e "INFINITY_THRIFT_PORT=${INFINITY_THRIFT_PORT}" >> docker/.env
+          echo -e "INFINITY_HTTP_PORT=${INFINITY_HTTP_PORT}" >> docker/.env
+          echo -e "INFINITY_PSQL_PORT=${INFINITY_PSQL_PORT}" >> docker/.env
+          echo -e "MYSQL_PORT=${MYSQL_PORT}" >> docker/.env
+          echo -e "MINIO_PORT=${MINIO_PORT}" >> docker/.env
+          echo -e "MINIO_CONSOLE_PORT=${MINIO_CONSOLE_PORT}" >> docker/.env
+          echo -e "REDIS_PORT=${REDIS_PORT}" >> docker/.env
+          echo -e "TEI_PORT=${TEI_PORT}" >> docker/.env
+          echo -e "KIBANA_PORT=${KIBANA_PORT}" >> docker/.env
+          echo -e "SVR_HTTP_PORT=${SVR_HTTP_PORT}" >> docker/.env
+          echo -e "ADMIN_SVR_HTTP_PORT=${ADMIN_SVR_HTTP_PORT}" >> docker/.env
+          echo -e "SVR_MCP_PORT=${SVR_MCP_PORT}" >> docker/.env
+          echo -e "SANDBOX_EXECUTOR_MANAGER_PORT=${SANDBOX_EXECUTOR_MANAGER_PORT}" >> docker/.env
+          echo -e "SVR_WEB_HTTP_PORT=${SVR_WEB_HTTP_PORT}" >> docker/.env
+          echo -e "SVR_WEB_HTTPS_PORT=${SVR_WEB_HTTPS_PORT}" >> docker/.env
+
+          echo -e "COMPOSE_PROFILES=\${COMPOSE_PROFILES},tei-cpu" >> docker/.env
+          echo -e "TEI_MODEL=BAAI/bge-small-en-v1.5" >> docker/.env
+          echo -e "RAGFLOW_IMAGE=${RAGFLOW_IMAGE}" >> docker/.env
+          echo "HOST_ADDRESS=http://host.docker.internal:${SVR_HTTP_PORT}" >> ${GITHUB_ENV}
+
+          sudo docker compose -f docker/docker-compose.yml -p ${GITHUB_RUN_ID} up -d
+          uv sync --python 3.10 --only-group test --no-default-groups --frozen && uv pip install sdk/python
+
+      - name: Run sdk tests against Elasticsearch
+        run: |
+          export http_proxy=""; export https_proxy=""; export no_proxy=""; export HTTP_PROXY=""; export HTTPS_PROXY=""; export NO_PROXY=""
+          until sudo docker exec ${RAGFLOW_CONTAINER} curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
+            echo "Waiting for service to be available..."
+            sleep 5
+          done
+          source .venv/bin/activate && pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_sdk_api
+
+      - name: Run frontend api tests against Elasticsearch
+        run: |
+          export http_proxy=""; export https_proxy=""; export no_proxy=""; export HTTP_PROXY=""; export HTTPS_PROXY=""; export NO_PROXY=""
+          until sudo docker exec ${RAGFLOW_CONTAINER} curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
+            echo "Waiting for service to be available..."
+            sleep 5
+          done
+          source .venv/bin/activate && pytest -s --tb=short sdk/python/test/test_frontend_api/get_email.py sdk/python/test/test_frontend_api/test_dataset.py
+          
+      - name: Run http api tests against Elasticsearch
+        run: |
+          export http_proxy=""; export https_proxy=""; export no_proxy=""; export HTTP_PROXY=""; export HTTPS_PROXY=""; export NO_PROXY=""
+          until sudo docker exec ${RAGFLOW_CONTAINER} curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
+            echo "Waiting for service to be available..."
+            sleep 5
+          done
+          source .venv/bin/activate && pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_http_api
+
+      - name: Stop ragflow:nightly
+        if: always()  # always run this step even if previous steps failed
+        run: |
+          sudo docker compose -f docker/docker-compose.yml -p ${GITHUB_RUN_ID} down -v || true
+          sudo docker ps -a --filter "label=com.docker.compose.project=${GITHUB_RUN_ID}" -q | xargs -r sudo docker rm -f
+
+      - name: Start ragflow:nightly
+        run: |
+          sed -i '1i DOC_ENGINE=infinity' docker/.env
+          sudo docker compose -f docker/docker-compose.yml -p ${GITHUB_RUN_ID} up -d
+
+      - name: Run sdk tests against Infinity
+        run: |
+          export http_proxy=""; export https_proxy=""; export no_proxy=""; export HTTP_PROXY=""; export HTTPS_PROXY=""; export NO_PROXY=""
+          until sudo docker exec ${RAGFLOW_CONTAINER} curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
+            echo "Waiting for service to be available..."
+            sleep 5
+          done
+          source .venv/bin/activate && DOC_ENGINE=infinity pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_sdk_api
+
+      - name: Run frontend api tests against Infinity
+        run: |
+          export http_proxy=""; export https_proxy=""; export no_proxy=""; export HTTP_PROXY=""; export HTTPS_PROXY=""; export NO_PROXY=""
+          until sudo docker exec ${RAGFLOW_CONTAINER} curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
+            echo "Waiting for service to be available..."
+            sleep 5
+          done
+          source .venv/bin/activate && DOC_ENGINE=infinity pytest -s --tb=short sdk/python/test/test_frontend_api/get_email.py sdk/python/test/test_frontend_api/test_dataset.py
+
+      - name: Run http api tests against Infinity
+        run: |
+          export http_proxy=""; export https_proxy=""; export no_proxy=""; export HTTP_PROXY=""; export HTTPS_PROXY=""; export NO_PROXY=""
+          until sudo docker exec ${RAGFLOW_CONTAINER} curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
+            echo "Waiting for service to be available..."
+            sleep 5
+          done
+          source .venv/bin/activate && DOC_ENGINE=infinity pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_http_api
+
+      - name: Stop ragflow:nightly
+        if: always()  # always run this step even if previous steps failed
+        run: |
+          # Sometimes `docker compose down` fail due to hang container, heavy load etc. Need to remove such containers to release resources(for example, listen ports).
+          sudo docker compose -f docker/docker-compose.yml -p ${GITHUB_RUN_ID} down -v || true
+          sudo docker ps -a --filter "label=com.docker.compose.project=${GITHUB_RUN_ID}" -q | xargs -r sudo docker rm -f
+          if [[ -n ${RAGFLOW_IMAGE} ]]; then
+            sudo docker rmi -f ${RAGFLOW_IMAGE}
+          fi

.gitignore

@@ -35,4 +35,163 @@ rag/res/deepdoc
 sdk/python/ragflow.egg-info/
 sdk/python/build/
 sdk/python/dist/
-sdk/python/ragflow_sdk.egg-info/
+sdk/python/ragflow_sdk.egg-info/
+
+# Exclude dep files
+libssl*.deb
+tika-server*.jar*
+cl100k_base.tiktoken
+chrome*
+huggingface.co/
+nltk_data/
+
+# Exclude hash-like temporary files like 9b5ad71b2ce5302211f9c61530b329a4922fc6a4
+*[0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f]*
+.lh/
+.venv
+docker/data
+
+
+#--------------------------------------------------#
+# The following was generated with gitignore.nvim: #
+#--------------------------------------------------#
+# Gitignore for the following technologies: Node
+
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+lerna-debug.log*
+.pnpm-debug.log*
+
+# Diagnostic reports (https://nodejs.org/api/report.html)
+report.[0-9]*.[0-9]*.[0-9]*.[0-9]*.json
+
+# Runtime data
+pids
+*.pid
+*.seed
+*.pid.lock
+
+# Directory for instrumented libs generated by jscoverage/JSCover
+lib-cov
+
+# Coverage directory used by tools like istanbul
+coverage
+*.lcov
+
+# nyc test coverage
+.nyc_output
+
+# Grunt intermediate storage (https://gruntjs.com/creating-plugins#storing-task-files)
+.grunt
+
+# Bower dependency directory (https://bower.io/)
+bower_components
+
+# node-waf configuration
+.lock-wscript
+
+# Compiled binary addons (https://nodejs.org/api/addons.html)
+build/Release
+
+# Dependency directories
+node_modules/
+jspm_packages/
+
+# Snowpack dependency directory (https://snowpack.dev/)
+web_modules/
+
+# TypeScript cache
+*.tsbuildinfo
+
+# Optional npm cache directory
+.npm
+
+# Optional eslint cache
+.eslintcache
+
+# Optional stylelint cache
+.stylelintcache
+
+# Microbundle cache
+.rpt2_cache/
+.rts2_cache_cjs/
+.rts2_cache_es/
+.rts2_cache_umd/
+
+# Optional REPL history
+.node_repl_history
+
+# Output of 'npm pack'
+*.tgz
+
+# Yarn Integrity file
+.yarn-integrity
+
+# dotenv environment variable files
+.env
+.env.development.local
+.env.test.local
+.env.production.local
+.env.local
+
+# parcel-bundler cache (https://parceljs.org/)
+.cache
+.parcel-cache
+
+# Next.js build output
+.next
+out
+
+# Nuxt.js build / generate output
+.nuxt
+dist
+ragflow_cli.egg-info
+# Gatsby files
+.cache/
+# Comment in the public line in if your project uses Gatsby and not Next.js
+# https://nextjs.org/blog/next-9-1#public-directory-support
+# public
+
+# vuepress build output
+.vuepress/dist
+
+# vuepress v2.x temp and cache directory
+.temp
+
+# Docusaurus cache and generated files
+.docusaurus
+
+# Serverless directories
+.serverless/
+
+# FuseBox cache
+.fusebox/
+
+# DynamoDB Local files
+.dynamodb/
+
+# TernJS port file
+.tern-port
+
+# Stores VSCode versions used for testing VSCode extensions
+.vscode-test
+
+# yarn v2
+.yarn/cache
+.yarn/unplugged
+.yarn/build-state.yml
+.yarn/install-state.gz
+.pnp.*
+
+# Serverless Webpack directories
+.webpack/
+
+# SvelteKit build / generate output
+.svelte-kit
+
+# Default backup dir
+backup

.pre-commit-config.yaml

@@ -0,0 +1,19 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.6.0
+    hooks:
+      - id: check-yaml
+      - id: check-json
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+      - id: check-case-conflict
+      - id: check-merge-conflict
+      - id: mixed-line-ending
+      - id: check-symlinks
+
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.11.6
+    hooks:
+      - id: ruff
+        args: [ --fix ]
+      - id: ruff-format

.trivyignore

@@ -0,0 +1,15 @@
+**/*.md
+**/*.min.js
+**/*.min.css
+**/*.svg
+**/*.png
+**/*.jpg
+**/*.jpeg
+**/*.gif
+**/*.woff
+**/*.woff2
+**/*.map
+**/*.webp
+**/*.ico
+**/*.ttf
+**/*.eot

CLAUDE.md

@@ -0,0 +1,116 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It's a full-stack application with:
+- Python backend (Flask-based API server)
+- React/TypeScript frontend (built with UmiJS)
+- Microservices architecture with Docker deployment
+- Multiple data stores (MySQL, Elasticsearch/Infinity, Redis, MinIO)
+
+## Architecture
+
+### Backend (`/api/`)
+- **Main Server**: `api/ragflow_server.py` - Flask application entry point
+- **Apps**: Modular Flask blueprints in `api/apps/` for different functionalities:
+  - `kb_app.py` - Knowledge base management
+  - `dialog_app.py` - Chat/conversation handling
+  - `document_app.py` - Document processing
+  - `canvas_app.py` - Agent workflow canvas
+  - `file_app.py` - File upload/management
+- **Services**: Business logic in `api/db/services/`
+- **Models**: Database models in `api/db/db_models.py`
+
+### Core Processing (`/rag/`)
+- **Document Processing**: `deepdoc/` - PDF parsing, OCR, layout analysis
+- **LLM Integration**: `rag/llm/` - Model abstractions for chat, embedding, reranking
+- **RAG Pipeline**: `rag/flow/` - Chunking, parsing, tokenization
+- **Graph RAG**: `graphrag/` - Knowledge graph construction and querying
+
+### Agent System (`/agent/`)
+- **Components**: Modular workflow components (LLM, retrieval, categorize, etc.)
+- **Templates**: Pre-built agent workflows in `agent/templates/`
+- **Tools**: External API integrations (Tavily, Wikipedia, SQL execution, etc.)
+
+### Frontend (`/web/`)
+- React/TypeScript with UmiJS framework
+- Ant Design + shadcn/ui components
+- State management with Zustand
+- Tailwind CSS for styling
+
+## Common Development Commands
+
+### Backend Development
+```bash
+# Install Python dependencies
+uv sync --python 3.10 --all-extras
+uv run download_deps.py
+pre-commit install
+
+# Start dependent services
+docker compose -f docker/docker-compose-base.yml up -d
+
+# Run backend (requires services to be running)
+source .venv/bin/activate
+export PYTHONPATH=$(pwd)
+bash docker/launch_backend_service.sh
+
+# Run tests
+uv run pytest
+
+# Linting
+ruff check
+ruff format
+```
+
+### Frontend Development
+```bash
+cd web
+npm install
+npm run dev        # Development server
+npm run build      # Production build
+npm run lint       # ESLint
+npm run test       # Jest tests
+```
+
+### Docker Development
+```bash
+# Full stack with Docker
+cd docker
+docker compose -f docker-compose.yml up -d
+
+# Check server status
+docker logs -f ragflow-server
+
+# Rebuild images
+docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .
+```
+
+## Key Configuration Files
+
+- `docker/.env` - Environment variables for Docker deployment
+- `docker/service_conf.yaml.template` - Backend service configuration
+- `pyproject.toml` - Python dependencies and project configuration
+- `web/package.json` - Frontend dependencies and scripts
+
+## Testing
+
+- **Python**: pytest with markers (p1/p2/p3 priority levels)
+- **Frontend**: Jest with React Testing Library
+- **API Tests**: HTTP API and SDK tests in `test/` and `sdk/python/test/`
+
+## Database Engines
+
+RAGFlow supports switching between Elasticsearch (default) and Infinity:
+- Set `DOC_ENGINE=infinity` in `docker/.env` to use Infinity
+- Requires container restart: `docker compose down -v && docker compose up -d`
+
+## Development Environment Requirements
+
+- Python 3.10-3.12
+- Node.js >=18.20.4
+- Docker & Docker Compose
+- uv package manager
+- 16GB+ RAM, 50GB+ disk space

Dockerfile

@@ -1,23 +1,200 @@
-FROM infiniflow/ragflow-base:v2.0
-USER  root
+# base stage
+FROM ubuntu:22.04 AS base
+USER root
+SHELL ["/bin/bash", "-c"]
+
+ARG NEED_MIRROR=0
 
 WORKDIR /ragflow
 
-ADD ./web ./web
-RUN cd ./web && npm i --force && npm run build
+# Copy models downloaded via download_deps.py
+RUN mkdir -p /ragflow/rag/res/deepdoc /root/.ragflow
+RUN --mount=type=bind,from=infiniflow/ragflow_deps:latest,source=/huggingface.co,target=/huggingface.co \
+    cp /huggingface.co/InfiniFlow/huqie/huqie.txt.trie /ragflow/rag/res/ && \
+    tar --exclude='.*' -cf - \
+        /huggingface.co/InfiniFlow/text_concat_xgb_v1.0 \
+        /huggingface.co/InfiniFlow/deepdoc \
+        | tar -xf - --strip-components=3 -C /ragflow/rag/res/deepdoc 
 
-ADD ./api ./api
-ADD ./conf ./conf
-ADD ./deepdoc ./deepdoc
-ADD ./rag ./rag
-ADD ./agent ./agent
-ADD ./graphrag ./graphrag
+# https://github.com/chrismattmann/tika-python
+# This is the only way to run python-tika without internet access. Without this set, the default is to check the tika version and pull latest every time from Apache.
+RUN --mount=type=bind,from=infiniflow/ragflow_deps:latest,source=/,target=/deps \
+    cp -r /deps/nltk_data /root/ && \
+    cp /deps/tika-server-standard-3.0.0.jar /deps/tika-server-standard-3.0.0.jar.md5 /ragflow/ && \
+    cp /deps/cl100k_base.tiktoken /ragflow/9b5ad71b2ce5302211f9c61530b329a4922fc6a4
+
+ENV TIKA_SERVER_JAR="file:///ragflow/tika-server-standard-3.0.0.jar"
+ENV DEBIAN_FRONTEND=noninteractive
+
+# Setup apt
+# Python package and implicit dependencies:
+# opencv-python: libglib2.0-0 libglx-mesa0 libgl1
+# aspose-slides: pkg-config libicu-dev libgdiplus         libssl1.1_1.1.1f-1ubuntu2_amd64.deb
+# python-pptx:   default-jdk                              tika-server-standard-3.0.0.jar
+# selenium:      libatk-bridge2.0-0                       chrome-linux64-121-0-6167-85
+# Building C extensions: libpython3-dev libgtk-4-1 libnss3 xdg-utils libgbm-dev
+RUN --mount=type=cache,id=ragflow_apt,target=/var/cache/apt,sharing=locked \
+    if [ "$NEED_MIRROR" == "1" ]; then \
+        sed -i 's|http://ports.ubuntu.com|http://mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list; \
+        sed -i 's|http://archive.ubuntu.com|http://mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list; \
+    fi; \
+    rm -f /etc/apt/apt.conf.d/docker-clean && \
+    echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache && \
+    chmod 1777 /tmp && \
+    apt update && \
+    apt --no-install-recommends install -y ca-certificates && \
+    apt update && \
+    apt install -y libglib2.0-0 libglx-mesa0 libgl1 && \
+    apt install -y pkg-config libicu-dev libgdiplus && \
+    apt install -y default-jdk && \
+    apt install -y libatk-bridge2.0-0 && \
+    apt install -y libpython3-dev libgtk-4-1 libnss3 xdg-utils libgbm-dev && \
+    apt install -y libjemalloc-dev && \
+    apt install -y python3-pip pipx nginx unzip curl wget git vim less && \
+    apt install -y ghostscript && \
+    apt install -y pandoc && \
+    apt install -y texlive
+
+RUN if [ "$NEED_MIRROR" == "1" ]; then \
+        pip3 config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple && \
+        pip3 config set global.trusted-host pypi.tuna.tsinghua.edu.cn; \
+        mkdir -p /etc/uv && \
+        echo "[[index]]" > /etc/uv/uv.toml && \
+        echo 'url = "https://pypi.tuna.tsinghua.edu.cn/simple"' >> /etc/uv/uv.toml && \
+        echo "default = true" >> /etc/uv/uv.toml; \
+    fi; \
+    pipx install uv
+
+ENV PYTHONDONTWRITEBYTECODE=1 DOTNET_SYSTEM_GLOBALIZATION_INVARIANT=1
+ENV PATH=/root/.local/bin:$PATH
+
+# nodejs 12.22 on Ubuntu 22.04 is too old
+RUN --mount=type=cache,id=ragflow_apt,target=/var/cache/apt,sharing=locked \
+    curl -fsSL https://deb.nodesource.com/setup_20.x | bash - && \
+    apt purge -y nodejs npm cargo && \
+    apt autoremove -y && \
+    apt update && \
+    apt install -y nodejs
+
+# A modern version of cargo is needed for the latest version of the Rust compiler.
+RUN apt update && apt install -y curl build-essential \
+    && if [ "$NEED_MIRROR" == "1" ]; then \
+         # Use TUNA mirrors for rustup/rust dist files
+         export RUSTUP_DIST_SERVER="https://mirrors.tuna.tsinghua.edu.cn/rustup"; \
+         export RUSTUP_UPDATE_ROOT="https://mirrors.tuna.tsinghua.edu.cn/rustup/rustup"; \
+         echo "Using TUNA mirrors for Rustup."; \
+       fi; \
+    # Force curl to use HTTP/1.1
+    curl --proto '=https' --tlsv1.2 --http1.1 -sSf https://sh.rustup.rs | bash -s -- -y --profile minimal \
+    && echo 'export PATH="/root/.cargo/bin:${PATH}"' >> /root/.bashrc
+
+ENV PATH="/root/.cargo/bin:${PATH}"
+
+RUN cargo --version && rustc --version
+
+# Add msssql ODBC driver
+# macOS ARM64 environment, install msodbcsql18.
+# general x86_64 environment, install msodbcsql17.
+RUN --mount=type=cache,id=ragflow_apt,target=/var/cache/apt,sharing=locked \
+    curl https://packages.microsoft.com/keys/microsoft.asc | apt-key add - && \
+    curl https://packages.microsoft.com/config/ubuntu/22.04/prod.list > /etc/apt/sources.list.d/mssql-release.list && \
+    apt update && \
+    arch="$(uname -m)"; \
+    if [ "$arch" = "arm64" ] || [ "$arch" = "aarch64" ]; then \
+        # ARM64 (macOS/Apple Silicon or Linux aarch64)
+        ACCEPT_EULA=Y apt install -y unixodbc-dev msodbcsql18; \
+    else \
+        # x86_64 or others
+        ACCEPT_EULA=Y apt install -y unixodbc-dev msodbcsql17; \
+    fi || \
+    { echo "Failed to install ODBC driver"; exit 1; }
+
+
+
+# Add dependencies of selenium
+RUN --mount=type=bind,from=infiniflow/ragflow_deps:latest,source=/chrome-linux64-121-0-6167-85,target=/chrome-linux64.zip \
+    unzip /chrome-linux64.zip && \
+    mv chrome-linux64 /opt/chrome && \
+    ln -s /opt/chrome/chrome /usr/local/bin/
+RUN --mount=type=bind,from=infiniflow/ragflow_deps:latest,source=/chromedriver-linux64-121-0-6167-85,target=/chromedriver-linux64.zip \
+    unzip -j /chromedriver-linux64.zip chromedriver-linux64/chromedriver && \
+    mv chromedriver /usr/local/bin/ && \
+    rm -f /usr/bin/google-chrome
+
+# https://forum.aspose.com/t/aspose-slides-for-net-no-usable-version-of-libssl-found-with-linux-server/271344/13
+# aspose-slides on linux/arm64 is unavailable
+RUN --mount=type=bind,from=infiniflow/ragflow_deps:latest,source=/,target=/deps \
+    if [ "$(uname -m)" = "x86_64" ]; then \
+        dpkg -i /deps/libssl1.1_1.1.1f-1ubuntu2_amd64.deb; \
+    elif [ "$(uname -m)" = "aarch64" ]; then \
+        dpkg -i /deps/libssl1.1_1.1.1f-1ubuntu2_arm64.deb; \
+    fi
+
+
+# builder stage
+FROM base AS builder
+USER root
+
+WORKDIR /ragflow
+
+# install dependencies from uv.lock file
+COPY pyproject.toml uv.lock ./
+
+# https://github.com/astral-sh/uv/issues/10462
+# uv records index url into uv.lock but doesn't failover among multiple indexes
+RUN --mount=type=cache,id=ragflow_uv,target=/root/.cache/uv,sharing=locked \
+    if [ "$NEED_MIRROR" == "1" ]; then \
+        sed -i 's|pypi.org|pypi.tuna.tsinghua.edu.cn|g' uv.lock; \
+    else \
+        sed -i 's|pypi.tuna.tsinghua.edu.cn|pypi.org|g' uv.lock; \
+    fi; \
+    uv sync --python 3.10 --frozen
+
+COPY web web
+COPY docs docs
+RUN --mount=type=cache,id=ragflow_npm,target=/root/.npm,sharing=locked \
+    cd web && npm install && npm run build
+
+COPY .git /ragflow/.git
+
+RUN version_info=$(git describe --tags --match=v* --first-parent --always); \
+    version_info="$version_info"; \
+    echo "RAGFlow version: $version_info"; \
+    echo $version_info > /ragflow/VERSION
+
+# production stage
+FROM base AS production
+USER root
+
+WORKDIR /ragflow
+
+# Copy Python environment and packages
+ENV VIRTUAL_ENV=/ragflow/.venv
+COPY --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV}
+ENV PATH="${VIRTUAL_ENV}/bin:${PATH}"
 
 ENV PYTHONPATH=/ragflow/
-ENV HF_ENDPOINT=https://hf-mirror.com
 
-ADD docker/entrypoint.sh ./entrypoint.sh
-ADD docker/.env ./
-RUN chmod +x ./entrypoint.sh
+COPY web web
+COPY admin admin
+COPY api api
+COPY conf conf
+COPY deepdoc deepdoc
+COPY rag rag
+COPY agent agent
+COPY graphrag graphrag
+COPY agentic_reasoning agentic_reasoning
+COPY pyproject.toml uv.lock ./
+COPY mcp mcp
+COPY plugin plugin
+COPY common common
 
-ENTRYPOINT ["./entrypoint.sh"]
+COPY docker/service_conf.yaml.template ./conf/service_conf.yaml.template
+COPY docker/entrypoint.sh ./
+RUN chmod +x ./entrypoint*.sh
+
+# Copy compiled web pages
+COPY --from=builder /ragflow/web/dist /ragflow/web/dist
+
+COPY --from=builder /ragflow/VERSION /ragflow/VERSION
+ENTRYPOINT ["./entrypoint.sh"]

Dockerfile.arm

@@ -1,43 +0,0 @@
-FROM python:3.11
-USER  root
-
-WORKDIR /ragflow
-
-COPY requirements_arm.txt /ragflow/requirements.txt
-
-
-RUN pip install nltk --default-timeout=10000
-
-RUN pip install -i https://mirrors.aliyun.com/pypi/simple/ --default-timeout=1000 -r requirements.txt &&\
-python -c "import nltk;nltk.download('punkt');nltk.download('wordnet')"
-
-RUN apt-get update && \
-    apt-get install -y curl gnupg && \
-    rm -rf /var/lib/apt/lists/*
-
-RUN curl -sL https://deb.nodesource.com/setup_20.x | bash - && \
-    apt-get install -y --fix-missing nodejs nginx ffmpeg libsm6 libxext6 libgl1
-
-RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
-ENV PATH="/root/.cargo/bin:${PATH}"
-
-RUN pip install graspologic
-
-ADD ./web ./web
-RUN cd ./web && npm i --force && npm run build
-
-ADD ./api ./api
-ADD ./conf ./conf
-ADD ./deepdoc ./deepdoc
-ADD ./rag ./rag
-ADD ./agent ./agent
-ADD ./graphrag ./graphrag
-
-ENV PYTHONPATH=/ragflow/
-ENV HF_ENDPOINT=https://hf-mirror.com
-
-ADD docker/entrypoint.sh ./entrypoint.sh
-ADD docker/.env ./
-RUN chmod +x ./entrypoint.sh
-
-ENTRYPOINT ["./entrypoint.sh"]

Dockerfile.cuda

@@ -1,27 +0,0 @@
-FROM infiniflow/ragflow-base:v2.0
-USER  root
-
-WORKDIR /ragflow
-
-## for cuda > 12.0
-RUN pip uninstall -y onnxruntime-gpu
-RUN pip install onnxruntime-gpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/
-
-
-ADD ./web ./web
-RUN cd ./web && npm i --force && npm run build
-
-ADD ./api ./api
-ADD ./conf ./conf
-ADD ./deepdoc ./deepdoc
-ADD ./rag ./rag
-ADD ./agent ./agent
-ADD ./graphrag ./graphrag
-
-ENV PYTHONPATH=/ragflow/
-ENV HF_ENDPOINT=https://hf-mirror.com
-
-ADD docker/entrypoint.sh ./entrypoint.sh
-RUN chmod +x ./entrypoint.sh
-
-ENTRYPOINT ["./entrypoint.sh"]

Dockerfile.deps

@@ -0,0 +1,10 @@
+# This builds an image that contains the resources needed by Dockerfile
+#
+FROM scratch
+
+# Copy resources downloaded via download_deps.py
+COPY chromedriver-linux64-121-0-6167-85 chrome-linux64-121-0-6167-85 cl100k_base.tiktoken libssl1.1_1.1.1f-1ubuntu2_amd64.deb libssl1.1_1.1.1f-1ubuntu2_arm64.deb tika-server-standard-3.0.0.jar tika-server-standard-3.0.0.jar.md5 libssl*.deb /
+
+COPY nltk_data /nltk_data
+
+COPY huggingface.co /huggingface.co

Dockerfile.scratch

@@ -1,56 +0,0 @@
-FROM ubuntu:22.04
-USER root
-
-WORKDIR /ragflow
-
-RUN apt-get update && apt-get install -y wget curl build-essential libopenmpi-dev
-
-RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh && \
-    bash ~/miniconda.sh -b -p /root/miniconda3 && \
-    rm ~/miniconda.sh && ln -s /root/miniconda3/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \
-    echo ". /root/miniconda3/etc/profile.d/conda.sh" >> ~/.bashrc && \
-    echo "conda activate base" >> ~/.bashrc
-
-ENV PATH /root/miniconda3/bin:$PATH
-
-RUN conda create -y --name py11 python=3.11
-
-ENV CONDA_DEFAULT_ENV py11
-ENV CONDA_PREFIX /root/miniconda3/envs/py11
-ENV PATH $CONDA_PREFIX/bin:$PATH
-
-RUN curl -sL https://deb.nodesource.com/setup_14.x | bash -
-RUN apt-get install -y nodejs
-
-RUN apt-get install -y nginx
-
-ADD ./web ./web
-ADD ./api ./api
-ADD ./conf ./conf
-ADD ./deepdoc ./deepdoc
-ADD ./rag ./rag
-ADD ./requirements.txt ./requirements.txt
-ADD ./agent ./agent
-ADD ./graphrag ./graphrag
-
-RUN apt install openmpi-bin openmpi-common libopenmpi-dev
-ENV LD_LIBRARY_PATH /usr/lib/x86_64-linux-gnu/openmpi/lib:$LD_LIBRARY_PATH
-RUN rm /root/miniconda3/envs/py11/compiler_compat/ld
-RUN cd ./web && npm i --force && npm run build
-RUN conda run -n py11 pip install -i https://mirrors.aliyun.com/pypi/simple/ -r ./requirements.txt
-
-RUN apt-get update && \
-    apt-get install -y libglib2.0-0 libgl1-mesa-glx && \
-    rm -rf /var/lib/apt/lists/*
-
-RUN conda run -n py11 pip install -i https://mirrors.aliyun.com/pypi/simple/ ollama
-RUN conda run -n py11 python -m nltk.downloader punkt
-RUN conda run -n py11 python -m nltk.downloader wordnet
-
-ENV PYTHONPATH=/ragflow/
-ENV HF_ENDPOINT=https://hf-mirror.com
-
-ADD docker/entrypoint.sh ./entrypoint.sh
-RUN chmod +x ./entrypoint.sh
-
-ENTRYPOINT ["./entrypoint.sh"]

Dockerfile.scratch.oc9

@@ -26,18 +26,20 @@ RUN dnf install -y nginx
 
 ADD ./web ./web
 ADD ./api ./api
+ADD ./docs ./docs
 ADD ./conf ./conf
 ADD ./deepdoc ./deepdoc
 ADD ./rag ./rag
 ADD ./requirements.txt ./requirements.txt
 ADD ./agent ./agent
 ADD ./graphrag ./graphrag
+ADD ./plugin ./plugin
 
 RUN dnf install -y openmpi openmpi-devel python3-openmpi
 ENV C_INCLUDE_PATH /usr/include/openmpi-x86_64:$C_INCLUDE_PATH
 ENV LD_LIBRARY_PATH /usr/lib64/openmpi/lib:$LD_LIBRARY_PATH
 RUN rm /root/miniconda3/envs/py11/compiler_compat/ld
-RUN cd ./web && npm i --force && npm run build
+RUN cd ./web && npm i && npm run build
 RUN conda run -n py11 pip install $(grep -ivE "mpi4py" ./requirements.txt) # without mpi4py==3.1.5
 RUN conda run -n py11 pip install redis
 
@@ -52,6 +54,7 @@ RUN conda run -n py11 python -m nltk.downloader wordnet
 ENV PYTHONPATH=/ragflow/
 ENV HF_ENDPOINT=https://hf-mirror.com
 
+COPY docker/service_conf.yaml.template ./conf/service_conf.yaml.template
 ADD docker/entrypoint.sh ./entrypoint.sh
 RUN chmod +x ./entrypoint.sh
 

Dockerfile_tei

@@ -0,0 +1,14 @@
+FROM ghcr.io/huggingface/text-embeddings-inference:cpu-1.8
+
+# uv tool install huggingface_hub
+# hf download --local-dir tei_data/BAAI/bge-small-en-v1.5 BAAI/bge-small-en-v1.5
+# hf download --local-dir tei_data/BAAI/bge-m3 BAAI/bge-m3
+# hf download --local-dir tei_data/Qwen/Qwen3-Embedding-0.6B Qwen/Qwen3-Embedding-0.6B
+COPY tei_data /data
+
+# curl -X POST http://localhost:6380/embed -H "Content-Type: application/json" -d '{"inputs": "Hello, world! This is a test sentence."}'
+# curl -X POST http://tei:80/embed -H "Content-Type: application/json" -d '{"inputs": "Hello, world! This is a test sentence."}'
+# [[-0.058816575,0.019564206,0.026697718,...]]
+
+# curl -X POST http://localhost:6380/v1/embeddings -H "Content-Type: application/json" -d '{"input": "Hello, world! This is a test sentence."}'
+# {"object":"list","data":[{"object":"embedding","embedding":[-0.058816575,0.019564206,...],"index":0}],"model":"BAAI/bge-small-en-v1.5","usage":{"prompt_tokens":12,"total_tokens":12}}

README.md

@@ -1,40 +1,59 @@
 <div align="center">
 <a href="https://demo.ragflow.io/">
-<img src="web/src/assets/logo-with-text.png" width="520" alt="ragflow logo">
+<img src="web/src/assets/logo-with-text.svg" width="520" alt="ragflow logo">
 </a>
 </div>
 
 <p align="center">
-  <a href="./README.md">English</a> |
-  <a href="./README_zh.md">简体中文</a> |
-  <a href="./README_ja.md">日本語</a> |
-  <a href="./README_ko.md">한국어</a>
+  <a href="./README.md"><img alt="README in English" src="https://img.shields.io/badge/English-DBEDFA"></a>
+  <a href="./README_zh.md"><img alt="简体中文版自述文件" src="https://img.shields.io/badge/简体中文-DFE0E5"></a>
+  <a href="./README_tzh.md"><img alt="繁體版中文自述文件" src="https://img.shields.io/badge/繁體中文-DFE0E5"></a>
+  <a href="./README_ja.md"><img alt="日本語のREADME" src="https://img.shields.io/badge/日本語-DFE0E5"></a>
+  <a href="./README_ko.md"><img alt="한국어" src="https://img.shields.io/badge/한국어-DFE0E5"></a>
+  <a href="./README_id.md"><img alt="Bahasa Indonesia" src="https://img.shields.io/badge/Bahasa Indonesia-DFE0E5"></a>
+  <a href="./README_pt_br.md"><img alt="Português(Brasil)" src="https://img.shields.io/badge/Português(Brasil)-DFE0E5"></a>
 </p>
 
 <p align="center">
+    <a href="https://x.com/intent/follow?screen_name=infiniflowai" target="_blank">
+        <img src="https://img.shields.io/twitter/follow/infiniflow?logo=X&color=%20%23f5f5f5" alt="follow on X(Twitter)">
+    </a>
+    <a href="https://demo.ragflow.io" target="_blank">
+        <img alt="Static Badge" src="https://img.shields.io/badge/Online-Demo-4e6b99">
+    </a>
+    <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
+        <img src="https://img.shields.io/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.22.1">
+    </a>
     <a href="https://github.com/infiniflow/ragflow/releases/latest">
         <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Latest%20Release" alt="Latest Release">
     </a>
-    <a href="https://demo.ragflow.io" target="_blank">
-        <img alt="Static Badge" src="https://img.shields.io/badge/Online-Demo-4e6b99"></a>
-    <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
-        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.11.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.11.0"></a>
     <a href="https://github.com/infiniflow/ragflow/blob/main/LICENSE">
-    <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?labelColor=d4eaf7&color=2e6cc4" alt="license">
-  </a>
+        <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?labelColor=d4eaf7&color=2e6cc4" alt="license">
+    </a>
+    <a href="https://deepwiki.com/infiniflow/ragflow">
+        <img alt="Ask DeepWiki" src="https://deepwiki.com/badge.svg">
+    </a>
 </p>
 
 <h4 align="center">
   <a href="https://ragflow.io/docs/dev/">Document</a> |
-  <a href="https://github.com/infiniflow/ragflow/issues/162">Roadmap</a> |
+  <a href="https://github.com/infiniflow/ragflow/issues/4214">Roadmap</a> |
   <a href="https://twitter.com/infiniflowai">Twitter</a> |
-  <a href="https://discord.gg/4XxujFgUN7">Discord</a> |
+  <a href="https://discord.gg/NjYzJD3GM3">Discord</a> |
   <a href="https://demo.ragflow.io">Demo</a>
 </h4>
 
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/ragflow-octoverse.png" width="1200"/>
+</div>
+
+<div align="center">
+<a href="https://trendshift.io/repositories/9064" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9064" alt="infiniflow%2Fragflow | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
+</div>
+
 <details open>
-<summary></b>📕 Table of Contents</b></summary>
-  
+<summary><b>📕 Table of Contents</b></summary>
+
 - 💡 [What is RAGFlow?](#-what-is-ragflow)
 - 🎮 [Demo](#-demo)
 - 📌 [Latest Updates](#-latest-updates)
@@ -42,8 +61,8 @@
 - 🔎 [System Architecture](#-system-architecture)
 - 🎬 [Get Started](#-get-started)
 - 🔧 [Configurations](#-configurations)
-- 🛠️ [Build from source](#-build-from-source)
-- 🛠️ [Launch service from source](#-launch-service-from-source)
+- 🔧 [Build a Docker image](#-build-a-docker-image)
+- 🔨 [Launch service from source for development](#-launch-service-from-source-for-development)
 - 📚 [Documentation](#-documentation)
 - 📜 [Roadmap](#-roadmap)
 - 🏄 [Community](#-community)
@@ -53,34 +72,44 @@
 
 ## 💡 What is RAGFlow?
 
-[RAGFlow](https://ragflow.io/) is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It offers a streamlined RAG workflow for businesses of any scale, combining LLM (Large Language Models) to provide truthful question-answering capabilities, backed by well-founded citations from various complex formatted data.
+[RAGFlow](https://ragflow.io/) is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs. It offers a streamlined RAG workflow adaptable to enterprises of any scale. Powered by a converged context engine and pre-built agent templates, RAGFlow enables developers to transform complex data into high-fidelity, production-ready AI systems with exceptional efficiency and precision.
 
 ## 🎮 Demo
 
 Try our demo at [https://demo.ragflow.io](https://demo.ragflow.io).
-<div align="center" style="margin-top:20px;margin-bottom:20px;">
-<img src="https://github.com/infiniflow/ragflow/assets/7248/2f6baa3e-1092-4f11-866d-36f6a9d075e5" width="1200"/>
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/b083d173-dadc-4ea9-bdeb-180d7df514eb" width="1200"/>
-</div>
 
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/chunking.gif" width="1200"/>
+<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/agentic-dark.gif" width="1200"/>
+</div>
 
 ## 🔥 Latest Updates
 
-- 2024-09-13 Adds search mode for knowledge base Q&A.
-- 2024-09-09 Adds a medical consultant agent template.
-- 2024-08-22 Support text to SQL statements through RAG.
-- 2024-08-02 Supports GraphRAG inspired by [graphrag](https://github.com/microsoft/graphrag) and mind map.
-- 2024-07-23 Supports audio file parsing.
-- 2024-07-08 Supports workflow based on [Graph](./agent/README.md).
-- 2024-06-27 Supports Markdown and Docx in the Q&A parsing method, extracting images from Docx files, extracting tables from Markdown files.
-- 2024-05-23 Supports [RAPTOR](https://arxiv.org/html/2401.18059v1) for better text retrieval.
+- 2025-11-19 Supports Gemini 3 Pro.
+- 2025-11-12 Supports data synchronization from Confluence, S3, Notion, Discord, Google Drive.
+- 2025-10-23 Supports MinerU & Docling as document parsing methods.
+- 2025-10-15 Supports orchestrable ingestion pipeline.
+- 2025-08-08 Supports OpenAI's latest GPT-5 series models.
+- 2025-08-01 Supports agentic workflow and MCP.
+- 2025-05-23 Adds a Python/JavaScript code executor component to Agent.
+- 2025-05-05 Supports cross-language query.
+- 2025-03-19 Supports using a multi-modal model to make sense of images within PDF or DOCX files.
 
+## 🎉 Stay Tuned
+
+⭐️ Star our repository to stay up-to-date with exciting new features and improvements! Get instant notifications for new
+releases! 🌟
+
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/user-attachments/assets/18c9707e-b8aa-4caf-a154-037089c105ba" width="1200"/>
+</div>
 
 ## 🌟 Key Features
 
 ### 🍭 **"Quality in, quality out"**
 
-- [Deep document understanding](./deepdoc/README.md)-based knowledge extraction from unstructured data with complicated formats.
+- [Deep document understanding](./deepdoc/README.md)-based knowledge extraction from unstructured data with complicated
+  formats.
 - Finds "needle in a data haystack" of literally unlimited tokens.
 
 ### 🍱 **Template-based chunking**
@@ -107,7 +136,7 @@ Try our demo at [https://demo.ragflow.io](https://demo.ragflow.io).
 ## 🔎 System Architecture
 
 <div align="center" style="margin-top:20px;margin-bottom:20px;">
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/d6ac5664-c237-4200-a7c2-a4a00691b485" width="1000"/>
+<img src="https://github.com/user-attachments/assets/31b0dd6f-ca4f-445a-9457-70cb44a381b2" width="1000"/>
 </div>
 
 ## 🎬 Get Started
@@ -118,7 +147,10 @@ Try our demo at [https://demo.ragflow.io](https://demo.ragflow.io).
 - RAM >= 16 GB
 - Disk >= 50 GB
 - Docker >= 24.0.0 & Docker Compose >= v2.26.1
-  > If you have not installed Docker on your local machine (Windows, Mac, or Linux), see [Install Docker Engine](https://docs.docker.com/engine/install/).
+- [gVisor](https://gvisor.dev/docs/user_guide/install/): Required only if you intend to use the code executor (sandbox) feature of RAGFlow.
+
+> [!TIP]
+> If you have not installed Docker on your local machine (Windows, Mac, or Linux), see [Install Docker Engine](https://docs.docker.com/engine/install/).
 
 ### 🚀 Start up the server
 
@@ -137,206 +169,232 @@ Try our demo at [https://demo.ragflow.io](https://demo.ragflow.io).
    > $ sudo sysctl -w vm.max_map_count=262144
    > ```
    >
-   > This change will be reset after a system reboot. To ensure your change remains permanent, add or update the `vm.max_map_count` value in **/etc/sysctl.conf** accordingly:
+   > This change will be reset after a system reboot. To ensure your change remains permanent, add or update the
+   > `vm.max_map_count` value in **/etc/sysctl.conf** accordingly:
    >
    > ```bash
    > vm.max_map_count=262144
    > ```
-
+   >
 2. Clone the repo:
 
    ```bash
    $ git clone https://github.com/infiniflow/ragflow.git
    ```
+3. Start up the server using the pre-built Docker images:
 
-3. Build the pre-built Docker images and start up the server:
+> [!CAUTION]
+> All Docker images are built for x86 platforms. We don't currently offer Docker images for ARM64.
+> If you are on an ARM64 platform, follow [this guide](https://ragflow.io/docs/dev/build_docker_image) to build a Docker image compatible with your system.
 
-   > Running the following commands automatically downloads the *dev* version RAGFlow Docker image. To download and run a specified Docker version, update `RAGFLOW_VERSION` in **docker/.env** to the intended version, for example `RAGFLOW_VERSION=v0.11.0`, before running the following commands.
+> The command below downloads the `v0.22.1` edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.22.1`, update the `RAGFLOW_IMAGE` variable accordingly in **docker/.env** before using `docker compose` to start the server.
 
-   ```bash
+```bash
    $ cd ragflow/docker
-   $ chmod +x ./entrypoint.sh
-   $ docker compose up -d
-   ```
+  
+   # git checkout v0.22.1
+   # Optional: use a stable tag (see releases: https://github.com/infiniflow/ragflow/releases)
+   # This steps ensures the **entrypoint.sh** file in the code matches the Docker image version.
    
+   # Use CPU for DeepDoc tasks:
+   $ docker compose -f docker-compose.yml up -d
 
-   > The core image is about 9 GB in size and may take a while to load.
+   # To use GPU to accelerate DeepDoc tasks:
+   # sed -i '1i DEVICE=gpu' .env
+   # docker compose -f docker-compose.yml up -d
+```
+
+> Note: Prior to `v0.22.0`, we provided both images with embedding models and slim images without embedding models. Details as follows:
+
+| RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
+| ----------------- | --------------- | --------------------- | ------------------------ |
+| v0.21.1           | &approx;9       | ✔️                    | Stable release           |
+| v0.21.1-slim      | &approx;2       | ❌                    | Stable release           |
+
+> Starting with `v0.22.0`, we ship only the slim edition and no longer append the **-slim** suffix to the image tag.
 
 4. Check the server status after having the server up and running:
 
    ```bash
-   $ docker logs -f ragflow-server
+   $ docker logs -f docker-ragflow-cpu-1
    ```
 
    _The following output confirms a successful launch of the system:_
 
    ```bash
-       ____                 ______ __
-      / __ \ ____ _ ____ _ / ____// /____  _      __
-     / /_/ // __ `// __ `// /_   / // __ \| | /| / /
-    / _, _// /_/ // /_/ // __/  / // /_/ /| |/ |/ /
-   /_/ |_| \__,_/ \__, //_/    /_/ \____/ |__/|__/
-                 /____/
+
+         ____   ___    ______ ______ __
+        / __ \ /   |  / ____// ____// /____  _      __
+       / /_/ // /| | / / __ / /_   / // __ \| | /| / /
+      / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ /
+     /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/
 
     * Running on all addresses (0.0.0.0)
-    * Running on http://127.0.0.1:9380
-    * Running on http://x.x.x.x:9380
-    INFO:werkzeug:Press CTRL+C to quit
    ```
-   > If you skip this confirmation step and directly log in to RAGFlow, your browser may prompt a `network abnormal` error because, at that moment, your RAGFlow may not be fully initialized.  
 
+   > If you skip this confirmation step and directly log in to RAGFlow, your browser may prompt a `network anormal`
+   > error because, at that moment, your RAGFlow may not be fully initialized.
+   >
 5. In your web browser, enter the IP address of your server and log in to RAGFlow.
-   > With the default settings, you only need to enter `http://IP_OF_YOUR_MACHINE` (**sans** port number) as the default HTTP serving port `80` can be omitted when using the default configurations.
-6. In [service_conf.yaml](./docker/service_conf.yaml), select the desired LLM factory in `user_default_llm` and update the `API_KEY` field with the corresponding API key.
+
+   > With the default settings, you only need to enter `http://IP_OF_YOUR_MACHINE` (**sans** port number) as the default
+   > HTTP serving port `80` can be omitted when using the default configurations.
+   >
+6. In [service_conf.yaml.template](./docker/service_conf.yaml.template), select the desired LLM factory in `user_default_llm` and update
+   the `API_KEY` field with the corresponding API key.
 
    > See [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) for more information.
+   >
 
-   _The show is now on!_
+   _The show is on!_
 
 ## 🔧 Configurations
 
 When it comes to system configurations, you will need to manage the following files:
 
-- [.env](./docker/.env): Keeps the fundamental setups for the system, such as `SVR_HTTP_PORT`, `MYSQL_PASSWORD`, and `MINIO_PASSWORD`.
-- [service_conf.yaml](./docker/service_conf.yaml): Configures the back-end services.
+- [.env](./docker/.env): Keeps the fundamental setups for the system, such as `SVR_HTTP_PORT`, `MYSQL_PASSWORD`, and
+  `MINIO_PASSWORD`.
+- [service_conf.yaml.template](./docker/service_conf.yaml.template): Configures the back-end services. The environment variables in this file will be automatically populated when the Docker container starts. Any environment variables set within the Docker container will be available for use, allowing you to customize service behavior based on the deployment environment.
 - [docker-compose.yml](./docker/docker-compose.yml): The system relies on [docker-compose.yml](./docker/docker-compose.yml) to start up.
 
-You must ensure that changes to the [.env](./docker/.env) file are in line with what are in the [service_conf.yaml](./docker/service_conf.yaml) file.
+> The [./docker/README](./docker/README.md) file provides a detailed description of the environment settings and service
+> configurations which can be used as `${ENV_VARS}` in the [service_conf.yaml.template](./docker/service_conf.yaml.template) file.
 
-> The [./docker/README](./docker/README.md) file provides a detailed description of the environment settings and service configurations, and you are REQUIRED to ensure that all environment settings listed in the [./docker/README](./docker/README.md) file are aligned with the corresponding configurations in the [service_conf.yaml](./docker/service_conf.yaml) file.
+To update the default HTTP serving port (80), go to [docker-compose.yml](./docker/docker-compose.yml) and change `80:80`
+to `<YOUR_SERVING_PORT>:80`.
 
-To update the default HTTP serving port (80), go to [docker-compose.yml](./docker/docker-compose.yml) and change `80:80` to `<YOUR_SERVING_PORT>:80`.
+Updates to the above configurations require a reboot of all containers to take effect:
 
-> Updates to all system configurations require a system reboot to take effect:
->
 > ```bash
-> $ docker-compose up -d
+> $ docker compose -f docker-compose.yml up -d
 > ```
 
-## 🛠️ Build from source
+### Switch doc engine from Elasticsearch to Infinity
 
-To build the Docker images from source:
+RAGFlow uses Elasticsearch by default for storing full text and vectors. To switch to [Infinity](https://github.com/infiniflow/infinity/), follow these steps:
+
+1. Stop all running containers:
+
+   ```bash
+   $ docker compose -f docker/docker-compose.yml down -v
+   ```
+
+> [!WARNING]
+> `-v` will delete the docker container volumes, and the existing data will be cleared.
+
+2. Set `DOC_ENGINE` in **docker/.env** to `infinity`.
+3. Start the containers:
+
+   ```bash
+   $ docker compose -f docker-compose.yml up -d
+   ```
+
+> [!WARNING]
+> Switching to Infinity on a Linux/arm64 machine is not yet officially supported.
+
+## 🔧 Build a Docker image
+
+This image is approximately 2 GB in size and relies on external LLM and embedding services.
 
 ```bash
-$ git clone https://github.com/infiniflow/ragflow.git
-$ cd ragflow/
-$ docker build -t infiniflow/ragflow:dev .
-$ cd ragflow/docker
-$ chmod +x ./entrypoint.sh
-$ docker compose up -d
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .
 ```
 
-## 🛠️ Launch service from source
+## 🔨 Launch service from source for development
 
-To launch the service from source:
-
-1. Clone the repository: 
+1. Install `uv` and `pre-commit`, or skip this step if they are already installed:
 
    ```bash
-   $ git clone https://github.com/infiniflow/ragflow.git
-   $ cd ragflow/
+   pipx install uv pre-commit
+   ```
+2. Clone the source code and install Python dependencies:
+
+   ```bash
+   git clone https://github.com/infiniflow/ragflow.git
+   cd ragflow/
+   uv sync --python 3.10 # install RAGFlow dependent python modules
+   uv run download_deps.py
+   pre-commit install
+   ```
+3. Launch the dependent services (MinIO, Elasticsearch, Redis, and MySQL) using Docker Compose:
+
+   ```bash
+   docker compose -f docker/docker-compose-base.yml up -d
    ```
 
-2. Create a virtual environment, ensuring that Anaconda or Miniconda is installed:
+   Add the following line to `/etc/hosts` to resolve all hosts specified in **docker/.env** to `127.0.0.1`:
+
+   ```
+   127.0.0.1       es01 infinity mysql minio redis sandbox-executor-manager
+   ```
+4. If you cannot access HuggingFace, set the `HF_ENDPOINT` environment variable to use a mirror site:
 
    ```bash
-   $ conda create -n ragflow python=3.11.0
-   $ conda activate ragflow
-   $ pip install -r requirements.txt
-   ```
-   
-   ```bash
-   # If your CUDA version is higher than 12.0, run the following additional commands:
-   $ pip uninstall -y onnxruntime-gpu
-   $ pip install onnxruntime-gpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/
-   ```
-
-3. Copy the entry script and configure environment variables:
-
-   ```bash
-   # Get the Python path:
-   $ which python
-   # Get the ragflow project path:
-   $ pwd
-   ```
-   
-   ```bash
-   $ cp docker/entrypoint.sh .
-   $ vi entrypoint.sh
-   ```
-
-   ```bash
-   # Adjust configurations according to your actual situation (the following two export commands are newly added):
-   # - Assign the result of `which python` to `PY`.
-   # - Assign the result of `pwd` to `PYTHONPATH`.
-   # - Comment out `LD_LIBRARY_PATH`, if it is configured.
-   # - Optional: Add Hugging Face mirror.
-   PY=${PY}
-   export PYTHONPATH=${PYTHONPATH}
    export HF_ENDPOINT=https://hf-mirror.com
    ```
-
-4. Launch the third-party services (MinIO, Elasticsearch, Redis, and MySQL):
+5. If your operating system does not have jemalloc, please install it as follows:
 
    ```bash
-   $ cd docker
-   $ docker compose -f docker-compose-base.yml up -d 
+   # Ubuntu
+   sudo apt-get install libjemalloc-dev
+   # CentOS
+   sudo yum install jemalloc
+   # OpenSUSE
+   sudo zypper install jemalloc
+   # macOS
+   sudo brew install jemalloc
+   ```
+6. Launch backend service:
+
+   ```bash
+   source .venv/bin/activate
+   export PYTHONPATH=$(pwd)
+   bash docker/launch_backend_service.sh
+   ```
+7. Install frontend dependencies:
+
+   ```bash
+   cd web
+   npm install
+   ```
+8. Launch frontend service:
+
+   ```bash
+   npm run dev
    ```
 
-5. Check the configuration files, ensuring that:
+   _The following output confirms a successful launch of the system:_
 
-   - The settings in **docker/.env** match those in **conf/service_conf.yaml**. 
-   - The IP addresses and ports for related services in **service_conf.yaml** match the local machine IP and ports exposed by the container.
-
-6. Launch the RAGFlow backend service:
+   ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
+9. Stop RAGFlow front-end and back-end service after development is complete:
 
    ```bash
-   $ chmod +x ./entrypoint.sh
-   $ bash ./entrypoint.sh
-   ```
-
-7. Launch the frontend service:
-
-   ```bash
-   $ cd web
-   $ npm install --registry=https://registry.npmmirror.com --force
-   $ vim .umirc.ts
-   # Update proxy.target to http://127.0.0.1:9380
-   $ npm run dev 
-   ```
-
-8. Deploy the frontend service:
-
-   ```bash
-   $ cd web
-   $ npm install --registry=https://registry.npmmirror.com --force
-   $ umi build
-   $ mkdir -p /ragflow/web
-   $ cp -r dist /ragflow/web
-   $ apt install nginx -y
-   $ cp ../docker/nginx/proxy.conf /etc/nginx
-   $ cp ../docker/nginx/nginx.conf /etc/nginx
-   $ cp ../docker/nginx/ragflow.conf /etc/nginx/conf.d
-   $ systemctl start nginx
+   pkill -f "ragflow_server.py|task_executor.py"
    ```
 
 ## 📚 Documentation
 
 - [Quickstart](https://ragflow.io/docs/dev/)
-- [User guide](https://ragflow.io/docs/dev/category/user-guides)
+- [Configuration](https://ragflow.io/docs/dev/configurations)
+- [Release notes](https://ragflow.io/docs/dev/release_notes)
+- [User guides](https://ragflow.io/docs/dev/category/guides)
+- [Developer guides](https://ragflow.io/docs/dev/category/developers)
 - [References](https://ragflow.io/docs/dev/category/references)
-- [FAQ](https://ragflow.io/docs/dev/faq)
+- [FAQs](https://ragflow.io/docs/dev/faq)
 
 ## 📜 Roadmap
 
-See the [RAGFlow Roadmap 2024](https://github.com/infiniflow/ragflow/issues/162)
+See the [RAGFlow Roadmap 2025](https://github.com/infiniflow/ragflow/issues/4214)
 
 ## 🏄 Community
 
-- [Discord](https://discord.gg/4XxujFgUN7)
+- [Discord](https://discord.gg/NjYzJD3GM3)
 - [Twitter](https://twitter.com/infiniflowai)
 - [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
 
 ## 🙌 Contributing
 
-RAGFlow flourishes via open-source collaboration. In this spirit, we embrace diverse contributions from the community. If you would like to be a part, review our [Contribution Guidelines](./docs/references/CONTRIBUTING.md) first.
+RAGFlow flourishes via open-source collaboration. In this spirit, we embrace diverse contributions from the community.
+If you would like to be a part, review our [Contribution Guidelines](https://ragflow.io/docs/dev/contributing) first.

README_id.md

@@ -0,0 +1,372 @@
+<div align="center">
+<a href="https://demo.ragflow.io/">
+<img src="web/src/assets/logo-with-text.svg" width="520" alt="Logo ragflow">
+</a>
+</div>
+
+<p align="center">
+  <a href="./README.md"><img alt="README in English" src="https://img.shields.io/badge/English-DFE0E5"></a>
+  <a href="./README_zh.md"><img alt="简体中文版自述文件" src="https://img.shields.io/badge/简体中文-DFE0E5"></a>
+  <a href="./README_tzh.md"><img alt="繁體中文版自述文件" src="https://img.shields.io/badge/繁體中文-DFE0E5"></a>
+  <a href="./README_ja.md"><img alt="日本語のREADME" src="https://img.shields.io/badge/日本語-DFE0E5"></a>
+  <a href="./README_ko.md"><img alt="한국어" src="https://img.shields.io/badge/한국어-DFE0E5"></a>
+  <a href="./README_id.md"><img alt="Bahasa Indonesia" src="https://img.shields.io/badge/Bahasa Indonesia-DBEDFA"></a>
+  <a href="./README_pt_br.md"><img alt="Português(Brasil)" src="https://img.shields.io/badge/Português(Brasil)-DFE0E5"></a>
+</p>
+
+<p align="center">
+    <a href="https://x.com/intent/follow?screen_name=infiniflowai" target="_blank">
+        <img src="https://img.shields.io/twitter/follow/infiniflow?logo=X&color=%20%23f5f5f5" alt="Ikuti di X (Twitter)">
+    </a>
+    <a href="https://demo.ragflow.io" target="_blank">
+        <img alt="Lencana Daring" src="https://img.shields.io/badge/Online-Demo-4e6b99">
+    </a>
+    <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
+        <img src="https://img.shields.io/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.22.1">
+    </a>
+    <a href="https://github.com/infiniflow/ragflow/releases/latest">
+        <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Rilis%20Terbaru" alt="Rilis Terbaru">
+    </a>
+    <a href="https://github.com/infiniflow/ragflow/blob/main/LICENSE">
+        <img height="21" src="https://img.shields.io/badge/Lisensi-Apache--2.0-ffffff?labelColor=d4eaf7&color=2e6cc4" alt="Lisensi">
+    </a>
+    <a href="https://deepwiki.com/infiniflow/ragflow">
+        <img alt="Ask DeepWiki" src="https://deepwiki.com/badge.svg">
+    </a>
+</p>
+
+<h4 align="center">
+  <a href="https://ragflow.io/docs/dev/">Dokumentasi</a> |
+  <a href="https://github.com/infiniflow/ragflow/issues/4214">Peta Jalan</a> |
+  <a href="https://twitter.com/infiniflowai">Twitter</a> |
+  <a href="https://discord.gg/NjYzJD3GM3">Discord</a> |
+  <a href="https://demo.ragflow.io">Demo</a>
+</h4>
+
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/ragflow-octoverse.png" width="1200"/>
+</div>
+
+<div align="center">
+<a href="https://trendshift.io/repositories/9064" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9064" alt="infiniflow%2Fragflow | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
+</div>
+
+<details open>
+<summary><b>📕 Daftar Isi </b> </summary>
+
+- 💡 [Apa Itu RAGFlow?](#-apa-itu-ragflow)
+- 🎮 [Demo](#-demo)
+- 📌 [Pembaruan Terbaru](#-pembaruan-terbaru)
+- 🌟 [Fitur Utama](#-fitur-utama)
+- 🔎 [Arsitektur Sistem](#-arsitektur-sistem)
+- 🎬 [Mulai](#-mulai)
+- 🔧 [Konfigurasi](#-konfigurasi)
+- 🔧 [Membangun Image Docker](#-membangun-docker-image)
+- 🔨 [Meluncurkan aplikasi dari Sumber untuk Pengembangan](#-meluncurkan-aplikasi-dari-sumber-untuk-pengembangan)
+- 📚 [Dokumentasi](#-dokumentasi)
+- 📜 [Peta Jalan](#-peta-jalan)
+- 🏄 [Komunitas](#-komunitas)
+- 🙌 [Kontribusi](#-kontribusi)
+
+</details>
+
+## 💡 Apa Itu RAGFlow?
+
+[RAGFlow](https://ragflow.io/) adalah mesin RAG (Retrieval-Augmented Generation) open-source terkemuka yang mengintegrasikan teknologi RAG mutakhir dengan kemampuan Agent untuk menciptakan lapisan kontekstual superior bagi LLM. Menyediakan alur kerja RAG yang efisien dan dapat diadaptasi untuk perusahaan segala skala. Didukung oleh mesin konteks terkonvergensi dan template Agent yang telah dipra-bangun, RAGFlow memungkinkan pengembang mengubah data kompleks menjadi sistem AI kesetiaan-tinggi dan siap-produksi dengan efisiensi dan presisi yang luar biasa.
+
+## 🎮 Demo
+
+Coba demo kami di [https://demo.ragflow.io](https://demo.ragflow.io).
+
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/chunking.gif" width="1200"/>
+<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/agentic-dark.gif" width="1200"/>
+</div>
+
+## 🔥 Pembaruan Terbaru
+
+- 2025-11-19 Mendukung Gemini 3 Pro.
+- 2025-11-12 Mendukung sinkronisasi data dari Confluence, S3, Notion, Discord, Google Drive.
+- 2025-10-23 Mendukung MinerU & Docling sebagai metode penguraian dokumen.
+- 2025-10-15 Dukungan untuk jalur data yang terorkestrasi.
+- 2025-08-08 Mendukung model seri GPT-5 terbaru dari OpenAI.
+- 2025-08-01 Mendukung alur kerja agen dan MCP.
+- 2025-05-23 Menambahkan komponen pelaksana kode Python/JS ke Agen.
+- 2025-05-05 Mendukung kueri lintas bahasa.
+- 2025-03-19 Mendukung penggunaan model multi-modal untuk memahami gambar di dalam file PDF atau DOCX.
+- 2024-12-18 Meningkatkan model Analisis Tata Letak Dokumen di DeepDoc.
+- 2024-08-22 Dukungan untuk teks ke pernyataan SQL melalui RAG.
+
+## 🎉 Tetap Terkini
+
+⭐️ Star repositori kami untuk tetap mendapat informasi tentang fitur baru dan peningkatan menarik! 🌟
+
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/user-attachments/assets/18c9707e-b8aa-4caf-a154-037089c105ba" width="1200"/>
+</div>
+
+## 🌟 Fitur Utama
+
+### 🍭 **"Kualitas Masuk, Kualitas Keluar"**
+
+- Ekstraksi pengetahuan berbasis pemahaman dokumen mendalam dari data tidak terstruktur dengan format yang rumit.
+- Menemukan "jarum di tumpukan data" dengan token yang hampir tidak terbatas.
+
+### 🍱 **Pemotongan Berbasis Template**
+
+- Cerdas dan dapat dijelaskan.
+- Banyak pilihan template yang tersedia.
+
+### 🌱 **Referensi yang Didasarkan pada Data untuk Mengurangi Hallusinasi**
+
+- Visualisasi pemotongan teks memungkinkan intervensi manusia.
+- Tampilan cepat referensi kunci dan referensi yang dapat dilacak untuk mendukung jawaban yang didasarkan pada fakta.
+
+### 🍔 **Kompatibilitas dengan Sumber Data Heterogen**
+
+- Mendukung Word, slide, excel, txt, gambar, salinan hasil scan, data terstruktur, halaman web, dan banyak lagi.
+
+### 🛀 **Alur Kerja RAG yang Otomatis dan Mudah**
+
+- Orkestrasi RAG yang ramping untuk bisnis kecil dan besar.
+- LLM yang dapat dikonfigurasi serta model embedding.
+- Peringkat ulang berpasangan dengan beberapa pengambilan ulang.
+- API intuitif untuk integrasi yang mudah dengan bisnis.
+
+## 🔎 Arsitektur Sistem
+
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/user-attachments/assets/31b0dd6f-ca4f-445a-9457-70cb44a381b2" width="1000"/>
+</div>
+
+## 🎬 Mulai
+
+### 📝 Prasyarat
+
+- CPU >= 4 inti
+- RAM >= 16 GB
+- Disk >= 50 GB
+- Docker >= 24.0.0 & Docker Compose >= v2.26.1
+- [gVisor](https://gvisor.dev/docs/user_guide/install/): Hanya diperlukan jika Anda ingin menggunakan fitur eksekutor kode (sandbox) dari RAGFlow.
+
+> [!TIP]
+> Jika Anda belum menginstal Docker di komputer lokal Anda (Windows, Mac, atau Linux), lihat [Install Docker Engine](https://docs.docker.com/engine/install/).
+
+### 🚀 Menjalankan Server
+
+1. Pastikan `vm.max_map_count` >= 262144:
+
+   > Untuk memeriksa nilai `vm.max_map_count`:
+   >
+   > ```bash
+   > $ sysctl vm.max_map_count
+   > ```
+   >
+   > Jika nilainya kurang dari 262144, setel ulang `vm.max_map_count` ke setidaknya 262144:
+   >
+   > ```bash
+   > # Dalam contoh ini, kita atur menjadi 262144:
+   > $ sudo sysctl -w vm.max_map_count=262144
+   > ```
+   >
+   > Perubahan ini akan hilang setelah sistem direboot. Untuk membuat perubahan ini permanen, tambahkan atau perbarui nilai
+   > `vm.max_map_count` di **/etc/sysctl.conf**:
+   >
+   > ```bash
+   > vm.max_map_count=262144
+   > ```
+   >
+2. Clone repositori:
+
+   ```bash
+   $ git clone https://github.com/infiniflow/ragflow.git
+   ```
+3. Bangun image Docker pre-built dan jalankan server:
+
+> [!CAUTION]
+> Semua gambar Docker dibangun untuk platform x86. Saat ini, kami tidak menawarkan gambar Docker untuk ARM64.
+> Jika Anda menggunakan platform ARM64, [silakan gunakan panduan ini untuk membangun gambar Docker yang kompatibel dengan sistem Anda](https://ragflow.io/docs/dev/build_docker_image).
+
+> Perintah di bawah ini mengunduh edisi v0.22.1 dari gambar Docker RAGFlow. Silakan merujuk ke tabel berikut untuk deskripsi berbagai edisi RAGFlow. Untuk mengunduh edisi RAGFlow yang berbeda dari v0.22.1, perbarui variabel RAGFLOW_IMAGE di docker/.env sebelum menggunakan docker compose untuk memulai server.
+
+```bash
+   $ cd ragflow/docker
+   
+   # git checkout v0.22.1
+   # Opsional: gunakan tag stabil (lihat releases: https://github.com/infiniflow/ragflow/releases)
+   # This steps ensures the **entrypoint.sh** file in the code matches the Docker image version.
+
+   # Use CPU for DeepDoc tasks:
+   $ docker compose -f docker-compose.yml up -d
+
+   # To use GPU to accelerate DeepDoc tasks:
+   # sed -i '1i DEVICE=gpu' .env
+   # docker compose -f docker-compose.yml up -d
+```
+
+> Catatan: Sebelum `v0.22.0`, kami menyediakan image dengan model embedding dan image slim tanpa model embedding. Detailnya sebagai berikut:
+
+| RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
+| ----------------- | --------------- | --------------------- | ------------------------ |
+| v0.21.1           | &approx;9       | ✔️                    | Stable release           |
+| v0.21.1-slim      | &approx;2       | ❌                    | Stable release           |
+
+> Mulai dari `v0.22.0`, kami hanya menyediakan edisi slim dan tidak lagi menambahkan akhiran **-slim** pada tag image.
+
+1. Periksa status server setelah server aktif dan berjalan:
+
+   ```bash
+   $ docker logs -f docker-ragflow-cpu-1
+   ```
+
+   _Output berikut menandakan bahwa sistem berhasil diluncurkan:_
+
+   ```bash
+
+         ____   ___    ______ ______ __
+        / __ \ /   |  / ____// ____// /____  _      __
+       / /_/ // /| | / / __ / /_   / // __ \| | /| / /
+      / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ /
+     /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/
+
+    * Running on all addresses (0.0.0.0)
+   ```
+
+   > Jika Anda melewatkan langkah ini dan langsung login ke RAGFlow, browser Anda mungkin menampilkan error `network anormal`
+   > karena RAGFlow mungkin belum sepenuhnya siap.
+   >
+2. Buka browser web Anda, masukkan alamat IP server Anda, dan login ke RAGFlow.
+
+   > Dengan pengaturan default, Anda hanya perlu memasukkan `http://IP_DEVICE_ANDA` (**tanpa** nomor port) karena
+   > port HTTP default `80` bisa dihilangkan saat menggunakan konfigurasi default.
+   >
+3. Dalam [service_conf.yaml.template](./docker/service_conf.yaml.template), pilih LLM factory yang diinginkan di `user_default_llm` dan perbarui
+   bidang `API_KEY` dengan kunci API yang sesuai.
+
+   > Lihat [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) untuk informasi lebih lanjut.
+   >
+
+   _Sistem telah siap digunakan!_
+
+## 🔧 Konfigurasi
+
+Untuk konfigurasi sistem, Anda perlu mengelola file-file berikut:
+
+- [.env](./docker/.env): Menyimpan pengaturan dasar sistem, seperti `SVR_HTTP_PORT`, `MYSQL_PASSWORD`, dan
+  `MINIO_PASSWORD`.
+- [service_conf.yaml.template](./docker/service_conf.yaml.template): Mengonfigurasi aplikasi backend.
+- [docker-compose.yml](./docker/docker-compose.yml): Sistem ini bergantung pada [docker-compose.yml](./docker/docker-compose.yml) untuk memulai.
+
+Untuk memperbarui port HTTP default (80), buka [docker-compose.yml](./docker/docker-compose.yml) dan ubah `80:80`
+menjadi `<YOUR_SERVING_PORT>:80`.
+
+Pembaruan konfigurasi ini memerlukan reboot semua kontainer agar efektif:
+
+> ```bash
+> $ docker compose -f docker-compose.yml up -d
+> ```
+
+## 🔧 Membangun Docker Image
+
+Image ini berukuran sekitar 2 GB dan bergantung pada aplikasi LLM eksternal dan embedding.
+
+```bash
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .
+```
+
+## 🔨 Menjalankan Aplikasi dari untuk Pengembangan
+
+1. Instal `uv` dan `pre-commit`, atau lewati langkah ini jika sudah terinstal:
+
+   ```bash
+   pipx install uv pre-commit
+   ```
+2. Clone kode sumber dan instal dependensi Python:
+
+   ```bash
+   git clone https://github.com/infiniflow/ragflow.git
+   cd ragflow/
+   uv sync --python 3.10 # install RAGFlow dependent python modules
+   uv run download_deps.py
+   pre-commit install
+   ```
+3. Jalankan aplikasi yang diperlukan (MinIO, Elasticsearch, Redis, dan MySQL) menggunakan Docker Compose:
+
+   ```bash
+   docker compose -f docker/docker-compose-base.yml up -d
+   ```
+
+   Tambahkan baris berikut ke `/etc/hosts` untuk memetakan semua host yang ditentukan di **conf/service_conf.yaml** ke `127.0.0.1`:
+
+   ```
+   127.0.0.1       es01 infinity mysql minio redis sandbox-executor-manager
+   ```
+4. Jika Anda tidak dapat mengakses HuggingFace, atur variabel lingkungan `HF_ENDPOINT` untuk menggunakan situs mirror:
+
+   ```bash
+   export HF_ENDPOINT=https://hf-mirror.com
+   ```
+5. Jika sistem operasi Anda tidak memiliki jemalloc, instal sebagai berikut:
+
+   ```bash
+   # ubuntu
+   sudo apt-get install libjemalloc-dev
+   # centos
+   sudo yum install jemalloc
+   # mac
+   sudo brew install jemalloc
+   ```
+6. Jalankan aplikasi backend:
+
+   ```bash
+   source .venv/bin/activate
+   export PYTHONPATH=$(pwd)
+   bash docker/launch_backend_service.sh
+   ```
+7. Instal dependensi frontend:
+
+   ```bash
+   cd web
+   npm install
+   ```
+8. Jalankan aplikasi frontend:
+
+   ```bash
+   npm run dev
+   ```
+
+   _Output berikut menandakan bahwa sistem berhasil diluncurkan:_
+
+   ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
+9. Hentikan layanan front-end dan back-end RAGFlow setelah pengembangan selesai:
+
+   ```bash
+   pkill -f "ragflow_server.py|task_executor.py"
+   ```
+
+## 📚 Dokumentasi
+
+- [Quickstart](https://ragflow.io/docs/dev/)
+- [Configuration](https://ragflow.io/docs/dev/configurations)
+- [Release notes](https://ragflow.io/docs/dev/release_notes)
+- [User guides](https://ragflow.io/docs/dev/category/guides)
+- [Developer guides](https://ragflow.io/docs/dev/category/developers)
+- [References](https://ragflow.io/docs/dev/category/references)
+- [FAQs](https://ragflow.io/docs/dev/faq)
+
+## 📜 Roadmap
+
+Lihat [Roadmap RAGFlow 2025](https://github.com/infiniflow/ragflow/issues/4214)
+
+## 🏄 Komunitas
+
+- [Discord](https://discord.gg/NjYzJD3GM3)
+- [Twitter](https://twitter.com/infiniflowai)
+- [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
+
+## 🙌 Kontribusi
+
+RAGFlow berkembang melalui kolaborasi open-source. Dalam semangat ini, kami menerima kontribusi dari komunitas.
+Jika Anda ingin berpartisipasi, tinjau terlebih dahulu [Panduan Kontribusi](https://ragflow.io/docs/dev/contributing).

README_ja.md

@@ -1,62 +1,90 @@
 <div align="center">
 <a href="https://demo.ragflow.io/">
-<img src="web/src/assets/logo-with-text.png" width="350" alt="ragflow logo">
+<img src="web/src/assets/logo-with-text.svg" width="350" alt="ragflow logo">
 </a>
 </div>
 
 <p align="center">
-  <a href="./README.md">English</a> |
-  <a href="./README_zh.md">简体中文</a> |
-  <a href="./README_ja.md">日本語</a> |
-  <a href="./README_ko.md">한국어</a>
+  <a href="./README.md"><img alt="README in English" src="https://img.shields.io/badge/English-DFE0E5"></a>
+  <a href="./README_zh.md"><img alt="简体中文版自述文件" src="https://img.shields.io/badge/简体中文-DFE0E5"></a>
+  <a href="./README_tzh.md"><img alt="繁體中文版自述文件" src="https://img.shields.io/badge/繁體中文-DFE0E5"></a>
+  <a href="./README_ja.md"><img alt="日本語のREADME" src="https://img.shields.io/badge/日本語-DBEDFA"></a>
+  <a href="./README_ko.md"><img alt="한국어" src="https://img.shields.io/badge/한국어-DFE0E5"></a>
+  <a href="./README_id.md"><img alt="Bahasa Indonesia" src="https://img.shields.io/badge/Bahasa Indonesia-DFE0E5"></a>
+  <a href="./README_pt_br.md"><img alt="Português(Brasil)" src="https://img.shields.io/badge/Português(Brasil)-DFE0E5"></a>
 </p>
 
 <p align="center">
+    <a href="https://x.com/intent/follow?screen_name=infiniflowai" target="_blank">
+        <img src="https://img.shields.io/twitter/follow/infiniflow?logo=X&color=%20%23f5f5f5" alt="follow on X(Twitter)">
+    </a>
+    <a href="https://demo.ragflow.io" target="_blank">
+        <img alt="Static Badge" src="https://img.shields.io/badge/Online-Demo-4e6b99">
+    </a>
+    <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
+        <img src="https://img.shields.io/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.22.1">
+    </a>
     <a href="https://github.com/infiniflow/ragflow/releases/latest">
         <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Latest%20Release" alt="Latest Release">
     </a>
-    <a href="https://demo.ragflow.io" target="_blank">
-        <img alt="Static Badge" src="https://img.shields.io/badge/Online-Demo-4e6b99"></a>
-    <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
-        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.11.0-brightgreen"
-            alt="docker pull infiniflow/ragflow:v0.11.0"></a>
-      <a href="https://github.com/infiniflow/ragflow/blob/main/LICENSE">
-    <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?labelColor=d4eaf7&color=2e6cc4" alt="license">
-  </a>
+    <a href="https://github.com/infiniflow/ragflow/blob/main/LICENSE">
+        <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?labelColor=d4eaf7&color=2e6cc4" alt="license">
+    </a>
+    <a href="https://deepwiki.com/infiniflow/ragflow">
+        <img alt="Ask DeepWiki" src="https://deepwiki.com/badge.svg">
+    </a>
 </p>
 
 <h4 align="center">
   <a href="https://ragflow.io/docs/dev/">Document</a> |
-  <a href="https://github.com/infiniflow/ragflow/issues/162">Roadmap</a> |
+  <a href="https://github.com/infiniflow/ragflow/issues/4214">Roadmap</a> |
   <a href="https://twitter.com/infiniflowai">Twitter</a> |
-  <a href="https://discord.gg/4XxujFgUN7">Discord</a> |
+  <a href="https://discord.gg/NjYzJD3GM3">Discord</a> |
   <a href="https://demo.ragflow.io">Demo</a>
 </h4>
 
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/ragflow-octoverse.png" width="1200"/>
+</div>
+
+<div align="center">
+<a href="https://trendshift.io/repositories/9064" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9064" alt="infiniflow%2Fragflow | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
+</div>
+
 ## 💡 RAGFlow とは？
 
-[RAGFlow](https://ragflow.io/) は、深い文書理解に基づいたオープンソースの RAG (Retrieval-Augmented Generation) エンジンである。LLM（大規模言語モデル）を組み合わせることで、様々な複雑なフォーマットのデータから根拠のある引用に裏打ちされた、信頼できる質問応答機能を実現し、あらゆる規模のビジネスに適した RAG ワークフローを提供します。
+[RAGFlow](https://ragflow.io/) は、先進的なRAG（Retrieval-Augmented Generation）技術と Agent 機能を融合し、大規模言語モデル（LLM）に優れたコンテキスト層を構築する最先端のオープンソース RAG エンジンです。あらゆる規模の企業に対応可能な合理化された RAG ワークフローを提供し、統合型コンテキストエンジンと事前構築されたAgentテンプレートにより、開発者が複雑なデータを驚異的な効率性と精度で高精細なプロダクションレディAIシステムへ変換することを可能にします。
 
 ## 🎮 Demo
 
 デモをお試しください：[https://demo.ragflow.io](https://demo.ragflow.io)。
-<div align="center" style="margin-top:20px;margin-bottom:20px;">
-<img src="https://github.com/infiniflow/ragflow/assets/7248/2f6baa3e-1092-4f11-866d-36f6a9d075e5" width="1200"/>
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/b083d173-dadc-4ea9-bdeb-180d7df514eb" width="1200"/>
-</div>
 
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/chunking.gif" width="1200"/>
+<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/agentic-dark.gif" width="1200"/>
+</div>
 
 ## 🔥 最新情報
 
-- 2024-09-13 ナレッジベース Q&A の検索モードを追加しました。
-- 2024-09-09 エージェントに医療相談テンプレートを追加しました。
+- 2025-11-19 Gemini 3 Proをサポートしています
+- 2025-11-12 Confluence、S3、Notion、Discord、Google Drive からのデータ同期をサポートします。
+- 2025-10-23 ドキュメント解析方法として MinerU と Docling をサポートします。
+- 2025-10-15 オーケストレーションされたデータパイプラインのサポート。
+- 2025-08-08 OpenAI の最新 GPT-5 シリーズモデルをサポートします。
+- 2025-08-01 エージェントワークフローとMCPをサポート。
+- 2025-05-23 エージェントに Python/JS コードエグゼキュータコンポーネントを追加しました。
+- 2025-05-05 言語間クエリをサポートしました。
+- 2025-03-19 PDFまたはDOCXファイル内の画像を理解するために、多モーダルモデルを使用することをサポートします。
+- 2024-12-18 DeepDoc のドキュメント レイアウト分析モデルをアップグレードします。
 - 2024-08-22 RAG を介して SQL ステートメントへのテキストをサポートします。
-- 2024-08-02 [graphrag](https://github.com/microsoft/graphrag) からインスピレーションを得た GraphRAG とマインド マップをサポートします。
-- 2024-07-23 音声ファイルの解析をサポートしました。
-- 2024-07-08 [Graph](./agent/README.md) ベースのワークフローをサポート
-- 2024-06-27 Q&A 解析メソッドで Markdown と Docx をサポートし、Docx ファイルから画像を抽出し、Markdown ファイルからテーブルを抽出します。
-- 2024-05-23 より良いテキスト検索のために [RAPTOR](https://arxiv.org/html/2401.18059v1) をサポート。
 
+## 🎉 続きを楽しみに
+
+⭐️ リポジトリをスター登録して、エキサイティングな新機能やアップデートを最新の状態に保ちましょう！すべての新しいリリースに関する即時通知を受け取れます！ 🌟
+
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/user-attachments/assets/18c9707e-b8aa-4caf-a154-037089c105ba" width="1200"/>
+</div>
 
 ## 🌟 主な特徴
 
@@ -89,7 +117,7 @@
 ## 🔎 システム構成
 
 <div align="center" style="margin-top:20px;margin-bottom:20px;">
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/d6ac5664-c237-4200-a7c2-a4a00691b485" width="1000"/>
+<img src="https://github.com/user-attachments/assets/31b0dd6f-ca4f-445a-9457-70cb44a381b2" width="1000"/>
 </div>
 
 ## 🎬 初期設定
@@ -100,7 +128,10 @@
 - RAM >= 16 GB
 - Disk >= 50 GB
 - Docker >= 24.0.0 & Docker Compose >= v2.26.1
-  > ローカルマシン（Windows、Mac、または Linux）に Docker をインストールしていない場合は、[Docker Engine のインストール](https://docs.docker.com/engine/install/) を参照してください。
+- [gVisor](https://gvisor.dev/docs/user_guide/install/): RAGFlowのコード実行（サンドボックス）機能を利用する場合のみ必要です。
+
+> [!TIP]
+> ローカルマシン（Windows、Mac、または Linux）に Docker をインストールしていない場合は、[Docker Engine のインストール](https://docs.docker.com/engine/install/) を参照してください。
 
 ### 🚀 サーバーを起動
 
@@ -124,53 +155,72 @@
    > ```bash
    > vm.max_map_count=262144
    > ```
-
+   >
 2. リポジトリをクローンする:
 
    ```bash
    $ git clone https://github.com/infiniflow/ragflow.git
    ```
-
 3. ビルド済みの Docker イメージをビルドし、サーバーを起動する:
 
-   ```bash
+> [!CAUTION]
+> 現在、公式に提供されているすべての Docker イメージは x86 アーキテクチャ向けにビルドされており、ARM64 用の Docker イメージは提供されていません。
+> ARM64 アーキテクチャのオペレーティングシステムを使用している場合は、[このドキュメント](https://ragflow.io/docs/dev/build_docker_image)を参照して Docker イメージを自分でビルドしてください。
+
+> 以下のコマンドは、RAGFlow Docker イメージの v0.22.1 エディションをダウンロードします。異なる RAGFlow エディションの説明については、以下の表を参照してください。v0.22.1 とは異なるエディションをダウンロードするには、docker/.env ファイルの RAGFLOW_IMAGE 変数を適宜更新し、docker compose を使用してサーバーを起動してください。
+
+```bash
    $ cd ragflow/docker
-   $ chmod +x ./entrypoint.sh
-   $ docker compose up -d
-   ```
 
-   > 上記のコマンドを実行すると、RAGFlowの開発版dockerイメージが自動的にダウンロードされます。 特定のバージョンのDockerイメージをダウンロードして実行したい場合は、docker/.envファイルのRAGFLOW_VERSION変数を見つけて、対応するバージョンに変更してください。 例えば、RAGFLOW_VERSION=v0.11.0として、上記のコマンドを実行してください。
+   # git checkout v0.22.1
+   # 任意: 安定版タグを利用 (一覧: https://github.com/infiniflow/ragflow/releases)
+   # この手順は、コード内の entrypoint.sh ファイルが Docker イメージのバージョンと一致していることを確認します。
 
-   > コアイメージのサイズは約 9 GB で、ロードに時間がかかる場合があります。
+   # Use CPU for DeepDoc tasks:
+   $ docker compose -f docker-compose.yml up -d
 
-4. サーバーを立ち上げた後、サーバーの状態を確認する:
+   # To use GPU to accelerate DeepDoc tasks:
+   # sed -i '1i DEVICE=gpu' .env
+   # docker compose -f docker-compose.yml up -d
+```
 
+> 注意：`v0.22.0` より前のバージョンでは、embedding モデルを含むイメージと、embedding モデルを含まない slim イメージの両方を提供していました。詳細は以下の通りです：
+
+| RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
+| ----------------- | --------------- | --------------------- | ------------------------ |
+| v0.21.1           | &approx;9       | ✔️                    | Stable release           |
+| v0.21.1-slim      | &approx;2       | ❌                    | Stable release           |
+
+> `v0.22.0` 以降、当プロジェクトでは slim エディションのみを提供し、イメージタグに **-slim** サフィックスを付けなくなりました。
+
+   1. サーバーを立ち上げた後、サーバーの状態を確認する:   
+   
    ```bash
-   $ docker logs -f ragflow-server
+   $ docker logs -f docker-ragflow-cpu-1
    ```
 
    _以下の出力は、システムが正常に起動したことを確認するものです:_
 
    ```bash
-       ____                 ______ __
-      / __ \ ____ _ ____ _ / ____// /____  _      __
-     / /_/ // __ `// __ `// /_   / // __ \| | /| / /
-    / _, _// /_/ // /_/ // __/  / // /_/ /| |/ |/ /
-   /_/ |_| \__,_/ \__, //_/    /_/ \____/ |__/|__/
-                 /____/
+        ____   ___    ______ ______ __
+       / __ \ /   |  / ____// ____// /____  _      __
+      / /_/ // /| | / / __ / /_   / // __ \| | /| / /
+     / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ /
+    /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/
 
     * Running on all addresses (0.0.0.0)
-    * Running on http://127.0.0.1:9380
-    * Running on http://x.x.x.x:9380
-    INFO:werkzeug:Press CTRL+C to quit
    ```
-   > もし確認ステップをスキップして直接 RAGFlow にログインした場合、その時点で RAGFlow が完全に初期化されていない可能性があるため、ブラウザーがネットワーク異常エラーを表示するかもしれません。
 
-5. ウェブブラウザで、プロンプトに従ってサーバーの IP アドレスを入力し、RAGFlow にログインします。
+   > もし確認ステップをスキップして直接 RAGFlow にログインした場合、その時点で RAGFlow が完全に初期化されていない可能性があるため、ブラウザーがネットワーク異常エラーを表示するかもしれません。
+   >
+2. ウェブブラウザで、プロンプトに従ってサーバーの IP アドレスを入力し、RAGFlow にログインします。
+
    > デフォルトの設定を使用する場合、デフォルトの HTTP サービングポート `80` は省略できるので、与えられたシナリオでは、`http://IP_OF_YOUR_MACHINE`（ポート番号は省略）だけを入力すればよい。
-6. [service_conf.yaml](./docker/service_conf.yaml) で、`user_default_llm` で希望の LLM ファクトリを選択し、`API_KEY` フィールドを対応する API キーで更新する。
+   >
+3. [service_conf.yaml.template](./docker/service_conf.yaml.template) で、`user_default_llm` で希望の LLM ファクトリを選択し、`API_KEY` フィールドを対応する API キーで更新する。
 
    > 詳しくは [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) を参照してください。
+   >
 
    _これで初期設定完了！ショーの開幕です！_
 
@@ -179,111 +229,143 @@
 システムコンフィグに関しては、以下のファイルを管理する必要がある:
 
 - [.env](./docker/.env): `SVR_HTTP_PORT`、`MYSQL_PASSWORD`、`MINIO_PASSWORD` などのシステムの基本設定を保持する。
-- [service_conf.yaml](./docker/service_conf.yaml): バックエンドのサービスを設定します。
+- [service_conf.yaml.template](./docker/service_conf.yaml.template): バックエンドのサービスを設定します。
 - [docker-compose.yml](./docker/docker-compose.yml): システムの起動は [docker-compose.yml](./docker/docker-compose.yml) に依存している。
 
-[.env](./docker/.env) ファイルの変更が [service_conf.yaml](./docker/service_conf.yaml) ファイルの内容と一致していることを確認する必要があります。
+[.env](./docker/.env) ファイルの変更が [service_conf.yaml.template](./docker/service_conf.yaml.template) ファイルの内容と一致していることを確認する必要があります。
 
-> [./docker/README](./docker/README.md) ファイルは環境設定とサービスコンフィグの詳細な説明を提供し、[./docker/README](./docker/README.md) ファイルに記載されている全ての環境設定が [service_conf.yaml](./docker/service_conf.yaml) ファイルの対応するコンフィグと一致していることを確認することが義務付けられています。
+> [./docker/README](./docker/README.md) ファイル ./docker/README には、service_conf.yaml.template ファイルで ${ENV_VARS} として使用できる環境設定とサービス構成の詳細な説明が含まれています。
 
 デフォルトの HTTP サービングポート(80)を更新するには、[docker-compose.yml](./docker/docker-compose.yml) にアクセスして、`80:80` を `<YOUR_SERVING_PORT>:80` に変更します。
 
 > すべてのシステム設定のアップデートを有効にするには、システムの再起動が必要です:
 >
 > ```bash
-> $ docker-compose up -d
+> $ docker compose -f docker-compose.yml up -d
 > ```
 
-## 🛠️ ソースからビルドする
+### Elasticsearch から Infinity にドキュメントエンジンを切り替えます
 
-ソースからDockerイメージをビルドするには:
+RAGFlow はデフォルトで Elasticsearch を使用して全文とベクトルを保存します。［Infinity］に切り替え（https://github.com/infiniflow/infinity/)、次の手順に従います。
+
+1. 実行中のすべてのコンテナを停止するには：
+
+   ```bash
+   $ docker compose -f docker/docker-compose.yml down -v
+   ```
+
+   Note: `-v` は docker コンテナのボリュームを削除し、既存のデータをクリアします。
+2. **docker/.env** の「DOC \_ ENGINE」を「infinity」に設定します。
+3. 起動コンテナ：
+
+   ```bash
+   $ docker compose -f docker-compose.yml up -d
+   ```
+
+   > [!WARNING]
+   > Linux/arm64 マシンでの Infinity への切り替えは正式にサポートされていません。
+   >
+
+## 🔧 ソースコードで Docker イメージを作成
+
+この Docker イメージのサイズは約 1GB で、外部の大モデルと埋め込みサービスに依存しています。
 
 ```bash
-$ git clone https://github.com/infiniflow/ragflow.git
-$ cd ragflow/
-$ docker build -t infiniflow/ragflow:v0.11.0 .
-$ cd ragflow/docker
-$ chmod +x ./entrypoint.sh
-$ docker compose up -d
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .
 ```
 
-## 🛠️ ソースコードからサービスを起動する方法
+## 🔨 ソースコードからサービスを起動する方法
 
-ソースコードからサービスを起動する場合は、以下の手順に従ってください:
+1. `uv` と `pre-commit` をインストールする。すでにインストールされている場合は、このステップをスキップしてください:
 
-1. リポジトリをクローンします
-```bash
-$ git clone https://github.com/infiniflow/ragflow.git
-$ cd ragflow/
-```
+   ```bash
+   pipx install uv pre-commit
+   ```
+2. ソースコードをクローンし、Python の依存関係をインストールする:
 
-2. 仮想環境を作成します（AnacondaまたはMinicondaがインストールされていることを確認してください）
-```bash
-$ conda create -n ragflow python=3.11.0
-$ conda activate ragflow
-$ pip install -r requirements.txt
-```
-CUDAのバージョンが12.0以上の場合、以下の追加コマンドを実行してください：
-```bash
-$ pip uninstall -y onnxruntime-gpu
-$ pip install onnxruntime-gpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/
-```
+   ```bash
+   git clone https://github.com/infiniflow/ragflow.git
+   cd ragflow/
+   uv sync --python 3.10 # install RAGFlow dependent python modules
+   uv run download_deps.py
+   pre-commit install
+   ```
+3. Docker Compose を使用して依存サービス（MinIO、Elasticsearch、Redis、MySQL）を起動する:
 
-3. エントリースクリプトをコピーし、環境変数を設定します
-```bash
-$ cp docker/entrypoint.sh .
-$ vi entrypoint.sh
-```
-以下のコマンドで Python のパスとragflowプロジェクトのパスを取得します：
-```bash
-$ which python
-$ pwd
-```
+   ```bash
+   docker compose -f docker/docker-compose-base.yml up -d
+   ```
 
-`which python` の出力を `PY` の値として、`pwd` の出力を `PYTHONPATH` の値として設定します。
+   `/etc/hosts` に以下の行を追加して、**conf/service_conf.yaml** に指定されたすべてのホストを `127.0.0.1` に解決します:
 
-`LD_LIBRARY_PATH` が既に設定されている場合は、コメントアウトできます。
+   ```
+   127.0.0.1       es01 infinity mysql minio redis sandbox-executor-manager
+   ```
+4. HuggingFace にアクセスできない場合は、`HF_ENDPOINT` 環境変数を設定してミラーサイトを使用してください:
 
-```bash
-# 実際の状況に応じて設定を調整してください。以下の二つの export は新たに追加された設定です
-PY=${PY}
-export PYTHONPATH=${PYTHONPATH}
-# オプション：Hugging Face ミラーを追加
-export HF_ENDPOINT=https://hf-mirror.com
-```
+   ```bash
+   export HF_ENDPOINT=https://hf-mirror.com
+   ```
+5. オペレーティングシステムにjemallocがない場合は、次のようにインストールします:
 
-4. 基本サービスを起動します
-```bash
-$ cd docker
-$ docker compose -f docker-compose-base.yml up -d 
-```
+   ```bash
+   # ubuntu
+   sudo apt-get install libjemalloc-dev
+   # centos
+   sudo yum install jemalloc
+   # mac
+   sudo brew install jemalloc
+   ```
+6. バックエンドサービスを起動する:
 
-5. 設定ファイルを確認します
-**docker/.env** 内の設定が**conf/service_conf.yaml**内の設定と一致していることを確認してください。**service_conf.yaml**内の関連サービスのIPアドレスとポートは、ローカルマシンのIPアドレスとコンテナが公開するポートに変更する必要があります。
+   ```bash
+   source .venv/bin/activate
+   export PYTHONPATH=$(pwd)
+   bash docker/launch_backend_service.sh
+   ```
+7. フロントエンドの依存関係をインストールする:
 
-6. サービスを起動します
-```bash
-$ chmod +x ./entrypoint.sh
-$ bash ./entrypoint.sh
-```
+   ```bash
+   cd web
+   npm install
+   ```
+8. フロントエンドサービスを起動する:
+
+   ```bash
+   npm run dev
+   ```
+
+   _以下の画面で、システムが正常に起動したことを示します:_
+
+   ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
+9. 開発が完了したら、RAGFlow のフロントエンド サービスとバックエンド サービスを停止します:
+
+   ```bash
+   pkill -f "ragflow_server.py|task_executor.py"
+   ```
 
 ## 📚 ドキュメンテーション
 
 - [Quickstart](https://ragflow.io/docs/dev/)
-- [User guide](https://ragflow.io/docs/dev/category/user-guides)
+- [Configuration](https://ragflow.io/docs/dev/configurations)
+- [Release notes](https://ragflow.io/docs/dev/release_notes)
+- [User guides](https://ragflow.io/docs/dev/category/guides)
+- [Developer guides](https://ragflow.io/docs/dev/category/developers)
 - [References](https://ragflow.io/docs/dev/category/references)
-- [FAQ](https://ragflow.io/docs/dev/faq)
+- [FAQs](https://ragflow.io/docs/dev/faq)
 
 ## 📜 ロードマップ
 
-[RAGFlow ロードマップ 2024](https://github.com/infiniflow/ragflow/issues/162) を参照
+[RAGFlow ロードマップ 2025](https://github.com/infiniflow/ragflow/issues/4214) を参照
 
 ## 🏄 コミュニティ
 
-- [Discord](https://discord.gg/4XxujFgUN7)
+- [Discord](https://discord.gg/NjYzJD3GM3)
 - [Twitter](https://twitter.com/infiniflowai)
 - [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
 
 ## 🙌 コントリビュート
 
-RAGFlow はオープンソースのコラボレーションによって発展してきました。この精神に基づき、私たちはコミュニティからの多様なコントリビュートを受け入れています。 参加を希望される方は、まず [コントリビューションガイド](./docs/references/CONTRIBUTING.md)をご覧ください。
+RAGFlow はオープンソースのコラボレーションによって発展してきました。この精神に基づき、私たちはコミュニティからの多様なコントリビュートを受け入れています。 参加を希望される方は、まず [コントリビューションガイド](https://ragflow.io/docs/dev/contributing)をご覧ください。

README_ko.md

@@ -1,116 +1,143 @@
 <div align="center">
 <a href="https://demo.ragflow.io/">
-<img src="web/src/assets/logo-with-text.png" width="520" alt="ragflow logo">
+<img src="web/src/assets/logo-with-text.svg" width="520" alt="ragflow logo">
 </a>
 </div>
 
 <p align="center">
-  <a href="./README.md">English</a> |
-  <a href="./README_zh.md">简体中文</a> |
-  <a href="./README_ja.md">日本語</a> |
-  <a href="./README_ko.md">한국어</a> |
+  <a href="./README.md"><img alt="README in English" src="https://img.shields.io/badge/English-DFE0E5"></a>
+  <a href="./README_zh.md"><img alt="简体中文版自述文件" src="https://img.shields.io/badge/简体中文-DFE0E5"></a>
+  <a href="./README_tzh.md"><img alt="繁體版中文自述文件" src="https://img.shields.io/badge/繁體中文-DFE0E5"></a>
+  <a href="./README_ja.md"><img alt="日本語のREADME" src="https://img.shields.io/badge/日本語-DFE0E5"></a>
+  <a href="./README_ko.md"><img alt="한국어" src="https://img.shields.io/badge/한국어-DBEDFA"></a>
+  <a href="./README_id.md"><img alt="Bahasa Indonesia" src="https://img.shields.io/badge/Bahasa Indonesia-DFE0E5"></a>
+  <a href="./README_pt_br.md"><img alt="Português(Brasil)" src="https://img.shields.io/badge/Português(Brasil)-DFE0E5"></a>
 </p>
 
 <p align="center">
+    <a href="https://x.com/intent/follow?screen_name=infiniflowai" target="_blank">
+        <img src="https://img.shields.io/twitter/follow/infiniflow?logo=X&color=%20%23f5f5f5" alt="follow on X(Twitter)">
+    </a>
+    <a href="https://demo.ragflow.io" target="_blank">
+        <img alt="Static Badge" src="https://img.shields.io/badge/Online-Demo-4e6b99">
+    </a>
+    <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
+        <img src="https://img.shields.io/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.22.1">
+    </a>
     <a href="https://github.com/infiniflow/ragflow/releases/latest">
         <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Latest%20Release" alt="Latest Release">
     </a>
-    <a href="https://demo.ragflow.io" target="_blank">
-        <img alt="Static Badge" src="https://img.shields.io/badge/Online-Demo-4e6b99"></a>
-    <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
-        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.11.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.11.0"></a>
     <a href="https://github.com/infiniflow/ragflow/blob/main/LICENSE">
-    <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?labelColor=d4eaf7&color=2e6cc4" alt="license">
-  </a>
+        <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?labelColor=d4eaf7&color=2e6cc4" alt="license">
+    </a>
+    <a href="https://deepwiki.com/infiniflow/ragflow">
+        <img alt="Ask DeepWiki" src="https://deepwiki.com/badge.svg">
+    </a>
 </p>
 
 <h4 align="center">
   <a href="https://ragflow.io/docs/dev/">Document</a> |
-  <a href="https://github.com/infiniflow/ragflow/issues/162">Roadmap</a> |
+  <a href="https://github.com/infiniflow/ragflow/issues/4214">Roadmap</a> |
   <a href="https://twitter.com/infiniflowai">Twitter</a> |
-  <a href="https://discord.gg/4XxujFgUN7">Discord</a> |
+  <a href="https://discord.gg/NjYzJD3GM3">Discord</a> |
   <a href="https://demo.ragflow.io">Demo</a>
 </h4>
 
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/ragflow-octoverse.png" width="1200"/>
+</div>
+
+<div align="center">
+<a href="https://trendshift.io/repositories/9064" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9064" alt="infiniflow%2Fragflow | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
+</div>
+
 
 ## 💡 RAGFlow란?
 
-[RAGFlow](https://ragflow.io/)는 심층 문서 이해에 기반한 오픈소스 RAG (Retrieval-Augmented Generation) 엔진입니다. 이 엔진은 대규모 언어 모델(LLM)과 결합하여 정확한 질문 응답 기능을 제공하며, 다양한 복잡한 형식의 데이터에서 신뢰할 수 있는 출처를 바탕으로 한 인용을 통해 이를 뒷받침합니다. RAGFlow는 규모에 상관없이 모든 기업에 최적화된 RAG 워크플로우를 제공합니다.
-
-
+[RAGFlow](https://ragflow.io/) 는 최첨단 RAG(Retrieval-Augmented Generation)와 Agent 기능을 융합하여 대규모 언어 모델(LLM)을 위한 우수한 컨텍스트 계층을 생성하는 선도적인 오픈소스 RAG 엔진입니다. 모든 규모의 기업에 적용 가능한 효율적인 RAG 워크플로를 제공하며, 통합 컨텍스트 엔진과 사전 구축된 Agent 템플릿을 통해 개발자들이 복잡한 데이터를 예외적인 효율성과 정밀도로 고급 구현도의 프로덕션 준비 완료 AI 시스템으로 변환할 수 있도록 지원합니다.
 
 ## 🎮 데모
-데모를 [https://demo.ragflow.io](https://demo.ragflow.io)에서 실행해 보세요.
-<div align="center" style="margin-top:20px;margin-bottom:20px;">
-<img src="https://github.com/infiniflow/ragflow/assets/7248/2f6baa3e-1092-4f11-866d-36f6a9d075e5" width="1200"/>
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/b083d173-dadc-4ea9-bdeb-180d7df514eb" width="1200"/>
-</div>
 
+데모를 [https://demo.ragflow.io](https://demo.ragflow.io)에서 실행해 보세요.
+
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/chunking.gif" width="1200"/>
+<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/agentic-dark.gif" width="1200"/>
+</div>
 
 ## 🔥 업데이트
 
-- 2024-09-13 지식베이스 Q&A 검색 모드를 추가합니다.
-  
-- 2024-09-09 Agent에 의료상담 템플릿을 추가하였습니다.
-  
+- 2025-11-19 Gemini 3 Pro를 지원합니다.
+- 2025-11-12 Confluence, S3, Notion, Discord, Google Drive에서 데이터 동기화를 지원합니다.
+- 2025-10-23 문서 파싱 방법으로 MinerU 및 Docling을 지원합니다.
+- 2025-10-15 조정된 데이터 파이프라인 지원.
+- 2025-08-08 OpenAI의 최신 GPT-5 시리즈 모델을 지원합니다.
+- 2025-08-01 에이전트 워크플로우와 MCP를 지원합니다.
+- 2025-05-23 Agent에 Python/JS 코드 실행기 구성 요소를 추가합니다.
+- 2025-05-05 언어 간 쿼리를 지원합니다.
+- 2025-03-19 PDF 또는 DOCX 파일 내의 이미지를 이해하기 위해 다중 모드 모델을 사용하는 것을 지원합니다.
+- 2024-12-18 DeepDoc의 문서 레이아웃 분석 모델 업그레이드.
 - 2024-08-22 RAG를 통해 SQL 문에 텍스트를 지원합니다.
-  
-- 2024-08-02: [graphrag](https://github.com/microsoft/graphrag)와 마인드맵에서 영감을 받은 GraphRAG를 지원합니다.
 
-- 2024-07-23: 오디오 파일 분석을 지원합니다.
-
-- 2024-07-08: [Graph](./agent/README.md)를 기반으로 한 워크플로우를 지원합니다.
-
-- 2024-06-27 Q&A 구문 분석 방식에서 Markdown 및 Docx를 지원하고, Docx 파일에서 이미지 추출, Markdown 파일에서 테이블 추출을 지원합니다.
-
-- 2024-05-23: 더 나은 텍스트 검색을 위해 [RAPTOR](https://arxiv.org/html/2401.18059v1)를 지원합니다.
+## 🎉 계속 지켜봐 주세요
 
+⭐️우리의 저장소를 즐겨찾기에 등록하여 흥미로운 새로운 기능과 업데이트를 최신 상태로 유지하세요! 모든 새로운 릴리스에 대한 즉시 알림을 받으세요! 🌟
 
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/user-attachments/assets/18c9707e-b8aa-4caf-a154-037089c105ba" width="1200"/>
+</div>
 
 ## 🌟 주요 기능
 
 ### 🍭 **"Quality in, quality out"**
+
 - [심층 문서 이해](./deepdoc/README.md)를 기반으로 복잡한 형식의 비정형 데이터에서 지식을 추출합니다.
 - 문자 그대로 무한한 토큰에서 "데이터 속의 바늘"을 찾아냅니다.
 
 ### 🍱 **템플릿 기반의 chunking**
+
 - 똑똑하고 설명 가능한 방식.
 - 다양한 템플릿 옵션을 제공합니다.
 
-
 ### 🌱 **할루시네이션을 줄인 신뢰할 수 있는 인용**
+
 - 텍스트 청킹을 시각화하여 사용자가 개입할 수 있도록 합니다.
 - 중요한 참고 자료와 추적 가능한 인용을 빠르게 확인하여 신뢰할 수 있는 답변을 지원합니다.
 
-
 ### 🍔 **다른 종류의 데이터 소스와의 호환성**
+
 - 워드, 슬라이드, 엑셀, 텍스트 파일, 이미지, 스캔본, 구조화된 데이터, 웹 페이지 등을 지원합니다.
 
 ### 🛀 **자동화되고 손쉬운 RAG 워크플로우**
+
 - 개인 및 대규모 비즈니스에 맞춘 효율적인 RAG 오케스트레이션.
 - 구성 가능한 LLM 및 임베딩 모델.
 - 다중 검색과 결합된 re-ranking.
 - 비즈니스와 원활하게 통합할 수 있는 직관적인 API.
 
-
 ## 🔎 시스템 아키텍처
 
 <div align="center" style="margin-top:20px;margin-bottom:20px;">
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/d6ac5664-c237-4200-a7c2-a4a00691b485" width="1000"/>
+<img src="https://github.com/user-attachments/assets/31b0dd6f-ca4f-445a-9457-70cb44a381b2" width="1000"/>
 </div>
 
 ## 🎬 시작하기
+
 ### 📝 사전 준비 사항
+
 - CPU >= 4 cores
 - RAM >= 16 GB
 - Disk >= 50 GB
 - Docker >= 24.0.0 & Docker Compose >= v2.26.1
-  > 로컬 머신(Windows, Mac, Linux)에 Docker가 설치되지 않은 경우, [Docker 엔진 설치]((https://docs.docker.com/engine/install/))를 참조하세요.
+- [gVisor](https://gvisor.dev/docs/user_guide/install/): RAGFlow의 코드 실행기(샌드박스) 기능을 사용하려는 경우에만 필요합니다.
 
+> [!TIP]
+> 로컬 머신(Windows, Mac, Linux)에 Docker가 설치되지 않은 경우, [Docker 엔진 설치](<(https://docs.docker.com/engine/install/)>)를 참조하세요.
 
 ### 🚀 서버 시작하기
 
 1. `vm.max_map_count`가 262144 이상인지 확인하세요:
+
    > `vm.max_map_count`의 값을 아래 명령어를 통해 확인하세요:
    >
    > ```bash
@@ -138,42 +165,60 @@
 
 3. 미리 빌드된 Docker 이미지를 생성하고 서버를 시작하세요:
 
-   > 다음 명령어를 실행하면 *dev* 버전의 RAGFlow Docker 이미지가 자동으로 다운로드됩니다. 특정 Docker 버전을 다운로드하고 실행하려면, **docker/.env** 파일에서 `RAGFLOW_VERSION`을 원하는 버전으로 업데이트한 후, 예를 들어 `RAGFLOW_VERSION=v0.11.0`로 업데이트 한 뒤, 다음 명령어를 실행하세요.
+> [!CAUTION]
+> 모든 Docker 이미지는 x86 플랫폼을 위해 빌드되었습니다. 우리는 현재 ARM64 플랫폼을 위한 Docker 이미지를 제공하지 않습니다.
+> ARM64 플랫폼을 사용 중이라면, [시스템과 호환되는 Docker 이미지를 빌드하려면 이 가이드를 사용해 주세요](https://ragflow.io/docs/dev/build_docker_image).
+
+   > 아래 명령어는 RAGFlow Docker 이미지의 v0.22.1 버전을 다운로드합니다. 다양한 RAGFlow 버전에 대한 설명은 다음 표를 참조하십시오. v0.22.1과 다른 RAGFlow 버전을 다운로드하려면, docker/.env 파일에서 RAGFLOW_IMAGE 변수를 적절히 업데이트한 후 docker compose를 사용하여 서버를 시작하십시오.
+
    ```bash
    $ cd ragflow/docker
-   $ chmod +x ./entrypoint.sh
-   $ docker compose up -d
-   ```
    
-   > 기본 이미지는 약 9GB 크기이며 로드하는 데 시간이 걸릴 수 있습니다.
+   # git checkout v0.22.1
+   # Optional: use a stable tag (see releases: https://github.com/infiniflow/ragflow/releases)
+   # 이 단계는 코드의 entrypoint.sh 파일이 Docker 이미지 버전과 일치하도록 보장합니다.
 
+   # Use CPU for DeepDoc tasks:
+   $ docker compose -f docker-compose.yml up -d
 
-4. 서버가 시작된 후 서버 상태를 확인하세요:
+   # To use GPU to accelerate DeepDoc tasks:
+   # sed -i '1i DEVICE=gpu' .env
+   # docker compose -f docker-compose.yml up -d
+```
+
+> 참고: `v0.22.0` 이전 버전에서는 embedding 모델이 포함된 이미지와 embedding 모델이 포함되지 않은 slim 이미지를 모두 제공했습니다. 자세한 내용은 다음과 같습니다:
+
+| RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
+| ----------------- | --------------- | --------------------- | ------------------------ |
+| v0.21.1           | &approx;9       | ✔️                    | Stable release           |
+| v0.21.1-slim      | &approx;2       | ❌                    | Stable release           |
+
+> `v0.22.0`부터는 slim 에디션만 배포하며 이미지 태그에 **-slim** 접미사를 더 이상 붙이지 않습니다.
+
+1. 서버가 시작된 후 서버 상태를 확인하세요:
 
    ```bash
-   $ docker logs -f ragflow-server
+   $ docker logs -f docker-ragflow-cpu-1
    ```
 
    _다음 출력 결과로 시스템이 성공적으로 시작되었음을 확인합니다:_
 
    ```bash
-       ____                 ______ __
-      / __ \ ____ _ ____ _ / ____// /____  _      __
-     / /_/ // __ `// __ `// /_   / // __ \| | /| / /
-    / _, _// /_/ // /_/ // __/  / // /_/ /| |/ |/ /
-   /_/ |_| \__,_/ \__, //_/    /_/ \____/ |__/|__/
-                 /____/
+        ____   ___    ______ ______ __
+       / __ \ /   |  / ____// ____// /____  _      __
+      / /_/ // /| | / / __ / /_   / // __ \| | /| / /
+     / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ /
+    /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/
 
     * Running on all addresses (0.0.0.0)
-    * Running on http://127.0.0.1:9380
-    * Running on http://x.x.x.x:9380
-    INFO:werkzeug:Press CTRL+C to quit
    ```
-   > 만약 확인 단계를 건너뛰고 바로 RAGFlow에 로그인하면, RAGFlow가 완전히 초기화되지 않았기 때문에 브라우저에서 `network abnormal` 오류가 발생할 수 있습니다.
 
-5. 웹 브라우저에 서버의 IP 주소를 입력하고 RAGFlow에 로그인하세요.
+   > 만약 확인 단계를 건너뛰고 바로 RAGFlow에 로그인하면, RAGFlow가 완전히 초기화되지 않았기 때문에 브라우저에서 `network anormal` 오류가 발생할 수 있습니다.
+
+2. 웹 브라우저에 서버의 IP 주소를 입력하고 RAGFlow에 로그인하세요.
    > 기본 설정을 사용할 경우, `http://IP_OF_YOUR_MACHINE`만 입력하면 됩니다 (포트 번호는 제외). 기본 HTTP 서비스 포트 `80`은 기본 구성으로 사용할 때 생략할 수 있습니다.
-6. [service_conf.yaml](./docker/service_conf.yaml) 파일에서 원하는 LLM 팩토리를 `user_default_llm`에 선택하고, `API_KEY` 필드를 해당 API 키로 업데이트하세요.
+3. [service_conf.yaml.template](./docker/service_conf.yaml.template) 파일에서 원하는 LLM 팩토리를 `user_default_llm`에 선택하고, `API_KEY` 필드를 해당 API 키로 업데이트하세요.
+
    > 자세한 내용은 [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup)를 참조하세요.
 
    _이제 쇼가 시작됩니다!_
@@ -183,143 +228,148 @@
 시스템 설정과 관련하여 다음 파일들을 관리해야 합니다:
 
 - [.env](./docker/.env): `SVR_HTTP_PORT`, `MYSQL_PASSWORD`, `MINIO_PASSWORD`와 같은 시스템의 기본 설정을 포함합니다.
-- [service_conf.yaml](./docker/service_conf.yaml): 백엔드 서비스를 구성합니다.
+- [service_conf.yaml.template](./docker/service_conf.yaml.template): 백엔드 서비스를 구성합니다.
 - [docker-compose.yml](./docker/docker-compose.yml): 시스템은 [docker-compose.yml](./docker/docker-compose.yml)을 사용하여 시작됩니다.
 
-[.env](./docker/.env) 파일의 변경 사항이 [service_conf.yaml](./docker/service_conf.yaml) 파일의 내용과 일치하도록 해야 합니다.
+[.env](./docker/.env) 파일의 변경 사항이 [service_conf.yaml.template](./docker/service_conf.yaml.template) 파일의 내용과 일치하도록 해야 합니다.
 
-> [./docker/README](./docker/README.md) 파일에는 환경 설정과 서비스 구성에 대한 자세한 설명이 있으며, [./docker/README](./docker/README.md) 파일에 나열된 모든 환경 설정이 [service_conf.yaml](./docker/service_conf.yaml) 파일의 해당 구성과 일치하도록 해야 합니다.
+> [./docker/README](./docker/README.md) 파일 ./docker/README은 service_conf.yaml.template 파일에서 ${ENV_VARS}로 사용할 수 있는 환경 설정과 서비스 구성에 대한 자세한 설명을 제공합니다.
 
 기본 HTTP 서비스 포트(80)를 업데이트하려면 [docker-compose.yml](./docker/docker-compose.yml) 파일에서 `80:80`을 `<YOUR_SERVING_PORT>:80`으로 변경하세요.
 
 > 모든 시스템 구성 업데이트는 적용되기 위해 시스템 재부팅이 필요합니다.
 >
 > ```bash
-> $ docker-compose up -d
+> $ docker compose -f docker-compose.yml up -d
 > ```
 
-## 🛠️ 소스에서 빌드하기
+### Elasticsearch 에서 Infinity 로 문서 엔진 전환
 
-Docker 이미지를 소스에서 빌드하려면:
+RAGFlow 는 기본적으로 Elasticsearch 를 사용하여 전체 텍스트 및 벡터를 저장합니다. [Infinity]로 전환(https://github.com/infiniflow/infinity/), 다음 절차를 따르십시오.
+
+1. 실행 중인 모든 컨테이너를 중지합니다.
+   ```bash
+   $docker compose-f docker/docker-compose.yml down -v
+   ```
+   Note: `-v` 는 docker 컨테이너의 볼륨을 삭제하고 기존 데이터를 지우며, 이 작업은 컨테이너를 중지하는 것과 동일합니다.
+2. **docker/.env**의 "DOC_ENGINE" 을 "infinity" 로 설정합니다.
+3. 컨테이너 부팅:
+   ```bash
+   $docker compose-f docker/docker-compose.yml up -d
+   ```
+   > [!WARNING]
+   > Linux/arm64 시스템에서 Infinity로 전환하는 것은 공식적으로 지원되지 않습니다.
+
+## 🔧 소스 코드로 Docker 이미지를 컴파일합니다
+
+이 Docker 이미지의 크기는 약 1GB이며, 외부 대형 모델과 임베딩 서비스에 의존합니다.
 
 ```bash
-$ git clone https://github.com/infiniflow/ragflow.git
-$ cd ragflow/
-$ docker build -t infiniflow/ragflow:dev .
-$ cd ragflow/docker
-$ chmod +x ./entrypoint.sh
-$ docker compose up -d
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .
 ```
 
+## 🔨 소스 코드로 서비스를 시작합니다.
 
-## 🛠️ 소스에서 서비스 시작하기
-
-서비스를 소스에서 시작하려면:
-
-1. 레포지토리를 클론하세요: 
+1. `uv` 와 `pre-commit` 을 설치하거나, 이미 설치된 경우 이 단계를 건너뜁니다:
 
    ```bash
-   $ git clone https://github.com/infiniflow/ragflow.git
-   $ cd ragflow/
+   pipx install uv pre-commit
    ```
 
-2. 가상 환경을 생성하고, Anaconda 또는 Miniconda가 설치되어 있는지 확인하세요:
-   ```bash
-   $ conda create -n ragflow python=3.11.0
-   $ conda activate ragflow
-   $ pip install -r requirements.txt
-   ```
-   
-   ```bash
-   # CUDA 버전이 12.0보다 높은 경우, 다음 명령어를 추가로 실행하세요:
-   $ pip uninstall -y onnxruntime-gpu
-   $ pip install onnxruntime-gpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/
-   ```
-
-3. 진입 스크립트를 복사하고 환경 변수를 설정하세요:
-   ```bash
-   # 파이썬 경로를 받아옵니다:
-   $ which python
-   # RAGFlow 프로젝트 경로를 받아옵니다:
-   $ pwd
-   ```
-   
-   ```bash
-   $ cp docker/entrypoint.sh .
-   $ vi entrypoint.sh
-   ```
+2. 소스 코드를 클론하고 Python 의존성을 설치합니다:
+
+   ```bash
+   git clone https://github.com/infiniflow/ragflow.git
+   cd ragflow/
+   uv sync --python 3.10 # install RAGFlow dependent python modules
+   uv run download_deps.py
+   pre-commit install
+   ```
+
+3. Docker Compose를 사용하여 의존 서비스(MinIO, Elasticsearch, Redis 및 MySQL)를 시작합니다:
+
+   ```bash
+   docker compose -f docker/docker-compose-base.yml up -d
+   ```
+
+   `/etc/hosts` 에 다음 줄을 추가하여 **conf/service_conf.yaml** 에 지정된 모든 호스트를 `127.0.0.1` 로 해결합니다:
+
+   ```
+   127.0.0.1       es01 infinity mysql minio redis sandbox-executor-manager
+   ```
+
+4. HuggingFace에 접근할 수 없는 경우, `HF_ENDPOINT` 환경 변수를 설정하여 미러 사이트를 사용하세요:
 
    ```bash
-   # 실제 상황에 맞게 설정 조정하기 (다음 두 개의 export 명령어는 새로 추가되었습니다):
-   # - `which python`의 결과를 `PY`에 할당합니다.
-   # - `pwd`의 결과를 `PYTHONPATH`에 할당합니다.
-   # - `LD_LIBRARY_PATH`가 설정되어 있는 경우 주석 처리합니다.
-   # - 선택 사항: Hugging Face 미러 추가.
-   PY=${PY}
-   export PYTHONPATH=${PYTHONPATH}
    export HF_ENDPOINT=https://hf-mirror.com
    ```
 
-4. 다른 서비스(MinIO, Elasticsearch, Redis, MySQL)를 시작하세요:
-   ```bash
-   $ cd docker
-   $ docker compose -f docker-compose-base.yml up -d 
-   ```
-
-5. 설정 파일을 확인하여 다음 사항을 확인하세요:
-  - **docker/.env**의 설정이 **conf/service_conf.yaml**의 설정과 일치하는지 확인합니다.
-  - **service_conf.yaml**의 관련 서비스에 대한 IP 주소와 포트가 로컬 머신의 IP 주소와 컨테이너에서 노출된 포트와 일치하는지 확인합니다.
-
-
-6. RAGFlow 백엔드 서비스를 시작합니다:
+5. 만약 운영 체제에 jemalloc이 없으면 다음 방식으로 설치하세요:
 
    ```bash
-   $ chmod +x ./entrypoint.sh
-   $ bash ./entrypoint.sh
+   # ubuntu
+   sudo apt-get install libjemalloc-dev
+   # centos
+   sudo yum install jemalloc
+   # mac
+   sudo brew install jemalloc
    ```
 
-7. 프론트엔드 서비스를 시작합니다:
+6. 백엔드 서비스를 시작합니다:
 
    ```bash
-   $ cd web
-   $ npm install --registry=https://registry.npmmirror.com --force
-   $ vim .umirc.ts
-   # proxy.target을 http://127.0.0.1:9380로 업데이트합니다.
-   $ npm run dev 
+   source .venv/bin/activate
+   export PYTHONPATH=$(pwd)
+   bash docker/launch_backend_service.sh
    ```
 
-8. 프론트엔드 서비스를 배포합니다:
+7. 프론트엔드 의존성을 설치합니다:
 
    ```bash
-   $ cd web
-   $ npm install --registry=https://registry.npmmirror.com --force
-   $ umi build
-   $ mkdir -p /ragflow/web
-   $ cp -r dist /ragflow/web
-   $ apt install nginx -y
-   $ cp ../docker/nginx/proxy.conf /etc/nginx
-   $ cp ../docker/nginx/nginx.conf /etc/nginx
-   $ cp ../docker/nginx/ragflow.conf /etc/nginx/conf.d
-   $ systemctl start nginx
+   cd web
+   npm install
    ```
 
+8. 프론트엔드 서비스를 시작합니다:
+
+   ```bash
+   npm run dev
+   ```
+
+   _다음 인터페이스는 시스템이 성공적으로 시작되었음을 나타냅니다:_
+
+   ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
+
+
+9. 개발이 완료된 후 RAGFlow 프론트엔드 및 백엔드 서비스를 중지합니다.
+
+   ```bash
+   pkill -f "ragflow_server.py|task_executor.py"
+   ```
+
+
 ## 📚 문서
 
 - [Quickstart](https://ragflow.io/docs/dev/)
-- [User guide](https://ragflow.io/docs/dev/category/user-guides)
+- [Configuration](https://ragflow.io/docs/dev/configurations)
+- [Release notes](https://ragflow.io/docs/dev/release_notes)
+- [User guides](https://ragflow.io/docs/dev/category/guides)
+- [Developer guides](https://ragflow.io/docs/dev/category/developers)
 - [References](https://ragflow.io/docs/dev/category/references)
-- [FAQ](https://ragflow.io/docs/dev/faq)
+- [FAQs](https://ragflow.io/docs/dev/faq)
 
 ## 📜 로드맵
 
-[RAGFlow 로드맵 2024](https://github.com/infiniflow/ragflow/issues/162)을 확인하세요.
+[RAGFlow 로드맵 2025](https://github.com/infiniflow/ragflow/issues/4214)을 확인하세요.
 
 ## 🏄 커뮤니티
 
-- [Discord](https://discord.gg/4XxujFgUN7)
+- [Discord](https://discord.gg/NjYzJD3GM3)
 - [Twitter](https://twitter.com/infiniflowai)
 - [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
 
 ## 🙌 컨트리뷰션
 
-RAGFlow는 오픈소스 협업을 통해 발전합니다. 이러한 정신을 바탕으로, 우리는 커뮤니티의 다양한 기여를 환영합니다. 참여하고 싶으시다면, 먼저 [가이드라인](./docs/references/CONTRIBUTING.md)을 검토해 주세요.
+RAGFlow는 오픈소스 협업을 통해 발전합니다. 이러한 정신을 바탕으로, 우리는 커뮤니티의 다양한 기여를 환영합니다. 참여하고 싶으시다면, 먼저 [가이드라인](https://ragflow.io/docs/dev/contributing)을 검토해 주세요.

README_pt_br.md

@@ -0,0 +1,389 @@
+<div align="center">
+<a href="https://demo.ragflow.io/">
+<img src="web/src/assets/logo-with-text.svg" width="520" alt="ragflow logo">
+</a>
+</div>
+
+<p align="center">
+  <a href="./README.md"><img alt="README in English" src="https://img.shields.io/badge/English-DFE0E5"></a>
+  <a href="./README_zh.md"><img alt="简体中文版自述文件" src="https://img.shields.io/badge/简体中文-DFE0E5"></a>
+  <a href="./README_tzh.md"><img alt="繁體版中文自述文件" src="https://img.shields.io/badge/繁體中文-DFE0E5"></a>
+  <a href="./README_ja.md"><img alt="日本語のREADME" src="https://img.shields.io/badge/日本語-DFE0E5"></a>
+  <a href="./README_ko.md"><img alt="한국어" src="https://img.shields.io/badge/한국어-DFE0E5"></a>
+  <a href="./README_id.md"><img alt="Bahasa Indonesia" src="https://img.shields.io/badge/Bahasa Indonesia-DFE0E5"></a>
+  <a href="./README_pt_br.md"><img alt="Português(Brasil)" src="https://img.shields.io/badge/Português(Brasil)-DBEDFA"></a>
+</p>
+
+<p align="center">
+    <a href="https://x.com/intent/follow?screen_name=infiniflowai" target="_blank">
+        <img src="https://img.shields.io/twitter/follow/infiniflow?logo=X&color=%20%23f5f5f5" alt="seguir no X(Twitter)">
+    </a>
+    <a href="https://demo.ragflow.io" target="_blank">
+        <img alt="Badge Estático" src="https://img.shields.io/badge/Online-Demo-4e6b99">
+    </a>
+    <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
+        <img src="https://img.shields.io/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.22.1">
+    </a>
+    <a href="https://github.com/infiniflow/ragflow/releases/latest">
+        <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Última%20Relese" alt="Última Versão">
+    </a>
+    <a href="https://github.com/infiniflow/ragflow/blob/main/LICENSE">
+        <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?labelColor=d4eaf7&color=2e6cc4" alt="licença">
+    </a>
+    <a href="https://deepwiki.com/infiniflow/ragflow">
+        <img alt="Ask DeepWiki" src="https://deepwiki.com/badge.svg">
+    </a>
+</p>
+
+<h4 align="center">
+  <a href="https://ragflow.io/docs/dev/">Documentação</a> |
+  <a href="https://github.com/infiniflow/ragflow/issues/4214">Roadmap</a> |
+  <a href="https://twitter.com/infiniflowai">Twitter</a> |
+  <a href="https://discord.gg/NjYzJD3GM3">Discord</a> |
+  <a href="https://demo.ragflow.io">Demo</a>
+</h4>
+
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/ragflow-octoverse.png" width="1200"/>
+</div>
+
+<div align="center">
+<a href="https://trendshift.io/repositories/9064" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9064" alt="infiniflow%2Fragflow | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
+</div>
+
+<details open>
+<summary><b>📕 Índice</b></summary>
+
+- 💡 [O que é o RAGFlow?](#-o-que-é-o-ragflow)
+- 🎮 [Demo](#-demo)
+- 📌 [Últimas Atualizações](#-últimas-atualizações)
+- 🌟 [Principais Funcionalidades](#-principais-funcionalidades)
+- 🔎 [Arquitetura do Sistema](#-arquitetura-do-sistema)
+- 🎬 [Primeiros Passos](#-primeiros-passos)
+- 🔧 [Configurações](#-configurações)
+- 🔧 [Construir uma imagem docker sem incorporar modelos](#-construir-uma-imagem-docker-sem-incorporar-modelos)
+- 🔧 [Construir uma imagem docker incluindo modelos](#-construir-uma-imagem-docker-incluindo-modelos)
+- 🔨 [Lançar serviço a partir do código-fonte para desenvolvimento](#-lançar-serviço-a-partir-do-código-fonte-para-desenvolvimento)
+- 📚 [Documentação](#-documentação)
+- 📜 [Roadmap](#-roadmap)
+- 🏄 [Comunidade](#-comunidade)
+- 🙌 [Contribuindo](#-contribuindo)
+
+</details>
+
+## 💡 O que é o RAGFlow?
+
+[RAGFlow](https://ragflow.io/) é um mecanismo de RAG (Retrieval-Augmented Generation) open-source líder que fusiona tecnologias RAG de ponta com funcionalidades Agent para criar uma camada contextual superior para LLMs. Oferece um fluxo de trabalho RAG otimizado adaptável a empresas de qualquer escala. Alimentado por um motor de contexto convergente e modelos Agent pré-construídos, o RAGFlow permite que desenvolvedores transformem dados complexos em sistemas de IA de alta fidelidade e pronto para produção com excepcional eficiência e precisão.
+
+## 🎮 Demo
+
+Experimente nossa demo em [https://demo.ragflow.io](https://demo.ragflow.io).
+
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/chunking.gif" width="1200"/>
+<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/agentic-dark.gif" width="1200"/>
+</div>
+
+## 🔥 Últimas Atualizações
+
+- 19-11-2025 Suporta Gemini 3 Pro.
+- 12-11-2025 Suporta a sincronização de dados do Confluence, S3, Notion, Discord e Google Drive.
+- 23-10-2025 Suporta MinerU e Docling como métodos de análise de documentos.
+- 15-10-2025 Suporte para pipelines de dados orquestrados.
+- 08-08-2025 Suporta a mais recente série GPT-5 da OpenAI.
+- 01-08-2025 Suporta fluxo de trabalho agente e MCP.
+- 23-05-2025 Adicione o componente executor de código Python/JS ao Agente.
+- 05-05-2025 Suporte a consultas entre idiomas.
+- 19-03-2025 Suporta o uso de um modelo multi-modal para entender imagens dentro de arquivos PDF ou DOCX.
+- 18-12-2024 Atualiza o modelo de Análise de Layout de Documentos no DeepDoc.
+- 22-08-2024 Suporta conversão de texto para comandos SQL via RAG.
+
+## 🎉 Fique Ligado
+
+⭐️ Dê uma estrela no nosso repositório para se manter atualizado com novas funcionalidades e melhorias empolgantes! Receba notificações instantâneas sobre novos lançamentos! 🌟
+
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/user-attachments/assets/18c9707e-b8aa-4caf-a154-037089c105ba" width="1200"/>
+</div>
+
+## 🌟 Principais Funcionalidades
+
+### 🍭 **"Qualidade entra, qualidade sai"**
+
+- Extração de conhecimento baseada em [entendimento profundo de documentos](./deepdoc/README.md) a partir de dados não estruturados com formatos complicados.
+- Encontra a "agulha no palheiro de dados" de literalmente tokens ilimitados.
+
+### 🍱 **Fragmentação baseada em templates**
+
+- Inteligente e explicável.
+- Muitas opções de templates para escolher.
+
+### 🌱 **Citações fundamentadas com menos alucinações**
+
+- Visualização da fragmentação de texto para permitir intervenção humana.
+- Visualização rápida das referências chave e citações rastreáveis para apoiar respostas fundamentadas.
+
+### 🍔 **Compatibilidade com fontes de dados heterogêneas**
+
+- Suporta Word, apresentações, excel, txt, imagens, cópias digitalizadas, dados estruturados, páginas da web e mais.
+
+### 🛀 **Fluxo de trabalho RAG automatizado e sem esforço**
+
+- Orquestração RAG simplificada voltada tanto para negócios pessoais quanto grandes empresas.
+- Modelos LLM e de incorporação configuráveis.
+- Múltiplas recuperações emparelhadas com reclassificação fundida.
+- APIs intuitivas para integração sem problemas com os negócios.
+
+## 🔎 Arquitetura do Sistema
+
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/user-attachments/assets/31b0dd6f-ca4f-445a-9457-70cb44a381b2" width="1000"/>
+</div>
+
+## 🎬 Primeiros Passos
+
+### 📝 Pré-requisitos
+
+- CPU >= 4 núcleos
+- RAM >= 16 GB
+- Disco >= 50 GB
+- Docker >= 24.0.0 & Docker Compose >= v2.26.1
+- [gVisor](https://gvisor.dev/docs/user_guide/install/): Necessário apenas se você pretende usar o recurso de executor de código (sandbox) do RAGFlow.
+
+> [!TIP]
+> Se você não instalou o Docker na sua máquina local (Windows, Mac ou Linux), veja [Instalar Docker Engine](https://docs.docker.com/engine/install/).
+
+### 🚀 Iniciar o servidor
+
+1. Certifique-se de que `vm.max_map_count` >= 262144:
+
+   > Para verificar o valor de `vm.max_map_count`:
+   >
+   > ```bash
+   > $ sysctl vm.max_map_count
+   > ```
+   >
+   > Se necessário, redefina `vm.max_map_count` para um valor de pelo menos 262144:
+   >
+   > ```bash
+   > # Neste caso, defina para 262144:
+   > $ sudo sysctl -w vm.max_map_count=262144
+   > ```
+   >
+   > Essa mudança será resetada após a reinicialização do sistema. Para garantir que a alteração permaneça permanente, adicione ou atualize o valor de `vm.max_map_count` em **/etc/sysctl.conf**:
+   >
+   > ```bash
+   > vm.max_map_count=262144
+   > ```
+   >
+2. Clone o repositório:
+
+   ```bash
+   $ git clone https://github.com/infiniflow/ragflow.git
+   ```
+3. Inicie o servidor usando as imagens Docker pré-compiladas:
+
+> [!CAUTION]
+> Todas as imagens Docker são construídas para plataformas x86. Atualmente, não oferecemos imagens Docker para ARM64.
+> Se você estiver usando uma plataforma ARM64, por favor, utilize [este guia](https://ragflow.io/docs/dev/build_docker_image) para construir uma imagem Docker compatível com o seu sistema.
+
+    > O comando abaixo baixa a edição`v0.22.1` da imagem Docker do RAGFlow. Consulte a tabela a seguir para descrições de diferentes edições do RAGFlow. Para baixar uma edição do RAGFlow diferente da `v0.22.1`, atualize a variável `RAGFLOW_IMAGE` conforme necessário no **docker/.env** antes de usar `docker compose` para iniciar o servidor.
+
+```bash
+   $ cd ragflow/docker
+   
+   # git checkout v0.22.1
+   # Opcional: use uma tag estável (veja releases: https://github.com/infiniflow/ragflow/releases)
+   # Esta etapa garante que o arquivo entrypoint.sh no código corresponda à versão da imagem do Docker.
+
+   # Use CPU for DeepDoc tasks:
+   $ docker compose -f docker-compose.yml up -d
+
+   # To use GPU to accelerate DeepDoc tasks:
+   # sed -i '1i DEVICE=gpu' .env
+   # docker compose -f docker-compose.yml up -d
+```
+
+> Nota: Antes da `v0.22.0`, fornecíamos imagens com modelos de embedding e imagens slim sem modelos de embedding. Detalhes a seguir:
+
+| RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
+| ----------------- | --------------- | --------------------- | ------------------------ |
+| v0.21.1           | &approx;9       | ✔️                    | Stable release           |
+| v0.21.1-slim      | &approx;2       | ❌                    | Stable release           |
+
+> A partir da `v0.22.0`, distribuímos apenas a edição slim e não adicionamos mais o sufixo **-slim** às tags das imagens.
+
+4. Verifique o status do servidor após tê-lo iniciado:
+
+   ```bash
+   $ docker logs -f docker-ragflow-cpu-1
+   ```
+
+   _O seguinte resultado confirma o lançamento bem-sucedido do sistema:_
+
+   ```bash
+        ____   ___    ______ ______ __
+       / __ \ /   |  / ____// ____// /____  _      __
+      / /_/ // /| | / / __ / /_   / // __ \| | /| / /
+     / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ /
+    /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/
+
+    * Rodando em todos os endereços (0.0.0.0)
+   ```
+
+   > Se você pular essa etapa de confirmação e acessar diretamente o RAGFlow, seu navegador pode exibir um erro `network anormal`, pois, nesse momento, seu RAGFlow pode não estar totalmente inicializado.
+   >
+5. No seu navegador, insira o endereço IP do seu servidor e faça login no RAGFlow.
+
+   > Com as configurações padrão, você só precisa digitar `http://IP_DO_SEU_MÁQUINA` (**sem** o número da porta), pois a porta HTTP padrão `80` pode ser omitida ao usar as configurações padrão.
+   >
+6. Em [service_conf.yaml.template](./docker/service_conf.yaml.template), selecione a fábrica LLM desejada em `user_default_llm` e atualize o campo `API_KEY` com a chave de API correspondente.
+
+   > Consulte [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) para mais informações.
+   >
+
+_O show está no ar!_
+
+## 🔧 Configurações
+
+Quando se trata de configurações do sistema, você precisará gerenciar os seguintes arquivos:
+
+- [.env](./docker/.env): Contém as configurações fundamentais para o sistema, como `SVR_HTTP_PORT`, `MYSQL_PASSWORD` e `MINIO_PASSWORD`.
+- [service_conf.yaml.template](./docker/service_conf.yaml.template): Configura os serviços de back-end. As variáveis de ambiente neste arquivo serão automaticamente preenchidas quando o contêiner Docker for iniciado. Quaisquer variáveis de ambiente definidas dentro do contêiner Docker estarão disponíveis para uso, permitindo personalizar o comportamento do serviço com base no ambiente de implantação.
+- [docker-compose.yml](./docker/docker-compose.yml): O sistema depende do [docker-compose.yml](./docker/docker-compose.yml) para iniciar.
+
+> O arquivo [./docker/README](./docker/README.md) fornece uma descrição detalhada das configurações do ambiente e dos serviços, que podem ser usadas como `${ENV_VARS}` no arquivo [service_conf.yaml.template](./docker/service_conf.yaml.template).
+
+Para atualizar a porta HTTP de serviço padrão (80), vá até [docker-compose.yml](./docker/docker-compose.yml) e altere `80:80` para `<SUA_PORTA_DE_SERVIÇO>:80`.
+
+Atualizações nas configurações acima exigem um reinício de todos os contêineres para que tenham efeito:
+
+> ```bash
+> $ docker compose -f docker-compose.yml up -d
+> ```
+
+### Mudar o mecanismo de documentos de Elasticsearch para Infinity
+
+O RAGFlow usa o Elasticsearch por padrão para armazenar texto completo e vetores. Para mudar para o [Infinity](https://github.com/infiniflow/infinity/), siga estas etapas:
+
+1. Pare todos os contêineres em execução:
+
+   ```bash
+   $ docker compose -f docker/docker-compose.yml down -v
+   ```
+
+   Note: `-v` irá deletar os volumes do contêiner, e os dados existentes serão apagados.
+2. Defina `DOC_ENGINE` no **docker/.env** para `infinity`.
+3. Inicie os contêineres:
+
+   ```bash
+   $ docker compose -f docker-compose.yml up -d
+   ```
+
+> [!ATENÇÃO]
+ > A mudança para o Infinity em uma máquina Linux/arm64 ainda não é oficialmente suportada.
+
+## 🔧 Criar uma imagem Docker
+
+Esta imagem tem cerca de 2 GB de tamanho e depende de serviços externos de LLM e incorporação.
+
+```bash
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .
+```
+
+## 🔨 Lançar o serviço a partir do código-fonte para desenvolvimento
+
+1. Instale o `uv` e o `pre-commit`, ou pule esta etapa se eles já estiverem instalados:
+
+   ```bash
+   pipx install uv pre-commit
+   ```
+2. Clone o código-fonte e instale as dependências Python:
+
+   ```bash
+   git clone https://github.com/infiniflow/ragflow.git
+   cd ragflow/
+   uv sync --python 3.10 # instala os módulos Python dependentes do RAGFlow
+   uv run download_deps.py
+   pre-commit install
+   ```
+3. Inicie os serviços dependentes (MinIO, Elasticsearch, Redis e MySQL) usando Docker Compose:
+
+   ```bash
+   docker compose -f docker/docker-compose-base.yml up -d
+   ```
+
+   Adicione a seguinte linha ao arquivo `/etc/hosts` para resolver todos os hosts especificados em **docker/.env** para `127.0.0.1`:
+
+   ```
+   127.0.0.1       es01 infinity mysql minio redis sandbox-executor-manager
+   ```
+4. Se não conseguir acessar o HuggingFace, defina a variável de ambiente `HF_ENDPOINT` para usar um site espelho:
+
+   ```bash
+   export HF_ENDPOINT=https://hf-mirror.com
+   ```
+5. Se o seu sistema operacional não tiver jemalloc, instale-o da seguinte maneira:
+
+   ```bash
+   # ubuntu
+   sudo apt-get install libjemalloc-dev
+   # centos
+   sudo yum instalar jemalloc
+   # mac
+   sudo brew install jemalloc
+   ```
+6. Lance o serviço de back-end:
+
+   ```bash
+   source .venv/bin/activate
+   export PYTHONPATH=$(pwd)
+   bash docker/launch_backend_service.sh
+   ```
+7. Instale as dependências do front-end:
+
+   ```bash
+   cd web
+   npm install
+   ```
+8. Lance o serviço de front-end:
+
+   ```bash
+   npm run dev
+   ```
+
+   _O seguinte resultado confirma o lançamento bem-sucedido do sistema:_
+
+   ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
+9. Pare os serviços de front-end e back-end do RAGFlow após a conclusão do desenvolvimento:
+
+   ```bash
+   pkill -f "ragflow_server.py|task_executor.py"
+   ```
+
+## 📚 Documentação
+
+- [Quickstart](https://ragflow.io/docs/dev/)
+- [Configuration](https://ragflow.io/docs/dev/configurations)
+- [Release notes](https://ragflow.io/docs/dev/release_notes)
+- [User guides](https://ragflow.io/docs/dev/category/guides)
+- [Developer guides](https://ragflow.io/docs/dev/category/developers)
+- [References](https://ragflow.io/docs/dev/category/references)
+- [FAQs](https://ragflow.io/docs/dev/faq)
+
+## 📜 Roadmap
+
+Veja o [RAGFlow Roadmap 2025](https://github.com/infiniflow/ragflow/issues/4214)
+
+## 🏄 Comunidade
+
+- [Discord](https://discord.gg/NjYzJD3GM3)
+- [Twitter](https://twitter.com/infiniflowai)
+- [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
+
+## 🙌 Contribuindo
+
+O RAGFlow prospera por meio da colaboração de código aberto. Com esse espírito, abraçamos contribuições diversas da comunidade.
+Se você deseja fazer parte, primeiro revise nossas [Diretrizes de Contribuição](https://ragflow.io/docs/dev/contributing).

README_tzh.md

@@ -0,0 +1,414 @@
+<div align="center">
+<a href="https://demo.ragflow.io/">
+<img src="web/src/assets/logo-with-text.svg" width="350" alt="ragflow logo">
+</a>
+</div>
+
+<p align="center">
+  <a href="./README.md"><img alt="README in English" src="https://img.shields.io/badge/English-DFE0E5"></a>
+  <a href="./README_zh.md"><img alt="简体中文版自述文件" src="https://img.shields.io/badge/简体中文-DFE0E5"></a>
+  <a href="./README_tzh.md"><img alt="繁體版中文自述文件" src="https://img.shields.io/badge/繁體中文-DBEDFA"></a>
+  <a href="./README_ja.md"><img alt="日本語のREADME" src="https://img.shields.io/badge/日本語-DFE0E5"></a>
+  <a href="./README_ko.md"><img alt="한국어" src="https://img.shields.io/badge/한국어-DFE0E5"></a>
+  <a href="./README_id.md"><img alt="Bahasa Indonesia" src="https://img.shields.io/badge/Bahasa Indonesia-DFE0E5"></a>
+  <a href="./README_pt_br.md"><img alt="Português(Brasil)" src="https://img.shields.io/badge/Português(Brasil)-DFE0E5"></a>
+</p>
+
+<p align="center">
+    <a href="https://x.com/intent/follow?screen_name=infiniflowai" target="_blank">
+        <img src="https://img.shields.io/twitter/follow/infiniflow?logo=X&color=%20%23f5f5f5" alt="follow on X(Twitter)">
+    </a>
+    <a href="https://demo.ragflow.io" target="_blank">
+        <img alt="Static Badge" src="https://img.shields.io/badge/Online-Demo-4e6b99">
+    </a>
+    <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
+        <img src="https://img.shields.io/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.22.1">
+    </a>
+    <a href="https://github.com/infiniflow/ragflow/releases/latest">
+        <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Latest%20Release" alt="Latest Release">
+    </a>
+    <a href="https://github.com/infiniflow/ragflow/blob/main/LICENSE">
+        <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?labelColor=d4eaf7&color=2e6cc4" alt="license">
+    </a>
+    <a href="https://deepwiki.com/infiniflow/ragflow">
+        <img alt="Ask DeepWiki" src="https://deepwiki.com/badge.svg">
+    </a>
+</p>
+
+<h4 align="center">
+  <a href="https://ragflow.io/docs/dev/">Document</a> |
+  <a href="https://github.com/infiniflow/ragflow/issues/4214">Roadmap</a> |
+  <a href="https://twitter.com/infiniflowai">Twitter</a> |
+  <a href="https://discord.gg/NjYzJD3GM3">Discord</a> |
+  <a href="https://demo.ragflow.io">Demo</a>
+</h4>
+
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/ragflow-octoverse.png" width="1200"/>
+</div>
+
+<div align="center">
+<a href="https://trendshift.io/repositories/9064" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9064" alt="infiniflow%2Fragflow | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
+</div>
+
+<details open>
+<summary><b>📕 目錄</b></summary>
+
+- 💡 [RAGFlow 是什麼？](#-RAGFlow-是什麼)
+- 🎮 [Demo-試用](#-demo-試用)
+- 📌 [近期更新](#-近期更新)
+- 🌟 [主要功能](#-主要功能)
+- 🔎 [系統架構](#-系統架構)
+- 🎬 [快速開始](#-快速開始)
+- 🔧 [系統配置](#-系統配置)
+- 🔨 [以原始碼啟動服務](#-以原始碼啟動服務)
+- 📚 [技術文檔](#-技術文檔)
+- 📜 [路線圖](#-路線圖)
+- 🏄 [貢獻指南](#-貢獻指南)
+- 🙌 [加入社區](#-加入社區)
+- 🤝 [商務合作](#-商務合作)
+
+</details>
+
+## 💡 RAGFlow 是什麼？
+
+[RAGFlow](https://ragflow.io/) 是一款領先的開源 RAG（Retrieval-Augmented Generation）引擎，通過融合前沿的 RAG 技術與 Agent 能力，為大型語言模型提供卓越的上下文層。它提供可適配任意規模企業的端到端 RAG 工作流，憑藉融合式上下文引擎與預置的 Agent 模板，助力開發者以極致效率與精度將複雜數據轉化為高可信、生產級的人工智能系統。
+
+## 🎮 Demo 試用
+
+請登入網址 [https://demo.ragflow.io](https://demo.ragflow.io) 試用 demo。
+
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/chunking.gif" width="1200"/>
+<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/agentic-dark.gif" width="1200"/>
+</div>
+
+## 🔥 近期更新
+
+- 2025-11-19 支援 Gemini 3 Pro.
+- 2025-11-12 支援從 Confluence、S3、Notion、Discord、Google Drive 進行資料同步。
+- 2025-10-23 支援 MinerU 和 Docling 作為文件解析方法。
+- 2025-10-15 支援可編排的資料管道。
+- 2025-08-08 支援 OpenAI 最新的 GPT-5 系列模型。
+- 2025-08-01 支援 agentic workflow 和 MCP
+- 2025-05-23 為 Agent 新增 Python/JS 程式碼執行器元件。
+- 2025-05-05 支援跨語言查詢。
+- 2025-03-19 PDF和DOCX中的圖支持用多模態大模型去解析得到描述.
+- 2024-12-18 升級了 DeepDoc 的文檔佈局分析模型。
+- 2024-08-22 支援用 RAG 技術實現從自然語言到 SQL 語句的轉換。
+
+## 🎉 關注項目
+
+⭐️ 點擊右上角的 Star 追蹤 RAGFlow，可以取得最新發布的即時通知 !🌟
+
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/user-attachments/assets/18c9707e-b8aa-4caf-a154-037089c105ba" width="1200"/>
+</div>
+
+## 🌟 主要功能
+
+### 🍭 **"Quality in, quality out"**
+
+- 基於[深度文件理解](./deepdoc/README.md)，能夠從各類複雜格式的非結構化資料中提取真知灼見。
+- 真正在無限上下文（token）的場景下快速完成大海撈針測試。
+
+### 🍱 **基於模板的文字切片**
+
+- 不只是智能，更重要的是可控可解釋。
+- 多種文字範本可供選擇
+
+### 🌱 **有理有據、最大程度降低幻覺（hallucination）**
+
+- 文字切片過程視覺化，支援手動調整。
+- 有理有據：答案提供關鍵引用的快照並支持追根溯源。
+
+### 🍔 **相容各類異質資料來源**
+
+- 支援豐富的文件類型，包括 Word 文件、PPT、excel 表格、txt 檔案、圖片、PDF、影印件、影印件、結構化資料、網頁等。
+
+### 🛀 **全程無憂、自動化的 RAG 工作流程**
+
+- 全面優化的 RAG 工作流程可以支援從個人應用乃至超大型企業的各類生態系統。
+- 大語言模型 LLM 以及向量模型皆支援配置。
+- 基於多路召回、融合重排序。
+- 提供易用的 API，可輕鬆整合到各類企業系統。
+
+## 🔎 系統架構
+
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/user-attachments/assets/31b0dd6f-ca4f-445a-9457-70cb44a381b2" width="1000"/>
+</div>
+
+## 🎬 快速開始
+
+### 📝 前提條件
+
+- CPU >= 4 核
+- RAM >= 16 GB
+- Disk >= 50 GB
+- Docker >= 24.0.0 & Docker Compose >= v2.26.1
+- [gVisor](https://gvisor.dev/docs/user_guide/install/): 僅在您打算使用 RAGFlow 的代碼執行器（沙箱）功能時才需要安裝。
+
+> [!TIP]
+> 如果你並沒有在本機安裝 Docker（Windows、Mac，或 Linux）, 可以參考文件 [Install Docker Engine](https://docs.docker.com/engine/install/) 自行安裝。
+
+### 🚀 啟動伺服器
+
+1. 確保 `vm.max_map_count` 不小於 262144：
+
+   > 如需確認 `vm.max_map_count` 的大小：
+   >
+   > ```bash
+   > $ sysctl vm.max_map_count
+   > ```
+   >
+   > 如果 `vm.max_map_count` 的值小於 262144，可以進行重設：
+   >
+   > ```bash
+   > # 這裡我們設為 262144:
+   > $ sudo sysctl -w vm.max_map_count=262144
+   > ```
+   >
+   > 你的改動會在下次系統重新啟動時被重置。如果希望做永久改動，還需要在 **/etc/sysctl.conf** 檔案裡把 `vm.max_map_count` 的值再相應更新一遍：
+   >
+   > ```bash
+   > vm.max_map_count=262144
+   > ```
+   >
+2. 克隆倉庫：
+
+   ```bash
+   $ git clone https://github.com/infiniflow/ragflow.git
+   ```
+3. 進入 **docker** 資料夾，利用事先編譯好的 Docker 映像啟動伺服器：
+
+> [!CAUTION]
+> 所有 Docker 映像檔都是為 x86 平台建置的。目前，我們不提供 ARM64 平台的 Docker 映像檔。
+> 如果您使用的是 ARM64 平台，請使用 [這份指南](https://ragflow.io/docs/dev/build_docker_image) 來建置適合您系統的 Docker 映像檔。
+
+> 執行以下指令會自動下載 RAGFlow Docker 映像 `v0.22.1`。請參考下表查看不同 Docker 發行版的說明。如需下載不同於 `v0.22.1` 的 Docker 映像，請在執行 `docker compose` 啟動服務之前先更新 **docker/.env** 檔案內的 `RAGFLOW_IMAGE` 變數。
+
+```bash
+   $ cd ragflow/docker
+   
+   # git checkout v0.22.1
+   # 可選：使用穩定版標籤（查看發佈：https://github.com/infiniflow/ragflow/releases）
+   # 此步驟確保程式碼中的 entrypoint.sh 檔案與 Docker 映像版本一致。
+
+   # Use CPU for DeepDoc tasks:
+   $ docker compose -f docker-compose.yml up -d
+
+   # To use GPU to accelerate DeepDoc tasks:
+   # sed -i '1i DEVICE=gpu' .env
+   # docker compose -f docker-compose.yml up -d
+```
+
+> 注意：在 `v0.22.0` 之前的版本，我們會同時提供包含 embedding 模型的映像和不含 embedding 模型的 slim 映像。具體如下：
+
+| RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
+| ----------------- | --------------- | --------------------- | ------------------------ |
+| v0.21.1           | &approx;9       | ✔️                    | Stable release           |
+| v0.21.1-slim      | &approx;2       | ❌                    | Stable release           |
+
+> 從 `v0.22.0` 開始，我們只發佈 slim 版本，並且不再在映像標籤後附加 **-slim** 後綴。
+
+> [!TIP]
+> 如果你遇到 Docker 映像檔拉不下來的問題，可以在 **docker/.env** 檔案內根據變數 `RAGFLOW_IMAGE` 的註解提示選擇華為雲或阿里雲的對應映像。
+>
+> - 華為雲鏡像名：`swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow`
+> - 阿里雲鏡像名：`registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow`
+
+4. 伺服器啟動成功後再次確認伺服器狀態：
+
+   ```bash
+   $ docker logs -f docker-ragflow-cpu-1
+   ```
+
+   _出現以下介面提示說明伺服器啟動成功：_
+
+   ```bash
+        ____   ___    ______ ______ __
+       / __ \ /   |  / ____// ____// /____  _      __
+      / /_/ // /| | / / __ / /_   / // __ \| | /| / /
+     / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ /
+    /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/
+
+    * Running on all addresses (0.0.0.0)
+   ```
+
+   > 如果您跳過這一步驟系統確認步驟就登入 RAGFlow，你的瀏覽器有可能會提示 `network anormal` 或 `網路異常`，因為 RAGFlow 可能並未完全啟動成功。
+   >
+5. 在你的瀏覽器中輸入你的伺服器對應的 IP 位址並登入 RAGFlow。
+
+   > 上面這個範例中，您只需輸入 http://IP_OF_YOUR_MACHINE 即可：未改動過設定則無需輸入連接埠（預設的 HTTP 服務連接埠 80）。
+   >
+6. 在 [service_conf.yaml.template](./docker/service_conf.yaml.template) 檔案的 `user_default_llm` 欄位設定 LLM factory，並在 `API_KEY` 欄填入和你選擇的大模型相對應的 API key。
+
+   > 詳見 [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup)。
+   >
+
+   _好戲開始，接著奏樂接著舞！ _
+
+## 🔧 系統配置
+
+系統配置涉及以下三份文件：
+
+- [.env](./docker/.env)：存放一些系統環境變量，例如 `SVR_HTTP_PORT`、`MYSQL_PASSWORD`、`MINIO_PASSWORD` 等。
+- [service_conf.yaml.template](./docker/service_conf.yaml.template)：設定各類別後台服務。
+- [docker-compose.yml](./docker/docker-compose.yml): 系統依賴該檔案完成啟動。
+
+請務必確保 [.env](./docker/.env) 檔案中的變數設定與 [service_conf.yaml.template](./docker/service_conf.yaml.template) 檔案中的設定保持一致！
+
+如果無法存取映像網站 hub.docker.com 或模型網站 huggingface.co，請依照 [.env](./docker/.env) 註解修改 `RAGFLOW_IMAGE` 和 `HF_ENDPOINT`。
+
+> [./docker/README](./docker/README.md) 解釋了 [service_conf.yaml.template](./docker/service_conf.yaml.template) 用到的環境變數設定和服務配置。
+
+如需更新預設的 HTTP 服務連接埠(80), 可以在[docker-compose.yml](./docker/docker-compose.yml) 檔案中將配置 `80:80` 改為 `<YOUR_SERVING_PORT>:80` 。
+
+> 所有系統配置都需要透過系統重新啟動生效：
+>
+> ```bash
+> $ docker compose -f docker-compose.yml up -d
+> ```
+
+###把文檔引擎從 Elasticsearch 切換成為 Infinity
+
+RAGFlow 預設使用 Elasticsearch 儲存文字和向量資料. 如果要切換為 [Infinity](https://github.com/infiniflow/infinity/), 可以按照下面步驟進行:
+
+1. 停止所有容器運作:
+
+   ```bash
+   $ docker compose -f docker/docker-compose.yml down -v
+   ```
+
+   Note: `-v` 將會刪除 docker 容器的 volumes，已有的資料會被清空。
+2. 設定 **docker/.env** 目錄中的 `DOC_ENGINE` 為 `infinity`.
+3. 啟動容器:
+
+   ```bash
+   $ docker compose -f docker-compose.yml up -d
+   ```
+
+> [!WARNING]
+> Infinity 目前官方並未正式支援在 Linux/arm64 架構下的機器上運行.
+
+## 🔧 原始碼編譯 Docker 映像
+
+本 Docker 映像大小約 2 GB 左右並且依賴外部的大模型和 embedding 服務。
+
+```bash
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .
+```
+
+## 🔨 以原始碼啟動服務
+
+1. 安裝 `uv` 和 `pre-commit`。如已安裝，可跳過此步驟：
+
+   ```bash
+   pipx install uv pre-commit
+   export UV_INDEX=https://mirrors.aliyun.com/pypi/simple
+   ```
+2. 下載原始碼並安裝 Python 依賴：
+
+   ```bash
+   git clone https://github.com/infiniflow/ragflow.git
+   cd ragflow/
+   uv sync --python 3.10 # install RAGFlow dependent python modules
+   uv run download_deps.py
+   pre-commit install
+   ```
+3. 透過 Docker Compose 啟動依賴的服務（MinIO, Elasticsearch, Redis, and MySQL）：
+
+   ```bash
+   docker compose -f docker/docker-compose-base.yml up -d
+   ```
+
+   在 `/etc/hosts` 中加入以下程式碼，將 **conf/service_conf.yaml** 檔案中的所有 host 位址都解析為 `127.0.0.1`：
+
+   ```
+   127.0.0.1       es01 infinity mysql minio redis sandbox-executor-manager
+   ```
+4. 如果無法存取 HuggingFace，可以把環境變數 `HF_ENDPOINT` 設為對應的鏡像網站：
+
+   ```bash
+   export HF_ENDPOINT=https://hf-mirror.com
+   ```
+5. 如果你的操作系统没有 jemalloc，请按照如下方式安装：
+
+   ```bash
+   # ubuntu
+   sudo apt-get install libjemalloc-dev
+   # centos
+   sudo yum install jemalloc
+   # mac
+   sudo brew install jemalloc
+   ```
+6. 啟動後端服務：
+
+   ```bash
+   source .venv/bin/activate
+   export PYTHONPATH=$(pwd)
+   bash docker/launch_backend_service.sh
+   ```
+7. 安裝前端依賴：
+
+   ```bash
+   cd web
+   npm install
+   ```
+8. 啟動前端服務：
+
+   ```bash
+   npm run dev
+   ```
+
+   以下界面說明系統已成功啟動：_
+
+   ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
+
+   ```
+
+   ```
+9. 開發完成後停止 RAGFlow 前端和後端服務：
+
+   ```bash
+   pkill -f "ragflow_server.py|task_executor.py"
+   ```
+
+## 📚 技術文檔
+
+- [Quickstart](https://ragflow.io/docs/dev/)
+- [Configuration](https://ragflow.io/docs/dev/configurations)
+- [Release notes](https://ragflow.io/docs/dev/release_notes)
+- [User guides](https://ragflow.io/docs/dev/category/guides)
+- [Developer guides](https://ragflow.io/docs/dev/category/developers)
+- [References](https://ragflow.io/docs/dev/category/references)
+- [FAQs](https://ragflow.io/docs/dev/faq)
+
+## 📜 路線圖
+
+詳見 [RAGFlow Roadmap 2025](https://github.com/infiniflow/ragflow/issues/4214) 。
+
+## 🏄 開源社群
+
+- [Discord](https://discord.gg/zd4qPW6t)
+- [Twitter](https://twitter.com/infiniflowai)
+- [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
+
+## 🙌 貢獻指南
+
+RAGFlow 只有透過開源協作才能蓬勃發展。秉持這項精神,我們歡迎來自社區的各種貢獻。如果您有意參與其中,請查閱我們的 [貢獻者指南](https://ragflow.io/docs/dev/contributing) 。
+
+## 🤝 商務合作
+
+- [預約諮詢](https://aao615odquw.feishu.cn/share/base/form/shrcnjw7QleretCLqh1nuPo1xxh)
+
+## 👥 加入社區
+
+掃二維碼加入 RAGFlow 小助手，進 RAGFlow 交流群。
+
+<p align="center">
+  <img src="https://github.com/infiniflow/ragflow/assets/7248/bccf284f-46f2-4445-9809-8f1030fb7585" width=50% height=50%>
+</p>

README_zh.md

@@ -1,60 +1,109 @@
 <div align="center">
 <a href="https://demo.ragflow.io/">
-<img src="web/src/assets/logo-with-text.png" width="350" alt="ragflow logo">
+<img src="web/src/assets/logo-with-text.svg" width="350" alt="ragflow logo">
 </a>
 </div>
 
 <p align="center">
-  <a href="./README.md">English</a> |
-  <a href="./README_zh.md">简体中文</a> |
-  <a href="./README_ja.md">日本語</a> |
-  <a href="./README_ko.md">한국어</a>
+  <a href="./README.md"><img alt="README in English" src="https://img.shields.io/badge/English-DFE0E5"></a>
+  <a href="./README_zh.md"><img alt="简体中文版自述文件" src="https://img.shields.io/badge/简体中文-DBEDFA"></a>
+  <a href="./README_tzh.md"><img alt="繁體版中文自述文件" src="https://img.shields.io/badge/繁體中文-DFE0E5"></a>
+  <a href="./README_ja.md"><img alt="日本語のREADME" src="https://img.shields.io/badge/日本語-DFE0E5"></a>
+  <a href="./README_ko.md"><img alt="한국어" src="https://img.shields.io/badge/한국어-DFE0E5"></a>
+  <a href="./README_id.md"><img alt="Bahasa Indonesia" src="https://img.shields.io/badge/Bahasa Indonesia-DFE0E5"></a>
+  <a href="./README_pt_br.md"><img alt="Português(Brasil)" src="https://img.shields.io/badge/Português(Brasil)-DFE0E5"></a>
 </p>
 
 <p align="center">
+    <a href="https://x.com/intent/follow?screen_name=infiniflowai" target="_blank">
+        <img src="https://img.shields.io/twitter/follow/infiniflow?logo=X&color=%20%23f5f5f5" alt="follow on X(Twitter)">
+    </a>
+    <a href="https://demo.ragflow.io" target="_blank">
+        <img alt="Static Badge" src="https://img.shields.io/badge/Online-Demo-4e6b99">
+    </a>
+    <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
+        <img src="https://img.shields.io/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.22.1">
+    </a>
     <a href="https://github.com/infiniflow/ragflow/releases/latest">
         <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Latest%20Release" alt="Latest Release">
     </a>
-    <a href="https://demo.ragflow.io" target="_blank">
-        <img alt="Static Badge" src="https://img.shields.io/badge/Online-Demo-4e6b99"></a>
-    <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
-        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.11.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.11.0"></a>
     <a href="https://github.com/infiniflow/ragflow/blob/main/LICENSE">
-    <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?labelColor=d4eaf7&color=2e6cc4" alt="license">
-  </a>
+        <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?labelColor=d4eaf7&color=2e6cc4" alt="license">
+    </a>
+    <a href="https://deepwiki.com/infiniflow/ragflow">
+        <img alt="Ask DeepWiki" src="https://deepwiki.com/badge.svg">
+    </a>
 </p>
 
 <h4 align="center">
   <a href="https://ragflow.io/docs/dev/">Document</a> |
-  <a href="https://github.com/infiniflow/ragflow/issues/162">Roadmap</a> |
+  <a href="https://github.com/infiniflow/ragflow/issues/4214">Roadmap</a> |
   <a href="https://twitter.com/infiniflowai">Twitter</a> |
-  <a href="https://discord.gg/4XxujFgUN7">Discord</a> |
+  <a href="https://discord.gg/NjYzJD3GM3">Discord</a> |
   <a href="https://demo.ragflow.io">Demo</a>
 </h4>
 
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/ragflow-octoverse.png" width="1200"/>
+</div>
+
+<div align="center">
+<a href="https://trendshift.io/repositories/9064" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9064" alt="infiniflow%2Fragflow | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
+</div>
+
+<details open>
+<summary><b>📕 目录</b></summary>
+
+- 💡 [RAGFlow 是什么？](#-RAGFlow-是什么)
+- 🎮 [Demo](#-demo)
+- 📌 [近期更新](#-近期更新)
+- 🌟 [主要功能](#-主要功能)
+- 🔎 [系统架构](#-系统架构)
+- 🎬 [快速开始](#-快速开始)
+- 🔧 [系统配置](#-系统配置)
+- 🔨 [以源代码启动服务](#-以源代码启动服务)
+- 📚 [技术文档](#-技术文档)
+- 📜 [路线图](#-路线图)
+- 🏄 [贡献指南](#-贡献指南)
+- 🙌 [加入社区](#-加入社区)
+- 🤝 [商务合作](#-商务合作)
+
+</details>
+
 ## 💡 RAGFlow 是什么？
 
-[RAGFlow](https://ragflow.io/) 是一款基于深度文档理解构建的开源 RAG（Retrieval-Augmented Generation）引擎。RAGFlow 可以为各种规模的企业及个人提供一套精简的 RAG 工作流程，结合大语言模型（LLM）针对用户各类不同的复杂格式数据提供可靠的问答以及有理有据的引用。
+[RAGFlow](https://ragflow.io/) 是一款领先的开源检索增强生成（RAG）引擎，通过融合前沿的 RAG 技术与 Agent 能力，为大型语言模型提供卓越的上下文层。它提供可适配任意规模企业的端到端 RAG 工作流，凭借融合式上下文引擎与预置的 Agent 模板，助力开发者以极致效率与精度将复杂数据转化为高可信、生产级的人工智能系统。
 
 ## 🎮 Demo 试用
 
 请登录网址 [https://demo.ragflow.io](https://demo.ragflow.io) 试用 demo。
-<div align="center" style="margin-top:20px;margin-bottom:20px;">
-<img src="https://github.com/infiniflow/ragflow/assets/7248/2f6baa3e-1092-4f11-866d-36f6a9d075e5" width="1200"/>
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/b083d173-dadc-4ea9-bdeb-180d7df514eb" width="1200"/>
-</div>
 
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/chunking.gif" width="1200"/>
+<img src="https://raw.githubusercontent.com/infiniflow/ragflow-docs/refs/heads/image/image/agentic-dark.gif" width="1200"/>
+</div>
 
 ## 🔥 近期更新
 
-- 2024-09-13 增加知识库问答搜索模式。
-- 2024-09-09 在 Agent 中加入医疗问诊模板。
-- 2024-08-22 支持用RAG技术实现从自然语言到SQL语句的转换。
-- 2024-08-02 支持 GraphRAG 启发于 [graphrag](https://github.com/microsoft/graphrag) 和思维导图。
-- 2024-07-23 支持解析音频文件。
-- 2024-07-08 支持 Agentic RAG: 基于 [Graph](./agent/README.md) 的工作流。
-- 2024-06-27 Q&A 解析方式支持 Markdown 文件和 Docx 文件，支持提取出 Docx 文件中的图片和 Markdown 文件中的表格。
-- 2024-05-23 实现 [RAPTOR](https://arxiv.org/html/2401.18059v1) 提供更好的文本检索。
+- 2025-11-19 支持 Gemini 3 Pro.
+- 2025-11-12 支持从 Confluence、S3、Notion、Discord、Google Drive 进行数据同步。
+- 2025-10-23 支持 MinerU 和 Docling 作为文档解析方法。
+- 2025-10-15 支持可编排的数据管道。
+- 2025-08-08 支持 OpenAI 最新的 GPT-5 系列模型。
+- 2025-08-01 支持 agentic workflow 和 MCP。
+- 2025-05-23 Agent 新增 Python/JS 代码执行器组件。
+- 2025-05-05 支持跨语言查询。
+- 2025-03-19 PDF 和 DOCX 中的图支持用多模态大模型去解析得到描述.
+- 2024-12-18 升级了 DeepDoc 的文档布局分析模型。
+- 2024-08-22 支持用 RAG 技术实现从自然语言到 SQL 语句的转换。
+
+## 🎉 关注项目
+
+⭐️ 点击右上角的 Star 关注 RAGFlow，可以获取最新发布的实时通知 !🌟
+
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/user-attachments/assets/18c9707e-b8aa-4caf-a154-037089c105ba" width="1200"/>
+</div>
 
 ## 🌟 主要功能
 
@@ -87,7 +136,7 @@
 ## 🔎 系统架构
 
 <div align="center" style="margin-top:20px;margin-bottom:20px;">
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/d6ac5664-c237-4200-a7c2-a4a00691b485" width="1000"/>
+<img src="https://github.com/user-attachments/assets/31b0dd6f-ca4f-445a-9457-70cb44a381b2" width="1000"/>
 </div>
 
 ## 🎬 快速开始
@@ -98,7 +147,10 @@
 - RAM >= 16 GB
 - Disk >= 50 GB
 - Docker >= 24.0.0 & Docker Compose >= v2.26.1
-  > 如果你并没有在本机安装 Docker（Windows、Mac，或者 Linux）, 可以参考文档 [Install Docker Engine](https://docs.docker.com/engine/install/) 自行安装。
+- [gVisor](https://gvisor.dev/docs/user_guide/install/): 仅在你打算使用 RAGFlow 的代码执行器（沙箱）功能时才需要安装。
+
+> [!TIP]
+> 如果你并没有在本机安装 Docker（Windows、Mac，或者 Linux）, 可以参考文档 [Install Docker Engine](https://docs.docker.com/engine/install/) 自行安装。
 
 ### 🚀 启动服务器
 
@@ -131,42 +183,65 @@
 
 3. 进入 **docker** 文件夹，利用提前编译好的 Docker 镜像启动服务器：
 
+> [!CAUTION]
+> 请注意，目前官方提供的所有 Docker 镜像均基于 x86 架构构建，并不提供基于 ARM64 的 Docker 镜像。
+> 如果你的操作系统是 ARM64 架构，请参考[这篇文档](https://ragflow.io/docs/dev/build_docker_image)自行构建 Docker 镜像。
+
+   > 运行以下命令会自动下载 RAGFlow Docker 镜像 `v0.22.1`。请参考下表查看不同 Docker 发行版的描述。如需下载不同于 `v0.22.1` 的 Docker 镜像，请在运行 `docker compose` 启动服务之前先更新 **docker/.env** 文件内的 `RAGFLOW_IMAGE` 变量。
+
    ```bash
    $ cd ragflow/docker
-   $ chmod +x ./entrypoint.sh
-   $ docker compose -f docker-compose-CN.yml up -d
+   
+   # git checkout v0.22.1
+   # 可选：使用稳定版本标签（查看发布：https://github.com/infiniflow/ragflow/releases）
+   # 这一步确保代码中的 entrypoint.sh 文件与 Docker 镜像的版本保持一致。
+
+   # Use CPU for DeepDoc tasks:
+   $ docker compose -f docker-compose.yml up -d
+
+   # To use GPU to accelerate DeepDoc tasks:
+   # sed -i '1i DEVICE=gpu' .env
+   # docker compose -f docker-compose.yml up -d
    ```
+   
+   > 注意：在 `v0.22.0` 之前的版本，我们会同时提供包含 embedding 模型的镜像和不含 embedding 模型的 slim 镜像。具体如下：
 
-   > 请注意，运行上述命令会自动下载 RAGFlow 的开发版本 docker 镜像。如果你想下载并运行特定版本的 docker 镜像，请在 docker/.env 文件中找到 RAGFLOW_VERSION 变量，将其改为对应版本。例如 RAGFLOW_VERSION=v0.11.0，然后运行上述命令。
+   | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
+   | ----------------- | --------------- | --------------------- | ------------------------ |
+   | v0.21.1           | &approx;9       | ✔️                    | Stable release           |
+   | v0.21.1-slim      | &approx;2       | ❌                    | Stable release           |
 
-   > 核心镜像文件大约 9 GB，可能需要一定时间拉取。请耐心等待。
+   > 从 `v0.22.0` 开始，我们只发布 slim 版本，并且不再在镜像标签后附加 **-slim** 后缀。
+
+   > [!TIP]
+   > 如果你遇到 Docker 镜像拉不下来的问题，可以在 **docker/.env** 文件内根据变量 `RAGFLOW_IMAGE` 的注释提示选择华为云或者阿里云的相应镜像。
+   >
+   > - 华为云镜像名：`swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow`
+   > - 阿里云镜像名：`registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow`
 
 4. 服务器启动成功后再次确认服务器状态：
 
    ```bash
-   $ docker logs -f ragflow-server
+   $ docker logs -f docker-ragflow-cpu-1
    ```
 
    _出现以下界面提示说明服务器启动成功：_
 
    ```bash
-       ____                 ______ __
-      / __ \ ____ _ ____ _ / ____// /____  _      __
-     / /_/ // __ `// __ `// /_   / // __ \| | /| / /
-    / _, _// /_/ // /_/ // __/  / // /_/ /| |/ |/ /
-   /_/ |_| \__,_/ \__, //_/    /_/ \____/ |__/|__/
-                 /____/
+        ____   ___    ______ ______ __
+       / __ \ /   |  / ____// ____// /____  _      __
+      / /_/ // /| | / / __ / /_   / // __ \| | /| / /
+     / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ /
+    /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/
 
     * Running on all addresses (0.0.0.0)
-    * Running on http://127.0.0.1:9380
-    * Running on http://x.x.x.x:9380
-    INFO:werkzeug:Press CTRL+C to quit
    ```
-   > 如果您跳过这一步系统确认步骤就登录 RAGFlow，你的浏览器有可能会提示 `network abnormal` 或 `网络异常`，因为 RAGFlow 可能并未完全启动成功。
+
+   > 如果您在没有看到上面的提示信息出来之前，就尝试登录 RAGFlow，你的浏览器有可能会提示 `network anormal` 或 `网络异常`。
 
 5. 在你的浏览器中输入你的服务器对应的 IP 地址并登录 RAGFlow。
    > 上面这个例子中，您只需输入 http://IP_OF_YOUR_MACHINE 即可：未改动过配置则无需输入端口（默认的 HTTP 服务端口 80）。
-6. 在 [service_conf.yaml](./docker/service_conf.yaml) 文件的 `user_default_llm` 栏配置 LLM factory，并在 `API_KEY` 栏填写和你选择的大模型相对应的 API key。
+6. 在 [service_conf.yaml.template](./docker/service_conf.yaml.template) 文件的 `user_default_llm` 栏配置 LLM factory，并在 `API_KEY` 栏填写和你选择的大模型相对应的 API key。
 
    > 详见 [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup)。
 
@@ -177,143 +252,157 @@
 系统配置涉及以下三份文件：
 
 - [.env](./docker/.env)：存放一些基本的系统环境变量，比如 `SVR_HTTP_PORT`、`MYSQL_PASSWORD`、`MINIO_PASSWORD` 等。
-- [service_conf.yaml](./docker/service_conf.yaml)：配置各类后台服务。
-- [docker-compose-CN.yml](./docker/docker-compose-CN.yml): 系统依赖该文件完成启动。
+- [service_conf.yaml.template](./docker/service_conf.yaml.template)：配置各类后台服务。
+- [docker-compose.yml](./docker/docker-compose.yml): 系统依赖该文件完成启动。
 
-请务必确保 [.env](./docker/.env) 文件中的变量设置与 [service_conf.yaml](./docker/service_conf.yaml) 文件中的配置保持一致！
+请务必确保 [.env](./docker/.env) 文件中的变量设置与 [service_conf.yaml.template](./docker/service_conf.yaml.template) 文件中的配置保持一致！
 
-> [./docker/README](./docker/README.md) 文件提供了环境变量设置和服务配置的详细信息。请**一定要**确保 [./docker/README](./docker/README.md) 文件当中列出来的环境变量的值与 [service_conf.yaml](./docker/service_conf.yaml) 文件当中的系统配置保持一致。
+如果不能访问镜像站点 hub.docker.com 或者模型站点 huggingface.co，请按照 [.env](./docker/.env) 注释修改 `RAGFLOW_IMAGE` 和 `HF_ENDPOINT`。
 
-如需更新默认的 HTTP 服务端口(80), 可以在 [docker-compose-CN.yml](./docker/docker-compose-CN.yml) 文件中将配置 `80:80` 改为 `<YOUR_SERVING_PORT>:80`。
+> [./docker/README](./docker/README.md) 解释了 [service_conf.yaml.template](./docker/service_conf.yaml.template) 用到的环境变量设置和服务配置。
+
+如需更新默认的 HTTP 服务端口(80), 可以在 [docker-compose.yml](./docker/docker-compose.yml) 文件中将配置 `80:80` 改为 `<YOUR_SERVING_PORT>:80`。
 
 > 所有系统配置都需要通过系统重启生效：
 >
 > ```bash
-> $ docker compose -f docker-compose-CN.yml up -d
+> $ docker compose -f docker-compose.yml up -d
 > ```
 
-## 🛠️ 源码编译、安装 Docker 镜像
+### 把文档引擎从 Elasticsearch 切换成为 Infinity
 
-如需从源码安装 Docker 镜像：
+RAGFlow 默认使用 Elasticsearch 存储文本和向量数据. 如果要切换为 [Infinity](https://github.com/infiniflow/infinity/), 可以按照下面步骤进行:
+
+1. 停止所有容器运行:
+
+   ```bash
+   $ docker compose -f docker/docker-compose.yml down -v
+   ```
+   Note: `-v` 将会删除 docker 容器的 volumes，已有的数据会被清空。
+
+2. 设置 **docker/.env** 目录中的 `DOC_ENGINE` 为 `infinity`.
+
+3. 启动容器:
+
+   ```bash
+   $ docker compose -f docker-compose.yml up -d
+   ```
+
+> [!WARNING]
+> Infinity 目前官方并未正式支持在 Linux/arm64 架构下的机器上运行.
+
+## 🔧 源码编译 Docker 镜像
+
+本 Docker 镜像大小约 2 GB 左右并且依赖外部的大模型和 embedding 服务。
 
 ```bash
-$ git clone https://github.com/infiniflow/ragflow.git
-$ cd ragflow/
-$ docker build -t infiniflow/ragflow:v0.11.0 .
-$ cd ragflow/docker
-$ chmod +x ./entrypoint.sh
-$ docker compose up -d
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .
 ```
 
-## 🛠️ 源码启动服务
+## 🔨 以源代码启动服务
 
-如需从源码启动服务，请参考以下步骤：
+1. 安装 `uv` 和 `pre-commit`。如已经安装，可跳过本步骤：
 
-1. 克隆仓库
+   ```bash
+   pipx install uv pre-commit
+   export UV_INDEX=https://mirrors.aliyun.com/pypi/simple
+   ```
 
-```bash
-$ git clone https://github.com/infiniflow/ragflow.git
-$ cd ragflow/
-```
+2. 下载源代码并安装 Python 依赖：
 
-2. 创建虚拟环境（确保已安装 Anaconda 或 Miniconda）
+   ```bash
+   git clone https://github.com/infiniflow/ragflow.git
+   cd ragflow/
+   uv sync --python 3.10 # install RAGFlow dependent python modules
+   uv run download_deps.py
+   pre-commit install
+   ```
 
-```bash
-$ conda create -n ragflow python=3.11.0
-$ conda activate ragflow
-$ pip install -r requirements.txt
-```
-如果 cuda > 12.0，需额外执行以下命令：
-```bash
-$ pip uninstall -y onnxruntime-gpu
-$ pip install onnxruntime-gpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/
-```
+3. 通过 Docker Compose 启动依赖的服务（MinIO, Elasticsearch, Redis, and MySQL）：
 
-3. 拷贝入口脚本并配置环境变量
+   ```bash
+   docker compose -f docker/docker-compose-base.yml up -d
+   ```
 
-```bash
-$ cp docker/entrypoint.sh .
-$ vi entrypoint.sh
-```
-使用以下命令获取python路径及ragflow项目路径：
-```bash
-$ which python
-$ pwd
-```
+   在 `/etc/hosts` 中添加以下代码，目的是将 **conf/service_conf.yaml** 文件中的所有 host 地址都解析为 `127.0.0.1`：
 
-将上述 `which python` 的输出作为 `PY` 的值，将 `pwd` 的输出作为 `PYTHONPATH` 的值。
+   ```
+   127.0.0.1       es01 infinity mysql minio redis sandbox-executor-manager
+   ```
+4. 如果无法访问 HuggingFace，可以把环境变量 `HF_ENDPOINT` 设成相应的镜像站点：
 
-`LD_LIBRARY_PATH` 如果环境已经配置好，可以注释掉。
+   ```bash
+   export HF_ENDPOINT=https://hf-mirror.com
+   ```
 
-```bash
-# 此处配置需要按照实际情况调整，两个 export 为新增配置
-PY=${PY}
-export PYTHONPATH=${PYTHONPATH}
-# 可选：添加 Hugging Face 镜像
-export HF_ENDPOINT=https://hf-mirror.com
-```
+5. 如果你的操作系统没有 jemalloc，请按照如下方式安装：
 
-4. 启动基础服务
+   ```bash
+   # ubuntu
+   sudo apt-get install libjemalloc-dev
+   # centos
+   sudo yum install jemalloc
+   # mac
+   sudo brew install jemalloc
+   ```
 
-```bash
-$ cd docker
-$ docker compose -f docker-compose-base.yml up -d 
-```
+6. 启动后端服务：
 
-5. 检查配置文件
-确保**docker/.env**中的配置与**conf/service_conf.yaml**中配置一致， **service_conf.yaml**中相关服务的IP地址与端口应该改成本机IP地址及容器映射出来的端口。
+   ```bash
+   source .venv/bin/activate
+   export PYTHONPATH=$(pwd)
+   bash docker/launch_backend_service.sh
+   ```
 
-6. 启动服务
+7. 安装前端依赖：
 
-```bash
-$ chmod +x ./entrypoint.sh
-$ bash ./entrypoint.sh
-```
+   ```bash
+   cd web
+   npm install
+   ```
 
-7. 启动WebUI服务
+8. 启动前端服务：
 
-```bash
-$ cd web
-$ npm install --registry=https://registry.npmmirror.com --force
-$ vim .umirc.ts
-# 修改proxy.target为http://127.0.0.1:9380
-$ npm run dev 
-```
+   ```bash
+   npm run dev
+   ```
+
+   _以下界面说明系统已经成功启动：_
+
+   ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
+
+9. 开发完成后停止 RAGFlow 前端和后端服务：
+
+   ```bash
+   pkill -f "ragflow_server.py|task_executor.py"
+   ```
 
-8. 部署WebUI服务
 
-```bash
-$ cd web
-$ npm install --registry=https://registry.npmmirror.com --force
-$ umi build
-$ mkdir -p /ragflow/web
-$ cp -r dist /ragflow/web
-$ apt install nginx -y
-$ cp ../docker/nginx/proxy.conf /etc/nginx
-$ cp ../docker/nginx/nginx.conf /etc/nginx
-$ cp ../docker/nginx/ragflow.conf /etc/nginx/conf.d
-$ systemctl start nginx
-```
 ## 📚 技术文档
 
 - [Quickstart](https://ragflow.io/docs/dev/)
-- [User guide](https://ragflow.io/docs/dev/category/user-guides)
+- [Configuration](https://ragflow.io/docs/dev/configurations)
+- [Release notes](https://ragflow.io/docs/dev/release_notes)
+- [User guides](https://ragflow.io/docs/dev/category/guides)
+- [Developer guides](https://ragflow.io/docs/dev/category/developers)
 - [References](https://ragflow.io/docs/dev/category/references)
-- [FAQ](https://ragflow.io/docs/dev/faq)
+- [FAQs](https://ragflow.io/docs/dev/faq)
 
 ## 📜 路线图
 
-详见 [RAGFlow Roadmap 2024](https://github.com/infiniflow/ragflow/issues/162) 。
+详见 [RAGFlow Roadmap 2025](https://github.com/infiniflow/ragflow/issues/4214) 。
 
 ## 🏄 开源社区
 
-- [Discord](https://discord.gg/4XxujFgUN7)
+- [Discord](https://discord.gg/zd4qPW6t)
 - [Twitter](https://twitter.com/infiniflowai)
 - [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
 
 ## 🙌 贡献指南
 
-RAGFlow 只有通过开源协作才能蓬勃发展。秉持这一精神,我们欢迎来自社区的各种贡献。如果您有意参与其中,请查阅我们的 [贡献者指南](./docs/references/CONTRIBUTING.md) 。
+RAGFlow 只有通过开源协作才能蓬勃发展。秉持这一精神,我们欢迎来自社区的各种贡献。如果您有意参与其中,请查阅我们的 [贡献者指南](https://ragflow.io/docs/dev/contributing) 。
 
 ## 🤝 商务合作
 
@@ -326,4 +415,3 @@ RAGFlow 只有通过开源协作才能蓬勃发展。秉持这一精神,我们
 <p align="center">
   <img src="https://github.com/infiniflow/ragflow/assets/7248/bccf284f-46f2-4445-9809-8f1030fb7585" width=50% height=50%>
 </p>
-

admin/build_cli_release.sh

@@ -0,0 +1,47 @@
+#!/bin/bash
+
+set -e
+
+echo "🚀 Start building..."
+echo "================================"
+
+PROJECT_NAME="ragflow-cli"
+
+RELEASE_DIR="release"
+BUILD_DIR="dist"
+SOURCE_DIR="src"
+PACKAGE_DIR="ragflow_cli"
+
+echo "🧹 Clean old build folder..."
+rm -rf release/
+
+echo "📁 Prepare source code..."
+mkdir release/$PROJECT_NAME/$SOURCE_DIR -p
+cp pyproject.toml release/$PROJECT_NAME/pyproject.toml
+cp README.md release/$PROJECT_NAME/README.md
+
+mkdir release/$PROJECT_NAME/$SOURCE_DIR/$PACKAGE_DIR -p
+cp admin_client.py release/$PROJECT_NAME/$SOURCE_DIR/$PACKAGE_DIR/admin_client.py
+
+if [ -d "release/$PROJECT_NAME/$SOURCE_DIR" ]; then
+    echo "✅ source dir: release/$PROJECT_NAME/$SOURCE_DIR"
+else
+    echo "❌ source dir not exist: release/$PROJECT_NAME/$SOURCE_DIR"
+    exit 1
+fi
+
+echo "🔨 Make build file..."
+cd release/$PROJECT_NAME
+export PYTHONPATH=$(pwd)
+python -m build
+
+echo "✅ check build result..."
+if [ -d "$BUILD_DIR" ]; then
+    echo "📦 Package generated:"
+    ls -la $BUILD_DIR/
+else
+    echo "❌ Build Failed: $BUILD_DIR not exist."
+    exit 1
+fi
+
+echo "🎉 Build finished successfully!"

admin/client/README.md

@@ -0,0 +1,136 @@
+# RAGFlow Admin Service & CLI
+
+### Introduction
+
+Admin Service is a dedicated management component designed to monitor, maintain, and administrate the RAGFlow system. It provides comprehensive tools for ensuring system stability, performing operational tasks, and managing users and permissions efficiently.
+
+The service offers real-time monitoring of critical components, including the RAGFlow server, Task Executor processes, and dependent services such as MySQL, Infinity, Elasticsearch, Redis, and MinIO. It automatically checks their health status, resource usage, and uptime, and performs restarts in case of failures to minimize downtime.
+
+For user and system management, it supports listing, creating, modifying, and deleting users and their associated resources like knowledge bases and Agents.
+
+Built with scalability and reliability in mind, the Admin Service ensures smooth system operation and simplifies maintenance workflows.
+
+It consists of a server-side Service and a command-line client (CLI), both implemented in Python. User commands are parsed using the Lark parsing toolkit.
+
+- **Admin Service**: A backend service that interfaces with the RAGFlow system to execute administrative operations and monitor its status.
+- **Admin CLI**: A command-line interface that allows users to connect to the Admin Service and issue commands for system management.
+
+
+
+### Starting the Admin Service
+
+#### Launching from source code
+
+1. Before start Admin Service, please make sure RAGFlow system is already started.
+
+2. Launch from source code:
+
+   ```bash
+   python admin/server/admin_server.py
+   ```
+   The service will start and listen for incoming connections from the CLI on the configured port. 
+
+#### Using docker image
+
+1. Before startup, please configure the `docker_compose.yml`  file to enable admin server:
+
+   ```bash
+   command:
+     - --enable-adminserver
+   ```
+
+2. Start the containers, the service will start and listen for incoming connections from the CLI on the configured port.
+
+
+
+### Using the Admin CLI
+
+1.  Ensure the Admin Service is running.
+2.  Install ragflow-cli.
+    ```bash
+    pip install ragflow-cli==0.22.1
+    ```
+3.  Launch the CLI client:
+    ```bash
+    ragflow-cli -h 127.0.0.1 -p 9381
+    ```
+    You will be prompted to enter the superuser's password to log in.
+    The default password is admin.
+
+    **Parameters:**
+    
+    - -h: RAGFlow admin server host address
+    
+    - -p: RAGFlow admin server port
+
+
+
+## Supported Commands
+
+Commands are case-insensitive and must be terminated with a semicolon (`;`).
+
+### Service Management Commands
+
+-   `LIST SERVICES;`
+    -   Lists all available services within the RAGFlow system.
+-   `SHOW SERVICE <id>;`
+    -   Shows detailed status information for the service identified by `<id>`.
+
+
+### User Management Commands
+
+-   `LIST USERS;`
+    -   Lists all users known to the system.
+-   `SHOW USER '<username>';`
+    -   Shows details and permissions for the specified user. The username must be enclosed in single or double quotes.
+
+- `CREATE USER <username> <password>;`
+  - Create user by username and password. The username and password must be enclosed in single or double quotes.
+
+-   `DROP USER '<username>';`
+    -   Removes the specified user from the system. Use with caution.
+-   `ALTER USER PASSWORD '<username>' '<new_password>';`
+    -   Changes the password for the specified user.
+-   `ALTER USER ACTIVE <username> <on/off>;`
+    -   Changes the user to active or inactive.
+
+
+### Data and Agent Commands
+
+-   `LIST DATASETS OF '<username>';`
+    -   Lists the datasets associated with the specified user.
+-   `LIST AGENTS OF '<username>';`
+    -   Lists the agents associated with the specified user.
+
+### Meta-Commands
+
+Meta-commands are prefixed with a backslash (`\`).
+
+-   `\?` or `\help`
+    -   Shows help information for the available commands.
+-   `\q` or `\quit`
+    -   Exits the CLI application.
+
+## Examples
+
+```commandline
+admin> list users;
++-------------------------------+------------------------+-----------+-------------+
+| create_date                   | email                  | is_active | nickname    |
++-------------------------------+------------------------+-----------+-------------+
+| Fri, 22 Nov 2024 16:03:41 GMT | jeffery@infiniflow.org | 1         | Jeffery     |
+| Fri, 22 Nov 2024 16:10:55 GMT | aya@infiniflow.org     | 1         | Waterdancer |
++-------------------------------+------------------------+-----------+-------------+
+
+admin> list services;
++-------------------------------------------------------------------------------------------+-----------+----+---------------+-------+----------------+
+| extra                                                                                     | host      | id | name          | port  | service_type   |
++-------------------------------------------------------------------------------------------+-----------+----+---------------+-------+----------------+
+| {}                                                                                        | 0.0.0.0   | 0  | ragflow_0     | 9380  | ragflow_server |
+| {'meta_type': 'mysql', 'password': 'infini_rag_flow', 'username': 'root'}                 | localhost | 1  | mysql         | 5455  | meta_data      |
+| {'password': 'infini_rag_flow', 'store_type': 'minio', 'user': 'rag_flow'}                | localhost | 2  | minio         | 9000  | file_store     |
+| {'password': 'infini_rag_flow', 'retrieval_type': 'elasticsearch', 'username': 'elastic'} | localhost | 3  | elasticsearch | 1200  | retrieval      |
+| {'db_name': 'default_db', 'retrieval_type': 'infinity'}                                   | localhost | 4  | infinity      | 23817 | retrieval      |
+| {'database': 1, 'mq_type': 'redis', 'password': 'infini_rag_flow'}                        | localhost | 5  | redis         | 6379  | message_queue  |
++-------------------------------------------------------------------------------------------+-----------+----+---------------+-------+----------------+
+```

admin/client/admin_client.py

@@ -0,0 +1,978 @@
+#
+#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+
+import argparse
+import base64
+from cmd import Cmd
+
+from Cryptodome.PublicKey import RSA
+from Cryptodome.Cipher import PKCS1_v1_5 as Cipher_pkcs1_v1_5
+from typing import Dict, List, Any
+from lark import Lark, Transformer, Tree
+import requests
+import getpass
+
+GRAMMAR = r"""
+start: command
+
+command: sql_command | meta_command
+
+sql_command: list_services
+           | show_service
+           | startup_service
+           | shutdown_service
+           | restart_service
+           | list_users
+           | show_user
+           | drop_user
+           | alter_user
+           | create_user
+           | activate_user
+           | list_datasets
+           | list_agents
+           | create_role
+           | drop_role
+           | alter_role
+           | list_roles
+           | show_role
+           | grant_permission
+           | revoke_permission
+           | alter_user_role
+           | show_user_permission
+           | show_version
+
+// meta command definition
+meta_command: "\\" meta_command_name [meta_args]
+
+meta_command_name: /[a-zA-Z?]+/
+meta_args: (meta_arg)+
+
+meta_arg: /[^\\s"']+/ | quoted_string
+
+// command definition
+
+LIST: "LIST"i
+SERVICES: "SERVICES"i
+SHOW: "SHOW"i
+CREATE: "CREATE"i
+SERVICE: "SERVICE"i
+SHUTDOWN: "SHUTDOWN"i
+STARTUP: "STARTUP"i
+RESTART: "RESTART"i
+USERS: "USERS"i
+DROP: "DROP"i
+USER: "USER"i
+ALTER: "ALTER"i
+ACTIVE: "ACTIVE"i
+PASSWORD: "PASSWORD"i
+DATASETS: "DATASETS"i
+OF: "OF"i
+AGENTS: "AGENTS"i
+ROLE: "ROLE"i
+ROLES: "ROLES"i
+DESCRIPTION: "DESCRIPTION"i
+GRANT: "GRANT"i
+REVOKE: "REVOKE"i
+ALL: "ALL"i
+PERMISSION: "PERMISSION"i
+TO: "TO"i
+FROM: "FROM"i
+FOR: "FOR"i
+RESOURCES: "RESOURCES"i
+ON: "ON"i
+SET: "SET"i
+VERSION: "VERSION"i
+
+list_services: LIST SERVICES ";"
+show_service: SHOW SERVICE NUMBER ";"
+startup_service: STARTUP SERVICE NUMBER ";"
+shutdown_service: SHUTDOWN SERVICE NUMBER ";"
+restart_service: RESTART SERVICE NUMBER ";"
+
+list_users: LIST USERS ";"
+drop_user: DROP USER quoted_string ";"
+alter_user: ALTER USER PASSWORD quoted_string quoted_string ";"
+show_user: SHOW USER quoted_string ";"
+create_user: CREATE USER quoted_string quoted_string ";"
+activate_user: ALTER USER ACTIVE quoted_string status ";"
+
+list_datasets: LIST DATASETS OF quoted_string ";"
+list_agents: LIST AGENTS OF quoted_string ";"
+
+create_role: CREATE ROLE identifier [DESCRIPTION quoted_string] ";"
+drop_role: DROP ROLE identifier ";"
+alter_role: ALTER ROLE identifier SET DESCRIPTION quoted_string ";"
+list_roles: LIST ROLES ";"
+show_role: SHOW ROLE identifier ";"
+
+grant_permission: GRANT action_list ON identifier TO ROLE identifier ";"
+revoke_permission: REVOKE action_list ON identifier FROM ROLE identifier ";"
+alter_user_role: ALTER USER quoted_string SET ROLE identifier ";"
+show_user_permission: SHOW USER PERMISSION quoted_string ";"
+
+show_version: SHOW VERSION ";"
+
+action_list: identifier ("," identifier)*
+
+identifier: WORD
+quoted_string: QUOTED_STRING
+status: WORD
+
+QUOTED_STRING: /'[^']+'/ | /"[^"]+"/
+WORD: /[a-zA-Z0-9_\-\.]+/
+NUMBER: /[0-9]+/
+
+%import common.WS
+%ignore WS
+"""
+
+
+class AdminTransformer(Transformer):
+
+    def start(self, items):
+        return items[0]
+
+    def command(self, items):
+        return items[0]
+
+    def list_services(self, items):
+        result = {'type': 'list_services'}
+        return result
+
+    def show_service(self, items):
+        service_id = int(items[2])
+        return {"type": "show_service", "number": service_id}
+
+    def startup_service(self, items):
+        service_id = int(items[2])
+        return {"type": "startup_service", "number": service_id}
+
+    def shutdown_service(self, items):
+        service_id = int(items[2])
+        return {"type": "shutdown_service", "number": service_id}
+
+    def restart_service(self, items):
+        service_id = int(items[2])
+        return {"type": "restart_service", "number": service_id}
+
+    def list_users(self, items):
+        return {"type": "list_users"}
+
+    def show_user(self, items):
+        user_name = items[2]
+        return {"type": "show_user", "user_name": user_name}
+
+    def drop_user(self, items):
+        user_name = items[2]
+        return {"type": "drop_user", "user_name": user_name}
+
+    def alter_user(self, items):
+        user_name = items[3]
+        new_password = items[4]
+        return {"type": "alter_user", "user_name": user_name, "password": new_password}
+
+    def create_user(self, items):
+        user_name = items[2]
+        password = items[3]
+        return {"type": "create_user", "user_name": user_name, "password": password, "role": "user"}
+
+    def activate_user(self, items):
+        user_name = items[3]
+        activate_status = items[4]
+        return {"type": "activate_user", "activate_status": activate_status, "user_name": user_name}
+
+    def list_datasets(self, items):
+        user_name = items[3]
+        return {"type": "list_datasets", "user_name": user_name}
+
+    def list_agents(self, items):
+        user_name = items[3]
+        return {"type": "list_agents", "user_name": user_name}
+
+    def create_role(self, items):
+        role_name = items[2]
+        if len(items) > 4:
+            description = items[4]
+            return {"type": "create_role", "role_name": role_name, "description": description}
+        else:
+            return {"type": "create_role", "role_name": role_name}
+
+    def drop_role(self, items):
+        role_name = items[2]
+        return {"type": "drop_role", "role_name": role_name}
+
+    def alter_role(self, items):
+        role_name = items[2]
+        description = items[5]
+        return {"type": "alter_role", "role_name": role_name, "description": description}
+
+    def list_roles(self, items):
+        return {"type": "list_roles"}
+
+    def show_role(self, items):
+        role_name = items[2]
+        return {"type": "show_role", "role_name": role_name}
+
+    def grant_permission(self, items):
+        action_list = items[1]
+        resource = items[3]
+        role_name = items[6]
+        return {"type": "grant_permission", "role_name": role_name, "resource": resource, "actions": action_list}
+
+    def revoke_permission(self, items):
+        action_list = items[1]
+        resource = items[3]
+        role_name = items[6]
+        return {
+            "type": "revoke_permission",
+            "role_name": role_name,
+            "resource": resource, "actions": action_list
+        }
+
+    def alter_user_role(self, items):
+        user_name = items[2]
+        role_name = items[5]
+        return {"type": "alter_user_role", "user_name": user_name, "role_name": role_name}
+
+    def show_user_permission(self, items):
+        user_name = items[3]
+        return {"type": "show_user_permission", "user_name": user_name}
+
+    def show_version(self, items):
+        return {"type": "show_version"}
+
+    def action_list(self, items):
+        return items
+
+    def meta_command(self, items):
+        command_name = str(items[0]).lower()
+        args = items[1:] if len(items) > 1 else []
+
+        # handle quoted parameter
+        parsed_args = []
+        for arg in args:
+            if hasattr(arg, 'value'):
+                parsed_args.append(arg.value)
+            else:
+                parsed_args.append(str(arg))
+
+        return {'type': 'meta', 'command': command_name, 'args': parsed_args}
+
+    def meta_command_name(self, items):
+        return items[0]
+
+    def meta_args(self, items):
+        return items
+
+
+def encrypt(input_string):
+    pub = '-----BEGIN PUBLIC KEY-----\nMIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEArq9XTUSeYr2+N1h3Afl/z8Dse/2yD0ZGrKwx+EEEcdsBLca9Ynmx3nIB5obmLlSfmskLpBo0UACBmB5rEjBp2Q2f3AG3Hjd4B+gNCG6BDaawuDlgANIhGnaTLrIqWrrcm4EMzJOnAOI1fgzJRsOOUEfaS318Eq9OVO3apEyCCt0lOQK6PuksduOjVxtltDav+guVAA068NrPYmRNabVKRNLJpL8w4D44sfth5RvZ3q9t+6RTArpEtc5sh5ChzvqPOzKGMXW83C95TxmXqpbK6olN4RevSfVjEAgCydH6HN6OhtOQEcnrU97r9H0iZOWwbw3pVrZiUkuRD1R56Wzs2wIDAQAB\n-----END PUBLIC KEY-----'
+    pub_key = RSA.importKey(pub)
+    cipher = Cipher_pkcs1_v1_5.new(pub_key)
+    cipher_text = cipher.encrypt(base64.b64encode(input_string.encode('utf-8')))
+    return base64.b64encode(cipher_text).decode("utf-8")
+
+
+def encode_to_base64(input_string):
+    base64_encoded = base64.b64encode(input_string.encode('utf-8'))
+    return base64_encoded.decode('utf-8')
+
+
+class AdminCLI(Cmd):
+    def __init__(self):
+        super().__init__()
+        self.parser = Lark(GRAMMAR, start='start', parser='lalr', transformer=AdminTransformer())
+        self.command_history = []
+        self.is_interactive = False
+        self.admin_account = "admin@ragflow.io"
+        self.admin_password: str = "admin"
+        self.session = requests.Session()
+        self.access_token: str = ""
+        self.host: str = ""
+        self.port: int = 0
+
+    intro = r"""Type "\h" for help."""
+    prompt = "admin> "
+
+    def onecmd(self, command: str) -> bool:
+        try:
+            result = self.parse_command(command)
+
+            if isinstance(result, dict):
+                if 'type' in result and result.get('type') == 'empty':
+                    return False
+
+            self.execute_command(result)
+
+            if isinstance(result, Tree):
+                return False
+
+            if result.get('type') == 'meta' and result.get('command') in ['q', 'quit', 'exit']:
+                return True
+
+        except KeyboardInterrupt:
+            print("\nUse '\\q' to quit")
+        except EOFError:
+            print("\nGoodbye!")
+            return True
+        return False
+
+    def emptyline(self) -> bool:
+        return False
+
+    def default(self, line: str) -> bool:
+        return self.onecmd(line)
+
+    def parse_command(self, command_str: str) -> dict[str, str]:
+        if not command_str.strip():
+            return {'type': 'empty'}
+
+        self.command_history.append(command_str)
+
+        try:
+            result = self.parser.parse(command_str)
+            return result
+        except Exception as e:
+            return {'type': 'error', 'message': f'Parse error: {str(e)}'}
+
+    def verify_admin(self, arguments: dict, single_command: bool):
+        self.host = arguments['host']
+        self.port = arguments['port']
+        print(f"Attempt to access ip: {self.host}, port: {self.port}")
+        url = f"http://{self.host}:{self.port}/api/v1/admin/login"
+
+        attempt_count = 3
+        if single_command:
+            attempt_count = 1
+
+        try_count = 0
+        while True:
+            try_count += 1
+            if try_count > attempt_count:
+                return False
+
+            if single_command:
+                admin_passwd = arguments['password']
+            else:
+                admin_passwd = getpass.getpass(f"password for {self.admin_account}: ").strip()
+            try:
+                self.admin_password = encrypt(admin_passwd)
+                response = self.session.post(url, json={'email': self.admin_account, 'password': self.admin_password})
+                if response.status_code == 200:
+                    res_json = response.json()
+                    error_code = res_json.get('code', -1)
+                    if error_code == 0:
+                        self.session.headers.update({
+                            'Content-Type': 'application/json',
+                            'Authorization': response.headers['Authorization'],
+                            'User-Agent': 'RAGFlow-CLI/0.22.1'
+                        })
+                        print("Authentication successful.")
+                        return True
+                    else:
+                        error_message = res_json.get('message', 'Unknown error')
+                        print(f"Authentication failed: {error_message}, try again")
+                        continue
+                else:
+                    print(f"Bad response，status: {response.status_code}, password is wrong")
+            except Exception as e:
+                print(str(e))
+                print(f"Can't access {self.host}, port: {self.port}")
+
+    def _format_service_detail_table(self, data):
+        if isinstance(data, list):
+            return data
+        if not all([isinstance(v, list) for v in data.values()]):
+            # normal table
+            return data
+        # handle task_executor heartbeats map, for example {'name': [{'done': 2, 'now': timestamp1}, {'done': 3, 'now': timestamp2}]
+        task_executor_list = []
+        for k, v in data.items():
+            # display latest status
+            heartbeats = sorted(v, key=lambda x: x["now"], reverse=True)
+            task_executor_list.append({
+                "task_executor_name": k,
+                **heartbeats[0],
+            } if heartbeats else {"task_executor_name": k})
+        return task_executor_list
+
+    def _print_table_simple(self, data):
+        if not data:
+            print("No data to print")
+            return
+        if isinstance(data, dict):
+            # handle single row data
+            data = [data]
+
+        columns = list(set().union(*(d.keys() for d in data)))
+        columns.sort()
+        col_widths = {}
+
+        def get_string_width(text):
+            half_width_chars = (
+                " !\"#$%&'()*+,-./0123456789:;<=>?@"
+                "ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`"
+                "abcdefghijklmnopqrstuvwxyz{|}~"
+                "\t\n\r"
+            )
+            width = 0
+            for char in text:
+                if char in half_width_chars:
+                    width += 1
+                else:
+                    width += 2
+            return width
+
+        for col in columns:
+            max_width = get_string_width(str(col))
+            for item in data:
+                value_len = get_string_width(str(item.get(col, '')))
+                if value_len > max_width:
+                    max_width = value_len
+            col_widths[col] = max(2, max_width)
+
+        # Generate delimiter
+        separator = "+" + "+".join(["-" * (col_widths[col] + 2) for col in columns]) + "+"
+
+        # Print header
+        print(separator)
+        header = "|" + "|".join([f" {col:<{col_widths[col]}} " for col in columns]) + "|"
+        print(header)
+        print(separator)
+
+        # Print data
+        for item in data:
+            row = "|"
+            for col in columns:
+                value = str(item.get(col, ''))
+                if get_string_width(value) > col_widths[col]:
+                    value = value[:col_widths[col] - 3] + "..."
+                row += f" {value:<{col_widths[col] - (get_string_width(value) - len(value))}} |"
+            print(row)
+
+        print(separator)
+
+    def run_interactive(self):
+
+        self.is_interactive = True
+        print("RAGFlow Admin command line interface - Type '\\?' for help, '\\q' to quit")
+
+        while True:
+            try:
+                command = input("admin> ").strip()
+                if not command:
+                    continue
+
+                print(f"command: {command}")
+                result = self.parse_command(command)
+                self.execute_command(result)
+
+                if isinstance(result, Tree):
+                    continue
+
+                if result.get('type') == 'meta' and result.get('command') in ['q', 'quit', 'exit']:
+                    break
+
+            except KeyboardInterrupt:
+                print("\nUse '\\q' to quit")
+            except EOFError:
+                print("\nGoodbye!")
+                break
+
+    def run_single_command(self, command: str):
+        result = self.parse_command(command)
+        self.execute_command(result)
+
+    def parse_connection_args(self, args: List[str]) -> Dict[str, Any]:
+        parser = argparse.ArgumentParser(description='Admin CLI Client', add_help=False)
+        parser.add_argument('-h', '--host', default='localhost', help='Admin service host')
+        parser.add_argument('-p', '--port', type=int, default=9381, help='Admin service port')
+        parser.add_argument('-w', '--password', default='admin', type=str, help='Superuser password')
+        parser.add_argument('command', nargs='?', help='Single command')
+        try:
+            parsed_args, remaining_args = parser.parse_known_args(args)
+            if remaining_args:
+                command = remaining_args[0]
+                return {
+                    'host': parsed_args.host,
+                    'port': parsed_args.port,
+                    'password': parsed_args.password,
+                    'command': command
+                }
+            else:
+                return {
+                    'host': parsed_args.host,
+                    'port': parsed_args.port,
+                }
+        except SystemExit:
+            return {'error': 'Invalid connection arguments'}
+
+    def execute_command(self, parsed_command: Dict[str, Any]):
+
+        command_dict: dict
+        if isinstance(parsed_command, Tree):
+            command_dict = parsed_command.children[0]
+        else:
+            if parsed_command['type'] == 'error':
+                print(f"Error: {parsed_command['message']}")
+                return
+            else:
+                command_dict = parsed_command
+
+        # print(f"Parsed command: {command_dict}")
+
+        command_type = command_dict['type']
+
+        match command_type:
+            case 'list_services':
+                self._handle_list_services(command_dict)
+            case 'show_service':
+                self._handle_show_service(command_dict)
+            case 'restart_service':
+                self._handle_restart_service(command_dict)
+            case 'shutdown_service':
+                self._handle_shutdown_service(command_dict)
+            case 'startup_service':
+                self._handle_startup_service(command_dict)
+            case 'list_users':
+                self._handle_list_users(command_dict)
+            case 'show_user':
+                self._handle_show_user(command_dict)
+            case 'drop_user':
+                self._handle_drop_user(command_dict)
+            case 'alter_user':
+                self._handle_alter_user(command_dict)
+            case 'create_user':
+                self._handle_create_user(command_dict)
+            case 'activate_user':
+                self._handle_activate_user(command_dict)
+            case 'list_datasets':
+                self._handle_list_datasets(command_dict)
+            case 'list_agents':
+                self._handle_list_agents(command_dict)
+            case 'create_role':
+                self._create_role(command_dict)
+            case 'drop_role':
+                self._drop_role(command_dict)
+            case 'alter_role':
+                self._alter_role(command_dict)
+            case 'list_roles':
+                self._list_roles(command_dict)
+            case 'show_role':
+                self._show_role(command_dict)
+            case 'grant_permission':
+                self._grant_permission(command_dict)
+            case 'revoke_permission':
+                self._revoke_permission(command_dict)
+            case 'alter_user_role':
+                self._alter_user_role(command_dict)
+            case 'show_user_permission':
+                self._show_user_permission(command_dict)
+            case 'show_version':
+                self._show_version(command_dict)
+            case 'meta':
+                self._handle_meta_command(command_dict)
+            case _:
+                print(f"Command '{command_type}' would be executed with API")
+
+    def _handle_list_services(self, command):
+        print("Listing all services")
+
+        url = f'http://{self.host}:{self.port}/api/v1/admin/services'
+        response = self.session.get(url)
+        res_json = response.json()
+        if response.status_code == 200:
+            self._print_table_simple(res_json['data'])
+        else:
+            print(f"Fail to get all services, code: {res_json['code']}, message: {res_json['message']}")
+
+    def _handle_show_service(self, command):
+        service_id: int = command['number']
+        print(f"Showing service: {service_id}")
+
+        url = f'http://{self.host}:{self.port}/api/v1/admin/services/{service_id}'
+        response = self.session.get(url)
+        res_json = response.json()
+        if response.status_code == 200:
+            res_data = res_json['data']
+            if 'status' in res_data and res_data['status'] == 'alive':
+                print(f"Service {res_data['service_name']} is alive, ")
+                if isinstance(res_data['message'], str):
+                    print(res_data['message'])
+                else:
+                    data = self._format_service_detail_table(res_data['message'])
+                    self._print_table_simple(data)
+            else:
+                print(f"Service {res_data['service_name']} is down, {res_data['message']}")
+        else:
+            print(f"Fail to show service, code: {res_json['code']}, message: {res_json['message']}")
+
+    def _handle_restart_service(self, command):
+        service_id: int = command['number']
+        print(f"Restart service {service_id}")
+
+    def _handle_shutdown_service(self, command):
+        service_id: int = command['number']
+        print(f"Shutdown service {service_id}")
+
+    def _handle_startup_service(self, command):
+        service_id: int = command['number']
+        print(f"Startup service {service_id}")
+
+    def _handle_list_users(self, command):
+        print("Listing all users")
+
+        url = f'http://{self.host}:{self.port}/api/v1/admin/users'
+        response = self.session.get(url)
+        res_json = response.json()
+        if response.status_code == 200:
+            self._print_table_simple(res_json['data'])
+        else:
+            print(f"Fail to get all users, code: {res_json['code']}, message: {res_json['message']}")
+
+    def _handle_show_user(self, command):
+        username_tree: Tree = command['user_name']
+        user_name: str = username_tree.children[0].strip("'\"")
+        print(f"Showing user: {user_name}")
+        url = f'http://{self.host}:{self.port}/api/v1/admin/users/{user_name}'
+        response = self.session.get(url)
+        res_json = response.json()
+        if response.status_code == 200:
+            table_data = res_json['data']
+            table_data.pop('avatar')
+            self._print_table_simple(table_data)
+        else:
+            print(f"Fail to get user {user_name}, code: {res_json['code']}, message: {res_json['message']}")
+
+    def _handle_drop_user(self, command):
+        username_tree: Tree = command['user_name']
+        user_name: str = username_tree.children[0].strip("'\"")
+        print(f"Drop user: {user_name}")
+        url = f'http://{self.host}:{self.port}/api/v1/admin/users/{user_name}'
+        response = self.session.delete(url)
+        res_json = response.json()
+        if response.status_code == 200:
+            print(res_json["message"])
+        else:
+            print(f"Fail to drop user, code: {res_json['code']}, message: {res_json['message']}")
+
+    def _handle_alter_user(self, command):
+        user_name_tree: Tree = command['user_name']
+        user_name: str = user_name_tree.children[0].strip("'\"")
+        password_tree: Tree = command['password']
+        password: str = password_tree.children[0].strip("'\"")
+        print(f"Alter user: {user_name}, password: {password}")
+        url = f'http://{self.host}:{self.port}/api/v1/admin/users/{user_name}/password'
+        response = self.session.put(url, json={'new_password': encrypt(password)})
+        res_json = response.json()
+        if response.status_code == 200:
+            print(res_json["message"])
+        else:
+            print(f"Fail to alter password, code: {res_json['code']}, message: {res_json['message']}")
+
+    def _handle_create_user(self, command):
+        user_name_tree: Tree = command['user_name']
+        user_name: str = user_name_tree.children[0].strip("'\"")
+        password_tree: Tree = command['password']
+        password: str = password_tree.children[0].strip("'\"")
+        role: str = command['role']
+        print(f"Create user: {user_name}, password: {password}, role: {role}")
+        url = f'http://{self.host}:{self.port}/api/v1/admin/users'
+        response = self.session.post(
+            url,
+            json={'user_name': user_name, 'password': encrypt(password), 'role': role}
+        )
+        res_json = response.json()
+        if response.status_code == 200:
+            self._print_table_simple(res_json['data'])
+        else:
+            print(f"Fail to create user {user_name}, code: {res_json['code']}, message: {res_json['message']}")
+
+    def _handle_activate_user(self, command):
+        user_name_tree: Tree = command['user_name']
+        user_name: str = user_name_tree.children[0].strip("'\"")
+        activate_tree: Tree = command['activate_status']
+        activate_status: str = activate_tree.children[0].strip("'\"")
+        if activate_status.lower() in ['on', 'off']:
+            print(f"Alter user {user_name} activate status, turn {activate_status.lower()}.")
+            url = f'http://{self.host}:{self.port}/api/v1/admin/users/{user_name}/activate'
+            response = self.session.put(url, json={'activate_status': activate_status})
+            res_json = response.json()
+            if response.status_code == 200:
+                print(res_json["message"])
+            else:
+                print(f"Fail to alter activate status, code: {res_json['code']}, message: {res_json['message']}")
+        else:
+            print(f"Unknown activate status: {activate_status}.")
+
+    def _handle_list_datasets(self, command):
+        username_tree: Tree = command['user_name']
+        user_name: str = username_tree.children[0].strip("'\"")
+        print(f"Listing all datasets of user: {user_name}")
+        url = f'http://{self.host}:{self.port}/api/v1/admin/users/{user_name}/datasets'
+        response = self.session.get(url)
+        res_json = response.json()
+        if response.status_code == 200:
+            table_data = res_json['data']
+            for t in table_data:
+                t.pop('avatar')
+            self._print_table_simple(table_data)
+        else:
+            print(f"Fail to get all datasets of {user_name}, code: {res_json['code']}, message: {res_json['message']}")
+
+    def _handle_list_agents(self, command):
+        username_tree: Tree = command['user_name']
+        user_name: str = username_tree.children[0].strip("'\"")
+        print(f"Listing all agents of user: {user_name}")
+        url = f'http://{self.host}:{self.port}/api/v1/admin/users/{user_name}/agents'
+        response = self.session.get(url)
+        res_json = response.json()
+        if response.status_code == 200:
+            table_data = res_json['data']
+            for t in table_data:
+                t.pop('avatar')
+            self._print_table_simple(table_data)
+        else:
+            print(f"Fail to get all agents of {user_name}, code: {res_json['code']}, message: {res_json['message']}")
+
+    def _create_role(self, command):
+        role_name_tree: Tree = command['role_name']
+        role_name: str = role_name_tree.children[0].strip("'\"")
+        desc_str: str = ''
+        if 'description' in command:
+            desc_tree: Tree = command['description']
+            desc_str = desc_tree.children[0].strip("'\"")
+
+        print(f"create role name: {role_name}, description: {desc_str}")
+        url = f'http://{self.host}:{self.port}/api/v1/admin/roles'
+        response = self.session.post(
+            url,
+            json={'role_name': role_name, 'description': desc_str}
+        )
+        res_json = response.json()
+        if response.status_code == 200:
+            self._print_table_simple(res_json['data'])
+        else:
+            print(f"Fail to create role {role_name}, code: {res_json['code']}, message: {res_json['message']}")
+
+    def _drop_role(self, command):
+        role_name_tree: Tree = command['role_name']
+        role_name: str = role_name_tree.children[0].strip("'\"")
+        print(f"drop role name: {role_name}")
+        url = f'http://{self.host}:{self.port}/api/v1/admin/roles/{role_name}'
+        response = self.session.delete(url)
+        res_json = response.json()
+        if response.status_code == 200:
+            self._print_table_simple(res_json['data'])
+        else:
+            print(f"Fail to drop role {role_name}, code: {res_json['code']}, message: {res_json['message']}")
+
+    def _alter_role(self, command):
+        role_name_tree: Tree = command['role_name']
+        role_name: str = role_name_tree.children[0].strip("'\"")
+        desc_tree: Tree = command['description']
+        desc_str: str = desc_tree.children[0].strip("'\"")
+
+        print(f"alter role name: {role_name}, description: {desc_str}")
+        url = f'http://{self.host}:{self.port}/api/v1/admin/roles/{role_name}'
+        response = self.session.put(
+            url,
+            json={'description': desc_str}
+        )
+        res_json = response.json()
+        if response.status_code == 200:
+            self._print_table_simple(res_json['data'])
+        else:
+            print(
+                f"Fail to update role {role_name} with description: {desc_str}, code: {res_json['code']}, message: {res_json['message']}")
+
+    def _list_roles(self, command):
+        print("Listing all roles")
+        url = f'http://{self.host}:{self.port}/api/v1/admin/roles'
+        response = self.session.get(url)
+        res_json = response.json()
+        if response.status_code == 200:
+            self._print_table_simple(res_json['data'])
+        else:
+            print(f"Fail to list roles, code: {res_json['code']}, message: {res_json['message']}")
+
+    def _show_role(self, command):
+        role_name_tree: Tree = command['role_name']
+        role_name: str = role_name_tree.children[0].strip("'\"")
+        print(f"show role: {role_name}")
+        url = f'http://{self.host}:{self.port}/api/v1/admin/roles/{role_name}/permission'
+        response = self.session.get(url)
+        res_json = response.json()
+        if response.status_code == 200:
+            self._print_table_simple(res_json['data'])
+        else:
+            print(f"Fail to list roles, code: {res_json['code']}, message: {res_json['message']}")
+
+    def _grant_permission(self, command):
+        role_name_tree: Tree = command['role_name']
+        role_name_str: str = role_name_tree.children[0].strip("'\"")
+        resource_tree: Tree = command['resource']
+        resource_str: str = resource_tree.children[0].strip("'\"")
+        action_tree_list: list = command['actions']
+        actions: list = []
+        for action_tree in action_tree_list:
+            action_str: str = action_tree.children[0].strip("'\"")
+            actions.append(action_str)
+        print(f"grant role_name: {role_name_str}, resource: {resource_str}, actions: {actions}")
+        url = f'http://{self.host}:{self.port}/api/v1/admin/roles/{role_name_str}/permission'
+        response = self.session.post(
+            url,
+            json={'actions': actions, 'resource': resource_str}
+        )
+        res_json = response.json()
+        if response.status_code == 200:
+            self._print_table_simple(res_json['data'])
+        else:
+            print(
+                f"Fail to grant role {role_name_str} with {actions} on {resource_str}, code: {res_json['code']}, message: {res_json['message']}")
+
+    def _revoke_permission(self, command):
+        role_name_tree: Tree = command['role_name']
+        role_name_str: str = role_name_tree.children[0].strip("'\"")
+        resource_tree: Tree = command['resource']
+        resource_str: str = resource_tree.children[0].strip("'\"")
+        action_tree_list: list = command['actions']
+        actions: list = []
+        for action_tree in action_tree_list:
+            action_str: str = action_tree.children[0].strip("'\"")
+            actions.append(action_str)
+        print(f"revoke role_name: {role_name_str}, resource: {resource_str}, actions: {actions}")
+        url = f'http://{self.host}:{self.port}/api/v1/admin/roles/{role_name_str}/permission'
+        response = self.session.delete(
+            url,
+            json={'actions': actions, 'resource': resource_str}
+        )
+        res_json = response.json()
+        if response.status_code == 200:
+            self._print_table_simple(res_json['data'])
+        else:
+            print(
+                f"Fail to revoke role {role_name_str} with {actions} on {resource_str}, code: {res_json['code']}, message: {res_json['message']}")
+
+    def _alter_user_role(self, command):
+        role_name_tree: Tree = command['role_name']
+        role_name_str: str = role_name_tree.children[0].strip("'\"")
+        user_name_tree: Tree = command['user_name']
+        user_name_str: str = user_name_tree.children[0].strip("'\"")
+        print(f"alter_user_role user_name: {user_name_str}, role_name: {role_name_str}")
+        url = f'http://{self.host}:{self.port}/api/v1/admin/users/{user_name_str}/role'
+        response = self.session.put(
+            url,
+            json={'role_name': role_name_str}
+        )
+        res_json = response.json()
+        if response.status_code == 200:
+            self._print_table_simple(res_json['data'])
+        else:
+            print(
+                f"Fail to alter user: {user_name_str} to role {role_name_str}, code: {res_json['code']}, message: {res_json['message']}")
+
+    def _show_user_permission(self, command):
+        user_name_tree: Tree = command['user_name']
+        user_name_str: str = user_name_tree.children[0].strip("'\"")
+        print(f"show_user_permission user_name: {user_name_str}")
+        url = f'http://{self.host}:{self.port}/api/v1/admin/users/{user_name_str}/permission'
+        response = self.session.get(url)
+        res_json = response.json()
+        if response.status_code == 200:
+            self._print_table_simple(res_json['data'])
+        else:
+            print(
+                f"Fail to show user: {user_name_str} permission, code: {res_json['code']}, message: {res_json['message']}")
+
+    def _show_version(self, command):
+        print("show_version")
+        url = f'http://{self.host}:{self.port}/api/v1/admin/version'
+        response = self.session.get(url)
+        res_json = response.json()
+        if response.status_code == 200:
+            self._print_table_simple(res_json['data'])
+        else:
+            print(f"Fail to show version, code: {res_json['code']}, message: {res_json['message']}")
+
+    def _handle_meta_command(self, command):
+        meta_command = command['command']
+        args = command.get('args', [])
+
+        if meta_command in ['?', 'h', 'help']:
+            self.show_help()
+        elif meta_command in ['q', 'quit', 'exit']:
+            print("Goodbye!")
+        else:
+            print(f"Meta command '{meta_command}' with args {args}")
+
+    def show_help(self):
+        """Help info"""
+        help_text = """
+Commands:
+  LIST SERVICES
+  SHOW SERVICE <service>
+  STARTUP SERVICE <service>
+  SHUTDOWN SERVICE <service>
+  RESTART SERVICE <service>
+  LIST USERS
+  SHOW USER <user>
+  DROP USER <user>
+  CREATE USER <user> <password>
+  ALTER USER PASSWORD <user> <new_password>
+  ALTER USER ACTIVE <user> <on/off>
+  LIST DATASETS OF <user>
+  LIST AGENTS OF <user>
+
+Meta Commands:
+  \\?, \\h, \\help     Show this help
+  \\q, \\quit, \\exit   Quit the CLI
+        """
+        print(help_text)
+
+
+def main():
+    import sys
+
+    cli = AdminCLI()
+
+    args = cli.parse_connection_args(sys.argv)
+    if 'error' in args:
+        print(f"Error: {args['error']}")
+        return
+
+    if 'command' in args:
+        if 'password' not in args:
+            print("Error: password is missing")
+            return
+        if cli.verify_admin(args, single_command=True):
+            command: str = args['command']
+            print(f"Run single command: {command}")
+            cli.run_single_command(command)
+    else:
+        if cli.verify_admin(args, single_command=False):
+            print(r"""
+                ____  ___   ______________                 ___       __          _     
+               / __ \/   | / ____/ ____/ /___ _      __   /   | ____/ /___ ___  (_)___ 
+              / /_/ / /| |/ / __/ /_  / / __ \ | /| / /  / /| |/ __  / __ `__ \/ / __ \
+             / _, _/ ___ / /_/ / __/ / / /_/ / |/ |/ /  / ___ / /_/ / / / / / / / / / /
+            /_/ |_/_/  |_\____/_/   /_/\____/|__/|__/  /_/  |_\__,_/_/ /_/ /_/_/_/ /_/ 
+            """)
+            cli.cmdloop()
+
+
+if __name__ == '__main__':
+    main()

admin/client/pyproject.toml

@@ -0,0 +1,24 @@
+[project]
+name = "ragflow-cli"
+version = "0.22.1"
+description = "Admin Service's client of [RAGFlow](https://github.com/infiniflow/ragflow). The Admin Service provides user management and system monitoring. "
+authors = [{ name = "Lynn", email = "lynn_inf@hotmail.com" }]
+license = { text = "Apache License, Version 2.0" }
+readme = "README.md"
+requires-python = ">=3.10,<3.13"
+dependencies = [
+    "requests>=2.30.0,<3.0.0",
+    "beartype>=0.18.5,<0.19.0",
+    "pycryptodomex>=3.10.0",
+    "lark>=1.1.0",
+]
+
+[dependency-groups]
+test = [
+    "pytest>=8.3.5",
+    "requests>=2.32.3",
+    "requests-toolbelt>=1.0.0",
+]
+
+[project.scripts]
+ragflow-cli = "admin_client:main"

admin/server/admin_server.py

@@ -0,0 +1,80 @@
+#
+#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+
+import os
+import signal
+import logging
+import time
+import threading
+import traceback
+
+from flask import Flask
+from flask_login import LoginManager
+from werkzeug.serving import run_simple
+from routes import admin_bp
+from common.log_utils import init_root_logger
+from common.constants import SERVICE_CONF
+from common.config_utils import show_configs
+from common import settings
+from config import load_configurations, SERVICE_CONFIGS
+from auth import init_default_admin, setup_auth
+from flask_session import Session
+from common.versions import get_ragflow_version
+
+stop_event = threading.Event()
+
+if __name__ == '__main__':
+    init_root_logger("admin_service")
+    logging.info(r"""
+        ____  ___   ______________                 ___       __          _     
+       / __ \/   | / ____/ ____/ /___ _      __   /   | ____/ /___ ___  (_)___ 
+      / /_/ / /| |/ / __/ /_  / / __ \ | /| / /  / /| |/ __  / __ `__ \/ / __ \
+     / _, _/ ___ / /_/ / __/ / / /_/ / |/ |/ /  / ___ / /_/ / / / / / / / / / /
+    /_/ |_/_/  |_\____/_/   /_/\____/|__/|__/  /_/  |_\__,_/_/ /_/ /_/_/_/ /_/ 
+    """)
+
+    app = Flask(__name__)
+    app.register_blueprint(admin_bp)
+    app.config["SESSION_PERMANENT"] = False
+    app.config["SESSION_TYPE"] = "filesystem"
+    app.config["MAX_CONTENT_LENGTH"] = int(
+        os.environ.get("MAX_CONTENT_LENGTH", 1024 * 1024 * 1024)
+    )
+    Session(app)
+    logging.info(f'RAGFlow version: {get_ragflow_version()}')
+    show_configs()
+    login_manager = LoginManager()
+    login_manager.init_app(app)
+    settings.init_settings()
+    setup_auth(login_manager)
+    init_default_admin()
+    SERVICE_CONFIGS.configs = load_configurations(SERVICE_CONF)
+
+    try:
+        logging.info("RAGFlow Admin service start...")
+        run_simple(
+            hostname="0.0.0.0",
+            port=9381,
+            application=app,
+            threaded=True,
+            use_reloader=False,
+            use_debugger=True,
+        )
+    except Exception:
+        traceback.print_exc()
+        stop_event.set()
+        time.sleep(1)
+        os.kill(os.getpid(), signal.SIGKILL)

admin/server/auth.py

@@ -0,0 +1,188 @@
+#
+#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+
+
+import logging
+import uuid
+from functools import wraps
+from datetime import datetime
+
+from flask import jsonify, request
+from flask_login import current_user, login_user
+from itsdangerous.url_safe import URLSafeTimedSerializer as Serializer
+
+from api.common.exceptions import AdminException, UserNotFoundError
+from api.common.base64 import encode_to_base64
+from api.db.services import UserService
+from common.constants import ActiveEnum, StatusEnum
+from api.utils.crypt import decrypt
+from common.misc_utils import get_uuid
+from common.time_utils import current_timestamp, datetime_format, get_format_time
+from common.connection_utils import sync_construct_response
+from common import settings
+
+
+def setup_auth(login_manager):
+    @login_manager.request_loader
+    def load_user(web_request):
+        jwt = Serializer(secret_key=settings.SECRET_KEY)
+        authorization = web_request.headers.get("Authorization")
+        if authorization:
+            try:
+                access_token = str(jwt.loads(authorization))
+
+                if not access_token or not access_token.strip():
+                    logging.warning("Authentication attempt with empty access token")
+                    return None
+
+                # Access tokens should be UUIDs (32 hex characters)
+                if len(access_token.strip()) < 32:
+                    logging.warning(f"Authentication attempt with invalid token format: {len(access_token)} chars")
+                    return None
+
+                user = UserService.query(
+                    access_token=access_token, status=StatusEnum.VALID.value
+                )
+                if user:
+                    if not user[0].access_token or not user[0].access_token.strip():
+                        logging.warning(f"User {user[0].email} has empty access_token in database")
+                        return None
+                    return user[0]
+                else:
+                    return None
+            except Exception as e:
+                logging.warning(f"load_user got exception {e}")
+                return None
+        else:
+            return None
+
+
+def init_default_admin():
+    # Verify that at least one active admin user exists. If not, create a default one.
+    users = UserService.query(is_superuser=True)
+    if not users:
+        default_admin = {
+            "id": uuid.uuid1().hex,
+            "password": encode_to_base64("admin"),
+            "nickname": "admin",
+            "is_superuser": True,
+            "email": "admin@ragflow.io",
+            "creator": "system",
+            "status": "1",
+        }
+        if not UserService.save(**default_admin):
+            raise AdminException("Can't init admin.", 500)
+    elif not any([u.is_active == ActiveEnum.ACTIVE.value for u in users]):
+        raise AdminException("No active admin. Please update 'is_active' in db manually.", 500)
+
+
+def check_admin_auth(func):
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        user = UserService.filter_by_id(current_user.id)
+        if not user:
+            raise UserNotFoundError(current_user.email)
+        if not user.is_superuser:
+            raise AdminException("Not admin", 403)
+        if user.is_active == ActiveEnum.INACTIVE.value:
+            raise AdminException(f"User {current_user.email} inactive", 403)
+
+        return func(*args, **kwargs)
+
+    return wrapper
+
+
+def login_admin(email: str, password: str):
+    """
+    :param email: admin email
+    :param password: string before decrypt
+    """
+    users = UserService.query(email=email)
+    if not users:
+        raise UserNotFoundError(email)
+    psw = decrypt(password)
+    user = UserService.query_user(email, psw)
+    if not user:
+        raise AdminException("Email and password do not match!")
+    if not user.is_superuser:
+        raise AdminException("Not admin", 403)
+    if user.is_active == ActiveEnum.INACTIVE.value:
+        raise AdminException(f"User {email} inactive", 403)
+
+    resp = user.to_json()
+    user.access_token = get_uuid()
+    login_user(user)
+    user.update_time = (current_timestamp(),)
+    user.update_date = (datetime_format(datetime.now()),)
+    user.last_login_time = get_format_time()
+    user.save()
+    msg = "Welcome back!"
+    return sync_construct_response(data=resp, auth=user.get_id(), message=msg)
+
+
+def check_admin(username: str, password: str):
+    users = UserService.query(email=username)
+    if not users:
+        logging.info(f"Username: {username} is not registered!")
+        user_info = {
+            "id": uuid.uuid1().hex,
+            "password": encode_to_base64("admin"),
+            "nickname": "admin",
+            "is_superuser": True,
+            "email": "admin@ragflow.io",
+            "creator": "system",
+            "status": "1",
+        }
+        if not UserService.save(**user_info):
+            raise AdminException("Can't init admin.", 500)
+
+    user = UserService.query_user(username, password)
+    if user:
+        return True
+    else:
+        return False
+
+
+def login_verify(f):
+    @wraps(f)
+    def decorated(*args, **kwargs):
+        auth = request.authorization
+        if not auth or 'username' not in auth.parameters or 'password' not in auth.parameters:
+            return jsonify({
+                "code": 401,
+                "message": "Authentication required",
+                "data": None
+            }), 200
+
+        username = auth.parameters['username']
+        password = auth.parameters['password']
+        try:
+            if not check_admin(username, password):
+                return jsonify({
+                    "code": 500,
+                    "message": "Access denied",
+                    "data": None
+                }), 200
+        except Exception as e:
+            error_msg = str(e)
+            return jsonify({
+                "code": 500,
+                "message": error_msg
+            }), 200
+
+        return f(*args, **kwargs)
+
+    return decorated

admin/server/config.py

@@ -0,0 +1,317 @@
+#
+#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+
+
+import logging
+import threading
+from enum import Enum
+
+from pydantic import BaseModel
+from typing import Any
+from common.config_utils import read_config
+from urllib.parse import urlparse
+
+
+class BaseConfig(BaseModel):
+    id: int
+    name: str
+    host: str
+    port: int
+    service_type: str
+    detail_func_name: str
+
+    def to_dict(self) -> dict[str, Any]:
+        return {'id': self.id, 'name': self.name, 'host': self.host, 'port': self.port,
+                'service_type': self.service_type}
+
+
+class ServiceConfigs:
+    configs = list[BaseConfig]
+
+    def __init__(self):
+        self.configs = []
+        self.lock = threading.Lock()
+
+
+SERVICE_CONFIGS = ServiceConfigs
+
+
+class ServiceType(Enum):
+    METADATA = "metadata"
+    RETRIEVAL = "retrieval"
+    MESSAGE_QUEUE = "message_queue"
+    RAGFLOW_SERVER = "ragflow_server"
+    TASK_EXECUTOR = "task_executor"
+    FILE_STORE = "file_store"
+
+
+class MetaConfig(BaseConfig):
+    meta_type: str
+
+    def to_dict(self) -> dict[str, Any]:
+        result = super().to_dict()
+        if 'extra' not in result:
+            result['extra'] = dict()
+        extra_dict = result['extra'].copy()
+        extra_dict['meta_type'] = self.meta_type
+        result['extra'] = extra_dict
+        return result
+
+
+class MySQLConfig(MetaConfig):
+    username: str
+    password: str
+
+    def to_dict(self) -> dict[str, Any]:
+        result = super().to_dict()
+        if 'extra' not in result:
+            result['extra'] = dict()
+        extra_dict = result['extra'].copy()
+        extra_dict['username'] = self.username
+        extra_dict['password'] = self.password
+        result['extra'] = extra_dict
+        return result
+
+
+class PostgresConfig(MetaConfig):
+
+    def to_dict(self) -> dict[str, Any]:
+        result = super().to_dict()
+        if 'extra' not in result:
+            result['extra'] = dict()
+        return result
+
+
+class RetrievalConfig(BaseConfig):
+    retrieval_type: str
+
+    def to_dict(self) -> dict[str, Any]:
+        result = super().to_dict()
+        if 'extra' not in result:
+            result['extra'] = dict()
+        extra_dict = result['extra'].copy()
+        extra_dict['retrieval_type'] = self.retrieval_type
+        result['extra'] = extra_dict
+        return result
+
+
+class InfinityConfig(RetrievalConfig):
+    db_name: str
+
+    def to_dict(self) -> dict[str, Any]:
+        result = super().to_dict()
+        if 'extra' not in result:
+            result['extra'] = dict()
+        extra_dict = result['extra'].copy()
+        extra_dict['db_name'] = self.db_name
+        result['extra'] = extra_dict
+        return result
+
+
+class ElasticsearchConfig(RetrievalConfig):
+    username: str
+    password: str
+
+    def to_dict(self) -> dict[str, Any]:
+        result = super().to_dict()
+        if 'extra' not in result:
+            result['extra'] = dict()
+        extra_dict = result['extra'].copy()
+        extra_dict['username'] = self.username
+        extra_dict['password'] = self.password
+        result['extra'] = extra_dict
+        return result
+
+
+class MessageQueueConfig(BaseConfig):
+    mq_type: str
+
+    def to_dict(self) -> dict[str, Any]:
+        result = super().to_dict()
+        if 'extra' not in result:
+            result['extra'] = dict()
+        extra_dict = result['extra'].copy()
+        extra_dict['mq_type'] = self.mq_type
+        result['extra'] = extra_dict
+        return result
+
+
+class RedisConfig(MessageQueueConfig):
+    database: int
+    password: str
+
+    def to_dict(self) -> dict[str, Any]:
+        result = super().to_dict()
+        if 'extra' not in result:
+            result['extra'] = dict()
+        extra_dict = result['extra'].copy()
+        extra_dict['database'] = self.database
+        extra_dict['password'] = self.password
+        result['extra'] = extra_dict
+        return result
+
+
+class RabbitMQConfig(MessageQueueConfig):
+
+    def to_dict(self) -> dict[str, Any]:
+        result = super().to_dict()
+        if 'extra' not in result:
+            result['extra'] = dict()
+        return result
+
+
+class RAGFlowServerConfig(BaseConfig):
+
+    def to_dict(self) -> dict[str, Any]:
+        result = super().to_dict()
+        if 'extra' not in result:
+            result['extra'] = dict()
+        return result
+
+
+class TaskExecutorConfig(BaseConfig):
+    message_queue_type: str
+
+    def to_dict(self) -> dict[str, Any]:
+        result = super().to_dict()
+        if 'extra' not in result:
+            result['extra'] = dict()
+        result['extra']['message_queue_type'] = self.message_queue_type
+        return result
+
+
+class FileStoreConfig(BaseConfig):
+    store_type: str
+
+    def to_dict(self) -> dict[str, Any]:
+        result = super().to_dict()
+        if 'extra' not in result:
+            result['extra'] = dict()
+        extra_dict = result['extra'].copy()
+        extra_dict['store_type'] = self.store_type
+        result['extra'] = extra_dict
+        return result
+
+
+class MinioConfig(FileStoreConfig):
+    user: str
+    password: str
+
+    def to_dict(self) -> dict[str, Any]:
+        result = super().to_dict()
+        if 'extra' not in result:
+            result['extra'] = dict()
+        extra_dict = result['extra'].copy()
+        extra_dict['user'] = self.user
+        extra_dict['password'] = self.password
+        result['extra'] = extra_dict
+        return result
+
+
+def load_configurations(config_path: str) -> list[BaseConfig]:
+    raw_configs = read_config(config_path)
+    configurations = []
+    ragflow_count = 0
+    id_count = 0
+    for k, v in raw_configs.items():
+        match k:
+            case "ragflow":
+                name: str = f'ragflow_{ragflow_count}'
+                host: str = v['host']
+                http_port: int = v['http_port']
+                config = RAGFlowServerConfig(id=id_count, name=name, host=host, port=http_port,
+                                             service_type="ragflow_server",
+                                             detail_func_name="check_ragflow_server_alive")
+                configurations.append(config)
+                id_count += 1
+            case "es":
+                name: str = 'elasticsearch'
+                url = v['hosts']
+                parsed = urlparse(url)
+                host: str = parsed.hostname
+                port: int = parsed.port
+                username: str = v.get('username')
+                password: str = v.get('password')
+                config = ElasticsearchConfig(id=id_count, name=name, host=host, port=port, service_type="retrieval",
+                                             retrieval_type="elasticsearch",
+                                             username=username, password=password,
+                                             detail_func_name="get_es_cluster_stats")
+                configurations.append(config)
+                id_count += 1
+
+            case "infinity":
+                name: str = 'infinity'
+                url = v['uri']
+                parts = url.split(':', 1)
+                host = parts[0]
+                port = int(parts[1])
+                database: str = v.get('db_name', 'default_db')
+                config = InfinityConfig(id=id_count, name=name, host=host, port=port, service_type="retrieval",
+                                        retrieval_type="infinity",
+                                        db_name=database, detail_func_name="get_infinity_status")
+                configurations.append(config)
+                id_count += 1
+            case "minio":
+                name: str = 'minio'
+                url = v['host']
+                parts = url.split(':', 1)
+                host = parts[0]
+                port = int(parts[1])
+                user = v.get('user')
+                password = v.get('password')
+                config = MinioConfig(id=id_count, name=name, host=host, port=port, user=user, password=password,
+                                     service_type="file_store",
+                                     store_type="minio", detail_func_name="check_minio_alive")
+                configurations.append(config)
+                id_count += 1
+            case "redis":
+                name: str = 'redis'
+                url = v['host']
+                parts = url.split(':', 1)
+                host = parts[0]
+                port = int(parts[1])
+                password = v.get('password')
+                db: int = v.get('db')
+                config = RedisConfig(id=id_count, name=name, host=host, port=port, password=password, database=db,
+                                     service_type="message_queue", mq_type="redis", detail_func_name="get_redis_info")
+                configurations.append(config)
+                id_count += 1
+            case "mysql":
+                name: str = 'mysql'
+                host: str = v.get('host')
+                port: int = v.get('port')
+                username = v.get('user')
+                password = v.get('password')
+                config = MySQLConfig(id=id_count, name=name, host=host, port=port, username=username, password=password,
+                                     service_type="meta_data", meta_type="mysql", detail_func_name="get_mysql_status")
+                configurations.append(config)
+                id_count += 1
+            case "admin":
+                pass
+            case "task_executor":
+                name: str = 'task_executor'
+                host: str = v.get('host', '')
+                port: int = v.get('port', 0)
+                message_queue_type: str = v.get('message_queue_type')
+                config = TaskExecutorConfig(id=id_count, name=name, host=host, port=port, message_queue_type=message_queue_type,
+                                            service_type="task_executor", detail_func_name="check_task_executor_alive")
+                configurations.append(config)
+                id_count += 1
+            case _:
+                logging.warning(f"Unknown configuration key: {k}")
+                continue
+
+    return configurations

admin/server/exceptions.py

@@ -0,0 +1,17 @@
+class AdminException(Exception):
+    def __init__(self, message, code=400):
+        super().__init__(message)
+        self.code = code
+        self.message = message
+
+class UserNotFoundError(AdminException):
+    def __init__(self, username):
+        super().__init__(f"User '{username}' not found", 404)
+
+class UserAlreadyExistsError(AdminException):
+    def __init__(self, username):
+        super().__init__(f"User '{username}' already exists", 409)
+
+class CannotDeleteAdminError(AdminException):
+    def __init__(self):
+        super().__init__("Cannot delete admin account", 403)

admin/server/models.py

@@ -0,0 +1,15 @@
+#
+#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#

admin/server/responses.py

@@ -0,0 +1,32 @@
+#
+#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from flask import jsonify
+
+
+def success_response(data=None, message="Success", code=0):
+    return jsonify({
+        "code": code,
+        "message": message,
+        "data": data
+    }), 200
+
+
+def error_response(message="Error", code=-1, data=None):
+    return jsonify({
+        "code": code,
+        "message": message,
+        "data": data
+    }), 400

admin/server/roles.py

@@ -0,0 +1,76 @@
+#
+#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import logging
+
+from typing import Dict, Any
+
+from api.common.exceptions import AdminException
+
+
+class RoleMgr:
+    @staticmethod
+    def create_role(role_name: str, description: str):
+        error_msg = f"not implement: create role: {role_name}, description: {description}"
+        logging.error(error_msg)
+        raise AdminException(error_msg)
+
+    @staticmethod
+    def update_role_description(role_name: str, description: str) -> Dict[str, Any]:
+        error_msg = f"not implement: update role: {role_name} with description: {description}"
+        logging.error(error_msg)
+        raise AdminException(error_msg)
+
+    @staticmethod
+    def delete_role(role_name: str) -> Dict[str, Any]:
+        error_msg = f"not implement: drop role: {role_name}"
+        logging.error(error_msg)
+        raise AdminException(error_msg)
+
+    @staticmethod
+    def list_roles() -> Dict[str, Any]:
+        error_msg = "not implement: list roles"
+        logging.error(error_msg)
+        raise AdminException(error_msg)
+
+    @staticmethod
+    def get_role_permission(role_name: str) -> Dict[str, Any]:
+        error_msg = f"not implement: show role {role_name}"
+        logging.error(error_msg)
+        raise AdminException(error_msg)
+
+    @staticmethod
+    def grant_role_permission(role_name: str, actions: list, resource: str) -> Dict[str, Any]:
+        error_msg = f"not implement: grant role {role_name} actions: {actions} on {resource}"
+        logging.error(error_msg)
+        raise AdminException(error_msg)
+
+    @staticmethod
+    def revoke_role_permission(role_name: str, actions: list, resource: str) -> Dict[str, Any]:
+        error_msg = f"not implement: revoke role {role_name} actions: {actions} on {resource}"
+        logging.error(error_msg)
+        raise AdminException(error_msg)
+
+    @staticmethod
+    def update_user_role(user_name: str, role_name: str) -> Dict[str, Any]:
+        error_msg = f"not implement: update user role: {user_name} to role {role_name}"
+        logging.error(error_msg)
+        raise AdminException(error_msg)
+
+    @staticmethod
+    def get_user_permission(user_name: str) -> Dict[str, Any]:
+        error_msg = f"not implement: get user permission: {user_name}"
+        logging.error(error_msg)
+        raise AdminException(error_msg)

admin/server/routes.py

@@ -0,0 +1,382 @@
+#
+#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+
+import secrets
+
+from flask import Blueprint, request
+from flask_login import current_user, login_required, logout_user
+
+from auth import login_verify, login_admin, check_admin_auth
+from responses import success_response, error_response
+from services import UserMgr, ServiceMgr, UserServiceMgr
+from roles import RoleMgr
+from api.common.exceptions import AdminException
+from common.versions import get_ragflow_version
+
+admin_bp = Blueprint('admin', __name__, url_prefix='/api/v1/admin')
+
+
+@admin_bp.route('/login', methods=['POST'])
+def login():
+    if not request.json:
+        return error_response('Authorize admin failed.' ,400)
+    try:
+        email = request.json.get("email", "")
+        password = request.json.get("password", "")
+        return login_admin(email, password)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+
+@admin_bp.route('/logout', methods=['GET'])
+@login_required
+def logout():
+    try:
+        current_user.access_token = f"INVALID_{secrets.token_hex(16)}"
+        current_user.save()
+        logout_user()
+        return success_response(True)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+
+@admin_bp.route('/auth', methods=['GET'])
+@login_verify
+def auth_admin():
+    try:
+        return success_response(None, "Admin is authorized", 0)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+
+@admin_bp.route('/users', methods=['GET'])
+@login_required
+@check_admin_auth
+def list_users():
+    try:
+        users = UserMgr.get_all_users()
+        return success_response(users, "Get all users", 0)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+
+@admin_bp.route('/users', methods=['POST'])
+@login_required
+@check_admin_auth
+def create_user():
+    try:
+        data = request.get_json()
+        if not data or 'username' not in data or 'password' not in data:
+            return error_response("Username and password are required", 400)
+
+        username = data['username']
+        password = data['password']
+        role = data.get('role', 'user')
+
+        res = UserMgr.create_user(username, password, role)
+        if res["success"]:
+            user_info = res["user_info"]
+            user_info.pop("password")  # do not return password
+            return success_response(user_info, "User created successfully")
+        else:
+            return error_response("create user failed")
+
+    except AdminException as e:
+        return error_response(e.message, e.code)
+    except Exception as e:
+        return error_response(str(e))
+
+
+@admin_bp.route('/users/<username>', methods=['DELETE'])
+@login_required
+@check_admin_auth
+def delete_user(username):
+    try:
+        res = UserMgr.delete_user(username)
+        if res["success"]:
+            return success_response(None, res["message"])
+        else:
+            return error_response(res["message"])
+
+    except AdminException as e:
+        return error_response(e.message, e.code)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+
+@admin_bp.route('/users/<username>/password', methods=['PUT'])
+@login_required
+@check_admin_auth
+def change_password(username):
+    try:
+        data = request.get_json()
+        if not data or 'new_password' not in data:
+            return error_response("New password is required", 400)
+
+        new_password = data['new_password']
+        msg = UserMgr.update_user_password(username, new_password)
+        return success_response(None, msg)
+
+    except AdminException as e:
+        return error_response(e.message, e.code)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+
+@admin_bp.route('/users/<username>/activate', methods=['PUT'])
+@login_required
+@check_admin_auth
+def alter_user_activate_status(username):
+    try:
+        data = request.get_json()
+        if not data or 'activate_status' not in data:
+            return error_response("Activation status is required", 400)
+        activate_status = data['activate_status']
+        msg = UserMgr.update_user_activate_status(username, activate_status)
+        return success_response(None, msg)
+    except AdminException as e:
+        return error_response(e.message, e.code)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+
+@admin_bp.route('/users/<username>', methods=['GET'])
+@login_required
+@check_admin_auth
+def get_user_details(username):
+    try:
+        user_details = UserMgr.get_user_details(username)
+        return success_response(user_details)
+
+    except AdminException as e:
+        return error_response(e.message, e.code)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+
+@admin_bp.route('/users/<username>/datasets', methods=['GET'])
+@login_required
+@check_admin_auth
+def get_user_datasets(username):
+    try:
+        datasets_list = UserServiceMgr.get_user_datasets(username)
+        return success_response(datasets_list)
+
+    except AdminException as e:
+        return error_response(e.message, e.code)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+
+@admin_bp.route('/users/<username>/agents', methods=['GET'])
+@login_required
+@check_admin_auth
+def get_user_agents(username):
+    try:
+        agents_list = UserServiceMgr.get_user_agents(username)
+        return success_response(agents_list)
+
+    except AdminException as e:
+        return error_response(e.message, e.code)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+
+@admin_bp.route('/services', methods=['GET'])
+@login_required
+@check_admin_auth
+def get_services():
+    try:
+        services = ServiceMgr.get_all_services()
+        return success_response(services, "Get all services", 0)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+
+@admin_bp.route('/service_types/<service_type>', methods=['GET'])
+@login_required
+@check_admin_auth
+def get_services_by_type(service_type_str):
+    try:
+        services = ServiceMgr.get_services_by_type(service_type_str)
+        return success_response(services)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+
+@admin_bp.route('/services/<service_id>', methods=['GET'])
+@login_required
+@check_admin_auth
+def get_service(service_id):
+    try:
+        services = ServiceMgr.get_service_details(service_id)
+        return success_response(services)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+
+@admin_bp.route('/services/<service_id>', methods=['DELETE'])
+@login_required
+@check_admin_auth
+def shutdown_service(service_id):
+    try:
+        services = ServiceMgr.shutdown_service(service_id)
+        return success_response(services)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+
+@admin_bp.route('/services/<service_id>', methods=['PUT'])
+@login_required
+@check_admin_auth
+def restart_service(service_id):
+    try:
+        services = ServiceMgr.restart_service(service_id)
+        return success_response(services)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+
+@admin_bp.route('/roles', methods=['POST'])
+@login_required
+@check_admin_auth
+def create_role():
+    try:
+        data = request.get_json()
+        if not data or 'role_name' not in data:
+            return error_response("Role name is required", 400)
+        role_name: str = data['role_name']
+        description: str = data['description']
+        res = RoleMgr.create_role(role_name, description)
+        return success_response(res)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+
+@admin_bp.route('/roles/<role_name>', methods=['PUT'])
+@login_required
+@check_admin_auth
+def update_role(role_name: str):
+    try:
+        data = request.get_json()
+        if not data or 'description' not in data:
+            return error_response("Role description is required", 400)
+        description: str = data['description']
+        res = RoleMgr.update_role_description(role_name, description)
+        return success_response(res)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+
+@admin_bp.route('/roles/<role_name>', methods=['DELETE'])
+@login_required
+@check_admin_auth
+def delete_role(role_name: str):
+    try:
+        res = RoleMgr.delete_role(role_name)
+        return success_response(res)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+
+@admin_bp.route('/roles', methods=['GET'])
+@login_required
+@check_admin_auth
+def list_roles():
+    try:
+        res = RoleMgr.list_roles()
+        return success_response(res)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+
+@admin_bp.route('/roles/<role_name>/permission', methods=['GET'])
+@login_required
+@check_admin_auth
+def get_role_permission(role_name: str):
+    try:
+        res = RoleMgr.get_role_permission(role_name)
+        return success_response(res)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+
+@admin_bp.route('/roles/<role_name>/permission', methods=['POST'])
+@login_required
+@check_admin_auth
+def grant_role_permission(role_name: str):
+    try:
+        data = request.get_json()
+        if not data or 'actions' not in data or 'resource' not in data:
+            return error_response("Permission is required", 400)
+        actions: list = data['actions']
+        resource: str = data['resource']
+        res = RoleMgr.grant_role_permission(role_name, actions, resource)
+        return success_response(res)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+
+@admin_bp.route('/roles/<role_name>/permission', methods=['DELETE'])
+@login_required
+@check_admin_auth
+def revoke_role_permission(role_name: str):
+    try:
+        data = request.get_json()
+        if not data or 'actions' not in data or 'resource' not in data:
+            return error_response("Permission is required", 400)
+        actions: list = data['actions']
+        resource: str = data['resource']
+        res = RoleMgr.revoke_role_permission(role_name, actions, resource)
+        return success_response(res)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+
+@admin_bp.route('/users/<user_name>/role', methods=['PUT'])
+@login_required
+@check_admin_auth
+def update_user_role(user_name: str):
+    try:
+        data = request.get_json()
+        if not data or 'role_name' not in data:
+            return error_response("Role name is required", 400)
+        role_name: str = data['role_name']
+        res = RoleMgr.update_user_role(user_name, role_name)
+        return success_response(res)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+
+@admin_bp.route('/users/<user_name>/permission', methods=['GET'])
+@login_required
+@check_admin_auth
+def get_user_permission(user_name: str):
+    try:
+        res = RoleMgr.get_user_permission(user_name)
+        return success_response(res)
+    except Exception as e:
+        return error_response(str(e), 500)
+
+@admin_bp.route('/version', methods=['GET'])
+@login_required
+@check_admin_auth
+def show_version():
+    try:
+        res = {"version": get_ragflow_version()}
+        return success_response(res)
+    except Exception as e:
+        return error_response(str(e), 500)

admin/server/services.py

@@ -0,0 +1,227 @@
+#
+#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import logging
+import re
+from werkzeug.security import check_password_hash
+from common.constants import ActiveEnum
+from api.db.services import UserService
+from api.db.joint_services.user_account_service import create_new_user, delete_user_data
+from api.db.services.canvas_service import UserCanvasService
+from api.db.services.user_service import TenantService
+from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.utils.crypt import decrypt
+from api.utils import health_utils
+
+from api.common.exceptions import AdminException, UserAlreadyExistsError, UserNotFoundError
+from config import SERVICE_CONFIGS
+
+
+class UserMgr:
+    @staticmethod
+    def get_all_users():
+        users = UserService.get_all_users()
+        result = []
+        for user in users:
+            result.append({
+                'email': user.email,
+                'nickname': user.nickname,
+                'create_date': user.create_date,
+                'is_active': user.is_active,
+                'is_superuser': user.is_superuser,
+            })
+        return result
+
+    @staticmethod
+    def get_user_details(username):
+        # use email to query
+        users = UserService.query_user_by_email(username)
+        result = []
+        for user in users:
+            result.append({
+                'avatar': user.avatar,
+                'email': user.email,
+                'language': user.language,
+                'last_login_time': user.last_login_time,
+                'is_active': user.is_active,
+                'is_anonymous': user.is_anonymous,
+                'login_channel': user.login_channel,
+                'status': user.status,
+                'is_superuser': user.is_superuser,
+                'create_date': user.create_date,
+                'update_date': user.update_date
+            })
+        return result
+
+    @staticmethod
+    def create_user(username, password, role="user") -> dict:
+        # Validate the email address
+        if not re.match(r"^[\w\._-]+@([\w_-]+\.)+[\w-]{2,}$", username):
+            raise AdminException(f"Invalid email address: {username}!")
+        # Check if the email address is already used
+        if UserService.query(email=username):
+            raise UserAlreadyExistsError(username)
+        # Construct user info data
+        user_info_dict = {
+            "email": username,
+            "nickname": "",  # ask user to edit it manually in settings.
+            "password": decrypt(password),
+            "login_channel": "password",
+            "is_superuser": role == "admin",
+        }
+        return create_new_user(user_info_dict)
+
+    @staticmethod
+    def delete_user(username):
+        # use email to delete
+        user_list = UserService.query_user_by_email(username)
+        if not user_list:
+            raise UserNotFoundError(username)
+        if len(user_list) > 1:
+            raise AdminException(f"Exist more than 1 user: {username}!")
+        usr = user_list[0]
+        return delete_user_data(usr.id)
+
+    @staticmethod
+    def update_user_password(username, new_password) -> str:
+        # use email to find user. check exist and unique.
+        user_list = UserService.query_user_by_email(username)
+        if not user_list:
+            raise UserNotFoundError(username)
+        elif len(user_list) > 1:
+            raise AdminException(f"Exist more than 1 user: {username}!")
+        # check new_password different from old.
+        usr = user_list[0]
+        psw = decrypt(new_password)
+        if check_password_hash(usr.password, psw):
+            return "Same password, no need to update!"
+        # update password
+        UserService.update_user_password(usr.id, psw)
+        return "Password updated successfully!"
+
+    @staticmethod
+    def update_user_activate_status(username, activate_status: str):
+        # use email to find user. check exist and unique.
+        user_list = UserService.query_user_by_email(username)
+        if not user_list:
+            raise UserNotFoundError(username)
+        elif len(user_list) > 1:
+            raise AdminException(f"Exist more than 1 user: {username}!")
+        # check activate status different from new
+        usr = user_list[0]
+        # format activate_status before handle
+        _activate_status = activate_status.lower()
+        target_status = {
+            'on': ActiveEnum.ACTIVE.value,
+            'off': ActiveEnum.INACTIVE.value,
+        }.get(_activate_status)
+        if not target_status:
+            raise AdminException(f"Invalid activate_status: {activate_status}")
+        if target_status == usr.is_active:
+            return f"User activate status is already {_activate_status}!"
+        # update is_active
+        UserService.update_user(usr.id, {"is_active": target_status})
+        return f"Turn {_activate_status} user activate status successfully!"
+
+
+class UserServiceMgr:
+
+    @staticmethod
+    def get_user_datasets(username):
+        # use email to find user.
+        user_list = UserService.query_user_by_email(username)
+        if not user_list:
+            raise UserNotFoundError(username)
+        elif len(user_list) > 1:
+            raise AdminException(f"Exist more than 1 user: {username}!")
+        # find tenants
+        usr = user_list[0]
+        tenants = TenantService.get_joined_tenants_by_user_id(usr.id)
+        tenant_ids = [m["tenant_id"] for m in tenants]
+        # filter permitted kb and owned kb
+        return KnowledgebaseService.get_all_kb_by_tenant_ids(tenant_ids, usr.id)
+
+    @staticmethod
+    def get_user_agents(username):
+        # use email to find user.
+        user_list = UserService.query_user_by_email(username)
+        if not user_list:
+            raise UserNotFoundError(username)
+        elif len(user_list) > 1:
+            raise AdminException(f"Exist more than 1 user: {username}!")
+        # find tenants
+        usr = user_list[0]
+        tenants = TenantService.get_joined_tenants_by_user_id(usr.id)
+        tenant_ids = [m["tenant_id"] for m in tenants]
+        # filter permitted agents and owned agents
+        res = UserCanvasService.get_all_agents_by_tenant_ids(tenant_ids, usr.id)
+        return [{
+            'title': r['title'],
+            'permission': r['permission'],
+            'canvas_category': r['canvas_category'].split('_')[0],
+            'avatar': r['avatar']
+        } for r in res]
+
+
+class ServiceMgr:
+
+    @staticmethod
+    def get_all_services():
+        result = []
+        configs = SERVICE_CONFIGS.configs
+        for service_id, config in enumerate(configs):
+            config_dict = config.to_dict()
+            try:
+                service_detail = ServiceMgr.get_service_details(service_id)
+                if "status" in service_detail:
+                    config_dict['status'] = service_detail['status']
+                else:
+                    config_dict['status'] = 'timeout'
+            except Exception as e:
+                logging.warning(f"Can't get service details, error: {e}")
+                config_dict['status'] = 'timeout'
+            if not config_dict['host']:
+                config_dict['host'] = '-'
+            if not config_dict['port']:
+                config_dict['port'] = '-'
+            result.append(config_dict)
+        return result
+
+    @staticmethod
+    def get_services_by_type(service_type_str: str):
+        raise AdminException("get_services_by_type: not implemented")
+
+    @staticmethod
+    def get_service_details(service_id: int):
+        service_idx = int(service_id)
+        configs = SERVICE_CONFIGS.configs
+        if service_idx < 0 or service_idx >= len(configs):
+            raise AdminException(f"invalid service_index: {service_idx}")
+
+        service_config = configs[service_idx]
+        service_info = {'name': service_config.name, 'detail_func_name': service_config.detail_func_name}
+
+        detail_func = getattr(health_utils, service_info.get('detail_func_name'))
+        res = detail_func()
+        res.update({'service_name': service_info.get('name')})
+        return res
+
+    @staticmethod
+    def shutdown_service(service_id: int):
+        raise AdminException("shutdown_service: not implemented")
+
+    @staticmethod
+    def restart_service(service_id: int):
+        raise AdminException("restart_service: not implemented")

agent/README.md

@@ -1,45 +0,0 @@
-English | [简体中文](./README_zh.md)
-
-# *Graph*
-
-
-## Introduction
-
-*Graph* is a mathematical concept which is composed of nodes and edges. 
-It is used to compose a complex work flow or agent. 
-And this graph is beyond the DAG that we can use circles to describe our agent or work flow.
-Under this folder, we propose a test tool ./test/client.py which can test the DSLs such as json files in folder ./test/dsl_examples.
-Please use this client at the same folder you start RAGFlow. If it's run by Docker, please go into the container before running the client.
-Otherwise, correct configurations in conf/service_conf.yaml is essential.
-
-```bash
-PYTHONPATH=path/to/ragflow python graph/test/client.py -h
-usage: client.py [-h] -s DSL -t TENANT_ID -m
-
-options:
-  -h, --help            show this help message and exit
-  -s DSL, --dsl DSL     input dsl
-  -t TENANT_ID, --tenant_id TENANT_ID
-                        Tenant ID
-  -m, --stream          Stream output
-```
-<div align="center" style="margin-top:20px;margin-bottom:20px;">
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/79179c5e-d4d6-464a-b6c4-5721cb329899" width="1000"/>
-</div>
-
-
-## How to gain a TENANT_ID in command line?
-<div align="center" style="margin-top:20px;margin-bottom:20px;">
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/419d8588-87b1-4ab8-ac49-2d1f047a4b97" width="600"/>
-</div>
-💡 We plan to display it here in the near future.
-<div align="center" style="margin-top:20px;margin-bottom:20px;">
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/c97915de-0091-46a5-afd9-e278946e5fe3" width="600"/>
-</div>
-
-
-## How to set 'kb_ids' for component 'Retrieval' in DSL?
-<div align="center" style="margin-top:20px;margin-bottom:20px;">
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/0a731534-cac8-49fd-8a92-ca247eeef66d" width="600"/>
-</div>
-

agent/README_zh.md

@@ -1,46 +0,0 @@
-[English](./README.md) | 简体中文
-
-# *Graph*
-
-
-## 简介
-
-"Graph"是一个由节点和边组成的数学概念。
-它被用来构建复杂的工作流或代理。
-这个图超越了有向无环图（DAG），我们可以使用循环来描述我们的代理或工作流。
-在这个文件夹下，我们提出了一个测试工具 ./test/client.py，
-它可以测试像文件夹./test/dsl_examples下一样的DSL文件。
-请在启动 RAGFlow 的同一文件夹中使用此客户端。如果它是通过 Docker 运行的，请在运行客户端之前进入容器。
-否则，正确配置 conf/service_conf.yaml 文件是必不可少的。
-
-```bash
-PYTHONPATH=path/to/ragflow python graph/test/client.py -h
-usage: client.py [-h] -s DSL -t TENANT_ID -m
-
-options:
-  -h, --help            show this help message and exit
-  -s DSL, --dsl DSL     input dsl
-  -t TENANT_ID, --tenant_id TENANT_ID
-                        Tenant ID
-  -m, --stream          Stream output
-```
-<div align="center" style="margin-top:20px;margin-bottom:20px;">
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/05924730-c427-495b-8ee4-90b8b2250681" width="1000"/>
-</div>
-
-
-## 命令行中的TENANT_ID如何获得?
-<div align="center" style="margin-top:20px;margin-bottom:20px;">
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/419d8588-87b1-4ab8-ac49-2d1f047a4b97" width="600"/>
-</div>
-💡 后面会展示在这里：
-<div align="center" style="margin-top:20px;margin-bottom:20px;">
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/c97915de-0091-46a5-afd9-e278946e5fe3" width="600"/>
-</div>
-
-
-## DSL里面的Retrieval组件的kb_ids怎么填?
-<div align="center" style="margin-top:20px;margin-bottom:20px;">
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/0a731534-cac8-49fd-8a92-ca247eeef66d" width="600"/>
-</div>
-

agent/__init__.py

@@ -0,0 +1,18 @@
+#
+#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+
+from beartype.claw import beartype_this_package
+beartype_this_package()

agent/canvas.py

@@ -13,92 +13,72 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-import importlib
+import base64
 import json
-import traceback
-from abc import ABC
+import logging
+import re
+import time
+from concurrent.futures import ThreadPoolExecutor
 from copy import deepcopy
 from functools import partial
-
-import pandas as pd
+from typing import Any, Union, Tuple
 
 from agent.component import component_class
 from agent.component.base import ComponentBase
-from agent.settings import flow_logger, DEBUG
+from api.db.services.task_service import has_canceled
+from common.misc_utils import get_uuid, hash_str2int
+from common.exceptions import TaskCanceledException
+from rag.prompts.generator import chunks_format
+from rag.utils.redis_conn import REDIS_CONN
 
-
-class Canvas(ABC):
+class Graph:
     """
-    dsl = {
-        "components": {
-            "begin": {
-                "obj":{
-                    "component_name": "Begin",
-                    "params": {},
-                },
-                "downstream": ["answer_0"],
-                "upstream": [],
-            },
-            "answer_0": {
-                "obj": {
-                    "component_name": "Answer",
-                    "params": {}
-                },
-                "downstream": ["retrieval_0"],
-                "upstream": ["begin", "generate_0"],
-            },
-            "retrieval_0": {
-                "obj": {
-                    "component_name": "Retrieval",
-                    "params": {}
-                },
-                "downstream": ["generate_0"],
-                "upstream": ["answer_0"],
-            },
-            "generate_0": {
-                "obj": {
-                    "component_name": "Generate",
-                    "params": {}
-                },
-                "downstream": ["answer_0"],
-                "upstream": ["retrieval_0"],
-            }
-        },
-        "history": [],
-        "messages": [],
-        "reference": [],
-        "path": [["begin"]],
-        "answer": []
-    }
-    """
-
-    def __init__(self, dsl: str, tenant_id=None):
-        self.path = []
-        self.history = []
-        self.messages = []
-        self.answer = []
-        self.components = {}
-        self.dsl = json.loads(dsl) if dsl else {
+        dsl = {
             "components": {
                 "begin": {
-                    "obj": {
+                    "obj":{
                         "component_name": "Begin",
-                        "params": {
-                            "prologue": "Hi there!"
-                        }
+                        "params": {},
                     },
-                    "downstream": [],
-                    "upstream": []
+                    "downstream": ["answer_0"],
+                    "upstream": [],
+                },
+                "retrieval_0": {
+                    "obj": {
+                        "component_name": "Retrieval",
+                        "params": {}
+                    },
+                    "downstream": ["generate_0"],
+                    "upstream": ["answer_0"],
+                },
+                "generate_0": {
+                    "obj": {
+                        "component_name": "Generate",
+                        "params": {}
+                    },
+                    "downstream": ["answer_0"],
+                    "upstream": ["retrieval_0"],
                 }
             },
             "history": [],
-            "messages": [],
-            "reference": [],
-            "path": [],
-            "answer": []
+            "path": ["begin"],
+            "retrieval": {"chunks": [], "doc_aggs": []},
+            "globals": {
+                "sys.query": "",
+                "sys.user_id": tenant_id,
+                "sys.conversation_turns": 0,
+                "sys.files": []
+            }
         }
+        """
+
+    def __init__(self, dsl: str, tenant_id=None, task_id=None):
+        self.path = []
+        self.components = {}
+        self.error = ""
+        self.dsl = json.loads(dsl)
         self._tenant_id = tenant_id
-        self._embed_id = ""
+        self.task_id = task_id if task_id else get_uuid()
         self.load()
 
     def load(self):
@@ -107,39 +87,28 @@ class Canvas(ABC):
         for k, cpn in self.components.items():
             cpn_nms.add(cpn["obj"]["component_name"])
 
-        assert "Begin" in cpn_nms, "There have to be an 'Begin' component."
-        assert "Answer" in cpn_nms, "There have to be an 'Answer' component."
-
         for k, cpn in self.components.items():
             cpn_nms.add(cpn["obj"]["component_name"])
             param = component_class(cpn["obj"]["component_name"] + "Param")()
             param.update(cpn["obj"]["params"])
-            param.check()
+            try:
+                param.check()
+            except Exception as e:
+                raise ValueError(self.get_component_name(k) + f": {e}")
+
             cpn["obj"] = component_class(cpn["obj"]["component_name"])(self, k, param)
-            if cpn["obj"].component_name == "Categorize":
-                for _, desc in param.category_description.items():
-                    if desc["to"] not in cpn["downstream"]:
-                        cpn["downstream"].append(desc["to"])
 
         self.path = self.dsl["path"]
-        self.history = self.dsl["history"]
-        self.messages = self.dsl["messages"]
-        self.answer = self.dsl["answer"]
-        self.reference = self.dsl["reference"]
-        self._embed_id = self.dsl.get("embed_id", "")
 
     def __str__(self):
         self.dsl["path"] = self.path
-        self.dsl["history"] = self.history
-        self.dsl["messages"] = self.messages
-        self.dsl["answer"] = self.answer
-        self.dsl["reference"] = self.reference
-        self.dsl["embed_id"] = self._embed_id
+        self.dsl["task_id"] = self.task_id
         dsl = {
             "components": {}
         }
         for k in self.dsl.keys():
-            if k in ["components"]:continue
+            if k in ["components"]:
+                continue
             dsl[k] = deepcopy(self.dsl[k])
 
         for k, cpn in self.components.items():
@@ -154,152 +123,528 @@ class Canvas(ABC):
 
     def reset(self):
         self.path = []
-        self.history = []
-        self.messages = []
-        self.answer = []
-        self.reference = []
         for k, cpn in self.components.items():
             self.components[k]["obj"].reset()
-        self._embed_id = ""
+        try:
+            REDIS_CONN.delete(f"{self.task_id}-logs")
+            REDIS_CONN.delete(f"{self.task_id}-cancel")
+        except Exception as e:
+            logging.exception(e)
+
+    def get_component_name(self, cid):
+        for n in self.dsl.get("graph", {}).get("nodes", []):
+            if cid == n["id"]:
+                return n["data"]["name"]
+        return ""
 
     def run(self, **kwargs):
-        ans = ""
-        if self.answer:
-            cpn_id = self.answer[0]
-            self.answer.pop(0)
-            try:
-                ans = self.components[cpn_id]["obj"].run(self.history, **kwargs)
-            except Exception as e:
-                ans = ComponentBase.be_output(str(e))
-            self.path[-1].append(cpn_id)
-            if kwargs.get("stream"):
-                assert isinstance(ans, partial)
-                return ans
-            self.history.append(("assistant", ans.to_dict("records")))
-            return ans
+        raise NotImplementedError()
 
-        if not self.path:
-            self.components["begin"]["obj"].run(self.history, **kwargs)
-            self.path.append(["begin"])
+    def get_component(self, cpn_id) -> Union[None, dict[str, Any]]:
+        return self.components.get(cpn_id)
 
-        self.path.append([])
-        ran = -1
+    def get_component_obj(self, cpn_id) -> ComponentBase:
+        return self.components.get(cpn_id)["obj"]
 
-        def prepare2run(cpns):
-            nonlocal ran, ans
-            for c in cpns:
-                if self.path[-1] and c == self.path[-1][-1]: continue
-                cpn = self.components[c]["obj"]
-                if cpn.component_name == "Answer":
-                    self.answer.append(c)
-                else:
-                    if DEBUG: print("RUN: ", c)
-                    if cpn.component_name == "Generate":
-                        cpids = cpn.get_dependent_components()
-                        if any([c not in self.path[-1] for c in cpids]):
-                            continue
-                    ans = cpn.run(self.history, **kwargs)
-                    self.path[-1].append(c)
-            ran += 1
+    def get_component_type(self, cpn_id) -> str:
+        return self.components.get(cpn_id)["obj"].component_name
 
-        prepare2run(self.components[self.path[-2][-1]]["downstream"])
-        while 0 <= ran < len(self.path[-1]):
-            if DEBUG: print(ran, self.path)
-            cpn_id = self.path[-1][ran]
-            cpn = self.get_component(cpn_id)
-            if not cpn["downstream"]: break
-
-            loop = self._find_loop()
-            if loop: raise OverflowError(f"Too much loops: {loop}")
-
-            if cpn["obj"].component_name.lower() in ["switch", "categorize", "relevant"]:
-                switch_out = cpn["obj"].output()[1].iloc[0, 0]
-                assert switch_out in self.components, \
-                    "{}'s output: {} not valid.".format(cpn_id, switch_out)
-                try:
-                    prepare2run([switch_out])
-                except Exception as e:
-                    for p in [c for p in self.path for c in p][::-1]:
-                        if p.lower().find("answer") >= 0:
-                            self.get_component(p)["obj"].set_exception(e)
-                            prepare2run([p])
-                            break
-                    traceback.print_exc()
-                    break
-                continue
-
-            try:
-                prepare2run(cpn["downstream"])
-            except Exception as e:
-                for p in [c for p in self.path for c in p][::-1]:
-                    if p.lower().find("answer") >= 0:
-                        self.get_component(p)["obj"].set_exception(e)
-                        prepare2run([p])
-                        break
-                traceback.print_exc()
-                break
-
-        if self.answer:
-            cpn_id = self.answer[0]
-            self.answer.pop(0)
-            ans = self.components[cpn_id]["obj"].run(self.history, **kwargs)
-            self.path[-1].append(cpn_id)
-            if kwargs.get("stream"):
-                assert isinstance(ans, partial)
-                return ans
-
-            self.history.append(("assistant", ans.to_dict("records")))
-
-        return ans
-
-    def get_component(self, cpn_id):
-        return self.components[cpn_id]
+    def get_component_input_form(self, cpn_id) -> dict:
+        return self.components.get(cpn_id)["obj"].get_input_form()
 
     def get_tenant_id(self):
         return self._tenant_id
 
+    def get_value_with_variable(self,value: str) -> Any:
+        pat = re.compile(r"\{* *\{([a-zA-Z:0-9]+@[A-Za-z0-9_.]+|sys\.[A-Za-z0-9_.]+|env\.[A-Za-z0-9_.]+)\} *\}*")
+        out_parts = []
+        last = 0
+
+        for m in pat.finditer(value):
+            out_parts.append(value[last:m.start()])
+            key = m.group(1)
+            v = self.get_variable_value(key)
+            if v is None:
+                rep = ""
+            elif isinstance(v, partial):
+                buf = []
+                for chunk in v():
+                    buf.append(chunk)
+                rep = "".join(buf)
+            elif isinstance(v, str):
+                rep = v
+            else:
+                rep = json.dumps(v, ensure_ascii=False)
+
+            out_parts.append(rep)
+            last = m.end()
+
+        out_parts.append(value[last:])
+        return("".join(out_parts))
+
+    def get_variable_value(self, exp: str) -> Any:
+        exp = exp.strip("{").strip("}").strip(" ").strip("{").strip("}")
+        if exp.find("@") < 0:
+            return self.globals[exp]
+        cpn_id, var_nm = exp.split("@")
+        cpn = self.get_component(cpn_id)
+        if not cpn:
+            raise Exception(f"Can't find variable: '{cpn_id}@{var_nm}'")
+        parts = var_nm.split(".", 1)
+        root_key = parts[0]
+        rest = parts[1] if len(parts) > 1 else ""
+        root_val = cpn["obj"].output(root_key)
+
+        if not rest:
+            return root_val
+        return self.get_variable_param_value(root_val,rest)
+
+    def get_variable_param_value(self, obj: Any, path: str) -> Any:
+        cur = obj
+        if not path:
+            return cur
+        for key in path.split('.'):
+            if cur is None:
+                return None
+            if isinstance(cur, str):
+                try:
+                    cur = json.loads(cur)
+                except Exception:
+                    return None
+            if isinstance(cur, dict):
+                cur = cur.get(key)
+            else:
+                cur = getattr(cur, key, None)
+        return cur
+    
+    def set_variable_value(self, exp: str,value):
+        exp = exp.strip("{").strip("}").strip(" ").strip("{").strip("}")
+        if exp.find("@") < 0:
+            self.globals[exp] = value
+            return
+        cpn_id, var_nm = exp.split("@")
+        cpn = self.get_component(cpn_id)
+        if not cpn:
+            raise Exception(f"Can't find variable: '{cpn_id}@{var_nm}'")
+        parts = var_nm.split(".", 1)
+        root_key = parts[0]
+        rest = parts[1] if len(parts) > 1 else ""
+        if not rest:
+            cpn["obj"].set_output(root_key, value)
+            return
+        root_val = cpn["obj"].output(root_key)
+        if not root_val:
+            root_val = {}
+        cpn["obj"].set_output(root_key, self.set_variable_param_value(root_val,rest,value))
+
+    def set_variable_param_value(self, obj: Any, path: str, value) -> Any:
+        cur = obj
+        keys = path.split('.')
+        if not path:
+            return value
+        for key in keys:
+            if key not in cur or not isinstance(cur[key], dict):
+                cur[key] = {}
+            cur = cur[key]
+        cur[keys[-1]] = value
+        return obj
+
+    def is_canceled(self) -> bool:
+        return has_canceled(self.task_id)
+
+    def cancel_task(self) -> bool:
+        try:
+            REDIS_CONN.set(f"{self.task_id}-cancel", "x")
+        except Exception as e:
+            logging.exception(e)
+            return False
+        return True
+
+
+class Canvas(Graph):
+
+    def __init__(self, dsl: str, tenant_id=None, task_id=None):
+        self.globals = {
+            "sys.query": "",
+            "sys.user_id": tenant_id,
+            "sys.conversation_turns": 0,
+            "sys.files": []
+        }
+        super().__init__(dsl, tenant_id, task_id)
+
+    def load(self):
+        super().load()
+        self.history = self.dsl["history"]
+        if "globals" in self.dsl:
+            self.globals = self.dsl["globals"]
+        else:
+            self.globals = {
+            "sys.query": "",
+            "sys.user_id": "",
+            "sys.conversation_turns": 0,
+            "sys.files": []
+        }
+
+        self.retrieval = self.dsl["retrieval"]
+        self.memory = self.dsl.get("memory", [])
+
+    def __str__(self):
+        self.dsl["history"] = self.history
+        self.dsl["retrieval"] = self.retrieval
+        self.dsl["memory"] = self.memory
+        return super().__str__()
+
+    def reset(self, mem=False):
+        super().reset()
+        if not mem:
+            self.history = []
+            self.retrieval = []
+            self.memory = []
+        for k in self.globals.keys():
+            if k.startswith("sys.") or k.startswith("env."):
+                if isinstance(self.globals[k], str):
+                    self.globals[k] = ""
+                elif isinstance(self.globals[k], int):
+                    self.globals[k] = 0
+                elif isinstance(self.globals[k], float):
+                    self.globals[k] = 0
+                elif isinstance(self.globals[k], list):
+                    self.globals[k] = []
+                elif isinstance(self.globals[k], dict):
+                    self.globals[k] = {}
+                else:
+                    self.globals[k] = None
+
+    async def run(self, **kwargs):
+        st = time.perf_counter()
+        self.message_id = get_uuid()
+        created_at = int(time.time())
+        self.add_user_input(kwargs.get("query"))
+        for k, cpn in self.components.items():
+            self.components[k]["obj"].reset(True)
+
+        if kwargs.get("webhook_payload"):
+            for k, cpn in self.components.items():
+                if self.components[k]["obj"].component_name.lower() == "webhook":
+                    for kk, vv in kwargs["webhook_payload"].items():
+                        self.components[k]["obj"].set_output(kk, vv)
+
+        for k in kwargs.keys():
+            if k in ["query", "user_id", "files"] and kwargs[k]:
+                if k == "files":
+                    self.globals[f"sys.{k}"] = self.get_files(kwargs[k])
+                else:
+                    self.globals[f"sys.{k}"] = kwargs[k]
+        if not self.globals["sys.conversation_turns"] :
+            self.globals["sys.conversation_turns"] = 0
+        self.globals["sys.conversation_turns"] += 1
+
+        def decorate(event, dt):
+            nonlocal created_at
+            return {
+                "event": event,
+                #"conversation_id": "f3cc152b-24b0-4258-a1a1-7d5e9fc8a115",
+                "message_id": self.message_id,
+                "created_at": created_at,
+                "task_id": self.task_id,
+                "data": dt
+            }
+
+        if not self.path or self.path[-1].lower().find("userfillup") < 0:
+            self.path.append("begin")
+            self.retrieval.append({"chunks": [], "doc_aggs": []})
+
+        if self.is_canceled():
+            msg = f"Task {self.task_id} has been canceled before starting."
+            logging.info(msg)
+            raise TaskCanceledException(msg)
+
+        yield decorate("workflow_started", {"inputs": kwargs.get("inputs")})
+        self.retrieval.append({"chunks": {}, "doc_aggs": {}})
+
+        def _run_batch(f, t):
+            if self.is_canceled():
+                msg = f"Task {self.task_id} has been canceled during batch execution."
+                logging.info(msg)
+                raise TaskCanceledException(msg)
+
+            with ThreadPoolExecutor(max_workers=5) as executor:
+                thr = []
+                i = f
+                while i < t:
+                    cpn = self.get_component_obj(self.path[i])
+                    if cpn.component_name.lower() in ["begin", "userfillup"]:
+                        thr.append(executor.submit(cpn.invoke, inputs=kwargs.get("inputs", {})))
+                        i += 1
+                    else:
+                        for _, ele in cpn.get_input_elements().items():
+                            if isinstance(ele, dict) and ele.get("_cpn_id") and ele.get("_cpn_id") not in self.path[:i] and self.path[0].lower().find("userfillup") < 0:
+                                self.path.pop(i)
+                                t -= 1
+                                break
+                        else:
+                            thr.append(executor.submit(cpn.invoke, **cpn.get_input()))
+                            i += 1
+                for t in thr:
+                    t.result()
+
+        def _node_finished(cpn_obj):
+            return decorate("node_finished",{
+                           "inputs": cpn_obj.get_input_values(),
+                           "outputs": cpn_obj.output(),
+                           "component_id": cpn_obj._id,
+                           "component_name": self.get_component_name(cpn_obj._id),
+                           "component_type": self.get_component_type(cpn_obj._id),
+                           "error": cpn_obj.error(),
+                           "elapsed_time": time.perf_counter() - cpn_obj.output("_created_time"),
+                           "created_at": cpn_obj.output("_created_time"),
+                       })
+
+        self.error = ""
+        idx = len(self.path) - 1
+        partials = []
+        while idx < len(self.path):
+            to = len(self.path)
+            for i in range(idx, to):
+                yield decorate("node_started", {
+                    "inputs": None, "created_at": int(time.time()),
+                    "component_id": self.path[i],
+                    "component_name": self.get_component_name(self.path[i]),
+                    "component_type": self.get_component_type(self.path[i]),
+                    "thoughts": self.get_component_thoughts(self.path[i])
+                })
+            _run_batch(idx, to)
+            to = len(self.path)
+            # post processing of components invocation
+            for i in range(idx, to):
+                cpn = self.get_component(self.path[i])
+                cpn_obj = self.get_component_obj(self.path[i])
+                if cpn_obj.component_name.lower() == "message":
+                    if isinstance(cpn_obj.output("content"), partial):
+                        _m = ""
+                        for m in cpn_obj.output("content")():
+                            if not m:
+                                continue
+                            if m == "<think>":
+                                yield decorate("message", {"content": "", "start_to_think": True})
+                            elif m == "</think>":
+                                yield decorate("message", {"content": "", "end_to_think": True})
+                            else:
+                                yield decorate("message", {"content": m})
+                                _m += m
+                        cpn_obj.set_output("content", _m)
+                        cite = re.search(r"\[ID:[ 0-9]+\]", _m)
+                    else:
+                        yield decorate("message", {"content": cpn_obj.output("content")})
+                        cite = re.search(r"\[ID:[ 0-9]+\]",  cpn_obj.output("content"))
+
+                    if isinstance(cpn_obj.output("attachment"), tuple):
+                        yield decorate("message", {"attachment": cpn_obj.output("attachment")})
+                        
+                    yield decorate("message_end", {"reference": self.get_reference() if cite else None})
+
+                    while partials:
+                        _cpn_obj = self.get_component_obj(partials[0])
+                        if isinstance(_cpn_obj.output("content"), partial):
+                            break
+                        yield _node_finished(_cpn_obj)
+                        partials.pop(0)
+
+                other_branch = False
+                if cpn_obj.error():
+                    ex = cpn_obj.exception_handler()
+                    if ex and ex["goto"]:
+                        self.path.extend(ex["goto"])
+                        other_branch = True
+                    elif ex and ex["default_value"]:
+                        yield decorate("message", {"content": ex["default_value"]})
+                        yield decorate("message_end", {})
+                    else:
+                        self.error = cpn_obj.error()
+
+                if cpn_obj.component_name.lower() != "iteration":
+                    if isinstance(cpn_obj.output("content"), partial):
+                        if self.error:
+                            cpn_obj.set_output("content", None)
+                            yield _node_finished(cpn_obj)
+                        else:
+                            partials.append(self.path[i])
+                    else:
+                        yield _node_finished(cpn_obj)
+
+                def _append_path(cpn_id):
+                    nonlocal other_branch
+                    if other_branch:
+                        return
+                    if self.path[-1] == cpn_id:
+                        return
+                    self.path.append(cpn_id)
+
+                def _extend_path(cpn_ids):
+                    nonlocal other_branch
+                    if other_branch:
+                        return
+                    for cpn_id in cpn_ids:
+                        _append_path(cpn_id)
+
+                if cpn_obj.component_name.lower() == "iterationitem" and cpn_obj.end():
+                    iter = cpn_obj.get_parent()
+                    yield _node_finished(iter)
+                    _extend_path(self.get_component(cpn["parent_id"])["downstream"])
+                elif cpn_obj.component_name.lower() in ["categorize", "switch"]:
+                    _extend_path(cpn_obj.output("_next"))
+                elif cpn_obj.component_name.lower() == "iteration":
+                    _append_path(cpn_obj.get_start())
+                elif not cpn["downstream"] and cpn_obj.get_parent():
+                    _append_path(cpn_obj.get_parent().get_start())
+                else:
+                    _extend_path(cpn["downstream"])
+
+            if self.error:
+                logging.error(f"Runtime Error: {self.error}")
+                break
+            idx = to
+
+            if any([self.get_component_obj(c).component_name.lower() == "userfillup" for c in self.path[idx:]]):
+                path = [c for c in self.path[idx:] if self.get_component(c)["obj"].component_name.lower() == "userfillup"]
+                path.extend([c for c in self.path[idx:] if self.get_component(c)["obj"].component_name.lower() != "userfillup"])
+                another_inputs = {}
+                tips = ""
+                for c in path:
+                    o = self.get_component_obj(c)
+                    if o.component_name.lower() == "userfillup":
+                        o.invoke()
+                        another_inputs.update(o.get_input_elements())
+                        if o.get_param("enable_tips"):
+                            tips = o.output("tips")
+                self.path = path
+                yield decorate("user_inputs", {"inputs": another_inputs, "tips": tips})
+                return
+        self.path = self.path[:idx]
+        if not self.error:
+            yield decorate("workflow_finished",
+                       {
+                           "inputs": kwargs.get("inputs"),
+                           "outputs": self.get_component_obj(self.path[-1]).output(),
+                           "elapsed_time": time.perf_counter() - st,
+                           "created_at": st,
+                       })
+            self.history.append(("assistant", self.get_component_obj(self.path[-1]).output()))
+        elif "Task has been canceled" in self.error:
+            yield decorate("workflow_finished",
+                       {
+                           "inputs": kwargs.get("inputs"),
+                           "outputs": "Task has been canceled",
+                           "elapsed_time": time.perf_counter() - st,
+                           "created_at": st,
+                       })
+
+    def is_reff(self, exp: str) -> bool:
+        exp = exp.strip("{").strip("}")
+        if exp.find("@") < 0:
+            return exp in self.globals
+        arr = exp.split("@")
+        if len(arr) != 2:
+            return False
+        if self.get_component(arr[0]) is None:
+            return False
+        return True
+
     def get_history(self, window_size):
         convs = []
-        for role, obj in self.history[(window_size + 1) * -1:]:
-            convs.append({"role": role, "content": (obj if role == "user" else
-                                                    '\n'.join(pd.DataFrame(obj)['content']))})
+        if window_size <= 0:
+            return convs
+        for role, obj in self.history[window_size * -2:]:
+            if isinstance(obj, dict):
+                convs.append({"role": role, "content": obj.get("content", "")})
+            else:
+                convs.append({"role": role, "content": str(obj)})
         return convs
 
     def add_user_input(self, question):
         self.history.append(("user", question))
 
-    def set_embedding_model(self, embed_id):
-        self._embed_id = embed_id
-
-    def get_embedding_model(self):
-        return self._embed_id
-
-    def _find_loop(self, max_loops=6):
-        path = self.path[-1][::-1]
-        if len(path) < 2: return False
-
-        for i in range(len(path)):
-            if path[i].lower().find("answer") >= 0:
-                path = path[:i]
-                break
-
-        if len(path) < 2: return False
-
-        for l in range(2, len(path) // 2):
-            pat = ",".join(path[0:l])
-            path_str = ",".join(path)
-            if len(pat) >= len(path_str): return False
-            loop = max_loops
-            while path_str.find(pat) == 0 and loop >= 0:
-                loop -= 1
-                if len(pat)+1 >= len(path_str):
-                    return False
-                path_str = path_str[len(pat)+1:]
-            if loop < 0:
-                pat = " => ".join([p.split(":")[0] for p in path[0:l]])
-                return pat + " => " + pat
-
-        return False
-
     def get_prologue(self):
         return self.components["begin"]["obj"]._param.prologue
+
+    def get_mode(self):
+        return self.components["begin"]["obj"]._param.mode
+
+    def set_global_param(self, **kwargs):
+        self.globals.update(kwargs)
+
+    def get_preset_param(self):
+        return self.components["begin"]["obj"]._param.inputs
+
+    def get_component_input_elements(self, cpnnm):
+        return self.components[cpnnm]["obj"].get_input_elements()
+
+    def get_files(self, files: Union[None, list[dict]]) -> list[str]:
+        from api.db.services.file_service import FileService
+        if not files:
+            return  []
+        def image_to_base64(file):
+            return "data:{};base64,{}".format(file["mime_type"],
+                                        base64.b64encode(FileService.get_blob(file["created_by"], file["id"])).decode("utf-8"))
+        exe = ThreadPoolExecutor(max_workers=5)
+        threads = []
+        for file in files:
+            if file["mime_type"].find("image") >=0:
+                threads.append(exe.submit(image_to_base64, file))
+                continue
+            threads.append(exe.submit(FileService.parse, file["name"], FileService.get_blob(file["created_by"], file["id"]), True, file["created_by"]))
+        return [th.result() for th in threads]
+
+    def tool_use_callback(self, agent_id: str, func_name: str, params: dict, result: Any, elapsed_time=None):
+        agent_ids = agent_id.split("-->")
+        agent_name = self.get_component_name(agent_ids[0])
+        path = agent_name if len(agent_ids) < 2 else agent_name+"-->"+"-->".join(agent_ids[1:])
+        try:
+            bin = REDIS_CONN.get(f"{self.task_id}-{self.message_id}-logs")
+            if bin:
+                obj = json.loads(bin.encode("utf-8"))
+                if obj[-1]["component_id"] == agent_ids[0]:
+                    obj[-1]["trace"].append({"path": path, "tool_name": func_name, "arguments": params, "result": result, "elapsed_time": elapsed_time})
+                else:
+                    obj.append({
+                    "component_id": agent_ids[0],
+                    "trace": [{"path": path, "tool_name": func_name, "arguments": params, "result": result, "elapsed_time": elapsed_time}]
+                })
+            else:
+                obj = [{
+                    "component_id": agent_ids[0],
+                    "trace": [{"path": path, "tool_name": func_name, "arguments": params, "result": result, "elapsed_time": elapsed_time}]
+                }]
+            REDIS_CONN.set_obj(f"{self.task_id}-{self.message_id}-logs", obj, 60*10)
+        except Exception as e:
+            logging.exception(e)
+
+    def add_reference(self, chunks: list[object], doc_infos: list[object]):
+        if not self.retrieval:
+            self.retrieval = [{"chunks": {}, "doc_aggs": {}}]
+
+        r = self.retrieval[-1]
+        for ck in chunks_format({"chunks": chunks}):
+            cid = hash_str2int(ck["id"], 500)
+            # cid = uuid.uuid5(uuid.NAMESPACE_DNS, ck["id"])
+            if cid not in r:
+                r["chunks"][cid] = ck
+
+        for doc in doc_infos:
+            if doc["doc_name"] not in r:
+                r["doc_aggs"][doc["doc_name"]] = doc
+
+    def get_reference(self):
+        if not self.retrieval:
+            return {"chunks": {}, "doc_aggs": {}}
+        return self.retrieval[-1]
+
+    def add_memory(self, user:str, assist:str, summ: str):
+        self.memory.append((user, assist, summ))
+
+    def get_memory(self) -> list[Tuple]:
+        return self.memory
+
+    def get_component_thoughts(self, cpn_id) -> str:
+        return self.components.get(cpn_id)["obj"].thoughts()
+

agent/component/__init__.py

@@ -1,35 +1,58 @@
+#
+#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import os
 import importlib
-from .begin import Begin, BeginParam
-from .generate import Generate, GenerateParam
-from .retrieval import Retrieval, RetrievalParam
-from .answer import Answer, AnswerParam
-from .categorize import Categorize, CategorizeParam
-from .switch import Switch, SwitchParam
-from .relevant import Relevant, RelevantParam
-from .message import Message, MessageParam
-from .rewrite import RewriteQuestion, RewriteQuestionParam
-from .keyword import KeywordExtract, KeywordExtractParam
-from .baidu import Baidu, BaiduParam
-from .duckduckgo import DuckDuckGo, DuckDuckGoParam
-from .wikipedia import Wikipedia, WikipediaParam
-from .pubmed import PubMed, PubMedParam
-from .arxiv import ArXiv, ArXivParam
-from .google import Google, GoogleParam
-from .bing import Bing, BingParam
-from .googlescholar import GoogleScholar, GoogleScholarParam
-from .deepl import DeepL, DeepLParam
-from .github import GitHub, GitHubParam
-from .baidufanyi import BaiduFanyi, BaiduFanyiParam
-from .qweather import QWeather, QWeatherParam
-from .exesql import ExeSQL, ExeSQLParam
-from .yahoofinance import YahooFinance, YahooFinanceParam
-from .wencai import WenCai, WenCaiParam
-from .jin10 import Jin10, Jin10Param
-from .tushare import TuShare, TuShareParam
-from .akshare import AkShare, AkShareParam
+import inspect
+from types import ModuleType
+from typing import Dict, Type
+
+_package_path = os.path.dirname(__file__)
+__all_classes: Dict[str, Type] = {}
+
+def _import_submodules() -> None:
+    for filename in os.listdir(_package_path): # noqa: F821
+        if filename.startswith("__") or not filename.endswith(".py") or filename.startswith("base"):
+            continue
+        module_name = filename[:-3]
+
+        try:
+            module = importlib.import_module(f".{module_name}", package=__name__)
+            _extract_classes_from_module(module)  # noqa: F821
+        except ImportError as e:
+            print(f"Warning: Failed to import module {module_name}: {str(e)}")
+
+def _extract_classes_from_module(module: ModuleType) -> None:
+    for name, obj in inspect.getmembers(module):
+        if (inspect.isclass(obj) and
+                obj.__module__ == module.__name__ and not name.startswith("_")):
+            __all_classes[name] = obj
+            globals()[name] = obj
+
+_import_submodules()
+
+__all__ = list(__all_classes.keys()) + ["__all_classes"]
+
+del _package_path, _import_submodules, _extract_classes_from_module
 
 
 def component_class(class_name):
-    m = importlib.import_module("agent.component")
-    c = getattr(m, class_name)
-    return c
+    for module_name in ["agent.component", "agent.tools", "rag.flow"]:
+        try:
+            return getattr(importlib.import_module(module_name), class_name)
+        except Exception:
+            # logging.warning(f"Can't import module: {module_name}, error: {e}")
+            pass
+    assert False, f"Can't import {class_name}"

agent/component/agent_with_tools.py

@@ -0,0 +1,381 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import logging
+import os
+import re
+from concurrent.futures import ThreadPoolExecutor
+from copy import deepcopy
+from functools import partial
+from typing import Any
+
+import json_repair
+from timeit import default_timer as timer
+from agent.tools.base import LLMToolPluginCallSession, ToolParamBase, ToolBase, ToolMeta
+from api.db.services.llm_service import LLMBundle
+from api.db.services.tenant_llm_service import TenantLLMService
+from api.db.services.mcp_server_service import MCPServerService
+from common.connection_utils import timeout
+from rag.prompts.generator import next_step, COMPLETE_TASK, analyze_task, \
+    citation_prompt, reflect, rank_memories, kb_prompt, citation_plus, full_question, message_fit_in
+from common.mcp_tool_call_conn import MCPToolCallSession, mcp_tool_metadata_to_openai_tool
+from agent.component.llm import LLMParam, LLM
+
+
+class AgentParam(LLMParam, ToolParamBase):
+    """
+    Define the Agent component parameters.
+    """
+
+    def __init__(self):
+        self.meta:ToolMeta = {
+                "name": "agent",
+                "description": "This is an agent for a specific task.",
+                "parameters": {
+                    "user_prompt": {
+                        "type": "string",
+                        "description": "This is the order you need to send to the agent.",
+                        "default": "",
+                        "required": True
+                    },
+                    "reasoning": {
+                        "type": "string",
+                        "description": (
+                            "Supervisor's reasoning for choosing the this agent. "
+                            "Explain why this agent is being invoked and what is expected of it."
+                        ),
+                        "required": True
+                    },
+                    "context": {
+                        "type": "string",
+                        "description": (
+                                "All relevant background information, prior facts, decisions, "
+                                "and state needed by the agent to solve the current query. "
+                                "Should be as detailed and self-contained as possible."
+                            ),
+                        "required": True
+                    },
+                }
+            }
+        super().__init__()
+        self.function_name = "agent"
+        self.tools = []
+        self.mcp = []
+        self.max_rounds = 5
+        self.description = ""
+
+
+class Agent(LLM, ToolBase):
+    component_name = "Agent"
+
+    def __init__(self, canvas, id, param: LLMParam):
+        LLM.__init__(self, canvas, id, param)
+        self.tools = {}
+        for cpn in self._param.tools:
+            cpn = self._load_tool_obj(cpn)
+            self.tools[cpn.get_meta()["function"]["name"]] = cpn
+
+        self.chat_mdl = LLMBundle(self._canvas.get_tenant_id(), TenantLLMService.llm_id2llm_type(self._param.llm_id), self._param.llm_id,
+                                  max_retries=self._param.max_retries,
+                                  retry_interval=self._param.delay_after_error,
+                                  max_rounds=self._param.max_rounds,
+                                  verbose_tool_use=True
+                                  )
+        self.tool_meta = [v.get_meta() for _,v in self.tools.items()]
+
+        for mcp in self._param.mcp:
+            _, mcp_server = MCPServerService.get_by_id(mcp["mcp_id"])
+            tool_call_session = MCPToolCallSession(mcp_server, mcp_server.variables)
+            for tnm, meta in mcp["tools"].items():
+                self.tool_meta.append(mcp_tool_metadata_to_openai_tool(meta))
+                self.tools[tnm] = tool_call_session
+        self.callback = partial(self._canvas.tool_use_callback, id)
+        self.toolcall_session = LLMToolPluginCallSession(self.tools, self.callback)
+        #self.chat_mdl.bind_tools(self.toolcall_session, self.tool_metas)
+
+    def _load_tool_obj(self, cpn: dict) -> object:
+        from agent.component import component_class
+        param = component_class(cpn["component_name"] + "Param")()
+        param.update(cpn["params"])
+        try:
+            param.check()
+        except Exception as e:
+            self.set_output("_ERROR", cpn["component_name"] + f" configuration error: {e}")
+            raise
+        cpn_id = f"{self._id}-->" + cpn.get("name", "").replace(" ", "_")
+        return component_class(cpn["component_name"])(self._canvas, cpn_id, param)
+
+    def get_meta(self) -> dict[str, Any]:
+        self._param.function_name= self._id.split("-->")[-1]
+        m = super().get_meta()
+        if hasattr(self._param, "user_prompt") and self._param.user_prompt:
+            m["function"]["parameters"]["properties"]["user_prompt"] = self._param.user_prompt
+        return m
+
+    def get_input_form(self) -> dict[str, dict]:
+        res = {}
+        for k, v in self.get_input_elements().items():
+            res[k] = {
+                "type": "line",
+                "name": v["name"]
+            }
+        for cpn in self._param.tools:
+            if not isinstance(cpn, LLM):
+                continue
+            res.update(cpn.get_input_form())
+        return res
+
+    @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 20*60)))
+    def _invoke(self, **kwargs):
+        if self.check_if_canceled("Agent processing"):
+            return
+
+        if kwargs.get("user_prompt"):
+            usr_pmt = ""
+            if kwargs.get("reasoning"):
+                usr_pmt += "\nREASONING:\n{}\n".format(kwargs["reasoning"])
+            if kwargs.get("context"):
+                usr_pmt += "\nCONTEXT:\n{}\n".format(kwargs["context"])
+            if usr_pmt:
+                usr_pmt += "\nQUERY:\n{}\n".format(str(kwargs["user_prompt"]))
+            else:
+                usr_pmt = str(kwargs["user_prompt"])
+            self._param.prompts = [{"role": "user", "content": usr_pmt}]
+
+        if not self.tools:
+            if self.check_if_canceled("Agent processing"):
+                return
+            return LLM._invoke(self, **kwargs)
+
+        prompt, msg, user_defined_prompt = self._prepare_prompt_variables()
+
+        downstreams = self._canvas.get_component(self._id)["downstream"] if self._canvas.get_component(self._id) else []
+        ex = self.exception_handler()
+        if any([self._canvas.get_component_obj(cid).component_name.lower()=="message" for cid in downstreams]) and not (ex and ex["goto"]):
+            self.set_output("content", partial(self.stream_output_with_tools, prompt, msg, user_defined_prompt))
+            return
+
+        _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(self.chat_mdl.max_length * 0.97))
+        use_tools = []
+        ans = ""
+        for delta_ans, tk in self._react_with_tools_streamly(prompt, msg, use_tools, user_defined_prompt):
+            if self.check_if_canceled("Agent processing"):
+                return
+            ans += delta_ans
+
+        if ans.find("**ERROR**") >= 0:
+            logging.error(f"Agent._chat got error. response: {ans}")
+            if self.get_exception_default_value():
+                self.set_output("content", self.get_exception_default_value())
+            else:
+                self.set_output("_ERROR", ans)
+            return
+
+        self.set_output("content", ans)
+        if use_tools:
+            self.set_output("use_tools", use_tools)
+        return ans
+
+    def stream_output_with_tools(self, prompt, msg, user_defined_prompt={}):
+        _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(self.chat_mdl.max_length * 0.97))
+        answer_without_toolcall = ""
+        use_tools = []
+        for delta_ans,_ in self._react_with_tools_streamly(prompt, msg, use_tools, user_defined_prompt):
+            if self.check_if_canceled("Agent streaming"):
+                return
+
+            if delta_ans.find("**ERROR**") >= 0:
+                if self.get_exception_default_value():
+                    self.set_output("content", self.get_exception_default_value())
+                    yield self.get_exception_default_value()
+                else:
+                    self.set_output("_ERROR", delta_ans)
+                    return
+            answer_without_toolcall += delta_ans
+            yield delta_ans
+
+        self.set_output("content", answer_without_toolcall)
+        if use_tools:
+            self.set_output("use_tools", use_tools)
+
+    def _gen_citations(self, text):
+        retrievals = self._canvas.get_reference()
+        retrievals = {"chunks": list(retrievals["chunks"].values()), "doc_aggs": list(retrievals["doc_aggs"].values())}
+        formated_refer = kb_prompt(retrievals, self.chat_mdl.max_length, True)
+        for delta_ans in self._generate_streamly([{"role": "system", "content": citation_plus("\n\n".join(formated_refer))},
+                                                  {"role": "user", "content": text}
+                                                  ]):
+            yield delta_ans
+
+    def _react_with_tools_streamly(self, prompt, history: list[dict], use_tools, user_defined_prompt={}):
+        token_count = 0
+        tool_metas = self.tool_meta
+        hist = deepcopy(history)
+        last_calling = ""
+        if len(hist) > 3:
+            st = timer()
+            user_request = full_question(messages=history, chat_mdl=self.chat_mdl)
+            self.callback("Multi-turn conversation optimization", {}, user_request, elapsed_time=timer()-st)
+        else:
+            user_request = history[-1]["content"]
+
+        def use_tool(name, args):
+            nonlocal hist, use_tools, token_count,last_calling,user_request
+            logging.info(f"{last_calling=} == {name=}")
+            # Summarize of function calling
+            #if all([
+            #    isinstance(self.toolcall_session.get_tool_obj(name), Agent),
+            #    last_calling,
+            #    last_calling != name
+            #]):
+            #    self.toolcall_session.get_tool_obj(name).add2system_prompt(f"The chat history with other agents are as following: \n" + self.get_useful_memory(user_request, str(args["user_prompt"]),user_defined_prompt))
+            last_calling = name
+            tool_response = self.toolcall_session.tool_call(name, args)
+            use_tools.append({
+                "name": name,
+                "arguments": args,
+                "results": tool_response
+            })
+            # self.callback("add_memory", {}, "...")
+            #self.add_memory(hist[-2]["content"], hist[-1]["content"], name, args, str(tool_response), user_defined_prompt)
+
+            return name, tool_response
+
+        def complete():
+            nonlocal hist
+            need2cite = self._param.cite and self._canvas.get_reference()["chunks"] and self._id.find("-->") < 0
+            cited = False
+            if hist[0]["role"] == "system" and need2cite:
+                if len(hist) < 7:
+                    hist[0]["content"] += citation_prompt()
+                    cited = True
+            yield "", token_count
+
+            _hist = hist
+            if len(hist) > 12:
+                _hist = [hist[0], hist[1], *hist[-10:]]
+            entire_txt = ""
+            for delta_ans in self._generate_streamly(_hist):
+                if not need2cite or cited:
+                    yield delta_ans, 0
+                entire_txt += delta_ans
+            if not need2cite or cited:
+                return
+
+            st = timer()
+            txt = ""
+            for delta_ans in self._gen_citations(entire_txt):
+                if self.check_if_canceled("Agent streaming"):
+                    return
+                yield delta_ans, 0
+                txt += delta_ans
+
+            self.callback("gen_citations", {}, txt, elapsed_time=timer()-st)
+
+        def append_user_content(hist, content):
+            if hist[-1]["role"] == "user":
+                hist[-1]["content"] += content
+            else:
+                hist.append({"role": "user", "content": content})
+
+        st = timer()
+        task_desc = analyze_task(self.chat_mdl, prompt, user_request, tool_metas, user_defined_prompt)
+        self.callback("analyze_task", {}, task_desc, elapsed_time=timer()-st)
+        for _ in range(self._param.max_rounds + 1):
+            if self.check_if_canceled("Agent streaming"):
+                return
+            response, tk = next_step(self.chat_mdl, hist, tool_metas, task_desc, user_defined_prompt)
+            # self.callback("next_step", {}, str(response)[:256]+"...")
+            token_count += tk
+            hist.append({"role": "assistant", "content": response})
+            try:
+                functions = json_repair.loads(re.sub(r"```.*", "", response))
+                if not isinstance(functions, list):
+                    raise TypeError(f"List should be returned, but `{functions}`")
+                for f in functions:
+                    if not isinstance(f, dict):
+                        raise TypeError(f"An object type should be returned, but `{f}`")
+                with ThreadPoolExecutor(max_workers=5) as executor:
+                    thr = []
+                    for func in functions:
+                        name = func["name"]
+                        args = func["arguments"]
+                        if name == COMPLETE_TASK:
+                            append_user_content(hist, f"Respond with a formal answer. FORGET(DO NOT mention) about `{COMPLETE_TASK}`. The language for the response MUST be as the same as the first user request.\n")
+                            for txt, tkcnt in complete():
+                                yield txt, tkcnt
+                            return
+
+                        thr.append(executor.submit(use_tool, name, args))
+
+                    st = timer()
+                    reflection = reflect(self.chat_mdl, hist, [th.result() for th in thr], user_defined_prompt)
+                    append_user_content(hist, reflection)
+                    self.callback("reflection", {}, str(reflection), elapsed_time=timer()-st)
+
+            except Exception as e:
+                logging.exception(msg=f"Wrong JSON argument format in LLM ReAct response: {e}")
+                e = f"\nTool call error, please correct the input parameter of response format and call it again.\n *** Exception ***\n{e}"
+                append_user_content(hist, str(e))
+
+        logging.warning( f"Exceed max rounds: {self._param.max_rounds}")
+        final_instruction = f"""
+{user_request}
+IMPORTANT: You have reached the conversation limit. Based on ALL the information and research you have gathered so far, please provide a DIRECT and COMPREHENSIVE final answer to the original request.
+Instructions:
+1. SYNTHESIZE all information collected during this conversation
+2. Provide a COMPLETE response using existing data - do not suggest additional research
+3. Structure your response as a FINAL DELIVERABLE, not a plan
+4. If information is incomplete, state what you found and provide the best analysis possible with available data
+5. DO NOT mention conversation limits or suggest further steps
+6. Focus on delivering VALUE with the information already gathered
+Respond immediately with your final comprehensive answer.
+        """
+        if self.check_if_canceled("Agent final instruction"):
+            return
+        append_user_content(hist, final_instruction)
+
+        for txt, tkcnt in complete():
+            yield txt, tkcnt
+
+    def get_useful_memory(self, goal: str, sub_goal:str, topn=3, user_defined_prompt:dict={}) -> str:
+        # self.callback("get_useful_memory", {"topn": 3}, "...")
+        mems = self._canvas.get_memory()
+        rank = rank_memories(self.chat_mdl, goal, sub_goal, [summ for (user, assist, summ) in mems], user_defined_prompt)
+        try:
+            rank = json_repair.loads(re.sub(r"```.*", "", rank))[:topn]
+            mems = [mems[r] for r in rank]
+            return "\n\n".join([f"User: {u}\nAgent: {a}" for u, a,_ in mems])
+        except Exception as e:
+            logging.exception(e)
+
+        return "Error occurred."
+
+    def reset(self, only_output=False):
+        """
+        Reset all tools if they have a reset method. This avoids errors for tools like MCPToolCallSession.
+        """
+        for k in self._param.outputs.keys():
+            self._param.outputs[k]["value"] = None
+            
+        for k, cpn in self.tools.items():
+            if hasattr(cpn, "reset") and callable(cpn.reset):
+                cpn.reset()
+        if only_output:
+            return
+        for k in self._param.inputs.keys():
+            self._param.inputs[k]["value"] = None
+        self._param.debug_inputs = {}
+

agent/component/answer.py

@@ -1,79 +0,0 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import random
-from abc import ABC
-from functools import partial
-
-import pandas as pd
-
-from agent.component.base import ComponentBase, ComponentParamBase
-
-
-class AnswerParam(ComponentParamBase):
-
-    """
-    Define the Answer component parameters.
-    """
-    def __init__(self):
-        super().__init__()
-        self.post_answers = []
-
-    def check(self):
-        return True
-
-
-class Answer(ComponentBase, ABC):
-    component_name = "Answer"
-
-    def _run(self, history, **kwargs):
-        if kwargs.get("stream"):
-            return partial(self.stream_output)
-
-        ans = self.get_input()
-        if self._param.post_answers:
-            ans = pd.concat([ans, pd.DataFrame([{"content": random.choice(self._param.post_answers)}])], ignore_index=False)
-        return ans
-
-    def stream_output(self):
-        res = None
-        if hasattr(self, "exception") and self.exception:
-            res = {"content": str(self.exception)}
-            self.exception = None
-            yield res
-            self.set_output(res)
-            return
-
-        stream = self.get_stream_input()
-        if isinstance(stream, pd.DataFrame):
-            res = stream
-            answer = ""
-            for ii, row in stream.iterrows():
-                answer += row.to_dict()["content"]
-                yield {"content": answer}
-        else:
-            for st in stream():
-                res = st
-                yield st
-        if self._param.post_answers:
-            res["content"] += random.choice(self._param.post_answers)
-            yield res
-
-        self.set_output(res)
-
-    def set_exception(self, e):
-        self.exception = e
-
-

agent/component/arxiv.py

@@ -1,69 +0,0 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from abc import ABC
-import arxiv
-import pandas as pd
-from agent.settings import DEBUG
-from agent.component.base import ComponentBase, ComponentParamBase
-
-
-class ArXivParam(ComponentParamBase):
-    """
-    Define the ArXiv component parameters.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.top_n = 6
-        self.sort_by = 'submittedDate'
-
-    def check(self):
-        self.check_positive_integer(self.top_n, "Top N")
-        self.check_valid_value(self.sort_by, "ArXiv Search Sort_by",
-                               ['submittedDate', 'lastUpdatedDate', 'relevance'])
-
-
-class ArXiv(ComponentBase, ABC):
-    component_name = "ArXiv"
-
-    def _run(self, history, **kwargs):
-        ans = self.get_input()
-        ans = " - ".join(ans["content"]) if "content" in ans else ""
-        if not ans:
-            return ArXiv.be_output("")
-
-        try:
-            sort_choices = {"relevance": arxiv.SortCriterion.Relevance,
-                            "lastUpdatedDate": arxiv.SortCriterion.LastUpdatedDate,
-                            'submittedDate': arxiv.SortCriterion.SubmittedDate}
-            arxiv_client = arxiv.Client()
-            search = arxiv.Search(
-                query=ans,
-                max_results=self._param.top_n,
-                sort_by=sort_choices[self._param.sort_by]
-            )
-            arxiv_res = [
-                {"content": 'Title: ' + i.title + '\nPdf_Url: <a href="' + i.pdf_url + '"></a> \nSummary: ' + i.summary} for
-                i in list(arxiv_client.results(search))]
-        except Exception as e:
-            return ArXiv.be_output("**ERROR**: " + str(e))
-
-        if not arxiv_res:
-            return ArXiv.be_output("")
-
-        df = pd.DataFrame(arxiv_res)
-        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
-        return df

agent/component/baidu.py

@@ -1,69 +0,0 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import random
-from abc import ABC
-from functools import partial
-import pandas as pd
-import requests
-import re
-from agent.settings import DEBUG
-from agent.component.base import ComponentBase, ComponentParamBase
-
-
-class BaiduParam(ComponentParamBase):
-    """
-    Define the Baidu component parameters.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.top_n = 10
-
-    def check(self):
-        self.check_positive_integer(self.top_n, "Top N")
-
-
-class Baidu(ComponentBase, ABC):
-    component_name = "Baidu"
-
-    def _run(self, history, **kwargs):
-        ans = self.get_input()
-        ans = " - ".join(ans["content"]) if "content" in ans else ""
-        if not ans:
-            return Baidu.be_output("")
-
-        try:
-            url = 'https://www.baidu.com/s?wd=' + ans + '&rn=' + str(self._param.top_n)
-            headers = {
-                'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.104 Safari/537.36'}
-            response = requests.get(url=url, headers=headers)
-
-            url_res = re.findall(r"'url': \\\"(.*?)\\\"}", response.text)
-            title_res = re.findall(r"'title': \\\"(.*?)\\\",\\n", response.text)
-            body_res = re.findall(r"\"contentText\":\"(.*?)\"", response.text)
-            baidu_res = [{"content": re.sub('<em>|</em>', '', '<a href="' + url + '">' + title + '</a>    ' + body)} for
-                         url, title, body in zip(url_res, title_res, body_res)]
-            del body_res, url_res, title_res
-        except Exception as e:
-            return Baidu.be_output("**ERROR**: " + str(e))
-
-        if not baidu_res:
-            return Baidu.be_output("")
-
-        df = pd.DataFrame(baidu_res)
-        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
-        return df
-

agent/component/baidufanyi.py

@@ -1,99 +0,0 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import random
-from abc import ABC
-import requests
-from agent.component.base import ComponentBase, ComponentParamBase
-from hashlib import md5
-
-
-class BaiduFanyiParam(ComponentParamBase):
-    """
-    Define the BaiduFanyi component parameters.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.appid = "xxx"
-        self.secret_key = "xxx"
-        self.trans_type = 'translate'
-        self.parameters = []
-        self.source_lang = 'auto'
-        self.target_lang = 'auto'
-        self.domain = 'finance'
-
-    def check(self):
-        self.check_positive_integer(self.top_n, "Top N")
-        self.check_empty(self.appid, "BaiduFanyi APPID")
-        self.check_empty(self.secret_key, "BaiduFanyi Secret Key")
-        self.check_valid_value(self.trans_type, "Translate type", ['translate', 'fieldtranslate'])
-        self.check_valid_value(self.trans_type, "Translate domain",
-                               ['it', 'finance', 'machinery', 'senimed', 'novel', 'academic', 'aerospace', 'wiki',
-                                'news', 'law', 'contract'])
-        self.check_valid_value(self.source_lang, "Source language",
-                               ['auto', 'zh', 'en', 'yue', 'wyw', 'jp', 'kor', 'fra', 'spa', 'th', 'ara', 'ru', 'pt',
-                                'de', 'it', 'el', 'nl', 'pl', 'bul', 'est', 'dan', 'fin', 'cs', 'rom', 'slo', 'swe',
-                                'hu', 'cht', 'vie'])
-        self.check_valid_value(self.target_lang, "Target language",
-                               ['auto', 'zh', 'en', 'yue', 'wyw', 'jp', 'kor', 'fra', 'spa', 'th', 'ara', 'ru', 'pt',
-                                'de', 'it', 'el', 'nl', 'pl', 'bul', 'est', 'dan', 'fin', 'cs', 'rom', 'slo', 'swe',
-                                'hu', 'cht', 'vie'])
-        self.check_valid_value(self.domain, "Translate field",
-                               ['it', 'finance', 'machinery', 'senimed', 'novel', 'academic', 'aerospace', 'wiki',
-                                'news', 'law', 'contract'])
-
-
-class BaiduFanyi(ComponentBase, ABC):
-    component_name = "BaiduFanyi"
-
-    def _run(self, history, **kwargs):
-
-        ans = self.get_input()
-        ans = " - ".join(ans["content"]) if "content" in ans else ""
-        if not ans:
-            return BaiduFanyi.be_output("")
-
-        try:
-            source_lang = self._param.source_lang
-            target_lang = self._param.target_lang
-            appid = self._param.appid
-            salt = random.randint(32768, 65536)
-            secret_key = self._param.secret_key
-
-            if self._param.trans_type == 'translate':
-                sign = md5((appid + ans + salt + secret_key).encode('utf-8')).hexdigest()
-                url = 'http://api.fanyi.baidu.com/api/trans/vip/translate?' + 'q=' + ans + '&from=' + source_lang + '&to=' + target_lang + '&appid=' + appid + '&salt=' + salt + '&sign=' + sign
-                headers = {"Content-Type": "application/x-www-form-urlencoded"}
-                response = requests.post(url=url, headers=headers).json()
-
-                if response.get('error_code'):
-                    BaiduFanyi.be_output("**Error**:" + response['error_msg'])
-
-                return BaiduFanyi.be_output(response['trans_result'][0]['dst'])
-            elif self._param.trans_type == 'fieldtranslate':
-                domain = self._param.domain
-                sign = md5((appid + ans + salt + domain + secret_key).encode('utf-8')).hexdigest()
-                url = 'http://api.fanyi.baidu.com/api/trans/vip/fieldtranslate?' + 'q=' + ans + '&from=' + source_lang + '&to=' + target_lang + '&appid=' + appid + '&salt=' + salt + '&domain=' + domain + '&sign=' + sign
-                headers = {"Content-Type": "application/x-www-form-urlencoded"}
-                response = requests.post(url=url, headers=headers).json()
-
-                if response.get('error_code'):
-                    BaiduFanyi.be_output("**Error**:" + response['error_msg'])
-
-                return BaiduFanyi.be_output(response['trans_result'][0]['dst'])
-
-        except Exception as e:
-            BaiduFanyi.be_output("**Error**:" + str(e))

agent/component/base.py

@@ -13,18 +13,20 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+
+import re
+import time
 from abc import ABC
 import builtins
 import json
 import os
-from copy import deepcopy
-from functools import partial
-from typing import List, Dict, Tuple, Union
-
+import logging
+from typing import Any, List, Union
 import pandas as pd
-
+import trio
 from agent import settings
-from agent.settings import flow_logger, DEBUG
+from common.connection_utils import timeout
+
 
 _FEEDED_DEPRECATED_PARAMS = "_feeded_deprecated_params"
 _DEPRECATED_PARAMS = "_deprecated_params"
@@ -34,8 +36,16 @@ _IS_RAW_CONF = "_is_raw_conf"
 
 class ComponentParamBase(ABC):
     def __init__(self):
-        self.output_var_name = "output"
-        self.message_history_window_size = 22
+        self.message_history_window_size = 13
+        self.inputs = {}
+        self.outputs = {}
+        self.description = ""
+        self.max_retries = 0
+        self.delay_after_error = 2.0
+        self.exception_method = None
+        self.exception_default_value = None
+        self.exception_goto = None
+        self.debug_inputs = {}
 
     def set_name(self, name: str):
         self._name = name
@@ -81,12 +91,19 @@ class ComponentParamBase(ABC):
         return {name: True for name in self.get_feeded_deprecated_params()}
 
     def __str__(self):
-
         return json.dumps(self.as_dict(), ensure_ascii=False)
 
     def as_dict(self):
         def _recursive_convert_obj_to_dict(obj):
             ret_dict = {}
+            if isinstance(obj, dict):
+                for k,v in obj.items():
+                    if isinstance(v, dict) or (v and type(v).__name__ not in dir(builtins)):
+                        ret_dict[k] = _recursive_convert_obj_to_dict(v)
+                    else:
+                        ret_dict[k] = v
+                return ret_dict
+
             for attr_name in list(obj.__dict__):
                 if attr_name in [_FEEDED_DEPRECATED_PARAMS, _DEPRECATED_PARAMS, _USER_FEEDED_PARAMS, _IS_RAW_CONF]:
                     continue
@@ -95,7 +112,7 @@ class ComponentParamBase(ABC):
                 if isinstance(attr, pd.DataFrame):
                     ret_dict[attr_name] = attr.to_dict()
                     continue
-                if attr and type(attr).__name__ not in dir(builtins):
+                if isinstance(attr, dict) or (attr and type(attr).__name__ not in dir(builtins)):
                     ret_dict[attr_name] = _recursive_convert_obj_to_dict(attr)
                 else:
                     ret_dict[attr_name] = attr
@@ -227,7 +244,7 @@ class ComponentParamBase(ABC):
 
                 if not value_legal:
                     raise ValueError(
-                        "Plase check runtime conf, {} = {} does not match user-parameter restriction".format(
+                        "Please check runtime conf, {} = {} does not match user-parameter restriction".format(
                             variable, value
                         )
                     )
@@ -359,13 +376,13 @@ class ComponentParamBase(ABC):
 
     def _warn_deprecated_param(self, param_name, descr):
         if self._deprecated_params_set.get(param_name):
-            flow_logger.warning(
+            logging.warning(
                 f"{descr} {param_name} is deprecated and ignored in this version."
             )
 
     def _warn_to_deprecate_param(self, param_name, descr, new_param):
         if self._deprecated_params_set.get(param_name):
-            flow_logger.warning(
+            logging.warning(
                 f"{descr} {param_name} will be deprecated in future release; "
                 f"please use {new_param} instead."
             )
@@ -375,6 +392,8 @@ class ComponentParamBase(ABC):
 
 class ComponentBase(ABC):
     component_name: str
+    thread_limiter = trio.CapacityLimiter(int(os.environ.get('MAX_CONCURRENT_CHATS', 10)))
+    variable_ref_patt = r"\{* *\{([a-zA-Z:0-9]+@[A-Za-z0-9_.]+|sys\.[A-Za-z0-9_.]+|env\.[A-Za-z0-9_.]+)\} *\}*"
 
     def __str__(self):
         """
@@ -388,105 +407,176 @@ class ComponentBase(ABC):
             "params": {}
         }}""".format(self.component_name,
                      self._param
-                     )
+        )
 
     def __init__(self, canvas, id, param: ComponentParamBase):
+        from agent.canvas import Graph  # Local import to avoid cyclic dependency
+        assert isinstance(canvas, Graph), "canvas must be an instance of Canvas"
         self._canvas = canvas
         self._id = id
         self._param = param
         self._param.check()
 
-    def run(self, history, **kwargs):
-        flow_logger.info("{}, history: {}, kwargs: {}".format(self, json.dumps(history, ensure_ascii=False),
-                                                              json.dumps(kwargs, ensure_ascii=False)))
+    def is_canceled(self) -> bool:
+        return self._canvas.is_canceled()
+
+    def check_if_canceled(self, message: str = "") -> bool:
+        if self.is_canceled():
+            task_id = getattr(self._canvas, 'task_id', 'unknown')
+            log_message = f"Task {task_id} has been canceled"
+            if message:
+                log_message += f" during {message}"
+            logging.info(log_message)
+            self.set_output("_ERROR", "Task has been canceled")
+            return True
+        return False
+
+    def invoke(self, **kwargs) -> dict[str, Any]:
+        self.set_output("_created_time", time.perf_counter())
         try:
-            res = self._run(history, **kwargs)
-            self.set_output(res)
+            self._invoke(**kwargs)
         except Exception as e:
-            self.set_output(pd.DataFrame([{"content": str(e)}]))
-            raise e
+            if self.get_exception_default_value():
+                self.set_exception_default_value()
+            else:
+                self.set_output("_ERROR", str(e))
+            logging.exception(e)
+        self._param.debug_inputs = {}
+        self.set_output("_elapsed_time", time.perf_counter() - self.output("_created_time"))
+        return self.output()
 
-        return res
-
-    def _run(self, history, **kwargs):
+    @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 10*60)))
+    def _invoke(self, **kwargs):
         raise NotImplementedError()
 
-    def output(self, allow_partial=True) -> Tuple[str, Union[pd.DataFrame, partial]]:
-        o = getattr(self._param, self._param.output_var_name)
-        if not isinstance(o, partial) and not isinstance(o, pd.DataFrame):
-            if not isinstance(o, list): o = [o]
-            o = pd.DataFrame(o)
+    def output(self, var_nm: str=None) -> Union[dict[str, Any], Any]:
+        if var_nm:
+            return self._param.outputs.get(var_nm, {}).get("value", "")
+        return {k: o.get("value") for k,o in self._param.outputs.items()}
 
-        if allow_partial or not isinstance(o, partial):
-            if not isinstance(o, partial) and not isinstance(o, pd.DataFrame):
-                return pd.DataFrame(o if isinstance(o, list) else [o])
-            return self._param.output_var_name, o
+    def set_output(self, key: str, value: Any):
+        if key not in self._param.outputs:
+            self._param.outputs[key] = {"value": None, "type": str(type(value))}
+        self._param.outputs[key]["value"] = value
 
-        outs = None
-        for oo in o():
-            if not isinstance(oo, pd.DataFrame):
-                outs = pd.DataFrame(oo if isinstance(oo, list) else [oo])
-            else: outs = oo
-        return self._param.output_var_name, outs
+    def error(self):
+        return self._param.outputs.get("_ERROR", {}).get("value")
 
-    def reset(self):
-        setattr(self._param, self._param.output_var_name, None)
+    def reset(self, only_output=False):
+        outputs: dict = self._param.outputs # for better performance
+        for k in outputs.keys():
+            outputs[k]["value"] = None
+        if only_output:
+            return
 
-    def set_output(self, v: pd.DataFrame):
-        setattr(self._param, self._param.output_var_name, v)
+        inputs: dict = self._param.inputs # for better performance
+        for k in inputs.keys():
+            inputs[k]["value"] = None
+        self._param.debug_inputs = {}
 
-    def get_input(self):
-        upstream_outs = []
-        reversed_cpnts = []
-        if len(self._canvas.path) > 1:
-            reversed_cpnts.extend(self._canvas.path[-2])
-        reversed_cpnts.extend(self._canvas.path[-1])
+    def get_input(self, key: str=None) -> Union[Any, dict[str, Any]]:
+        if key:
+            return self._param.inputs.get(key, {}).get("value")
 
-        if DEBUG: print(self.component_name, reversed_cpnts[::-1])
-        for u in reversed_cpnts[::-1]:
-            if self.get_component_name(u) in ["switch"]: continue
-            if self.component_name.lower() == "generate" and self.get_component_name(u) == "retrieval":
-                o = self._canvas.get_component(u)["obj"].output(allow_partial=False)[1]
-                if o is not None:
-                    upstream_outs.append(o)
-                    continue
-            if u not in self._canvas.get_component(self._id)["upstream"]: continue
-            if self.component_name.lower().find("switch") < 0 \
-                    and self.get_component_name(u) in ["relevant", "categorize"]:
+        res = {}
+        for var, o in self.get_input_elements().items():
+            v = self.get_param(var)
+            if v is None:
                 continue
-            if u.lower().find("answer") >= 0:
-                for r, c in self._canvas.history[::-1]:
-                    if r == "user":
-                        upstream_outs.append(pd.DataFrame([{"content": c}]))
-                        break
-                break
-            if self.component_name.lower().find("answer") >= 0 and self.get_component_name(u) in ["relevant"]:
-                continue
-            o = self._canvas.get_component(u)["obj"].output(allow_partial=False)[1]
-            if o is not None:
-                upstream_outs.append(o)
-            break
+            if isinstance(v, str) and self._canvas.is_reff(v):
+                self.set_input_value(var, self._canvas.get_variable_value(v))
+            else:
+                self.set_input_value(var, v)
+            res[var] = self.get_input_value(var)
+        return res
 
-        if upstream_outs:
-            df = pd.concat(upstream_outs, ignore_index=True)
-            if "content" in df:
-                df = df.drop_duplicates(subset=['content']).reset_index(drop=True)
-            return df
-        return pd.DataFrame()
+    def get_input_values(self) -> Union[Any, dict[str, Any]]:
+        if self._param.debug_inputs:
+            return self._param.debug_inputs
 
-    def get_stream_input(self):
-        reversed_cpnts = []
-        if len(self._canvas.path) > 1:
-            reversed_cpnts.extend(self._canvas.path[-2])
-        reversed_cpnts.extend(self._canvas.path[-1])
+        return {var: self.get_input_value(var) for var, o in self.get_input_elements().items()}
 
-        for u in reversed_cpnts[::-1]:
-            if self.get_component_name(u) in ["switch", "answer"]: continue
-            return self._canvas.get_component(u)["obj"].output()[1]
+    def get_input_elements_from_text(self, txt: str) -> dict[str, dict[str, str]]:
+        res = {}
+        for r in re.finditer(self.variable_ref_patt, txt, flags=re.IGNORECASE|re.DOTALL):
+            exp = r.group(1)
+            cpn_id, var_nm = exp.split("@") if exp.find("@")>0 else ("", exp)
+            res[exp] = {
+                "name": (self._canvas.get_component_name(cpn_id) +f"@{var_nm}") if cpn_id else exp,
+                "value": self._canvas.get_variable_value(exp),
+                "_retrival": self._canvas.get_variable_value(f"{cpn_id}@_references") if cpn_id else None,
+                "_cpn_id": cpn_id
+            }
+        return res
+
+    def get_input_elements(self) -> dict[str, Any]:
+        return self._param.inputs
+
+    def get_input_form(self) -> dict[str, dict]:
+        return self._param.get_input_form()
+
+    def set_input_value(self, key: str, value: Any) -> None:
+        if key not in self._param.inputs:
+            self._param.inputs[key] = {"value": None}
+        self._param.inputs[key]["value"] = value
+
+    def get_input_value(self, key: str) -> Any:
+        if key not in self._param.inputs:
+            return None
+        return self._param.inputs[key].get("value")
+
+    def get_component_name(self, cpn_id) -> str:
+        return self._canvas.get_component(cpn_id)["obj"].component_name.lower()
+
+    def get_param(self, name):
+        if hasattr(self._param, name):
+            return getattr(self._param, name)
+        return None
+
+    def debug(self, **kwargs):
+        return self._invoke(**kwargs)
+
+    def get_parent(self) -> Union[object, None]:
+        pid = self._canvas.get_component(self._id).get("parent_id")
+        if not pid:
+            return None
+        return self._canvas.get_component(pid)["obj"]
+
+    def get_upstream(self) -> List[str]:
+        cpn_nms = self._canvas.get_component(self._id)['upstream']
+        return cpn_nms
+
+    def get_downstream(self) -> List[str]:
+        cpn_nms = self._canvas.get_component(self._id)['downstream']
+        return cpn_nms
 
     @staticmethod
-    def be_output(v):
-        return pd.DataFrame([{"content": v}])
+    def string_format(content: str, kv: dict[str, str]) -> str:
+        for n, v in kv.items():
+            def repl(_match, val=v):
+                return str(val) if val is not None else ""
+            content = re.sub(
+                r"\{%s\}" % re.escape(n),
+                repl,
+                content
+            )
+        return content
 
-    def get_component_name(self, cpn_id):
-        return self._canvas.get_component(cpn_id)["obj"].component_name.lower()
+    def exception_handler(self):
+        if not self._param.exception_method:
+            return None
+        return {
+            "goto": self._param.exception_goto,
+            "default_value": self._param.exception_default_value
+        }
+
+    def get_exception_default_value(self):
+        if self._param.exception_method != "comment":
+            return ""
+        return self._param.exception_default_value
+
+    def set_exception_default_value(self):
+        self.set_output("result", self.get_exception_default_value())
+
+    def thoughts(self) -> str:
+        raise NotImplementedError()

agent/component/begin.py

@@ -13,36 +13,46 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-from functools import partial
-import pandas as pd
-from agent.component.base import ComponentBase, ComponentParamBase
+from agent.component.fillup import UserFillUpParam, UserFillUp
 
 
-class BeginParam(ComponentParamBase):
+class BeginParam(UserFillUpParam):
 
     """
     Define the Begin component parameters.
     """
     def __init__(self):
         super().__init__()
+        self.mode = "conversational"
         self.prologue = "Hi! I'm your smart assistant. What can I do for you?"
 
     def check(self):
-        return True
+        self.check_valid_value(self.mode, "The 'mode' should be either `conversational` or `task`", ["conversational", "task"])
+
+    def get_input_form(self) -> dict[str, dict]:
+        return getattr(self, "inputs")
 
 
-class Begin(ComponentBase):
+class Begin(UserFillUp):
     component_name = "Begin"
 
-    def _run(self, history, **kwargs):
-        if kwargs.get("stream"):
-            return partial(self.stream_output)
-        return pd.DataFrame([{"content": self._param.prologue}])
-
-    def stream_output(self):
-        res = {"content": self._param.prologue}
-        yield res
-        self.set_output(res)
+    def _invoke(self, **kwargs):
+        if self.check_if_canceled("Begin processing"):
+            return
 
+        for k, v in kwargs.get("inputs", {}).items():
+            if self.check_if_canceled("Begin processing"):
+                return
 
+            if isinstance(v, dict) and v.get("type", "").lower().find("file") >=0:
+                if v.get("optional") and v.get("value", None) is None:
+                    v = None
+                else:
+                    v = self._canvas.get_files([v["value"]])
+            else:
+                v = v.get("value")
+            self.set_output(k, v)
+            self.set_input_value(k, v)
 
+    def thoughts(self) -> str:
+        return ""

agent/component/bing.py

@@ -1,85 +0,0 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from abc import ABC
-import requests
-import pandas as pd
-from agent.settings import DEBUG
-from agent.component.base import ComponentBase, ComponentParamBase
-
-
-class BingParam(ComponentParamBase):
-    """
-    Define the Bing component parameters.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.top_n = 10
-        self.channel = "Webpages"
-        self.api_key = "YOUR_ACCESS_KEY"
-        self.country = "CN"
-        self.language = "en"
-
-    def check(self):
-        self.check_positive_integer(self.top_n, "Top N")
-        self.check_valid_value(self.channel, "Bing Web Search or Bing News", ["Webpages", "News"])
-        self.check_empty(self.api_key, "Bing subscription key")
-        self.check_valid_value(self.country, "Bing Country",
-                               ['AR', 'AU', 'AT', 'BE', 'BR', 'CA', 'CL', 'DK', 'FI', 'FR', 'DE', 'HK', 'IN', 'ID',
-                                'IT', 'JP', 'KR', 'MY', 'MX', 'NL', 'NZ', 'NO', 'CN', 'PL', 'PT', 'PH', 'RU', 'SA',
-                                'ZA', 'ES', 'SE', 'CH', 'TW', 'TR', 'GB', 'US'])
-        self.check_valid_value(self.language, "Bing Languages",
-                               ['ar', 'eu', 'bn', 'bg', 'ca', 'ns', 'nt', 'hr', 'cs', 'da', 'nl', 'en', 'gb', 'et',
-                                'fi', 'fr', 'gl', 'de', 'gu', 'he', 'hi', 'hu', 'is', 'it', 'jp', 'kn', 'ko', 'lv',
-                                'lt', 'ms', 'ml', 'mr', 'nb', 'pl', 'br', 'pt', 'pa', 'ro', 'ru', 'sr', 'sk', 'sl',
-                                'es', 'sv', 'ta', 'te', 'th', 'tr', 'uk', 'vi'])
-
-
-class Bing(ComponentBase, ABC):
-    component_name = "Bing"
-
-    def _run(self, history, **kwargs):
-        ans = self.get_input()
-        ans = " - ".join(ans["content"]) if "content" in ans else ""
-        if not ans:
-            return Bing.be_output("")
-
-        try:
-            headers = {"Ocp-Apim-Subscription-Key": self._param.api_key, 'Accept-Language': self._param.language}
-            params = {"q": ans, "textDecorations": True, "textFormat": "HTML", "cc": self._param.country,
-                      "answerCount": 1, "promote": self._param.channel}
-            if self._param.channel == "Webpages":
-                response = requests.get("https://api.bing.microsoft.com/v7.0/search", headers=headers, params=params)
-                response.raise_for_status()
-                search_results = response.json()
-                bing_res = [{"content": '<a href="' + i["url"] + '">' + i["name"] + '</a>    ' + i["snippet"]} for i in
-                            search_results["webPages"]["value"]]
-            elif self._param.channel == "News":
-                response = requests.get("https://api.bing.microsoft.com/v7.0/news/search", headers=headers,
-                                        params=params)
-                response.raise_for_status()
-                search_results = response.json()
-                bing_res = [{"content": '<a href="' + i["url"] + '">' + i["name"] + '</a>    ' + i["description"]} for i
-                            in search_results['news']['value']]
-        except Exception as e:
-            return Bing.be_output("**ERROR**: " + str(e))
-
-        if not bing_res:
-            return Bing.be_output("")
-
-        df = pd.DataFrame(bing_res)
-        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
-        return df

agent/component/categorize.py

@@ -13,75 +13,136 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import logging
+import os
+import re
 from abc import ABC
-from api.db import LLMType
+
+from common.constants import LLMType
 from api.db.services.llm_service import LLMBundle
-from agent.component import GenerateParam, Generate
-from agent.settings import DEBUG
+from agent.component.llm import LLMParam, LLM
+from common.connection_utils import timeout
+from rag.llm.chat_model import ERROR_PREFIX
 
 
-class CategorizeParam(GenerateParam):
+class CategorizeParam(LLMParam):
 
     """
-    Define the Categorize component parameters.
+    Define the categorize component parameters.
     """
     def __init__(self):
         super().__init__()
         self.category_description = {}
-        self.prompt = ""
+        self.query = "sys.query"
+        self.message_history_window_size = 1
+        self.update_prompt()
 
     def check(self):
-        super().check()
+        self.check_positive_integer(self.message_history_window_size, "[Categorize] Message window size > 0")
         self.check_empty(self.category_description, "[Categorize] Category examples")
         for k, v in self.category_description.items():
-            if not k: raise ValueError(f"[Categorize] Category name can not be empty!")
-            if not v.get("to"): raise ValueError(f"[Categorize] 'To' of category {k} can not be empty!")
+            if not k:
+                raise ValueError("[Categorize] Category name can not be empty!")
+            if not v.get("to"):
+                raise ValueError(f"[Categorize] 'To' of category {k} can not be empty!")
 
-    def get_prompt(self):
+    def get_input_form(self) -> dict[str, dict]:
+        return {
+            "query": {
+                "type": "line",
+                "name": "Query"
+            }
+        }
+
+    def update_prompt(self):
         cate_lines = []
         for c, desc in self.category_description.items():
-            for l in desc.get("examples", "").split("\n"):
-                if not l: continue
-                cate_lines.append("Question: {}\tCategory: {}".format(l, c))
+            for line in desc.get("examples", []):
+                if not line:
+                    continue
+                cate_lines.append("USER: \"" + re.sub(r"\n", "    ", line, flags=re.DOTALL) + "\" → "+c)
+
         descriptions = []
         for c, desc in self.category_description.items():
             if desc.get("description"):
                 descriptions.append(
-                    "--------------------\nCategory: {}\nDescription: {}\n".format(c, desc["description"]))
+                    "\n------\nCategory: {}\nDescription: {}".format(c, desc["description"]))
 
-        self.prompt = """
-        You're a text classifier. You need to categorize the user’s questions into {} categories, 
-        namely: {}
-        Here's description of each category:
-        {}
+        self.sys_prompt = """
+You are an advanced classification system that categorizes user questions into specific types. Analyze the input question and classify it into ONE of the following categories:
+{}
 
-        You could learn from the following examples:
-        {}
-        You could learn from the above examples.
-        Just mention the category names, no need for any additional words.
-        """.format(
-            len(self.category_description.keys()),
-            "/".join(list(self.category_description.keys())),
-            "\n".join(descriptions),
-            "- ".join(cate_lines)
+Here's description of each category:
+ - {}
+
+---- Instructions ----
+ - Consider both explicit mentions and implied context
+ - Prioritize the most specific applicable category
+ - Return only the category name without explanations
+ - Use "Other" only when no other category fits
+
+ """.format(
+            "\n - ".join(list(self.category_description.keys())),
+            "\n".join(descriptions)
         )
-        return self.prompt
+
+        if cate_lines:
+            self.sys_prompt += """
+---- Examples ----
+{}
+""".format("\n".join(cate_lines))
 
 
-class Categorize(Generate, ABC):
+class Categorize(LLM, ABC):
     component_name = "Categorize"
 
-    def _run(self, history, **kwargs):
-        input = self.get_input()
-        input = "Question: " + ("; ".join(input["content"]) if "content" in input else "") + "Category: "
+    @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 10*60)))
+    def _invoke(self, **kwargs):
+        if self.check_if_canceled("Categorize processing"):
+            return
+
+        msg = self._canvas.get_history(self._param.message_history_window_size)
+        if not msg:
+            msg = [{"role": "user", "content": ""}]
+        if kwargs.get("sys.query"):
+            msg[-1]["content"] = kwargs["sys.query"]
+            self.set_input_value("sys.query", kwargs["sys.query"])
+        else:
+            msg[-1]["content"] = self._canvas.get_variable_value(self._param.query)
+            self.set_input_value(self._param.query, msg[-1]["content"])
+        self._param.update_prompt()
         chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id)
-        ans = chat_mdl.chat(self._param.get_prompt(), [{"role": "user", "content": input}],
-                            self._param.gen_conf())
-        if DEBUG: print(ans, ":::::::::::::::::::::::::::::::::", input)
+
+        user_prompt = """
+---- Real Data ----
+{} →
+""".format(" | ".join(["{}: \"{}\"".format(c["role"].upper(), re.sub(r"\n", "", c["content"], flags=re.DOTALL)) for c in msg]))
+
+        if self.check_if_canceled("Categorize processing"):
+            return
+
+        ans = chat_mdl.chat(self._param.sys_prompt, [{"role": "user", "content": user_prompt}], self._param.gen_conf())
+        logging.info(f"input: {user_prompt}, answer: {str(ans)}")
+        if ERROR_PREFIX in ans:
+            raise Exception(ans)
+
+        if self.check_if_canceled("Categorize processing"):
+            return
+
+        # Count the number of times each category appears in the answer.
+        category_counts = {}
         for c in self._param.category_description.keys():
-            if ans.lower().find(c.lower()) >= 0:
-                return Categorize.be_output(self._param.category_description[c]["to"])
+            count = ans.lower().count(c.lower())
+            category_counts[c] = count
 
-        return Categorize.be_output(list(self._param.category_description.items())[-1][1]["to"])
+        cpn_ids = list(self._param.category_description.items())[-1][1]["to"]
+        max_category = list(self._param.category_description.keys())[0]
+        if any(category_counts.values()):
+            max_category = max(category_counts.items(), key=lambda x: x[1])[0]
+            cpn_ids = self._param.category_description[max_category]["to"]
 
+        self.set_output("category_name", max_category)
+        self.set_output("_next", cpn_ids)
 
+    def thoughts(self) -> str:
+        return "Which should it falls into {}? ...".format(",".join([f"`{c}`" for c, _ in self._param.category_description.items()]))

agent/component/cite.py

@@ -1,75 +0,0 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from abc import ABC
-
-import pandas as pd
-
-from api.db import LLMType
-from api.db.services.knowledgebase_service import KnowledgebaseService
-from api.db.services.llm_service import LLMBundle
-from api.settings import retrievaler
-from agent.component.base import ComponentBase, ComponentParamBase
-
-
-class CiteParam(ComponentParamBase):
-
-    """
-    Define the Retrieval component parameters.
-    """
-    def __init__(self):
-        super().__init__()
-        self.cite_sources = []
-
-    def check(self):
-        self.check_empty(self.cite_source, "Please specify where you want to cite from.")
-
-
-class Cite(ComponentBase, ABC):
-    component_name = "Cite"
-
-    def _run(self, history, **kwargs):
-        input = "\n- ".join(self.get_input()["content"])
-        sources = [self._canvas.get_component(cpn_id).output()[1] for cpn_id in self._param.cite_source]
-        query = []
-        for role, cnt in history[::-1][:self._param.message_history_window_size]:
-            if role != "user":continue
-            query.append(cnt)
-        query = "\n".join(query)
-
-        kbs = KnowledgebaseService.get_by_ids(self._param.kb_ids)
-        if not kbs:
-            raise ValueError("Can't find knowledgebases by {}".format(self._param.kb_ids))
-        embd_nms = list(set([kb.embd_id for kb in kbs]))
-        assert len(embd_nms) == 1, "Knowledge bases use different embedding models."
-
-        embd_mdl = LLMBundle(kbs[0].tenant_id, LLMType.EMBEDDING, embd_nms[0])
-
-        rerank_mdl = None
-        if self._param.rerank_id:
-            rerank_mdl = LLMBundle(kbs[0].tenant_id, LLMType.RERANK, self._param.rerank_id)
-
-        kbinfos = retrievaler.retrieval(query, embd_mdl, kbs[0].tenant_id, self._param.kb_ids,
-                                        1, self._param.top_n,
-                                        self._param.similarity_threshold, 1 - self._param.keywords_similarity_weight,
-                                        aggs=False, rerank_mdl=rerank_mdl)
-
-        if not kbinfos["chunks"]: return pd.DataFrame()
-        df = pd.DataFrame(kbinfos["chunks"])
-        df["content"] = df["content_with_weight"]
-        del df["content_with_weight"]
-        return df
-
-

agent/component/data_operations.py

@@ -0,0 +1,218 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from abc import ABC
+import ast
+import os
+from agent.component.base import ComponentBase, ComponentParamBase
+from api.utils.api_utils import timeout
+
+class DataOperationsParam(ComponentParamBase):
+    """
+    Define the Data Operations component parameters.
+    """
+    def __init__(self):
+        super().__init__()
+        self.query = []
+        self.operations = "literal_eval"
+        self.select_keys = []
+        self.filter_values=[]
+        self.updates=[]
+        self.remove_keys=[]
+        self.rename_keys=[]
+        self.outputs = {
+            "result": {
+                "value": [],
+                "type": "Array of Object"
+            }
+        }
+    
+    def check(self):
+        self.check_valid_value(self.operations, "Support operations", ["select_keys", "literal_eval","combine","filter_values","append_or_update","remove_keys","rename_keys"])
+    
+    
+
+class DataOperations(ComponentBase,ABC):
+    component_name = "DataOperations"
+
+    def get_input_form(self) -> dict[str, dict]:
+        return {
+            k: {"name": o.get("name", ""), "type": "line"}
+            for input_item in (self._param.query or [])
+            for k, o in self.get_input_elements_from_text(input_item).items()
+        }
+
+    @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 10*60)))
+    def _invoke(self, **kwargs):
+        self.input_objects=[]
+        inputs = getattr(self._param, "query", None)
+        if not isinstance(inputs, (list, tuple)):
+            inputs = [inputs]
+        for input_ref in inputs:
+            input_object=self._canvas.get_variable_value(input_ref)
+            self.set_input_value(input_ref, input_object)
+            if input_object is None:
+                continue
+            if isinstance(input_object,dict):
+                self.input_objects.append(input_object)
+            elif isinstance(input_object,list):
+                self.input_objects.extend(x for x in input_object if isinstance(x, dict))
+            else:
+                continue
+        if self._param.operations == "select_keys":
+            self._select_keys()
+        elif self._param.operations == "recursive_eval":
+            self._literal_eval()
+        elif self._param.operations == "combine":
+            self._combine()
+        elif self._param.operations == "filter_values":
+            self._filter_values()
+        elif self._param.operations == "append_or_update":
+            self._append_or_update()
+        elif self._param.operations == "remove_keys":
+            self._remove_keys()
+        else:
+            self._rename_keys()
+    
+    def _select_keys(self):
+        filter_criteria: list[str] = self._param.select_keys
+        results = [{key: value for key, value in data_dict.items() if key in filter_criteria} for data_dict in self.input_objects]
+        self.set_output("result", results)
+
+
+    def _recursive_eval(self, data):
+        if isinstance(data, dict):
+            return {k: self.recursive_eval(v) for k, v in data.items()}
+        if isinstance(data, list):
+            return [self.recursive_eval(item) for item in data]
+        if isinstance(data, str):
+            try:
+                if (
+                    data.strip().startswith(("{", "[", "(", "'", '"'))
+                    or data.strip().lower() in ("true", "false", "none")
+                    or data.strip().replace(".", "").isdigit()
+                ):
+                    return ast.literal_eval(data)
+            except (ValueError, SyntaxError, TypeError, MemoryError):
+                return data
+            else:
+                return data
+        return data
+    
+    def _literal_eval(self):
+        self.set_output("result", self._recursive_eval(self.input_objects))
+
+    def _combine(self):
+        result={}
+        for obj in self.input_objects:
+            for key, value in obj.items():
+                if key not in result:
+                    result[key] = value
+                elif isinstance(result[key], list):
+                    if isinstance(value, list):
+                        result[key].extend(value)
+                    else:
+                        result[key].append(value)
+                else:
+                    result[key] = (
+                        [result[key], value] if not isinstance(value, list) else [result[key], *value]
+                    )
+        self.set_output("result", result)
+    
+    def norm(self,v):
+        s = "" if v is None else str(v)
+        return s
+    
+    def match_rule(self, obj, rule):
+        key = rule.get("key")
+        op = (rule.get("operator") or "equals").lower()
+        target = self.norm(rule.get("value"))
+        target = self._canvas.get_value_with_variable(target) or target
+        if key not in obj:
+            return False
+        val = obj.get(key, None)
+        v = self.norm(val)
+        if op == "=":
+            return v == target
+        if op == "≠":
+            return v != target
+        if op == "contains":
+            return target in v
+        if op == "start with":
+            return v.startswith(target)
+        if op == "end with":
+            return v.endswith(target)
+        return False
+        
+    def _filter_values(self):
+        results=[]
+        rules = (getattr(self._param, "filter_values", None) or [])
+        for obj in self.input_objects:
+            if not rules:
+                results.append(obj)
+                continue
+            if all(self.match_rule(obj, r) for r in rules):
+                results.append(obj)
+        self.set_output("result", results)
+            
+                
+    def _append_or_update(self):
+        results=[]
+        updates = getattr(self._param, "updates", []) or [] 
+        for obj in self.input_objects:
+            new_obj = dict(obj)
+            for item in updates:
+                if not isinstance(item, dict):
+                    continue
+                k = (item.get("key") or "").strip()
+                if not k:
+                    continue
+                new_obj[k] = self._canvas.get_value_with_variable(item.get("value")) or item.get("value")
+            results.append(new_obj)
+        self.set_output("result", results)
+
+    def _remove_keys(self):
+        results = []
+        remove_keys = getattr(self._param, "remove_keys", []) or []
+
+        for obj in (self.input_objects or []):
+            new_obj = dict(obj)
+            for k in remove_keys:
+                if not isinstance(k, str):
+                    continue
+                new_obj.pop(k, None)
+            results.append(new_obj)
+        self.set_output("result", results)
+
+    def _rename_keys(self):
+        results = []
+        rename_pairs = getattr(self._param, "rename_keys", []) or []
+
+        for obj in (self.input_objects or []):
+            new_obj = dict(obj)
+            for pair in rename_pairs:
+                if not isinstance(pair, dict):
+                    continue
+                old = (pair.get("old_key") or "").strip()
+                new = (pair.get("new_key") or "").strip()
+                if not old or not new or old == new:
+                    continue
+                if old in new_obj:
+                    new_obj[new] = new_obj.pop(old)
+            results.append(new_obj)
+        self.set_output("result", results)
+
+    def thoughts(self) -> str:
+        return "DataOperation in progress"

agent/component/duckduckgo.py

@@ -1,66 +0,0 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from abc import ABC
-from duckduckgo_search import DDGS
-import pandas as pd
-from agent.settings import DEBUG
-from agent.component.base import ComponentBase, ComponentParamBase
-
-
-class DuckDuckGoParam(ComponentParamBase):
-    """
-    Define the DuckDuckGo component parameters.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.top_n = 10
-        self.channel = "text"
-
-    def check(self):
-        self.check_positive_integer(self.top_n, "Top N")
-        self.check_valid_value(self.channel, "Web Search or News", ["text", "news"])
-
-
-class DuckDuckGo(ComponentBase, ABC):
-    component_name = "DuckDuckGo"
-
-    def _run(self, history, **kwargs):
-        ans = self.get_input()
-        ans = " - ".join(ans["content"]) if "content" in ans else ""
-        if not ans:
-            return DuckDuckGo.be_output("")
-
-        try:
-            if self._param.channel == "text":
-                with DDGS() as ddgs:
-                    # {'title': '', 'href': '', 'body': ''}
-                    duck_res = [{"content": '<a href="' + i["href"] + '">' + i["title"] + '</a>    ' + i["body"]} for i
-                                in ddgs.text(ans, max_results=self._param.top_n)]
-            elif self._param.channel == "news":
-                with DDGS() as ddgs:
-                    # {'date': '', 'title': '', 'body': '', 'url': '', 'image': '', 'source': ''}
-                    duck_res = [{"content": '<a href="' + i["url"] + '">' + i["title"] + '</a>    ' + i["body"]} for i
-                                in ddgs.news(ans, max_results=self._param.top_n)]
-        except Exception as e:
-            return DuckDuckGo.be_output("**ERROR**: " + str(e))
-
-        if not duck_res:
-            return DuckDuckGo.be_output("")
-
-        df = pd.DataFrame(duck_res)
-        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
-        return df

agent/component/exesql.py

@@ -1,99 +0,0 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from abc import ABC
-import re
-import pandas as pd
-from peewee import MySQLDatabase, PostgresqlDatabase
-from agent.component.base import ComponentBase, ComponentParamBase
-
-
-class ExeSQLParam(ComponentParamBase):
-    """
-    Define the ExeSQL component parameters.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.db_type = "mysql"
-        self.database = ""
-        self.username = ""
-        self.host = ""
-        self.port = 3306
-        self.password = ""
-        self.loop = 3
-        self.top_n = 30
-
-    def check(self):
-        self.check_valid_value(self.db_type, "Choose DB type", ['mysql', 'postgresql', 'mariadb'])
-        self.check_empty(self.database, "Database name")
-        self.check_empty(self.username, "database username")
-        self.check_empty(self.host, "IP Address")
-        self.check_positive_integer(self.port, "IP Port")
-        self.check_empty(self.password, "Database password")
-        self.check_positive_integer(self.top_n, "Number of records")
-
-
-class ExeSQL(ComponentBase, ABC):
-    component_name = "ExeSQL"
-
-    def _run(self, history, **kwargs):
-        if not hasattr(self, "_loop"):
-            setattr(self, "_loop", 0)
-        if self._loop >= self._param.loop:
-            self._loop = 0
-            raise Exception("Maximum loop time exceeds. Can't query the correct data via SQL statement.")
-        self._loop += 1
-
-        ans = self.get_input()
-        ans = "".join(ans["content"]) if "content" in ans else ""
-        ans = re.sub(r'^.*?SELECT ', 'SELECT ', repr(ans), flags=re.IGNORECASE)
-        ans = re.sub(r';.*?SELECT ', '; SELECT ', ans, flags=re.IGNORECASE)
-        ans = re.sub(r';[^;]*$', r';', ans)
-        if not ans:
-            raise Exception("SQL statement not found!")
-
-        if self._param.db_type in ["mysql", "mariadb"]:
-            db = MySQLDatabase(self._param.database, user=self._param.username, host=self._param.host,
-                               port=self._param.port, password=self._param.password)
-        elif self._param.db_type == 'postgresql':
-            db = PostgresqlDatabase(self._param.database, user=self._param.username, host=self._param.host,
-                                    port=self._param.port, password=self._param.password)
-
-        try:
-            db.connect()
-        except Exception as e:
-            raise Exception("Database Connection Failed! \n" + str(e))
-        sql_res = []
-        for single_sql in re.split(r';', ans.replace(r"\n", " ")):
-            if not single_sql:
-                continue
-            try:
-                query = db.execute_sql(single_sql)
-                if query.rowcount == 0:
-                    sql_res.append({"content": "\nTotal: " + str(query.rowcount) + "\n No record in the database!"})
-                    continue
-                single_res = pd.DataFrame([i for i in query.fetchmany(size=self._param.top_n)])
-                single_res.columns = [i[0] for i in query.description]
-                sql_res.append({"content": "\nTotal: " + str(query.rowcount) + "\n" + single_res.to_markdown()})
-            except Exception as e:
-                sql_res.append({"content": "**Error**:" + str(e) + "\nError SQL Statement:" + single_sql})
-                pass
-        db.close()
-
-        if not sql_res:
-            return ExeSQL.be_output("")
-
-        return pd.DataFrame(sql_res)

agent/component/fillup.py

@@ -0,0 +1,69 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import json
+import re
+from functools import partial
+
+from agent.component.base import ComponentParamBase, ComponentBase
+
+
+class UserFillUpParam(ComponentParamBase):
+
+    def __init__(self):
+        super().__init__()
+        self.enable_tips = True
+        self.tips = "Please fill up the form"
+
+    def check(self) -> bool:
+        return True
+
+
+class UserFillUp(ComponentBase):
+    component_name = "UserFillUp"
+
+    def _invoke(self, **kwargs):
+        if self.check_if_canceled("UserFillUp processing"):
+            return
+
+        if self._param.enable_tips:
+            content = self._param.tips
+            for k, v in self.get_input_elements_from_text(self._param.tips).items():
+                v = v["value"]
+                ans = ""
+                if isinstance(v, partial):
+                    for t in v():
+                        ans += t
+                elif isinstance(v, list):
+                    ans = ",".join([str(vv) for vv in v])
+                elif not isinstance(v, str):
+                    try:
+                        ans = json.dumps(v, ensure_ascii=False)
+                    except Exception:
+                        pass
+                else:
+                    ans = v
+                if not ans:
+                    ans = ""
+                content = re.sub(r"\{%s\}"%k, ans, content)
+
+            self.set_output("tips", content)
+        for k, v in kwargs.get("inputs", {}).items():
+            if self.check_if_canceled("UserFillUp processing"):
+                return
+            self.set_output(k, v)
+
+    def thoughts(self) -> str:
+        return "Waiting for your input..."

agent/component/generate.py

@@ -1,155 +0,0 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import re
-from functools import partial
-import pandas as pd
-from api.db import LLMType
-from api.db.services.llm_service import LLMBundle
-from api.settings import retrievaler
-from agent.component.base import ComponentBase, ComponentParamBase
-
-
-class GenerateParam(ComponentParamBase):
-    """
-    Define the Generate component parameters.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.llm_id = ""
-        self.prompt = ""
-        self.max_tokens = 0
-        self.temperature = 0
-        self.top_p = 0
-        self.presence_penalty = 0
-        self.frequency_penalty = 0
-        self.cite = True
-        self.parameters = []
-
-    def check(self):
-        self.check_decimal_float(self.temperature, "[Generate] Temperature")
-        self.check_decimal_float(self.presence_penalty, "[Generate] Presence penalty")
-        self.check_decimal_float(self.frequency_penalty, "[Generate] Frequency penalty")
-        self.check_nonnegative_number(self.max_tokens, "[Generate] Max tokens")
-        self.check_decimal_float(self.top_p, "[Generate] Top P")
-        self.check_empty(self.llm_id, "[Generate] LLM")
-        # self.check_defined_type(self.parameters, "Parameters", ["list"])
-
-    def gen_conf(self):
-        conf = {}
-        if self.max_tokens > 0: conf["max_tokens"] = self.max_tokens
-        if self.temperature > 0: conf["temperature"] = self.temperature
-        if self.top_p > 0: conf["top_p"] = self.top_p
-        if self.presence_penalty > 0: conf["presence_penalty"] = self.presence_penalty
-        if self.frequency_penalty > 0: conf["frequency_penalty"] = self.frequency_penalty
-        return conf
-
-
-class Generate(ComponentBase):
-    component_name = "Generate"
-
-    def get_dependent_components(self):
-        cpnts = [para["component_id"] for para in self._param.parameters]
-        return cpnts
-
-    def set_cite(self, retrieval_res, answer):
-        retrieval_res = retrieval_res.dropna(subset=["vector", "content_ltks"]).reset_index(drop=True)
-        if "empty_response" in retrieval_res.columns:
-            retrieval_res["empty_response"].fillna("", inplace=True)
-        answer, idx = retrievaler.insert_citations(answer, [ck["content_ltks"] for _, ck in retrieval_res.iterrows()],
-                                                   [ck["vector"] for _, ck in retrieval_res.iterrows()],
-                                                   LLMBundle(self._canvas.get_tenant_id(), LLMType.EMBEDDING,
-                                                             self._canvas.get_embedding_model()), tkweight=0.7,
-                                                   vtweight=0.3)
-        doc_ids = set([])
-        recall_docs = []
-        for i in idx:
-            did = retrieval_res.loc[int(i), "doc_id"]
-            if did in doc_ids: continue
-            doc_ids.add(did)
-            recall_docs.append({"doc_id": did, "doc_name": retrieval_res.loc[int(i), "docnm_kwd"]})
-
-        del retrieval_res["vector"]
-        del retrieval_res["content_ltks"]
-
-        reference = {
-            "chunks": [ck.to_dict() for _, ck in retrieval_res.iterrows()],
-            "doc_aggs": recall_docs
-        }
-
-        if answer.lower().find("invalid key") >= 0 or answer.lower().find("invalid api") >= 0:
-            answer += " Please set LLM API-Key in 'User Setting -> Model Providers -> API-Key'"
-        res = {"content": answer, "reference": reference}
-
-        return res
-
-    def _run(self, history, **kwargs):
-        chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id)
-        prompt = self._param.prompt
-
-        retrieval_res = self.get_input()
-        input = ("  - " + "\n  - ".join(retrieval_res["content"])) if "content" in retrieval_res else ""
-        for para in self._param.parameters:
-            cpn = self._canvas.get_component(para["component_id"])["obj"]
-            _, out = cpn.output(allow_partial=False)
-            if "content" not in out.columns:
-                kwargs[para["key"]] = "Nothing"
-            else:
-                kwargs[para["key"]] = "  - " + "\n  - ".join(out["content"])
-
-        kwargs["input"] = input
-        for n, v in kwargs.items():
-            prompt = re.sub(r"\{%s\}" % n, re.escape(str(v)), prompt)
-
-        downstreams = self._canvas.get_component(self._id)["downstream"]
-        if kwargs.get("stream") and len(downstreams) == 1 and self._canvas.get_component(downstreams[0])[
-            "obj"].component_name.lower() == "answer":
-            return partial(self.stream_output, chat_mdl, prompt, retrieval_res)
-
-        if "empty_response" in retrieval_res.columns and not "".join(retrieval_res["content"]):
-            res = {"content": "\n- ".join(retrieval_res["empty_response"]) if "\n- ".join(
-                retrieval_res["empty_response"]) else "Nothing found in knowledgebase!", "reference": []}
-            return Generate.be_output(res)
-
-        ans = chat_mdl.chat(prompt, self._canvas.get_history(self._param.message_history_window_size),
-                            self._param.gen_conf())
-        if self._param.cite and "content_ltks" in retrieval_res.columns and "vector" in retrieval_res.columns:
-            df = self.set_cite(retrieval_res, ans)
-            return pd.DataFrame(df)
-
-        return Generate.be_output(ans)
-
-    def stream_output(self, chat_mdl, prompt, retrieval_res):
-        res = None
-        if "empty_response" in retrieval_res.columns and not "".join(retrieval_res["content"]):
-            res = {"content": "\n- ".join(retrieval_res["empty_response"]) if "\n- ".join(
-                retrieval_res["empty_response"]) else "Nothing found in knowledgebase!", "reference": []}
-            yield res
-            self.set_output(res)
-            return
-
-        answer = ""
-        for ans in chat_mdl.chat_streamly(prompt, self._canvas.get_history(self._param.message_history_window_size),
-                                          self._param.gen_conf()):
-            res = {"content": ans, "reference": []}
-            answer = ans
-            yield res
-
-        if self._param.cite and "content_ltks" in retrieval_res.columns and "vector" in retrieval_res.columns:
-            res = self.set_cite(retrieval_res, answer)
-            yield res
-
-        self.set_output(res)

agent/component/github.py

@@ -1,61 +0,0 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from abc import ABC
-import pandas as pd
-import requests
-from agent.settings import DEBUG
-from agent.component.base import ComponentBase, ComponentParamBase
-
-
-class GitHubParam(ComponentParamBase):
-    """
-    Define the GitHub component parameters.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.top_n = 10
-
-    def check(self):
-        self.check_positive_integer(self.top_n, "Top N")
-
-
-class GitHub(ComponentBase, ABC):
-    component_name = "GitHub"
-
-    def _run(self, history, **kwargs):
-        ans = self.get_input()
-        ans = " - ".join(ans["content"]) if "content" in ans else ""
-        if not ans:
-            return GitHub.be_output("")
-
-        try:
-            url = 'https://api.github.com/search/repositories?q=' + ans + '&sort=stars&order=desc&per_page=' + str(
-                self._param.top_n)
-            headers = {"Content-Type": "application/vnd.github+json", "X-GitHub-Api-Version": '2022-11-28'}
-            response = requests.get(url=url, headers=headers).json()
-
-            github_res = [{"content": '<a href="' + i["html_url"] + '">' + i["name"] + '</a>' + str(
-                i["description"]) + '\n stars:' + str(i['watchers'])} for i in response['items']]
-        except Exception as e:
-            return GitHub.be_output("**ERROR**: " + str(e))
-
-        if not github_res:
-            return GitHub.be_output("")
-
-        df = pd.DataFrame(github_res)
-        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
-        return df

agent/component/googlescholar.py

@@ -1,70 +0,0 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from abc import ABC
-import pandas as pd
-from agent.settings import DEBUG
-from agent.component.base import ComponentBase, ComponentParamBase
-from scholarly import scholarly
-
-
-class GoogleScholarParam(ComponentParamBase):
-    """
-    Define the GoogleScholar component parameters.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.top_n = 6
-        self.sort_by = 'relevance'
-        self.year_low = None
-        self.year_high = None
-        self.patents = True
-
-    def check(self):
-        self.check_positive_integer(self.top_n, "Top N")
-        self.check_valid_value(self.sort_by, "GoogleScholar Sort_by", ['date', 'relevance'])
-        self.check_boolean(self.patents, "Whether or not to include patents, defaults to True")
-
-
-class GoogleScholar(ComponentBase, ABC):
-    component_name = "GoogleScholar"
-
-    def _run(self, history, **kwargs):
-        ans = self.get_input()
-        ans = " - ".join(ans["content"]) if "content" in ans else ""
-        if not ans:
-            return GoogleScholar.be_output("")
-
-        scholar_client = scholarly.search_pubs(ans, patents=self._param.patents, year_low=self._param.year_low,
-                                               year_high=self._param.year_high, sort_by=self._param.sort_by)
-        scholar_res = []
-        for i in range(self._param.top_n):
-            try:
-                pub = next(scholar_client)
-                scholar_res.append({"content": 'Title: ' + pub['bib']['title'] + '\n_Url: <a href="' + pub[
-                    'pub_url'] + '"></a> ' + "\n author: " + ",".join(pub['bib']['author']) + '\n Abstract: ' + pub[
-                                                   'bib'].get('abstract', 'no abstract')})
-
-            except StopIteration or Exception as e:
-                print("**ERROR** " + str(e))
-                break
-
-        if not scholar_res:
-            return GoogleScholar.be_output("")
-
-        df = pd.DataFrame(scholar_res)
-        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
-        return df

agent/component/invoke.py

@@ -0,0 +1,144 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import json
+import logging
+import os
+import re
+import time
+from abc import ABC
+
+import requests
+
+from agent.component.base import ComponentBase, ComponentParamBase
+from common.connection_utils import timeout
+from deepdoc.parser import HtmlParser
+
+
+class InvokeParam(ComponentParamBase):
+    """
+    Define the Crawler component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.proxy = None
+        self.headers = ""
+        self.method = "get"
+        self.variables = []
+        self.url = ""
+        self.timeout = 60
+        self.clean_html = False
+        self.datatype = "json"  # New parameter to determine data posting type
+
+    def check(self):
+        self.check_valid_value(self.method.lower(), "Type of content from the crawler", ["get", "post", "put"])
+        self.check_empty(self.url, "End point URL")
+        self.check_positive_integer(self.timeout, "Timeout time in second")
+        self.check_boolean(self.clean_html, "Clean HTML")
+        self.check_valid_value(self.datatype.lower(), "Data post type", ["json", "formdata"])  # Check for valid datapost value
+
+
+class Invoke(ComponentBase, ABC):
+    component_name = "Invoke"
+
+    @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 3)))
+    def _invoke(self, **kwargs):
+        if self.check_if_canceled("Invoke processing"):
+            return
+
+        args = {}
+        for para in self._param.variables:
+            if para.get("value"):
+                args[para["key"]] = para["value"]
+            else:
+                args[para["key"]] = self._canvas.get_variable_value(para["ref"])
+
+        url = self._param.url.strip()
+
+        def replace_variable(match):
+            var_name = match.group(1)
+            try:
+                value = self._canvas.get_variable_value(var_name)
+                return str(value or "")
+            except Exception:
+                return ""
+
+        # {base_url} or {component_id@variable_name}
+        url = re.sub(r"\{([a-zA-Z_][a-zA-Z0-9_.@-]*)\}", replace_variable, url)
+
+        if url.find("http") != 0:
+            url = "http://" + url
+
+        method = self._param.method.lower()
+        headers = {}
+        if self._param.headers:
+            headers = json.loads(self._param.headers)
+        proxies = None
+        if re.sub(r"https?:?/?/?", "", self._param.proxy):
+            proxies = {"http": self._param.proxy, "https": self._param.proxy}
+
+        last_e = ""
+        for _ in range(self._param.max_retries + 1):
+            if self.check_if_canceled("Invoke processing"):
+                return
+
+            try:
+                if method == "get":
+                    response = requests.get(url=url, params=args, headers=headers, proxies=proxies, timeout=self._param.timeout)
+                    if self._param.clean_html:
+                        sections = HtmlParser()(None, response.content)
+                        self.set_output("result", "\n".join(sections))
+                    else:
+                        self.set_output("result", response.text)
+
+                if method == "put":
+                    if self._param.datatype.lower() == "json":
+                        response = requests.put(url=url, json=args, headers=headers, proxies=proxies, timeout=self._param.timeout)
+                    else:
+                        response = requests.put(url=url, data=args, headers=headers, proxies=proxies, timeout=self._param.timeout)
+                    if self._param.clean_html:
+                        sections = HtmlParser()(None, response.content)
+                        self.set_output("result", "\n".join(sections))
+                    else:
+                        self.set_output("result", response.text)
+
+                if method == "post":
+                    if self._param.datatype.lower() == "json":
+                        response = requests.post(url=url, json=args, headers=headers, proxies=proxies, timeout=self._param.timeout)
+                    else:
+                        response = requests.post(url=url, data=args, headers=headers, proxies=proxies, timeout=self._param.timeout)
+                    if self._param.clean_html:
+                        self.set_output("result", "\n".join(sections))
+                    else:
+                        self.set_output("result", response.text)
+
+                return self.output("result")
+            except Exception as e:
+                if self.check_if_canceled("Invoke processing"):
+                    return
+
+                last_e = e
+                logging.exception(f"Http request error: {e}")
+                time.sleep(self._param.delay_after_error)
+
+        if last_e:
+            self.set_output("_ERROR", str(last_e))
+            return f"Http request error: {last_e}"
+
+        assert False, self.output()
+
+    def thoughts(self) -> str:
+        return "Waiting for the server respond..."

agent/component/iteration.py

@@ -0,0 +1,71 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from abc import ABC
+from agent.component.base import ComponentBase, ComponentParamBase
+
+"""
+class VariableModel(BaseModel):
+    data_type: Annotated[Literal["string", "number", "Object", "Boolean", "Array<string>", "Array<number>", "Array<object>", "Array<boolean>"], Field(default="Array<string>")]
+    input_mode: Annotated[Literal["constant", "variable"], Field(default="constant")]
+    value: Annotated[Any, Field(default=None)]
+    model_config = ConfigDict(extra="forbid")
+"""
+
+class IterationParam(ComponentParamBase):
+    """
+    Define the Iteration component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.items_ref = ""
+        self.variable={}
+
+    def get_input_form(self) -> dict[str, dict]:
+        return {
+            "items": {
+                "type": "json",
+                "name": "Items"
+            }
+        }
+
+    def check(self):
+        return True
+
+
+class Iteration(ComponentBase, ABC):
+    component_name = "Iteration"
+
+    def get_start(self):
+        for cid in self._canvas.components.keys():
+            if self._canvas.get_component(cid)["obj"].component_name.lower() != "iterationitem":
+                continue
+            if self._canvas.get_component(cid)["parent_id"] == self._id:
+                return cid
+
+    def _invoke(self, **kwargs):
+        if self.check_if_canceled("Iteration processing"):
+            return
+
+        arr = self._canvas.get_variable_value(self._param.items_ref)
+        if not isinstance(arr, list):
+            self.set_output("_ERROR", self._param.items_ref + " must be an array, but its type is "+str(type(arr)))
+
+    def thoughts(self) -> str:
+        return "Need to process {} items.".format(len(self._canvas.get_variable_value(self._param.items_ref)))
+
+
+

agent/component/iterationitem.py

@@ -0,0 +1,91 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from abc import ABC
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class IterationItemParam(ComponentParamBase):
+    """
+    Define the IterationItem component parameters.
+    """
+    def check(self):
+        return True
+
+
+class IterationItem(ComponentBase, ABC):
+    component_name = "IterationItem"
+
+    def __init__(self, canvas, id, param: ComponentParamBase):
+        super().__init__(canvas, id, param)
+        self._idx = 0
+
+    def _invoke(self, **kwargs):
+        if self.check_if_canceled("IterationItem processing"):
+            return
+
+        parent = self.get_parent()
+        arr = self._canvas.get_variable_value(parent._param.items_ref)
+        if not isinstance(arr, list):
+            self._idx = -1
+            raise Exception(parent._param.items_ref + " must be an array, but its type is "+str(type(arr)))
+
+        if self._idx > 0:
+            if self.check_if_canceled("IterationItem processing"):
+                return
+            self.output_collation()
+
+        if self._idx >= len(arr):
+            self._idx = -1
+            return
+
+        if self.check_if_canceled("IterationItem processing"):
+            return
+
+        self.set_output("item", arr[self._idx])
+        self.set_output("index", self._idx)
+
+        self._idx += 1
+
+    def output_collation(self):
+        pid = self.get_parent()._id
+        for cid in self._canvas.components.keys():
+            obj = self._canvas.get_component_obj(cid)
+            p = obj.get_parent()
+            if not p:
+                continue
+            if p._id != pid:
+                continue
+
+            if p.component_name.lower() in ["categorize", "message", "switch", "userfillup", "interationitem"]:
+                continue
+
+            for k, o in p._param.outputs.items():
+                if "ref" not in o:
+                    continue
+                _cid, var = o["ref"].split("@")
+                if _cid != cid:
+                    continue
+                res = p.output(k)
+                if not res:
+                    res = []
+                res.append(obj.output(var))
+                p.set_output(k, res)
+
+    def end(self):
+        return self._idx == -1
+
+    def thoughts(self) -> str:
+        return "Next turn..."

agent/component/keyword.py

@@ -1,65 +0,0 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import re
-from abc import ABC
-from api.db import LLMType
-from api.db.services.llm_service import LLMBundle
-from agent.component import GenerateParam, Generate
-from agent.settings import DEBUG
-
-
-class KeywordExtractParam(GenerateParam):
-    """
-    Define the KeywordExtract component parameters.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.top_n = 1
-
-    def check(self):
-        super().check()
-        self.check_positive_integer(self.top_n, "Top N")
-
-    def get_prompt(self):
-        self.prompt = """
-- Role: You're a question analyzer. 
-- Requirements: 
-  - Summarize user's question, and give top %s important keyword/phrase.
-  - Use comma as a delimiter to separate keywords/phrases.
-- Answer format: (in language of user's question)
-  - keyword: 
-""" % self.top_n
-        return self.prompt
-
-
-class KeywordExtract(Generate, ABC):
-    component_name = "KeywordExtract"
-
-    def _run(self, history, **kwargs):
-        q = ""
-        for r, c in self._canvas.history[::-1]:
-            if r == "user":
-                q += c
-                break
-
-        chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id)
-        ans = chat_mdl.chat(self._param.get_prompt(), [{"role": "user", "content": q}],
-                            self._param.gen_conf())
-
-        ans = re.sub(r".*keyword:", "", ans).strip()
-        if DEBUG: print(ans, ":::::::::::::::::::::::::::::::::")
-        return KeywordExtract.be_output(ans)

agent/component/list_operations.py

@@ -0,0 +1,168 @@
+from abc import ABC
+import os
+from agent.component.base import ComponentBase, ComponentParamBase
+from api.utils.api_utils import timeout
+
+class ListOperationsParam(ComponentParamBase):
+    """
+    Define the List Operations component parameters.
+    """
+    def __init__(self):
+        super().__init__()
+        self.query = ""
+        self.operations = "topN"
+        self.n=0
+        self.sort_method = "asc"
+        self.filter = {
+            "operator": "=",
+            "value": ""
+        }
+        self.outputs = {
+            "result": {
+                "value": [],
+                "type": "Array of ?"
+            },
+            "first": {
+                "value": "",
+                "type": "?"
+            },
+            "last": {
+                "value": "",
+                "type": "?"
+            }
+        }
+    
+    def check(self):
+        self.check_empty(self.query, "query")
+        self.check_valid_value(self.operations, "Support operations", ["topN","head","tail","filter","sort","drop_duplicates"])
+
+    def get_input_form(self) -> dict[str, dict]:
+        return {}
+    
+
+class ListOperations(ComponentBase,ABC):
+    component_name = "ListOperations"
+
+    @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 10*60)))
+    def _invoke(self, **kwargs):
+        self.input_objects=[]
+        inputs = getattr(self._param, "query", None)
+        self.inputs = self._canvas.get_variable_value(inputs)
+        if not isinstance(self.inputs, list):
+            raise TypeError("The input of List Operations should be an array.")
+        self.set_input_value(inputs, self.inputs)
+        if self._param.operations == "topN":
+            self._topN()
+        elif self._param.operations == "head":
+            self._head()
+        elif self._param.operations == "tail":
+            self._tail()
+        elif self._param.operations == "filter":
+            self._filter()
+        elif self._param.operations == "sort":
+            self._sort()
+        elif self._param.operations == "drop_duplicates":
+            self._drop_duplicates()
+
+
+    def _coerce_n(self):
+        try:
+            return int(getattr(self._param, "n", 0))
+        except Exception:
+            return 0
+        
+    def _set_outputs(self, outputs):
+        self._param.outputs["result"]["value"] = outputs
+        self._param.outputs["first"]["value"] = outputs[0] if outputs else None
+        self._param.outputs["last"]["value"]  = outputs[-1] if outputs else None
+        
+    def _topN(self):
+        n = self._coerce_n()
+        if n < 1:
+            outputs = []
+        else:
+            n = min(n, len(self.inputs))
+            outputs = self.inputs[:n]
+        self._set_outputs(outputs)
+
+    def _head(self):
+        n = self._coerce_n()
+        if 1 <= n <= len(self.inputs):
+            outputs = [self.inputs[n - 1]]
+        else:
+            outputs = []
+        self._set_outputs(outputs)
+
+    def _tail(self):
+        n = self._coerce_n()
+        if 1 <= n <= len(self.inputs):
+            outputs = [self.inputs[-n]]
+        else:
+            outputs = []
+        self._set_outputs(outputs)
+
+    def _filter(self):
+        self._set_outputs([i for i in self.inputs if self._eval(self._norm(i),self._param.filter["operator"],self._param.filter["value"])])
+
+    def _norm(self,v):
+        s = "" if v is None else str(v)
+        return s
+    
+    def _eval(self, v, operator, value):
+        if operator == "=":
+            return v == value
+        elif operator == "≠":
+            return v != value
+        elif operator == "contains":
+            return value in v
+        elif operator == "start with":
+            return v.startswith(value)
+        elif operator == "end with":
+            return v.endswith(value)
+        else:
+            return False
+
+    def _sort(self):
+        items = self.inputs or []
+        method = getattr(self._param, "sort_method", "asc") or "asc"
+        reverse = method == "desc"
+
+        if not items:
+            self._set_outputs([])
+            return
+
+        first = items[0]
+
+        if isinstance(first, dict):
+            outputs = sorted(
+                items,
+                key=lambda x: self._hashable(x),
+                reverse=reverse,
+            )
+        else:
+            outputs = sorted(items, reverse=reverse)
+
+        self._set_outputs(outputs)
+
+    def _drop_duplicates(self):
+        seen = set()
+        outs = []
+        for item in self.inputs:
+            k = self._hashable(item)
+            if k in seen:
+                continue
+            seen.add(k)
+            outs.append(item)
+        self._set_outputs(outs)
+
+    def _hashable(self,x):
+        if isinstance(x, dict):
+            return tuple(sorted((k, self._hashable(v)) for k, v in x.items()))
+        if isinstance(x, (list, tuple)):
+            return tuple(self._hashable(v) for v in x)
+        if isinstance(x, set):
+            return tuple(sorted(self._hashable(v) for v in x))
+        return x
+    
+    def thoughts(self) -> str:
+        return "ListOperation in progress"

agent/component/llm.py

@@ -0,0 +1,302 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import json
+import logging
+import os
+import re
+from copy import deepcopy
+from typing import Any, Generator
+import json_repair
+from functools import partial
+from common.constants import LLMType
+from api.db.services.llm_service import LLMBundle
+from api.db.services.tenant_llm_service import TenantLLMService
+from agent.component.base import ComponentBase, ComponentParamBase
+from common.connection_utils import timeout
+from rag.prompts.generator import tool_call_summary, message_fit_in, citation_prompt, structured_output_prompt
+
+
+class LLMParam(ComponentParamBase):
+    """
+    Define the LLM component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.llm_id = ""
+        self.sys_prompt = ""
+        self.prompts = [{"role": "user", "content": "{sys.query}"}]
+        self.max_tokens = 0
+        self.temperature = 0
+        self.top_p = 0
+        self.presence_penalty = 0
+        self.frequency_penalty = 0
+        self.output_structure = None
+        self.cite = True
+        self.visual_files_var = None
+
+    def check(self):
+        self.check_decimal_float(float(self.temperature), "[Agent] Temperature")
+        self.check_decimal_float(float(self.presence_penalty), "[Agent] Presence penalty")
+        self.check_decimal_float(float(self.frequency_penalty), "[Agent] Frequency penalty")
+        self.check_nonnegative_number(int(self.max_tokens), "[Agent] Max tokens")
+        self.check_decimal_float(float(self.top_p), "[Agent] Top P")
+        self.check_empty(self.llm_id, "[Agent] LLM")
+        self.check_empty(self.sys_prompt, "[Agent] System prompt")
+        self.check_empty(self.prompts, "[Agent] User prompt")
+
+    def gen_conf(self):
+        conf = {}
+        def get_attr(nm):
+            try:
+                return getattr(self, nm)
+            except Exception:
+                pass
+
+        if int(self.max_tokens) > 0 and get_attr("maxTokensEnabled"):
+            conf["max_tokens"] = int(self.max_tokens)
+        if float(self.temperature) > 0 and get_attr("temperatureEnabled"):
+            conf["temperature"] = float(self.temperature)
+        if float(self.top_p) > 0 and get_attr("topPEnabled"):
+            conf["top_p"] = float(self.top_p)
+        if float(self.presence_penalty) > 0 and get_attr("presencePenaltyEnabled"):
+            conf["presence_penalty"] = float(self.presence_penalty)
+        if float(self.frequency_penalty) > 0 and get_attr("frequencyPenaltyEnabled"):
+            conf["frequency_penalty"] = float(self.frequency_penalty)
+        return conf
+
+
+class LLM(ComponentBase):
+    component_name = "LLM"
+
+    def __init__(self, canvas, component_id, param: ComponentParamBase):
+        super().__init__(canvas, component_id, param)
+        self.chat_mdl = LLMBundle(self._canvas.get_tenant_id(), TenantLLMService.llm_id2llm_type(self._param.llm_id),
+                                  self._param.llm_id, max_retries=self._param.max_retries,
+                                  retry_interval=self._param.delay_after_error
+                                  )
+        self.imgs = []
+
+    def get_input_form(self) -> dict[str, dict]:
+        res = {}
+        for k, v in self.get_input_elements().items():
+            res[k] = {
+                "type": "line",
+                "name": v["name"]
+            }
+        return res
+
+    def get_input_elements(self) -> dict[str, Any]:
+        res = self.get_input_elements_from_text(self._param.sys_prompt)
+        if isinstance(self._param.prompts, str):
+            self._param.prompts = [{"role": "user", "content": self._param.prompts}]
+        for prompt in self._param.prompts:
+            d = self.get_input_elements_from_text(prompt["content"])
+            res.update(d)
+        return res
+
+    def set_debug_inputs(self, inputs: dict[str, dict]):
+        self._param.debug_inputs = inputs
+
+    def add2system_prompt(self, txt):
+        self._param.sys_prompt += txt
+
+    def _sys_prompt_and_msg(self, msg, args):
+        if isinstance(self._param.prompts, str):
+            self._param.prompts = [{"role": "user", "content": self._param.prompts}]
+        for p in self._param.prompts:
+            if msg and msg[-1]["role"] == p["role"]:
+                continue
+            p = deepcopy(p)
+            p["content"] = self.string_format(p["content"], args)
+            msg.append(p)
+        return msg, self.string_format(self._param.sys_prompt, args)
+
+    def _prepare_prompt_variables(self):
+        if self._param.visual_files_var:
+            self.imgs = self._canvas.get_variable_value(self._param.visual_files_var)
+            if not self.imgs:
+                self.imgs = []
+            self.imgs = [img for img in self.imgs if img[:len("data:image/")] == "data:image/"]
+            if self.imgs and TenantLLMService.llm_id2llm_type(self._param.llm_id) == LLMType.CHAT.value:
+                self.chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.IMAGE2TEXT.value,
+                                          self._param.llm_id, max_retries=self._param.max_retries,
+                                          retry_interval=self._param.delay_after_error
+                                          )
+
+
+        args = {}
+        vars = self.get_input_elements() if not self._param.debug_inputs else self._param.debug_inputs
+        for k, o in vars.items():
+            args[k] = o["value"]
+            if not isinstance(args[k], str):
+                try:
+                    args[k] = json.dumps(args[k], ensure_ascii=False)
+                except Exception:
+                    args[k] = str(args[k])
+            self.set_input_value(k, args[k])
+
+        msg, sys_prompt = self._sys_prompt_and_msg(self._canvas.get_history(self._param.message_history_window_size)[:-1], args)
+        user_defined_prompt, sys_prompt = self._extract_prompts(sys_prompt)
+        if self._param.cite and self._canvas.get_reference()["chunks"]:
+            sys_prompt += citation_prompt(user_defined_prompt)
+
+        return sys_prompt, msg, user_defined_prompt
+
+    def _extract_prompts(self, sys_prompt):
+        pts = {}
+        for tag in ["TASK_ANALYSIS", "PLAN_GENERATION", "REFLECTION", "CONTEXT_SUMMARY", "CONTEXT_RANKING", "CITATION_GUIDELINES"]:
+            r = re.search(rf"<{tag}>(.*?)</{tag}>", sys_prompt, flags=re.DOTALL|re.IGNORECASE)
+            if not r:
+                continue
+            pts[tag.lower()] = r.group(1)
+            sys_prompt = re.sub(rf"<{tag}>(.*?)</{tag}>", "", sys_prompt, flags=re.DOTALL|re.IGNORECASE)
+        return pts, sys_prompt
+
+    def _generate(self, msg:list[dict], **kwargs) -> str:
+        if not self.imgs:
+            return self.chat_mdl.chat(msg[0]["content"], msg[1:], self._param.gen_conf(), **kwargs)
+        return self.chat_mdl.chat(msg[0]["content"], msg[1:], self._param.gen_conf(), images=self.imgs, **kwargs)
+
+    def _generate_streamly(self, msg:list[dict], **kwargs) -> Generator[str, None, None]:
+        ans = ""
+        last_idx = 0
+        endswith_think = False
+        def delta(txt):
+            nonlocal ans, last_idx, endswith_think
+            delta_ans = txt[last_idx:]
+            ans = txt
+
+            if delta_ans.find("<think>") == 0:
+                last_idx += len("<think>")
+                return "<think>"
+            elif delta_ans.find("<think>") > 0:
+                delta_ans = txt[last_idx:last_idx+delta_ans.find("<think>")]
+                last_idx += delta_ans.find("<think>")
+                return delta_ans
+            elif delta_ans.endswith("</think>"):
+                endswith_think = True
+            elif endswith_think:
+                endswith_think = False
+                return "</think>"
+
+            last_idx = len(ans)
+            if ans.endswith("</think>"):
+                last_idx -= len("</think>")
+            return re.sub(r"(<think>|</think>)", "", delta_ans)
+
+        if not self.imgs:
+            for txt in self.chat_mdl.chat_streamly(msg[0]["content"], msg[1:], self._param.gen_conf(), **kwargs):
+                yield delta(txt)
+        else:
+            for txt in self.chat_mdl.chat_streamly(msg[0]["content"], msg[1:], self._param.gen_conf(), images=self.imgs, **kwargs):
+                yield delta(txt)
+
+    @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 10*60)))
+    def _invoke(self, **kwargs):
+        if self.check_if_canceled("LLM processing"):
+            return
+
+        def clean_formated_answer(ans: str) -> str:
+            ans = re.sub(r"^.*</think>", "", ans, flags=re.DOTALL)
+            ans = re.sub(r"^.*```json", "", ans, flags=re.DOTALL)
+            return re.sub(r"```\n*$", "", ans, flags=re.DOTALL)
+
+        prompt, msg, _ = self._prepare_prompt_variables()
+        error: str = ""
+        output_structure=None
+        try:
+            output_structure = self._param.outputs['structured']
+        except Exception:
+            pass
+        if output_structure and isinstance(output_structure, dict) and output_structure.get("properties"):
+            schema=json.dumps(output_structure, ensure_ascii=False, indent=2)
+            prompt += structured_output_prompt(schema)
+            for _ in range(self._param.max_retries+1):
+                if self.check_if_canceled("LLM processing"):
+                    return
+
+                _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(self.chat_mdl.max_length * 0.97))
+                error = ""
+                ans = self._generate(msg)
+                msg.pop(0)
+                if ans.find("**ERROR**") >= 0:
+                    logging.error(f"LLM response error: {ans}")
+                    error = ans
+                    continue
+                try:
+                    self.set_output("structured", json_repair.loads(clean_formated_answer(ans)))
+                    return
+                except Exception:
+                    msg.append({"role": "user", "content": "The answer can't not be parsed as JSON"})
+                    error = "The answer can't not be parsed as JSON"
+            if error:
+                self.set_output("_ERROR", error)
+            return
+
+        downstreams = self._canvas.get_component(self._id)["downstream"] if self._canvas.get_component(self._id) else []
+        ex = self.exception_handler()
+        if any([self._canvas.get_component_obj(cid).component_name.lower()=="message" for cid in downstreams]) and not (ex and ex["goto"]):
+            self.set_output("content", partial(self._stream_output, prompt, msg))
+            return
+
+        for _ in range(self._param.max_retries+1):
+            if self.check_if_canceled("LLM processing"):
+                return
+
+            _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(self.chat_mdl.max_length * 0.97))
+            error = ""
+            ans = self._generate(msg)
+            msg.pop(0)
+            if ans.find("**ERROR**") >= 0:
+                logging.error(f"LLM response error: {ans}")
+                error = ans
+                continue
+            self.set_output("content", ans)
+            break
+
+        if error:
+            if self.get_exception_default_value():
+                self.set_output("content", self.get_exception_default_value())
+            else:
+                self.set_output("_ERROR", error)
+
+    def _stream_output(self, prompt, msg):
+        _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(self.chat_mdl.max_length * 0.97))
+        answer = ""
+        for ans in self._generate_streamly(msg):
+            if self.check_if_canceled("LLM streaming"):
+                return
+
+            if ans.find("**ERROR**") >= 0:
+                if self.get_exception_default_value():
+                    self.set_output("content", self.get_exception_default_value())
+                    yield self.get_exception_default_value()
+                else:
+                    self.set_output("_ERROR", ans)
+                return
+            yield ans
+            answer += ans
+        self.set_output("content", answer)
+
+    def add_memory(self, user:str, assist:str, func_name: str, params: dict, results: str, user_defined_prompt:dict={}):
+        summ = tool_call_summary(self.chat_mdl, func_name, params, results, user_defined_prompt)
+        logging.info(f"[MEMORY]: {summ}")
+        self._canvas.add_memory(user, assist, summ)
+
+    def thoughts(self) -> str:
+        _, msg,_ = self._prepare_prompt_variables()
+        return "⌛Give me a moment—starting from: \n\n" + re.sub(r"(User's query:|[\\]+)", '', msg[-1]['content'], flags=re.DOTALL) + "\n\nI’ll figure out our best next move."

agent/component/message.py

@@ -13,41 +13,229 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import json
+import os
 import random
-from abc import ABC
+import re
+import logging
+import tempfile
 from functools import partial
+from typing import Any
+
 from agent.component.base import ComponentBase, ComponentParamBase
+from jinja2 import Template as Jinja2Template
+
+from common.connection_utils import timeout
+from common.misc_utils import get_uuid
+from common import settings
 
 
 class MessageParam(ComponentParamBase):
-
     """
     Define the Message component parameters.
     """
     def __init__(self):
         super().__init__()
-        self.messages = []
+        self.content = []
+        self.stream = True
+        self.output_format = None  # default output format
+        self.outputs = {
+            "content": {
+                "type": "str"
+            }
+        }
 
     def check(self):
-        self.check_empty(self.messages, "[Message]")
+        self.check_empty(self.content, "[Message] Content")
+        self.check_boolean(self.stream, "[Message] stream")
         return True
 
 
-class Message(ComponentBase, ABC):
+class Message(ComponentBase):
     component_name = "Message"
 
-    def _run(self, history, **kwargs):
-        if kwargs.get("stream"):
-            return partial(self.stream_output)
+    def get_input_elements(self) -> dict[str, Any]:
+        return self.get_input_elements_from_text("".join(self._param.content))
 
-        return Message.be_output(random.choice(self._param.messages))
+    def get_kwargs(self, script:str, kwargs:dict = {}, delimiter:str=None) -> tuple[str, dict[str, str | list | Any]]:
+        for k,v in self.get_input_elements_from_text(script).items():
+            if k in kwargs:
+                continue
+            v = v["value"]
+            if not v:
+                v = ""
+            ans = ""
+            if isinstance(v, partial):
+                for t in v():
+                    ans += t
+            elif isinstance(v, list) and delimiter:
+                ans = delimiter.join([str(vv) for vv in v])
+            elif not isinstance(v, str):
+                try:
+                    ans = json.dumps(v, ensure_ascii=False)
+                except Exception:
+                    pass
+            else:
+                ans = v
+            if not ans:
+                ans = ""
+            kwargs[k] = ans
+            self.set_input_value(k, ans)
 
-    def stream_output(self):
-        res = None
-        if self._param.messages:
-            res = {"content": random.choice(self._param.messages)}
-            yield res
+        _kwargs = {}
+        for n, v in kwargs.items():
+            _n = re.sub("[@:.]", "_", n)
+            script = re.sub(r"\{%s\}" % re.escape(n), _n, script)
+            _kwargs[_n] = v
+        return script, _kwargs
 
-        self.set_output(res)
+    def _stream(self, rand_cnt:str):
+        s = 0
+        all_content = ""
+        cache = {}
+        for r in re.finditer(self.variable_ref_patt, rand_cnt, flags=re.DOTALL):
+            if self.check_if_canceled("Message streaming"):
+                return
 
+            all_content += rand_cnt[s: r.start()]
+            yield rand_cnt[s: r.start()]
+            s = r.end()
+            exp = r.group(1)
+            if exp in cache:
+                yield cache[exp]
+                all_content += cache[exp]
+                continue
 
+            v = self._canvas.get_variable_value(exp)
+            if v is None:
+                v = ""
+            if isinstance(v, partial):
+                cnt = ""
+                for t in v():
+                    if self.check_if_canceled("Message streaming"):
+                        return
+
+                    all_content += t
+                    cnt += t
+                    yield t
+                self.set_input_value(exp, cnt)
+                continue
+            elif not isinstance(v, str):
+                try:
+                    v = json.dumps(v, ensure_ascii=False)
+                except Exception:
+                    v = str(v)
+            yield v
+            self.set_input_value(exp, v)
+            all_content += v
+            cache[exp] = v
+
+        if s < len(rand_cnt):
+            if self.check_if_canceled("Message streaming"):
+                return
+
+            all_content += rand_cnt[s: ]
+            yield rand_cnt[s: ]
+
+        self.set_output("content", all_content)
+        self._convert_content(all_content)
+
+    def _is_jinjia2(self, content:str) -> bool:
+        patt = [
+            r"\{%.*%\}", "{{", "}}"
+        ]
+        return any([re.search(p, content) for p in patt])
+
+    @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 10*60)))
+    def _invoke(self, **kwargs):
+        if self.check_if_canceled("Message processing"):
+            return
+
+        rand_cnt = random.choice(self._param.content)
+        if self._param.stream and not self._is_jinjia2(rand_cnt):
+            self.set_output("content", partial(self._stream, rand_cnt))
+            return
+
+        rand_cnt, kwargs = self.get_kwargs(rand_cnt, kwargs)
+        template = Jinja2Template(rand_cnt)
+        try:
+            content = template.render(kwargs)
+        except Exception:
+            pass
+
+        if self.check_if_canceled("Message processing"):
+            return
+
+        for n, v in kwargs.items():
+            content = re.sub(n, v, content)
+
+        self.set_output("content", content)
+        self._convert_content(content)
+
+    def thoughts(self) -> str:
+        return ""
+
+    def _convert_content(self, content):
+        if not self._param.output_format:
+            return
+
+        import pypandoc
+        doc_id = get_uuid()
+        
+        if self._param.output_format.lower() not in {"markdown", "html", "pdf", "docx"}:
+            self._param.output_format = "markdown"
+
+        try:
+            if self._param.output_format in {"markdown", "html"}:
+                if isinstance(content, str):
+                    converted = pypandoc.convert_text(
+                        content,
+                        to=self._param.output_format,
+                        format="markdown",
+                    )
+                else:
+                    converted = pypandoc.convert_file(
+                        content,
+                        to=self._param.output_format,
+                        format="markdown",
+                    )
+
+                binary_content = converted.encode("utf-8")
+
+            else:  # pdf, docx
+                with tempfile.NamedTemporaryFile(suffix=f".{self._param.output_format}", delete=False) as tmp:
+                    tmp_name = tmp.name
+
+                try:
+                    if isinstance(content, str):
+                        pypandoc.convert_text(
+                            content,
+                            to=self._param.output_format,
+                            format="markdown",
+                            outputfile=tmp_name,
+                        )
+                    else:
+                        pypandoc.convert_file(
+                            content,
+                            to=self._param.output_format,
+                            format="markdown",
+                            outputfile=tmp_name,
+                        )
+
+                    with open(tmp_name, "rb") as f:
+                        binary_content = f.read()
+
+                finally:
+                    if os.path.exists(tmp_name):
+                        os.remove(tmp_name)
+
+            settings.STORAGE_IMPL.put(self._canvas._tenant_id, doc_id, binary_content)
+            self.set_output("attachment", {
+                "doc_id":doc_id, 
+                "format":self._param.output_format, 
+                "file_name":f"{doc_id[:8]}.{self._param.output_format}"})
+
+            logging.info(f"Converted content uploaded as {doc_id} (format={self._param.output_format})")
+
+        except Exception as e:
+            logging.error(f"Error converting content to {self._param.output_format}: {e}")

agent/component/pubmed.py

@@ -1,69 +0,0 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from abc import ABC
-from Bio import Entrez
-import re
-import pandas as pd
-import xml.etree.ElementTree as ET
-from agent.settings import DEBUG
-from agent.component.base import ComponentBase, ComponentParamBase
-
-
-class PubMedParam(ComponentParamBase):
-    """
-    Define the PubMed component parameters.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.top_n = 5
-        self.email = "A.N.Other@example.com"
-
-    def check(self):
-        self.check_positive_integer(self.top_n, "Top N")
-
-
-class PubMed(ComponentBase, ABC):
-    component_name = "PubMed"
-
-    def _run(self, history, **kwargs):
-        ans = self.get_input()
-        ans = " - ".join(ans["content"]) if "content" in ans else ""
-        if not ans:
-            return PubMed.be_output("")
-
-        try:
-            Entrez.email = self._param.email
-            pubmedids = Entrez.read(Entrez.esearch(db='pubmed', retmax=self._param.top_n, term=ans))['IdList']
-            pubmedcnt = ET.fromstring(re.sub(r'<(/?)b>|<(/?)i>', '', Entrez.efetch(db='pubmed', id=",".join(pubmedids),
-                                                                                   retmode="xml").read().decode(
-                "utf-8")))
-            pubmed_res = [{"content": 'Title:' + child.find("MedlineCitation").find("Article").find(
-                "ArticleTitle").text + '\nUrl:<a href=" https://pubmed.ncbi.nlm.nih.gov/' + child.find(
-                "MedlineCitation").find("PMID").text + '">' + '</a>\n' + 'Abstract:' + (
-                                          child.find("MedlineCitation").find("Article").find("Abstract").find(
-                                              "AbstractText").text if child.find("MedlineCitation").find(
-                                              "Article").find("Abstract") else "No abstract available")} for child in
-                          pubmedcnt.findall("PubmedArticle")]
-        except Exception as e:
-            return PubMed.be_output("**ERROR**: " + str(e))
-
-        if not pubmed_res:
-            return PubMed.be_output("")
-
-        df = pd.DataFrame(pubmed_res)
-        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
-        return df

agent/component/relevant.py

@@ -1,80 +0,0 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from abc import ABC
-from api.db import LLMType
-from api.db.services.llm_service import LLMBundle
-from agent.component import GenerateParam, Generate
-from rag.utils import num_tokens_from_string, encoder
-
-
-class RelevantParam(GenerateParam):
-
-    """
-    Define the Relevant component parameters.
-    """
-    def __init__(self):
-        super().__init__()
-        self.prompt = ""
-        self.yes = ""
-        self.no = ""
-
-    def check(self):
-        super().check()
-        self.check_empty(self.yes, "[Relevant] 'Yes'")
-        self.check_empty(self.no, "[Relevant] 'No'")
-
-    def get_prompt(self):
-        self.prompt = """
-        You are a grader assessing relevance of a retrieved document to a user question. 
-        It does not need to be a stringent test. The goal is to filter out erroneous retrievals.
-        If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. 
-        Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.
-        No other words needed except 'yes' or 'no'.
-        """
-        return self.prompt
-
-
-class Relevant(Generate, ABC):
-    component_name = "Relevant"
-
-    def _run(self, history, **kwargs):
-        q = ""
-        for r, c in self._canvas.history[::-1]:
-            if r == "user":
-                q = c
-                break
-        ans = self.get_input()
-        ans = " - ".join(ans["content"]) if "content" in ans else ""
-        if not ans:
-            return Relevant.be_output(self._param.no)
-        ans = "Documents: \n" + ans
-        ans = f"Question: {q}\n" + ans
-        chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id)
-
-        if num_tokens_from_string(ans) >= chat_mdl.max_length - 4:
-            ans = encoder.decode(encoder.encode(ans)[:chat_mdl.max_length - 4])
-
-        ans = chat_mdl.chat(self._param.get_prompt(), [{"role": "user", "content": ans}],
-                            self._param.gen_conf())
-
-        print(ans, ":::::::::::::::::::::::::::::::::")
-        if ans.lower().find("yes") >= 0:
-            return Relevant.be_output(self._param.yes)
-        if ans.lower().find("no") >= 0:
-            return Relevant.be_output(self._param.no)
-        assert False, f"Relevant component got: {ans}"
-
-

agent/component/retrieval.py

@@ -1,89 +0,0 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from abc import ABC
-
-import pandas as pd
-
-from api.db import LLMType
-from api.db.services.knowledgebase_service import KnowledgebaseService
-from api.db.services.llm_service import LLMBundle
-from api.settings import retrievaler
-from agent.component.base import ComponentBase, ComponentParamBase
-
-
-class RetrievalParam(ComponentParamBase):
-
-    """
-    Define the Retrieval component parameters.
-    """
-    def __init__(self):
-        super().__init__()
-        self.similarity_threshold = 0.2
-        self.keywords_similarity_weight = 0.5
-        self.top_n = 8
-        self.top_k = 1024
-        self.kb_ids = []
-        self.rerank_id = ""
-        self.empty_response = ""
-
-    def check(self):
-        self.check_decimal_float(self.similarity_threshold, "[Retrieval] Similarity threshold")
-        self.check_decimal_float(self.keywords_similarity_weight, "[Retrieval] Keywords similarity weight")
-        self.check_positive_number(self.top_n, "[Retrieval] Top N")
-        self.check_empty(self.kb_ids, "[Retrieval] Knowledge bases")
-
-
-class Retrieval(ComponentBase, ABC):
-    component_name = "Retrieval"
-
-    def _run(self, history, **kwargs):
-        query = []
-        for role, cnt in history[::-1][:self._param.message_history_window_size]:
-            if role != "user":continue
-            query.append(cnt)
-        # query = "\n".join(query)
-        query = query[0]
-        kbs = KnowledgebaseService.get_by_ids(self._param.kb_ids)
-        if not kbs:
-            raise ValueError("Can't find knowledgebases by {}".format(self._param.kb_ids))
-        embd_nms = list(set([kb.embd_id for kb in kbs]))
-        assert len(embd_nms) == 1, "Knowledge bases use different embedding models."
-
-        embd_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.EMBEDDING, embd_nms[0])
-        self._canvas.set_embedding_model(embd_nms[0])
-
-        rerank_mdl = None
-        if self._param.rerank_id:
-            rerank_mdl = LLMBundle(kbs[0].tenant_id, LLMType.RERANK, self._param.rerank_id)
-
-        kbinfos = retrievaler.retrieval(query, embd_mdl, kbs[0].tenant_id, self._param.kb_ids,
-                                        1, self._param.top_n,
-                                        self._param.similarity_threshold, 1 - self._param.keywords_similarity_weight,
-                                        aggs=False, rerank_mdl=rerank_mdl)
-
-        if not kbinfos["chunks"]:
-            df = Retrieval.be_output("")
-            if self._param.empty_response and self._param.empty_response.strip():
-                df["empty_response"] = self._param.empty_response
-            return df
-
-        df = pd.DataFrame(kbinfos["chunks"])
-        df["content"] = df["content_with_weight"]
-        del df["content_with_weight"]
-        print(">>>>>>>>>>>>>>>>>>>>>>>>>>\n", query, df)
-        return df
-
-

agent/component/rewrite.py

@@ -1,72 +0,0 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from abc import ABC
-from api.db import LLMType
-from api.db.services.llm_service import LLMBundle
-from agent.component import GenerateParam, Generate
-
-
-class RewriteQuestionParam(GenerateParam):
-
-    """
-    Define the QuestionRewrite component parameters.
-    """
-    def __init__(self):
-        super().__init__()
-        self.temperature = 0.9
-        self.prompt = ""
-        self.loop = 1
-
-    def check(self):
-        super().check()
-
-    def get_prompt(self):
-        self.prompt = """
-        You are an expert at query expansion to generate a paraphrasing of a question.
-        I can't retrieval relevant information from the knowledge base by using user's question directly.     
-        You need to expand or paraphrase user's question by multiple ways such as using synonyms words/phrase, 
-        writing the abbreviation in its entirety, adding some extra descriptions or explanations, 
-        changing the way of expression, translating the original question into another language (English/Chinese), etc. 
-        And return 5 versions of question and one is from translation.
-        Just list the question. No other words are needed.
-        """
-        return self.prompt
-
-
-class RewriteQuestion(Generate, ABC):
-    component_name = "RewriteQuestion"
-
-    def _run(self, history, **kwargs):
-        if not hasattr(self, "_loop"):
-            setattr(self, "_loop", 0)
-        if self._loop >= self._param.loop:
-            self._loop = 0
-            raise Exception("Sorry! Nothing relevant found.")
-        self._loop += 1
-        q = "Question: "
-        for r, c in self._canvas.history[::-1]:
-            if r == "user":
-                q += c
-                break
-
-        chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id)
-        ans = chat_mdl.chat(self._param.get_prompt(), [{"role": "user", "content": q}],
-                            self._param.gen_conf())
-
-        print(ans, ":::::::::::::::::::::::::::::::::")
-        return RewriteQuestion.be_output(ans)
-
-

agent/component/string_transform.py

@@ -0,0 +1,115 @@
+#
+#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import os
+import re
+from abc import ABC
+from typing import Any
+
+from jinja2 import Template as Jinja2Template
+from agent.component.base import ComponentParamBase
+from common.connection_utils import timeout
+from .message import Message
+
+
+class StringTransformParam(ComponentParamBase):
+    """
+    Define the code sandbox component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.method = "split"
+        self.script = ""
+        self.split_ref = ""
+        self.delimiters = [","]
+        self.outputs = {"result": {"value": "", "type": "string"}}
+
+    def check(self):
+        self.check_valid_value(self.method, "Support method", ["split", "merge"])
+        self.check_empty(self.delimiters, "delimiters")
+
+
+class StringTransform(Message, ABC):
+    component_name = "StringTransform"
+
+    def get_input_elements(self) -> dict[str, Any]:
+        return self.get_input_elements_from_text(self._param.script)
+
+    def get_input_form(self) -> dict[str, dict]:
+        if self._param.method == "split":
+            return {
+                "line": {
+                    "name": "String",
+                    "type": "line"
+                }
+            }
+        return {k: {
+            "name": o["name"],
+            "type": "line"
+        } for k, o in self.get_input_elements_from_text(self._param.script).items()}
+
+    @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 10*60)))
+    def _invoke(self, **kwargs):
+        if self.check_if_canceled("StringTransform processing"):
+            return
+
+        if self._param.method == "split":
+            self._split(kwargs.get("line"))
+        else:
+            self._merge(kwargs)
+
+    def _split(self, line:str|None = None):
+        if self.check_if_canceled("StringTransform split processing"):
+            return
+
+        var = self._canvas.get_variable_value(self._param.split_ref) if not line else line
+        if not var:
+            var = ""
+        assert isinstance(var, str), "The input variable is not a string: {}".format(type(var))
+        self.set_input_value(self._param.split_ref, var)
+
+        res = []
+        for i,s in enumerate(re.split(r"(%s)"%("|".join([re.escape(d) for d in self._param.delimiters])), var, flags=re.DOTALL)):
+            if i % 2 == 1:
+                continue
+            res.append(s)
+        self.set_output("result", res)
+
+    def _merge(self, kwargs:dict[str, str] = {}):
+        if self.check_if_canceled("StringTransform merge processing"):
+            return
+
+        script = self._param.script
+        script, kwargs = self.get_kwargs(script, kwargs, self._param.delimiters[0])
+
+        if self._is_jinjia2(script):
+            template = Jinja2Template(script)
+            try:
+                script = template.render(kwargs)
+            except Exception:
+                pass
+
+        for k,v in kwargs.items():
+            if not v:
+                v = ""
+            script = re.sub(k, lambda match: v, script)
+
+        self.set_output("result", script)
+
+    def thoughts(self) -> str:
+        return f"It's {self._param.method}ing."
+
+

agent/component/switch.py

@@ -13,8 +13,13 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import numbers
+import os
 from abc import ABC
+from typing import Any
+
 from agent.component.base import ComponentBase, ComponentParamBase
+from common.connection_utils import timeout
 
 
 class SwitchParam(ComponentParamBase):
@@ -34,57 +39,64 @@ class SwitchParam(ComponentParamBase):
         }
         """
         self.conditions = []
-        self.end_cpn_id = "answer:0"
+        self.end_cpn_ids = []
         self.operators = ['contains', 'not contains', 'start with', 'end with', 'empty', 'not empty', '=', '≠', '>',
                           '<', '≥', '≤']
 
     def check(self):
         self.check_empty(self.conditions, "[Switch] conditions")
         for cond in self.conditions:
-            if not cond["to"]: raise ValueError(f"[Switch] 'To' can not be empty!")
+            if not cond["to"]:
+                raise ValueError("[Switch] 'To' can not be empty!")
+        self.check_empty(self.end_cpn_ids, "[Switch] the ELSE/Other destination can not be empty.")
 
+    def get_input_form(self) -> dict[str, dict]:
+        return {
+            "urls": {
+                "name": "URLs",
+                "type": "line"
+            }
+        }
 
 class Switch(ComponentBase, ABC):
     component_name = "Switch"
 
-    def _run(self, history, **kwargs):
+    @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 3)))
+    def _invoke(self, **kwargs):
+        if self.check_if_canceled("Switch processing"):
+            return
+
         for cond in self._param.conditions:
+            if self.check_if_canceled("Switch processing"):
+                return
 
-            if len(cond["items"]) == 1:
-                out = self._canvas.get_component(cond["items"][0]["cpn_id"])["obj"].output()[1]
-                cpn_input = "" if "content" not in out.columns else " ".join(out["content"])
-                if self.process_operator(cpn_input, cond["items"][0]["operator"], cond["items"][0]["value"]):
-                    return Switch.be_output(cond["to"])
-                continue
-
-            if cond["logical_operator"] == "and":
-                res = True
-                for item in cond["items"]:
-                    out = self._canvas.get_component(item["cpn_id"])["obj"].output()[1]
-                    cpn_input = "" if "content" not in out.columns else " ".join(out["content"])
-                    if not self.process_operator(cpn_input, item["operator"], item["value"]):
-                        res = False
-                        break
-                if res:
-                    return Switch.be_output(cond["to"])
-                continue
-
-            res = False
+            res = []
             for item in cond["items"]:
-                out = self._canvas.get_component(item["cpn_id"])["obj"].output()[1]
-                cpn_input = "" if "content" not in out.columns else " ".join(out["content"])
-                if self.process_operator(cpn_input, item["operator"], item["value"]):
-                    res = True
-                    break
-            if res:
-                return Switch.be_output(cond["to"])
+                if self.check_if_canceled("Switch processing"):
+                    return
 
-        return Switch.be_output(self._param.end_cpn_id)
+                if not item["cpn_id"]:
+                    continue
+                cpn_v = self._canvas.get_variable_value(item["cpn_id"])
+                self.set_input_value(item["cpn_id"], cpn_v)
+                operatee = item.get("value", "")
+                if isinstance(cpn_v, numbers.Number):
+                    operatee = float(operatee)
+                res.append(self.process_operator(cpn_v, item["operator"], operatee))
+                if cond["logical_operator"] != "and" and any(res):
+                    self.set_output("next", [self._canvas.get_component_name(cpn_id) for cpn_id in cond["to"]])
+                    self.set_output("_next", cond["to"])
+                    return
 
-    def process_operator(self, input: str, operator: str, value: str) -> bool:
-        if not isinstance(input, str) or not isinstance(value, str):
-            raise ValueError('Invalid input or value type: string')
+            if all(res):
+                self.set_output("next", [self._canvas.get_component_name(cpn_id) for cpn_id in cond["to"]])
+                self.set_output("_next", cond["to"])
+                return
 
+        self.set_output("next", [self._canvas.get_component_name(cpn_id) for cpn_id in self._param.end_cpn_ids])
+        self.set_output("_next", self._param.end_cpn_ids)
+
+    def process_operator(self, input: Any, operator: str, value: Any) -> bool:
         if operator == "contains":
             return True if value.lower() in input.lower() else False
         elif operator == "not contains":
@@ -104,22 +116,25 @@ class Switch(ComponentBase, ABC):
         elif operator == ">":
             try:
                 return True if float(input) > float(value) else False
-            except Exception as e:
+            except Exception:
                 return True if input > value else False
         elif operator == "<":
             try:
                 return True if float(input) < float(value) else False
-            except Exception as e:
+            except Exception:
                 return True if input < value else False
         elif operator == "≥":
             try:
                 return True if float(input) >= float(value) else False
-            except Exception as e:
+            except Exception:
                 return True if input >= value else False
         elif operator == "≤":
             try:
                 return True if float(input) <= float(value) else False
-            except Exception as e:
+            except Exception:
                 return True if input <= value else False
 
         raise ValueError('Not supported operator' + operator)
+
+    def thoughts(self) -> str:
+        return "I’m weighing a few options and will pick the next step shortly."

agent/component/varaiable_aggregator.py

@@ -0,0 +1,84 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+from typing import Any
+import os
+
+from common.connection_utils import timeout
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class VariableAggregatorParam(ComponentParamBase):
+    """
+    Parameters for VariableAggregator
+
+    - groups: list of dicts {"group_name": str, "variables": [variable selectors]}
+    """
+
+    def __init__(self):
+        super().__init__()
+        # each group expects: {"group_name": str, "variables": List[str]}
+        self.groups = []
+
+    def check(self):
+        self.check_empty(self.groups, "[VariableAggregator] groups")
+        for g in self.groups:
+            if not g.get("group_name"):
+                raise ValueError("[VariableAggregator] group_name can not be empty!")
+            if not g.get("variables"):
+                raise ValueError(
+                    f"[VariableAggregator] variables of group `{g.get('group_name')}` can not be empty"
+                )
+            if not isinstance(g.get("variables"), list):
+                raise ValueError(
+                    f"[VariableAggregator] variables of group `{g.get('group_name')}` should be a list of strings"
+                )
+
+    def get_input_form(self) -> dict[str, dict]:
+        return {
+            "variables": {
+                "name": "Variables",
+                "type": "list",
+            }
+        }
+
+
+class VariableAggregator(ComponentBase):
+    component_name = "VariableAggregator"
+
+    @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 3)))
+    def _invoke(self, **kwargs):
+        # Group mode: for each group, pick the first available variable
+        for group in self._param.groups:
+            gname = group.get("group_name")
+
+            # record candidate selectors within this group
+            self.set_input_value(f"{gname}.variables", list(group.get("variables", [])))
+            for selector in group.get("variables", []):
+                val = self._canvas.get_variable_value(selector['value'])
+                if val:
+                    self.set_output(gname, val)
+                    break
+            
+    @staticmethod
+    def _to_object(value: Any) -> Any:
+        # Try to convert value to serializable object if it has to_object()
+        try:
+            return value.to_object()  # type: ignore[attr-defined]
+        except Exception:
+            return value
+
+    def thoughts(self) -> str:
+        return "Aggregating variables from canvas and grouping as configured."

agent/component/variable_assigner.py

@@ -0,0 +1,192 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from abc import ABC
+import os
+import numbers
+from agent.component.base import ComponentBase, ComponentParamBase
+from api.utils.api_utils import timeout
+
+class VariableAssignerParam(ComponentParamBase):
+    """
+    Define the Variable Assigner component parameters.
+    """
+    def __init__(self):
+        super().__init__()
+        self.variables=[]
+
+    def check(self):
+        return True
+    
+    def get_input_form(self) -> dict[str, dict]:
+        return {
+            "items": {
+                "type": "json",
+                "name": "Items"
+            }
+        }
+
+class VariableAssigner(ComponentBase,ABC):
+    component_name = "VariableAssigner"
+
+    @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 10*60)))
+    def _invoke(self, **kwargs):
+        if not isinstance(self._param.variables,list):
+            return
+        else:
+            for item in self._param.variables:
+                if any([not item.get("variable"), not item.get("operator"), not item.get("parameter")]):
+                    assert "Variable is not complete."
+                variable=item["variable"]
+                operator=item["operator"]
+                parameter=item["parameter"]
+                variable_value=self._canvas.get_variable_value(variable)
+                new_variable=self._operate(variable_value,operator,parameter)
+                self._canvas.set_variable_value(variable, new_variable)
+
+    def _operate(self,variable,operator,parameter):
+        if operator == "overwrite":
+            return self._overwrite(parameter)
+        elif operator == "clear":
+            return self._clear(variable)
+        elif operator == "set":
+            return self._set(variable,parameter)
+        elif operator == "append":
+            return self._append(variable,parameter)
+        elif operator == "extend":
+            return self._extend(variable,parameter)
+        elif operator == "remove_first":
+            return self._remove_first(variable)
+        elif operator == "remove_last":
+            return self._remove_last(variable)
+        elif operator == "+=":
+            return self._add(variable,parameter)
+        elif operator == "-=":
+            return self._subtract(variable,parameter)
+        elif operator == "*=":
+            return self._multiply(variable,parameter)
+        elif operator == "/=":
+            return self._divide(variable,parameter)
+        else:
+            return
+    
+    def _overwrite(self,parameter):
+        return self._canvas.get_variable_value(parameter)
+
+    def _clear(self,variable):
+        if isinstance(variable,list):
+            return []
+        elif isinstance(variable,str):
+            return ""
+        elif isinstance(variable,dict):
+            return {}
+        elif isinstance(variable,int):
+            return 0
+        elif isinstance(variable,float):
+            return 0.0
+        elif isinstance(variable,bool):
+            return False
+        else:
+            return None
+
+    def _set(self,variable,parameter):
+        if variable is None:
+            return self._canvas.get_value_with_variable(parameter)
+        elif isinstance(variable,str):
+            return self._canvas.get_value_with_variable(parameter)
+        elif isinstance(variable,bool):
+            return parameter
+        elif isinstance(variable,int):
+            return parameter
+        elif isinstance(variable,float):
+            return parameter
+        else:
+            return parameter
+
+    def _append(self,variable,parameter):
+        parameter=self._canvas.get_variable_value(parameter)
+        if variable is None:
+            variable=[]
+        if not isinstance(variable,list):
+            return "ERROR:VARIABLE_NOT_LIST"
+        elif len(variable)!=0 and not isinstance(parameter,type(variable[0])):
+            return "ERROR:PARAMETER_NOT_LIST_ELEMENT_TYPE"
+        else:
+            variable.append(parameter)
+            return variable
+
+    def _extend(self,variable,parameter):
+        parameter=self._canvas.get_variable_value(parameter)
+        if variable is None:
+            variable=[]
+        if not isinstance(variable,list):
+            return "ERROR:VARIABLE_NOT_LIST"
+        elif not isinstance(parameter,list):
+            return "ERROR:PARAMETER_NOT_LIST"
+        elif len(variable)!=0 and len(parameter)!=0 and not isinstance(parameter[0],type(variable[0])):
+            return "ERROR:PARAMETER_NOT_LIST_ELEMENT_TYPE"
+        else:
+            return variable + parameter
+
+    def _remove_first(self,variable):
+        if len(variable)==0:
+            return variable
+        if not isinstance(variable,list):
+            return "ERROR:VARIABLE_NOT_LIST"
+        else:
+            return variable[1:]
+
+    def _remove_last(self,variable):
+        if len(variable)==0:
+            return variable
+        if not isinstance(variable,list):
+            return "ERROR:VARIABLE_NOT_LIST"
+        else:
+            return variable[:-1]
+
+    def is_number(self, value):
+        if isinstance(value, bool):
+            return False
+        return isinstance(value, numbers.Number)
+
+    def _add(self,variable,parameter):
+        if self.is_number(variable) and self.is_number(parameter):
+            return variable + parameter
+        else:
+            return "ERROR:VARIABLE_NOT_NUMBER or PARAMETER_NOT_NUMBER"
+
+    def _subtract(self,variable,parameter):
+        if self.is_number(variable) and self.is_number(parameter):
+            return variable - parameter
+        else:
+            return "ERROR:VARIABLE_NOT_NUMBER or PARAMETER_NOT_NUMBER"
+
+    def _multiply(self,variable,parameter):
+        if self.is_number(variable) and self.is_number(parameter):
+            return variable * parameter
+        else:
+            return "ERROR:VARIABLE_NOT_NUMBER or PARAMETER_NOT_NUMBER"
+
+    def _divide(self,variable,parameter):
+        if self.is_number(variable) and self.is_number(parameter):
+            if  parameter==0:
+                return "ERROR:DIVIDE_BY_ZERO"
+            else:
+                return variable/parameter
+        else:
+            return  "ERROR:VARIABLE_NOT_NUMBER or PARAMETER_NOT_NUMBER"
+
+    def thoughts(self) -> str:
+        return "Assign variables from canvas."

agent/component/webhook.py

@@ -0,0 +1,38 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from agent.component.base import ComponentParamBase, ComponentBase
+
+
+class WebhookParam(ComponentParamBase):
+
+    """
+    Define the Begin component parameters.
+    """
+    def __init__(self):
+        super().__init__()
+
+    def get_input_form(self) -> dict[str, dict]:
+        return getattr(self, "inputs")
+
+
+class Webhook(ComponentBase):
+    component_name = "Webhook"
+
+    def _invoke(self, **kwargs):
+        pass
+
+    def thoughts(self) -> str:
+        return ""

agent/component/wencai.py

@@ -1,74 +0,0 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from abc import ABC
-import pandas as pd
-import pywencai
-from agent.component.base import ComponentBase, ComponentParamBase
-
-
-class WenCaiParam(ComponentParamBase):
-    """
-    Define the WenCai component parameters.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.top_n = 10
-        self.query_type = "stock"
-
-    def check(self):
-        self.check_positive_integer(self.top_n, "Top N")
-        self.check_valid_value(self.query_type, "Query type",
-                               ['stock', 'zhishu', 'fund', 'hkstock', 'usstock', 'threeboard', 'conbond', 'insurance',
-                                'futures', 'lccp',
-                                'foreign_exchange'])
-
-
-class WenCai(ComponentBase, ABC):
-    component_name = "WenCai"
-
-    def _run(self, history, **kwargs):
-        ans = self.get_input()
-        ans = ",".join(ans["content"]) if "content" in ans else ""
-        if not ans:
-            return WenCai.be_output("")
-
-        try:
-            wencai_res = []
-            res = pywencai.get(query=ans, query_type=self._param.query_type, perpage=self._param.top_n)
-            if isinstance(res, pd.DataFrame):
-                wencai_res.append({"content": res.to_markdown()})
-            if isinstance(res, dict):
-                for item in res.items():
-                    if isinstance(item[1], list):
-                        wencai_res.append({"content": item[0] + "\n" + pd.DataFrame(item[1]).to_markdown()})
-                        continue
-                    if isinstance(item[1], str):
-                        wencai_res.append({"content": item[0] + "\n" + item[1]})
-                        continue
-                    if isinstance(item[1], dict):
-                        if "meta" in item[1].keys():
-                            continue
-                        wencai_res.append({"content": pd.DataFrame.from_dict(item[1], orient='index').to_markdown()})
-                        continue
-                    wencai_res.append({"content": item[0] + "\n" + str(item[1])})
-        except Exception as e:
-            return WenCai.be_output("**ERROR**: " + str(e))
-
-        if not wencai_res:
-            return WenCai.be_output("")
-
-        return pd.DataFrame(wencai_res)

agent/component/wikipedia.py

@@ -1,69 +0,0 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import random
-from abc import ABC
-from functools import partial
-import wikipedia
-import pandas as pd
-from agent.settings import DEBUG
-from agent.component.base import ComponentBase, ComponentParamBase
-
-
-class WikipediaParam(ComponentParamBase):
-    """
-    Define the Wikipedia component parameters.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.top_n = 10
-        self.language = "en"
-
-    def check(self):
-        self.check_positive_integer(self.top_n, "Top N")
-        self.check_valid_value(self.language, "Wikipedia languages",
-                               ['af', 'pl', 'ar', 'ast', 'az', 'bg', 'nan', 'bn', 'be', 'ca', 'cs', 'cy', 'da', 'de',
-                                'et', 'el', 'en', 'es', 'eo', 'eu', 'fa', 'fr', 'gl', 'ko', 'hy', 'hi', 'hr', 'id',
-                                'it', 'he', 'ka', 'lld', 'la', 'lv', 'lt', 'hu', 'mk', 'arz', 'ms', 'min', 'my', 'nl',
-                                'ja', 'nb', 'nn', 'ce', 'uz', 'pt', 'kk', 'ro', 'ru', 'ceb', 'sk', 'sl', 'sr', 'sh',
-                                'fi', 'sv', 'ta', 'tt', 'th', 'tg', 'azb', 'tr', 'uk', 'ur', 'vi', 'war', 'zh', 'yue'])
-
-
-class Wikipedia(ComponentBase, ABC):
-    component_name = "Wikipedia"
-
-    def _run(self, history, **kwargs):
-        ans = self.get_input()
-        ans = " - ".join(ans["content"]) if "content" in ans else ""
-        if not ans:
-            return Wikipedia.be_output("")
-
-        try:
-            wiki_res = []
-            wikipedia.set_lang(self._param.language)
-            wiki_engine = wikipedia
-            for wiki_key in wiki_engine.search(ans, results=self._param.top_n):
-                page = wiki_engine.page(title=wiki_key, auto_suggest=False)
-                wiki_res.append({"content": '<a href="' + page.url + '">' + page.title + '</a> ' + page.summary})
-        except Exception as e:
-            return Wikipedia.be_output("**ERROR**: " + str(e))
-
-        if not wiki_res:
-            return Wikipedia.be_output("")
-
-        df = pd.DataFrame(wiki_res)
-        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
-        return df

agent/component/yahoofinance.py

@@ -1,83 +0,0 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-from abc import ABC
-import pandas as pd
-from agent.component.base import ComponentBase, ComponentParamBase
-import yfinance as yf
-
-
-class YahooFinanceParam(ComponentParamBase):
-    """
-    Define the YahooFinance component parameters.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.info = True
-        self.history = False
-        self.count = False
-        self.financials = False
-        self.income_stmt = False
-        self.balance_sheet = False
-        self.cash_flow_statement = False
-        self.news = True
-
-    def check(self):
-        self.check_boolean(self.info, "get all stock info")
-        self.check_boolean(self.history, "get historical market data")
-        self.check_boolean(self.count, "show share count")
-        self.check_boolean(self.financials, "show financials")
-        self.check_boolean(self.income_stmt, "income statement")
-        self.check_boolean(self.balance_sheet, "balance sheet")
-        self.check_boolean(self.cash_flow_statement, "cash flow statement")
-        self.check_boolean(self.news, "show news")
-
-
-class YahooFinance(ComponentBase, ABC):
-    component_name = "YahooFinance"
-
-    def _run(self, history, **kwargs):
-        ans = self.get_input()
-        ans = "".join(ans["content"]) if "content" in ans else ""
-        if not ans:
-            return YahooFinance.be_output("")
-
-        yohoo_res = []
-        try:
-            msft = yf.Ticker(ans)
-            if self._param.info:
-                yohoo_res.append({"content": "info:\n" + pd.Series(msft.info).to_markdown() + "\n"})
-            if self._param.history:
-                yohoo_res.append({"content": "history:\n" + msft.history().to_markdown() + "\n"})
-            if self._param.financials:
-                yohoo_res.append({"content": "calendar:\n" + pd.DataFrame(msft.calendar).to_markdown() + "\n"})
-            if self._param.balance_sheet:
-                yohoo_res.append({"content": "balance sheet:\n" + msft.balance_sheet.to_markdown() + "\n"})
-                yohoo_res.append(
-                    {"content": "quarterly balance sheet:\n" + msft.quarterly_balance_sheet.to_markdown() + "\n"})
-            if self._param.cash_flow_statement:
-                yohoo_res.append({"content": "cash flow statement:\n" + msft.cashflow.to_markdown() + "\n"})
-                yohoo_res.append(
-                    {"content": "quarterly cash flow statement:\n" + msft.quarterly_cashflow.to_markdown() + "\n"})
-            if self._param.news:
-                yohoo_res.append({"content": "news:\n" + pd.DataFrame(msft.news).to_markdown() + "\n"})
-        except Exception as e:
-            print("**ERROR** " + str(e))
-
-        if not yohoo_res:
-            return YahooFinance.be_output("")
-
-        return pd.DataFrame(yohoo_res)

agent/settings.py

@@ -1,5 +1,5 @@
 #
-#  Copyright 2019 The FATE Authors. All Rights Reserved.
+#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
 #  you may not use this file except in compliance with the License.
@@ -13,22 +13,6 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-# Logger
-import os
 
-from api.utils.file_utils import get_project_base_directory
-from api.utils.log_utils import LoggerFactory, getLogger
-
-DEBUG = 0
-LoggerFactory.set_directory(
-    os.path.join(
-        get_project_base_directory(),
-        "logs",
-        "flow"))
-# {CRITICAL: 50, FATAL:50, ERROR:40, WARNING:30, WARN:30, INFO:20, DEBUG:10, NOTSET:0}
-LoggerFactory.LEVEL = 30
-
-flow_logger = getLogger("flow")
-database_logger = getLogger("database")
 FLOAT_ZERO = 1e-8
 PARAM_MAXDEPTH = 5

agent/templates/generate_SEO_blog.json

@@ -0,0 +1,908 @@
+{
+    "id": 8,
+    "title": {
+        "en": "Generate SEO Blog",
+        "de": "SEO Blog generieren",
+        "zh": "生成SEO博客"},
+    "description": {
+        "en": "This is a multi-agent version of the SEO blog generation workflow. It simulates a small team of AI “writers”, where each agent plays a specialized role — just like a real editorial team.",
+        "de": "Dies ist eine Multi-Agenten-Version des Workflows zur Erstellung von SEO-Blogs. Sie simuliert ein kleines Team von KI-„Autoren“, in dem jeder Agent eine spezielle Rolle übernimmt – genau wie in einem echten Redaktionsteam.",
+        "zh": "多智能体架构可根据简单的用户输入自动生成完整的SEO博客文章。模拟小型“作家”团队，其中每个智能体扮演一个专业角色——就像真正的编辑团队。"},
+    "canvas_type": "Agent",
+    "dsl": {
+            "components": {
+                "Agent:LuckyApplesGrab": {
+                    "downstream": [
+                        "Message:ModernSwansThrow"
+                    ],
+                    "obj": {
+                        "component_name": "Agent",
+                        "params": {
+                            "delay_after_error": 1,
+                            "description": "",
+                            "exception_comment": "",
+                            "exception_default_value": "",
+                            "exception_goto": [],
+                            "exception_method": null,
+                            "frequencyPenaltyEnabled": false,
+                            "frequency_penalty": 0.5,
+                            "llm_id": "deepseek-chat@DeepSeek",
+                            "maxTokensEnabled": false,
+                            "max_retries": 3,
+                            "max_rounds": 3,
+                            "max_tokens": 4096,
+                            "mcp": [],
+                            "message_history_window_size": 12,
+                            "outputs": {
+                                "content": {
+                                    "type": "string",
+                                    "value": ""
+                                }
+                            },
+                            "parameter": "Precise",
+                            "presencePenaltyEnabled": false,
+                            "presence_penalty": 0.5,
+                            "prompts": [
+                                {
+                                    "content": "The user query is {sys.query}",
+                                    "role": "user"
+                                }
+                            ],
+                            "sys_prompt": "# Role\n\nYou are the **Lead Agent**, responsible for initiating the multi-agent SEO blog generation process. You will receive the user\u2019s topic and blog goal, interpret the intent, and coordinate the downstream writing agents.\n\n# Goals\n\n1. Parse the user's initial input.\n\n2. Generate a high-level blog intent summary and writing plan.\n\n3. Provide clear instructions to the following Sub_Agents:\n\n   - `Outline Agent` \u2192 Create the blog outline.\n\n   - `Body Agent` \u2192 Write all sections based on outline.\n\n   - `Editor Agent` \u2192 Polish and finalize the blog post.\n\n4. Merge outputs into a complete, readable blog draft in Markdown format.\n\n# Input\n\nYou will receive:\n\n- Blog topic\n\n- Target audience\n\n- Blog goal (e.g., SEO, education, product marketing)\n\n# Output Format\n\n```markdown\n\n## Parsed Writing Plan\n\n- **Topic**: [Extracted from user input]\n\n- **Audience**: [Summarized from user input]\n\n- **Intent**: [Inferred goal and style]\n\n- **Blog Type**: [e.g., Tutorial / Informative Guide / Marketing Content]\n\n- **Long-tail Keywords**: \n\n  - keyword 1\n\n  - keyword 2\n\n  - keyword 3\n\n  - ...\n\n## Instructions for Outline Agent\n\nPlease generate a structured outline including H2 and H3 headings. Assign 1\u20132 relevant keywords to each section. Keep it aligned with the user\u2019s intent and audience level.\n\n## Instructions for Body Agent\n\nWrite the full content based on the outline. Each section should be concise (500\u2013600 words), informative, and optimized for SEO. Use `Tavily Search` only when additional examples or context are needed.\n\n## Instructions for Editor Agent\n\nReview and refine the combined content. Improve transitions, ensure keyword integration, and add a meta title + meta description. Maintain Markdown formatting.\n\n\n## Guides\n\n- Do not generate blog content directly.\n\n- Focus on correct intent recognition and instruction generation.\n\n- Keep communication to downstream agents simple, scoped, and accurate.\n\n\n## Input Examples (and how to handle them)\n\nInput: \"I want to write about RAGFlow.\"\n\u2192 Output: Informative Guide, Audience: AI developers, Intent: explain what RAGFlow is and its use cases\n\nInput: \"Need a blog to promote our prompt design tool.\"\n\u2192 Output: Marketing Content, Audience: product managers or tool adopters, Intent: raise awareness and interest in the product\n\nInput: \"How to get more Google traffic using AI\"\n\u2192 Output: How-to, Audience: SEO marketers, Intent: guide readers on applying AI for SEO growth",
+                            "temperature": "0.1",
+                            "temperatureEnabled": true,
+                            "tools": [
+                                {
+                                    "component_name": "Agent",
+                                    "id": "Agent:SlickSpidersTurn",
+                                    "name": "Outline Agent",
+                                    "params": {
+                                        "delay_after_error": 1,
+                                        "description": "Generates a clear and SEO-friendly blog outline using H2/H3 headings based on the topic, audience, and intent provided by the lead agent. Each section includes suggested keywords for optimized downstream writing.\n",
+                                        "exception_comment": "",
+                                        "exception_default_value": "",
+                                        "exception_goto": [],
+                                        "exception_method": null,
+                                        "frequencyPenaltyEnabled": false,
+                                        "frequency_penalty": 0.3,
+                                        "llm_id": "deepseek-chat@DeepSeek",
+                                        "maxTokensEnabled": false,
+                                        "max_retries": 3,
+                                        "max_rounds": 2,
+                                        "max_tokens": 4096,
+                                        "mcp": [],
+                                        "message_history_window_size": 12,
+                                        "outputs": {
+                                            "content": {
+                                                "type": "string",
+                                                "value": ""
+                                            }
+                                        },
+                                        "parameter": "Balance",
+                                        "presencePenaltyEnabled": false,
+                                        "presence_penalty": 0.2,
+                                        "prompts": [
+                                            {
+                                                "content": "{sys.query}",
+                                                "role": "user"
+                                            }
+                                        ],
+                                        "sys_prompt": "# Role\n\nYou are the **Outline Agent**, a sub-agent in a multi-agent SEO blog writing system. You operate under the instruction of the `Lead Agent`, and your sole responsibility is to create a clear, well-structured, and SEO-optimized blog outline.\n\n# Tool Access:\n\n- You have access to a search tool called `Tavily Search`.\n\n- If you are unsure how to structure a section, you may call this tool to search for related blog outlines or content from Google.\n\n- Do not overuse it. Your job is to extract **structure**, not to write paragraphs.\n\n\n# Goals\n\n1. Create a well-structured outline with appropriate H2 and H3 headings.\n\n2. Ensure logical flow from introduction to conclusion.\n\n3. Assign 1\u20132 suggested long-tail keywords to each major section for SEO alignment.\n\n4. Make the structure suitable for downstream paragraph writing.\n\n\n\n\n#Note\n\n- Use concise, scannable section titles.\n\n- Do not write full paragraphs.\n\n- Prioritize clarity, logical progression, and SEO alignment.\n\n\n\n- If the blog type is \u201cTutorial\u201d or \u201cHow-to\u201d, include step-based sections.\n\n\n# Input\n\nYou will receive:\n\n- Writing Type (e.g., Tutorial, Informative Guide)\n\n- Target Audience\n\n- User Intent Summary\n\n- 3\u20135 long-tail keywords\n\n\nUse this information to design a structure that both informs readers and maximizes search engine visibility.\n\n# Output Format\n\n```markdown\n\n## Blog Title (suggested)\n\n[Give a short, SEO-friendly title suggestion]\n\n## Outline\n\n### Introduction\n\n- Purpose of the article\n\n- Brief context\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 1]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 2]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 3]\n\n- [Optional H3 Subsection Title A]\n\n  - [Explanation of sub-point]\n\n- [Optional H3 Subsection Title B]\n\n  - [Explanation of sub-point]\n\n- **Suggested keywords**: [keyword1]\n\n### Conclusion\n\n- Recap key takeaways\n\n- Optional CTA (Call to Action)\n\n- **Suggested keywords**: [keyword3]\n\n",
+                                        "temperature": 0.5,
+                                        "temperatureEnabled": true,
+                                        "tools": [
+                                            {
+                                                "component_name": "TavilySearch",
+                                                "name": "TavilySearch",
+                                                "params": {
+                                                    "api_key": "",
+                                                    "days": 7,
+                                                    "exclude_domains": [],
+                                                    "include_answer": false,
+                                                    "include_domains": [],
+                                                    "include_image_descriptions": false,
+                                                    "include_images": false,
+                                                    "include_raw_content": true,
+                                                    "max_results": 5,
+                                                    "outputs": {
+                                                        "formalized_content": {
+                                                            "type": "string",
+                                                            "value": ""
+                                                        },
+                                                        "json": {
+                                                            "type": "Array<Object>",
+                                                            "value": []
+                                                        }
+                                                    },
+                                                    "query": "sys.query",
+                                                    "search_depth": "basic",
+                                                    "topic": "general"
+                                                }
+                                            }
+                                        ],
+                                        "topPEnabled": false,
+                                        "top_p": 0.85,
+                                        "user_prompt": "This is the order you need to send to the agent.",
+                                        "visual_files_var": ""
+                                    }
+                                },
+                                {
+                                    "component_name": "Agent",
+                                    "id": "Agent:IcyPawsRescue",
+                                    "name": "Body Agent",
+                                    "params": {
+                                        "delay_after_error": 1,
+                                        "description": "Writes the full blog content section-by-section following the outline structure. It integrates target keywords naturally and uses Tavily Search only when additional facts or examples are needed.\n",
+                                        "exception_comment": "",
+                                        "exception_default_value": "",
+                                        "exception_goto": [],
+                                        "exception_method": null,
+                                        "frequencyPenaltyEnabled": false,
+                                        "frequency_penalty": 0.5,
+                                        "llm_id": "deepseek-chat@DeepSeek",
+                                        "maxTokensEnabled": false,
+                                        "max_retries": 3,
+                                        "max_rounds": 3,
+                                        "max_tokens": 4096,
+                                        "mcp": [],
+                                        "message_history_window_size": 12,
+                                        "outputs": {
+                                            "content": {
+                                                "type": "string",
+                                                "value": ""
+                                            }
+                                        },
+                                        "parameter": "Precise",
+                                        "presencePenaltyEnabled": false,
+                                        "presence_penalty": 0.5,
+                                        "prompts": [
+                                            {
+                                                "content": "{sys.query}",
+                                                "role": "user"
+                                            }
+                                        ],
+                                        "sys_prompt": "# Role\n\nYou are the **Body Agent**, a sub-agent in a multi-agent SEO blog writing system. You operate under the instruction of the `Lead Agent`, and your job is to write the full blog content based on the outline created by the `OutlineWriter_Agent`.\n\n\n\n# Tool Access:\n\nYou can use the `Tavily Search` tool to retrieve relevant content, statistics, or examples to support each section you're writing.\n\nUse it **only** when the provided outline lacks enough information, or if the section requires factual grounding.\n\nAlways cite the original link or indicate source where possible.\n\n\n# Goals\n\n1. Write each section (based on H2/H3 structure) as a complete and natural blog paragraph.\n\n2. Integrate the suggested long-tail keywords naturally into each section.\n\n3. When appropriate, use the `Tavily Search` tool to enrich your writing with relevant facts, examples, or quotes.\n\n4. Ensure each section is clear, engaging, and informative, suitable for both human readers and search engines.\n\n\n# Style Guidelines\n\n- Write in a tone appropriate to the audience. Be explanatory, not promotional, unless it's a marketing blog.\n\n- Avoid generic filler content. Prioritize clarity, structure, and value.\n\n- Ensure SEO keywords are embedded seamlessly, not forcefully.\n\n\n\n- Maintain writing rhythm. Vary sentence lengths. Use transitions between ideas.\n\n\n# Input\n\n\nYou will receive:\n\n- Blog title\n\n- Structured outline (including section titles, keywords, and descriptions)\n\n- Target audience\n\n- Blog type and user intent\n\nYou must **follow the outline strictly**. Write content **section-by-section**, based on the structure.\n\n\n# Output Format\n\n```markdown\n\n## H2: [Section Title]\n\n[Your generated content for this section \u2014 500-600 words, using keywords naturally.]\n\n",
+                                        "temperature": 0.2,
+                                        "temperatureEnabled": true,
+                                        "tools": [
+                                            {
+                                                "component_name": "TavilySearch",
+                                                "name": "TavilySearch",
+                                                "params": {
+                                                    "api_key": "",
+                                                    "days": 7,
+                                                    "exclude_domains": [],
+                                                    "include_answer": false,
+                                                    "include_domains": [],
+                                                    "include_image_descriptions": false,
+                                                    "include_images": false,
+                                                    "include_raw_content": true,
+                                                    "max_results": 5,
+                                                    "outputs": {
+                                                        "formalized_content": {
+                                                            "type": "string",
+                                                            "value": ""
+                                                        },
+                                                        "json": {
+                                                            "type": "Array<Object>",
+                                                            "value": []
+                                                        }
+                                                    },
+                                                    "query": "sys.query",
+                                                    "search_depth": "basic",
+                                                    "topic": "general"
+                                                }
+                                            }
+                                        ],
+                                        "topPEnabled": false,
+                                        "top_p": 0.75,
+                                        "user_prompt": "This is the order you need to send to the agent.",
+                                        "visual_files_var": ""
+                                    }
+                                },
+                                {
+                                    "component_name": "Agent",
+                                    "id": "Agent:TenderAdsAllow",
+                                    "name": "Editor Agent",
+                                    "params": {
+                                        "delay_after_error": 1,
+                                        "description": "Polishes and finalizes the entire blog post. Enhances clarity, checks keyword usage, improves flow, and generates a meta title and description for SEO. Operates after all sections are completed.\n\n",
+                                        "exception_comment": "",
+                                        "exception_default_value": "",
+                                        "exception_goto": [],
+                                        "exception_method": null,
+                                        "frequencyPenaltyEnabled": false,
+                                        "frequency_penalty": 0.5,
+                                        "llm_id": "deepseek-chat@DeepSeek",
+                                        "maxTokensEnabled": false,
+                                        "max_retries": 3,
+                                        "max_rounds": 2,
+                                        "max_tokens": 4096,
+                                        "mcp": [],
+                                        "message_history_window_size": 12,
+                                        "outputs": {
+                                            "content": {
+                                                "type": "string",
+                                                "value": ""
+                                            }
+                                        },
+                                        "parameter": "Precise",
+                                        "presencePenaltyEnabled": false,
+                                        "presence_penalty": 0.5,
+                                        "prompts": [
+                                            {
+                                                "content": "{sys.query}",
+                                                "role": "user"
+                                            }
+                                        ],
+                                        "sys_prompt": "# Role\n\nYou are the **Editor Agent**, the final agent in a multi-agent SEO blog writing workflow. You are responsible for finalizing the blog post for both human readability and SEO effectiveness.\n\n# Goals\n\n1. Polish the entire blog content for clarity, coherence, and style.\n\n2. Improve transitions between sections, ensure logical flow.\n\n3. Verify that keywords are used appropriately and effectively.\n\n4. Conduct a lightweight SEO audit \u2014 checking keyword density, structure (H1/H2/H3), and overall searchability.\n\n\n\n## Integration Responsibilities\n\n- Maintain alignment with Lead Agent's original intent and audience\n\n- Preserve the structure and keyword strategy from Outline Agent\n\n- Enhance and polish Body Agent's content without altering core information\n\n# Style Guidelines\n\n- Be precise. Avoid bloated or vague language.\n\n- Maintain an informative and engaging tone, suitable to the target audience.\n\n- Do not remove keywords unless absolutely necessary for clarity.\n\n- Ensure paragraph flow and section continuity.\n\n\n\n# Input\n\nYou will receive:\n\n- Full blog content, written section-by-section\n\n- Original outline with suggested keywords\n\n- Target audience and writing type\n\n# Output Format\n\n```markdown\n\n[The revised, fully polished blog post content goes here.]\n",
+                                        "temperature": 0.2,
+                                        "temperatureEnabled": true,
+                                        "tools": [],
+                                        "topPEnabled": false,
+                                        "top_p": 0.75,
+                                        "user_prompt": "This is the order you need to send to the agent.",
+                                        "visual_files_var": ""
+                                    }
+                                }
+                            ],
+                            "topPEnabled": false,
+                            "top_p": 0.75,
+                            "user_prompt": "",
+                            "visual_files_var": ""
+                        }
+                    },
+                    "upstream": [
+                        "begin"
+                    ]
+                },
+                "Message:ModernSwansThrow": {
+                    "downstream": [],
+                    "obj": {
+                        "component_name": "Message",
+                        "params": {
+                            "content": [
+                                "{Agent:LuckyApplesGrab@content}"
+                            ]
+                        }
+                    },
+                    "upstream": [
+                        "Agent:LuckyApplesGrab"
+                    ]
+                },
+                "begin": {
+                    "downstream": [
+                        "Agent:LuckyApplesGrab"
+                    ],
+                    "obj": {
+                        "component_name": "Begin",
+                        "params": {
+                            "enablePrologue": true,
+                            "inputs": {},
+                            "mode": "conversational",
+                            "prologue": "Hi! I'm your SEO blog assistant.\n\nTo get started, please tell me:\n1. What topic you want the blog to cover\n2. Who is the target audience\n3. What you hope to achieve with this blog (e.g., SEO traffic, teaching beginners, promoting a product)\n"
+                        }
+                    },
+                    "upstream": []
+                }
+            },
+            "globals": {
+                "sys.conversation_turns": 0,
+                "sys.files": [],
+                "sys.query": "",
+                "sys.user_id": ""
+            },
+            "graph": {
+                "edges": [
+                    {
+                        "data": {
+                            "isHovered": false
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+                        "id": "xy-edge__beginstart-Agent:LuckyApplesGrabend",
+                        "source": "begin",
+                        "sourceHandle": "start",
+                        "target": "Agent:LuckyApplesGrab",
+                        "targetHandle": "end"
+                    },
+                    {
+                        "data": {
+                            "isHovered": false
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+                        "id": "xy-edge__Agent:LuckyApplesGrabstart-Message:ModernSwansThrowend",
+                        "source": "Agent:LuckyApplesGrab",
+                        "sourceHandle": "start",
+                        "target": "Message:ModernSwansThrow",
+                        "targetHandle": "end"
+                    },
+                    {
+                        "data": {
+                            "isHovered": false
+                        },
+                        "id": "xy-edge__Agent:LuckyApplesGrabagentBottom-Agent:SlickSpidersTurnagentTop",
+                        "source": "Agent:LuckyApplesGrab",
+                        "sourceHandle": "agentBottom",
+                        "target": "Agent:SlickSpidersTurn",
+                        "targetHandle": "agentTop"
+                    },
+                    {
+                        "data": {
+                            "isHovered": false
+                        },
+                        "id": "xy-edge__Agent:LuckyApplesGrabagentBottom-Agent:IcyPawsRescueagentTop",
+                        "source": "Agent:LuckyApplesGrab",
+                        "sourceHandle": "agentBottom",
+                        "target": "Agent:IcyPawsRescue",
+                        "targetHandle": "agentTop"
+                    },
+                    {
+                        "data": {
+                            "isHovered": false
+                        },
+                        "id": "xy-edge__Agent:LuckyApplesGrabagentBottom-Agent:TenderAdsAllowagentTop",
+                        "source": "Agent:LuckyApplesGrab",
+                        "sourceHandle": "agentBottom",
+                        "target": "Agent:TenderAdsAllow",
+                        "targetHandle": "agentTop"
+                    },
+                    {
+                        "data": {
+                            "isHovered": false
+                        },
+                        "id": "xy-edge__Agent:SlickSpidersTurntool-Tool:ThreeWallsRingend",
+                        "source": "Agent:SlickSpidersTurn",
+                        "sourceHandle": "tool",
+                        "target": "Tool:ThreeWallsRing",
+                        "targetHandle": "end"
+                    },
+                    {
+                        "data": {
+                            "isHovered": false
+                        },
+                        "id": "xy-edge__Agent:IcyPawsRescuetool-Tool:FloppyJokesItchend",
+                        "source": "Agent:IcyPawsRescue",
+                        "sourceHandle": "tool",
+                        "target": "Tool:FloppyJokesItch",
+                        "targetHandle": "end"
+                    }
+                ],
+                "nodes": [
+                    {
+                        "data": {
+                            "form": {
+                                "enablePrologue": true,
+                                "inputs": {},
+                                "mode": "conversational",
+                                "prologue": "Hi! I'm your SEO blog assistant.\n\nTo get started, please tell me:\n1. What topic you want the blog to cover\n2. Who is the target audience\n3. What you hope to achieve with this blog (e.g., SEO traffic, teaching beginners, promoting a product)\n"
+                            },
+                            "label": "Begin",
+                            "name": "begin"
+                        },
+                        "dragging": false,
+                        "id": "begin",
+                        "measured": {
+                            "height": 48,
+                            "width": 200
+                        },
+                        "position": {
+                            "x": 38.19445084117184,
+                            "y": 183.9781832844475
+                        },
+                        "selected": false,
+                        "sourcePosition": "left",
+                        "targetPosition": "right",
+                        "type": "beginNode"
+                    },
+                    {
+                        "data": {
+                            "form": {
+                                "delay_after_error": 1,
+                                "description": "",
+                                "exception_comment": "",
+                                "exception_default_value": "",
+                                "exception_goto": [],
+                                "exception_method": null,
+                                "frequencyPenaltyEnabled": false,
+                                "frequency_penalty": 0.5,
+                                "llm_id": "deepseek-chat@DeepSeek",
+                                "maxTokensEnabled": false,
+                                "max_retries": 3,
+                                "max_rounds": 3,
+                                "max_tokens": 4096,
+                                "mcp": [],
+                                "message_history_window_size": 12,
+                                "outputs": {
+                                    "content": {
+                                        "type": "string",
+                                        "value": ""
+                                    }
+                                },
+                                "parameter": "Precise",
+                                "presencePenaltyEnabled": false,
+                                "presence_penalty": 0.5,
+                                "prompts": [
+                                    {
+                                        "content": "The user query is {sys.query}",
+                                        "role": "user"
+                                    }
+                                ],
+                                "sys_prompt": "# Role\n\nYou are the **Lead Agent**, responsible for initiating the multi-agent SEO blog generation process. You will receive the user\u2019s topic and blog goal, interpret the intent, and coordinate the downstream writing agents.\n\n# Goals\n\n1. Parse the user's initial input.\n\n2. Generate a high-level blog intent summary and writing plan.\n\n3. Provide clear instructions to the following Sub_Agents:\n\n   - `Outline Agent` \u2192 Create the blog outline.\n\n   - `Body Agent` \u2192 Write all sections based on outline.\n\n   - `Editor Agent` \u2192 Polish and finalize the blog post.\n\n4. Merge outputs into a complete, readable blog draft in Markdown format.\n\n# Input\n\nYou will receive:\n\n- Blog topic\n\n- Target audience\n\n- Blog goal (e.g., SEO, education, product marketing)\n\n# Output Format\n\n```markdown\n\n## Parsed Writing Plan\n\n- **Topic**: [Extracted from user input]\n\n- **Audience**: [Summarized from user input]\n\n- **Intent**: [Inferred goal and style]\n\n- **Blog Type**: [e.g., Tutorial / Informative Guide / Marketing Content]\n\n- **Long-tail Keywords**: \n\n  - keyword 1\n\n  - keyword 2\n\n  - keyword 3\n\n  - ...\n\n## Instructions for Outline Agent\n\nPlease generate a structured outline including H2 and H3 headings. Assign 1\u20132 relevant keywords to each section. Keep it aligned with the user\u2019s intent and audience level.\n\n## Instructions for Body Agent\n\nWrite the full content based on the outline. Each section should be concise (500\u2013600 words), informative, and optimized for SEO. Use `Tavily Search` only when additional examples or context are needed.\n\n## Instructions for Editor Agent\n\nReview and refine the combined content. Improve transitions, ensure keyword integration, and add a meta title + meta description. Maintain Markdown formatting.\n\n\n## Guides\n\n- Do not generate blog content directly.\n\n- Focus on correct intent recognition and instruction generation.\n\n- Keep communication to downstream agents simple, scoped, and accurate.\n\n\n## Input Examples (and how to handle them)\n\nInput: \"I want to write about RAGFlow.\"\n\u2192 Output: Informative Guide, Audience: AI developers, Intent: explain what RAGFlow is and its use cases\n\nInput: \"Need a blog to promote our prompt design tool.\"\n\u2192 Output: Marketing Content, Audience: product managers or tool adopters, Intent: raise awareness and interest in the product\n\nInput: \"How to get more Google traffic using AI\"\n\u2192 Output: How-to, Audience: SEO marketers, Intent: guide readers on applying AI for SEO growth",
+                                "temperature": "0.1",
+                                "temperatureEnabled": true,
+                                "tools": [],
+                                "topPEnabled": false,
+                                "top_p": 0.75,
+                                "user_prompt": "",
+                                "visual_files_var": ""
+                            },
+                            "label": "Agent",
+                            "name": "Lead Agent"
+                        },
+                        "id": "Agent:LuckyApplesGrab",
+                        "measured": {
+                            "height": 84,
+                            "width": 200
+                        },
+                        "position": {
+                            "x": 350,
+                            "y": 200
+                        },
+                        "selected": false,
+                        "sourcePosition": "right",
+                        "targetPosition": "left",
+                        "type": "agentNode"
+                    },
+                    {
+                        "data": {
+                            "form": {
+                                "content": [
+                                    "{Agent:LuckyApplesGrab@content}"
+                                ]
+                            },
+                            "label": "Message",
+                            "name": "Response"
+                        },
+                        "dragging": false,
+                        "id": "Message:ModernSwansThrow",
+                        "measured": {
+                            "height": 56,
+                            "width": 200
+                        },
+                        "position": {
+                            "x": 669.394830760932,
+                            "y": 190.72421137520644
+                        },
+                        "selected": false,
+                        "sourcePosition": "right",
+                        "targetPosition": "left",
+                        "type": "messageNode"
+                    },
+                    {
+                        "data": {
+                            "form": {
+                                "delay_after_error": 1,
+                                "description": "Generates a clear and SEO-friendly blog outline using H2/H3 headings based on the topic, audience, and intent provided by the lead agent. Each section includes suggested keywords for optimized downstream writing.\n",
+                                "exception_comment": "",
+                                "exception_default_value": "",
+                                "exception_goto": [],
+                                "exception_method": null,
+                                "frequencyPenaltyEnabled": false,
+                                "frequency_penalty": 0.3,
+                                "llm_id": "deepseek-chat@DeepSeek",
+                                "maxTokensEnabled": false,
+                                "max_retries": 3,
+                                "max_rounds": 2,
+                                "max_tokens": 4096,
+                                "mcp": [],
+                                "message_history_window_size": 12,
+                                "outputs": {
+                                    "content": {
+                                        "type": "string",
+                                        "value": ""
+                                    }
+                                },
+                                "parameter": "Balance",
+                                "presencePenaltyEnabled": false,
+                                "presence_penalty": 0.2,
+                                "prompts": [
+                                    {
+                                        "content": "{sys.query}",
+                                        "role": "user"
+                                    }
+                                ],
+                                "sys_prompt": "# Role\n\nYou are the **Outline Agent**, a sub-agent in a multi-agent SEO blog writing system. You operate under the instruction of the `Lead Agent`, and your sole responsibility is to create a clear, well-structured, and SEO-optimized blog outline.\n\n# Tool Access:\n\n- You have access to a search tool called `Tavily Search`.\n\n- If you are unsure how to structure a section, you may call this tool to search for related blog outlines or content from Google.\n\n- Do not overuse it. Your job is to extract **structure**, not to write paragraphs.\n\n\n# Goals\n\n1. Create a well-structured outline with appropriate H2 and H3 headings.\n\n2. Ensure logical flow from introduction to conclusion.\n\n3. Assign 1\u20132 suggested long-tail keywords to each major section for SEO alignment.\n\n4. Make the structure suitable for downstream paragraph writing.\n\n\n\n\n#Note\n\n- Use concise, scannable section titles.\n\n- Do not write full paragraphs.\n\n- Prioritize clarity, logical progression, and SEO alignment.\n\n\n\n- If the blog type is \u201cTutorial\u201d or \u201cHow-to\u201d, include step-based sections.\n\n\n# Input\n\nYou will receive:\n\n- Writing Type (e.g., Tutorial, Informative Guide)\n\n- Target Audience\n\n- User Intent Summary\n\n- 3\u20135 long-tail keywords\n\n\nUse this information to design a structure that both informs readers and maximizes search engine visibility.\n\n# Output Format\n\n```markdown\n\n## Blog Title (suggested)\n\n[Give a short, SEO-friendly title suggestion]\n\n## Outline\n\n### Introduction\n\n- Purpose of the article\n\n- Brief context\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 1]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 2]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 3]\n\n- [Optional H3 Subsection Title A]\n\n  - [Explanation of sub-point]\n\n- [Optional H3 Subsection Title B]\n\n  - [Explanation of sub-point]\n\n- **Suggested keywords**: [keyword1]\n\n### Conclusion\n\n- Recap key takeaways\n\n- Optional CTA (Call to Action)\n\n- **Suggested keywords**: [keyword3]\n\n",
+                                "temperature": 0.5,
+                                "temperatureEnabled": true,
+                                "tools": [
+                                    {
+                                        "component_name": "TavilySearch",
+                                        "name": "TavilySearch",
+                                        "params": {
+                                            "api_key": "",
+                                            "days": 7,
+                                            "exclude_domains": [],
+                                            "include_answer": false,
+                                            "include_domains": [],
+                                            "include_image_descriptions": false,
+                                            "include_images": false,
+                                            "include_raw_content": true,
+                                            "max_results": 5,
+                                            "outputs": {
+                                                "formalized_content": {
+                                                    "type": "string",
+                                                    "value": ""
+                                                },
+                                                "json": {
+                                                    "type": "Array<Object>",
+                                                    "value": []
+                                                }
+                                            },
+                                            "query": "sys.query",
+                                            "search_depth": "basic",
+                                            "topic": "general"
+                                        }
+                                    }
+                                ],
+                                "topPEnabled": false,
+                                "top_p": 0.85,
+                                "user_prompt": "This is the order you need to send to the agent.",
+                                "visual_files_var": ""
+                            },
+                            "label": "Agent",
+                            "name": "Outline Agent"
+                        },
+                        "dragging": false,
+                        "id": "Agent:SlickSpidersTurn",
+                        "measured": {
+                            "height": 84,
+                            "width": 200
+                        },
+                        "position": {
+                            "x": 100.60137004146719,
+                            "y": 411.67654846431367
+                        },
+                        "selected": false,
+                        "sourcePosition": "right",
+                        "targetPosition": "left",
+                        "type": "agentNode"
+                    },
+                    {
+                        "data": {
+                            "form": {
+                                "delay_after_error": 1,
+                                "description": "Writes the full blog content section-by-section following the outline structure. It integrates target keywords naturally and uses Tavily Search only when additional facts or examples are needed.\n",
+                                "exception_comment": "",
+                                "exception_default_value": "",
+                                "exception_goto": [],
+                                "exception_method": null,
+                                "frequencyPenaltyEnabled": false,
+                                "frequency_penalty": 0.5,
+                                "llm_id": "deepseek-chat@DeepSeek",
+                                "maxTokensEnabled": false,
+                                "max_retries": 3,
+                                "max_rounds": 3,
+                                "max_tokens": 4096,
+                                "mcp": [],
+                                "message_history_window_size": 12,
+                                "outputs": {
+                                    "content": {
+                                        "type": "string",
+                                        "value": ""
+                                    }
+                                },
+                                "parameter": "Precise",
+                                "presencePenaltyEnabled": false,
+                                "presence_penalty": 0.5,
+                                "prompts": [
+                                    {
+                                        "content": "{sys.query}",
+                                        "role": "user"
+                                    }
+                                ],
+                                "sys_prompt": "# Role\n\nYou are the **Body Agent**, a sub-agent in a multi-agent SEO blog writing system. You operate under the instruction of the `Lead Agent`, and your job is to write the full blog content based on the outline created by the `OutlineWriter_Agent`.\n\n\n\n# Tool Access:\n\nYou can use the `Tavily Search` tool to retrieve relevant content, statistics, or examples to support each section you're writing.\n\nUse it **only** when the provided outline lacks enough information, or if the section requires factual grounding.\n\nAlways cite the original link or indicate source where possible.\n\n\n# Goals\n\n1. Write each section (based on H2/H3 structure) as a complete and natural blog paragraph.\n\n2. Integrate the suggested long-tail keywords naturally into each section.\n\n3. When appropriate, use the `Tavily Search` tool to enrich your writing with relevant facts, examples, or quotes.\n\n4. Ensure each section is clear, engaging, and informative, suitable for both human readers and search engines.\n\n\n# Style Guidelines\n\n- Write in a tone appropriate to the audience. Be explanatory, not promotional, unless it's a marketing blog.\n\n- Avoid generic filler content. Prioritize clarity, structure, and value.\n\n- Ensure SEO keywords are embedded seamlessly, not forcefully.\n\n\n\n- Maintain writing rhythm. Vary sentence lengths. Use transitions between ideas.\n\n\n# Input\n\n\nYou will receive:\n\n- Blog title\n\n- Structured outline (including section titles, keywords, and descriptions)\n\n- Target audience\n\n- Blog type and user intent\n\nYou must **follow the outline strictly**. Write content **section-by-section**, based on the structure.\n\n\n# Output Format\n\n```markdown\n\n## H2: [Section Title]\n\n[Your generated content for this section \u2014 500-600 words, using keywords naturally.]\n\n",
+                                "temperature": 0.2,
+                                "temperatureEnabled": true,
+                                "tools": [
+                                    {
+                                        "component_name": "TavilySearch",
+                                        "name": "TavilySearch",
+                                        "params": {
+                                            "api_key": "",
+                                            "days": 7,
+                                            "exclude_domains": [],
+                                            "include_answer": false,
+                                            "include_domains": [],
+                                            "include_image_descriptions": false,
+                                            "include_images": false,
+                                            "include_raw_content": true,
+                                            "max_results": 5,
+                                            "outputs": {
+                                                "formalized_content": {
+                                                    "type": "string",
+                                                    "value": ""
+                                                },
+                                                "json": {
+                                                    "type": "Array<Object>",
+                                                    "value": []
+                                                }
+                                            },
+                                            "query": "sys.query",
+                                            "search_depth": "basic",
+                                            "topic": "general"
+                                        }
+                                    }
+                                ],
+                                "topPEnabled": false,
+                                "top_p": 0.75,
+                                "user_prompt": "This is the order you need to send to the agent.",
+                                "visual_files_var": ""
+                            },
+                            "label": "Agent",
+                            "name": "Body Agent"
+                        },
+                        "dragging": false,
+                        "id": "Agent:IcyPawsRescue",
+                        "measured": {
+                            "height": 84,
+                            "width": 200
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+                        "position": {
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+                        "selected": false,
+                        "sourcePosition": "right",
+                        "targetPosition": "left",
+                        "type": "agentNode"
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+                    {
+                        "data": {
+                            "form": {
+                                "delay_after_error": 1,
+                                "description": "Polishes and finalizes the entire blog post. Enhances clarity, checks keyword usage, improves flow, and generates a meta title and description for SEO. Operates after all sections are completed.\n\n",
+                                "exception_comment": "",
+                                "exception_default_value": "",
+                                "exception_goto": [],
+                                "exception_method": null,
+                                "frequencyPenaltyEnabled": false,
+                                "frequency_penalty": 0.5,
+                                "llm_id": "deepseek-chat@DeepSeek",
+                                "maxTokensEnabled": false,
+                                "max_retries": 3,
+                                "max_rounds": 2,
+                                "max_tokens": 4096,
+                                "mcp": [],
+                                "message_history_window_size": 12,
+                                "outputs": {
+                                    "content": {
+                                        "type": "string",
+                                        "value": ""
+                                    }
+                                },
+                                "parameter": "Precise",
+                                "presencePenaltyEnabled": false,
+                                "presence_penalty": 0.5,
+                                "prompts": [
+                                    {
+                                        "content": "{sys.query}",
+                                        "role": "user"
+                                    }
+                                ],
+                                "sys_prompt": "# Role\n\nYou are the **Editor Agent**, the final agent in a multi-agent SEO blog writing workflow. You are responsible for finalizing the blog post for both human readability and SEO effectiveness.\n\n# Goals\n\n1. Polish the entire blog content for clarity, coherence, and style.\n\n2. Improve transitions between sections, ensure logical flow.\n\n3. Verify that keywords are used appropriately and effectively.\n\n4. Conduct a lightweight SEO audit \u2014 checking keyword density, structure (H1/H2/H3), and overall searchability.\n\n\n\n## Integration Responsibilities\n\n- Maintain alignment with Lead Agent's original intent and audience\n\n- Preserve the structure and keyword strategy from Outline Agent\n\n- Enhance and polish Body Agent's content without altering core information\n\n# Style Guidelines\n\n- Be precise. Avoid bloated or vague language.\n\n- Maintain an informative and engaging tone, suitable to the target audience.\n\n- Do not remove keywords unless absolutely necessary for clarity.\n\n- Ensure paragraph flow and section continuity.\n\n\n\n# Input\n\nYou will receive:\n\n- Full blog content, written section-by-section\n\n- Original outline with suggested keywords\n\n- Target audience and writing type\n\n# Output Format\n\n```markdown\n\n[The revised, fully polished blog post content goes here.]\n",
+                                "temperature": 0.2,
+                                "temperatureEnabled": true,
+                                "tools": [],
+                                "topPEnabled": false,
+                                "top_p": 0.75,
+                                "user_prompt": "This is the order you need to send to the agent.",
+                                "visual_files_var": ""
+                            },
+                            "label": "Agent",
+                            "name": "Editor Agent"
+                        },
+                        "dragging": false,
+                        "id": "Agent:TenderAdsAllow",
+                        "measured": {
+                            "height": 84,
+                            "width": 200
+                        },
+                        "position": {
+                            "x": 730.8513124709204,
+                            "y": 327.351197329827
+                        },
+                        "selected": false,
+                        "sourcePosition": "right",
+                        "targetPosition": "left",
+                        "type": "agentNode"
+                    },
+                    {
+                        "data": {
+                            "form": {
+                                "description": "This is an agent for a specific task.",
+                                "user_prompt": "This is the order you need to send to the agent."
+                            },
+                            "label": "Tool",
+                            "name": "flow.tool_0"
+                        },
+                        "dragging": false,
+                        "id": "Tool:ThreeWallsRing",
+                        "measured": {
+                            "height": 48,
+                            "width": 200
+                        },
+                        "position": {
+                            "x": -26.93431957115564,
+                            "y": 531.4384641920368
+                        },
+                        "selected": false,
+                        "sourcePosition": "right",
+                        "targetPosition": "left",
+                        "type": "toolNode"
+                    },
+                    {
+                        "data": {
+                            "form": {
+                                "description": "This is an agent for a specific task.",
+                                "user_prompt": "This is the order you need to send to the agent."
+                            },
+                            "label": "Tool",
+                            "name": "flow.tool_1"
+                        },
+                        "dragging": false,
+                        "id": "Tool:FloppyJokesItch",
+                        "measured": {
+                            "height": 48,
+                            "width": 200
+                        },
+                        "position": {
+                            "x": 414.6786783453011,
+                            "y": 499.39483076093194
+                        },
+                        "selected": false,
+                        "sourcePosition": "right",
+                        "targetPosition": "left",
+                        "type": "toolNode"
+                    },
+                    {
+                        "data": {
+                            "form": {
+                                "text": "This is a multi-agent version of the SEO blog generation workflow. It simulates a small team of AI \u201cwriters\u201d, where each agent plays a specialized role \u2014 just like a real editorial team.\n\nInstead of one AI doing everything in order, this version uses a **Lead Agent** to assign tasks to different sub-agents, who then write and edit the blog in parallel. The Lead Agent manages everything and produces the final output.\n\n### Why use multi-agent format?\n\n- Better control over each stage of writing  \n- Easier to reuse agents across tasks  \n- More human-like workflow (planning \u2192 writing \u2192 editing \u2192 publishing)  \n- Easier to scale and customize for advanced users\n\n### Flow Summary:\n\n1. `LeadWriter_Agent` takes your input and creates a plan\n2. It sends that plan to:\n   - `OutlineWriter_Agent`: build blog structure\n   - `BodyWriter_Agent`: write full content\n   - `FinalEditor_Agent`: polish and finalize\n3. `LeadWriter_Agent` collects all results and outputs the final blog post\n"
+                            },
+                            "label": "Note",
+                            "name": "Workflow Overall Description"
+                        },
+                        "dragHandle": ".note-drag-handle",
+                        "dragging": false,
+                        "height": 208,
+                        "id": "Note:ElevenVansInvent",
+                        "measured": {
+                            "height": 208,
+                            "width": 518
+                        },
+                        "position": {
+                            "x": -336.6586460874556,
+                            "y": 113.43253511344867
+                        },
+                        "resizing": false,
+                        "selected": false,
+                        "sourcePosition": "right",
+                        "targetPosition": "left",
+                        "type": "noteNode",
+                        "width": 518
+                    },
+                    {
+                        "data": {
+                            "form": {
+                                "text": "**Purpose**:  \nThis is the central agent that controls the entire writing process.\n\n**What it does**:\n- Reads your blog topic and intent\n- Generates a clear writing plan (topic, audience, goal, keywords)\n- Sends instructions to all sub-agents\n- Waits for their responses and checks quality\n- If any section is missing or weak, it can request a rewrite\n- Finally, it assembles all parts into a complete blog and sends it back to you\n"
+                            },
+                            "label": "Note",
+                            "name": "Lead Agent"
+                        },
+                        "dragHandle": ".note-drag-handle",
+                        "dragging": false,
+                        "height": 146,
+                        "id": "Note:EmptyClubsGreet",
+                        "measured": {
+                            "height": 146,
+                            "width": 334
+                        },
+                        "position": {
+                            "x": 390.1408623279084,
+                            "y": 2.6521144030202493
+                        },
+                        "resizing": false,
+                        "selected": false,
+                        "sourcePosition": "right",
+                        "targetPosition": "left",
+                        "type": "noteNode",
+                        "width": 334
+                    },
+                    {
+                        "data": {
+                            "form": {
+                                "text": "**Purpose**:  \nThis agent is responsible for building the blog's structure. It creates an outline that shows what the article will cover and how it's organized.\n\n**What it does**:\n- Suggests a blog title that matches the topic and keywords  \n- Breaks the article into sections using H2 and H3 headers  \n- Adds a short description of what each section should include  \n- Assigns SEO keywords to each section for better search visibility  \n- Uses search data (via Tavily Search) to find how similar blogs are structured"
+                            },
+                            "label": "Note",
+                            "name": "Outline Agent"
+                        },
+                        "dragHandle": ".note-drag-handle",
+                        "dragging": false,
+                        "height": 157,
+                        "id": "Note:CurlyTigersDouble",
+                        "measured": {
+                            "height": 157,
+                            "width": 394
+                        },
+                        "position": {
+                            "x": -60.03139680691618,
+                            "y": 595.8208080534818
+                        },
+                        "resizing": false,
+                        "selected": false,
+                        "sourcePosition": "right",
+                        "targetPosition": "left",
+                        "type": "noteNode",
+                        "width": 394
+                    },
+                    {
+                        "data": {
+                            "form": {
+                                "text": "**Purpose**:  \nThis agent is in charge of writing the full blog content, section by section, based on the outline it receives.\n\n**What it does**:\n- Takes each section heading from the outline (H2 / H3)\n- Writes a complete paragraph (150\u2013220 words) under each section\n- Naturally includes the keywords provided for that section\n- Uses the Tavily Search tool to add real-world examples, definitions, or facts if needed\n- Makes sure each section is clear, useful, and easy to read\n"
+                            },
+                            "label": "Note",
+                            "name": "Body Agent"
+                        },
+                        "dragHandle": ".note-drag-handle",
+                        "dragging": false,
+                        "height": 164,
+                        "id": "Note:StrongKingsCamp",
+                        "measured": {
+                            "height": 164,
+                            "width": 408
+                        },
+                        "position": {
+                            "x": 446.54943226110845,
+                            "y": 590.9443887062529
+                        },
+                        "resizing": false,
+                        "selected": false,
+                        "sourcePosition": "right",
+                        "targetPosition": "left",
+                        "type": "noteNode",
+                        "width": 408
+                    },
+                    {
+                        "data": {
+                            "form": {
+                                "text": "**Purpose**:  \nThis agent reviews, polishes, and finalizes the blog post written by the BodyWriter_Agent. It ensures everything is clean, smooth, and SEO-compliant.\n\n**What it does**:\n- Improves grammar, sentence flow, and transitions  \n- Makes sure the content reads naturally and professionally  \n- Checks whether keywords are present and well integrated (but not overused)  \n- Verifies that the structure follows the correct H1/H2/H3 format  \n"
+                            },
+                            "label": "Note",
+                            "name": "Editor Agent"
+                        },
+                        "dragHandle": ".note-drag-handle",
+                        "dragging": false,
+                        "height": 147,
+                        "id": "Note:OpenOttersShow",
+                        "measured": {
+                            "height": 147,
+                            "width": 357
+                        },
+                        "position": {
+                            "x": 976.6858726228803,
+                            "y": 422.7404806291804
+                        },
+                        "resizing": false,
+                        "selected": false,
+                        "sourcePosition": "right",
+                        "targetPosition": "left",
+                        "type": "noteNode",
+                        "width": 357
+                    }
+                ]
+            },
+            "history": [],
+            "messages": [],
+            "path": [],
+            "retrieval": []
+        },
+    "avatar": "data:image/jpeg;base64,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"
+}

agent/templates/knowledge_base_report.json

@@ -0,0 +1,333 @@
+{
+    "id": 20,
+    "title": {
+        "en": "Report Agent Using Knowledge Base",
+        "de": "Berichtsagent mit Wissensdatenbank",
+        "zh": "知识库检索智能体"},
+    "description": {
+        "en": "A report generation assistant using local knowledge base, with advanced capabilities in task planning, reasoning, and reflective analysis. Recommended for academic research paper Q&A",
+        "de": "Ein Berichtsgenerierungsassistent, der eine lokale Wissensdatenbank nutzt, mit erweiterten Fähigkeiten in Aufgabenplanung, Schlussfolgerung und reflektierender Analyse. Empfohlen für akademische Forschungspapier-Fragen und -Antworten.",
+        "zh": "一个使用本地知识库的报告生成助手，具备高级能力，包括任务规划、推理和反思性分析。推荐用于学术研究论文问答。"},
+    "canvas_type": "Agent",
+    "dsl": {
+        "components": {
+            "Agent:NewPumasLick": {
+                "downstream": [
+                    "Message:OrangeYearsShine"
+                ],
+                "obj": {
+                    "component_name": "Agent",
+                    "params": {
+                        "delay_after_error": 1,
+                        "description": "",
+                        "exception_comment": "",
+                        "exception_default_value": "",
+                        "exception_goto": [],
+                        "exception_method": null,
+                        "frequencyPenaltyEnabled": false,
+                        "frequency_penalty": 0.5,
+                        "llm_id": "qwen3-235b-a22b-instruct-2507@Tongyi-Qianwen",
+                        "maxTokensEnabled": true,
+                        "max_retries": 3,
+                        "max_rounds": 3,
+                        "max_tokens": 128000,
+                        "mcp": [],
+                        "message_history_window_size": 12,
+                        "outputs": {
+                            "content": {
+                                "type": "string",
+                                "value": ""
+                            }
+                        },
+                        "parameter": "Precise",
+                        "presencePenaltyEnabled": false,
+                        "presence_penalty": 0.5,
+                        "prompts": [
+                            {
+                                "content": "# User Query\n {sys.query}",
+                                "role": "user"
+                            }
+                        ],
+                        "sys_prompt": "## Role & Task\nYou are a **\u201cKnowledge Base Retrieval Q\\&A Agent\u201d** whose goal is to break down the user\u2019s question into retrievable subtasks, and then produce a multi-source-verified, structured, and actionable research report using the internal knowledge base.\n## Execution Framework (Detailed Steps & Key Points)\n1. **Assessment & Decomposition**\n   * Actions:\n     * Automatically extract: main topic, subtopics, entities (people/organizations/products/technologies), time window, geographic/business scope.\n     * Output as a list: N facts/data points that must be collected (*N* ranges from 5\u201320 depending on question complexity).\n2. **Query Type Determination (Rule-Based)**\n   * Example rules:\n     * If the question involves a single issue but requests \u201cmethod comparison/multiple explanations\u201d \u2192 use **depth-first**.\n     * If the question can naturally be split into \u22653 independent sub-questions \u2192 use **breadth-first**.\n     * If the question can be answered by a single fact/specification/definition \u2192 use **simple query**.\n3. **Research Plan Formulation**\n   * Depth-first: define 3\u20135 perspectives (methodology/stakeholders/time dimension/technical route, etc.), assign search keywords, target document types, and output format for each perspective.\n   * Breadth-first: list subtasks, prioritize them, and assign search terms.\n   * Simple query: directly provide the search sentence and required fields.\n4. **Retrieval Execution**\n   * After retrieval: perform coverage check (does it contain the key facts?) and quality check (source diversity, authority, latest update time).\n   * If standards are not met, automatically loop: rewrite queries (synonyms/cross-domain terms) and retry \u22643 times, or flag as requiring external search.\n5. **Integration & Reasoning**\n   * Build the answer using a **fact\u2013evidence\u2013reasoning** chain. For each conclusion, attach 1\u20132 strongest pieces of evidence.\n---\n## Quality Gate Checklist (Verify at Each Stage)\n* **Stage 1 (Decomposition)**:\n  * [ ] Key concepts and expected outputs identified\n  * [ ] Required facts/data points listed\n* **Stage 2 (Retrieval)**:\n  * [ ] Meets quality standards (see above)\n  * [ ] If not met: execute query iteration\n* **Stage 3 (Generation)**:\n  * [ ] Each conclusion has at least one direct evidence source\n  * [ ] State assumptions/uncertainties\n  * [ ] Provide next-step suggestions or experiment/retrieval plans\n  * [ ] Final length and depth match user expectations (comply with word count/format if specified)\n---\n## Core Principles\n1. **Strict reliance on the knowledge base**: answers must be **fully bounded** by the content retrieved from the knowledge base.\n2. **No fabrication**: do not generate, infer, or create information that is not explicitly present in the knowledge base.\n3. **Accuracy first**: prefer incompleteness over inaccurate content.\n4. **Output format**:\n   * Hierarchically clear modular structure\n   * Logical grouping according to the MECE principle\n   * Professionally presented formatting\n   * Step-by-step cognitive guidance\n   * Reasonable use of headings and dividers for clarity\n   * *Italicize* key parameters\n   * **Bold** critical information\n5. **LaTeX formula requirements**:\n   * Inline formulas: start and end with `$`\n   * Block formulas: start and end with `$$`, each `$$` on its own line\n   * Block formula content must comply with LaTeX math syntax\n   * Verify formula correctness\n---\n## Additional Notes (Interaction & Failure Strategy)\n* If the knowledge base does not cover critical facts: explicitly inform the user (with sample wording)\n* For time-sensitive issues: enforce time filtering in the search request, and indicate the latest retrieval date in the answer.\n* Language requirement: answer in the user\u2019s preferred language\n",
+                        "temperature": "0.1",
+                        "temperatureEnabled": true,
+                        "tools": [
+                            {
+                                "component_name": "Retrieval",
+                                "name": "Retrieval",
+                                "params": {
+                                    "cross_languages": [],
+                                    "description": "",
+                                    "empty_response": "",
+                                    "kb_ids": [],
+                                    "keywords_similarity_weight": 0.7,
+                                    "outputs": {
+                                        "formalized_content": {
+                                            "type": "string",
+                                            "value": ""
+                                        }
+                                    },
+                                    "rerank_id": "",
+                                    "similarity_threshold": 0.2,
+                                    "top_k": 1024,
+                                    "top_n": 8,
+                                    "use_kg": false
+                                }
+                            }
+                        ],
+                        "topPEnabled": false,
+                        "top_p": 0.75,
+                        "user_prompt": "",
+                        "visual_files_var": ""
+                    }
+                },
+                "upstream": [
+                    "begin"
+                ]
+            },
+            "Message:OrangeYearsShine": {
+                "downstream": [],
+                "obj": {
+                    "component_name": "Message",
+                    "params": {
+                        "content": [
+                            "{Agent:NewPumasLick@content}"
+                        ]
+                    }
+                },
+                "upstream": [
+                    "Agent:NewPumasLick"
+                ]
+            },
+            "begin": {
+                "downstream": [
+                    "Agent:NewPumasLick"
+                ],
+                "obj": {
+                    "component_name": "Begin",
+                    "params": {
+                        "enablePrologue": true,
+                        "inputs": {},
+                        "mode": "conversational",
+                        "prologue": "\u4f60\u597d\uff01 \u6211\u662f\u4f60\u7684\u52a9\u7406\uff0c\u6709\u4ec0\u4e48\u53ef\u4ee5\u5e2e\u5230\u4f60\u7684\u5417\uff1f"
+                    }
+                },
+                "upstream": []
+            }
+        },
+        "globals": {
+            "sys.conversation_turns": 0,
+            "sys.files": [],
+            "sys.query": "",
+            "sys.user_id": ""
+        },
+        "graph": {
+            "edges": [
+                {
+                    "data": {
+                        "isHovered": false
+                    },
+                    "id": "xy-edge__beginstart-Agent:NewPumasLickend",
+                    "source": "begin",
+                    "sourceHandle": "start",
+                    "target": "Agent:NewPumasLick",
+                    "targetHandle": "end"
+                },
+                {
+                    "data": {
+                        "isHovered": false
+                    },
+                    "id": "xy-edge__Agent:NewPumasLickstart-Message:OrangeYearsShineend",
+                    "markerEnd": "logo",
+                    "source": "Agent:NewPumasLick",
+                    "sourceHandle": "start",
+                    "style": {
+                        "stroke": "rgba(91, 93, 106, 1)",
+                        "strokeWidth": 1
+                    },
+                    "target": "Message:OrangeYearsShine",
+                    "targetHandle": "end",
+                    "type": "buttonEdge",
+                    "zIndex": 1001
+                },
+                {
+                    "data": {
+                        "isHovered": false
+                    },
+                    "id": "xy-edge__Agent:NewPumasLicktool-Tool:AllBirdsNailend",
+                    "selected": false,
+                    "source": "Agent:NewPumasLick",
+                    "sourceHandle": "tool",
+                    "target": "Tool:AllBirdsNail",
+                    "targetHandle": "end"
+                }
+            ],
+            "nodes": [
+                {
+                    "data": {
+                        "form": {
+                            "enablePrologue": true,
+                            "inputs": {},
+                            "mode": "conversational",
+                            "prologue": "\u4f60\u597d\uff01 \u6211\u662f\u4f60\u7684\u52a9\u7406\uff0c\u6709\u4ec0\u4e48\u53ef\u4ee5\u5e2e\u5230\u4f60\u7684\u5417\uff1f"
+                        },
+                        "label": "Begin",
+                        "name": "begin"
+                    },
+                    "dragging": false,
+                    "id": "begin",
+                    "measured": {
+                        "height": 48,
+                        "width": 200
+                    },
+                    "position": {
+                        "x": -9.569875358221438,
+                        "y": 205.84018385864917
+                    },
+                    "selected": false,
+                    "sourcePosition": "left",
+                    "targetPosition": "right",
+                    "type": "beginNode"
+                },
+                {
+                    "data": {
+                        "form": {
+                            "content": [
+                                "{Agent:NewPumasLick@content}"
+                            ]
+                        },
+                        "label": "Message",
+                        "name": "Response"
+                    },
+                    "dragging": false,
+                    "id": "Message:OrangeYearsShine",
+                    "measured": {
+                        "height": 56,
+                        "width": 200
+                    },
+                    "position": {
+                        "x": 734.4061285881053,
+                        "y": 199.9706031723009
+                    },
+                    "selected": false,
+                    "sourcePosition": "right",
+                    "targetPosition": "left",
+                    "type": "messageNode"
+                },
+                {
+                    "data": {
+                        "form": {
+                            "delay_after_error": 1,
+                            "description": "",
+                            "exception_comment": "",
+                            "exception_default_value": "",
+                            "exception_goto": [],
+                            "exception_method": null,
+                            "frequencyPenaltyEnabled": false,
+                            "frequency_penalty": 0.5,
+                            "llm_id": "qwen3-235b-a22b-instruct-2507@Tongyi-Qianwen",
+                            "maxTokensEnabled": true,
+                            "max_retries": 3,
+                            "max_rounds": 3,
+                            "max_tokens": 128000,
+                            "mcp": [],
+                            "message_history_window_size": 12,
+                            "outputs": {
+                                "content": {
+                                    "type": "string",
+                                    "value": ""
+                                }
+                            },
+                            "parameter": "Precise",
+                            "presencePenaltyEnabled": false,
+                            "presence_penalty": 0.5,
+                            "prompts": [
+                                {
+                                    "content": "# User Query\n {sys.query}",
+                                    "role": "user"
+                                }
+                            ],
+                            "sys_prompt": "## Role & Task\nYou are a **\u201cKnowledge Base Retrieval Q\\&A Agent\u201d** whose goal is to break down the user\u2019s question into retrievable subtasks, and then produce a multi-source-verified, structured, and actionable research report using the internal knowledge base.\n## Execution Framework (Detailed Steps & Key Points)\n1. **Assessment & Decomposition**\n   * Actions:\n     * Automatically extract: main topic, subtopics, entities (people/organizations/products/technologies), time window, geographic/business scope.\n     * Output as a list: N facts/data points that must be collected (*N* ranges from 5\u201320 depending on question complexity).\n2. **Query Type Determination (Rule-Based)**\n   * Example rules:\n     * If the question involves a single issue but requests \u201cmethod comparison/multiple explanations\u201d \u2192 use **depth-first**.\n     * If the question can naturally be split into \u22653 independent sub-questions \u2192 use **breadth-first**.\n     * If the question can be answered by a single fact/specification/definition \u2192 use **simple query**.\n3. **Research Plan Formulation**\n   * Depth-first: define 3\u20135 perspectives (methodology/stakeholders/time dimension/technical route, etc.), assign search keywords, target document types, and output format for each perspective.\n   * Breadth-first: list subtasks, prioritize them, and assign search terms.\n   * Simple query: directly provide the search sentence and required fields.\n4. **Retrieval Execution**\n   * After retrieval: perform coverage check (does it contain the key facts?) and quality check (source diversity, authority, latest update time).\n   * If standards are not met, automatically loop: rewrite queries (synonyms/cross-domain terms) and retry \u22643 times, or flag as requiring external search.\n5. **Integration & Reasoning**\n   * Build the answer using a **fact\u2013evidence\u2013reasoning** chain. For each conclusion, attach 1\u20132 strongest pieces of evidence.\n---\n## Quality Gate Checklist (Verify at Each Stage)\n* **Stage 1 (Decomposition)**:\n  * [ ] Key concepts and expected outputs identified\n  * [ ] Required facts/data points listed\n* **Stage 2 (Retrieval)**:\n  * [ ] Meets quality standards (see above)\n  * [ ] If not met: execute query iteration\n* **Stage 3 (Generation)**:\n  * [ ] Each conclusion has at least one direct evidence source\n  * [ ] State assumptions/uncertainties\n  * [ ] Provide next-step suggestions or experiment/retrieval plans\n  * [ ] Final length and depth match user expectations (comply with word count/format if specified)\n---\n## Core Principles\n1. **Strict reliance on the knowledge base**: answers must be **fully bounded** by the content retrieved from the knowledge base.\n2. **No fabrication**: do not generate, infer, or create information that is not explicitly present in the knowledge base.\n3. **Accuracy first**: prefer incompleteness over inaccurate content.\n4. **Output format**:\n   * Hierarchically clear modular structure\n   * Logical grouping according to the MECE principle\n   * Professionally presented formatting\n   * Step-by-step cognitive guidance\n   * Reasonable use of headings and dividers for clarity\n   * *Italicize* key parameters\n   * **Bold** critical information\n5. **LaTeX formula requirements**:\n   * Inline formulas: start and end with `$`\n   * Block formulas: start and end with `$$`, each `$$` on its own line\n   * Block formula content must comply with LaTeX math syntax\n   * Verify formula correctness\n---\n## Additional Notes (Interaction & Failure Strategy)\n* If the knowledge base does not cover critical facts: explicitly inform the user (with sample wording)\n* For time-sensitive issues: enforce time filtering in the search request, and indicate the latest retrieval date in the answer.\n* Language requirement: answer in the user\u2019s preferred language\n",
+                            "temperature": "0.1",
+                            "temperatureEnabled": true,
+                            "tools": [
+                                {
+                                    "component_name": "Retrieval",
+                                    "name": "Retrieval",
+                                    "params": {
+                                        "cross_languages": [],
+                                        "description": "",
+                                        "empty_response": "",
+                                        "kb_ids": [],
+                                        "keywords_similarity_weight": 0.7,
+                                        "outputs": {
+                                            "formalized_content": {
+                                                "type": "string",
+                                                "value": ""
+                                            }
+                                        },
+                                        "rerank_id": "",
+                                        "similarity_threshold": 0.2,
+                                        "top_k": 1024,
+                                        "top_n": 8,
+                                        "use_kg": false
+                                    }
+                                }
+                            ],
+                            "topPEnabled": false,
+                            "top_p": 0.75,
+                            "user_prompt": "",
+                            "visual_files_var": ""
+                        },
+                        "label": "Agent",
+                        "name": "Knowledge Base Agent"
+                    },
+                    "dragging": false,
+                    "id": "Agent:NewPumasLick",
+                    "measured": {
+                        "height": 84,
+                        "width": 200
+                    },
+                    "position": {
+                        "x": 347.00048227952215,
+                        "y": 186.49109364794631
+                    },
+                    "selected": false,
+                    "sourcePosition": "right",
+                    "targetPosition": "left",
+                    "type": "agentNode"
+                },
+                {
+                    "data": {
+                        "form": {
+                            "description": "This is an agent for a specific task.",
+                            "user_prompt": "This is the order you need to send to the agent."
+                        },
+                        "label": "Tool",
+                        "name": "flow.tool_10"
+                    },
+                    "dragging": false,
+                    "id": "Tool:AllBirdsNail",
+                    "measured": {
+                        "height": 48,
+                        "width": 200
+                    },
+                    "position": {
+                        "x": 220.24819746977118,
+                        "y": 403.31576836482583
+                    },
+                    "selected": false,
+                    "sourcePosition": "right",
+                    "targetPosition": "left",
+                    "type": "toolNode"
+                }
+            ]
+        },
+        "history": [],
+        "memory": [],
+        "messages": [],
+        "path": [],
+        "retrieval": []
+    },
+    "avatar": "data:image/png;base64,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"
+}

agent/templates/knowledge_base_report_r.json

@@ -0,0 +1,333 @@
+{
+    "id": 21,
+    "title": {
+        "en": "Report Agent Using Knowledge Base",
+        "de": "Berichtsagent mit Wissensdatenbank",
+        "zh": "知识库检索智能体"},
+    "description": {
+        "en": "A report generation assistant using local knowledge base, with advanced capabilities in task planning, reasoning, and reflective analysis. Recommended for academic research paper Q&A",
+        "de": "Ein Berichtsgenerierungsassistent, der eine lokale Wissensdatenbank nutzt, mit erweiterten Fähigkeiten in Aufgabenplanung, Schlussfolgerung und reflektierender Analyse. Empfohlen für akademische Forschungspapier-Fragen und -Antworten.",
+        "zh": "一个使用本地知识库的报告生成助手，具备高级能力，包括任务规划、推理和反思性分析。推荐用于学术研究论文问答。"},
+    "canvas_type": "Recommended",
+    "dsl": {
+        "components": {
+            "Agent:NewPumasLick": {
+                "downstream": [
+                    "Message:OrangeYearsShine"
+                ],
+                "obj": {
+                    "component_name": "Agent",
+                    "params": {
+                        "delay_after_error": 1,
+                        "description": "",
+                        "exception_comment": "",
+                        "exception_default_value": "",
+                        "exception_goto": [],
+                        "exception_method": null,
+                        "frequencyPenaltyEnabled": false,
+                        "frequency_penalty": 0.5,
+                        "llm_id": "qwen3-235b-a22b-instruct-2507@Tongyi-Qianwen",
+                        "maxTokensEnabled": true,
+                        "max_retries": 3,
+                        "max_rounds": 3,
+                        "max_tokens": 128000,
+                        "mcp": [],
+                        "message_history_window_size": 12,
+                        "outputs": {
+                            "content": {
+                                "type": "string",
+                                "value": ""
+                            }
+                        },
+                        "parameter": "Precise",
+                        "presencePenaltyEnabled": false,
+                        "presence_penalty": 0.5,
+                        "prompts": [
+                            {
+                                "content": "# User Query\n {sys.query}",
+                                "role": "user"
+                            }
+                        ],
+                        "sys_prompt": "## Role & Task\nYou are a **\u201cKnowledge Base Retrieval Q\\&A Agent\u201d** whose goal is to break down the user\u2019s question into retrievable subtasks, and then produce a multi-source-verified, structured, and actionable research report using the internal knowledge base.\n## Execution Framework (Detailed Steps & Key Points)\n1. **Assessment & Decomposition**\n   * Actions:\n     * Automatically extract: main topic, subtopics, entities (people/organizations/products/technologies), time window, geographic/business scope.\n     * Output as a list: N facts/data points that must be collected (*N* ranges from 5\u201320 depending on question complexity).\n2. **Query Type Determination (Rule-Based)**\n   * Example rules:\n     * If the question involves a single issue but requests \u201cmethod comparison/multiple explanations\u201d \u2192 use **depth-first**.\n     * If the question can naturally be split into \u22653 independent sub-questions \u2192 use **breadth-first**.\n     * If the question can be answered by a single fact/specification/definition \u2192 use **simple query**.\n3. **Research Plan Formulation**\n   * Depth-first: define 3\u20135 perspectives (methodology/stakeholders/time dimension/technical route, etc.), assign search keywords, target document types, and output format for each perspective.\n   * Breadth-first: list subtasks, prioritize them, and assign search terms.\n   * Simple query: directly provide the search sentence and required fields.\n4. **Retrieval Execution**\n   * After retrieval: perform coverage check (does it contain the key facts?) and quality check (source diversity, authority, latest update time).\n   * If standards are not met, automatically loop: rewrite queries (synonyms/cross-domain terms) and retry \u22643 times, or flag as requiring external search.\n5. **Integration & Reasoning**\n   * Build the answer using a **fact\u2013evidence\u2013reasoning** chain. For each conclusion, attach 1\u20132 strongest pieces of evidence.\n---\n## Quality Gate Checklist (Verify at Each Stage)\n* **Stage 1 (Decomposition)**:\n  * [ ] Key concepts and expected outputs identified\n  * [ ] Required facts/data points listed\n* **Stage 2 (Retrieval)**:\n  * [ ] Meets quality standards (see above)\n  * [ ] If not met: execute query iteration\n* **Stage 3 (Generation)**:\n  * [ ] Each conclusion has at least one direct evidence source\n  * [ ] State assumptions/uncertainties\n  * [ ] Provide next-step suggestions or experiment/retrieval plans\n  * [ ] Final length and depth match user expectations (comply with word count/format if specified)\n---\n## Core Principles\n1. **Strict reliance on the knowledge base**: answers must be **fully bounded** by the content retrieved from the knowledge base.\n2. **No fabrication**: do not generate, infer, or create information that is not explicitly present in the knowledge base.\n3. **Accuracy first**: prefer incompleteness over inaccurate content.\n4. **Output format**:\n   * Hierarchically clear modular structure\n   * Logical grouping according to the MECE principle\n   * Professionally presented formatting\n   * Step-by-step cognitive guidance\n   * Reasonable use of headings and dividers for clarity\n   * *Italicize* key parameters\n   * **Bold** critical information\n5. **LaTeX formula requirements**:\n   * Inline formulas: start and end with `$`\n   * Block formulas: start and end with `$$`, each `$$` on its own line\n   * Block formula content must comply with LaTeX math syntax\n   * Verify formula correctness\n---\n## Additional Notes (Interaction & Failure Strategy)\n* If the knowledge base does not cover critical facts: explicitly inform the user (with sample wording)\n* For time-sensitive issues: enforce time filtering in the search request, and indicate the latest retrieval date in the answer.\n* Language requirement: answer in the user\u2019s preferred language\n",
+                        "temperature": "0.1",
+                        "temperatureEnabled": true,
+                        "tools": [
+                            {
+                                "component_name": "Retrieval",
+                                "name": "Retrieval",
+                                "params": {
+                                    "cross_languages": [],
+                                    "description": "",
+                                    "empty_response": "",
+                                    "kb_ids": [],
+                                    "keywords_similarity_weight": 0.7,
+                                    "outputs": {
+                                        "formalized_content": {
+                                            "type": "string",
+                                            "value": ""
+                                        }
+                                    },
+                                    "rerank_id": "",
+                                    "similarity_threshold": 0.2,
+                                    "top_k": 1024,
+                                    "top_n": 8,
+                                    "use_kg": false
+                                }
+                            }
+                        ],
+                        "topPEnabled": false,
+                        "top_p": 0.75,
+                        "user_prompt": "",
+                        "visual_files_var": ""
+                    }
+                },
+                "upstream": [
+                    "begin"
+                ]
+            },
+            "Message:OrangeYearsShine": {
+                "downstream": [],
+                "obj": {
+                    "component_name": "Message",
+                    "params": {
+                        "content": [
+                            "{Agent:NewPumasLick@content}"
+                        ]
+                    }
+                },
+                "upstream": [
+                    "Agent:NewPumasLick"
+                ]
+            },
+            "begin": {
+                "downstream": [
+                    "Agent:NewPumasLick"
+                ],
+                "obj": {
+                    "component_name": "Begin",
+                    "params": {
+                        "enablePrologue": true,
+                        "inputs": {},
+                        "mode": "conversational",
+                        "prologue": "\u4f60\u597d\uff01 \u6211\u662f\u4f60\u7684\u52a9\u7406\uff0c\u6709\u4ec0\u4e48\u53ef\u4ee5\u5e2e\u5230\u4f60\u7684\u5417\uff1f"
+                    }
+                },
+                "upstream": []
+            }
+        },
+        "globals": {
+            "sys.conversation_turns": 0,
+            "sys.files": [],
+            "sys.query": "",
+            "sys.user_id": ""
+        },
+        "graph": {
+            "edges": [
+                {
+                    "data": {
+                        "isHovered": false
+                    },
+                    "id": "xy-edge__beginstart-Agent:NewPumasLickend",
+                    "source": "begin",
+                    "sourceHandle": "start",
+                    "target": "Agent:NewPumasLick",
+                    "targetHandle": "end"
+                },
+                {
+                    "data": {
+                        "isHovered": false
+                    },
+                    "id": "xy-edge__Agent:NewPumasLickstart-Message:OrangeYearsShineend",
+                    "markerEnd": "logo",
+                    "source": "Agent:NewPumasLick",
+                    "sourceHandle": "start",
+                    "style": {
+                        "stroke": "rgba(91, 93, 106, 1)",
+                        "strokeWidth": 1
+                    },
+                    "target": "Message:OrangeYearsShine",
+                    "targetHandle": "end",
+                    "type": "buttonEdge",
+                    "zIndex": 1001
+                },
+                {
+                    "data": {
+                        "isHovered": false
+                    },
+                    "id": "xy-edge__Agent:NewPumasLicktool-Tool:AllBirdsNailend",
+                    "selected": false,
+                    "source": "Agent:NewPumasLick",
+                    "sourceHandle": "tool",
+                    "target": "Tool:AllBirdsNail",
+                    "targetHandle": "end"
+                }
+            ],
+            "nodes": [
+                {
+                    "data": {
+                        "form": {
+                            "enablePrologue": true,
+                            "inputs": {},
+                            "mode": "conversational",
+                            "prologue": "\u4f60\u597d\uff01 \u6211\u662f\u4f60\u7684\u52a9\u7406\uff0c\u6709\u4ec0\u4e48\u53ef\u4ee5\u5e2e\u5230\u4f60\u7684\u5417\uff1f"
+                        },
+                        "label": "Begin",
+                        "name": "begin"
+                    },
+                    "dragging": false,
+                    "id": "begin",
+                    "measured": {
+                        "height": 48,
+                        "width": 200
+                    },
+                    "position": {
+                        "x": -9.569875358221438,
+                        "y": 205.84018385864917
+                    },
+                    "selected": false,
+                    "sourcePosition": "left",
+                    "targetPosition": "right",
+                    "type": "beginNode"
+                },
+                {
+                    "data": {
+                        "form": {
+                            "content": [
+                                "{Agent:NewPumasLick@content}"
+                            ]
+                        },
+                        "label": "Message",
+                        "name": "Response"
+                    },
+                    "dragging": false,
+                    "id": "Message:OrangeYearsShine",
+                    "measured": {
+                        "height": 56,
+                        "width": 200
+                    },
+                    "position": {
+                        "x": 734.4061285881053,
+                        "y": 199.9706031723009
+                    },
+                    "selected": false,
+                    "sourcePosition": "right",
+                    "targetPosition": "left",
+                    "type": "messageNode"
+                },
+                {
+                    "data": {
+                        "form": {
+                            "delay_after_error": 1,
+                            "description": "",
+                            "exception_comment": "",
+                            "exception_default_value": "",
+                            "exception_goto": [],
+                            "exception_method": null,
+                            "frequencyPenaltyEnabled": false,
+                            "frequency_penalty": 0.5,
+                            "llm_id": "qwen3-235b-a22b-instruct-2507@Tongyi-Qianwen",
+                            "maxTokensEnabled": true,
+                            "max_retries": 3,
+                            "max_rounds": 3,
+                            "max_tokens": 128000,
+                            "mcp": [],
+                            "message_history_window_size": 12,
+                            "outputs": {
+                                "content": {
+                                    "type": "string",
+                                    "value": ""
+                                }
+                            },
+                            "parameter": "Precise",
+                            "presencePenaltyEnabled": false,
+                            "presence_penalty": 0.5,
+                            "prompts": [
+                                {
+                                    "content": "# User Query\n {sys.query}",
+                                    "role": "user"
+                                }
+                            ],
+                            "sys_prompt": "## Role & Task\nYou are a **\u201cKnowledge Base Retrieval Q\\&A Agent\u201d** whose goal is to break down the user\u2019s question into retrievable subtasks, and then produce a multi-source-verified, structured, and actionable research report using the internal knowledge base.\n## Execution Framework (Detailed Steps & Key Points)\n1. **Assessment & Decomposition**\n   * Actions:\n     * Automatically extract: main topic, subtopics, entities (people/organizations/products/technologies), time window, geographic/business scope.\n     * Output as a list: N facts/data points that must be collected (*N* ranges from 5\u201320 depending on question complexity).\n2. **Query Type Determination (Rule-Based)**\n   * Example rules:\n     * If the question involves a single issue but requests \u201cmethod comparison/multiple explanations\u201d \u2192 use **depth-first**.\n     * If the question can naturally be split into \u22653 independent sub-questions \u2192 use **breadth-first**.\n     * If the question can be answered by a single fact/specification/definition \u2192 use **simple query**.\n3. **Research Plan Formulation**\n   * Depth-first: define 3\u20135 perspectives (methodology/stakeholders/time dimension/technical route, etc.), assign search keywords, target document types, and output format for each perspective.\n   * Breadth-first: list subtasks, prioritize them, and assign search terms.\n   * Simple query: directly provide the search sentence and required fields.\n4. **Retrieval Execution**\n   * After retrieval: perform coverage check (does it contain the key facts?) and quality check (source diversity, authority, latest update time).\n   * If standards are not met, automatically loop: rewrite queries (synonyms/cross-domain terms) and retry \u22643 times, or flag as requiring external search.\n5. **Integration & Reasoning**\n   * Build the answer using a **fact\u2013evidence\u2013reasoning** chain. For each conclusion, attach 1\u20132 strongest pieces of evidence.\n---\n## Quality Gate Checklist (Verify at Each Stage)\n* **Stage 1 (Decomposition)**:\n  * [ ] Key concepts and expected outputs identified\n  * [ ] Required facts/data points listed\n* **Stage 2 (Retrieval)**:\n  * [ ] Meets quality standards (see above)\n  * [ ] If not met: execute query iteration\n* **Stage 3 (Generation)**:\n  * [ ] Each conclusion has at least one direct evidence source\n  * [ ] State assumptions/uncertainties\n  * [ ] Provide next-step suggestions or experiment/retrieval plans\n  * [ ] Final length and depth match user expectations (comply with word count/format if specified)\n---\n## Core Principles\n1. **Strict reliance on the knowledge base**: answers must be **fully bounded** by the content retrieved from the knowledge base.\n2. **No fabrication**: do not generate, infer, or create information that is not explicitly present in the knowledge base.\n3. **Accuracy first**: prefer incompleteness over inaccurate content.\n4. **Output format**:\n   * Hierarchically clear modular structure\n   * Logical grouping according to the MECE principle\n   * Professionally presented formatting\n   * Step-by-step cognitive guidance\n   * Reasonable use of headings and dividers for clarity\n   * *Italicize* key parameters\n   * **Bold** critical information\n5. **LaTeX formula requirements**:\n   * Inline formulas: start and end with `$`\n   * Block formulas: start and end with `$$`, each `$$` on its own line\n   * Block formula content must comply with LaTeX math syntax\n   * Verify formula correctness\n---\n## Additional Notes (Interaction & Failure Strategy)\n* If the knowledge base does not cover critical facts: explicitly inform the user (with sample wording)\n* For time-sensitive issues: enforce time filtering in the search request, and indicate the latest retrieval date in the answer.\n* Language requirement: answer in the user\u2019s preferred language\n",
+                            "temperature": "0.1",
+                            "temperatureEnabled": true,
+                            "tools": [
+                                {
+                                    "component_name": "Retrieval",
+                                    "name": "Retrieval",
+                                    "params": {
+                                        "cross_languages": [],
+                                        "description": "",
+                                        "empty_response": "",
+                                        "kb_ids": [],
+                                        "keywords_similarity_weight": 0.7,
+                                        "outputs": {
+                                            "formalized_content": {
+                                                "type": "string",
+                                                "value": ""
+                                            }
+                                        },
+                                        "rerank_id": "",
+                                        "similarity_threshold": 0.2,
+                                        "top_k": 1024,
+                                        "top_n": 8,
+                                        "use_kg": false
+                                    }
+                                }
+                            ],
+                            "topPEnabled": false,
+                            "top_p": 0.75,
+                            "user_prompt": "",
+                            "visual_files_var": ""
+                        },
+                        "label": "Agent",
+                        "name": "Knowledge Base Agent"
+                    },
+                    "dragging": false,
+                    "id": "Agent:NewPumasLick",
+                    "measured": {
+                        "height": 84,
+                        "width": 200
+                    },
+                    "position": {
+                        "x": 347.00048227952215,
+                        "y": 186.49109364794631
+                    },
+                    "selected": false,
+                    "sourcePosition": "right",
+                    "targetPosition": "left",
+                    "type": "agentNode"
+                },
+                {
+                    "data": {
+                        "form": {
+                            "description": "This is an agent for a specific task.",
+                            "user_prompt": "This is the order you need to send to the agent."
+                        },
+                        "label": "Tool",
+                        "name": "flow.tool_10"
+                    },
+                    "dragging": false,
+                    "id": "Tool:AllBirdsNail",
+                    "measured": {
+                        "height": 48,
+                        "width": 200
+                    },
+                    "position": {
+                        "x": 220.24819746977118,
+                        "y": 403.31576836482583
+                    },
+                    "selected": false,
+                    "sourcePosition": "right",
+                    "targetPosition": "left",
+                    "type": "toolNode"
+                }
+            ]
+        },
+        "history": [],
+        "memory": [],
+        "messages": [],
+        "path": [],
+        "retrieval": []
+    },
+    "avatar": "data:image/png;base64,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"
+}