Refa: knowledge_graph chunk method is deprecated (#7220)

### What problem does this PR solve?

The knowledge_graph chunk method is deprecated and should no longer be
used. #7184.

### Type of change

- [x] Refactoring

.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 +1,118 @@
+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", "overseas" ]
+    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.MY_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.MY_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
+
+      # https://github.com/marketplace/actions/docker-login
+      - name: Login to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: infiniflow
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+
+      # https://github.com/marketplace/actions/build-and-push-docker-images
+      - name: Build and push full image
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          push: true
+          tags: infiniflow/ragflow:${{ env.RELEASE_TAG }}
+          file: Dockerfile
+          platforms: linux/amd64
+
+      # https://github.com/marketplace/actions/build-and-push-docker-images
+      - name: Build and push slim image
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          push: true
+          tags: infiniflow/ragflow:${{ env.RELEASE_TAG }}-slim
+          file: Dockerfile
+          build-args: LIGHTEN=1
+          platforms: linux/amd64
+
+      - name: Build ragflow-sdk
+        if: startsWith(github.ref, 'refs/tags/v')
+        run: |
+          cd sdk/python && \
+          uv build
+
+      - name: Publish package distributions to PyPI
+        if: startsWith(github.ref, 'refs/tags/v')
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          packages-dir: sdk/python/dist/
+          password: ${{ secrets.PYPI_API_TOKEN }}
+          verbose: true

.github/workflows/tests.yml

@@ -0,0 +1,153 @@
+name: tests
+
+on:
+  push:
+    branches:
+      - 'main'
+      - '*.*.*'
+    paths-ignore:
+      - 'docs/**'
+      - '*.md'
+      - '*.mdx'
+  pull_request:
+    types: [ opened, synchronize, reopened, labeled ]
+    paths-ignore:
+      - 'docs/**'
+      - '*.md'
+      - '*.mdx'
+
+# 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' || contains(github.event.pull_request.labels.*.name, 'ci') }}
+    runs-on: [ "self-hosted", "debug" ]
+    steps:
+      # https://github.com/hmarr/debug-action
+      #- uses: hmarr/debug-action@v2
+
+      - name: Show who triggered this workflow
+        run: |
+          echo "Workflow triggered by ${{ github.event_name }}"
+
+      - name: Ensure workspace ownership
+        run: echo "chown -R $USER $GITHUB_WORKSPACE" && sudo chown -R $USER $GITHUB_WORKSPACE
+
+      # https://github.com/actions/checkout/issues/1781
+      - name: Check out code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          fetch-tags: true
+
+      # https://github.com/astral-sh/ruff-action
+      - name: Static check with Ruff
+        uses: astral-sh/ruff-action@v2
+        with:
+          version: ">=0.8.2"
+          args: "check"
+
+      - name: Build ragflow:nightly-slim
+        run: |
+          RUNNER_WORKSPACE_PREFIX=${RUNNER_WORKSPACE_PREFIX:-$HOME}
+          sudo docker pull ubuntu:22.04
+          sudo docker build --progress=plain --build-arg LIGHTEN=1 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
+
+      - name: Build ragflow:nightly
+        run: |
+          sudo docker build --progress=plain --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly .
+
+      - name: Start ragflow:nightly-slim
+        run: |
+          echo -e "\nRAGFLOW_IMAGE=infiniflow/ragflow:nightly-slim" >> docker/.env
+          sudo docker compose -f docker/docker-compose.yml up -d
+
+      - name: Stop ragflow:nightly-slim
+        if: always()  # always run this step even if previous steps failed
+        run: |
+          sudo docker compose -f docker/docker-compose.yml down -v
+
+      - name: Start ragflow:nightly
+        run: |
+          echo -e "\nRAGFLOW_IMAGE=infiniflow/ragflow:nightly" >> docker/.env
+          sudo docker compose -f docker/docker-compose.yml up -d
+
+      - 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=""
+          export HOST_ADDRESS=http://host.docker.internal:9380
+          until sudo docker exec ragflow-server curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
+            echo "Waiting for service to be available..."
+            sleep 5
+          done
+          cd sdk/python && uv sync --python 3.10 --frozen && uv pip install . && source .venv/bin/activate && cd test/test_sdk_api && pytest -s --tb=short get_email.py t_dataset.py t_chat.py t_session.py t_document.py t_chunk.py
+
+      - 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=""
+          export HOST_ADDRESS=http://host.docker.internal:9380
+          until sudo docker exec ragflow-server curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
+            echo "Waiting for service to be available..."
+            sleep 5
+          done
+          cd sdk/python && uv sync --python 3.10 --frozen && uv pip install . && source .venv/bin/activate && cd test/test_frontend_api && pytest -s --tb=short get_email.py 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=""
+          export HOST_ADDRESS=http://host.docker.internal:9380
+          until sudo docker exec ragflow-server curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
+            echo "Waiting for service to be available..."
+            sleep 5
+          done
+          cd sdk/python && uv sync --python 3.10 --frozen && uv pip install . && source .venv/bin/activate && cd test/test_http_api && pytest -s --tb=short -m "not slow"
+
+      - name: Stop ragflow:nightly
+        if: always()  # always run this step even if previous steps failed
+        run: |
+          sudo docker compose -f docker/docker-compose.yml down -v
+
+      - name: Start ragflow:nightly
+        run: |
+          sudo DOC_ENGINE=infinity docker compose -f docker/docker-compose.yml 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=""
+          export HOST_ADDRESS=http://host.docker.internal:9380
+          until sudo docker exec ragflow-server curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
+            echo "Waiting for service to be available..."
+            sleep 5
+          done
+          cd sdk/python && uv sync --python 3.10 --frozen && uv pip install . && source .venv/bin/activate && cd test/test_sdk_api && pytest -s --tb=short get_email.py t_dataset.py t_chat.py t_session.py t_document.py t_chunk.py
+
+      - 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=""
+          export HOST_ADDRESS=http://host.docker.internal:9380
+          until sudo docker exec ragflow-server curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
+            echo "Waiting for service to be available..."
+            sleep 5
+          done
+          cd sdk/python && uv sync --python 3.10 --frozen && uv pip install . && source .venv/bin/activate && cd test/test_frontend_api && pytest -s --tb=short get_email.py 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=""
+          export HOST_ADDRESS=http://host.docker.internal:9380
+          until sudo docker exec ragflow-server curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
+            echo "Waiting for service to be available..."
+            sleep 5
+          done
+          cd sdk/python && uv sync --python 3.10 --frozen && uv pip install . && source .venv/bin/activate && cd test/test_http_api && DOC_ENGINE=infinity pytest -s --tb=short -m "not slow"
+
+      - name: Stop ragflow:nightly
+        if: always()  # always run this step even if previous steps failed
+        run: |
+          sudo DOC_ENGINE=infinity docker compose -f docker/docker-compose.yml down -v

.gitignore

@@ -35,4 +35,10 @@ 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/
+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/

CONTRIBUTING.md

@@ -1,16 +1,10 @@
----
-sidebar_position: 0
-slug: /contribution_guidelines
----
-
 # Contribution guidelines
 
-Thanks for wanting to contribute to RAGFlow. This document offers guidlines and major considerations for submitting your contributions. 
+This document offers guidelines and major considerations for submitting your contributions to RAGFlow.
 
 - To report a bug, file a [GitHub issue](https://github.com/infiniflow/ragflow/issues/new/choose) with us.
 - For further questions, you can explore existing discussions or initiate a new one in [Discussions](https://github.com/orgs/infiniflow/discussions).
 
-
 ## What you can contribute
 
 The list below mentions some contributions you can make, but it is not a complete list.
@@ -27,7 +21,7 @@ The list below mentions some contributions you can make, but it is not a complet
 ### General workflow
 
 1. Fork our GitHub repository.
-2. Clone your fork to your local machine: 
+2. Clone your fork to your local machine:
 `git clone git@github.com:<yourname>/ragflow.git`
 3. Create a local branch: 
 `git checkout -b my-branch`
@@ -39,14 +33,16 @@ The list below mentions some contributions you can make, but it is not a complet
 
 ### Before filing a PR
 
-- Consider splitting a large PR into multiple smaller, standalone PRs to keep a traceable development history. 
+- Consider splitting a large PR into multiple smaller, standalone PRs to keep a traceable development history.
 - Ensure that your PR addresses just one issue, or keep any unrelated changes small.
 - Add test cases when contributing new features. They demonstrate that your code functions correctly and protect against potential issues from future changes.
-### Describing your PR 
+
+### Describing your PR
 
 - Ensure that your PR title is concise and clear, providing all the required information.
-- Refer to a corresponding GitHub issue in your PR description if applicable. 
+- Refer to a corresponding GitHub issue in your PR description if applicable.
 - Include sufficient design details for *breaking changes* or *API changes* in your description.
 
 ### Reviewing & merging a PR
-- Ensure that your PR passes all Continuous Integration (CI) tests before merging it.
+
+Ensure that your PR passes all Continuous Integration (CI) tests before merging it.

Dockerfile

@@ -1,23 +1,211 @@
-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
+ARG LIGHTEN=0
+ENV LIGHTEN=${LIGHTEN}
 
 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 
+RUN --mount=type=bind,from=infiniflow/ragflow_deps:latest,source=/huggingface.co,target=/huggingface.co \
+    if [ "$LIGHTEN" != "1" ]; then \
+        (tar -cf - \
+            /huggingface.co/BAAI/bge-large-zh-v1.5 \
+            /huggingface.co/maidalun1020/bce-embedding-base_v1 \
+            | tar -xf - --strip-components=2 -C /root/.ragflow) \
+    fi
 
-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 [ "$NEED_MIRROR" == "1" ]; then \
+        sed -i 's|http://ports.ubuntu.com|http://mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list; \
+        sed -i 's|http://archive.ubuntu.com|http://mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list; \
+    fi; \
+    rm -f /etc/apt/apt.conf.d/docker-clean && \
+    echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache && \
+    chmod 1777 /tmp && \
+    apt update && \
+    apt --no-install-recommends install -y ca-certificates && \
+    apt update && \
+    apt install -y libglib2.0-0 libglx-mesa0 libgl1 && \
+    apt install -y pkg-config libicu-dev libgdiplus && \
+    apt install -y default-jdk && \
+    apt install -y libatk-bridge2.0-0 && \
+    apt install -y libpython3-dev libgtk-4-1 libnss3 xdg-utils libgbm-dev && \
+    apt install -y libjemalloc-dev && \
+    apt install -y python3-pip pipx nginx unzip curl wget git vim less
+
+RUN if [ "$NEED_MIRROR" == "1" ]; then \
+        pip3 config set global.index-url https://mirrors.aliyun.com/pypi/simple && \
+        pip3 config set global.trusted-host mirrors.aliyun.com; \
+        mkdir -p /etc/uv && \
+        echo "[[index]]" > /etc/uv/uv.toml && \
+        echo 'url = "https://mirrors.aliyun.com/pypi/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|mirrors.aliyun.com/pypi|g' uv.lock; \
+    else \
+        sed -i 's|mirrors.aliyun.com/pypi|pypi.org|g' uv.lock; \
+    fi; \
+    if [ "$LIGHTEN" == "1" ]; then \
+        uv sync --python 3.10 --frozen; \
+    else \
+        uv sync --python 3.10 --frozen --all-extras; \
+    fi
+
+COPY web web
+COPY docs docs
+RUN --mount=type=cache,id=ragflow_npm,target=/root/.npm,sharing=locked \
+    cd web && npm install && npm run build
+
+COPY .git /ragflow/.git
+
+RUN version_info=$(git describe --tags --match=v* --first-parent --always); \
+    if [ "$LIGHTEN" == "1" ]; then \
+        version_info="$version_info slim"; \
+    else \
+        version_info="$version_info full"; \
+    fi; \
+    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 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
 
-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

@@ -1,34 +0,0 @@
-FROM python:3.11
-USER  root
-
-WORKDIR /ragflow
-
-COPY requirements_arm.txt /ragflow/requirements.txt
-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
-
-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

@@ -26,6 +26,7 @@ RUN dnf install -y nginx
 
 ADD ./web ./web
 ADD ./api ./api
+ADD ./docs ./docs
 ADD ./conf ./conf
 ADD ./deepdoc ./deepdoc
 ADD ./rag ./rag
@@ -37,7 +38,7 @@ 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 +53,7 @@ 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
 

README.md

@@ -7,34 +7,42 @@
 <p align="center">
   <a href="./README.md">English</a> |
   <a href="./README_zh.md">简体中文</a> |
+  <a href="./README_tzh.md">繁体中文</a> |
   <a href="./README_ja.md">日本語</a> |
-  <a href="./README_ko.md">한국어</a>
+  <a href="./README_ko.md">한국어</a> |
+  <a href="./README_id.md">Bahasa Indonesia</a> |
+  <a href="/README_pt_br.md">Português (Brasil)</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/badge/docker_pull-ragflow:v0.18.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.18.0">
+    </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:v0.10.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.10.0"></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>
 </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>
 
 <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 +50,9 @@
 - 🔎 [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 without embedding models](#-build-a-docker-image-without-embedding-models)
+- 🔧 [Build a docker image including embedding models](#-build-a-docker-image-including-embedding-models)
+- 🔨 [Launch service from source for development](#-launch-service-from-source-for-development)
 - 📚 [Documentation](#-documentation)
 - 📜 [Roadmap](#-roadmap)
 - 🏄 [Community](#-community)
@@ -53,43 +62,44 @@
 
 ## 💡 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 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.
 
 ## 🎮 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"/>
+<img src="https://github.com/user-attachments/assets/504bbbf1-c9f7-4d83-8cc5-e9cb63c26db6" width="1200"/>
 </div>
 
-
 ## 🔥 Latest Updates
 
+- 2025-03-19 Supports using a multi-modal model to make sense of images within PDF or DOCX files.
+- 2025-02-28 Combined with Internet search (Tavily), supports reasoning like Deep Research for any LLMs.
+- 2025-01-26 Optimizes knowledge graph extraction and application, offering various configuration options.
+- 2024-12-18 Upgrades Document Layout Analysis model in DeepDoc.
+- 2024-11-01 Adds keyword extraction and related question generation to the parsed chunks to improve the accuracy of retrieval.
 - 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.
 
-- 2024-07-21 Supports more LLMs (LocalAI, OpenRouter, StepFun, and Nvidia).
+## 🎉 Stay Tuned
 
-- 2024-07-18 Adds more components (Wikipedia, PubMed, Baidu, and Duckduckgo) to the graph.
+⭐️ Star our repository to stay up-to-date with exciting new features and improvements! Get instant notifications for new
+releases! 🌟
 
-- 2024-07-08 Supports workflow based on [Graph](./graph/README.md).
-- 2024-06-27 Supports Markdown and Docx in the Q&A parsing method. 
-- 2024-06-27 Supports extracting images from Docx files. 
-- 2024-06-27 Supports extracting tables from Markdown files.
-- 2024-06-06 Supports [Self-RAG](https://huggingface.co/papers/2310.11511), which is enabled by default in dialog settings.
-- 2024-05-30 Integrates [BCE](https://github.com/netease-youdao/BCEmbedding) and [BGE](https://github.com/FlagOpen/FlagEmbedding) reranker models.
-- 2024-05-23 Supports [RAPTOR](https://arxiv.org/html/2401.18059v1) for better text retrieval.
-- 2024-05-15 Integrates OpenAI GPT-4o.
+<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**
@@ -127,7 +137,8 @@ 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/).
+  > 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
 
@@ -146,7 +157,8 @@ 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
@@ -158,18 +170,29 @@ Try our demo at [https://demo.ragflow.io](https://demo.ragflow.io).
    $ git clone https://github.com/infiniflow/ragflow.git
    ```
 
-3. Build the pre-built Docker images and start up the server:
+3. Start up the server using the pre-built Docker images:
 
-   > 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=v0.10.0`, before running the following commands.
+> [!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.
+
+   > The command below downloads the `v0.18.0-slim` edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.18.0-slim`, update the `RAGFLOW_IMAGE` variable accordingly in **docker/.env** before using `docker compose` to start the server. For example: set `RAGFLOW_IMAGE=infiniflow/ragflow:v0.18.0` for the full edition `v0.18.0`.
 
    ```bash
    $ cd ragflow/docker
-   $ chmod +x ./entrypoint.sh
-   $ docker compose up -d
-   ```
-   
+   # Use CPU for embedding and 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 embedding and DeepDoc tasks:
+   # docker compose -f docker-compose-gpu.yml up -d
+   ```
+
+   | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
+   |-------------------|-----------------|-----------------------|--------------------------|
+   | v0.18.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
+   | v0.18.0-slim      | &approx;2       | ❌                   | Stable release            |
+   | nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
+   | nightly-slim      | &approx;2       | ❌                   | _Unstable_ nightly build  |
 
 4. Check the server status after having the server up and running:
 
@@ -180,172 +203,172 @@ Try our demo at [https://demo.ragflow.io](https://demo.ragflow.io).
    _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 anomaly` 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 without embedding models
+
+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 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
 ```
 
-## 🛠️ Launch service from source
+## 🔧 Build a Docker image including embedding models
 
-To launch the service from source:
+This image is approximately 9 GB in size. As it includes embedding models, it relies on external LLM services only.
 
-1. Clone the repository: 
+```bash
+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 for development
+
+1. Install uv, or skip this step if it is already installed:
 
    ```bash
-   $ git clone https://github.com/infiniflow/ragflow.git
-   $ cd ragflow/
+   pipx install uv
    ```
 
-2. Create a virtual environment, ensuring that Anaconda or Miniconda is installed:
+2. Clone the source code and install Python dependencies:
 
    ```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/
+   git clone https://github.com/infiniflow/ragflow.git
+   cd ragflow/
+   uv sync --python 3.10 --all-extras # install RAGFlow dependent python modules
    ```
 
-3. Copy the entry script and configure environment variables:
+3. Launch the dependent services (MinIO, Elasticsearch, Redis, and MySQL) using Docker Compose:
 
    ```bash
-   # Get the Python path:
-   $ which python
-   # Get the ragflow project path:
-   $ pwd
-   ```
-   
-   ```bash
-   $ cp docker/entrypoint.sh .
-   $ vi entrypoint.sh
+   docker compose -f docker/docker-compose-base.yml up -d
    ```
 
+   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
+   ```
+
+4. If you cannot access HuggingFace, set the `HF_ENDPOINT` environment variable to use a mirror site:
+
    ```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. Launch backend service:
 
    ```bash
-   $ cd docker
-   $ docker compose -f docker-compose-base.yml up -d 
+   source .venv/bin/activate
+   export PYTHONPATH=$(pwd)
+   bash docker/launch_backend_service.sh
    ```
 
-5. Check the configuration files, ensuring that:
-
-   - 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:
+6. Install frontend dependencies:
+   ```bash
+   cd web
+   npm install
+   ```
+7. Launch frontend service:
 
    ```bash
-   $ chmod +x ./entrypoint.sh
-   $ bash ./entrypoint.sh
+   npm run dev
    ```
 
-7. Launch the frontend service:
+   _The following output confirms a successful launch of the system:_
 
-   ```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
-   ```
+   ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
 
 ## 📚 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](./CONTRIBUTING.md) first.

README_id.md

@@ -0,0 +1,340 @@
+<div align="center">
+<a href="https://demo.ragflow.io/">
+<img src="web/src/assets/logo-with-text.png" width="520" alt="Logo ragflow">
+</a>
+</div>
+
+<p align="center">
+  <a href="./README.md">English</a> |
+  <a href="./README_zh.md">简体中文</a> |
+  <a href="./README_tzh.md">繁体中文</a> |
+  <a href="./README_ja.md">日本語</a> |
+  <a href="./README_ko.md">한국어</a> |
+  <a href="./README_id.md">Bahasa Indonesia</a> |
+  <a href="/README_pt_br.md">Português (Brasil)</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/badge/docker_pull-ragflow:v0.18.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.18.0">
+    </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>
+</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>
+
+<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 tanpa Model Embedding](#-membangun-image-docker-tanpa-model-embedding)
+- 🔧 [Membangun Image Docker dengan Model Embedding](#-membangun-image-docker-dengan-model-embedding)
+- 🔨 [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 berbasis pemahaman dokumen yang mendalam. Platform ini menyediakan alur kerja RAG yang efisien untuk bisnis dengan berbagai skala, menggabungkan LLM (Large Language Models) untuk menyediakan kemampuan tanya-jawab yang benar dan didukung oleh referensi dari data terstruktur kompleks.
+
+## 🎮 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://github.com/infiniflow/ragflow/assets/7248/2f6baa3e-1092-4f11-866d-36f6a9d075e5" width="1200"/>
+<img src="https://github.com/user-attachments/assets/504bbbf1-c9f7-4d83-8cc5-e9cb63c26db6" width="1200"/>
+</div>
+
+## 🔥 Pembaruan Terbaru
+
+- 2025-03-19 Mendukung penggunaan model multi-modal untuk memahami gambar di dalam file PDF atau DOCX.
+- 2025-02-28 dikombinasikan dengan pencarian Internet (TAVILY), mendukung penelitian mendalam untuk LLM apa pun.
+- 2025-01-26 Optimalkan ekstraksi dan penerapan grafik pengetahuan dan sediakan berbagai opsi konfigurasi.
+- 2024-12-18 Meningkatkan model Analisis Tata Letak Dokumen di DeepDoc.
+- 2024-11-01 Penambahan ekstraksi kata kunci dan pembuatan pertanyaan terkait untuk meningkatkan akurasi pengambilan.
+- 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/infiniflow/ragflow/assets/12318111/d6ac5664-c237-4200-a7c2-a4a00691b485" width="1000"/>
+</div>
+
+## 🎬 Mulai
+
+### 📝 Prasyarat
+
+- CPU >= 4 inti
+- RAM >= 16 GB
+- Disk >= 50 GB
+- Docker >= 24.0.0 & Docker Compose >= v2.26.1
+
+### 🚀 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 v0.18.0-slim dari gambar Docker RAGFlow. Silakan merujuk ke tabel berikut untuk deskripsi berbagai edisi RAGFlow. Untuk mengunduh edisi RAGFlow yang berbeda dari v0.18.0-slim, perbarui variabel RAGFLOW_IMAGE di docker/.env sebelum menggunakan docker compose untuk memulai server. Misalnya, atur RAGFLOW_IMAGE=infiniflow/ragflow:v0.18.0 untuk edisi lengkap v0.18.0.
+
+```bash
+$ cd ragflow/docker
+# Use CPU for embedding and DeepDoc tasks:
+$ docker compose -f docker-compose.yml up -d
+
+# To use GPU to accelerate embedding and DeepDoc tasks:
+# docker compose -f docker-compose-gpu.yml up -d
+```
+
+| RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
+| ----------------- | --------------- | --------------------- | ------------------------ |
+| v0.18.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
+| v0.18.0-slim      | &approx;2       | ❌                    | Stable release           |
+| nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
+| nightly-slim      | &approx;2       | ❌                    | _Unstable_ nightly build |
+
+1. Periksa status server setelah server aktif dan berjalan:
+
+   ```bash
+   $ docker logs -f ragflow-server
+   ```
+
+   _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 tanpa Model Embedding
+
+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 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
+```
+
+## 🔧 Membangun Docker Image Termasuk Model Embedding
+
+Image ini berukuran sekitar 9 GB. Karena sudah termasuk model embedding, ia hanya bergantung pada aplikasi LLM eksternal.
+
+```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, atau lewati langkah ini jika sudah terinstal:
+
+   ```bash
+   pipx install uv
+   ```
+
+2. Clone kode sumber dan instal dependensi Python:
+
+   ```bash
+   git clone https://github.com/infiniflow/ragflow.git
+   cd ragflow/
+   uv sync --python 3.10 --all-extras # install RAGFlow dependent python modules
+   ```
+
+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
+   ```
+
+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. Jalankan aplikasi backend:
+
+   ```bash
+   source .venv/bin/activate
+   export PYTHONPATH=$(pwd)
+   bash docker/launch_backend_service.sh
+   ```
+
+6. Instal dependensi frontend:
+   ```bash
+   cd web
+   npm install
+   ```
+7. 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)
+
+## 📚 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](./CONTRIBUTING.md).

README_ja.md

@@ -7,29 +7,36 @@
 <p align="center">
   <a href="./README.md">English</a> |
   <a href="./README_zh.md">简体中文</a> |
+  <a href="./README_tzh.md">繁体中文</a> |
   <a href="./README_ja.md">日本語</a> |
-  <a href="./README_ko.md">한국어</a>
+  <a href="./README_ko.md">한국어</a> |
+  <a href="./README_id.md">Bahasa Indonesia</a> |
+  <a href="/README_pt_br.md">Português (Brasil)</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/badge/docker_pull-ragflow:v0.18.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.18.0">
+    </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:v0.10.0-brightgreen"
-            alt="docker pull infiniflow/ragflow:v0.10.0"></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>
 </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>
 
@@ -40,27 +47,28 @@
 ## 🎮 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"/>
+<img src="https://github.com/user-attachments/assets/504bbbf1-c9f7-4d83-8cc5-e9cb63c26db6" width="1200"/>
 </div>
 
-
 ## 🔥 最新情報
 
+- 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。
+- 2025-02-28 インターネット検索 (TAVILY) と組み合わせて、あらゆる LLM の詳細な調査をサポートします。
+- 2025-01-26 ナレッジ グラフの抽出と適用を最適化し、さまざまな構成オプションを提供します。
+- 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。
+- 2024-11-01 再現の精度を向上させるために、解析されたチャンクにキーワード抽出と関連質問の生成を追加しました。
 - 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。
-- 2024-07-21 より多くの LLM サプライヤー (LocalAI/OpenRouter/StepFun/Nvidia) をサポートします。
-- 2024-07-18 グラフにコンポーネント(Wikipedia/PubMed/Baidu/Duckduckgo)を追加しました。
-- 2024-07-08 [Graph](./graph/README.md) ベースのワークフローをサポート
-- 2024-06-27 Q&A解析方式はMarkdownファイルとDocxファイルをサポートしています。
-- 2024-06-27 Docxファイルからの画像の抽出をサポートします。
-- 2024-06-27 Markdownファイルからテーブルを抽出することをサポートします。
-- 2024-06-06 会話設定でデフォルトでチェックされている [Self-RAG](https://huggingface.co/papers/2310.11511) をサポートします。
-- 2024-05-30 [BCE](https://github.com/netease-youdao/BCEmbedding) 、[BGE](https://github.com/FlagOpen/FlagEmbedding) reranker を統合。
-- 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。
-- 2024-05-15 OpenAI GPT-4oを統合しました。
+
+## 🎉 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
 
@@ -137,17 +145,29 @@
 
 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:
 
+> [!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 v0.18.0-slim 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。v0.18.0-slim 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。例えば、完全版 v0.18.0 をダウンロードするには、RAGFLOW_IMAGE=infiniflow/ragflow:v0.18.0 と設定します。
+
    ```bash
    $ cd ragflow/docker
-   $ chmod +x ./entrypoint.sh
-   $ docker compose up -d
+   # Use CPU for embedding and DeepDoc tasks:
+   $ docker compose -f docker-compose.yml up -d
+
+   # To use GPU to accelerate embedding and DeepDoc tasks:
+   # docker compose -f docker-compose-gpu.yml 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=v0.10.0として、上記のコマンドを実行してください。
+   | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
+   | ----------------- | --------------- | --------------------- | ------------------------ |
+   | v0.18.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
+   | v0.18.0-slim      | &approx;2       | ❌                    | Stable release           |
+   | nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
+   | nightly-slim      | &approx;2       | ❌                     | _Unstable_ nightly build |
 
-   > 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。
-
-4. サーバーを立ち上げた後、サーバーの状態を確認する:
+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
@@ -156,23 +176,20 @@
    _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。
+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。
 
@@ -183,111 +200,136 @@
 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（埋め込みモデルなし）
+
+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:v0.10.0 .
-$ 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 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
 ```
 
-## 🛠️ ソースコードからサービスを起動する方法
+## 🔧 ソースコードをコンパイルした Docker イメージ（埋め込みモデルを含む）
 
-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. リポジトリをクローンします
-```bash
-$ git clone https://github.com/infiniflow/ragflow.git
-$ cd ragflow/
-```
-
-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/
-```
-
-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
-```
-
-`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。
-
-`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。
+この Docker のサイズは約 9GB で、埋め込みモデルを含むため、外部の大モデルサービスのみが必要です。
 
 ```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
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .
 ```
 
-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 
-```
+## 🔨 ソースコードからサービスを起動する方法
 
-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。
+1. uv 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:
 
-6. サービスを起動します
-```bash
-$ chmod +x ./entrypoint.sh
-$ bash ./entrypoint.sh
-```
+   ```bash
+   pipx install uv
+   ```
+
+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:
+
+   ```bash
+   git clone https://github.com/infiniflow/ragflow.git
+   cd ragflow/
+   uv sync --python 3.10 --all-extras # install RAGFlow dependent python modules
+   ```
+
+3. Docker Compose を使用して依存サービス（MinIO、Elasticsearch、Redis、MySQL）を起動する:
+
+   ```bash
+   docker compose -f docker/docker-compose-base.yml up -d
+   ```
+
+   `/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:
+
+   ```
+   127.0.0.1       es01 infinity mysql minio redis
+   ```
+
+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
+   export HF_ENDPOINT=https://hf-mirror.com
+   ```
+
+5. バックエンドサービスを起動する:
+
+   ```bash
+   source .venv/bin/activate
+   export PYTHONPATH=$(pwd)
+   bash docker/launch_backend_service.sh
+   ```
+
+6. フロントエンドの依存関係をインストールする:
+   ```bash
+   cd web
+   npm install
+   ```
+7. 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)
 
 ## 📚 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](./CONTRIBUTING.md)をご覧ください。

README_ko.md

@@ -7,101 +7,97 @@
 <p align="center">
   <a href="./README.md">English</a> |
   <a href="./README_zh.md">简体中文</a> |
+  <a href="./README_tzh.md">繁体中文</a> |
   <a href="./README_ja.md">日本語</a> |
   <a href="./README_ko.md">한국어</a> |
+  <a href="./README_id.md">Bahasa Indonesia</a> |
+  <a href="/README_pt_br.md">Português (Brasil)</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/badge/docker_pull-ragflow:v0.18.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.18.0">
+    </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:v0.10.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.10.0"></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>
 </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>
 
-
 ## 💡 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.
 
-
-
 ## 🎮 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"/>
+<img src="https://github.com/user-attachments/assets/504bbbf1-c9f7-4d83-8cc5-e9cb63c26db6" width="1200"/>
 </div>
 
-
 ## 🔥 업데이트
 
+- 2025-03-19 PDF 또는 DOCX 파일 내의 이미지를 이해하기 위해 다중 모드 모델을 사용하는 것을 지원합니다.
+- 2025-02-28 인터넷 검색(TAVILY)과 결합되어 모든 LLM에 대한 심층 연구를 지원합니다.
+- 2025-01-26 지식 그래프 추출 및 적용을 최적화하고 다양한 구성 옵션을 제공합니다.
+- 2024-12-18 DeepDoc의 문서 레이아웃 분석 모델 업그레이드.
+- 2024-11-01 파싱된 청크에 키워드 추출 및 관련 질문 생성을 추가하여 재현율을 향상시킵니다.
 - 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.
+## 🎉 계속 지켜봐 주세요
 
-- 2024-07-21: 더 많은 LLMs(LocalAI, OpenRouter, StepFun, Nvidia)를 지원합니다.
-
-- 2024-07-18: 그래프에 더 많은 구성요소(Wikipedia, PubMed, Baidu, Duckduckgo)를 추가합니다.
-
-- 2024-07-08: [Graph](./graph/README.md)를 기반으로 한 워크플로우를 지원합니다.
-
-- 2024-06-27: Q&A 분석 방법에서 Markdown과 Docx를 지원합니다.
-
-- 2024-06-27: Docx 파일에서 이미지 추출을 지원합니다.
-
-- 2024-06-27: Markdown 파일에서 표 추출을 지원합니다.
-
-- 2024-06-06: 대화 설정에서 기본으로 [Self-RAG](https://huggingface.co/papers/2310.11511)를 지원합니다.
-
-- 2024-05-30: [BCE](https://github.com/netease-youdao/BCEmbedding) 및 [BGE](https://github.com/FlagOpen/FlagEmbedding) reranker 모델을 통합합니다.
-
-- 2024-05-23: 더 나은 텍스트 검색을 위해 [RAPTOR](https://arxiv.org/html/2401.18059v1)를 지원합니다.
-
-- 2024-05-15: OpenAI GPT-4o를 통합합니다.
+⭐️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;">
@@ -109,17 +105,19 @@
 </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.
-
+  > 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
@@ -147,17 +145,29 @@
 
 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=v0.10.0`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 v0.18.0-slim 버전을 다운로드합니다. 다양한 RAGFlow 버전에 대한 설명은 다음 표를 참조하십시오. v0.18.0-slim과 다른 RAGFlow 버전을 다운로드하려면, docker/.env 파일에서 RAGFLOW_IMAGE 변수를 적절히 업데이트한 후 docker compose를 사용하여 서버를 시작하십시오. 예를 들어, 전체 버전인 v0.18.0을 다운로드하려면 RAGFLOW_IMAGE=infiniflow/ragflow:v0.18.0로 설정합니다.
+
    ```bash
    $ cd ragflow/docker
-   $ chmod +x ./entrypoint.sh
-   $ docker compose up -d
+   # Use CPU for embedding and DeepDoc tasks:
+   $ docker compose -f docker-compose.yml up -d
+
+   # To use GPU to accelerate embedding and DeepDoc tasks:
+   # docker compose -f docker-compose-gpu.yml up -d
    ```
-   
-   > 기본 이미지는 약 9GB 크기이며 로드하는 데 시간이 걸릴 수 있습니다.
 
+   | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
+   | ----------------- | --------------- | --------------------- | ------------------------ |
+   | v0.18.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
+   | v0.18.0-slim      | &approx;2       | ❌                    | Stable release           |
+   | nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
+   | nightly-slim      | &approx;2       | ❌                     | _Unstable_ nightly build |
 
-4. 서버가 시작된 후 서버 상태를 확인하세요:
+1. 서버가 시작된 후 서버 상태를 확인하세요:
 
    ```bash
    $ docker logs -f ragflow-server
@@ -166,23 +176,21 @@
    _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 anomaly` 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!_
@@ -192,143 +200,135 @@
 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(임베딩 모델 포함하지 않음)
+
+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 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
 ```
 
+## 🔧 소스 코드로 Docker 이미지를 컴파일합니다(임베딩 모델 포함)
 
-## 🛠️ 소스에서 서비스 시작하기
+이 Docker의 크기는 약 9GB이며, 이미 임베딩 모델을 포함하고 있으므로 외부 대형 모델 서비스에만 의존하면 됩니다.
 
-서비스를 소스에서 시작하려면:
+```bash
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .
+```
 
-1. 레포지토리를 클론하세요: 
+## 🔨 소스 코드로 서비스를 시작합니다.
+
+1. uv를 설치하거나 이미 설치된 경우 이 단계를 건너뜁니다:
 
    ```bash
-   $ git clone https://github.com/infiniflow/ragflow.git
-   $ cd ragflow/
+   pipx install uv
    ```
 
-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 --all-extras # install RAGFlow dependent python modules
+   ```
+
+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
+   ```
+
+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. 백엔드 서비스를 시작합니다:
 
    ```bash
-   $ chmod +x ./entrypoint.sh
-   $ bash ./entrypoint.sh
+   source .venv/bin/activate
+   export PYTHONPATH=$(pwd)
+   bash docker/launch_backend_service.sh
    ```
 
+6. 프론트엔드 의존성을 설치합니다:
+   ```bash
+   cd web
+   npm install
+   ```
 7. 프론트엔드 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 
+   npm run dev
    ```
 
-8. 프론트엔드 서비스를 배포합니다:
+   _다음 인터페이스는 시스템이 성공적으로 시작되었음을 나타냅니다:_
 
-   ```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
-   ```
+   ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
 
 ## 📚 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](./CONTRIBUTING.md)을 검토해 주세요.

README_pt_br.md

@@ -0,0 +1,361 @@
+<div align="center">
+<a href="https://demo.ragflow.io/">
+<img src="web/src/assets/logo-with-text.png" 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_tzh.md">繁体中文</a> |
+  <a href="./README_ja.md">日本語</a> |
+  <a href="./README_ko.md">한국어</a> |
+  <a href="./README_id.md">Bahasa Indonesia</a> |
+  <a href="/README_pt_br.md">Português (Brasil)</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/badge/docker_pull-ragflow:v0.18.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.18.0">
+    </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>
+</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>
+
+<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 RAG (Geração Aumentada por Recuperação) de código aberto baseado em entendimento profundo de documentos. Ele oferece um fluxo de trabalho RAG simplificado para empresas de qualquer porte, combinando LLMs (Modelos de Linguagem de Grande Escala) para fornecer capacidades de perguntas e respostas verídicas, respaldadas por citações bem fundamentadas de diversos dados complexos formatados.
+
+## 🎮 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://github.com/infiniflow/ragflow/assets/7248/2f6baa3e-1092-4f11-866d-36f6a9d075e5" width="1200"/>
+<img src="https://github.com/user-attachments/assets/504bbbf1-c9f7-4d83-8cc5-e9cb63c26db6" width="1200"/>
+</div>
+
+## 🔥 Últimas Atualizações
+
+- 19-03-2025 Suporta o uso de um modelo multi-modal para entender imagens dentro de arquivos PDF ou DOCX.
+- 28-02-2025 combinado com a pesquisa na Internet (T AVI LY), suporta pesquisas profundas para qualquer LLM.
+- 26-01-2025 Otimize a extração e aplicação de gráficos de conhecimento e forneça uma variedade de opções de configuração.
+- 18-12-2024 Atualiza o modelo de Análise de Layout de Documentos no DeepDoc.
+- 01-11-2024 Adiciona extração de palavras-chave e geração de perguntas relacionadas aos blocos analisados para melhorar a precisão da recuperação.
+- 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/infiniflow/ragflow/assets/12318111/d6ac5664-c237-4200-a7c2-a4a00691b485" 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
+  > 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 `v0.18.0-slim` 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 `v0.18.0-slim`, atualize a variável `RAGFLOW_IMAGE` conforme necessário no **docker/.env** antes de usar `docker compose` para iniciar o servidor. Por exemplo: defina `RAGFLOW_IMAGE=infiniflow/ragflow:v0.18.0` para a edição completa `v0.18.0`.
+
+    ```bash
+    $ cd ragflow/docker
+    # Use CPU for embedding and DeepDoc tasks:
+    $ docker compose -f docker-compose.yml up -d
+
+    # To use GPU to accelerate embedding and DeepDoc tasks:
+    # docker compose -f docker-compose-gpu.yml up -d
+    ```
+
+    | Tag da imagem RAGFlow | Tamanho da imagem (GB) | Possui modelos de incorporação? | Estável?                 |
+    | --------------------- | ---------------------- | ------------------------------- | ------------------------ |
+    | v0.18.0               | ~9                     | :heavy_check_mark:              | Lançamento estável       |
+    | v0.18.0-slim          | ~2                     | ❌                              | Lançamento estável       |
+    | nightly               | ~9                     | :heavy_check_mark:              | _Instável_ build noturno |
+    | nightly-slim          | ~2                     | ❌                               | _Instável_ build noturno |
+
+4.  Verifique o status do servidor após tê-lo iniciado:
+
+    ```bash
+    $ docker logs -f ragflow-server
+    ```
+
+    _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 sem modelos de incorporação
+
+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 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
+```
+
+## 🔧 Criar uma imagem Docker incluindo modelos de incorporação
+
+Esta imagem tem cerca de 9 GB de tamanho. Como inclui modelos de incorporação, depende apenas de serviços externos de LLM.
+
+```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`, ou pule esta etapa se ele já estiver instalado:
+
+   ```bash
+   pipx install uv
+   ```
+
+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 --all-extras # instala os módulos Python dependentes do RAGFlow
+   ```
+
+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
+   ```
+
+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. Lance o serviço de back-end:
+
+   ```bash
+   source .venv/bin/activate
+   export PYTHONPATH=$(pwd)
+   bash docker/launch_backend_service.sh
+   ```
+
+6. Instale as dependências do front-end:
+
+   ```bash
+   cd web
+   npm install
+   ```
+
+7. 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)
+
+## 📚 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](./CONTRIBUTING.md).

README_tzh.md

@@ -0,0 +1,361 @@
+<div align="center">
+<a href="https://demo.ragflow.io/">
+<img src="web/src/assets/logo-with-text.png" 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_id.md">Bahasa Indonesia</a> |
+  <a href="/README_pt_br.md">Português (Brasil)</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/badge/docker_pull-ragflow:v0.18.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.18.0">
+    </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>
+</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>
+
+## 💡 RAGFlow 是什麼？
+
+[RAGFlow](https://ragflow.io/) 是一款基於深度文件理解所建構的開源 RAG（Retrieval-Augmented Generation）引擎。 RAGFlow 可以為各種規模的企業及個人提供一套精簡的 RAG 工作流程，結合大語言模型（LLM）針對用戶各類不同的複雜格式數據提供可靠的問答以及有理有據的引用。
+
+## 🎮 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://github.com/infiniflow/ragflow/assets/7248/2f6baa3e-1092-4f11-866d-36f6a9d075e5" width="1200"/>
+<img src="https://github.com/user-attachments/assets/504bbbf1-c9f7-4d83-8cc5-e9cb63c26db6" width="1200"/>
+</div>
+
+## 🔥 近期更新
+
+- 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.
+- 2025-02-28 結合網路搜尋（Tavily），對於任意大模型實現類似 Deep Research 的推理功能.
+- 2025-01-26 最佳化知識圖譜的擷取與應用，提供了多種配置選擇。
+- 2024-12-18 升級了 DeepDoc 的文檔佈局分析模型。
+- 2024-11-01 對解析後的 chunk 加入關鍵字抽取和相關問題產生以提高回想的準確度。
+- 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/infiniflow/ragflow/assets/12318111/d6ac5664-c237-4200-a7c2-a4a00691b485" 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
+  > 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 slim Docker 映像 `v0.18.0-slim`。請參考下表查看不同 Docker 發行版的說明。如需下載不同於 `v0.18.0-slim` 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。例如，你可以透過設定 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.18.0` 來下載 RAGFlow 鏡像的 `v0.18.0` 完整發行版。
+
+   ```bash
+   $ cd ragflow/docker
+   # Use CPU for embedding and DeepDoc tasks:
+   $ docker compose -f docker-compose.yml up -d
+
+   # To use GPU to accelerate embedding and DeepDoc tasks:
+   # docker compose -f docker-compose-gpu.yml up -d
+   ```
+
+   | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
+   | ----------------- | --------------- | --------------------- | ------------------------ |
+   | v0.18.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
+   | v0.18.0-slim      | &approx;2       | ❌                    | Stable release           |
+   | nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
+   | nightly-slim      | &approx;2       | ❌                     | _Unstable_ nightly build |
+
+   > [!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 ragflow-server
+   ```
+
+   _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（不含 embedding 模型）
+
+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 --build-arg LIGHTEN=1 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
+```
+
+## 🔧 原始碼編譯 Docker 映像（包含 embedding 模型）
+
+本 Docker 大小約 9 GB 左右。由於已包含 embedding 模型，所以只需依賴外部的大模型服務即可。
+
+```bash
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+docker build --platform linux/amd64 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly .
+```
+
+## 🔨 以原始碼啟動服務
+
+1. 安裝 uv。如已安裝，可跳過此步驟：
+
+   ```bash
+   pipx install uv
+   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 --all-extras # install RAGFlow dependent python modules
+   ```
+
+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
+   ```
+
+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.啟動後端服務：
+『`bash
+source .venv/bin/activate
+export PYTHONPATH=$(pwd)
+bash docker/launch_backend_service.sh
+
+```
+
+6. 安裝前端依賴：
+『`bash
+cd web
+npm install
+```
+
+7. 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)
+   ```
+
+## 📚 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](./CONTRIBUTING.md) 。
+
+## 🤝 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

@@ -7,28 +7,36 @@
 <p align="center">
   <a href="./README.md">English</a> |
   <a href="./README_zh.md">简体中文</a> |
+  <a href="./README_tzh.md">繁体中文</a> |
   <a href="./README_ja.md">日本語</a> |
-  <a href="./README_ko.md">한국어</a>
+  <a href="./README_ko.md">한국어</a> |
+  <a href="./README_id.md">Bahasa Indonesia</a> |
+  <a href="/README_pt_br.md">Português (Brasil)</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/badge/docker_pull-ragflow:v0.18.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.18.0">
+    </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:v0.10.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.10.0"></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>
 </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>
 
@@ -39,27 +47,28 @@
 ## 🎮 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"/>
+<img src="https://github.com/user-attachments/assets/504bbbf1-c9f7-4d83-8cc5-e9cb63c26db6" width="1200"/>
 </div>
 
-
 ## 🔥 近期更新
 
-- 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。
-- 2024-07-21 支持更多的大模型供应商(LocalAI/OpenRouter/StepFun/Nvidia)。
-- 2024-07-18 在Graph中支持算子：Wikipedia、PubMed、Baidu和Duckduckgo。
-- 2024-07-08 支持 Agentic RAG: 基于 [Graph](./graph/README.md) 的工作流。
-- 2024-06-27 Q&A 解析方式支持 Markdown 文件和 Docx 文件。
-- 2024-06-27 支持提取出 Docx 文件中的图片。
-- 2024-06-27 支持提取出 Markdown 文件中的表格。
-- 2024-06-06 支持 [Self-RAG](https://huggingface.co/papers/2310.11511) ，在对话设置里面默认勾选。
-- 2024-05-30 集成 [BCE](https://github.com/netease-youdao/BCEmbedding) 和 [BGE](https://github.com/FlagOpen/FlagEmbedding) 重排序模型。
-- 2024-05-23 实现 [RAPTOR](https://arxiv.org/html/2401.18059v1) 提供更好的文本检索。
-- 2024-05-15 集成大模型 OpenAI GPT-4o。
+- 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.
+- 2025-02-28 结合互联网搜索（Tavily），对于任意大模型实现类似 Deep Research 的推理功能.
+- 2025-01-26 优化知识图谱的提取和应用，提供了多种配置选择。
+- 2024-12-18 升级了 DeepDoc 的文档布局分析模型。
+- 2024-11-01 对解析后的 chunk 加入关键词抽取和相关问题生成以提高召回的准确度。
+- 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
 
@@ -136,15 +145,33 @@
 
 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 slim Docker 镜像 `v0.18.0-slim`。请参考下表查看不同 Docker 发行版的描述。如需下载不同于 `v0.18.0-slim` 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。比如，你可以通过设置 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.18.0` 来下载 RAGFlow 镜像的 `v0.18.0` 完整发行版。
+
    ```bash
    $ cd ragflow/docker
-   $ chmod +x ./entrypoint.sh
-   $ docker compose -f docker-compose-CN.yml up -d
+   # Use CPU for embedding and DeepDoc tasks:
+   $ docker compose -f docker-compose.yml up -d
+
+   # To use GPU to accelerate embedding and DeepDoc tasks:
+   # docker compose -f docker-compose-gpu.yml up -d
    ```
 
-   > 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=v0.10.0，然后运行上述命令。
+   | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
+   | ----------------- | --------------- | --------------------- | ------------------------ |
+   | v0.18.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
+   | v0.18.0-slim      | &approx;2       | ❌                    | Stable release           |
+   | nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
+   | nightly-slim      | &approx;2       | ❌                     | _Unstable_ nightly build |
 
-   > 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。
+   > [!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：
 
@@ -155,23 +182,20 @@
    _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 anomaly` 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)。
 
@@ -182,143 +206,150 @@
 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（不含 embedding 模型）
+
+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:v0.10.0 .
-$ 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 --build-arg LIGHTEN=1 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
 ```
 
-## 🛠️ 源码启动服务
+## 🔧 源码编译 Docker 镜像（包含 embedding 模型）
 
-如需从源码启动服务，请参考以下步骤：
-
-1. 克隆仓库
+本 Docker 大小约 9 GB 左右。由于已包含 embedding 模型，所以只需依赖外部的大模型服务即可。
 
 ```bash
-$ git clone https://github.com/infiniflow/ragflow.git
-$ cd ragflow/
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+docker build --platform linux/amd64 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly .
 ```
 
-2. 创建虚拟环境（确保已安装 Anaconda 或 Miniconda）
+## 🔨 以源代码启动服务
 
-```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/
-```
+1. 安装 uv。如已经安装，可跳过本步骤：
 
-3. 拷贝入口脚本并配置环境变量
+   ```bash
+   pipx install uv
+   export UV_INDEX=https://mirrors.aliyun.com/pypi/simple
+   ```
 
-```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
-```
+2. 下载源代码并安装 Python 依赖：
 
-将上述 `which python` 的输出作为 `PY` 的值，将 `pwd` 的输出作为 `PYTHONPATH` 的值。
+   ```bash
+   git clone https://github.com/infiniflow/ragflow.git
+   cd ragflow/
+   uv sync --python 3.10 --all-extras # install RAGFlow dependent python modules
+   ```
 
-`LD_LIBRARY_PATH` 如果环境已经配置好，可以注释掉。
+3. 通过 Docker Compose 启动依赖的服务（MinIO, Elasticsearch, Redis, and MySQL）：
 
-```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
-```
+   ```bash
+   docker compose -f docker/docker-compose-base.yml up -d
+   ```
 
-4. 启动基础服务
+   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`：
 
-```bash
-$ cd docker
-$ docker compose -f docker-compose-base.yml up -d 
-```
+   ```
+   127.0.0.1       es01 infinity mysql minio redis
+   ```
+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：
 
-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
+   export HF_ENDPOINT=https://hf-mirror.com
+   ```
 
-6. 启动服务
+5. 启动后端服务：
 
-```bash
-$ chmod +x ./entrypoint.sh
-$ bash ./entrypoint.sh
-```
+   ```bash
+   source .venv/bin/activate
+   export PYTHONPATH=$(pwd)
+   bash docker/launch_backend_service.sh
+   ```
 
-7. 启动WebUI服务
+6. 安装前端依赖：
+   ```bash
+   cd web
+   npm install
+   ```
+7. 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
+   ```
 
-8. 部署WebUI服务
+   _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)
+8. 开发完成后停止 RAGFlow 服务
+   停止 RAGFlow 前端和后端服务：
+   ```bash
+   pkill -f "ragflow_server.py|task_executor.py"
+   ```
 
-```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](./CONTRIBUTING.md) 。
 
 ## 🤝 商务合作
 
@@ -331,4 +362,3 @@ 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>
-

SECURITY.md

@@ -18,7 +18,7 @@ main
 ### Actual behavior
 
 The restricted_loads function at [api/utils/__init__.py#L215](https://github.com/infiniflow/ragflow/blob/main/api/utils/__init__.py#L215) is still vulnerable leading via code execution.
-The main reson is that numpy module has a numpy.f2py.diagnose.run_command function directly execute commands, but the restricted_loads function allows users import functions in module numpy.
+The main reason is that numpy module has a numpy.f2py.diagnose.run_command function directly execute commands, but the restricted_loads function allows users import functions in module numpy.
 
 
 ### Steps to reproduce

agent/README.md

@@ -10,7 +10,7 @@ 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.
+Otherwise, correct configurations in service_conf.yaml is essential.
 
 ```bash
 PYTHONPATH=path/to/ragflow python graph/test/client.py -h

agent/README_zh.md

@@ -11,7 +11,7 @@
 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。
+否则，正确配置 service_conf.yaml 文件是必不可少的。
 
 ```bash
 PYTHONPATH=path/to/ragflow python graph/test/client.py -h

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

@@ -13,10 +13,8 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-import importlib
+import logging
 import json
-import traceback
-from abc import ABC
 from copy import deepcopy
 from functools import partial
 
@@ -24,10 +22,9 @@ import pandas as pd
 
 from agent.component import component_class
 from agent.component.base import ComponentBase
-from agent.settings import flow_logger, DEBUG
 
 
-class Canvas(ABC):
+class Canvas:
     """
     dsl = {
         "components": {
@@ -88,7 +85,8 @@ class Canvas(ABC):
                         }
                     },
                     "downstream": [],
-                    "upstream": []
+                    "upstream": [],
+                    "parent_id": ""
                 }
             },
             "history": [],
@@ -139,7 +137,8 @@ class Canvas(ABC):
             "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():
@@ -162,8 +161,13 @@ class Canvas(ABC):
             self.components[k]["obj"].reset()
         self._embed_id = ""
 
+    def get_component_name(self, cid):
+        for n in self.dsl["graph"]["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)
@@ -173,71 +177,107 @@ class Canvas(ABC):
                 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
+                for an in ans():
+                    yield an
+            else:
+                yield ans
+            return
 
         if not self.path:
             self.components["begin"]["obj"].run(self.history, **kwargs)
             self.path.append(["begin"])
 
         self.path.append([])
+
         ran = -1
+        waiting = []
+        without_dependent_checking = []
 
         def prepare2run(cpns):
             nonlocal ran, ans
             for c in cpns:
-                if self.path[-1] and c == self.path[-1][-1]: continue
+                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":
+                    logging.debug(f"Canvas.prepare2run: {c}")
+                    if c not in without_dependent_checking:
                         cpids = cpn.get_dependent_components()
-                        if any([c not in self.path[-1] for c in cpids]):
+                        if any([cc not in self.path[-1] for cc in cpids]):
+                            if c not in waiting:
+                                waiting.append(c)
                             continue
-                    ans = cpn.run(self.history, **kwargs)
+                    yield "*'{}'* is running...🕞".format(self.get_component_name(c))
+
+                    if cpn.component_name.lower() == "iteration":
+                        st_cpn = cpn.get_start()
+                        assert st_cpn, "Start component not found for Iteration."
+                        if not st_cpn["obj"].end():
+                            cpn = st_cpn["obj"]
+                            c = cpn._id
+
+                    try:
+                        ans = cpn.run(self.history, **kwargs)
+                    except Exception as e:
+                        logging.exception(f"Canvas.run got exception: {e}")
+                        self.path[-1].append(c)
+                        ran += 1
+                        raise e
                     self.path[-1].append(c)
+
             ran += 1
 
-        prepare2run(self.components[self.path[-2][-1]]["downstream"])
+        downstream = self.components[self.path[-2][-1]]["downstream"]
+        if not downstream and self.components[self.path[-2][-1]].get("parent_id"):
+            cid = self.path[-2][-1]
+            pid = self.components[cid]["parent_id"]
+            o, _ = self.components[cid]["obj"].output(allow_partial=False)
+            oo, _ = self.components[pid]["obj"].output(allow_partial=False)
+            self.components[pid]["obj"].set_output(pd.concat([oo, o], ignore_index=True).dropna())
+            downstream = [pid]
+
+        for m in prepare2run(downstream):
+            yield {"content": m, "running_status": True}
+
         while 0 <= ran < len(self.path[-1]):
-            if DEBUG: print(ran, self.path)
+            logging.debug(f"Canvas.run: {ran} {self.path}")
             cpn_id = self.path[-1][ran]
             cpn = self.get_component(cpn_id)
-            if not cpn["downstream"]: break
+            if not any([cpn["downstream"], cpn.get("parent_id"), waiting]):
+                break
 
             loop = self._find_loop()
-            if loop: raise OverflowError(f"Too much loops: {loop}")
+            if loop:
+                raise OverflowError(f"Too much loops: {loop}")
 
+            downstream = []
             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
+                downstream = [switch_out]
+            else:
+                downstream = cpn["downstream"]
 
-            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 not downstream and cpn.get("parent_id"):
+                pid = cpn["parent_id"]
+                _, o = cpn["obj"].output(allow_partial=False)
+                _, oo = self.components[pid]["obj"].output(allow_partial=False)
+                self.components[pid]["obj"].set_output(pd.concat([oo.dropna(axis=1), o.dropna(axis=1)], ignore_index=True).dropna())
+                downstream = [pid]
+
+            for m in prepare2run(downstream):
+                yield {"content": m, "running_status": True}
+
+            if ran >= len(self.path[-1]) and waiting:
+                without_dependent_checking = waiting
+                waiting = []
+                for m in prepare2run(without_dependent_checking):
+                    yield {"content": m, "running_status": True}
+                without_dependent_checking = []
+                ran -= 1
 
         if self.answer:
             cpn_id = self.answer[0]
@@ -246,11 +286,13 @@ class Canvas(ABC):
             self.path[-1].append(cpn_id)
             if kwargs.get("stream"):
                 assert isinstance(ans, partial)
-                return ans
+                for an in ans():
+                    yield an
+            else:
+                yield ans
 
-            self.history.append(("assistant", ans.to_dict("records")))
-
-        return ans
+        else:
+            raise Exception("The dialog flow has no way to interact with you. Please add an 'Interact' component to the end of the flow.")
 
     def get_component(self, cpn_id):
         return self.components[cpn_id]
@@ -260,9 +302,11 @@ class Canvas(ABC):
 
     def get_history(self, window_size):
         convs = []
-        for role, obj in self.history[window_size * -2:]:
-            convs.append({"role": role, "content": (obj if role == "user" else
-                                                    '\n'.join(pd.DataFrame(obj)['content']))})
+        for role, obj in self.history[window_size * -1:]:
+            if isinstance(obj, list) and obj and all([isinstance(o, dict) for o in obj]):
+                convs.append({"role": role, "content": '\n'.join([str(s.get("content", "")) for s in obj])})
+            else:
+                convs.append({"role": role, "content": str(obj)})
         return convs
 
     def add_user_input(self, question):
@@ -276,19 +320,22 @@ class Canvas(ABC):
 
     def _find_loop(self, max_loops=6):
         path = self.path[-1][::-1]
-        if len(path) < 2: return False
+        if len(path) < 2:
+            return False
 
         for i in range(len(path)):
-            if path[i].lower().find("answer") >= 0:
+            if path[i].lower().find("answer") == 0 or path[i].lower().find("iterationitem") == 0:
                 path = path[:i]
                 break
 
-        if len(path) < 2: return False
+        if len(path) < 2:
+            return False
 
-        for l in range(2, len(path) // 2):
-            pat = ",".join(path[0:l])
+        for loc in range(2, len(path) // 2):
+            pat = ",".join(path[0:loc])
             path_str = ",".join(path)
-            if len(pat) >= len(path_str): return False
+            if len(pat) >= len(path_str):
+                return False
             loop = max_loops
             while path_str.find(pat) == 0 and loop >= 0:
                 loop -= 1
@@ -296,7 +343,23 @@ class Canvas(ABC):
                     return False
                 path_str = path_str[len(pat)+1:]
             if loop < 0:
-                pat = " => ".join([p.split(":")[0] for p in path[0:l]])
+                pat = " => ".join([p.split(":")[0] for p in path[0:loc]])
                 return pat + " => " + pat
 
         return False
+
+    def get_prologue(self):
+        return self.components["begin"]["obj"]._param.prologue
+
+    def set_global_param(self, **kwargs):
+        for k, v in kwargs.items():
+            for q in self.components["begin"]["obj"]._param.query:
+                if k != q["key"]:
+                    continue
+                q["value"] = v
+
+    def get_preset_param(self):
+        return self.components["begin"]["obj"]._param.query
+
+    def get_component_input_elements(self, cpnnm):
+        return self.components[cpnnm]["obj"].get_input_elements()

agent/component/__init__.py

@@ -1,3 +1,19 @@
+#
+#  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 importlib
 from .begin import Begin, BeginParam
 from .generate import Generate, GenerateParam
@@ -9,6 +25,7 @@ from .relevant import Relevant, RelevantParam
 from .message import Message, MessageParam
 from .rewrite import RewriteQuestion, RewriteQuestionParam
 from .keyword import KeywordExtract, KeywordExtractParam
+from .concentrator import Concentrator, ConcentratorParam
 from .baidu import Baidu, BaiduParam
 from .duckduckgo import DuckDuckGo, DuckDuckGoParam
 from .wikipedia import Wikipedia, WikipediaParam
@@ -22,8 +39,95 @@ 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
+from .crawler import Crawler, CrawlerParam
+from .invoke import Invoke, InvokeParam
+from .template import Template, TemplateParam
+from .email import Email, EmailParam
+from .iteration import Iteration, IterationParam
+from .iterationitem import IterationItem, IterationItemParam
+
 
 def component_class(class_name):
     m = importlib.import_module("agent.component")
     c = getattr(m, class_name)
     return c
+
+
+__all__ = [
+    "Begin",
+    "BeginParam",
+    "Generate",
+    "GenerateParam",
+    "Retrieval",
+    "RetrievalParam",
+    "Answer",
+    "AnswerParam",
+    "Categorize",
+    "CategorizeParam",
+    "Switch",
+    "SwitchParam",
+    "Relevant",
+    "RelevantParam",
+    "Message",
+    "MessageParam",
+    "RewriteQuestion",
+    "RewriteQuestionParam",
+    "KeywordExtract",
+    "KeywordExtractParam",
+    "Concentrator",
+    "ConcentratorParam",
+    "Baidu",
+    "BaiduParam",
+    "DuckDuckGo",
+    "DuckDuckGoParam",
+    "Wikipedia",
+    "WikipediaParam",
+    "PubMed",
+    "PubMedParam",
+    "ArXiv",
+    "ArXivParam",
+    "Google",
+    "GoogleParam",
+    "Bing",
+    "BingParam",
+    "GoogleScholar",
+    "GoogleScholarParam",
+    "DeepL",
+    "DeepLParam",
+    "GitHub",
+    "GitHubParam",
+    "BaiduFanyi",
+    "BaiduFanyiParam",
+    "QWeather",
+    "QWeatherParam",
+    "ExeSQL",
+    "ExeSQLParam",
+    "YahooFinance",
+    "YahooFinanceParam",
+    "WenCai",
+    "WenCaiParam",
+    "Jin10",
+    "Jin10Param",
+    "TuShare",
+    "TuShareParam",
+    "AkShare",
+    "AkShareParam",
+    "Crawler",
+    "CrawlerParam",
+    "Invoke",
+    "InvokeParam",
+    "Iteration",
+    "IterationParam",
+    "IterationItem",
+    "IterationItemParam",
+    "Template",
+    "TemplateParam",
+    "Email",
+    "EmailParam",
+    "component_class"
+]

agent/component/akshare.py

@@ -0,0 +1,56 @@
+#
+#  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
+
+
+class AkShareParam(ComponentParamBase):
+    """
+    Define the AkShare component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 10
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+
+
+class AkShare(ComponentBase, ABC):
+    component_name = "AkShare"
+
+    def _run(self, history, **kwargs):
+        import akshare as ak
+        ans = self.get_input()
+        ans = ",".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return AkShare.be_output("")
+
+        try:
+            ak_res = []
+            stock_news_em_df = ak.stock_news_em(symbol=ans)
+            stock_news_em_df = stock_news_em_df.head(self._param.top_n)
+            ak_res = [{"content": '<a href="' + i["新闻链接"] + '">' + i["新闻标题"] + '</a>\n 新闻内容: ' + i[
+                "新闻内容"] + " \n发布时间:" + i["发布时间"] + " \n文章来源: " + i["文章来源"]} for index, i in stock_news_em_df.iterrows()]
+        except Exception as e:
+            return AkShare.be_output("**ERROR**: " + str(e))
+
+        if not ak_res:
+            return AkShare.be_output("")
+
+        return pd.DataFrame(ak_res)

agent/component/answer.py

@@ -16,6 +16,7 @@
 import random
 from abc import ABC
 from functools import partial
+from typing import Tuple, Union
 
 import pandas as pd
 
@@ -76,4 +77,13 @@ class Answer(ComponentBase, ABC):
     def set_exception(self, e):
         self.exception = e
 
+    def output(self, allow_partial=True) -> Tuple[str, Union[pd.DataFrame, partial]]:
+        if allow_partial:
+            return super.output()
+
+        for r, c in self._canvas.history[::-1]:
+            if r == "user":
+                return self._param.output_var_name, pd.DataFrame([{"content": c}])
+
+        self._param.output_var_name, pd.DataFrame([])
 

agent/component/arxiv.py

@@ -13,13 +13,12 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import logging
 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.
@@ -65,5 +64,5 @@ class ArXiv(ComponentBase, ABC):
             return ArXiv.be_output("")
 
         df = pd.DataFrame(arxiv_res)
-        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
+        logging.debug(f"df: {str(df)}")
         return df

agent/component/baidu.py

@@ -13,13 +13,11 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-import random
+import logging
 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
 
 
@@ -46,7 +44,7 @@ class Baidu(ComponentBase, ABC):
             return Baidu.be_output("")
 
         try:
-            url = 'https://www.baidu.com/s?wd=' + ans + '&rn=' + str(self._param.top_n)
+            url = 'http://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)
@@ -64,6 +62,6 @@ class Baidu(ComponentBase, ABC):
             return Baidu.be_output("")
 
         df = pd.DataFrame(baidu_res)
-        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
+        logging.debug(f"df: {str(df)}")
         return df
 

agent/component/baidufanyi.py

@@ -36,13 +36,9 @@ class BaiduFanyiParam(ComponentParamBase):
         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',
@@ -97,3 +93,4 @@ class BaiduFanyi(ComponentBase, ABC):
 
         except Exception as e:
             BaiduFanyi.be_output("**Error**:" + str(e))
+    

agent/component/base.py

@@ -17,14 +17,13 @@ from abc import ABC
 import builtins
 import json
 import os
-from copy import deepcopy
+import logging
 from functools import partial
-from typing import List, Dict, Tuple, Union
+from typing import Tuple, Union
 
 import pandas as pd
 
 from agent import settings
-from agent.settings import flow_logger, DEBUG
 
 _FEEDED_DEPRECATED_PARAMS = "_feeded_deprecated_params"
 _DEPRECATED_PARAMS = "_deprecated_params"
@@ -36,6 +35,9 @@ class ComponentParamBase(ABC):
     def __init__(self):
         self.output_var_name = "output"
         self.message_history_window_size = 22
+        self.query = []
+        self.inputs = []
+        self.debug_inputs = []
 
     def set_name(self, name: str):
         self._name = name
@@ -81,7 +83,6 @@ 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):
@@ -359,13 +360,13 @@ 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."
             )
@@ -383,22 +384,41 @@ class ComponentBase(ABC):
             "params": {}
         }
         """
+        out = getattr(self._param, self._param.output_var_name)
+        if isinstance(out, pd.DataFrame) and "chunks" in out:
+            del out["chunks"]
+            setattr(self._param, self._param.output_var_name, out)
+
         return """{{
             "component_name": "{}",
-            "params": {}
+            "params": {},
+            "output": {},
+            "inputs": {}
         }}""".format(self.component_name,
-                     self._param
-                     )
+                     self._param,
+                     json.dumps(json.loads(str(self._param)).get("output", {}), ensure_ascii=False),
+                     json.dumps(json.loads(str(self._param)).get("inputs", []), ensure_ascii=False)
+        )
 
     def __init__(self, canvas, id, param: ComponentParamBase):
+        from agent.canvas import Canvas  # Local import to avoid cyclic dependency
+        assert isinstance(canvas, Canvas), "canvas must be an instance of Canvas"
         self._canvas = canvas
         self._id = id
         self._param = param
         self._param.check()
 
+    def get_dependent_components(self):
+        cpnts = set([para["component_id"].split("@")[0] for para in self._param.query \
+                     if para.get("component_id") \
+                     and para["component_id"].lower().find("answer") < 0 \
+                     and para["component_id"].lower().find("begin") < 0])
+        return list(cpnts)
+
     def run(self, history, **kwargs):
-        flow_logger.info("{}, history: {}, kwargs: {}".format(self, json.dumps(history, ensure_ascii=False),
+        logging.debug("{}, history: {}, kwargs: {}".format(self, json.dumps(history, ensure_ascii=False),
                                                               json.dumps(kwargs, ensure_ascii=False)))
+        self._param.debug_inputs = []
         try:
             res = self._run(history, **kwargs)
             self.set_output(res)
@@ -413,75 +433,153 @@ class ComponentBase(ABC):
 
     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)
+        if not isinstance(o, partial):
+            if not isinstance(o, pd.DataFrame):
+                if isinstance(o, list):
+                    return self._param.output_var_name, pd.DataFrame(o).dropna()
+                if o is None:
+                    return self._param.output_var_name, pd.DataFrame()
+                return self._param.output_var_name, pd.DataFrame([{"content": str(o)}])
+            return self._param.output_var_name, o
 
         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 pd.DataFrame(o if isinstance(o, list) else [o]).dropna()
             return self._param.output_var_name, o
 
         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
+                outs = pd.DataFrame(oo if isinstance(oo, list) else [oo]).dropna()
+            else:
+                outs = oo.dropna()
         return self._param.output_var_name, outs
 
     def reset(self):
         setattr(self._param, self._param.output_var_name, None)
+        self._param.inputs = []
 
-    def set_output(self, v: pd.DataFrame):
+    def set_output(self, v):
         setattr(self._param, self._param.output_var_name, v)
 
     def get_input(self):
-        upstream_outs = []
+        if self._param.debug_inputs:
+            return pd.DataFrame([{"content": v["value"]} for v in self._param.debug_inputs if v.get("value")])
+
         reversed_cpnts = []
         if len(self._canvas.path) > 1:
             reversed_cpnts.extend(self._canvas.path[-2])
         reversed_cpnts.extend(self._canvas.path[-1])
+        up_cpns = self.get_upstream()
+        reversed_up_cpnts = [cpn for cpn in reversed_cpnts if cpn in up_cpns]
 
-        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._param.query:
+            self._param.inputs = []
+            outs = []
+            for q in self._param.query:
+                if q.get("component_id"):
+                    if q["component_id"].split("@")[0].lower().find("begin") >= 0:
+                        cpn_id, key = q["component_id"].split("@")
+                        for p in self._canvas.get_component(cpn_id)["obj"]._param.query:
+                            if p["key"] == key:
+                                outs.append(pd.DataFrame([{"content": p.get("value", "")}]))
+                                self._param.inputs.append({"component_id": q["component_id"],
+                                                           "content": p.get("value", "")})
+                                break
+                        else:
+                            assert False, f"Can't find parameter '{key}' for {cpn_id}"
+                        continue
+
+                    if q["component_id"].lower().find("answer") == 0:
+                        txt = []
+                        for r, c in self._canvas.history[::-1][:self._param.message_history_window_size][::-1]:
+                            txt.append(f"{r.upper()}:{c}")
+                        txt = "\n".join(txt)
+                        self._param.inputs.append({"content": txt, "component_id": q["component_id"]})
+                        outs.append(pd.DataFrame([{"content": txt}]))
+                        continue
+
+                    outs.append(self._canvas.get_component(q["component_id"])["obj"].output(allow_partial=False)[1])
+                    self._param.inputs.append({"component_id": q["component_id"],
+                                               "content": "\n".join(
+                                                   [str(d["content"]) for d in outs[-1].to_dict('records')])})
+                elif q.get("value"):
+                    self._param.inputs.append({"component_id": None, "content": q["value"]})
+                    outs.append(pd.DataFrame([{"content": q["value"]}]))
+            if outs:
+                df = pd.concat(outs, ignore_index=True)
+                if "content" in df:
+                    df = df.drop_duplicates(subset=['content']).reset_index(drop=True)
+                return df
+
+        upstream_outs = []
+
+        for u in reversed_up_cpnts[::-1]:
+            if self.get_component_name(u) in ["switch", "concentrator"]:
+                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:
+                    o["component_id"] = u
                     upstream_outs.append(o)
                     continue
-            if u not in self._canvas.get_component(self._id)["upstream"]: continue
+            #if self.component_name.lower()!="answer" and 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"]:
                 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}]))
+                        upstream_outs.append(pd.DataFrame([{"content": c, "component_id": u}]))
                         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:
+                o["component_id"] = u
                 upstream_outs.append(o)
             break
 
-        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()
+        assert upstream_outs, "Can't inference the where the component input is. Please identify whose output is this component's input."
+
+        df = pd.concat(upstream_outs, ignore_index=True)
+        if "content" in df:
+            df = df.drop_duplicates(subset=['content']).reset_index(drop=True)
+
+        self._param.inputs = []
+        for _, r in df.iterrows():
+            self._param.inputs.append({"component_id": r["component_id"], "content": r["content"]})
+
+        return df
+
+    def get_input_elements(self):
+        assert self._param.query, "Please verify the input parameters first."
+        eles = []
+        for q in self._param.query:
+            if q.get("component_id"):
+                cpn_id = q["component_id"]
+                if cpn_id.split("@")[0].lower().find("begin") >= 0:
+                    cpn_id, key = cpn_id.split("@")
+                    eles.extend(self._canvas.get_component(cpn_id)["obj"]._param.query)
+                    continue
+
+                eles.append({"name": self._canvas.get_component_name(cpn_id), "key": cpn_id})
+            else:
+                eles.append({"key": q["value"], "name": q["value"], "value": q["value"]})
+        return eles
 
     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])
+        up_cpns = self.get_upstream()
+        reversed_up_cpnts = [cpn for cpn in reversed_cpnts if cpn in up_cpns]
 
-        for u in reversed_cpnts[::-1]:
-            if self.get_component_name(u) in ["switch", "answer"]: continue
+        for u in reversed_up_cpnts[::-1]:
+            if self.get_component_name(u) in ["switch", "answer"]:
+                continue
             return self._canvas.get_component(u)["obj"].output()[1]
 
     @staticmethod
@@ -490,3 +588,14 @@ class ComponentBase(ABC):
 
     def get_component_name(self, cpn_id):
         return self._canvas.get_component(cpn_id)["obj"].component_name.lower()
+
+    def debug(self, **kwargs):
+        return self._run([], **kwargs)
+
+    def get_parent(self):
+        pid = self._canvas.get_component(self._id)["parent_id"]
+        return self._canvas.get_component(pid)["obj"]
+
+    def get_upstream(self):
+        cpn_nms = self._canvas.get_component(self._id)['upstream']
+        return cpn_nms

agent/component/begin.py

@@ -26,6 +26,7 @@ class BeginParam(ComponentParamBase):
     def __init__(self):
         super().__init__()
         self.prologue = "Hi! I'm your smart assistant. What can I do for you?"
+        self.query = []
 
     def check(self):
         return True
@@ -42,7 +43,7 @@ class Begin(ComponentBase):
     def stream_output(self):
         res = {"content": self._param.prologue}
         yield res
-        self.set_output(res)
+        self.set_output(self.be_output(res))
 
 
 

agent/component/bing.py

@@ -13,13 +13,12 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import logging
 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.
@@ -81,5 +80,5 @@ class Bing(ComponentBase, ABC):
             return Bing.be_output("")
 
         df = pd.DataFrame(bing_res)
-        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
+        logging.debug(f"df: {str(df)}")
         return df

agent/component/categorize.py

@@ -13,11 +13,11 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import logging
 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 CategorizeParam(GenerateParam):
@@ -34,36 +34,46 @@ class CategorizeParam(GenerateParam):
         super().check()
         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_prompt(self, chat_hist):
         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", "").split("\n"):
+                if not line:
+                    continue
+                cate_lines.append("USER: {}\nCategory: {}".format(line, c))
         descriptions = []
         for c, desc in self.category_description.items():
             if desc.get("description"):
                 descriptions.append(
-                    "--------------------\nCategory: {}\nDescription: {}\n".format(c, desc["description"]))
+                    "\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:
-        {}
+Role: You're a text classifier. 
+Task: You need to categorize the user’s questions into {} categories, namely: {}
 
-        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.
+Here's description of each category:
+{}
+
+You could learn from the following examples:
+{}
+You could learn from the above examples.
+
+Requirements:
+- Just mention the category names, no need for any additional words.
+
+---- Real Data ----
+USER: {}\n
         """.format(
             len(self.category_description.keys()),
             "/".join(list(self.category_description.keys())),
             "\n".join(descriptions),
-            "- ".join(cate_lines)
+            "\n\n- ".join(cate_lines),
+            chat_hist
         )
         return self.prompt
 
@@ -73,15 +83,26 @@ class Categorize(Generate, ABC):
 
     def _run(self, history, **kwargs):
         input = self.get_input()
-        input = "Question: " + ("; ".join(input["content"]) if "content" in input else "") + "Category: "
+        input = " - ".join(input["content"]) if "content" in input else ""
         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}],
+        ans = chat_mdl.chat(self._param.get_prompt(input), [{"role": "user", "content": "\nCategory: "}],
                             self._param.gen_conf())
-        if DEBUG: print(ans, ":::::::::::::::::::::::::::::::::", input)
+        logging.debug(f"input: {input}, answer: {str(ans)}")    
+        # 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
+            
+        # If a category is found, return the category with the highest count.
+        if any(category_counts.values()):
+            max_category = max(category_counts.items(), key=lambda x: x[1])
+            return Categorize.be_output(self._param.category_description[max_category[0]]["to"])
 
-        return Categorize.be_output(self._param.category_description.items()[-1][1]["to"])
+        return Categorize.be_output(list(self._param.category_description.items())[-1][1]["to"])
 
+    def debug(self, **kwargs):
+        df = self._run([], **kwargs)
+        cpn_id = df.iloc[0, 0]
+        return Categorize.be_output(self._canvas.get_component_name(cpn_id))
 

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/concentrator.py

@@ -0,0 +1,36 @@
+#
+#  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 ConcentratorParam(ComponentParamBase):
+    """
+    Define the Concentrator component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+
+    def check(self):
+        return True
+
+
+class Concentrator(ComponentBase, ABC):
+    component_name = "Concentrator"
+
+    def _run(self, history, **kwargs):
+        return Concentrator.be_output("")

agent/component/crawler.py

@@ -0,0 +1,67 @@
+#
+#  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 asyncio
+from crawl4ai import AsyncWebCrawler
+from agent.component.base import ComponentBase, ComponentParamBase
+from api.utils.web_utils import is_valid_url
+
+
+class CrawlerParam(ComponentParamBase):
+    """
+    Define the Crawler component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.proxy = None
+        self.extract_type = "markdown"
+    
+    def check(self):
+        self.check_valid_value(self.extract_type, "Type of content from the crawler", ['html', 'markdown', 'content'])
+
+
+class Crawler(ComponentBase, ABC):
+    component_name = "Crawler"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not is_valid_url(ans):
+            return Crawler.be_output("URL not valid")
+        try:
+            result = asyncio.run(self.get_web(ans))
+
+            return Crawler.be_output(result)
+            
+        except Exception as e:
+            return Crawler.be_output(f"An unexpected error occurred: {str(e)}")
+
+    async def get_web(self, url):
+        proxy = self._param.proxy if self._param.proxy else None
+        async with AsyncWebCrawler(verbose=True, proxy=proxy) as crawler:
+            result = await crawler.arun(
+                url=url,
+                bypass_cache=True
+            )
+            
+            if self._param.extract_type == 'html':
+                return result.cleaned_html
+            elif self._param.extract_type == 'markdown':
+                return result.markdown
+            elif self._param.extract_type == 'content':
+                result.extracted_content
+            return result.markdown

agent/component/deepl.py

@@ -14,7 +14,6 @@
 #  limitations under the License.
 #
 from abc import ABC
-import re
 from agent.component.base import ComponentBase, ComponentParamBase
 import deepl
 

agent/component/duckduckgo.py

@@ -13,10 +13,10 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import logging
 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
 
 
@@ -62,5 +62,5 @@ class DuckDuckGo(ComponentBase, ABC):
             return DuckDuckGo.be_output("")
 
         df = pd.DataFrame(duck_res)
-        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
+        logging.debug("df: {df}")
         return df

agent/component/email.py

@@ -0,0 +1,141 @@
+#
+#  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 json
+import smtplib
+import logging
+from email.mime.text import MIMEText
+from email.mime.multipart import MIMEMultipart
+from email.header import Header
+from email.utils import formataddr
+from agent.component.base import ComponentBase, ComponentParamBase
+
+class EmailParam(ComponentParamBase):
+    """
+    Define the Email component parameters.
+    """
+    def __init__(self):
+        super().__init__()
+        # Fixed configuration parameters
+        self.smtp_server = ""  # SMTP server address
+        self.smtp_port = 465  # SMTP port
+        self.email = ""  # Sender email
+        self.password = ""  # Email authorization code
+        self.sender_name = ""  # Sender name
+
+    def check(self):
+        # Check required parameters
+        self.check_empty(self.smtp_server, "SMTP Server")
+        self.check_empty(self.email, "Email")
+        self.check_empty(self.password, "Password")
+        self.check_empty(self.sender_name, "Sender Name")
+
+class Email(ComponentBase, ABC):
+    component_name = "Email"
+    
+    def _run(self, history, **kwargs):
+        # Get upstream component output and parse JSON
+        ans = self.get_input()
+        content = "".join(ans["content"]) if "content" in ans else ""
+        if not content:
+            return Email.be_output("No content to send")
+
+        success = False
+        try:
+            # Parse JSON string passed from upstream
+            email_data = json.loads(content)
+            
+            # Validate required fields
+            if "to_email" not in email_data:
+                return Email.be_output("Missing required field: to_email")
+
+            # Create email object
+            msg = MIMEMultipart('alternative')
+            
+            # Properly handle sender name encoding
+            msg['From'] = formataddr((str(Header(self._param.sender_name,'utf-8')), self._param.email))
+            msg['To'] = email_data["to_email"]
+            if "cc_email" in email_data and email_data["cc_email"]:
+                msg['Cc'] = email_data["cc_email"]
+            msg['Subject'] = Header(email_data.get("subject", "No Subject"), 'utf-8').encode()
+
+            # Use content from email_data or default content
+            email_content = email_data.get("content", "No content provided")
+            # msg.attach(MIMEText(email_content, 'plain', 'utf-8'))
+            msg.attach(MIMEText(email_content, 'html', 'utf-8'))
+
+            # Connect to SMTP server and send
+            logging.info(f"Connecting to SMTP server {self._param.smtp_server}:{self._param.smtp_port}")
+            
+            context = smtplib.ssl.create_default_context()
+            with smtplib.SMTP(self._param.smtp_server, self._param.smtp_port) as server:
+                server.ehlo()
+                server.starttls(context=context)
+                server.ehlo()
+                # Login
+                logging.info(f"Attempting to login with email: {self._param.email}")
+                server.login(self._param.email, self._param.password)
+                
+                # Get all recipient list
+                recipients = [email_data["to_email"]]
+                if "cc_email" in email_data and email_data["cc_email"]:
+                    recipients.extend(email_data["cc_email"].split(','))
+                
+                # Send email
+                logging.info(f"Sending email to recipients: {recipients}")
+                try:
+                    server.send_message(msg, self._param.email, recipients)
+                    success = True
+                except Exception as e:
+                    logging.error(f"Error during send_message: {str(e)}")
+                    # Try alternative method
+                    server.sendmail(self._param.email, recipients, msg.as_string())
+                    success = True
+                
+                try:
+                    server.quit()
+                except Exception as e:
+                    # Ignore errors when closing connection
+                    logging.warning(f"Non-fatal error during connection close: {str(e)}")
+
+            if success:
+                return Email.be_output("Email sent successfully")
+
+        except json.JSONDecodeError:
+            error_msg = "Invalid JSON format in input"
+            logging.error(error_msg)
+            return Email.be_output(error_msg)
+            
+        except smtplib.SMTPAuthenticationError:
+            error_msg = "SMTP Authentication failed. Please check your email and authorization code."
+            logging.error(error_msg)
+            return Email.be_output(f"Failed to send email: {error_msg}")
+            
+        except smtplib.SMTPConnectError:
+            error_msg = f"Failed to connect to SMTP server {self._param.smtp_server}:{self._param.smtp_port}"
+            logging.error(error_msg)
+            return Email.be_output(f"Failed to send email: {error_msg}")
+            
+        except smtplib.SMTPException as e:
+            error_msg = f"SMTP error occurred: {str(e)}"
+            logging.error(error_msg)
+            return Email.be_output(f"Failed to send email: {error_msg}")
+            
+        except Exception as e:
+            error_msg = f"Unexpected error: {str(e)}"
+            logging.error(error_msg)
+            return Email.be_output(f"Failed to send email: {error_msg}") 

agent/component/exesql.py

@@ -15,12 +15,17 @@
 #
 from abc import ABC
 import re
+from copy import deepcopy
+
 import pandas as pd
-from peewee import MySQLDatabase, PostgresqlDatabase
-from agent.component.base import ComponentBase, ComponentParamBase
+import pymysql
+import psycopg2
+from agent.component import GenerateParam, Generate
+import pyodbc
+import logging
 
 
-class ExeSQLParam(ComponentParamBase):
+class ExeSQLParam(GenerateParam):
     """
     Define the ExeSQL component parameters.
     """
@@ -37,60 +42,113 @@ class ExeSQLParam(ComponentParamBase):
         self.top_n = 30
 
     def check(self):
-        self.check_valid_value(self.db_type, "Choose DB type", ['mysql', 'postgresql', 'mariadb'])
+        super().check()
+        self.check_valid_value(self.db_type, "Choose DB type", ['mysql', 'postgresql', 'mariadb', 'mssql'])
         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")
+        if self.database == "rag_flow":
+            if self.host == "ragflow-mysql":
+                raise ValueError("For the security reason, it dose not support database named rag_flow.")
+            if self.password == "infini_rag_flow":
+                raise ValueError("For the security reason, it dose not support database named rag_flow.")
 
 
-class ExeSQL(ComponentBase, ABC):
+class ExeSQL(Generate, 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)
+    def _refactor(self, ans):
+        ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
+        match = re.search(r"```sql\s*(.*?)\s*```", ans, re.DOTALL)
+        if match:
+            ans = match.group(1)  # Query content
+            return ans
+        else:
+            print("no markdown")
+        ans = re.sub(r'^.*?SELECT ', 'SELECT ', (ans), flags=re.IGNORECASE)
         ans = re.sub(r';.*?SELECT ', '; SELECT ', ans, flags=re.IGNORECASE)
         ans = re.sub(r';[^;]*$', r';', ans)
         if not ans:
-            return ExeSQL.be_output("SQL statement not found!")
+            raise Exception("SQL statement not found!")
+        return ans
 
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = "".join([str(a) for a in ans["content"]]) if "content" in ans else ""
+        ans = self._refactor(ans)
         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)
+            db = pymysql.connect(db=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)
-
+            db = psycopg2.connect(dbname=self._param.database, user=self._param.username, host=self._param.host,
+                                  port=self._param.port, password=self._param.password)
+        elif self._param.db_type == 'mssql':
+            conn_str = (
+                    r'DRIVER={ODBC Driver 17 for SQL Server};'
+                    r'SERVER=' + self._param.host + ',' + str(self._param.port) + ';'
+                    r'DATABASE=' + self._param.database + ';'
+                    r'UID=' + self._param.username + ';'
+                    r'PWD=' + self._param.password
+            )
+            db = pyodbc.connect(conn_str)
         try:
-            db.connect()
+            cursor = db.cursor()
         except Exception as e:
-            return ExeSQL.be_output("**Error**: \nDatabase Connection Failed! \n" + str(e))
+            raise Exception("Database Connection Failed! \n" + str(e))
+        if not hasattr(self, "_loop"):
+            setattr(self, "_loop", 0)
+            self._loop += 1
+        input_list = re.split(r';', ans.replace(r"\n", " "))
         sql_res = []
-        for single_sql in re.split(r';', ans):
-            if not single_sql:
-                continue
-            try:
-                query = db.execute_sql(single_sql)
-                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
+        for i in range(len(input_list)):
+            single_sql = input_list[i]
+            while self._loop <= self._param.loop:
+                self._loop += 1
+                if not single_sql:
+                    break
+                try:
+                    cursor.execute(single_sql)
+                    if cursor.rowcount == 0:
+                        sql_res.append({"content": "No record in the database!"})
+                        break
+                    if self._param.db_type == 'mssql':
+                        single_res = pd.DataFrame.from_records(cursor.fetchmany(self._param.top_n),
+                                                               columns=[desc[0] for desc in cursor.description])
+                    else:
+                        single_res = pd.DataFrame([i for i in cursor.fetchmany(self._param.top_n)])
+                        single_res.columns = [i[0] for i in cursor.description]
+                    sql_res.append({"content": single_res.to_markdown(index=False, floatfmt=".6f")})
+                    break
+                except Exception as e:
+                    single_sql = self._regenerate_sql(single_sql, str(e), **kwargs)
+                    single_sql = self._refactor(single_sql)
+                    if self._loop > self._param.loop:
+                        sql_res.append({"content": "Can't query the correct data via SQL statement."})
         db.close()
-
         if not sql_res:
-            return ExeSQL.be_output("No record in the database!")
-
+            return ExeSQL.be_output("")
         return pd.DataFrame(sql_res)
+
+    def _regenerate_sql(self, failed_sql, error_message, **kwargs):
+        prompt = f'''
+        ## You are the Repair SQL Statement Helper, please modify the original SQL statement based on the SQL query error report.
+        ## The original SQL statement is as follows:{failed_sql}.
+        ## The contents of the SQL query error report is as follows:{error_message}.
+        ## Answer only the modified SQL statement. Please do not give any explanation, just answer the code.
+'''
+        self._param.prompt = prompt
+        kwargs_ = deepcopy(kwargs)
+        kwargs_["stream"] = False
+        response = Generate._run(self, [], **kwargs_)
+        try:
+            regenerated_sql = response.loc[0, "content"]
+            return regenerated_sql
+        except Exception as e:
+            logging.error(f"Failed to regenerate SQL: {e}")
+            return None
+
+    def debug(self, **kwargs):
+        return self._run([], **kwargs)

agent/component/generate.py

@@ -13,13 +13,16 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import json
 import re
 from functools import partial
 import pandas as pd
 from api.db import LLMType
+from api.db.services.conversation_service import structure_answer
 from api.db.services.llm_service import LLMBundle
-from api.settings import retrievaler
+from api import settings
 from agent.component.base import ComponentBase, ComponentParamBase
+from rag.prompts import message_fit_in
 
 
 class GenerateParam(ComponentParamBase):
@@ -50,11 +53,16 @@ class GenerateParam(ComponentParamBase):
 
     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
+        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
 
 
@@ -62,58 +70,118 @@ class Generate(ComponentBase):
     component_name = "Generate"
 
     def get_dependent_components(self):
-        cpnts = [para["component_id"] for para in self._param.parameters]
-        return cpnts
+        inputs = self.get_input_elements()
+        cpnts = set([i["key"] for i in inputs[1:] if i["key"].lower().find("answer") < 0 and i["key"].lower().find("begin") < 0])
+        return list(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)
+        chunks = json.loads(retrieval_res["chunks"][0])
+        answer, idx = settings.retrievaler.insert_citations(answer,
+                                                            [ck["content_ltks"] for ck in chunks],
+                                                            [ck["vector"] for ck in chunks],
+                                                            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
+            did = chunks[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"]})
+            recall_docs.append({"doc_id": did, "doc_name": chunks[int(i)]["docnm_kwd"]})
 
-        del retrieval_res["vector"]
-        del retrieval_res["content_ltks"]
+        for c in chunks:
+            del c["vector"]
+            del c["content_ltks"]
 
         reference = {
-            "chunks": [ck.to_dict() for _, ck in retrieval_res.iterrows()],
+            "chunks": chunks,
             "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'"
+            answer += " Please set LLM API-Key in 'User Setting -> Model providers -> API-Key'"
         res = {"content": answer, "reference": reference}
+        res = structure_answer(None, res, "", "")
 
         return res
 
+    def get_input_elements(self):
+        key_set = set([])
+        res = [{"key": "user", "name": "Input your question here:"}]
+        for r in re.finditer(r"\{([a-z]+[:@][a-z0-9_-]+)\}", self._param.prompt, flags=re.IGNORECASE):
+            cpn_id = r.group(1)
+            if cpn_id in key_set:
+                continue
+            if cpn_id.lower().find("begin@") == 0:
+                cpn_id, key = cpn_id.split("@")
+                for p in self._canvas.get_component(cpn_id)["obj"]._param.query:
+                    if p["key"] != key:
+                        continue
+                    res.append({"key": r.group(1), "name": p["name"]})
+                    key_set.add(r.group(1))
+                continue
+            cpn_nm = self._canvas.get_component_name(cpn_id)
+            if not cpn_nm:
+                continue
+            res.append({"key": cpn_id, "name": cpn_nm})
+            key_set.add(cpn_id)
+        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"]
+        retrieval_res = []
+        self._param.inputs = []
+        for para in self.get_input_elements()[1:]:
+            if para["key"].lower().find("begin@") == 0:
+                cpn_id, key = para["key"].split("@")
+                for p in self._canvas.get_component(cpn_id)["obj"]._param.query:
+                    if p["key"] == key:
+                        kwargs[para["key"]] = p.get("value", "")
+                        self._param.inputs.append(
+                            {"component_id": para["key"], "content": kwargs[para["key"]]})
+                        break
+                else:
+                    assert False, f"Can't find parameter '{key}' for {cpn_id}"
+                continue
+
+            component_id = para["key"]
+            cpn = self._canvas.get_component(component_id)["obj"]
+            if cpn.component_name.lower() == "answer":
+                hist = self._canvas.get_history(1)
+                if hist:
+                    hist = hist[0]["content"]
+                else:
+                    hist = ""
+                kwargs[para["key"]] = hist
+                continue
             _, out = cpn.output(allow_partial=False)
             if "content" not in out.columns:
-                kwargs[para["key"]] = "Nothing"
+                kwargs[para["key"]] = ""
             else:
-                kwargs[para["key"]] = "  - " + "\n  - ".join(out["content"])
+                if cpn.component_name.lower() == "retrieval":
+                    retrieval_res.append(out)
+                kwargs[para["key"]] = "  - " + "\n - ".join([o if isinstance(o, str) else str(o) for o in out["content"]])
+            self._param.inputs.append({"component_id": para["key"], "content": kwargs[para["key"]]})
+
+        if retrieval_res:
+            retrieval_res = pd.concat(retrieval_res, ignore_index=True)
+        else:
+            retrieval_res = pd.DataFrame([])
 
-        kwargs["input"] = input
         for n, v in kwargs.items():
-            # prompt = re.sub(r"\{%s\}"%n, re.escape(str(v)), prompt)
-            prompt = re.sub(r"\{%s\}" % n, str(v), prompt)
+            prompt = re.sub(r"\{%s\}" % re.escape(n), str(v).replace("\\", " "), prompt)
+
+        if not self._param.inputs and prompt.find("{input}") >= 0:
+            retrieval_res = self.get_input()
+            input = ("  - " + "\n  - ".join(
+                [c for c in retrieval_res["content"] if isinstance(c, str)])) if "content" in retrieval_res else ""
+            prompt = re.sub(r"\{input\}", re.escape(input), prompt)
 
         downstreams = self._canvas.get_component(self._id)["downstream"]
         if kwargs.get("stream") and len(downstreams) == 1 and self._canvas.get_component(downstreams[0])[
@@ -121,36 +189,64 @@ class Generate(ComponentBase):
             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)
+            empty_res = "\n- ".join([str(t) for t in retrieval_res["empty_response"] if str(t)])
+            res = {"content": empty_res if empty_res else "Nothing found in knowledgebase!", "reference": []}
+            return pd.DataFrame([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)
+        msg = self._canvas.get_history(self._param.message_history_window_size)
+        if len(msg) < 1:
+            msg.append({"role": "user", "content": "Output: "})
+        _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(chat_mdl.max_length * 0.97))
+        if len(msg) < 2:
+            msg.append({"role": "user", "content": "Output: "})
+        ans = chat_mdl.chat(msg[0]["content"], msg[1:], self._param.gen_conf())
+        ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
+
+        if self._param.cite and "chunks" in retrieval_res.columns:
+            res = self.set_cite(retrieval_res, ans)
+            return pd.DataFrame([res])
 
         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": []}
+            empty_res = "\n- ".join([str(t) for t in retrieval_res["empty_response"] if str(t)])
+            res = {"content": empty_res if empty_res else "Nothing found in knowledgebase!", "reference": []}
             yield res
             self.set_output(res)
             return
 
+        msg = self._canvas.get_history(self._param.message_history_window_size)
+        if msg and msg[0]['role'] == 'assistant':
+            msg.pop(0)
+        if len(msg) < 1:
+            msg.append({"role": "user", "content": "Output: "})
+        _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(chat_mdl.max_length * 0.97))
+        if len(msg) < 2:
+            msg.append({"role": "user", "content": "Output: "})
         answer = ""
-        for ans in chat_mdl.chat_streamly(prompt, self._canvas.get_history(self._param.message_history_window_size),
-                                          self._param.gen_conf()):
+        for ans in chat_mdl.chat_streamly(msg[0]["content"], msg[1:], 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:
+        if self._param.cite and "chunks" in retrieval_res.columns:
             res = self.set_cite(retrieval_res, answer)
             yield res
 
-        self.set_output(res)
+        self.set_output(Generate.be_output(res))
+
+    def debug(self, **kwargs):
+        chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id)
+        prompt = self._param.prompt
+
+        for para in self._param.debug_inputs:
+            kwargs[para["key"]] = para.get("value", "")
+
+        for n, v in kwargs.items():
+            prompt = re.sub(r"\{%s\}" % re.escape(n), str(v).replace("\\", " "), prompt)
+
+        u = kwargs.get("user")
+        ans = chat_mdl.chat(prompt, [{"role": "user", "content": u if u else "Output: "}], self._param.gen_conf())
+        return pd.DataFrame([ans])

agent/component/github.py

@@ -13,10 +13,10 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import logging
 from abc import ABC
 import pandas as pd
 import requests
-from agent.settings import DEBUG
 from agent.component.base import ComponentBase, ComponentParamBase
 
 
@@ -57,5 +57,5 @@ class GitHub(ComponentBase, ABC):
             return GitHub.be_output("")
 
         df = pd.DataFrame(github_res)
-        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
+        logging.debug(f"df: {df}")
         return df

agent/component/google.py

@@ -13,10 +13,10 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import logging
 from abc import ABC
 from serpapi import GoogleSearch
 import pandas as pd
-from agent.settings import DEBUG
 from agent.component.base import ComponentBase, ComponentParamBase
 
 
@@ -85,12 +85,12 @@ class Google(ComponentBase, ABC):
                  "hl": self._param.language, "num": self._param.top_n})
             google_res = [{"content": '<a href="' + i["link"] + '">' + i["title"] + '</a>    ' + i["snippet"]} for i in
                           client.get_dict()["organic_results"]]
-        except Exception as e:
+        except Exception:
             return Google.be_output("**ERROR**: Existing Unavailable Parameters!")
 
         if not google_res:
             return Google.be_output("")
 
         df = pd.DataFrame(google_res)
-        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
+        logging.debug(f"df: {df}")
         return df

agent/component/googlescholar.py

@@ -13,9 +13,9 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import logging
 from abc import ABC
 import pandas as pd
-from agent.settings import DEBUG
 from agent.component.base import ComponentBase, ComponentParamBase
 from scholarly import scholarly
 
@@ -58,13 +58,13 @@ class GoogleScholar(ComponentBase, ABC):
                     '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))
+            except StopIteration or Exception:
+                logging.exception("GoogleScholar")
                 break
 
         if not scholar_res:
             return GoogleScholar.be_output("")
 
         df = pd.DataFrame(scholar_res)
-        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
+        logging.debug(f"df: {df}")
         return df

agent/component/invoke.py

@@ -0,0 +1,132 @@
+#
+#  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 abc import ABC
+import requests
+from deepdoc.parser import HtmlParser
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+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"
+
+    def _run(self, history, **kwargs):
+        args = {}
+        for para in self._param.variables:
+            if para.get("component_id"):
+                if '@' in para["component_id"]:
+                    component = para["component_id"].split('@')[0]
+                    field = para["component_id"].split('@')[1]
+                    cpn = self._canvas.get_component(component)["obj"]
+                    for param in cpn._param.query:
+                        if param["key"] == field:
+                            if "value" in param:
+                                args[para["key"]] = param["value"]
+                else:
+                    cpn = self._canvas.get_component(para["component_id"])["obj"]
+                    if cpn.component_name.lower() == "answer":
+                        args[para["key"]] = self._canvas.get_history(1)[0]["content"]
+                        continue
+                    _, out = cpn.output(allow_partial=False)
+                    if not out.empty:
+                        args[para["key"]] = "\n".join(out["content"])
+            else:
+                args[para["key"]] = para["value"]
+
+        url = self._param.url.strip()
+        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}
+
+        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)
+                return Invoke.be_output("\n".join(sections))
+
+            return Invoke.be_output(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)
+                return Invoke.be_output("\n".join(sections))
+            return Invoke.be_output(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:
+                sections = HtmlParser()(None, response.content)
+                return Invoke.be_output("\n".join(sections))
+            return Invoke.be_output(response.text)

agent/component/iteration.py

@@ -0,0 +1,45 @@
+#
+#  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 IterationParam(ComponentParamBase):
+    """
+    Define the Iteration component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.delimiter = ","
+
+    def check(self):
+        self.check_empty(self.delimiter, "Delimiter")
+
+
+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 self._canvas.get_component(cid)
+
+    def _run(self, history, **kwargs):
+        return self.output(allow_partial=False)[1]
+

agent/component/iterationitem.py

@@ -0,0 +1,53 @@
+#
+#  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
+
+
+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 _run(self, history, **kwargs):
+        parent = self.get_parent()
+        ans = parent.get_input()
+        ans = parent._param.delimiter.join(ans["content"]) if "content" in ans else ""
+        ans = [a.strip() for a in ans.split(parent._param.delimiter)]
+        if not ans:
+            self._idx = -1
+            return pd.DataFrame()
+
+        df = pd.DataFrame([{"content": ans[self._idx]}])
+        self._idx += 1
+        if self._idx >= len(ans):
+            self._idx = -1
+        return df
+
+    def end(self):
+        return self._idx == -1
+

agent/component/jin10.py

@@ -0,0 +1,130 @@
+#
+#  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
+from abc import ABC
+import pandas as pd
+import requests
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class Jin10Param(ComponentParamBase):
+    """
+    Define the Jin10 component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.type = "flash"
+        self.secret_key = "xxx"
+        self.flash_type = '1'
+        self.calendar_type = 'cj'
+        self.calendar_datatype = 'data'
+        self.symbols_type = 'GOODS'
+        self.symbols_datatype = 'symbols'
+        self.contain = ""
+        self.filter = ""
+
+    def check(self):
+        self.check_valid_value(self.type, "Type", ['flash', 'calendar', 'symbols', 'news'])
+        self.check_valid_value(self.flash_type, "Flash Type", ['1', '2', '3', '4', '5'])
+        self.check_valid_value(self.calendar_type, "Calendar Type", ['cj', 'qh', 'hk', 'us'])
+        self.check_valid_value(self.calendar_datatype, "Calendar DataType", ['data', 'event', 'holiday'])
+        self.check_valid_value(self.symbols_type, "Symbols Type", ['GOODS', 'FOREX', 'FUTURE', 'CRYPTO'])
+        self.check_valid_value(self.symbols_datatype, 'Symbols DataType', ['symbols', 'quotes'])
+
+
+class Jin10(ComponentBase, ABC):
+    component_name = "Jin10"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return Jin10.be_output("")
+
+        jin10_res = []
+        headers = {'secret-key': self._param.secret_key}
+        try:
+            if self._param.type == "flash":
+                params = {
+                    'category': self._param.flash_type,
+                    'contain': self._param.contain,
+                    'filter': self._param.filter
+                }
+                response = requests.get(
+                    url='https://open-data-api.jin10.com/data-api/flash?category=' + self._param.flash_type,
+                    headers=headers, data=json.dumps(params))
+                response = response.json()
+                for i in response['data']:
+                    jin10_res.append({"content": i['data']['content']})
+            if self._param.type == "calendar":
+                params = {
+                    'category': self._param.calendar_type
+                }
+                response = requests.get(
+                    url='https://open-data-api.jin10.com/data-api/calendar/' + self._param.calendar_datatype + '?category=' + self._param.calendar_type,
+                    headers=headers, data=json.dumps(params))
+
+                response = response.json()
+                jin10_res.append({"content": pd.DataFrame(response['data']).to_markdown()})
+            if self._param.type == "symbols":
+                params = {
+                    'type': self._param.symbols_type
+                }
+                if self._param.symbols_datatype == "quotes":
+                    params['codes'] = 'BTCUSD'
+                response = requests.get(
+                    url='https://open-data-api.jin10.com/data-api/' + self._param.symbols_datatype + '?type=' + self._param.symbols_type,
+                    headers=headers, data=json.dumps(params))
+                response = response.json()
+                if self._param.symbols_datatype == "symbols":
+                    for i in response['data']:
+                        i['Commodity Code'] = i['c']
+                        i['Stock Exchange'] = i['e']
+                        i['Commodity Name'] = i['n']
+                        i['Commodity Type'] = i['t']
+                        del i['c'], i['e'], i['n'], i['t']
+                if self._param.symbols_datatype == "quotes":
+                    for i in response['data']:
+                        i['Selling Price'] = i['a']
+                        i['Buying Price'] = i['b']
+                        i['Commodity Code'] = i['c']
+                        i['Stock Exchange'] = i['e']
+                        i['Highest Price'] = i['h']
+                        i['Yesterday’s Closing Price'] = i['hc']
+                        i['Lowest Price'] = i['l']
+                        i['Opening Price'] = i['o']
+                        i['Latest Price'] = i['p']
+                        i['Market Quote Time'] = i['t']
+                        del i['a'], i['b'], i['c'], i['e'], i['h'], i['hc'], i['l'], i['o'], i['p'], i['t']
+                jin10_res.append({"content": pd.DataFrame(response['data']).to_markdown()})
+            if self._param.type == "news":
+                params = {
+                    'contain': self._param.contain,
+                    'filter': self._param.filter
+                }
+                response = requests.get(
+                    url='https://open-data-api.jin10.com/data-api/news',
+                    headers=headers, data=json.dumps(params))
+                response = response.json()
+                jin10_res.append({"content": pd.DataFrame(response['data']).to_markdown()})
+        except Exception as e:
+            return Jin10.be_output("**ERROR**: " + str(e))
+
+        if not jin10_res:
+            return Jin10.be_output("")
+
+        return pd.DataFrame(jin10_res)

agent/component/keyword.py

@@ -13,12 +13,12 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import logging
 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):
@@ -50,16 +50,17 @@ 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
+        query = self.get_input()
+        query = str(query["content"][0]) if "content" in query else ""
 
         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}],
+        ans = chat_mdl.chat(self._param.get_prompt(), [{"role": "user", "content": query}],
                             self._param.gen_conf())
 
+        ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
         ans = re.sub(r".*keyword:", "", ans).strip()
-        if DEBUG: print(ans, ":::::::::::::::::::::::::::::::::")
+        logging.debug(f"ans: {ans}")
         return KeywordExtract.be_output(ans)
+
+    def debug(self, **kwargs):
+        return self._run([], **kwargs)

agent/component/pubmed.py

@@ -13,11 +13,12 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import logging
 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
 
 
@@ -47,12 +48,15 @@ class PubMed(ComponentBase, ABC):
         try:
             Entrez.email = self._param.email
             pubmedids = Entrez.read(Entrez.esearch(db='pubmed', retmax=self._param.top_n, term=ans))['IdList']
-            pubmedcnt = ET.fromstring(
-                Entrez.efetch(db='pubmed', id=",".join(pubmedids), retmode="xml").read().decode("utf-8"))
+            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} for child in
+                "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))
@@ -61,5 +65,5 @@ class PubMed(ComponentBase, ABC):
             return PubMed.be_output("")
 
         df = pd.DataFrame(pubmed_res)
-        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
+        logging.debug(f"df: {df}")
         return df

agent/component/qweather.py

@@ -51,7 +51,7 @@ class QWeatherParam(ComponentParamBase):
                                ['zh', 'zh-hant', 'en', 'de', 'es', 'fr', 'it', 'ja', 'ko', 'ru', 'hi', 'th', 'ar', 'pt',
                                 'bn', 'ms', 'nl', 'el', 'la', 'sv', 'id', 'pl', 'tr', 'cs', 'et', 'vi', 'fil', 'fi',
                                 'he', 'is', 'nb'])
-        self.check_vaild_value(self.time_period, "Time period", ['now', '3d', '7d', '10d', '15d', '30d'])
+        self.check_valid_value(self.time_period, "Time period", ['now', '3d', '7d', '10d', '15d', '30d'])
 
 
 class QWeather(ComponentBase, ABC):

agent/component/relevant.py

@@ -13,6 +13,7 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import logging
 from abc import ABC
 from api.db import LLMType
 from api.db.services.llm_service import LLMBundle
@@ -70,11 +71,13 @@ class Relevant(Generate, ABC):
         ans = chat_mdl.chat(self._param.get_prompt(), [{"role": "user", "content": ans}],
                             self._param.gen_conf())
 
-        print(ans, ":::::::::::::::::::::::::::::::::")
+        logging.debug(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}"
 
+    def debug(self, **kwargs):
+        return self._run([], **kwargs)
 

agent/component/retrieval.py

@@ -13,6 +13,8 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import json
+import logging
 from abc import ABC
 
 import pandas as pd
@@ -20,8 +22,11 @@ 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 api import settings
 from agent.component.base import ComponentBase, ComponentParamBase
+from rag.app.tag import label_question
+from rag.prompts import kb_prompt
+from rag.utils.tavily_conn import Tavily
 
 
 class RetrievalParam(ComponentParamBase):
@@ -38,51 +43,69 @@ class RetrievalParam(ComponentParamBase):
         self.kb_ids = []
         self.rerank_id = ""
         self.empty_response = ""
+        self.tavily_api_key = ""
+        self.use_kg = False
 
     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_decimal_float(self.keywords_similarity_weight, "[Retrieval] Keyword 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 = self.get_input()
+        query = str(query["content"][0]) if "content" in query else ""
         kbs = KnowledgebaseService.get_by_ids(self._param.kb_ids)
         if not kbs:
-            raise ValueError("Can't find knowledgebases by {}".format(self._param.kb_ids))
+            return Retrieval.be_output("")
+
         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])
+        embd_mdl = None
+        if embd_nms:
+            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,
+        if kbs:
+            kbinfos = settings.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)
+                                        aggs=False, rerank_mdl=rerank_mdl,
+                                        rank_feature=label_question(query, kbs))
+        else:
+            kbinfos = {"chunks": [], "doc_aggs": []}
+
+        if self._param.use_kg and kbs:
+            ck = settings.kg_retrievaler.retrieval(query,
+                                                   [kbs[0].tenant_id],
+                                                   self._param.kb_ids,
+                                                   embd_mdl,
+                                                   LLMBundle(kbs[0].tenant_id, LLMType.CHAT))
+            if ck["content_with_weight"]:
+                kbinfos["chunks"].insert(0, ck)
+
+        if self._param.tavily_api_key:
+            tav = Tavily(self._param.tavily_api_key)
+            tav_res = tav.retrieve_chunks(query)
+            kbinfos["chunks"].extend(tav_res["chunks"])
+            kbinfos["doc_aggs"].extend(tav_res["doc_aggs"])
 
         if not kbinfos["chunks"]:
             df = Retrieval.be_output("")
-            df["empty_response"] = self._param.empty_response
+            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
+        df = pd.DataFrame({"content": kb_prompt(kbinfos, 200000), "chunks": json.dumps(kbinfos["chunks"])})
+        logging.debug("{} {}".format(query, df))
+        return df.dropna()
 
 

agent/component/rewrite.py

@@ -14,59 +14,81 @@
 #  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.prompts import full_question
 
 
 class RewriteQuestionParam(GenerateParam):
-
     """
     Define the QuestionRewrite component parameters.
     """
+
     def __init__(self):
         super().__init__()
         self.temperature = 0.9
         self.prompt = ""
-        self.loop = 1
+        self.language = ""
 
     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("Maximum loop time exceeds. Can't find relevant information.")
-        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, ":::::::::::::::::::::::::::::::::")
+        hist = self._canvas.get_history(self._param.message_history_window_size)
+        query = self.get_input()
+        query = str(query["content"][0]) if "content" in query else ""
+        messages = [h for h in hist if h["role"]!="system"]
+        if messages[-1]["role"] != "user":
+            messages.append({"role": "user", "content": query})
+        ans = full_question(self._canvas.get_tenant_id(), self._param.llm_id, messages, self.gen_lang(self._param.language))
+        self._canvas.history.pop()
+        self._canvas.history.append(("user", ans))
         return RewriteQuestion.be_output(ans)
 
-
+    @staticmethod
+    def gen_lang(language):
+        # convert code lang to language word for the prompt
+        language_dict = {'af': 'Afrikaans', 'ak': 'Akan', 'sq': 'Albanian', 'ws': 'Samoan', 'am': 'Amharic',
+                         'ar': 'Arabic', 'hy': 'Armenian', 'az': 'Azerbaijani', 'eu': 'Basque', 'be': 'Belarusian',
+                         'bem': 'Bemba', 'bn': 'Bengali', 'bh': 'Bihari',
+                         'xx-bork': 'Bork', 'bs': 'Bosnian', 'br': 'Breton', 'bg': 'Bulgarian', 'bt': 'Bhutani',
+                         'km': 'Cambodian', 'ca': 'Catalan', 'chr': 'Cherokee', 'ny': 'Chichewa', 'zh-cn': 'Chinese',
+                         'zh-tw': 'Chinese', 'co': 'Corsican',
+                         'hr': 'Croatian', 'cs': 'Czech', 'da': 'Danish', 'nl': 'Dutch', 'xx-elmer': 'Elmer',
+                         'en': 'English', 'eo': 'Esperanto', 'et': 'Estonian', 'ee': 'Ewe', 'fo': 'Faroese',
+                         'tl': 'Filipino', 'fi': 'Finnish', 'fr': 'French',
+                         'fy': 'Frisian', 'gaa': 'Ga', 'gl': 'Galician', 'ka': 'Georgian', 'de': 'German',
+                         'el': 'Greek', 'kl': 'Greenlandic', 'gn': 'Guarani', 'gu': 'Gujarati', 'xx-hacker': 'Hacker',
+                         'ht': 'Haitian Creole', 'ha': 'Hausa', 'haw': 'Hawaiian',
+                         'iw': 'Hebrew', 'hi': 'Hindi', 'hu': 'Hungarian', 'is': 'Icelandic', 'ig': 'Igbo',
+                         'id': 'Indonesian', 'ia': 'Interlingua', 'ga': 'Irish', 'it': 'Italian', 'ja': 'Japanese',
+                         'jw': 'Javanese', 'kn': 'Kannada', 'kk': 'Kazakh', 'rw': 'Kinyarwanda',
+                         'rn': 'Kirundi', 'xx-klingon': 'Klingon', 'kg': 'Kongo', 'ko': 'Korean', 'kri': 'Krio',
+                         'ku': 'Kurdish', 'ckb': 'Kurdish (Sorani)', 'ky': 'Kyrgyz', 'lo': 'Laothian', 'la': 'Latin',
+                         'lv': 'Latvian', 'ln': 'Lingala', 'lt': 'Lithuanian',
+                         'loz': 'Lozi', 'lg': 'Luganda', 'ach': 'Luo', 'mk': 'Macedonian', 'mg': 'Malagasy',
+                         'ms': 'Malay', 'ml': 'Malayalam', 'mt': 'Maltese', 'mv': 'Maldivian', 'mi': 'Maori',
+                         'mr': 'Marathi', 'mfe': 'Mauritian Creole', 'mo': 'Moldavian', 'mn': 'Mongolian',
+                         'sr-me': 'Montenegrin', 'my': 'Burmese', 'ne': 'Nepali', 'pcm': 'Nigerian Pidgin',
+                         'nso': 'Northern Sotho', 'no': 'Norwegian', 'nn': 'Norwegian Nynorsk', 'oc': 'Occitan',
+                         'or': 'Oriya', 'om': 'Oromo', 'ps': 'Pashto', 'fa': 'Persian',
+                         'xx-pirate': 'Pirate', 'pl': 'Polish', 'pt': 'Portuguese', 'pt-br': 'Portuguese (Brazilian)',
+                         'pt-pt': 'Portuguese (Portugal)', 'pa': 'Punjabi', 'qu': 'Quechua', 'ro': 'Romanian',
+                         'rm': 'Romansh', 'nyn': 'Runyankole', 'ru': 'Russian', 'gd': 'Scots Gaelic',
+                         'sr': 'Serbian', 'sh': 'Serbo-Croatian', 'st': 'Sesotho', 'tn': 'Setswana',
+                         'crs': 'Seychellois Creole', 'sn': 'Shona', 'sd': 'Sindhi', 'si': 'Sinhalese', 'sk': 'Slovak',
+                         'sl': 'Slovenian', 'so': 'Somali', 'es': 'Spanish', 'es-419': 'Spanish (Latin America)',
+                         'su': 'Sundanese',
+                         'sw': 'Swahili', 'sv': 'Swedish', 'tg': 'Tajik', 'ta': 'Tamil', 'tt': 'Tatar', 'te': 'Telugu',
+                         'th': 'Thai', 'ti': 'Tigrinya', 'to': 'Tongan', 'lua': 'Tshiluba', 'tum': 'Tumbuka',
+                         'tr': 'Turkish', 'tk': 'Turkmen', 'tw': 'Twi',
+                         'ug': 'Uyghur', 'uk': 'Ukrainian', 'ur': 'Urdu', 'uz': 'Uzbek', 'vu': 'Vanuatu',
+                         'vi': 'Vietnamese', 'cy': 'Welsh', 'wo': 'Wolof', 'xh': 'Xhosa', 'yi': 'Yiddish',
+                         'yo': 'Yoruba', 'zu': 'Zulu'}
+        if language in language_dict:
+            return language_dict[language]
+        else:
+            return ""

agent/component/switch.py

@@ -41,44 +41,48 @@ class SwitchParam(ComponentParamBase):
     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 cond["logical_operator"] not in ["and", "or"] and len(cond["items"]) > 1:
-                raise ValueError(f"[Switch] Please set logical_operator correctly!")
+            if not cond["to"]:
+                raise ValueError("[Switch] 'To' can not be empty!")
 
 
 class Switch(ComponentBase, ABC):
     component_name = "Switch"
 
+    def get_dependent_components(self):
+        res = []
+        for cond in self._param.conditions:
+            for item in cond["items"]:
+                if not item["cpn_id"]:
+                    continue
+                if item["cpn_id"].lower().find("begin") >= 0 or item["cpn_id"].lower().find("answer") >= 0:
+                    continue
+                cid = item["cpn_id"].split("@")[0]
+                res.append(cid)
+
+        return list(set(res))
+
     def _run(self, history, **kwargs):
         for cond in self._param.conditions:
-
-            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:
+                if not item["cpn_id"]:
+                    continue
+                cid = item["cpn_id"].split("@")[0]
+                if item["cpn_id"].find("@") > 0:
+                    cpn_id, key = item["cpn_id"].split("@")
+                    for p in self._canvas.get_component(cid)["obj"]._param.query:
+                        if p["key"] == key:
+                            res.append(self.process_operator(p.get("value",""), item["operator"], item.get("value", "")))
+                            break
+                else:
+                    out = self._canvas.get_component(cid)["obj"].output(allow_partial=False)[1]
+                    cpn_input = "" if "content" not in out.columns else " ".join([str(s) for s in out["content"]])
+                    res.append(self.process_operator(cpn_input, item["operator"], item.get("value", "")))
+
+                if cond["logical_operator"] != "and" and any(res):
+                    return Switch.be_output(cond["to"])
+
+            if all(res):
                 return Switch.be_output(cond["to"])
 
         return Switch.be_output(self._param.end_cpn_id)
@@ -106,22 +110,22 @@ 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)
+        raise ValueError('Not supported operator' + operator)

agent/component/template.py

@@ -0,0 +1,134 @@
+#
+#  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 agent.component.base import ComponentBase, ComponentParamBase
+from jinja2 import Template as Jinja2Template
+
+
+class TemplateParam(ComponentParamBase):
+    """
+    Define the Generate component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.content = ""
+        self.parameters = []
+
+    def check(self):
+        self.check_empty(self.content, "[Template] Content")
+        return True
+
+
+class Template(ComponentBase):
+    component_name = "Template"
+
+    def get_dependent_components(self):
+        inputs = self.get_input_elements()
+        cpnts = set([i["key"] for i in inputs if i["key"].lower().find("answer") < 0 and i["key"].lower().find("begin") < 0])
+        return list(cpnts)
+
+    def get_input_elements(self):
+        key_set = set([])
+        res = []
+        for r in re.finditer(r"\{([a-z]+[:@][a-z0-9_-]+)\}", self._param.content, flags=re.IGNORECASE):
+            cpn_id = r.group(1)
+            if cpn_id in key_set:
+                continue
+            if cpn_id.lower().find("begin@") == 0:
+                cpn_id, key = cpn_id.split("@")
+                for p in self._canvas.get_component(cpn_id)["obj"]._param.query:
+                    if p["key"] != key:
+                        continue
+                    res.append({"key": r.group(1), "name": p["name"]})
+                    key_set.add(r.group(1))
+                continue
+            cpn_nm = self._canvas.get_component_name(cpn_id)
+            if not cpn_nm:
+                continue
+            res.append({"key": cpn_id, "name": cpn_nm})
+            key_set.add(cpn_id)
+        return res
+
+    def _run(self, history, **kwargs):
+        content = self._param.content
+
+        self._param.inputs = []
+        for para in self.get_input_elements():
+            if para["key"].lower().find("begin@") == 0:
+                cpn_id, key = para["key"].split("@")
+                for p in self._canvas.get_component(cpn_id)["obj"]._param.query:
+                    if p["key"] == key:
+                        value = p.get("value", "")
+                        self.make_kwargs(para, kwargs, value)
+                        break
+                else:
+                    assert False, f"Can't find parameter '{key}' for {cpn_id}"
+                continue
+
+            component_id = para["key"]
+            cpn = self._canvas.get_component(component_id)["obj"]
+            if cpn.component_name.lower() == "answer":
+                hist = self._canvas.get_history(1)
+                if hist:
+                    hist = hist[0]["content"]
+                else:
+                    hist = ""
+                self.make_kwargs(para, kwargs, hist)
+                continue
+
+            _, out = cpn.output(allow_partial=False)
+
+            result = ""
+            if "content" in out.columns:
+                result = "\n".join(
+                    [o if isinstance(o, str) else str(o) for o in out["content"]]
+                )
+
+            self.make_kwargs(para, kwargs, result)
+
+        template = Jinja2Template(content)
+
+        try:
+            content = template.render(kwargs)
+        except Exception:
+            pass
+
+        for n, v in kwargs.items():
+            if not isinstance(v, str):
+                try:
+                    v = json.dumps(v, ensure_ascii=False)
+                except Exception:
+                    pass
+            content = re.sub(
+                r"\{%s\}" % re.escape(n), v, content
+            )
+            content = re.sub(
+                r"(#+)", r" \1 ", content
+            )
+
+        return Template.be_output(content)
+
+    def make_kwargs(self, para, kwargs, value):
+        self._param.inputs.append(
+            {"component_id": para["key"], "content": value}
+        )
+        try:
+            value = json.loads(value)
+        except Exception:
+            pass
+        kwargs[para["key"]] = value

agent/component/tushare.py

@@ -0,0 +1,72 @@
+#
+#  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
+from abc import ABC
+import pandas as pd
+import time
+import requests
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class TuShareParam(ComponentParamBase):
+    """
+    Define the TuShare component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.token = "xxx"
+        self.src = "eastmoney"
+        self.start_date = "2024-01-01 09:00:00"
+        self.end_date = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
+        self.keyword = ""
+
+    def check(self):
+        self.check_valid_value(self.src, "Quick News Source",
+                               ["sina", "wallstreetcn", "10jqka", "eastmoney", "yuncaijing", "fenghuang", "jinrongjie"])
+
+
+class TuShare(ComponentBase, ABC):
+    component_name = "TuShare"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = ",".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return TuShare.be_output("")
+
+        try:
+            tus_res = []
+            params = {
+                "api_name": "news",
+                "token": self._param.token,
+                "params": {"src": self._param.src, "start_date": self._param.start_date,
+                           "end_date": self._param.end_date}
+            }
+            response = requests.post(url="http://api.tushare.pro", data=json.dumps(params).encode('utf-8'))
+            response = response.json()
+            if response['code'] != 0:
+                return TuShare.be_output(response['msg'])
+            df = pd.DataFrame(response['data']['items'])
+            df.columns = response['data']['fields']
+            tus_res.append({"content": (df[df['content'].str.contains(self._param.keyword, case=False)]).to_markdown()})
+        except Exception as e:
+            return TuShare.be_output("**ERROR**: " + str(e))
+
+        if not tus_res:
+            return TuShare.be_output("")
+
+        return pd.DataFrame(tus_res)

agent/component/wencai.py

@@ -0,0 +1,80 @@
+#
+#  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
+                    if isinstance(item[1], pd.DataFrame):
+                        if "image_url" in item[1].columns:
+                            continue
+                        wencai_res.append({"content": item[1].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

@@ -13,12 +13,10 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-import random
+import logging
 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
 
 
@@ -65,5 +63,5 @@ class Wikipedia(ComponentBase, ABC):
             return Wikipedia.be_output("")
 
         df = pd.DataFrame(wiki_res)
-        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
+        logging.debug(f"df: {df}")
         return df

agent/component/yahoofinance.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.
+#
+import logging
+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:
+            logging.exception("YahooFinance got exception")
+
+        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/test/client.py

@@ -43,6 +43,7 @@ if __name__ == '__main__':
         else:
             print(ans["content"])
 
-        if DEBUG: print(canvas.path)
+        if DEBUG:
+            print(canvas.path)
         question = input("\n==================== User =====================\n> ")
         canvas.add_user_input(question)

agent/test/dsl_examples/categorize.json

@@ -26,20 +26,48 @@
                       "category_description": {
                         "product_related": {
                           "description": "The question is about the product usage, appearance and how it works.",
-                          "examples": "Why it always beaming?\nHow to install it onto the wall?\nIt leaks, what to do?"
+                          "examples": "Why it always beaming?\nHow to install it onto the wall?\nIt leaks, what to do?",
+                          "to": "message:0"
                         },
                         "others": {
                           "description": "The question is not about the product usage, appearance and how it works.",
-                          "examples": "How are you doing?\nWhat is your name?\nAre you a robot?\nWhat's the weather?\nWill it rain?"
+                          "examples": "How are you doing?\nWhat is your name?\nAre you a robot?\nWhat's the weather?\nWill it rain?",
+                          "to": "message:1"
                         }
                       }
                     }
                 },
-                "downstream": [],
+                "downstream": ["message:0","message:1"],
                 "upstream": ["answer:0"]
+            },
+            "message:0": {
+                "obj": {
+                    "component_name": "Message",
+                    "params": {
+                      "messages": [
+                        "Message 0!!!!!!!"
+                      ]
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": ["categorize:0"]
+            },
+            "message:1": {
+                "obj": {
+                    "component_name": "Message",
+                    "params": {
+                      "messages": [
+                        "Message 1!!!!!!!"
+                      ]
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": ["categorize:0"]
             }
   },
   "history": [],
+  "messages": [],
   "path": [],
+  "reference": [],
   "answer": []
-}
+}

agent/test/dsl_examples/concentrator_message.json

@@ -0,0 +1,113 @@
+{
+  "components": {
+            "begin": {
+                "obj":{
+                    "component_name": "Begin",
+                    "params": {
+                      "prologue": "Hi there!"
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": []
+            },
+            "answer:0": {
+                "obj": {
+                    "component_name": "Answer",
+                    "params": {}
+                },
+                "downstream": ["categorize:0"],
+                "upstream": ["begin"]
+            },
+            "categorize:0": {
+                "obj": {
+                    "component_name": "Categorize",
+                    "params": {
+                      "llm_id": "deepseek-chat",
+                      "category_description": {
+                        "product_related": {
+                          "description": "The question is about the product usage, appearance and how it works.",
+                          "examples": "Why it always beaming?\nHow to install it onto the wall?\nIt leaks, what to do?",
+                          "to": "concentrator:0"
+                        },
+                        "others": {
+                          "description": "The question is not about the product usage, appearance and how it works.",
+                          "examples": "How are you doing?\nWhat is your name?\nAre you a robot?\nWhat's the weather?\nWill it rain?",
+                          "to": "concentrator:1"
+                        }
+                      }
+                    }
+                },
+                "downstream": ["concentrator:0","concentrator:1"],
+                "upstream": ["answer:0"]
+            },
+            "concentrator:0": {
+                "obj": {
+                    "component_name": "Concentrator",
+                    "params": {}
+                },
+                "downstream": ["message:0"],
+                "upstream": ["categorize:0"]
+            },
+            "concentrator:1": {
+                "obj": {
+                    "component_name": "Concentrator",
+                    "params": {}
+                },
+                "downstream": ["message:1_0","message:1_1","message:1_2"],
+                "upstream": ["categorize:0"]
+            },
+            "message:0": {
+                "obj": {
+                    "component_name": "Message",
+                    "params": {
+                      "messages": [
+                        "Message 0_0!!!!!!!"
+                      ]
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": ["concentrator:0"]
+            },
+            "message:1_0": {
+                "obj": {
+                    "component_name": "Message",
+                    "params": {
+                      "messages": [
+                        "Message 1_0!!!!!!!"
+                      ]
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": ["concentrator:1"]
+            },
+            "message:1_1": {
+                "obj": {
+                    "component_name": "Message",
+                    "params": {
+                      "messages": [
+                        "Message 1_1!!!!!!!"
+                      ]
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": ["concentrator:1"]
+            },
+            "message:1_2": {
+                "obj": {
+                    "component_name": "Message",
+                    "params": {
+                      "messages": [
+                        "Message 1_2!!!!!!!"
+                      ]
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": ["concentrator:1"]
+            }
+  },
+  "history": [],
+  "messages": [],
+  "path": [],
+  "reference": [],
+  "answer": []
+}

agentic_reasoning/__init__.py

@@ -0,0 +1 @@
+from .deep_research import DeepResearcher as DeepResearcher

agentic_reasoning/deep_research.py

@@ -0,0 +1,223 @@
+#
+#  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 re
+from functools import partial
+from agentic_reasoning.prompts import BEGIN_SEARCH_QUERY, BEGIN_SEARCH_RESULT, END_SEARCH_RESULT, MAX_SEARCH_LIMIT, \
+    END_SEARCH_QUERY, REASON_PROMPT, RELEVANT_EXTRACTION_PROMPT
+from api.db.services.llm_service import LLMBundle
+from rag.nlp import extract_between
+from rag.prompts import kb_prompt
+from rag.utils.tavily_conn import Tavily
+
+
+class DeepResearcher:
+    def __init__(self,
+                 chat_mdl: LLMBundle,
+                 prompt_config: dict,
+                 kb_retrieve: partial = None,
+                 kg_retrieve: partial = None
+                 ):
+        self.chat_mdl = chat_mdl
+        self.prompt_config = prompt_config
+        self._kb_retrieve = kb_retrieve
+        self._kg_retrieve = kg_retrieve
+
+    @staticmethod
+    def _remove_query_tags(text):
+        """Remove query tags from text"""
+        pattern = re.escape(BEGIN_SEARCH_QUERY) + r"(.*?)" + re.escape(END_SEARCH_QUERY)
+        return re.sub(pattern, "", text)
+
+    @staticmethod
+    def _remove_result_tags(text):
+        """Remove result tags from text"""
+        pattern = re.escape(BEGIN_SEARCH_RESULT) + r"(.*?)" + re.escape(END_SEARCH_RESULT)
+        return re.sub(pattern, "", text)
+
+    def _generate_reasoning(self, msg_history):
+        """Generate reasoning steps"""
+        query_think = ""
+        if msg_history[-1]["role"] != "user":
+            msg_history.append({"role": "user", "content": "Continues reasoning with the new information.\n"})
+        else:
+            msg_history[-1]["content"] += "\n\nContinues reasoning with the new information.\n"
+            
+        for ans in self.chat_mdl.chat_streamly(REASON_PROMPT, msg_history, {"temperature": 0.7}):
+            ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
+            if not ans:
+                continue
+            query_think = ans
+            yield query_think
+        return query_think
+
+    def _extract_search_queries(self, query_think, question, step_index):
+        """Extract search queries from thinking"""
+        queries = extract_between(query_think, BEGIN_SEARCH_QUERY, END_SEARCH_QUERY)
+        if not queries and step_index == 0:
+            # If this is the first step and no queries are found, use the original question as the query
+            queries = [question]
+        return queries
+
+    def _truncate_previous_reasoning(self, all_reasoning_steps):
+        """Truncate previous reasoning steps to maintain a reasonable length"""
+        truncated_prev_reasoning = ""
+        for i, step in enumerate(all_reasoning_steps):
+            truncated_prev_reasoning += f"Step {i + 1}: {step}\n\n"
+
+        prev_steps = truncated_prev_reasoning.split('\n\n')
+        if len(prev_steps) <= 5:
+            truncated_prev_reasoning = '\n\n'.join(prev_steps)
+        else:
+            truncated_prev_reasoning = ''
+            for i, step in enumerate(prev_steps):
+                if i == 0 or i >= len(prev_steps) - 4 or BEGIN_SEARCH_QUERY in step or BEGIN_SEARCH_RESULT in step:
+                    truncated_prev_reasoning += step + '\n\n'
+                else:
+                    if truncated_prev_reasoning[-len('\n\n...\n\n'):] != '\n\n...\n\n':
+                        truncated_prev_reasoning += '...\n\n'
+        
+        return truncated_prev_reasoning.strip('\n')
+
+    def _retrieve_information(self, search_query):
+        """Retrieve information from different sources"""
+        # 1. Knowledge base retrieval
+        kbinfos = self._kb_retrieve(question=search_query) if self._kb_retrieve else {"chunks": [], "doc_aggs": []}
+        
+        # 2. Web retrieval (if Tavily API is configured)
+        if self.prompt_config.get("tavily_api_key"):
+            tav = Tavily(self.prompt_config["tavily_api_key"])
+            tav_res = tav.retrieve_chunks(search_query)
+            kbinfos["chunks"].extend(tav_res["chunks"])
+            kbinfos["doc_aggs"].extend(tav_res["doc_aggs"])
+        
+        # 3. Knowledge graph retrieval (if configured)
+        if self.prompt_config.get("use_kg") and self._kg_retrieve:
+            ck = self._kg_retrieve(question=search_query)
+            if ck["content_with_weight"]:
+                kbinfos["chunks"].insert(0, ck)
+                
+        return kbinfos
+
+    def _update_chunk_info(self, chunk_info, kbinfos):
+        """Update chunk information for citations"""
+        if not chunk_info["chunks"]:
+            # If this is the first retrieval, use the retrieval results directly
+            for k in chunk_info.keys():
+                chunk_info[k] = kbinfos[k]
+        else:
+            # Merge newly retrieved information, avoiding duplicates
+            cids = [c["chunk_id"] for c in chunk_info["chunks"]]
+            for c in kbinfos["chunks"]:
+                if c["chunk_id"] not in cids:
+                    chunk_info["chunks"].append(c)
+                    
+            dids = [d["doc_id"] for d in chunk_info["doc_aggs"]]
+            for d in kbinfos["doc_aggs"]:
+                if d["doc_id"] not in dids:
+                    chunk_info["doc_aggs"].append(d)
+
+    def _extract_relevant_info(self, truncated_prev_reasoning, search_query, kbinfos):
+        """Extract and summarize relevant information"""
+        summary_think = ""
+        for ans in self.chat_mdl.chat_streamly(
+                RELEVANT_EXTRACTION_PROMPT.format(
+                    prev_reasoning=truncated_prev_reasoning,
+                    search_query=search_query,
+                    document="\n".join(kb_prompt(kbinfos, 4096))
+                ),
+                [{"role": "user",
+                  "content": f'Now you should analyze each web page and find helpful information based on the current search query "{search_query}" and previous reasoning steps.'}],
+                {"temperature": 0.7}):
+            ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
+            if not ans:
+                continue
+            summary_think = ans
+            yield summary_think
+        
+        return summary_think
+
+    def thinking(self, chunk_info: dict, question: str):
+        executed_search_queries = []
+        msg_history = [{"role": "user", "content": f'Question:\"{question}\"\n'}]
+        all_reasoning_steps = []
+        think = "<think>"
+        
+        for step_index in range(MAX_SEARCH_LIMIT + 1):
+            # Check if the maximum search limit has been reached
+            if step_index == MAX_SEARCH_LIMIT - 1:
+                summary_think = f"\n{BEGIN_SEARCH_RESULT}\nThe maximum search limit is exceeded. You are not allowed to search.\n{END_SEARCH_RESULT}\n"
+                yield {"answer": think + summary_think + "</think>", "reference": {}, "audio_binary": None}
+                all_reasoning_steps.append(summary_think)
+                msg_history.append({"role": "assistant", "content": summary_think})
+                break
+
+            # Step 1: Generate reasoning
+            query_think = ""
+            for ans in self._generate_reasoning(msg_history):
+                query_think = ans
+                yield {"answer": think + self._remove_query_tags(query_think) + "</think>", "reference": {}, "audio_binary": None}
+
+            think += self._remove_query_tags(query_think)
+            all_reasoning_steps.append(query_think)
+            
+            # Step 2: Extract search queries
+            queries = self._extract_search_queries(query_think, question, step_index)
+            if not queries and step_index > 0:
+                # If not the first step and no queries, end the search process
+                break
+
+            # Process each search query
+            for search_query in queries:
+                logging.info(f"[THINK]Query: {step_index}. {search_query}")
+                msg_history.append({"role": "assistant", "content": search_query})
+                think += f"\n\n> {step_index + 1}. {search_query}\n\n"
+                yield {"answer": think + "</think>", "reference": {}, "audio_binary": None}
+
+                # Check if the query has already been executed
+                if search_query in executed_search_queries:
+                    summary_think = f"\n{BEGIN_SEARCH_RESULT}\nYou have searched this query. Please refer to previous results.\n{END_SEARCH_RESULT}\n"
+                    yield {"answer": think + summary_think + "</think>", "reference": {}, "audio_binary": None}
+                    all_reasoning_steps.append(summary_think)
+                    msg_history.append({"role": "user", "content": summary_think})
+                    think += summary_think
+                    continue
+                
+                executed_search_queries.append(search_query)
+                
+                # Step 3: Truncate previous reasoning steps
+                truncated_prev_reasoning = self._truncate_previous_reasoning(all_reasoning_steps)
+                
+                # Step 4: Retrieve information
+                kbinfos = self._retrieve_information(search_query)
+                
+                # Step 5: Update chunk information
+                self._update_chunk_info(chunk_info, kbinfos)
+                
+                # Step 6: Extract relevant information
+                think += "\n\n"
+                summary_think = ""
+                for ans in self._extract_relevant_info(truncated_prev_reasoning, search_query, kbinfos):
+                    summary_think = ans
+                    yield {"answer": think + self._remove_result_tags(summary_think) + "</think>", "reference": {}, "audio_binary": None}
+
+                all_reasoning_steps.append(summary_think)
+                msg_history.append(
+                    {"role": "user", "content": f"\n\n{BEGIN_SEARCH_RESULT}{summary_think}{END_SEARCH_RESULT}\n\n"})
+                think += self._remove_result_tags(summary_think)
+                logging.info(f"[THINK]Summary: {step_index}. {summary_think}")
+
+        yield think + "</think>"

agentic_reasoning/prompts.py

@@ -0,0 +1,113 @@
+#
+#  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.
+#
+
+BEGIN_SEARCH_QUERY = "<|begin_search_query|>"
+END_SEARCH_QUERY = "<|end_search_query|>"
+BEGIN_SEARCH_RESULT = "<|begin_search_result|>"
+END_SEARCH_RESULT = "<|end_search_result|>"
+MAX_SEARCH_LIMIT = 6
+
+REASON_PROMPT = (
+        "You are a reasoning assistant with the ability to perform dataset searches to help "
+        "you answer the user's question accurately. You have special tools:\n\n"
+        f"- To perform a search: write {BEGIN_SEARCH_QUERY} your query here {END_SEARCH_QUERY}.\n"
+        f"Then, the system will search and analyze relevant content, then provide you with helpful information in the format {BEGIN_SEARCH_RESULT} ...search results... {END_SEARCH_RESULT}.\n\n"
+        f"You can repeat the search process multiple times if necessary. The maximum number of search attempts is limited to {MAX_SEARCH_LIMIT}.\n\n"
+        "Once you have all the information you need, continue your reasoning.\n\n"
+        "-- Example 1 --\n" ########################################
+        "Question: \"Are both the directors of Jaws and Casino Royale from the same country?\"\n"
+        "Assistant:\n"
+        f"    {BEGIN_SEARCH_QUERY}Who is the director of Jaws?{END_SEARCH_QUERY}\n\n"
+        "User:\n"
+        f"    {BEGIN_SEARCH_RESULT}\nThe director of Jaws is Steven Spielberg...\n{END_SEARCH_RESULT}\n\n"
+        "Continues reasoning with the new information.\n"
+        "Assistant:\n"
+        f"    {BEGIN_SEARCH_QUERY}Where is Steven Spielberg from?{END_SEARCH_QUERY}\n\n"
+        "User:\n"
+        f"    {BEGIN_SEARCH_RESULT}\nSteven Allan Spielberg is an American filmmaker...\n{END_SEARCH_RESULT}\n\n"
+        "Continues reasoning with the new information...\n\n"
+        "Assistant:\n"
+        f"    {BEGIN_SEARCH_QUERY}Who is the director of Casino Royale?{END_SEARCH_QUERY}\n\n"
+        "User:\n"
+        f"    {BEGIN_SEARCH_RESULT}\nCasino Royale is a 2006 spy film directed by Martin Campbell...\n{END_SEARCH_RESULT}\n\n"
+        "Continues reasoning with the new information...\n\n"
+        "Assistant:\n"
+        f"    {BEGIN_SEARCH_QUERY}Where is Martin Campbell from?{END_SEARCH_QUERY}\n\n"
+        "User:\n"
+        f"    {BEGIN_SEARCH_RESULT}\nMartin Campbell (born 24 October 1943) is a New Zealand film and television director...\n{END_SEARCH_RESULT}\n\n"
+        "Continues reasoning with the new information...\n\n"
+        "Assistant:\nIt's enough to answer the question\n"
+
+        "-- Example 2 --\n" #########################################
+        "Question: \"When was the founder of craigslist born?\"\n"
+        "Assistant:\n"
+        f"    {BEGIN_SEARCH_QUERY}Who was the founder of craigslist?{END_SEARCH_QUERY}\n\n"
+        "User:\n"
+        f"    {BEGIN_SEARCH_RESULT}\nCraigslist was founded by Craig Newmark...\n{END_SEARCH_RESULT}\n\n"
+        "Continues reasoning with the new information.\n"
+        "Assistant:\n"
+        f"    {BEGIN_SEARCH_QUERY} When was Craig Newmark born?{END_SEARCH_QUERY}\n\n"
+        "User:\n"
+        f"    {BEGIN_SEARCH_RESULT}\nCraig Newmark was born on December 6, 1952...\n{END_SEARCH_RESULT}\n\n"
+        "Continues reasoning with the new information...\n\n"
+        "Assistant:\nIt's enough to answer the question\n"
+        "**Remember**:\n"
+        f"- You have a dataset to search, so you just provide a proper search query.\n"
+        f"- Use {BEGIN_SEARCH_QUERY} to request a dataset search and end with {END_SEARCH_QUERY}.\n"
+        "- The language of query MUST be as the same as 'Question' or 'search result'.\n"
+        "- If no helpful information can be found, rewrite the search query to be less and precise keywords.\n"
+        "- When done searching, continue your reasoning.\n\n"
+        'Please answer the following question. You should think step by step to solve it.\n\n'
+    )
+
+RELEVANT_EXTRACTION_PROMPT = """**Task Instruction:**
+
+    You are tasked with reading and analyzing web pages based on the following inputs: **Previous Reasoning Steps**, **Current Search Query**, and **Searched Web Pages**. Your objective is to extract relevant and helpful information for **Current Search Query** from the **Searched Web Pages** and seamlessly integrate this information into the **Previous Reasoning Steps** to continue reasoning for the original question.
+
+    **Guidelines:**
+
+    1. **Analyze the Searched Web Pages:**
+    - Carefully review the content of each searched web page.
+    - Identify factual information that is relevant to the **Current Search Query** and can aid in the reasoning process for the original question.
+
+    2. **Extract Relevant Information:**
+    - Select the information from the Searched Web Pages that directly contributes to advancing the **Previous Reasoning Steps**.
+    - Ensure that the extracted information is accurate and relevant.
+
+    3. **Output Format:**
+    - **If the web pages provide helpful information for current search query:** Present the information beginning with `**Final Information**` as shown below.
+    - The language of query **MUST BE** as the same as 'Search Query' or 'Web Pages'.\n"
+    **Final Information**
+
+    [Helpful information]
+
+    - **If the web pages do not provide any helpful information for current search query:** Output the following text.
+
+    **Final Information**
+
+    No helpful information found.
+
+    **Inputs:**
+    - **Previous Reasoning Steps:**  
+    {prev_reasoning}
+
+    - **Current Search Query:**  
+    {search_query}
+
+    - **Searched Web Pages:**  
+    {document}
+
+    """

api/__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()

api/apps/__init__.py

@@ -13,14 +13,16 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-import logging
 import os
 import sys
+import logging
 from importlib.util import module_from_spec, spec_from_file_location
 from pathlib import Path
 from flask import Blueprint, Flask
 from werkzeug.wrappers.request import Request
 from flask_cors import CORS
+from flasgger import Swagger
+from itsdangerous.url_safe import URLSafeTimedSerializer as Serializer
 
 from api.db import StatusEnum
 from api.db.db_models import close_connection
@@ -29,32 +31,60 @@ from api.utils import CustomJSONEncoder, commands
 
 from flask_session import Session
 from flask_login import LoginManager
-from api.settings import SECRET_KEY, stat_logger
-from api.settings import API_VERSION, access_logger
+from api import settings
 from api.utils.api_utils import server_error_response
-from itsdangerous.url_safe import URLSafeTimedSerializer as Serializer
+from api.constants import API_VERSION
 
-__all__ = ['app']
-
-
-logger = logging.getLogger('flask.app')
-for h in access_logger.handlers:
-    logger.addHandler(h)
+__all__ = ["app"]
 
 Request.json = property(lambda self: self.get_json(force=True, silent=True))
 
 app = Flask(__name__)
-CORS(app, supports_credentials=True,max_age=2592000)
+
+# Add this at the beginning of your file to configure Swagger UI
+swagger_config = {
+    "headers": [],
+    "specs": [
+        {
+            "endpoint": "apispec",
+            "route": "/apispec.json",
+            "rule_filter": lambda rule: True,  # Include all endpoints
+            "model_filter": lambda tag: True,  # Include all models
+        }
+    ],
+    "static_url_path": "/flasgger_static",
+    "swagger_ui": True,
+    "specs_route": "/apidocs/",
+}
+
+swagger = Swagger(
+    app,
+    config=swagger_config,
+    template={
+        "swagger": "2.0",
+        "info": {
+            "title": "RAGFlow API",
+            "description": "",
+            "version": "1.0.0",
+        },
+        "securityDefinitions": {
+            "ApiKeyAuth": {"type": "apiKey", "name": "Authorization", "in": "header"}
+        },
+    },
+)
+
+CORS(app, supports_credentials=True, max_age=2592000)
 app.url_map.strict_slashes = False
 app.json_encoder = CustomJSONEncoder
 app.errorhandler(Exception)(server_error_response)
 
-
 ## convince for dev and debug
-#app.config["LOGIN_DISABLED"] = True
+# app.config["LOGIN_DISABLED"] = True
 app.config["SESSION_PERMANENT"] = False
 app.config["SESSION_TYPE"] = "filesystem"
-app.config['MAX_CONTENT_LENGTH'] = int(os.environ.get("MAX_CONTENT_LENGTH", 128 * 1024 * 1024))
+app.config["MAX_CONTENT_LENGTH"] = int(
+    os.environ.get("MAX_CONTENT_LENGTH", 1024 * 1024 * 1024)
+)
 
 Session(app)
 login_manager = LoginManager()
@@ -64,17 +94,23 @@ commands.register_commands(app)
 
 
 def search_pages_path(pages_dir):
-    app_path_list = [path for path in pages_dir.glob('*_app.py') if not path.name.startswith('.')]
-    api_path_list = [path for path in pages_dir.glob('*_api.py') if not path.name.startswith('.')]
+    app_path_list = [
+        path for path in pages_dir.glob("*_app.py") if not path.name.startswith(".")
+    ]
+    api_path_list = [
+        path for path in pages_dir.glob("*sdk/*.py") if not path.name.startswith(".")
+    ]
     app_path_list.extend(api_path_list)
     return app_path_list
 
 
 def register_page(page_path):
-    path = f'{page_path}'
+    path = f"{page_path}"
 
-    page_name = page_path.stem.rstrip('_api') if "_api" in path else page_path.stem.rstrip('_app')
-    module_name = '.'.join(page_path.parts[page_path.parts.index('api'):-1] + (page_name,))
+    page_name = page_path.stem.rstrip("_app")
+    module_name = ".".join(
+        page_path.parts[page_path.parts.index("api"): -1] + (page_name,)
+    )
 
     spec = spec_from_file_location(module_name, page_path)
     page = module_from_spec(spec)
@@ -82,8 +118,11 @@ def register_page(page_path):
     page.manager = Blueprint(page_name, module_name)
     sys.modules[module_name] = page
     spec.loader.exec_module(page)
-    page_name = getattr(page, 'page_name', page_name)
-    url_prefix = f'/api/{API_VERSION}/{page_name}' if "_api" in path else f'/{API_VERSION}/{page_name}'
+    page_name = getattr(page, "page_name", page_name)
+    sdk_path = "\\sdk\\" if sys.platform.startswith("win") else "/sdk/"
+    url_prefix = (
+        f"/api/{API_VERSION}" if sdk_path in path else f"/{API_VERSION}/{page_name}"
+    )
 
     app.register_blueprint(page.manager, url_prefix=url_prefix)
     return url_prefix
@@ -91,30 +130,31 @@ def register_page(page_path):
 
 pages_dir = [
     Path(__file__).parent,
-    Path(__file__).parent.parent / 'api' / 'apps', # FIXME: ragflow/api/api/apps, can be remove?
+    Path(__file__).parent.parent / "api" / "apps",
+    Path(__file__).parent.parent / "api" / "apps" / "sdk",
 ]
 
 client_urls_prefix = [
-    register_page(path)
-    for dir in pages_dir
-    for path in search_pages_path(dir)
+    register_page(path) for dir in pages_dir for path in search_pages_path(dir)
 ]
 
 
 @login_manager.request_loader
 def load_user(web_request):
-    jwt = Serializer(secret_key=SECRET_KEY)
+    jwt = Serializer(secret_key=settings.SECRET_KEY)
     authorization = web_request.headers.get("Authorization")
     if authorization:
         try:
             access_token = str(jwt.loads(authorization))
-            user = UserService.query(access_token=access_token, status=StatusEnum.VALID.value)
+            user = UserService.query(
+                access_token=access_token, status=StatusEnum.VALID.value
+            )
             if user:
                 return user[0]
             else:
                 return None
         except Exception as e:
-            stat_logger.exception(e)
+            logging.warning(f"load_user got exception {e}")
             return None
     else:
         return None
@@ -122,4 +162,4 @@ def load_user(web_request):
 
 @app.teardown_request
 def _db_close(exc):
-    close_connection()
+    close_connection()

api/apps/api_app.py

@@ -22,7 +22,7 @@ from api.db.services.llm_service import TenantLLMService
 from flask_login import login_required, current_user
 
 from api.db import FileType, LLMType, ParserType, FileSource
-from api.db.db_models import APIToken, API4Conversation, Task, File
+from api.db.db_models import APIToken, Task, File
 from api.db.services import duplicate_name
 from api.db.services.api_service import APITokenService, API4ConversationService
 from api.db.services.dialog_service import DialogService, chat
@@ -32,33 +32,29 @@ from api.db.services.file_service import FileService
 from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.db.services.task_service import queue_tasks, TaskService
 from api.db.services.user_service import UserTenantService
-from api.settings import RetCode, retrievaler
+from api import settings
 from api.utils import get_uuid, current_timestamp, datetime_format
-from api.utils.api_utils import server_error_response, get_data_error_result, get_json_result, validate_request
-from itsdangerous import URLSafeTimedSerializer
+from api.utils.api_utils import server_error_response, get_data_error_result, get_json_result, validate_request, \
+    generate_confirmation_token
 
 from api.utils.file_utils import filename_type, thumbnail
-from rag.nlp import keyword_extraction
-from rag.utils.minio_conn import MINIO
+from rag.app.tag import label_question
+from rag.prompts import keyword_extraction
+from rag.utils.storage_factory import STORAGE_IMPL
 
-from api.db.services.canvas_service import CanvasTemplateService, UserCanvasService
+from api.db.services.canvas_service import UserCanvasService
 from agent.canvas import Canvas
 from functools import partial
 
 
-def generate_confirmation_token(tenent_id):
-    serializer = URLSafeTimedSerializer(tenent_id)
-    return "ragflow-" + serializer.dumps(get_uuid(), salt=tenent_id)[2:34]
-
-
-@manager.route('/new_token', methods=['POST'])
+@manager.route('/new_token', methods=['POST'])  # noqa: F821
 @login_required
 def new_token():
     req = request.json
     try:
         tenants = UserTenantService.query(user_id=current_user.id)
         if not tenants:
-            return get_data_error_result(retmsg="Tenant not found!")
+            return get_data_error_result(message="Tenant not found!")
 
         tenant_id = tenants[0].tenant_id
         obj = {"tenant_id": tenant_id, "token": generate_confirmation_token(tenant_id),
@@ -74,20 +70,20 @@ def new_token():
             obj["dialog_id"] = req["dialog_id"]
 
         if not APITokenService.save(**obj):
-            return get_data_error_result(retmsg="Fail to new a dialog!")
+            return get_data_error_result(message="Fail to new a dialog!")
 
         return get_json_result(data=obj)
     except Exception as e:
         return server_error_response(e)
 
 
-@manager.route('/token_list', methods=['GET'])
+@manager.route('/token_list', methods=['GET'])  # noqa: F821
 @login_required
 def token_list():
     try:
         tenants = UserTenantService.query(user_id=current_user.id)
         if not tenants:
-            return get_data_error_result(retmsg="Tenant not found!")
+            return get_data_error_result(message="Tenant not found!")
 
         id = request.args["dialog_id"] if "dialog_id" in request.args else request.args["canvas_id"]
         objs = APITokenService.query(tenant_id=tenants[0].tenant_id, dialog_id=id)
@@ -96,7 +92,7 @@ def token_list():
         return server_error_response(e)
 
 
-@manager.route('/rm', methods=['POST'])
+@manager.route('/rm', methods=['POST'])  # noqa: F821
 @validate_request("tokens", "tenant_id")
 @login_required
 def rm():
@@ -110,13 +106,13 @@ def rm():
         return server_error_response(e)
 
 
-@manager.route('/stats', methods=['GET'])
+@manager.route('/stats', methods=['GET'])  # noqa: F821
 @login_required
 def stats():
     try:
         tenants = UserTenantService.query(user_id=current_user.id)
         if not tenants:
-            return get_data_error_result(retmsg="Tenant not found!")
+            return get_data_error_result(message="Tenant not found!")
         objs = API4ConversationService.stats(
             tenants[0].tenant_id,
             request.args.get(
@@ -141,24 +137,26 @@ def stats():
         return server_error_response(e)
 
 
-@manager.route('/new_conversation', methods=['GET'])
+@manager.route('/new_conversation', methods=['GET'])  # noqa: F821
 def set_conversation():
     token = request.headers.get('Authorization').split()[1]
     objs = APIToken.query(token=token)
     if not objs:
         return get_json_result(
-            data=False, retmsg='Token is not valid!"', retcode=RetCode.AUTHENTICATION_ERROR)
-    req = request.json
+            data=False, message='Authentication error: API key is invalid!"', code=settings.RetCode.AUTHENTICATION_ERROR)
     try:
         if objs[0].source == "agent":
-            e, c = UserCanvasService.get_by_id(objs[0].dialog_id)
+            e, cvs = UserCanvasService.get_by_id(objs[0].dialog_id)
             if not e:
                 return server_error_response("canvas not found.")
+            if not isinstance(cvs.dsl, str):
+                cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False)
+            canvas = Canvas(cvs.dsl, objs[0].tenant_id)
             conv = {
                 "id": get_uuid(),
-                "dialog_id": c.id,
+                "dialog_id": cvs.id,
                 "user_id": request.args.get("user_id", ""),
-                "message": [{"role": "assistant", "content": "Hi there!"}],
+                "message": [{"role": "assistant", "content": canvas.get_prologue()}],
                 "source": "agent"
             }
             API4ConversationService.save(**conv)
@@ -166,7 +164,7 @@ def set_conversation():
         else:
             e, dia = DialogService.get_by_id(objs[0].dialog_id)
             if not e:
-                return get_data_error_result(retmsg="Dialog not found")
+                return get_data_error_result(message="Dialog not found")
             conv = {
                 "id": get_uuid(),
                 "dialog_id": dia.id,
@@ -179,19 +177,20 @@ def set_conversation():
         return server_error_response(e)
 
 
-@manager.route('/completion', methods=['POST'])
+@manager.route('/completion', methods=['POST'])  # noqa: F821
 @validate_request("conversation_id", "messages")
 def completion():
     token = request.headers.get('Authorization').split()[1]
     objs = APIToken.query(token=token)
     if not objs:
         return get_json_result(
-            data=False, retmsg='Token is not valid!"', retcode=RetCode.AUTHENTICATION_ERROR)
+            data=False, message='Authentication error: API key is invalid!"', code=settings.RetCode.AUTHENTICATION_ERROR)
     req = request.json
     e, conv = API4ConversationService.get_by_id(req["conversation_id"])
     if not e:
-        return get_data_error_result(retmsg="Conversation not found!")
-    if "quote" not in req: req["quote"] = False
+        return get_data_error_result(message="Conversation not found!")
+    if "quote" not in req:
+        req["quote"] = False
 
     msg = []
     for m in req["messages"]:
@@ -199,15 +198,19 @@ def completion():
             continue
         if m["role"] == "assistant" and not msg:
             continue
-        msg.append({"role": m["role"], "content": m["content"]})
+        msg.append(m)
+    if not msg[-1].get("id"):
+        msg[-1]["id"] = get_uuid()
+    message_id = msg[-1]["id"]
 
     def fillin_conv(ans):
-        nonlocal conv
+        nonlocal conv, message_id
         if not conv.reference:
             conv.reference.append(ans["reference"])
         else:
             conv.reference[-1] = ans["reference"]
-        conv.message[-1] = {"role": "assistant", "content": ans["answer"]}
+        conv.message[-1] = {"role": "assistant", "content": ans["answer"], "id": message_id}
+        ans["id"] = message_id
 
     def rename_field(ans):
         reference = ans['reference']
@@ -233,7 +236,7 @@ def completion():
 
             if not conv.reference:
                 conv.reference = []
-            conv.message.append({"role": "assistant", "content": ""})
+            conv.message.append({"role": "assistant", "content": "", "id": message_id})
             conv.reference.append({"chunks": [], "doc_aggs": []})
 
             final_ans = {"reference": [], "content": ""}
@@ -257,19 +260,20 @@ def completion():
                             ans = {"answer": ans["content"], "reference": ans.get("reference", [])}
                             fillin_conv(ans)
                             rename_field(ans)
-                            yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": ans},
+                            yield "data:" + json.dumps({"code": 0, "message": "", "data": ans},
                                                        ensure_ascii=False) + "\n\n"
 
-                        canvas.messages.append({"role": "assistant", "content": final_ans["content"]})
+                        canvas.messages.append({"role": "assistant", "content": final_ans["content"], "id": message_id})
+                        canvas.history.append(("assistant", final_ans["content"]))
                         if final_ans.get("reference"):
                             canvas.reference.append(final_ans["reference"])
                         cvs.dsl = json.loads(str(canvas))
                         API4ConversationService.append_message(conv.id, conv.to_dict())
                     except Exception as e:
-                        yield "data:" + json.dumps({"retcode": 500, "retmsg": str(e),
+                        yield "data:" + json.dumps({"code": 500, "message": str(e),
                                                     "data": {"answer": "**ERROR**: " + str(e), "reference": []}},
                                                    ensure_ascii=False) + "\n\n"
-                    yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": True}, ensure_ascii=False) + "\n\n"
+                    yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n"
 
                 resp = Response(sse(), mimetype="text/event-stream")
                 resp.headers.add_header("Cache-control", "no-cache")
@@ -279,7 +283,7 @@ def completion():
                 return resp
 
             final_ans["content"] = "\n".join(answer["content"]) if "content" in answer else ""
-            canvas.messages.append({"role": "assistant", "content": final_ans["content"]})
+            canvas.messages.append({"role": "assistant", "content": final_ans["content"], "id": message_id})
             if final_ans.get("reference"):
                 canvas.reference.append(final_ans["reference"])
             cvs.dsl = json.loads(str(canvas))
@@ -289,18 +293,18 @@ def completion():
             API4ConversationService.append_message(conv.id, conv.to_dict())
             rename_field(result)
             return get_json_result(data=result)
-        
-        #******************For dialog******************
+
+        # ******************For dialog******************
         conv.message.append(msg[-1])
         e, dia = DialogService.get_by_id(conv.dialog_id)
         if not e:
-            return get_data_error_result(retmsg="Dialog not found!")
+            return get_data_error_result(message="Dialog not found!")
         del req["conversation_id"]
         del req["messages"]
 
         if not conv.reference:
             conv.reference = []
-        conv.message.append({"role": "assistant", "content": ""})
+        conv.message.append({"role": "assistant", "content": "", "id": message_id})
         conv.reference.append({"chunks": [], "doc_aggs": []})
 
         def stream():
@@ -309,14 +313,14 @@ def completion():
                 for ans in chat(dia, msg, True, **req):
                     fillin_conv(ans)
                     rename_field(ans)
-                    yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": ans},
+                    yield "data:" + json.dumps({"code": 0, "message": "", "data": ans},
                                                ensure_ascii=False) + "\n\n"
                 API4ConversationService.append_message(conv.id, conv.to_dict())
             except Exception as e:
-                yield "data:" + json.dumps({"retcode": 500, "retmsg": str(e),
+                yield "data:" + json.dumps({"code": 500, "message": str(e),
                                             "data": {"answer": "**ERROR**: " + str(e), "reference": []}},
                                            ensure_ascii=False) + "\n\n"
-            yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": True}, ensure_ascii=False) + "\n\n"
+            yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n"
 
         if req.get("stream", True):
             resp = Response(stream(), mimetype="text/event-stream")
@@ -325,7 +329,7 @@ def completion():
             resp.headers.add_header("X-Accel-Buffering", "no")
             resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8")
             return resp
-            
+
         answer = None
         for ans in chat(dia, msg, **req):
             answer = ans
@@ -339,15 +343,25 @@ def completion():
         return server_error_response(e)
 
 
-@manager.route('/conversation/<conversation_id>', methods=['GET'])
+@manager.route('/conversation/<conversation_id>', methods=['GET'])  # noqa: F821
 # @login_required
 def get(conversation_id):
+    token = request.headers.get('Authorization').split()[1]
+    objs = APIToken.query(token=token)
+    if not objs:
+        return get_json_result(
+            data=False, message='Authentication error: API key is invalid!"', code=settings.RetCode.AUTHENTICATION_ERROR)
+
     try:
         e, conv = API4ConversationService.get_by_id(conversation_id)
         if not e:
-            return get_data_error_result(retmsg="Conversation not found!")
+            return get_data_error_result(message="Conversation not found!")
 
         conv = conv.to_dict()
+        if token != APIToken.query(dialog_id=conv['dialog_id'])[0].token:
+            return get_json_result(data=False, message='Authentication error: API key is invalid for this conversation_id!"',
+                                   code=settings.RetCode.AUTHENTICATION_ERROR)
+
         for referenct_i in conv['reference']:
             if referenct_i is None or len(referenct_i) == 0:
                 continue
@@ -360,14 +374,14 @@ def get(conversation_id):
         return server_error_response(e)
 
 
-@manager.route('/document/upload', methods=['POST'])
+@manager.route('/document/upload', methods=['POST'])  # noqa: F821
 @validate_request("kb_name")
 def upload():
     token = request.headers.get('Authorization').split()[1]
     objs = APIToken.query(token=token)
     if not objs:
         return get_json_result(
-            data=False, retmsg='Token is not valid!"', retcode=RetCode.AUTHENTICATION_ERROR)
+            data=False, message='Authentication error: API key is invalid!"', code=settings.RetCode.AUTHENTICATION_ERROR)
 
     kb_name = request.form.get("kb_name").strip()
     tenant_id = objs[0].tenant_id
@@ -376,19 +390,19 @@ def upload():
         e, kb = KnowledgebaseService.get_by_name(kb_name, tenant_id)
         if not e:
             return get_data_error_result(
-                retmsg="Can't find this knowledgebase!")
+                message="Can't find this knowledgebase!")
         kb_id = kb.id
     except Exception as e:
         return server_error_response(e)
 
     if 'file' not in request.files:
         return get_json_result(
-            data=False, retmsg='No file part!', retcode=RetCode.ARGUMENT_ERROR)
+            data=False, message='No file part!', code=settings.RetCode.ARGUMENT_ERROR)
 
     file = request.files['file']
     if file.filename == '':
         return get_json_result(
-            data=False, retmsg='No file selected!', retcode=RetCode.ARGUMENT_ERROR)
+            data=False, message='No file selected!', code=settings.RetCode.ARGUMENT_ERROR)
 
     root_folder = FileService.get_root_folder(tenant_id)
     pf_id = root_folder["id"]
@@ -399,7 +413,7 @@ def upload():
     try:
         if DocumentService.get_doc_count(kb.tenant_id) >= int(os.environ.get('MAX_FILE_NUM_PER_USER', 8192)):
             return get_data_error_result(
-                retmsg="Exceed the maximum file number of a free user!")
+                message="Exceed the maximum file number of a free user!")
 
         filename = duplicate_name(
             DocumentService.query,
@@ -408,13 +422,13 @@ def upload():
         filetype = filename_type(filename)
         if not filetype:
             return get_data_error_result(
-                retmsg="This type of file has not been supported yet!")
+                message="This type of file has not been supported yet!")
 
         location = filename
-        while MINIO.obj_exist(kb_id, location):
+        while STORAGE_IMPL.obj_exist(kb_id, location):
             location += "_"
         blob = request.files['file'].read()
-        MINIO.put(kb_id, location, blob)
+        STORAGE_IMPL.put(kb_id, location, blob)
         doc = {
             "id": get_uuid(),
             "kb_id": kb.id,
@@ -438,6 +452,8 @@ def upload():
             doc["parser_id"] = ParserType.AUDIO.value
         if re.search(r"\.(ppt|pptx|pages)$", filename):
             doc["parser_id"] = ParserType.PRESENTATION.value
+        if re.search(r"\.(eml)$", filename):
+            doc["parser_id"] = ParserType.EMAIL.value
 
         doc_result = DocumentService.insert(doc)
         FileService.add_file_from_kb(doc, kb_folder["id"], kb.tenant_id)
@@ -455,52 +471,52 @@ def upload():
                 # if str(req["run"]) == TaskStatus.CANCEL.value:
                 tenant_id = DocumentService.get_tenant_id(doc["id"])
                 if not tenant_id:
-                    return get_data_error_result(retmsg="Tenant not found!")
+                    return get_data_error_result(message="Tenant not found!")
 
                 # e, doc = DocumentService.get_by_id(doc["id"])
                 TaskService.filter_delete([Task.doc_id == doc["id"]])
                 e, doc = DocumentService.get_by_id(doc["id"])
                 doc = doc.to_dict()
                 doc["tenant_id"] = tenant_id
-                bucket, name = File2DocumentService.get_minio_address(doc_id=doc["id"])
-                queue_tasks(doc, bucket, name)
+                bucket, name = File2DocumentService.get_storage_address(doc_id=doc["id"])
+                queue_tasks(doc, bucket, name, 0)
             except Exception as e:
                 return server_error_response(e)
 
     return get_json_result(data=doc_result.to_json())
 
 
-@manager.route('/document/upload_and_parse', methods=['POST'])
+@manager.route('/document/upload_and_parse', methods=['POST'])  # noqa: F821
 @validate_request("conversation_id")
 def upload_parse():
     token = request.headers.get('Authorization').split()[1]
     objs = APIToken.query(token=token)
     if not objs:
         return get_json_result(
-            data=False, retmsg='Token is not valid!"', retcode=RetCode.AUTHENTICATION_ERROR)
+            data=False, message='Authentication error: API key is invalid!"', code=settings.RetCode.AUTHENTICATION_ERROR)
 
     if 'file' not in request.files:
         return get_json_result(
-            data=False, retmsg='No file part!', retcode=RetCode.ARGUMENT_ERROR)
+            data=False, message='No file part!', code=settings.RetCode.ARGUMENT_ERROR)
 
     file_objs = request.files.getlist('file')
     for file_obj in file_objs:
         if file_obj.filename == '':
             return get_json_result(
-                data=False, retmsg='No file selected!', retcode=RetCode.ARGUMENT_ERROR)
+                data=False, message='No file selected!', code=settings.RetCode.ARGUMENT_ERROR)
 
     doc_ids = doc_upload_and_parse(request.form.get("conversation_id"), file_objs, objs[0].tenant_id)
     return get_json_result(data=doc_ids)
 
 
-@manager.route('/list_chunks', methods=['POST'])
+@manager.route('/list_chunks', methods=['POST'])  # noqa: F821
 # @login_required
 def list_chunks():
     token = request.headers.get('Authorization').split()[1]
     objs = APIToken.query(token=token)
     if not objs:
         return get_json_result(
-            data=False, retmsg='Token is not valid!"', retcode=RetCode.AUTHENTICATION_ERROR)
+            data=False, message='Authentication error: API key is invalid!"', code=settings.RetCode.AUTHENTICATION_ERROR)
 
     req = request.json
 
@@ -514,15 +530,16 @@ def list_chunks():
             doc_id = req['doc_id']
         else:
             return get_json_result(
-                data=False, retmsg="Can't find doc_name or doc_id"
+                data=False, message="Can't find doc_name or doc_id"
             )
+        kb_ids = KnowledgebaseService.get_kb_ids(tenant_id)
 
-        res = retrievaler.chunk_list(doc_id=doc_id, tenant_id=tenant_id)
+        res = settings.retrievaler.chunk_list(doc_id, tenant_id, kb_ids)
         res = [
             {
                 "content": res_item["content_with_weight"],
                 "doc_name": res_item["docnm_kwd"],
-                "img_id": res_item["img_id"]
+                "image_id": res_item["img_id"]
             } for res_item in res
         ]
 
@@ -532,14 +549,14 @@ def list_chunks():
     return get_json_result(data=res)
 
 
-@manager.route('/list_kb_docs', methods=['POST'])
+@manager.route('/list_kb_docs', methods=['POST'])  # noqa: F821
 # @login_required
 def list_kb_docs():
     token = request.headers.get('Authorization').split()[1]
     objs = APIToken.query(token=token)
     if not objs:
         return get_json_result(
-            data=False, retmsg='Token is not valid!"', retcode=RetCode.AUTHENTICATION_ERROR)
+            data=False, message='Authentication error: API key is invalid!"', code=settings.RetCode.AUTHENTICATION_ERROR)
 
     req = request.json
     tenant_id = objs[0].tenant_id
@@ -549,7 +566,7 @@ def list_kb_docs():
         e, kb = KnowledgebaseService.get_by_name(kb_name, tenant_id)
         if not e:
             return get_data_error_result(
-                retmsg="Can't find this knowledgebase!")
+                message="Can't find this knowledgebase!")
         kb_id = kb.id
 
     except Exception as e:
@@ -571,28 +588,29 @@ def list_kb_docs():
     except Exception as e:
         return server_error_response(e)
 
-@manager.route('/document/infos', methods=['POST'])
+
+@manager.route('/document/infos', methods=['POST'])  # noqa: F821
 @validate_request("doc_ids")
 def docinfos():
     token = request.headers.get('Authorization').split()[1]
     objs = APIToken.query(token=token)
     if not objs:
         return get_json_result(
-            data=False, retmsg='Token is not valid!"', retcode=RetCode.AUTHENTICATION_ERROR)
+            data=False, message='Authentication error: API key is invalid!"', code=settings.RetCode.AUTHENTICATION_ERROR)
     req = request.json
     doc_ids = req["doc_ids"]
     docs = DocumentService.get_by_ids(doc_ids)
     return get_json_result(data=list(docs.dicts()))
 
 
-@manager.route('/document', methods=['DELETE'])
+@manager.route('/document', methods=['DELETE'])  # noqa: F821
 # @login_required
 def document_rm():
     token = request.headers.get('Authorization').split()[1]
     objs = APIToken.query(token=token)
     if not objs:
         return get_json_result(
-            data=False, retmsg='Token is not valid!"', retcode=RetCode.AUTHENTICATION_ERROR)
+            data=False, message='Authentication error: API key is invalid!"', code=settings.RetCode.AUTHENTICATION_ERROR)
 
     tenant_id = objs[0].tenant_id
     req = request.json
@@ -604,7 +622,7 @@ def document_rm():
 
         if not doc_ids:
             return get_json_result(
-                data=False, retmsg="Can't find doc_names or doc_ids"
+                data=False, message="Can't find doc_names or doc_ids"
             )
 
     except Exception as e:
@@ -619,32 +637,32 @@ def document_rm():
         try:
             e, doc = DocumentService.get_by_id(doc_id)
             if not e:
-                return get_data_error_result(retmsg="Document not found!")
+                return get_data_error_result(message="Document not found!")
             tenant_id = DocumentService.get_tenant_id(doc_id)
             if not tenant_id:
-                return get_data_error_result(retmsg="Tenant not found!")
+                return get_data_error_result(message="Tenant not found!")
 
-            b, n = File2DocumentService.get_minio_address(doc_id=doc_id)
+            b, n = File2DocumentService.get_storage_address(doc_id=doc_id)
 
             if not DocumentService.remove_document(doc, tenant_id):
                 return get_data_error_result(
-                    retmsg="Database error (Document removal)!")
+                    message="Database error (Document removal)!")
 
             f2d = File2DocumentService.get_by_document_id(doc_id)
             FileService.filter_delete([File.source_type == FileSource.KNOWLEDGEBASE, File.id == f2d[0].file_id])
             File2DocumentService.delete_by_document_id(doc_id)
 
-            MINIO.rm(b, n)
+            STORAGE_IMPL.rm(b, n)
         except Exception as e:
             errors += str(e)
 
     if errors:
-        return get_json_result(data=False, retmsg=errors, retcode=RetCode.SERVER_ERROR)
+        return get_json_result(data=False, message=errors, code=settings.RetCode.SERVER_ERROR)
 
     return get_json_result(data=True)
 
 
-@manager.route('/completion_aibotk', methods=['POST'])
+@manager.route('/completion_aibotk', methods=['POST'])  # noqa: F821
 @validate_request("Authorization", "conversation_id", "word")
 def completion_faq():
     import base64
@@ -654,36 +672,101 @@ def completion_faq():
     objs = APIToken.query(token=token)
     if not objs:
         return get_json_result(
-            data=False, retmsg='Token is not valid!"', retcode=RetCode.AUTHENTICATION_ERROR)
+            data=False, message='Authentication error: API key is invalid!"', code=settings.RetCode.AUTHENTICATION_ERROR)
 
     e, conv = API4ConversationService.get_by_id(req["conversation_id"])
     if not e:
-        return get_data_error_result(retmsg="Conversation not found!")
-    if "quote" not in req: req["quote"] = True
+        return get_data_error_result(message="Conversation not found!")
+    if "quote" not in req:
+        req["quote"] = True
 
     msg = []
     msg.append({"role": "user", "content": req["word"]})
+    if not msg[-1].get("id"):
+        msg[-1]["id"] = get_uuid()
+    message_id = msg[-1]["id"]
+
+    def fillin_conv(ans):
+        nonlocal conv, message_id
+        if not conv.reference:
+            conv.reference.append(ans["reference"])
+        else:
+            conv.reference[-1] = ans["reference"]
+        conv.message[-1] = {"role": "assistant", "content": ans["answer"], "id": message_id}
+        ans["id"] = message_id
 
     try:
+        if conv.source == "agent":
+            conv.message.append(msg[-1])
+            e, cvs = UserCanvasService.get_by_id(conv.dialog_id)
+            if not e:
+                return server_error_response("canvas not found.")
+
+            if not isinstance(cvs.dsl, str):
+                cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False)
+
+            if not conv.reference:
+                conv.reference = []
+            conv.message.append({"role": "assistant", "content": "", "id": message_id})
+            conv.reference.append({"chunks": [], "doc_aggs": []})
+
+            final_ans = {"reference": [], "doc_aggs": []}
+            canvas = Canvas(cvs.dsl, objs[0].tenant_id)
+
+            canvas.messages.append(msg[-1])
+            canvas.add_user_input(msg[-1]["content"])
+            answer = canvas.run(stream=False)
+
+            assert answer is not None, "Nothing. Is it over?"
+
+            data_type_picture = {
+                "type": 3,
+                "url": "base64 content"
+            }
+            data = [
+                {
+                    "type": 1,
+                    "content": ""
+                }
+            ]
+            final_ans["content"] = "\n".join(answer["content"]) if "content" in answer else ""
+            canvas.messages.append({"role": "assistant", "content": final_ans["content"], "id": message_id})
+            if final_ans.get("reference"):
+                canvas.reference.append(final_ans["reference"])
+            cvs.dsl = json.loads(str(canvas))
+
+            ans = {"answer": final_ans["content"], "reference": final_ans.get("reference", [])}
+            data[0]["content"] += re.sub(r'##\d\$\$', '', ans["answer"])
+            fillin_conv(ans)
+            API4ConversationService.append_message(conv.id, conv.to_dict())
+
+            chunk_idxs = [int(match[2]) for match in re.findall(r'##\d\$\$', ans["answer"])]
+            for chunk_idx in chunk_idxs[:1]:
+                if ans["reference"]["chunks"][chunk_idx]["img_id"]:
+                    try:
+                        bkt, nm = ans["reference"]["chunks"][chunk_idx]["img_id"].split("-")
+                        response = STORAGE_IMPL.get(bkt, nm)
+                        data_type_picture["url"] = base64.b64encode(response).decode('utf-8')
+                        data.append(data_type_picture)
+                        break
+                    except Exception as e:
+                        return server_error_response(e)
+
+            response = {"code": 200, "msg": "success", "data": data}
+            return response
+
+        # ******************For dialog******************
         conv.message.append(msg[-1])
         e, dia = DialogService.get_by_id(conv.dialog_id)
         if not e:
-            return get_data_error_result(retmsg="Dialog not found!")
+            return get_data_error_result(message="Dialog not found!")
         del req["conversation_id"]
 
         if not conv.reference:
             conv.reference = []
-        conv.message.append({"role": "assistant", "content": ""})
+        conv.message.append({"role": "assistant", "content": "", "id": message_id})
         conv.reference.append({"chunks": [], "doc_aggs": []})
 
-        def fillin_conv(ans):
-            nonlocal conv
-            if not conv.reference:
-                conv.reference.append(ans["reference"])
-            else:
-                conv.reference[-1] = ans["reference"]
-            conv.message[-1] = {"role": "assistant", "content": ans["answer"]}
-
         data_type_picture = {
             "type": 3,
             "url": "base64 content"
@@ -707,7 +790,7 @@ def completion_faq():
             if ans["reference"]["chunks"][chunk_idx]["img_id"]:
                 try:
                     bkt, nm = ans["reference"]["chunks"][chunk_idx]["img_id"].split("-")
-                    response = MINIO.get(bkt, nm)
+                    response = STORAGE_IMPL.get(bkt, nm)
                     data_type_picture["url"] = base64.b64encode(response).decode('utf-8')
                     data.append(data_type_picture)
                     break
@@ -721,17 +804,17 @@ def completion_faq():
         return server_error_response(e)
 
 
-@manager.route('/retrieval', methods=['POST'])
+@manager.route('/retrieval', methods=['POST'])  # noqa: F821
 @validate_request("kb_id", "question")
 def retrieval():
     token = request.headers.get('Authorization').split()[1]
     objs = APIToken.query(token=token)
     if not objs:
         return get_json_result(
-            data=False, retmsg='Token is not valid!"', retcode=RetCode.AUTHENTICATION_ERROR)
+            data=False, message='Authentication error: API key is invalid!"', code=settings.RetCode.AUTHENTICATION_ERROR)
 
     req = request.json
-    kb_ids = req.get("kb_id",[])
+    kb_ids = req.get("kb_id", [])
     doc_ids = req.get("doc_ids", [])
     question = req.get("question")
     page = int(req.get("page", 1))
@@ -745,26 +828,27 @@ def retrieval():
         embd_nms = list(set([kb.embd_id for kb in kbs]))
         if len(embd_nms) != 1:
             return get_json_result(
-                data=False, retmsg='Knowledge bases use different embedding models or does not exist."', retcode=RetCode.AUTHENTICATION_ERROR)
+                data=False, message='Knowledge bases use different embedding models or does not exist."',
+                code=settings.RetCode.AUTHENTICATION_ERROR)
 
         embd_mdl = TenantLLMService.model_instance(
             kbs[0].tenant_id, LLMType.EMBEDDING.value, llm_name=kbs[0].embd_id)
         rerank_mdl = None
         if req.get("rerank_id"):
             rerank_mdl = TenantLLMService.model_instance(
-            kbs[0].tenant_id, LLMType.RERANK.value, llm_name=req["rerank_id"])
+                kbs[0].tenant_id, LLMType.RERANK.value, llm_name=req["rerank_id"])
         if req.get("keyword", False):
             chat_mdl = TenantLLMService.model_instance(kbs[0].tenant_id, LLMType.CHAT)
             question += keyword_extraction(chat_mdl, question)
-        ranks = retrievaler.retrieval(question, embd_mdl, kbs[0].tenant_id, kb_ids, page, size,
-            similarity_threshold, vector_similarity_weight, top,
-            doc_ids, rerank_mdl=rerank_mdl)
+        ranks = settings.retrievaler.retrieval(question, embd_mdl, kbs[0].tenant_id, kb_ids, page, size,
+                                               similarity_threshold, vector_similarity_weight, top,
+                                               doc_ids, rerank_mdl=rerank_mdl,
+                                               rank_feature=label_question(question, kbs))
         for c in ranks["chunks"]:
-            if "vector" in c:
-                del c["vector"]
+            c.pop("vector", None)
         return get_json_result(data=ranks)
     except Exception as e:
         if str(e).find("not_found") > 0:
-            return get_json_result(data=False, retmsg=f'No chunk found! Check the chunk status please!',
-                                   retcode=RetCode.DATA_ERROR)
-        return server_error_response(e)
+            return get_json_result(data=False, message='No chunk found! Check the chunk status please!',
+                                   code=settings.RetCode.DATA_ERROR)
+        return server_error_response(e)

api/apps/canvas_app.py

@@ -14,23 +14,28 @@
 #  limitations under the License.
 #
 import json
-from functools import partial
+import traceback
 from flask import request, Response
 from flask_login import login_required, current_user
 from api.db.services.canvas_service import CanvasTemplateService, UserCanvasService
+from api.db.services.user_service import TenantService
+from api.db.services.user_canvas_version import UserCanvasVersionService
+from api.settings import RetCode
 from api.utils import get_uuid
-from api.utils.api_utils import get_json_result, server_error_response, validate_request
+from api.utils.api_utils import get_json_result, server_error_response, validate_request, get_data_error_result
 from agent.canvas import Canvas
 from peewee import MySQLDatabase, PostgresqlDatabase
+from api.db.db_models import APIToken
+import logging
+import time
 
-
-@manager.route('/templates', methods=['GET'])
+@manager.route('/templates', methods=['GET'])  # noqa: F821
 @login_required
 def templates():
     return get_json_result(data=[c.to_dict() for c in CanvasTemplateService.get_all()])
 
 
-@manager.route('/list', methods=['GET'])
+@manager.route('/list', methods=['GET'])  # noqa: F821
 @login_required
 def canvas_list():
     return get_json_result(data=sorted([c.to_dict() for c in \
@@ -38,46 +43,73 @@ def canvas_list():
                            )
 
 
-@manager.route('/rm', methods=['POST'])
+@manager.route('/rm', methods=['POST'])  # noqa: F821
 @validate_request("canvas_ids")
 @login_required
 def rm():
     for i in request.json["canvas_ids"]:
+        if not UserCanvasService.query(user_id=current_user.id,id=i):
+            return get_json_result(
+                data=False, message='Only owner of canvas authorized for this operation.',
+                code=RetCode.OPERATING_ERROR)
         UserCanvasService.delete_by_id(i)
     return get_json_result(data=True)
 
 
-@manager.route('/set', methods=['POST'])
+@manager.route('/set', methods=['POST'])  # noqa: F821
 @validate_request("dsl", "title")
 @login_required
 def save():
     req = request.json
     req["user_id"] = current_user.id
-    if not isinstance(req["dsl"], str): req["dsl"] = json.dumps(req["dsl"], ensure_ascii=False)
-
+    if not isinstance(req["dsl"], str):
+        req["dsl"] = json.dumps(req["dsl"], ensure_ascii=False)
     req["dsl"] = json.loads(req["dsl"])
     if "id" not in req:
         if UserCanvasService.query(user_id=current_user.id, title=req["title"].strip()):
-            return server_error_response(ValueError("Duplicated title."))
+            return get_data_error_result(message=f"{req['title'].strip()} already exists.")
         req["id"] = get_uuid()
         if not UserCanvasService.save(**req):
-            return server_error_response("Fail to save canvas.")
+            return get_data_error_result(message="Fail to save canvas.")
     else:
+        if not UserCanvasService.query(user_id=current_user.id, id=req["id"]):
+            return get_json_result(
+                data=False, message='Only owner of canvas authorized for this operation.',
+                code=RetCode.OPERATING_ERROR)
         UserCanvasService.update_by_id(req["id"], req)
-
+    # save version    
+    UserCanvasVersionService.insert( user_canvas_id=req["id"], dsl=req["dsl"], title="{0}_{1}".format(req["title"], time.strftime("%Y_%m_%d_%H_%M_%S")))
+    UserCanvasVersionService.delete_all_versions(req["id"])
     return get_json_result(data=req)
 
+ 
 
-@manager.route('/get/<canvas_id>', methods=['GET'])
+
+@manager.route('/get/<canvas_id>', methods=['GET'])  # noqa: F821
 @login_required
 def get(canvas_id):
+    e, c = UserCanvasService.get_by_tenant_id(canvas_id)
+    logging.info(f"get canvas_id: {canvas_id} c: {c}")
+    if not e:
+        return get_data_error_result(message="canvas not found.")
+    return get_json_result(data=c)
+
+@manager.route('/getsse/<canvas_id>', methods=['GET'])  # type: ignore # noqa: F821
+def getsse(canvas_id):
+    token = request.headers.get('Authorization').split()
+    if len(token) != 2:
+        return get_data_error_result(message='Authorization is not valid!"')
+    token = token[1]
+    objs = APIToken.query(beta=token)
+    if not objs:
+        return get_data_error_result(message='Authentication error: API key is invalid!"')
     e, c = UserCanvasService.get_by_id(canvas_id)
     if not e:
-        return server_error_response("canvas not found.")
+        return get_data_error_result(message="canvas not found.")
     return get_json_result(data=c.to_dict())
 
 
-@manager.route('/completion', methods=['POST'])
+@manager.route('/completion', methods=['POST'])  # noqa: F821
 @validate_request("id")
 @login_required
 def run():
@@ -85,46 +117,59 @@ def run():
     stream = req.get("stream", True)
     e, cvs = UserCanvasService.get_by_id(req["id"])
     if not e:
-        return server_error_response("canvas not found.")
+        return get_data_error_result(message="canvas not found.")
+    if not UserCanvasService.query(user_id=current_user.id, id=req["id"]):
+        return get_json_result(
+            data=False, message='Only owner of canvas authorized for this operation.',
+            code=RetCode.OPERATING_ERROR)
 
     if not isinstance(cvs.dsl, str):
         cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False)
 
     final_ans = {"reference": [], "content": ""}
+    message_id = req.get("message_id", get_uuid())
     try:
         canvas = Canvas(cvs.dsl, current_user.id)
         if "message" in req:
-            canvas.messages.append({"role": "user", "content": req["message"]})
+            canvas.messages.append({"role": "user", "content": req["message"], "id": message_id})
             canvas.add_user_input(req["message"])
-        answer = canvas.run(stream=stream)
-        print(canvas)
     except Exception as e:
         return server_error_response(e)
 
-    assert answer is not None, "Nothing. Is it over?"
-
     if stream:
-        assert isinstance(answer, partial), "Nothing. Is it over?"
-
         def sse():
             nonlocal answer, cvs
             try:
-                for ans in answer():
+                for ans in canvas.run(stream=True):
+                    if ans.get("running_status"):
+                        yield "data:" + json.dumps({"code": 0, "message": "",
+                                                    "data": {"answer": ans["content"],
+                                                             "running_status": True}},
+                                                   ensure_ascii=False) + "\n\n"
+                        continue
                     for k in ans.keys():
                         final_ans[k] = ans[k]
                     ans = {"answer": ans["content"], "reference": ans.get("reference", [])}
-                    yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": ans}, ensure_ascii=False) + "\n\n"
+                    yield "data:" + json.dumps({"code": 0, "message": "", "data": ans}, ensure_ascii=False) + "\n\n"
 
-                canvas.messages.append({"role": "assistant", "content": final_ans["content"]})
+                canvas.messages.append({"role": "assistant", "content": final_ans["content"], "id": message_id})
+                canvas.history.append(("assistant", final_ans["content"]))
+                if not canvas.path[-1]:
+                    canvas.path.pop(-1)
                 if final_ans.get("reference"):
                     canvas.reference.append(final_ans["reference"])
                 cvs.dsl = json.loads(str(canvas))
                 UserCanvasService.update_by_id(req["id"], cvs.to_dict())
             except Exception as e:
-                yield "data:" + json.dumps({"retcode": 500, "retmsg": str(e),
+                cvs.dsl = json.loads(str(canvas))
+                if not canvas.path[-1]:
+                    canvas.path.pop(-1)
+                UserCanvasService.update_by_id(req["id"], cvs.to_dict())
+                traceback.print_exc()
+                yield "data:" + json.dumps({"code": 500, "message": str(e),
                                             "data": {"answer": "**ERROR**: " + str(e), "reference": []}},
                                            ensure_ascii=False) + "\n\n"
-            yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": True}, ensure_ascii=False) + "\n\n"
+            yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n"
 
         resp = Response(sse(), mimetype="text/event-stream")
         resp.headers.add_header("Cache-control", "no-cache")
@@ -133,16 +178,19 @@ def run():
         resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8")
         return resp
 
-    final_ans["content"] = "\n".join(answer["content"]) if "content" in answer else ""
-    canvas.messages.append({"role": "assistant", "content": final_ans["content"]})
-    if final_ans.get("reference"):
-        canvas.reference.append(final_ans["reference"])
-    cvs.dsl = json.loads(str(canvas))
-    UserCanvasService.update_by_id(req["id"], cvs.to_dict())
-    return get_json_result(data={"answer": final_ans["content"], "reference": final_ans.get("reference", [])})
+    for answer in canvas.run(stream=False):
+        if answer.get("running_status"):
+            continue
+        final_ans["content"] = "\n".join(answer["content"]) if "content" in answer else ""
+        canvas.messages.append({"role": "assistant", "content": final_ans["content"], "id": message_id})
+        if final_ans.get("reference"):
+            canvas.reference.append(final_ans["reference"])
+        cvs.dsl = json.loads(str(canvas))
+        UserCanvasService.update_by_id(req["id"], cvs.to_dict())
+        return get_json_result(data={"answer": final_ans["content"], "reference": final_ans.get("reference", [])})
 
 
-@manager.route('/reset', methods=['POST'])
+@manager.route('/reset', methods=['POST'])  # noqa: F821
 @validate_request("id")
 @login_required
 def reset():
@@ -150,7 +198,11 @@ def reset():
     try:
         e, user_canvas = UserCanvasService.get_by_id(req["id"])
         if not e:
-            return server_error_response("canvas not found.")
+            return get_data_error_result(message="canvas not found.")
+        if not UserCanvasService.query(user_id=current_user.id, id=req["id"]):
+            return get_json_result(
+                data=False, message='Only owner of canvas authorized for this operation.',
+                code=RetCode.OPERATING_ERROR)
 
         canvas = Canvas(json.dumps(user_canvas.dsl), current_user.id)
         canvas.reset()
@@ -161,7 +213,51 @@ def reset():
         return server_error_response(e)
 
 
-@manager.route('/test_db_connect', methods=['POST'])
+@manager.route('/input_elements', methods=['GET'])  # noqa: F821
+@login_required
+def input_elements():
+    cvs_id = request.args.get("id")
+    cpn_id = request.args.get("component_id")
+    try:
+        e, user_canvas = UserCanvasService.get_by_id(cvs_id)
+        if not e:
+            return get_data_error_result(message="canvas not found.")
+        if not UserCanvasService.query(user_id=current_user.id, id=cvs_id):
+            return get_json_result(
+                data=False, message='Only owner of canvas authorized for this operation.',
+                code=RetCode.OPERATING_ERROR)
+
+        canvas = Canvas(json.dumps(user_canvas.dsl), current_user.id)
+        return get_json_result(data=canvas.get_component_input_elements(cpn_id))
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/debug', methods=['POST'])  # noqa: F821
+@validate_request("id", "component_id", "params")
+@login_required
+def debug():
+    req = request.json
+    for p in req["params"]:
+        assert p.get("key")
+    try:
+        e, user_canvas = UserCanvasService.get_by_id(req["id"])
+        if not e:
+            return get_data_error_result(message="canvas not found.")
+        if not UserCanvasService.query(user_id=current_user.id, id=req["id"]):
+            return get_json_result(
+                data=False, message='Only owner of canvas authorized for this operation.',
+                code=RetCode.OPERATING_ERROR)
+
+        canvas = Canvas(json.dumps(user_canvas.dsl), current_user.id)
+        canvas.get_component(req["component_id"])["obj"]._param.debug_inputs = req["params"]
+        df = canvas.get_component(req["component_id"])["obj"].debug()
+        return get_json_result(data=df.to_dict(orient="records"))
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/test_db_connect', methods=['POST'])  # noqa: F821
 @validate_request("db_type", "database", "username", "host", "port", "password")
 @login_required
 def test_db_connect():
@@ -173,8 +269,84 @@ def test_db_connect():
         elif req["db_type"] == 'postgresql':
             db = PostgresqlDatabase(req["database"], user=req["username"], host=req["host"], port=req["port"],
                                     password=req["password"])
-        db.connect()
+        elif req["db_type"] == 'mssql':
+            import pyodbc
+            connection_string = (
+                f"DRIVER={{ODBC Driver 17 for SQL Server}};"
+                f"SERVER={req['host']},{req['port']};"
+                f"DATABASE={req['database']};"
+                f"UID={req['username']};"
+                f"PWD={req['password']};"
+            )
+            db = pyodbc.connect(connection_string)
+            cursor = db.cursor()
+            cursor.execute("SELECT 1")
+            cursor.close()
+        else:
+            return server_error_response("Unsupported database type.")
+        if req["db_type"] != 'mssql':
+            db.connect()
         db.close()
+        
         return get_json_result(data="Database Connection Successful!")
     except Exception as e:
         return server_error_response(e)
+#api get list version dsl of canvas
+@manager.route('/getlistversion/<canvas_id>', methods=['GET'])  # noqa: F821
+@login_required
+def getlistversion(canvas_id):
+    try:
+        list =sorted([c.to_dict() for c in UserCanvasVersionService.list_by_canvas_id(canvas_id)], key=lambda x: x["update_time"]*-1)
+        return get_json_result(data=list)
+    except Exception as e:
+        return get_data_error_result(message=f"Error getting history files: {e}")
+#api get version dsl of canvas
+@manager.route('/getversion/<version_id>', methods=['GET'])  # noqa: F821
+@login_required
+def getversion( version_id):
+    try:
+      
+        e, version = UserCanvasVersionService.get_by_id(version_id)
+        if version:
+            return get_json_result(data=version.to_dict())
+    except Exception as e:
+        return get_json_result(data=f"Error getting history file: {e}")
+@manager.route('/listteam', methods=['GET'])  # noqa: F821
+@login_required
+def list_kbs():
+    keywords = request.args.get("keywords", "")
+    page_number = int(request.args.get("page", 1))
+    items_per_page = int(request.args.get("page_size", 150))
+    orderby = request.args.get("orderby", "create_time")
+    desc = request.args.get("desc", True)
+    try:
+        tenants = TenantService.get_joined_tenants_by_user_id(current_user.id)
+        kbs, total = UserCanvasService.get_by_tenant_ids(
+            [m["tenant_id"] for m in tenants], current_user.id, page_number,
+            items_per_page, orderby, desc, keywords)
+        return get_json_result(data={"kbs": kbs, "total": total})
+    except Exception as e:
+        return server_error_response(e)
+@manager.route('/setting', methods=['POST'])  # noqa: F821
+@validate_request("id", "title", "permission")
+@login_required
+def setting():
+    req = request.json
+    req["user_id"] = current_user.id
+    e,flow = UserCanvasService.get_by_id(req["id"])
+    if not e:
+        return get_data_error_result(message="canvas not found.")
+    flow = flow.to_dict()
+    flow["title"] = req["title"]
+    if req["description"]:
+        flow["description"] = req["description"]
+    if req["permission"]:
+        flow["permission"] = req["permission"]
+    if req["avatar"]:
+        flow["avatar"] = req["avatar"]
+    if not UserCanvasService.query(user_id=current_user.id, id=req["id"]):
+        return get_json_result(
+            data=False, message='Only owner of canvas authorized for this operation.',
+            code=RetCode.OPERATING_ERROR)
+    num= UserCanvasService.update_by_id(req["id"], flow)
+    return get_json_result(data=num)

api/apps/chunk_app.py

@@ -15,29 +15,29 @@
 #
 import datetime
 import json
-import traceback
 
 from flask import request
 from flask_login import login_required, current_user
-from elasticsearch_dsl import Q
 
 from rag.app.qa import rmPrefix, beAdoc
-from rag.nlp import search, rag_tokenizer, keyword_extraction
-from rag.utils.es_conn import ELASTICSEARCH
+from rag.app.tag import label_question
+from rag.nlp import search, rag_tokenizer
+from rag.prompts import keyword_extraction
+from rag.settings import PAGERANK_FLD
 from rag.utils import rmSpace
 from api.db import LLMType, ParserType
 from api.db.services.knowledgebase_service import KnowledgebaseService
-from api.db.services.llm_service import TenantLLMService
+from api.db.services.llm_service import LLMBundle
 from api.db.services.user_service import UserTenantService
 from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
 from api.db.services.document_service import DocumentService
-from api.settings import RetCode, retrievaler, kg_retrievaler
+from api import settings
 from api.utils.api_utils import get_json_result
-import hashlib
+import xxhash
 import re
 
 
-@manager.route('/list', methods=['POST'])
+@manager.route('/list', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("doc_id")
 def list_chunk():
@@ -49,16 +49,17 @@ def list_chunk():
     try:
         tenant_id = DocumentService.get_tenant_id(req["doc_id"])
         if not tenant_id:
-            return get_data_error_result(retmsg="Tenant not found!")
+            return get_data_error_result(message="Tenant not found!")
         e, doc = DocumentService.get_by_id(doc_id)
         if not e:
-            return get_data_error_result(retmsg="Document not found!")
+            return get_data_error_result(message="Document not found!")
+        kb_ids = KnowledgebaseService.get_kb_ids(tenant_id)
         query = {
             "doc_ids": [doc_id], "page": page, "size": size, "question": question, "sort": True
         }
         if "available_int" in req:
             query["available_int"] = int(req["available_int"])
-        sres = retrievaler.search(query, search.index_name(tenant_id))
+        sres = settings.retrievaler.search(query, search.index_name(tenant_id), kb_ids, highlight=True)
         res = {"total": sres.total, "chunks": [], "doc": doc.to_dict()}
         for id in sres.ids:
             d = {
@@ -69,60 +70,56 @@ def list_chunk():
                 "doc_id": sres.field[id]["doc_id"],
                 "docnm_kwd": sres.field[id]["docnm_kwd"],
                 "important_kwd": sres.field[id].get("important_kwd", []),
-                "img_id": sres.field[id].get("img_id", ""),
-                "available_int": sres.field[id].get("available_int", 1),
-                "positions": sres.field[id].get("position_int", "").split("\t")
+                "question_kwd": sres.field[id].get("question_kwd", []),
+                "image_id": sres.field[id].get("img_id", ""),
+                "available_int": int(sres.field[id].get("available_int", 1)),
+                "positions": sres.field[id].get("position_int", []),
             }
-            if len(d["positions"]) % 5 == 0:
-                poss = []
-                for i in range(0, len(d["positions"]), 5):
-                    poss.append([float(d["positions"][i]), float(d["positions"][i + 1]), float(d["positions"][i + 2]),
-                                 float(d["positions"][i + 3]), float(d["positions"][i + 4])])
-                d["positions"] = poss
+            assert isinstance(d["positions"], list)
+            assert len(d["positions"]) == 0 or (isinstance(d["positions"][0], list) and len(d["positions"][0]) == 5)
             res["chunks"].append(d)
         return get_json_result(data=res)
     except Exception as e:
         if str(e).find("not_found") > 0:
-            return get_json_result(data=False, retmsg=f'No chunk found!',
-                                   retcode=RetCode.DATA_ERROR)
+            return get_json_result(data=False, message='No chunk found!',
+                                   code=settings.RetCode.DATA_ERROR)
         return server_error_response(e)
 
 
-@manager.route('/get', methods=['GET'])
+@manager.route('/get', methods=['GET'])  # noqa: F821
 @login_required
 def get():
     chunk_id = request.args["chunk_id"]
     try:
         tenants = UserTenantService.query(user_id=current_user.id)
         if not tenants:
-            return get_data_error_result(retmsg="Tenant not found!")
-        res = ELASTICSEARCH.get(
-            chunk_id, search.index_name(
-                tenants[0].tenant_id))
-        if not res.get("found"):
-            return server_error_response("Chunk not found")
-        id = res["_id"]
-        res = res["_source"]
-        res["chunk_id"] = id
+            return get_data_error_result(message="Tenant not found!")
+        for tenant in tenants:
+            kb_ids = KnowledgebaseService.get_kb_ids(tenant.tenant_id)
+            chunk = settings.docStoreConn.get(chunk_id, search.index_name(tenant.tenant_id), kb_ids)
+            if chunk:
+                break
+        if chunk is None:
+            return server_error_response(Exception("Chunk not found"))
+
         k = []
-        for n in res.keys():
+        for n in chunk.keys():
             if re.search(r"(_vec$|_sm_|_tks|_ltks)", n):
                 k.append(n)
         for n in k:
-            del res[n]
+            del chunk[n]
 
-        return get_json_result(data=res)
+        return get_json_result(data=chunk)
     except Exception as e:
         if str(e).find("NotFoundError") >= 0:
-            return get_json_result(data=False, retmsg=f'Chunk not found!',
-                                   retcode=RetCode.DATA_ERROR)
+            return get_json_result(data=False, message='Chunk not found!',
+                                   code=settings.RetCode.DATA_ERROR)
         return server_error_response(e)
 
 
-@manager.route('/set', methods=['POST'])
+@manager.route('/set', methods=['POST'])  # noqa: F821
 @login_required
-@validate_request("doc_id", "chunk_id", "content_with_weight",
-                  "important_kwd")
+@validate_request("doc_id", "chunk_id", "content_with_weight")
 def set():
     req = request.json
     d = {
@@ -130,74 +127,80 @@ def set():
         "content_with_weight": req["content_with_weight"]}
     d["content_ltks"] = rag_tokenizer.tokenize(req["content_with_weight"])
     d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
-    d["important_kwd"] = req["important_kwd"]
-    d["important_tks"] = rag_tokenizer.tokenize(" ".join(req["important_kwd"]))
+    if "important_kwd" in req:
+        d["important_kwd"] = req["important_kwd"]
+        d["important_tks"] = rag_tokenizer.tokenize(" ".join(req["important_kwd"]))
+    if "question_kwd" in req:
+        d["question_kwd"] = req["question_kwd"]
+        d["question_tks"] = rag_tokenizer.tokenize("\n".join(req["question_kwd"]))
+    if "tag_kwd" in req:
+        d["tag_kwd"] = req["tag_kwd"]
+    if "tag_feas" in req:
+        d["tag_feas"] = req["tag_feas"]
     if "available_int" in req:
         d["available_int"] = req["available_int"]
 
     try:
         tenant_id = DocumentService.get_tenant_id(req["doc_id"])
         if not tenant_id:
-            return get_data_error_result(retmsg="Tenant not found!")
+            return get_data_error_result(message="Tenant not found!")
 
         embd_id = DocumentService.get_embd_id(req["doc_id"])
-        embd_mdl = TenantLLMService.model_instance(
-            tenant_id, LLMType.EMBEDDING.value, embd_id)
+        embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING, embd_id)
 
         e, doc = DocumentService.get_by_id(req["doc_id"])
         if not e:
-            return get_data_error_result(retmsg="Document not found!")
+            return get_data_error_result(message="Document not found!")
 
         if doc.parser_id == ParserType.QA:
             arr = [
                 t for t in re.split(
                     r"[\n\t]",
                     req["content_with_weight"]) if len(t) > 1]
-            if len(arr) != 2:
-                return get_data_error_result(
-                    retmsg="Q&A must be separated by TAB/ENTER key.")
-            q, a = rmPrefix(arr[0]), rmPrefix(arr[1])
-            d = beAdoc(d, arr[0], arr[1], not any(
+            q, a = rmPrefix(arr[0]), rmPrefix("\n".join(arr[1:]))
+            d = beAdoc(d, q, a, not any(
                 [rag_tokenizer.is_chinese(t) for t in q + a]))
 
-        v, c = embd_mdl.encode([doc.name, req["content_with_weight"]])
+        v, c = embd_mdl.encode([doc.name, req["content_with_weight"] if not d.get("question_kwd") else "\n".join(d["question_kwd"])])
         v = 0.1 * v[0] + 0.9 * v[1] if doc.parser_id != ParserType.QA else v[1]
         d["q_%d_vec" % len(v)] = v.tolist()
-        ELASTICSEARCH.upsert([d], search.index_name(tenant_id))
+        settings.docStoreConn.update({"id": req["chunk_id"]}, d, search.index_name(tenant_id), doc.kb_id)
         return get_json_result(data=True)
     except Exception as e:
         return server_error_response(e)
 
 
-@manager.route('/switch', methods=['POST'])
+@manager.route('/switch', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("chunk_ids", "available_int", "doc_id")
 def switch():
     req = request.json
     try:
-        tenant_id = DocumentService.get_tenant_id(req["doc_id"])
-        if not tenant_id:
-            return get_data_error_result(retmsg="Tenant not found!")
-        if not ELASTICSEARCH.upsert([{"id": i, "available_int": int(req["available_int"])} for i in req["chunk_ids"]],
-                                    search.index_name(tenant_id)):
-            return get_data_error_result(retmsg="Index updating failure")
+        e, doc = DocumentService.get_by_id(req["doc_id"])
+        if not e:
+            return get_data_error_result(message="Document not found!")
+        for cid in req["chunk_ids"]:
+            if not settings.docStoreConn.update({"id": cid},
+                                                {"available_int": int(req["available_int"])},
+                                                search.index_name(DocumentService.get_tenant_id(req["doc_id"])),
+                                                doc.kb_id):
+                return get_data_error_result(message="Index updating failure")
         return get_json_result(data=True)
     except Exception as e:
         return server_error_response(e)
 
 
-@manager.route('/rm', methods=['POST'])
+@manager.route('/rm', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("chunk_ids", "doc_id")
 def rm():
     req = request.json
     try:
-        if not ELASTICSEARCH.deleteByQuery(
-                Q("ids", values=req["chunk_ids"]), search.index_name(current_user.id)):
-            return get_data_error_result(retmsg="Index updating failure")
         e, doc = DocumentService.get_by_id(req["doc_id"])
         if not e:
-            return get_data_error_result(retmsg="Document not found!")
+            return get_data_error_result(message="Document not found!")
+        if not settings.docStoreConn.delete({"id": req["chunk_ids"]}, search.index_name(current_user.id), doc.kb_id):
+            return get_data_error_result(message="Index updating failure")
         deleted_chunk_ids = req["chunk_ids"]
         chunk_number = len(deleted_chunk_ids)
         DocumentService.decrement_chunk_num(doc.id, doc.kb_id, 1, chunk_number, 0)
@@ -206,42 +209,48 @@ def rm():
         return server_error_response(e)
 
 
-@manager.route('/create', methods=['POST'])
+@manager.route('/create', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("doc_id", "content_with_weight")
 def create():
     req = request.json
-    md5 = hashlib.md5()
-    md5.update((req["content_with_weight"] + req["doc_id"]).encode("utf-8"))
-    chunck_id = md5.hexdigest()
+    chunck_id = xxhash.xxh64((req["content_with_weight"] + req["doc_id"]).encode("utf-8")).hexdigest()
     d = {"id": chunck_id, "content_ltks": rag_tokenizer.tokenize(req["content_with_weight"]),
          "content_with_weight": req["content_with_weight"]}
     d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
     d["important_kwd"] = req.get("important_kwd", [])
     d["important_tks"] = rag_tokenizer.tokenize(" ".join(req.get("important_kwd", [])))
+    d["question_kwd"] = req.get("question_kwd", [])
+    d["question_tks"] = rag_tokenizer.tokenize("\n".join(req.get("question_kwd", [])))
     d["create_time"] = str(datetime.datetime.now()).replace("T", " ")[:19]
     d["create_timestamp_flt"] = datetime.datetime.now().timestamp()
 
     try:
         e, doc = DocumentService.get_by_id(req["doc_id"])
         if not e:
-            return get_data_error_result(retmsg="Document not found!")
+            return get_data_error_result(message="Document not found!")
         d["kb_id"] = [doc.kb_id]
         d["docnm_kwd"] = doc.name
+        d["title_tks"] = rag_tokenizer.tokenize(doc.name)
         d["doc_id"] = doc.id
 
         tenant_id = DocumentService.get_tenant_id(req["doc_id"])
         if not tenant_id:
-            return get_data_error_result(retmsg="Tenant not found!")
+            return get_data_error_result(message="Tenant not found!")
+
+        e, kb = KnowledgebaseService.get_by_id(doc.kb_id)
+        if not e:
+            return get_data_error_result(message="Knowledgebase not found!")
+        if kb.pagerank:
+            d[PAGERANK_FLD] = kb.pagerank
 
         embd_id = DocumentService.get_embd_id(req["doc_id"])
-        embd_mdl = TenantLLMService.model_instance(
-            tenant_id, LLMType.EMBEDDING.value, embd_id)
+        embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING.value, embd_id)
 
-        v, c = embd_mdl.encode([doc.name, req["content_with_weight"]])
+        v, c = embd_mdl.encode([doc.name, req["content_with_weight"] if not d["question_kwd"] else "\n".join(d["question_kwd"])])
         v = 0.1 * v[0] + 0.9 * v[1]
         d["q_%d_vec" % len(v)] = v.tolist()
-        ELASTICSEARCH.upsert([d], search.index_name(tenant_id))
+        settings.docStoreConn.insert([d], search.index_name(tenant_id), doc.kb_id)
 
         DocumentService.increment_chunk_num(
             doc.id, doc.kb_id, c, 1, 0)
@@ -250,7 +259,7 @@ def create():
         return server_error_response(e)
 
 
-@manager.route('/retrieval_test', methods=['POST'])
+@manager.route('/retrieval_test', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("kb_id", "question")
 def retrieval_test():
@@ -258,61 +267,106 @@ def retrieval_test():
     page = int(req.get("page", 1))
     size = int(req.get("size", 30))
     question = req["question"]
-    kb_id = req["kb_id"]
+    kb_ids = req["kb_id"]
+    if isinstance(kb_ids, str):
+        kb_ids = [kb_ids]
     doc_ids = req.get("doc_ids", [])
-    similarity_threshold = float(req.get("similarity_threshold", 0.2))
+    similarity_threshold = float(req.get("similarity_threshold", 0.0))
     vector_similarity_weight = float(req.get("vector_similarity_weight", 0.3))
+    use_kg = req.get("use_kg", False)
     top = int(req.get("top_k", 1024))
-    try:
-        e, kb = KnowledgebaseService.get_by_id(kb_id)
-        if not e:
-            return get_data_error_result(retmsg="Knowledgebase not found!")
+    tenant_ids = []
 
-        embd_mdl = TenantLLMService.model_instance(
-            kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id)
+    try:
+        tenants = UserTenantService.query(user_id=current_user.id)
+        for kb_id in kb_ids:
+            for tenant in tenants:
+                if KnowledgebaseService.query(
+                        tenant_id=tenant.tenant_id, id=kb_id):
+                    tenant_ids.append(tenant.tenant_id)
+                    break
+            else:
+                return get_json_result(
+                    data=False, message='Only owner of knowledgebase authorized for this operation.',
+                    code=settings.RetCode.OPERATING_ERROR)
+
+        e, kb = KnowledgebaseService.get_by_id(kb_ids[0])
+        if not e:
+            return get_data_error_result(message="Knowledgebase not found!")
+
+        embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id)
 
         rerank_mdl = None
         if req.get("rerank_id"):
-            rerank_mdl = TenantLLMService.model_instance(
-                kb.tenant_id, LLMType.RERANK.value, llm_name=req["rerank_id"])
+            rerank_mdl = LLMBundle(kb.tenant_id, LLMType.RERANK.value, llm_name=req["rerank_id"])
 
         if req.get("keyword", False):
-            chat_mdl = TenantLLMService.model_instance(kb.tenant_id, LLMType.CHAT)
+            chat_mdl = LLMBundle(kb.tenant_id, LLMType.CHAT)
             question += keyword_extraction(chat_mdl, question)
 
-        retr = retrievaler if kb.parser_id != ParserType.KG else kg_retrievaler
-        ranks = retr.retrieval(question, embd_mdl, kb.tenant_id, [kb_id], page, size,
+        labels = label_question(question, [kb])
+        ranks = settings.retrievaler.retrieval(question, embd_mdl, tenant_ids, kb_ids, page, size,
                                similarity_threshold, vector_similarity_weight, top,
-                               doc_ids, rerank_mdl=rerank_mdl)
+                               doc_ids, rerank_mdl=rerank_mdl, highlight=req.get("highlight"),
+                               rank_feature=labels
+                               )
+        if use_kg:
+            ck = settings.kg_retrievaler.retrieval(question,
+                                                   tenant_ids,
+                                                   kb_ids,
+                                                   embd_mdl,
+                                                   LLMBundle(kb.tenant_id, LLMType.CHAT))
+            if ck["content_with_weight"]:
+                ranks["chunks"].insert(0, ck)
+
         for c in ranks["chunks"]:
-            if "vector" in c:
-                del c["vector"]
+            c.pop("vector", None)
+        ranks["labels"] = labels
 
         return get_json_result(data=ranks)
     except Exception as e:
         if str(e).find("not_found") > 0:
-            return get_json_result(data=False, retmsg=f'No chunk found! Check the chunk status please!',
-                                   retcode=RetCode.DATA_ERROR)
+            return get_json_result(data=False, message='No chunk found! Check the chunk status please!',
+                                   code=settings.RetCode.DATA_ERROR)
         return server_error_response(e)
 
 
-@manager.route('/knowledge_graph', methods=['GET'])
+@manager.route('/knowledge_graph', methods=['GET'])  # noqa: F821
 @login_required
 def knowledge_graph():
     doc_id = request.args["doc_id"]
+    tenant_id = DocumentService.get_tenant_id(doc_id)
+    kb_ids = KnowledgebaseService.get_kb_ids(tenant_id)
     req = {
-        "doc_ids":[doc_id],
+        "doc_ids": [doc_id],
         "knowledge_graph_kwd": ["graph", "mind_map"]
     }
-    tenant_id = DocumentService.get_tenant_id(doc_id)
-    sres = retrievaler.search(req, search.index_name(tenant_id))
+    sres = settings.retrievaler.search(req, search.index_name(tenant_id), kb_ids)
     obj = {"graph": {}, "mind_map": {}}
     for id in sres.ids[:2]:
         ty = sres.field[id]["knowledge_graph_kwd"]
         try:
-            obj[ty] = json.loads(sres.field[id]["content_with_weight"])
-        except Exception as e:
-            print(traceback.format_exc(), flush=True)
+            content_json = json.loads(sres.field[id]["content_with_weight"])
+        except Exception:
+            continue
+
+        if ty == 'mind_map':
+            node_dict = {}
+
+            def repeat_deal(content_json, node_dict):
+                if 'id' in content_json:
+                    if content_json['id'] in node_dict:
+                        node_name = content_json['id']
+                        content_json['id'] += f"({node_dict[content_json['id']]})"
+                        node_dict[node_name] += 1
+                    else:
+                        node_dict[content_json['id']] = 1
+                if 'children' in content_json and content_json['children']:
+                    for item in content_json['children']:
+                        repeat_deal(item, node_dict)
+
+            repeat_deal(content_json, node_dict)
+
+        obj[ty] = content_json
 
     return get_json_result(data=obj)
-

api/apps/conversation_app.py

@@ -13,30 +13,43 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-from copy import deepcopy
-from flask import request, Response
-from flask_login import login_required
-from api.db.services.dialog_service import DialogService, ConversationService, chat
-from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
-from api.utils import get_uuid
-from api.utils.api_utils import get_json_result
 import json
+import re
+import traceback
+from copy import deepcopy
+
+import trio
+from flask import Response, request
+from flask_login import current_user, login_required
+
+from api import settings
+from api.db import LLMType
+from api.db.db_models import APIToken
+from api.db.services.conversation_service import ConversationService, structure_answer
+from api.db.services.dialog_service import DialogService, ask, chat
+from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.db.services.llm_service import LLMBundle, TenantService
+from api.db.services.user_service import UserTenantService
+from api.utils.api_utils import get_data_error_result, get_json_result, server_error_response, validate_request
+from graphrag.general.mind_map_extractor import MindMapExtractor
+from rag.app.tag import label_question
 
 
-@manager.route('/set', methods=['POST'])
+@manager.route("/set", methods=["POST"])  # noqa: F821
 @login_required
 def set_conversation():
     req = request.json
     conv_id = req.get("conversation_id")
-    if conv_id:
+    is_new = req.get("is_new")
+    del req["is_new"]
+    if not is_new:
         del req["conversation_id"]
         try:
             if not ConversationService.update_by_id(conv_id, req):
-                return get_data_error_result(retmsg="Conversation not found!")
+                return get_data_error_result(message="Conversation not found!")
             e, conv = ConversationService.get_by_id(conv_id)
             if not e:
-                return get_data_error_result(
-                    retmsg="Fail to update a conversation!")
+                return get_data_error_result(message="Fail to update a conversation!")
             conv = conv.to_dict()
             return get_json_result(data=conv)
         except Exception as e:
@@ -45,116 +58,177 @@ def set_conversation():
     try:
         e, dia = DialogService.get_by_id(req["dialog_id"])
         if not e:
-            return get_data_error_result(retmsg="Dialog not found")
-        conv = {
-            "id": get_uuid(),
-            "dialog_id": req["dialog_id"],
-            "name": req.get("name", "New conversation"),
-            "message": [{"role": "assistant", "content": dia.prompt_config["prologue"]}]
-        }
+            return get_data_error_result(message="Dialog not found")
+        conv = {"id": conv_id, "dialog_id": req["dialog_id"], "name": req.get("name", "New conversation"), "message": [{"role": "assistant", "content": dia.prompt_config["prologue"]}]}
         ConversationService.save(**conv)
-        e, conv = ConversationService.get_by_id(conv["id"])
-        if not e:
-            return get_data_error_result(retmsg="Fail to new a conversation!")
-        conv = conv.to_dict()
         return get_json_result(data=conv)
     except Exception as e:
         return server_error_response(e)
 
 
-@manager.route('/get', methods=['GET'])
+@manager.route("/get", methods=["GET"])  # noqa: F821
 @login_required
 def get():
     conv_id = request.args["conversation_id"]
     try:
         e, conv = ConversationService.get_by_id(conv_id)
         if not e:
-            return get_data_error_result(retmsg="Conversation not found!")
+            return get_data_error_result(message="Conversation not found!")
+        tenants = UserTenantService.query(user_id=current_user.id)
+        avatar = None
+        for tenant in tenants:
+            dialog = DialogService.query(tenant_id=tenant.tenant_id, id=conv.dialog_id)
+            if dialog and len(dialog) > 0:
+                avatar = dialog[0].icon
+                break
+        else:
+            return get_json_result(data=False, message="Only owner of conversation authorized for this operation.", code=settings.RetCode.OPERATING_ERROR)
+
+        def get_value(d, k1, k2):
+            return d.get(k1, d.get(k2))
+
+        for ref in conv.reference:
+            if isinstance(ref, list):
+                continue
+            ref["chunks"] = [
+                {
+                    "id": get_value(ck, "chunk_id", "id"),
+                    "content": get_value(ck, "content", "content_with_weight"),
+                    "document_id": get_value(ck, "doc_id", "document_id"),
+                    "document_name": get_value(ck, "docnm_kwd", "document_name"),
+                    "dataset_id": get_value(ck, "kb_id", "dataset_id"),
+                    "image_id": get_value(ck, "image_id", "img_id"),
+                    "positions": get_value(ck, "positions", "position_int"),
+                }
+                for ck in ref.get("chunks", [])
+            ]
+
         conv = conv.to_dict()
+        conv["avatar"] = avatar
         return get_json_result(data=conv)
     except Exception as e:
         return server_error_response(e)
 
 
-@manager.route('/rm', methods=['POST'])
+@manager.route("/getsse/<dialog_id>", methods=["GET"])  # type: ignore # noqa: F821
+def getsse(dialog_id):
+    token = request.headers.get("Authorization").split()
+    if len(token) != 2:
+        return get_data_error_result(message='Authorization is not valid!"')
+    token = token[1]
+    objs = APIToken.query(beta=token)
+    if not objs:
+        return get_data_error_result(message='Authentication error: API key is invalid!"')
+    try:
+        e, conv = DialogService.get_by_id(dialog_id)
+        if not e:
+            return get_data_error_result(message="Dialog not found!")
+        conv = conv.to_dict()
+        conv["avatar"] = conv["icon"]
+        del conv["icon"]
+        return get_json_result(data=conv)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route("/rm", methods=["POST"])  # noqa: F821
 @login_required
 def rm():
     conv_ids = request.json["conversation_ids"]
     try:
         for cid in conv_ids:
+            exist, conv = ConversationService.get_by_id(cid)
+            if not exist:
+                return get_data_error_result(message="Conversation not found!")
+            tenants = UserTenantService.query(user_id=current_user.id)
+            for tenant in tenants:
+                if DialogService.query(tenant_id=tenant.tenant_id, id=conv.dialog_id):
+                    break
+            else:
+                return get_json_result(data=False, message="Only owner of conversation authorized for this operation.", code=settings.RetCode.OPERATING_ERROR)
             ConversationService.delete_by_id(cid)
         return get_json_result(data=True)
     except Exception as e:
         return server_error_response(e)
 
 
-@manager.route('/list', methods=['GET'])
+@manager.route("/list", methods=["GET"])  # noqa: F821
 @login_required
 def list_convsersation():
     dialog_id = request.args["dialog_id"]
     try:
-        convs = ConversationService.query(
-            dialog_id=dialog_id,
-            order_by=ConversationService.model.create_time,
-            reverse=True)
+        if not DialogService.query(tenant_id=current_user.id, id=dialog_id):
+            return get_json_result(data=False, message="Only owner of dialog authorized for this operation.", code=settings.RetCode.OPERATING_ERROR)
+        convs = ConversationService.query(dialog_id=dialog_id, order_by=ConversationService.model.create_time, reverse=True)
+
         convs = [d.to_dict() for d in convs]
         return get_json_result(data=convs)
     except Exception as e:
         return server_error_response(e)
 
 
-@manager.route('/completion', methods=['POST'])
+@manager.route("/completion", methods=["POST"])  # noqa: F821
 @login_required
-#@validate_request("conversation_id", "messages")
+@validate_request("conversation_id", "messages")
 def completion():
     req = request.json
-    #req = {"conversation_id": "9aaaca4c11d311efa461fa163e197198", "messages": [
-    #    {"role": "user", "content": "上海有吗？"}
-    #]}
     msg = []
     for m in req["messages"]:
         if m["role"] == "system":
             continue
         if m["role"] == "assistant" and not msg:
             continue
-        msg.append({"role": m["role"], "content": m["content"]})
-        if "doc_ids" in m:
-            msg[-1]["doc_ids"] = m["doc_ids"]
+        msg.append(m)
+    message_id = msg[-1].get("id")
     try:
         e, conv = ConversationService.get_by_id(req["conversation_id"])
         if not e:
-            return get_data_error_result(retmsg="Conversation not found!")
-        conv.message.append(deepcopy(msg[-1]))
+            return get_data_error_result(message="Conversation not found!")
+        conv.message = deepcopy(req["messages"])
         e, dia = DialogService.get_by_id(conv.dialog_id)
         if not e:
-            return get_data_error_result(retmsg="Dialog not found!")
+            return get_data_error_result(message="Dialog not found!")
         del req["conversation_id"]
         del req["messages"]
 
         if not conv.reference:
             conv.reference = []
-        conv.message.append({"role": "assistant", "content": ""})
-        conv.reference.append({"chunks": [], "doc_aggs": []})
+        else:
 
-        def fillin_conv(ans):
-            nonlocal conv
-            if not conv.reference:
-                conv.reference.append(ans["reference"])
-            else: conv.reference[-1] = ans["reference"]
-            conv.message[-1] = {"role": "assistant", "content": ans["answer"]}
+            def get_value(d, k1, k2):
+                return d.get(k1, d.get(k2))
+
+            for ref in conv.reference:
+                if isinstance(ref, list):
+                    continue
+                ref["chunks"] = [
+                    {
+                        "id": get_value(ck, "chunk_id", "id"),
+                        "content": get_value(ck, "content", "content_with_weight"),
+                        "document_id": get_value(ck, "doc_id", "document_id"),
+                        "document_name": get_value(ck, "docnm_kwd", "document_name"),
+                        "dataset_id": get_value(ck, "kb_id", "dataset_id"),
+                        "image_id": get_value(ck, "image_id", "img_id"),
+                        "positions": get_value(ck, "positions", "position_int"),
+                    }
+                    for ck in ref.get("chunks", [])
+                ]
+
+        if not conv.reference:
+            conv.reference = []
+        conv.reference.append({"chunks": [], "doc_aggs": []})
 
         def stream():
             nonlocal dia, msg, req, conv
             try:
                 for ans in chat(dia, msg, True, **req):
-                    fillin_conv(ans)
-                    yield "data:"+json.dumps({"retcode": 0, "retmsg": "", "data": ans}, ensure_ascii=False) + "\n\n"
+                    ans = structure_answer(conv, ans, message_id, conv.id)
+                    yield "data:" + json.dumps({"code": 0, "message": "", "data": ans}, ensure_ascii=False) + "\n\n"
                 ConversationService.update_by_id(conv.id, conv.to_dict())
             except Exception as e:
-                yield "data:" + json.dumps({"retcode": 500, "retmsg": str(e),
-                                            "data": {"answer": "**ERROR**: "+str(e), "reference": []}},
-                                           ensure_ascii=False) + "\n\n"
-            yield "data:"+json.dumps({"retcode": 0, "retmsg": "", "data": True}, ensure_ascii=False) + "\n\n"
+                traceback.print_exc()
+                yield "data:" + json.dumps({"code": 500, "message": str(e), "data": {"answer": "**ERROR**: " + str(e), "reference": []}}, ensure_ascii=False) + "\n\n"
+            yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n"
 
         if req.get("stream", True):
             resp = Response(stream(), mimetype="text/event-stream")
@@ -167,11 +241,193 @@ def completion():
         else:
             answer = None
             for ans in chat(dia, msg, **req):
-                answer = ans
-                fillin_conv(ans)
+                answer = structure_answer(conv, ans, message_id, req["conversation_id"])
                 ConversationService.update_by_id(conv.id, conv.to_dict())
                 break
             return get_json_result(data=answer)
     except Exception as e:
         return server_error_response(e)
 
+
+@manager.route("/tts", methods=["POST"])  # noqa: F821
+@login_required
+def tts():
+    req = request.json
+    text = req["text"]
+
+    tenants = TenantService.get_info_by(current_user.id)
+    if not tenants:
+        return get_data_error_result(message="Tenant not found!")
+
+    tts_id = tenants[0]["tts_id"]
+    if not tts_id:
+        return get_data_error_result(message="No default TTS model is set")
+
+    tts_mdl = LLMBundle(tenants[0]["tenant_id"], LLMType.TTS, tts_id)
+
+    def stream_audio():
+        try:
+            for txt in re.split(r"[，。/《》？；：！\n\r:;]+", text):
+                for chunk in tts_mdl.tts(txt):
+                    yield chunk
+        except Exception as e:
+            yield ("data:" + json.dumps({"code": 500, "message": str(e), "data": {"answer": "**ERROR**: " + str(e)}}, ensure_ascii=False)).encode("utf-8")
+
+    resp = Response(stream_audio(), mimetype="audio/mpeg")
+    resp.headers.add_header("Cache-Control", "no-cache")
+    resp.headers.add_header("Connection", "keep-alive")
+    resp.headers.add_header("X-Accel-Buffering", "no")
+
+    return resp
+
+
+@manager.route("/delete_msg", methods=["POST"])  # noqa: F821
+@login_required
+@validate_request("conversation_id", "message_id")
+def delete_msg():
+    req = request.json
+    e, conv = ConversationService.get_by_id(req["conversation_id"])
+    if not e:
+        return get_data_error_result(message="Conversation not found!")
+
+    conv = conv.to_dict()
+    for i, msg in enumerate(conv["message"]):
+        if req["message_id"] != msg.get("id", ""):
+            continue
+        assert conv["message"][i + 1]["id"] == req["message_id"]
+        conv["message"].pop(i)
+        conv["message"].pop(i)
+        conv["reference"].pop(max(0, i // 2 - 1))
+        break
+
+    ConversationService.update_by_id(conv["id"], conv)
+    return get_json_result(data=conv)
+
+
+@manager.route("/thumbup", methods=["POST"])  # noqa: F821
+@login_required
+@validate_request("conversation_id", "message_id")
+def thumbup():
+    req = request.json
+    e, conv = ConversationService.get_by_id(req["conversation_id"])
+    if not e:
+        return get_data_error_result(message="Conversation not found!")
+    up_down = req.get("thumbup")
+    feedback = req.get("feedback", "")
+    conv = conv.to_dict()
+    for i, msg in enumerate(conv["message"]):
+        if req["message_id"] == msg.get("id", "") and msg.get("role", "") == "assistant":
+            if up_down:
+                msg["thumbup"] = True
+                if "feedback" in msg:
+                    del msg["feedback"]
+            else:
+                msg["thumbup"] = False
+                if feedback:
+                    msg["feedback"] = feedback
+            break
+
+    ConversationService.update_by_id(conv["id"], conv)
+    return get_json_result(data=conv)
+
+
+@manager.route("/ask", methods=["POST"])  # noqa: F821
+@login_required
+@validate_request("question", "kb_ids")
+def ask_about():
+    req = request.json
+    uid = current_user.id
+
+    def stream():
+        nonlocal req, uid
+        try:
+            for ans in ask(req["question"], req["kb_ids"], uid):
+                yield "data:" + json.dumps({"code": 0, "message": "", "data": ans}, ensure_ascii=False) + "\n\n"
+        except Exception as e:
+            yield "data:" + json.dumps({"code": 500, "message": str(e), "data": {"answer": "**ERROR**: " + str(e), "reference": []}}, ensure_ascii=False) + "\n\n"
+        yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n"
+
+    resp = Response(stream(), mimetype="text/event-stream")
+    resp.headers.add_header("Cache-control", "no-cache")
+    resp.headers.add_header("Connection", "keep-alive")
+    resp.headers.add_header("X-Accel-Buffering", "no")
+    resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8")
+    return resp
+
+
+@manager.route("/mindmap", methods=["POST"])  # noqa: F821
+@login_required
+@validate_request("question", "kb_ids")
+def mindmap():
+    req = request.json
+    kb_ids = req["kb_ids"]
+    e, kb = KnowledgebaseService.get_by_id(kb_ids[0])
+    if not e:
+        return get_data_error_result(message="Knowledgebase not found!")
+
+    embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING, llm_name=kb.embd_id)
+    chat_mdl = LLMBundle(current_user.id, LLMType.CHAT)
+    question = req["question"]
+    ranks = settings.retrievaler.retrieval(question, embd_mdl, kb.tenant_id, kb_ids, 1, 12, 0.3, 0.3, aggs=False, rank_feature=label_question(question, [kb]))
+    mindmap = MindMapExtractor(chat_mdl)
+    mind_map = trio.run(mindmap, [c["content_with_weight"] for c in ranks["chunks"]])
+    mind_map = mind_map.output
+    if "error" in mind_map:
+        return server_error_response(Exception(mind_map["error"]))
+    return get_json_result(data=mind_map)
+
+
+@manager.route("/related_questions", methods=["POST"])  # noqa: F821
+@login_required
+@validate_request("question")
+def related_questions():
+    req = request.json
+    question = req["question"]
+    chat_mdl = LLMBundle(current_user.id, LLMType.CHAT)
+    prompt = """
+Role: You are an AI language model assistant tasked with generating 5-10 related questions based on a user’s original query. These questions should help expand the search query scope and improve search relevance.
+
+Instructions:
+	Input: You are provided with a user’s question.
+	Output: Generate 5-10 alternative questions that are related to the original user question. These alternatives should help retrieve a broader range of relevant documents from a vector database.
+	Context: Focus on rephrasing the original question in different ways, making sure the alternative questions are diverse but still connected to the topic of the original query. Do not create overly obscure, irrelevant, or unrelated questions.
+	Fallback: If you cannot generate any relevant alternatives, do not return any questions.
+	Guidance:
+	1. Each alternative should be unique but still relevant to the original query.
+	2. Keep the phrasing clear, concise, and easy to understand.
+	3. Avoid overly technical jargon or specialized terms unless directly relevant.
+	4. Ensure that each question contributes towards improving search results by broadening the search angle, not narrowing it.
+
+Example:
+Original Question: What are the benefits of electric vehicles?
+
+Alternative Questions:
+	1. How do electric vehicles impact the environment?
+	2. What are the advantages of owning an electric car?
+	3. What is the cost-effectiveness of electric vehicles?
+	4. How do electric vehicles compare to traditional cars in terms of fuel efficiency?
+	5. What are the environmental benefits of switching to electric cars?
+	6. How do electric vehicles help reduce carbon emissions?
+	7. Why are electric vehicles becoming more popular?
+	8. What are the long-term savings of using electric vehicles?
+	9. How do electric vehicles contribute to sustainability?
+	10. What are the key benefits of electric vehicles for consumers?
+
+Reason:
+	Rephrasing the original query into multiple alternative questions helps the user explore different aspects of their search topic, improving the quality of search results.
+	These questions guide the search engine to provide a more comprehensive set of relevant documents.
+"""
+    ans = chat_mdl.chat(
+        prompt,
+        [
+            {
+                "role": "user",
+                "content": f"""
+Keywords: {question}
+Related search terms:
+    """,
+            }
+        ],
+        {"temperature": 0.9},
+    )
+    return get_json_result(data=[re.sub(r"^[0-9]\. ", "", a) for a in ans.split("\n") if re.match(r"^[0-9]\. ", a)])

api/apps/dataset_api.py

@@ -1,878 +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 os
-import pathlib
-import re
-import warnings
-from functools import partial
-from io import BytesIO
-
-from elasticsearch_dsl import Q
-from flask import request, send_file
-from flask_login import login_required, current_user
-from httpx import HTTPError
-
-from api.contants import NAME_LENGTH_LIMIT
-from api.db import FileType, ParserType, FileSource, TaskStatus
-from api.db import StatusEnum
-from api.db.db_models import File
-from api.db.services import duplicate_name
-from api.db.services.document_service import DocumentService
-from api.db.services.file2document_service import File2DocumentService
-from api.db.services.file_service import FileService
-from api.db.services.knowledgebase_service import KnowledgebaseService
-from api.db.services.user_service import TenantService
-from api.settings import RetCode
-from api.utils import get_uuid
-from api.utils.api_utils import construct_json_result, construct_error_response
-from api.utils.api_utils import construct_result, validate_request
-from api.utils.file_utils import filename_type, thumbnail
-from rag.app import book, laws, manual, naive, one, paper, presentation, qa, resume, table, picture, audio, email
-from rag.nlp import search
-from rag.utils.es_conn import ELASTICSEARCH
-from rag.utils.minio_conn import MINIO
-
-MAXIMUM_OF_UPLOADING_FILES = 256
-
-
-# ------------------------------ create a dataset ---------------------------------------
-
-@manager.route("/", methods=["POST"])
-@login_required  # use login
-@validate_request("name")  # check name key
-def create_dataset():
-    # Check if Authorization header is present
-    authorization_token = request.headers.get("Authorization")
-    if not authorization_token:
-        return construct_json_result(code=RetCode.AUTHENTICATION_ERROR, message="Authorization header is missing.")
-
-    # TODO: Login or API key
-    # objs = APIToken.query(token=authorization_token)
-    #
-    # # Authorization error
-    # if not objs:
-    #     return construct_json_result(code=RetCode.AUTHENTICATION_ERROR, message="Token is invalid.")
-    #
-    # tenant_id = objs[0].tenant_id
-
-    tenant_id = current_user.id
-    request_body = request.json
-
-    # In case that there's no name
-    if "name" not in request_body:
-        return construct_json_result(code=RetCode.DATA_ERROR, message="Expected 'name' field in request body")
-
-    dataset_name = request_body["name"]
-
-    # empty dataset_name
-    if not dataset_name:
-        return construct_json_result(code=RetCode.DATA_ERROR, message="Empty dataset name")
-
-    # In case that there's space in the head or the tail
-    dataset_name = dataset_name.strip()
-
-    # In case that the length of the name exceeds the limit
-    dataset_name_length = len(dataset_name)
-    if dataset_name_length > NAME_LENGTH_LIMIT:
-        return construct_json_result(
-            code=RetCode.DATA_ERROR,
-            message=f"Dataset name: {dataset_name} with length {dataset_name_length} exceeds {NAME_LENGTH_LIMIT}!")
-
-    # In case that there are other fields in the data-binary
-    if len(request_body.keys()) > 1:
-        name_list = []
-        for key_name in request_body.keys():
-            if key_name != "name":
-                name_list.append(key_name)
-        return construct_json_result(code=RetCode.DATA_ERROR,
-                                     message=f"fields: {name_list}, are not allowed in request body.")
-
-    # If there is a duplicate name, it will modify it to make it unique
-    request_body["name"] = duplicate_name(
-        KnowledgebaseService.query,
-        name=dataset_name,
-        tenant_id=tenant_id,
-        status=StatusEnum.VALID.value)
-    try:
-        request_body["id"] = get_uuid()
-        request_body["tenant_id"] = tenant_id
-        request_body["created_by"] = tenant_id
-        exist, t = TenantService.get_by_id(tenant_id)
-        if not exist:
-            return construct_result(code=RetCode.AUTHENTICATION_ERROR, message="Tenant not found.")
-        request_body["embd_id"] = t.embd_id
-        if not KnowledgebaseService.save(**request_body):
-            # failed to create new dataset
-            return construct_result()
-        return construct_json_result(code=RetCode.SUCCESS,
-                                     data={"dataset_name": request_body["name"], "dataset_id": request_body["id"]})
-    except Exception as e:
-        return construct_error_response(e)
-
-
-# -----------------------------list datasets-------------------------------------------------------
-
-@manager.route("/", methods=["GET"])
-@login_required
-def list_datasets():
-    offset = request.args.get("offset", 0)
-    count = request.args.get("count", -1)
-    orderby = request.args.get("orderby", "create_time")
-    desc = request.args.get("desc", True)
-    try:
-        tenants = TenantService.get_joined_tenants_by_user_id(current_user.id)
-        datasets = KnowledgebaseService.get_by_tenant_ids_by_offset(
-            [m["tenant_id"] for m in tenants], current_user.id, int(offset), int(count), orderby, desc)
-        return construct_json_result(data=datasets, code=RetCode.SUCCESS, message=f"List datasets successfully!")
-    except Exception as e:
-        return construct_error_response(e)
-    except HTTPError as http_err:
-        return construct_json_result(http_err)
-
-
-# ---------------------------------delete a dataset ----------------------------
-
-@manager.route("/<dataset_id>", methods=["DELETE"])
-@login_required
-def remove_dataset(dataset_id):
-    try:
-        datasets = KnowledgebaseService.query(created_by=current_user.id, id=dataset_id)
-
-        # according to the id, searching for the dataset
-        if not datasets:
-            return construct_json_result(message=f"The dataset cannot be found for your current account.",
-                                         code=RetCode.OPERATING_ERROR)
-
-        # Iterating the documents inside the dataset
-        for doc in DocumentService.query(kb_id=dataset_id):
-            if not DocumentService.remove_document(doc, datasets[0].tenant_id):
-                # the process of deleting failed
-                return construct_json_result(code=RetCode.DATA_ERROR,
-                                             message="There was an error during the document removal process. "
-                                                     "Please check the status of the RAGFlow server and try the removal again.")
-            # delete the other files
-            f2d = File2DocumentService.get_by_document_id(doc.id)
-            FileService.filter_delete([File.source_type == FileSource.KNOWLEDGEBASE, File.id == f2d[0].file_id])
-            File2DocumentService.delete_by_document_id(doc.id)
-
-        # delete the dataset
-        if not KnowledgebaseService.delete_by_id(dataset_id):
-            return construct_json_result(code=RetCode.DATA_ERROR,
-                                         message="There was an error during the dataset removal process. "
-                                                 "Please check the status of the RAGFlow server and try the removal again.")
-        # success
-        return construct_json_result(code=RetCode.SUCCESS, message=f"Remove dataset: {dataset_id} successfully")
-    except Exception as e:
-        return construct_error_response(e)
-
-
-# ------------------------------ get details of a dataset ----------------------------------------
-
-@manager.route("/<dataset_id>", methods=["GET"])
-@login_required
-def get_dataset(dataset_id):
-    try:
-        dataset = KnowledgebaseService.get_detail(dataset_id)
-        if not dataset:
-            return construct_json_result(code=RetCode.DATA_ERROR, message="Can't find this dataset!")
-        return construct_json_result(data=dataset, code=RetCode.SUCCESS)
-    except Exception as e:
-        return construct_json_result(e)
-
-
-# ------------------------------ update a dataset --------------------------------------------
-
-@manager.route("/<dataset_id>", methods=["PUT"])
-@login_required
-def update_dataset(dataset_id):
-    req = request.json
-    try:
-        # the request cannot be empty
-        if not req:
-            return construct_json_result(code=RetCode.DATA_ERROR, message="Please input at least one parameter that "
-                                                                          "you want to update!")
-        # check whether the dataset can be found
-        if not KnowledgebaseService.query(created_by=current_user.id, id=dataset_id):
-            return construct_json_result(message=f"Only the owner of knowledgebase is authorized for this operation!",
-                                         code=RetCode.OPERATING_ERROR)
-
-        exist, dataset = KnowledgebaseService.get_by_id(dataset_id)
-        # check whether there is this dataset
-        if not exist:
-            return construct_json_result(code=RetCode.DATA_ERROR, message="This dataset cannot be found!")
-
-        if "name" in req:
-            name = req["name"].strip()
-            # check whether there is duplicate name
-            if name.lower() != dataset.name.lower() \
-                    and len(KnowledgebaseService.query(name=name, tenant_id=current_user.id,
-                                                       status=StatusEnum.VALID.value)) > 1:
-                return construct_json_result(code=RetCode.DATA_ERROR,
-                                             message=f"The name: {name.lower()} is already used by other "
-                                                     f"datasets. Please choose a different name.")
-
-        dataset_updating_data = {}
-        chunk_num = req.get("chunk_num")
-        # modify the value of 11 parameters
-
-        # 2 parameters: embedding id and chunk method
-        # only if chunk_num is 0, the user can update the embedding id
-        if req.get("embedding_model_id"):
-            if chunk_num == 0:
-                dataset_updating_data["embd_id"] = req["embedding_model_id"]
-            else:
-                return construct_json_result(code=RetCode.DATA_ERROR,
-                                             message="You have already parsed the document in this "
-                                                     "dataset, so you cannot change the embedding "
-                                                     "model.")
-        # only if chunk_num is 0, the user can update the chunk_method
-        if "chunk_method" in req:
-            type_value = req["chunk_method"]
-            if is_illegal_value_for_enum(type_value, ParserType):
-                return construct_json_result(message=f"Illegal value {type_value} for 'chunk_method' field.",
-                                             code=RetCode.DATA_ERROR)
-            if chunk_num != 0:
-                construct_json_result(code=RetCode.DATA_ERROR, message="You have already parsed the document "
-                                                                       "in this dataset, so you cannot "
-                                                                       "change the chunk method.")
-            dataset_updating_data["parser_id"] = req["template_type"]
-
-        # convert the photo parameter to avatar
-        if req.get("photo"):
-            dataset_updating_data["avatar"] = req["photo"]
-
-        # layout_recognize
-        if "layout_recognize" in req:
-            if "parser_config" not in dataset_updating_data:
-                dataset_updating_data['parser_config'] = {}
-            dataset_updating_data['parser_config']['layout_recognize'] = req['layout_recognize']
-
-        # TODO: updating use_raptor needs to construct a class
-
-        # 6 parameters
-        for key in ["name", "language", "description", "permission", "id", "token_num"]:
-            if key in req:
-                dataset_updating_data[key] = req.get(key)
-
-        # update
-        if not KnowledgebaseService.update_by_id(dataset.id, dataset_updating_data):
-            return construct_json_result(code=RetCode.OPERATING_ERROR, message="Failed to update! "
-                                                                               "Please check the status of RAGFlow "
-                                                                               "server and try again!")
-
-        exist, dataset = KnowledgebaseService.get_by_id(dataset.id)
-        if not exist:
-            return construct_json_result(code=RetCode.DATA_ERROR, message="Failed to get the dataset "
-                                                                          "using the dataset ID.")
-
-        return construct_json_result(data=dataset.to_json(), code=RetCode.SUCCESS)
-    except Exception as e:
-        return construct_error_response(e)
-
-
-# --------------------------------content management ----------------------------------------------
-
-# ----------------------------upload files-----------------------------------------------------
-@manager.route("/<dataset_id>/documents/", methods=["POST"])
-@login_required
-def upload_documents(dataset_id):
-    # no files
-    if not request.files:
-        return construct_json_result(
-            message="There is no file!", code=RetCode.ARGUMENT_ERROR)
-
-    # the number of uploading files exceeds the limit
-    file_objs = request.files.getlist("file")
-    num_file_objs = len(file_objs)
-
-    if num_file_objs > MAXIMUM_OF_UPLOADING_FILES:
-        return construct_json_result(code=RetCode.DATA_ERROR, message=f"You try to upload {num_file_objs} files, "
-                                                                      f"which exceeds the maximum number of uploading files: {MAXIMUM_OF_UPLOADING_FILES}")
-
-    # no dataset
-    exist, dataset = KnowledgebaseService.get_by_id(dataset_id)
-    if not exist:
-        return construct_json_result(message="Can't find this dataset", code=RetCode.DATA_ERROR)
-
-    for file_obj in file_objs:
-        file_name = file_obj.filename
-        # no name
-        if not file_name:
-            return construct_json_result(
-                message="There is a file without name!", code=RetCode.ARGUMENT_ERROR)
-
-        # TODO: support the remote files
-        if 'http' in file_name:
-            return construct_json_result(code=RetCode.ARGUMENT_ERROR, message="Remote files have not unsupported.")
-
-    # get the root_folder
-    root_folder = FileService.get_root_folder(current_user.id)
-    # get the id of the root_folder
-    parent_file_id = root_folder["id"]  # document id
-    # this is for the new user, create '.knowledgebase' file
-    FileService.init_knowledgebase_docs(parent_file_id, current_user.id)
-    # go inside this folder, get the kb_root_folder
-    kb_root_folder = FileService.get_kb_folder(current_user.id)
-    # link the file management to the kb_folder
-    kb_folder = FileService.new_a_file_from_kb(dataset.tenant_id, dataset.name, kb_root_folder["id"])
-
-    # grab all the errs
-    err = []
-    MAX_FILE_NUM_PER_USER = int(os.environ.get("MAX_FILE_NUM_PER_USER", 0))
-    uploaded_docs_json = []
-    for file in file_objs:
-        try:
-            # TODO: get this value from the database as some tenants have this limit while others don't
-            if MAX_FILE_NUM_PER_USER > 0 and DocumentService.get_doc_count(dataset.tenant_id) >= MAX_FILE_NUM_PER_USER:
-                return construct_json_result(code=RetCode.DATA_ERROR,
-                                             message="Exceed the maximum file number of a free user!")
-            # deal with the duplicate name
-            filename = duplicate_name(
-                DocumentService.query,
-                name=file.filename,
-                kb_id=dataset.id)
-
-            # deal with the unsupported type
-            filetype = filename_type(filename)
-            if filetype == FileType.OTHER.value:
-                return construct_json_result(code=RetCode.DATA_ERROR,
-                                             message="This type of file has not been supported yet!")
-
-            # upload to the minio
-            location = filename
-            while MINIO.obj_exist(dataset_id, location):
-                location += "_"
-
-            blob = file.read()
-
-            # the content is empty, raising a warning
-            if blob == b'':
-                warnings.warn(f"[WARNING]: The content of the file {filename} is empty.")
-
-            MINIO.put(dataset_id, location, blob)
-
-            doc = {
-                "id": get_uuid(),
-                "kb_id": dataset.id,
-                "parser_id": dataset.parser_id,
-                "parser_config": dataset.parser_config,
-                "created_by": current_user.id,
-                "type": filetype,
-                "name": filename,
-                "location": location,
-                "size": len(blob),
-                "thumbnail": thumbnail(filename, blob)
-            }
-            if doc["type"] == FileType.VISUAL:
-                doc["parser_id"] = ParserType.PICTURE.value
-            if doc["type"] == FileType.AURAL:
-                doc["parser_id"] = ParserType.AUDIO.value
-            if re.search(r"\.(ppt|pptx|pages)$", filename):
-                doc["parser_id"] = ParserType.PRESENTATION.value
-            DocumentService.insert(doc)
-
-            FileService.add_file_from_kb(doc, kb_folder["id"], dataset.tenant_id)
-            uploaded_docs_json.append(doc)
-        except Exception as e:
-            err.append(file.filename + ": " + str(e))
-
-    if err:
-        # return all the errors
-        return construct_json_result(message="\n".join(err), code=RetCode.SERVER_ERROR)
-    # success
-    return construct_json_result(data=uploaded_docs_json, code=RetCode.SUCCESS)
-
-
-# ----------------------------delete a file-----------------------------------------------------
-@manager.route("/<dataset_id>/documents/<document_id>", methods=["DELETE"])
-@login_required
-def delete_document(document_id, dataset_id):  # string
-    # get the root folder
-    root_folder = FileService.get_root_folder(current_user.id)
-    # parent file's id
-    parent_file_id = root_folder["id"]
-    # consider the new user
-    FileService.init_knowledgebase_docs(parent_file_id, current_user.id)
-    # store all the errors that may have
-    errors = ""
-    try:
-        # whether there is this document
-        exist, doc = DocumentService.get_by_id(document_id)
-        if not exist:
-            return construct_json_result(message=f"Document {document_id} not found!", code=RetCode.DATA_ERROR)
-        # whether this doc is authorized by this tenant
-        tenant_id = DocumentService.get_tenant_id(document_id)
-        if not tenant_id:
-            return construct_json_result(
-                message=f"You cannot delete this document {document_id} due to the authorization"
-                        f" reason!", code=RetCode.AUTHENTICATION_ERROR)
-
-        # get the doc's id and location
-        real_dataset_id, location = File2DocumentService.get_minio_address(doc_id=document_id)
-
-        if real_dataset_id != dataset_id:
-            return construct_json_result(message=f"The document {document_id} is not in the dataset: {dataset_id}, "
-                                                 f"but in the dataset: {real_dataset_id}.", code=RetCode.ARGUMENT_ERROR)
-
-        # there is an issue when removing
-        if not DocumentService.remove_document(doc, tenant_id):
-            return construct_json_result(
-                message="There was an error during the document removal process. Please check the status of the "
-                        "RAGFlow server and try the removal again.", code=RetCode.OPERATING_ERROR)
-
-        # fetch the File2Document record associated with the provided document ID.
-        file_to_doc = File2DocumentService.get_by_document_id(document_id)
-        # delete the associated File record.
-        FileService.filter_delete([File.source_type == FileSource.KNOWLEDGEBASE, File.id == file_to_doc[0].file_id])
-        # delete the File2Document record itself using the document ID. This removes the
-        # association between the document and the file after the File record has been deleted.
-        File2DocumentService.delete_by_document_id(document_id)
-
-        # delete it from minio
-        MINIO.rm(dataset_id, location)
-    except Exception as e:
-        errors += str(e)
-    if errors:
-        return construct_json_result(data=False, message=errors, code=RetCode.SERVER_ERROR)
-
-    return construct_json_result(data=True, code=RetCode.SUCCESS)
-
-
-# ----------------------------list files-----------------------------------------------------
-@manager.route('/<dataset_id>/documents/', methods=['GET'])
-@login_required
-def list_documents(dataset_id):
-    if not dataset_id:
-        return construct_json_result(
-            data=False, message="Lack of 'dataset_id'", code=RetCode.ARGUMENT_ERROR)
-
-    # searching keywords
-    keywords = request.args.get("keywords", "")
-
-    offset = request.args.get("offset", 0)
-    count = request.args.get("count", -1)
-    order_by = request.args.get("order_by", "create_time")
-    descend = request.args.get("descend", True)
-    try:
-        docs, total = DocumentService.list_documents_in_dataset(dataset_id, int(offset), int(count), order_by,
-                                                                descend, keywords)
-
-        return construct_json_result(data={"total": total, "docs": docs}, message=RetCode.SUCCESS)
-    except Exception as e:
-        return construct_error_response(e)
-
-
-# ----------------------------update: enable rename-----------------------------------------------------
-@manager.route("/<dataset_id>/documents/<document_id>", methods=["PUT"])
-@login_required
-def update_document(dataset_id, document_id):
-    req = request.json
-    try:
-        legal_parameters = set()
-        legal_parameters.add("name")
-        legal_parameters.add("enable")
-        legal_parameters.add("template_type")
-
-        for key in req.keys():
-            if key not in legal_parameters:
-                return construct_json_result(code=RetCode.ARGUMENT_ERROR, message=f"{key} is an illegal parameter.")
-
-        # The request body cannot be empty
-        if not req:
-            return construct_json_result(
-                code=RetCode.DATA_ERROR,
-                message="Please input at least one parameter that you want to update!")
-
-        # Check whether there is this dataset
-        exist, dataset = KnowledgebaseService.get_by_id(dataset_id)
-        if not exist:
-            return construct_json_result(code=RetCode.DATA_ERROR, message=f"This dataset {dataset_id} cannot be found!")
-
-        # The document does not exist
-        exist, document = DocumentService.get_by_id(document_id)
-        if not exist:
-            return construct_json_result(message=f"This document {document_id} cannot be found!",
-                                         code=RetCode.ARGUMENT_ERROR)
-
-        # Deal with the different keys
-        updating_data = {}
-        if "name" in req:
-            new_name = req["name"]
-            updating_data["name"] = new_name
-            # Check whether the new_name is suitable
-            # 1. no name value
-            if not new_name:
-                return construct_json_result(code=RetCode.DATA_ERROR, message="There is no new name.")
-
-            # 2. In case that there's space in the head or the tail
-            new_name = new_name.strip()
-
-            # 3. Check whether the new_name has the same extension of file as before
-            if pathlib.Path(new_name.lower()).suffix != pathlib.Path(
-                    document.name.lower()).suffix:
-                return construct_json_result(
-                    data=False,
-                    message="The extension of file cannot be changed",
-                    code=RetCode.ARGUMENT_ERROR)
-
-            # 4. Check whether the new name has already been occupied by other file
-            for d in DocumentService.query(name=new_name, kb_id=document.kb_id):
-                if d.name == new_name:
-                    return construct_json_result(
-                        message="Duplicated document name in the same dataset.",
-                        code=RetCode.ARGUMENT_ERROR)
-
-        if "enable" in req:
-            enable_value = req["enable"]
-            if is_illegal_value_for_enum(enable_value, StatusEnum):
-                return construct_json_result(message=f"Illegal value {enable_value} for 'enable' field.",
-                                             code=RetCode.DATA_ERROR)
-            updating_data["status"] = enable_value
-
-        # TODO: Chunk-method - update parameters inside the json object parser_config
-        if "template_type" in req:
-            type_value = req["template_type"]
-            if is_illegal_value_for_enum(type_value, ParserType):
-                return construct_json_result(message=f"Illegal value {type_value} for 'template_type' field.",
-                                             code=RetCode.DATA_ERROR)
-            updating_data["parser_id"] = req["template_type"]
-
-        # The process of updating
-        if not DocumentService.update_by_id(document_id, updating_data):
-            return construct_json_result(
-                code=RetCode.OPERATING_ERROR,
-                message="Failed to update document in the database! "
-                        "Please check the status of RAGFlow server and try again!")
-
-        # name part: file service
-        if "name" in req:
-            # Get file by document id
-            file_information = File2DocumentService.get_by_document_id(document_id)
-            if file_information:
-                exist, file = FileService.get_by_id(file_information[0].file_id)
-                FileService.update_by_id(file.id, {"name": req["name"]})
-
-        exist, document = DocumentService.get_by_id(document_id)
-
-        # Success
-        return construct_json_result(data=document.to_json(), message="Success", code=RetCode.SUCCESS)
-    except Exception as e:
-        return construct_error_response(e)
-
-
-# Helper method to judge whether it's an illegal value
-def is_illegal_value_for_enum(value, enum_class):
-    return value not in enum_class.__members__.values()
-
-
-# ----------------------------download a file-----------------------------------------------------
-@manager.route("/<dataset_id>/documents/<document_id>", methods=["GET"])
-@login_required
-def download_document(dataset_id, document_id):
-    try:
-        # Check whether there is this dataset
-        exist, _ = KnowledgebaseService.get_by_id(dataset_id)
-        if not exist:
-            return construct_json_result(code=RetCode.DATA_ERROR,
-                                         message=f"This dataset '{dataset_id}' cannot be found!")
-
-        # Check whether there is this document
-        exist, document = DocumentService.get_by_id(document_id)
-        if not exist:
-            return construct_json_result(message=f"This document '{document_id}' cannot be found!",
-                                         code=RetCode.ARGUMENT_ERROR)
-
-        # The process of downloading
-        doc_id, doc_location = File2DocumentService.get_minio_address(doc_id=document_id)  # minio address
-        file_stream = MINIO.get(doc_id, doc_location)
-        if not file_stream:
-            return construct_json_result(message="This file is empty.", code=RetCode.DATA_ERROR)
-
-        file = BytesIO(file_stream)
-
-        # Use send_file with a proper filename and MIME type
-        return send_file(
-            file,
-            as_attachment=True,
-            download_name=document.name,
-            mimetype='application/octet-stream'  # Set a default MIME type
-        )
-
-    # Error
-    except Exception as e:
-        return construct_error_response(e)
-
-
-# ----------------------------start parsing a document-----------------------------------------------------
-# helper method for parsing
-# callback method
-def doc_parse_callback(doc_id, prog=None, msg=""):
-    cancel = DocumentService.do_cancel(doc_id)
-    if cancel:
-        raise Exception("The parsing process has been cancelled!")
-
-"""
-def doc_parse(binary, doc_name, parser_name, tenant_id, doc_id):
-    match parser_name:
-        case "book":
-            book.chunk(doc_name, binary=binary, callback=partial(doc_parse_callback, doc_id))
-        case "laws":
-            laws.chunk(doc_name, binary=binary, callback=partial(doc_parse_callback, doc_id))
-        case "manual":
-            manual.chunk(doc_name, binary=binary, callback=partial(doc_parse_callback, doc_id))
-        case "naive":
-            # It's the mode by default, which is general in the front-end
-            naive.chunk(doc_name, binary=binary, callback=partial(doc_parse_callback, doc_id))
-        case "one":
-            one.chunk(doc_name, binary=binary, callback=partial(doc_parse_callback, doc_id))
-        case "paper":
-            paper.chunk(doc_name, binary=binary, callback=partial(doc_parse_callback, doc_id))
-        case "picture":
-            picture.chunk(doc_name, binary=binary, tenant_id=tenant_id, lang="Chinese",
-                          callback=partial(doc_parse_callback, doc_id))
-        case "presentation":
-            presentation.chunk(doc_name, binary=binary, callback=partial(doc_parse_callback, doc_id))
-        case "qa":
-            qa.chunk(doc_name, binary=binary, callback=partial(doc_parse_callback, doc_id))
-        case "resume":
-            resume.chunk(doc_name, binary=binary, callback=partial(doc_parse_callback, doc_id))
-        case "table":
-            table.chunk(doc_name, binary=binary, callback=partial(doc_parse_callback, doc_id))
-        case "audio":
-            audio.chunk(doc_name, binary=binary, callback=partial(doc_parse_callback, doc_id))
-        case "email":
-            email.chunk(doc_name, binary=binary, callback=partial(doc_parse_callback, doc_id))
-        case _:
-            return False
-
-    return True
-    """
-
-
-@manager.route("/<dataset_id>/documents/<document_id>/status", methods=["POST"])
-@login_required
-def parse_document(dataset_id, document_id):
-    try:
-        # valid dataset
-        exist, _ = KnowledgebaseService.get_by_id(dataset_id)
-        if not exist:
-            return construct_json_result(code=RetCode.DATA_ERROR,
-                                         message=f"This dataset '{dataset_id}' cannot be found!")
-
-        return parsing_document_internal(document_id)
-
-    except Exception as e:
-        return construct_error_response(e)
-
-
-# ----------------------------start parsing documents-----------------------------------------------------
-@manager.route("/<dataset_id>/documents/status", methods=["POST"])
-@login_required
-def parse_documents(dataset_id):
-    doc_ids = request.json["doc_ids"]
-    try:
-        exist, _ = KnowledgebaseService.get_by_id(dataset_id)
-        if not exist:
-            return construct_json_result(code=RetCode.DATA_ERROR,
-                                         message=f"This dataset '{dataset_id}' cannot be found!")
-        # two conditions
-        if not doc_ids:
-            # documents inside the dataset
-            docs, total = DocumentService.list_documents_in_dataset(dataset_id, 0, -1, "create_time",
-                                                                    True, "")
-            doc_ids = [doc["id"] for doc in docs]
-
-        message = ""
-        # for loop
-        for id in doc_ids:
-            res = parsing_document_internal(id)
-            res_body = res.json
-            if res_body["code"] == RetCode.SUCCESS:
-                message += res_body["message"]
-            else:
-                return res
-        return construct_json_result(data=True, code=RetCode.SUCCESS, message=message)
-
-    except Exception as e:
-        return construct_error_response(e)
-
-
-# helper method for parsing the document
-def parsing_document_internal(id):
-    message = ""
-    try:
-        # Check whether there is this document
-        exist, document = DocumentService.get_by_id(id)
-        if not exist:
-            return construct_json_result(message=f"This document '{id}' cannot be found!",
-                                         code=RetCode.ARGUMENT_ERROR)
-
-        tenant_id = DocumentService.get_tenant_id(id)
-        if not tenant_id:
-            return construct_json_result(message="Tenant not found!", code=RetCode.AUTHENTICATION_ERROR)
-
-        info = {"run": "1", "progress": 0}
-        info["progress_msg"] = ""
-        info["chunk_num"] = 0
-        info["token_num"] = 0
-
-        DocumentService.update_by_id(id, info)
-
-        ELASTICSEARCH.deleteByQuery(Q("match", doc_id=id), idxnm=search.index_name(tenant_id))
-
-        _, doc_attributes = DocumentService.get_by_id(id)
-        doc_attributes = doc_attributes.to_dict()
-        doc_id = doc_attributes["id"]
-
-        bucket, doc_name = File2DocumentService.get_minio_address(doc_id=doc_id)
-        binary = MINIO.get(bucket, doc_name)
-        parser_name = doc_attributes["parser_id"]
-        if binary:
-            res = doc_parse(binary, doc_name, parser_name, tenant_id, doc_id)
-            if res is False:
-                message += f"The parser id: {parser_name} of the document {doc_id} is not supported; "
-        else:
-            message += f"Empty data in the document: {doc_name}; "
-        # failed in parsing
-        if doc_attributes["status"] == TaskStatus.FAIL.value:
-            message += f"Failed in parsing the document: {doc_id}; "
-        return construct_json_result(code=RetCode.SUCCESS, message=message)
-    except Exception as e:
-        return construct_error_response(e)
-
-
-# ----------------------------stop parsing a doc-----------------------------------------------------
-@manager.route("<dataset_id>/documents/<document_id>/status", methods=["DELETE"])
-@login_required
-def stop_parsing_document(dataset_id, document_id):
-    try:
-        # valid dataset
-        exist, _ = KnowledgebaseService.get_by_id(dataset_id)
-        if not exist:
-            return construct_json_result(code=RetCode.DATA_ERROR,
-                                         message=f"This dataset '{dataset_id}' cannot be found!")
-
-        return stop_parsing_document_internal(document_id)
-
-    except Exception as e:
-        return construct_error_response(e)
-
-
-# ----------------------------stop parsing docs-----------------------------------------------------
-@manager.route("<dataset_id>/documents/status", methods=["DELETE"])
-@login_required
-def stop_parsing_documents(dataset_id):
-    doc_ids = request.json["doc_ids"]
-    try:
-        # valid dataset?
-        exist, _ = KnowledgebaseService.get_by_id(dataset_id)
-        if not exist:
-            return construct_json_result(code=RetCode.DATA_ERROR,
-                                         message=f"This dataset '{dataset_id}' cannot be found!")
-        if not doc_ids:
-            # documents inside the dataset
-            docs, total = DocumentService.list_documents_in_dataset(dataset_id, 0, -1, "create_time",
-                                                                        True, "")
-            doc_ids = [doc["id"] for doc in docs]
-
-        message = ""
-        # for loop
-        for id in doc_ids:
-            res = stop_parsing_document_internal(id)
-            res_body = res.json
-            if res_body["code"] == RetCode.SUCCESS:
-                message += res_body["message"]
-            else:
-                return res
-        return construct_json_result(data=True, code=RetCode.SUCCESS, message=message)
-
-    except Exception as e:
-        return construct_error_response(e)
-
-
-# Helper method
-def stop_parsing_document_internal(document_id):
-    try:
-        # valid doc?
-        exist, doc = DocumentService.get_by_id(document_id)
-        if not exist:
-            return construct_json_result(message=f"This document '{document_id}' cannot be found!",
-                                         code=RetCode.ARGUMENT_ERROR)
-        doc_attributes = doc.to_dict()
-
-        # only when the status is parsing, we need to stop it
-        if doc_attributes["status"] == TaskStatus.RUNNING.value:
-            tenant_id = DocumentService.get_tenant_id(document_id)
-            if not tenant_id:
-                return construct_json_result(message="Tenant not found!", code=RetCode.AUTHENTICATION_ERROR)
-
-            # update successfully?
-            if not DocumentService.update_by_id(document_id, {"status": "2"}):  # cancel
-                return construct_json_result(
-                    code=RetCode.OPERATING_ERROR,
-                    message="There was an error during the stopping parsing the document process. "
-                            "Please check the status of the RAGFlow server and try the update again."
-                )
-
-            _, doc_attributes = DocumentService.get_by_id(document_id)
-            doc_attributes = doc_attributes.to_dict()
-
-            # failed in stop parsing
-            if doc_attributes["status"] == TaskStatus.RUNNING.value:
-                return construct_json_result(message=f"Failed in parsing the document: {document_id}; ", code=RetCode.SUCCESS)
-        return construct_json_result(code=RetCode.SUCCESS, message="")
-    except Exception as e:
-        return construct_error_response(e)
-
-
-# ----------------------------show the status of the file-----------------------------------------------------
-@manager.route("/<dataset_id>/documents/<document_id>/status", methods=["GET"])
-@login_required
-def show_parsing_status(dataset_id, document_id):
-    try:
-        # valid dataset
-        exist, _ = KnowledgebaseService.get_by_id(dataset_id)
-        if not exist:
-            return construct_json_result(code=RetCode.DATA_ERROR,
-                                         message=f"This dataset: '{dataset_id}' cannot be found!")
-        # valid document
-        exist, _ = DocumentService.get_by_id(document_id)
-        if not exist:
-            return construct_json_result(code=RetCode.DATA_ERROR,
-                                         message=f"This document: '{document_id}' is not a valid document.")
-
-        _, doc = DocumentService.get_by_id(document_id)  # get doc object
-        doc_attributes = doc.to_dict()
-
-        return construct_json_result(
-            data={"progress": doc_attributes["progress"], "status": TaskStatus(doc_attributes["status"]).name},
-            code=RetCode.SUCCESS
-        )
-    except Exception as e:
-        return construct_error_response(e)
-
-# ----------------------------list the chunks of the file-----------------------------------------------------
-
-# -- --------------------------delete the chunk-----------------------------------------------------
-
-# ----------------------------edit the status of the chunk-----------------------------------------------------
-
-# ----------------------------insert a new chunk-----------------------------------------------------
-
-# ----------------------------upload a file-----------------------------------------------------
-
-# ----------------------------get a specific chunk-----------------------------------------------------
-
-# ----------------------------retrieval test-----------------------------------------------------

api/apps/dialog_app.py

@@ -18,28 +18,30 @@ from flask import request
 from flask_login import login_required, current_user
 from api.db.services.dialog_service import DialogService
 from api.db import StatusEnum
+from api.db.services.llm_service import TenantLLMService
 from api.db.services.knowledgebase_service import KnowledgebaseService
-from api.db.services.user_service import TenantService
+from api.db.services.user_service import TenantService, UserTenantService
+from api import settings
 from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
 from api.utils import get_uuid
 from api.utils.api_utils import get_json_result
 
 
-@manager.route('/set', methods=['POST'])
+@manager.route('/set', methods=['POST'])  # noqa: F821
 @login_required
 def set_dialog():
     req = request.json
     dialog_id = req.get("dialog_id")
     name = req.get("name", "New Dialog")
-    description = req.get("description", "A helpful Dialog")
+    description = req.get("description", "A helpful dialog")
     icon = req.get("icon", "")
     top_n = req.get("top_n", 6)
     top_k = req.get("top_k", 1024)
     rerank_id = req.get("rerank_id", "")
-    if not rerank_id: req["rerank_id"] = ""
+    if not rerank_id:
+        req["rerank_id"] = ""
     similarity_threshold = req.get("similarity_threshold", 0.1)
     vector_similarity_weight = req.get("vector_similarity_weight", 0.3)
-    if vector_similarity_weight is None: vector_similarity_weight = 0.3
     llm_setting = req.get("llm_setting", {})
     default_prompt = {
         "system": """你是一个智能助手，请总结知识库的内容来回答问题，请列举知识库中的数据详细回答。当所有知识库内容都与问题无关时，你的回答必须包括“知识库中未找到您要的答案！”这句话。回答需要考虑聊天历史。
@@ -56,33 +58,31 @@ def set_dialog():
 
     if not prompt_config["system"]:
         prompt_config["system"] = default_prompt["system"]
-    # if len(prompt_config["parameters"]) < 1:
-    #     prompt_config["parameters"] = default_prompt["parameters"]
-    # for p in prompt_config["parameters"]:
-    #     if p["key"] == "knowledge":break
-    # else: prompt_config["parameters"].append(default_prompt["parameters"][0])
 
     for p in prompt_config["parameters"]:
         if p["optional"]:
             continue
         if prompt_config["system"].find("{%s}" % p["key"]) < 0:
             return get_data_error_result(
-                retmsg="Parameter '{}' is not used".format(p["key"]))
+                message="Parameter '{}' is not used".format(p["key"]))
 
     try:
         e, tenant = TenantService.get_by_id(current_user.id)
         if not e:
-            return get_data_error_result(retmsg="Tenant not found!")
+            return get_data_error_result(message="Tenant not found!")
+        kbs = KnowledgebaseService.get_by_ids(req.get("kb_ids", []))
+        embd_ids = [TenantLLMService.split_model_name_and_factory(kb.embd_id)[0] for kb in kbs]  # remove vendor suffix for comparison
+        embd_count = len(set(embd_ids))
+        if embd_count > 1:
+            return get_data_error_result(message=f'Datasets use different embedding models: {[kb.embd_id for kb in kbs]}"')
+
         llm_id = req.get("llm_id", tenant.llm_id)
         if not dialog_id:
-            if not req.get("kb_ids"):
-                return get_data_error_result(
-                    retmsg="Fail! Please select knowledgebase!")
             dia = {
                 "id": get_uuid(),
                 "tenant_id": current_user.id,
                 "name": name,
-                "kb_ids": req["kb_ids"],
+                "kb_ids": req.get("kb_ids", []),
                 "description": description,
                 "llm_id": llm_id,
                 "llm_setting": llm_setting,
@@ -95,35 +95,33 @@ def set_dialog():
                 "icon": icon
             }
             if not DialogService.save(**dia):
-                return get_data_error_result(retmsg="Fail to new a dialog!")
-            e, dia = DialogService.get_by_id(dia["id"])
-            if not e:
-                return get_data_error_result(retmsg="Fail to new a dialog!")
-            return get_json_result(data=dia.to_json())
+                return get_data_error_result(message="Fail to new a dialog!")
+            return get_json_result(data=dia)
         else:
             del req["dialog_id"]
             if "kb_names" in req:
                 del req["kb_names"]
             if not DialogService.update_by_id(dialog_id, req):
-                return get_data_error_result(retmsg="Dialog not found!")
+                return get_data_error_result(message="Dialog not found!")
             e, dia = DialogService.get_by_id(dialog_id)
             if not e:
-                return get_data_error_result(retmsg="Fail to update a dialog!")
+                return get_data_error_result(message="Fail to update a dialog!")
             dia = dia.to_dict()
+            dia.update(req)
             dia["kb_ids"], dia["kb_names"] = get_kb_names(dia["kb_ids"])
             return get_json_result(data=dia)
     except Exception as e:
         return server_error_response(e)
 
 
-@manager.route('/get', methods=['GET'])
+@manager.route('/get', methods=['GET'])  # noqa: F821
 @login_required
 def get():
     dialog_id = request.args["dialog_id"]
     try:
         e, dia = DialogService.get_by_id(dialog_id)
         if not e:
-            return get_data_error_result(retmsg="Dialog not found!")
+            return get_data_error_result(message="Dialog not found!")
         dia = dia.to_dict()
         dia["kb_ids"], dia["kb_names"] = get_kb_names(dia["kb_ids"])
         return get_json_result(data=dia)
@@ -142,7 +140,7 @@ def get_kb_names(kb_ids):
     return ids, nms
 
 
-@manager.route('/list', methods=['GET'])
+@manager.route('/list', methods=['GET'])  # noqa: F821
 @login_required
 def list_dialogs():
     try:
@@ -159,14 +157,24 @@ def list_dialogs():
         return server_error_response(e)
 
 
-@manager.route('/rm', methods=['POST'])
+@manager.route('/rm', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("dialog_ids")
 def rm():
     req = request.json
+    dialog_list=[]
+    tenants = UserTenantService.query(user_id=current_user.id)
     try:
-        DialogService.update_many_by_id(
-            [{"id": id, "status": StatusEnum.INVALID.value} for id in req["dialog_ids"]])
+        for id in req["dialog_ids"]:
+            for tenant in tenants:
+                if DialogService.query(tenant_id=tenant.tenant_id, id=id):
+                    break
+            else:
+                return get_json_result(
+                    data=False, message='Only owner of dialog authorized for this operation.',
+                    code=settings.RetCode.OPERATING_ERROR)
+            dialog_list.append({"id": id,"status":StatusEnum.INVALID.value})
+        DialogService.update_many_by_id(dialog_list)
         return get_json_result(data=True)
     except Exception as e:
         return server_error_response(e)

api/apps/document_app.py

@@ -13,94 +13,93 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License
 #
-import datetime
-import hashlib
 import json
-import os
+import os.path
 import pathlib
 import re
-import traceback
-from concurrent.futures import ThreadPoolExecutor
-from copy import deepcopy
-from io import BytesIO
 
 import flask
-from elasticsearch_dsl import Q
 from flask import request
 from flask_login import login_required, current_user
 
-from api.db.db_models import Task, File
-from api.db.services.dialog_service import DialogService, ConversationService
+from deepdoc.parser.html_parser import RAGFlowHtmlParser
+from rag.nlp import search
+
+from api.db import FileType, TaskStatus, ParserType, FileSource
+from api.db.db_models import File, Task
 from api.db.services.file2document_service import File2DocumentService
 from api.db.services.file_service import FileService
-from api.db.services.llm_service import LLMBundle
-from api.db.services.task_service import TaskService, queue_tasks
-from api.db.services.user_service import TenantService
-from graphrag.mind_map_extractor import MindMapExtractor
-from rag.app import naive
-from rag.nlp import search
-from rag.utils.es_conn import ELASTICSEARCH
+from api.db.services.task_service import queue_tasks
+from api.db.services.user_service import UserTenantService
 from api.db.services import duplicate_name
 from api.db.services.knowledgebase_service import KnowledgebaseService
-from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
-from api.utils import get_uuid
-from api.db import FileType, TaskStatus, ParserType, FileSource, LLMType
+from api.db.services.task_service import TaskService
 from api.db.services.document_service import DocumentService, doc_upload_and_parse
-from api.settings import RetCode, stat_logger
+from api.utils.api_utils import (
+    server_error_response,
+    get_data_error_result,
+    validate_request,
+)
+from api.utils import get_uuid
+from api import settings
 from api.utils.api_utils import get_json_result
-from rag.utils.minio_conn import MINIO
+from rag.utils.storage_factory import STORAGE_IMPL
 from api.utils.file_utils import filename_type, thumbnail, get_project_base_directory
 from api.utils.web_utils import html2pdf, is_valid_url
+from api.constants import IMG_BASE64_PREFIX
 
 
-@manager.route('/upload', methods=['POST'])
+@manager.route('/upload', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("kb_id")
 def upload():
     kb_id = request.form.get("kb_id")
     if not kb_id:
         return get_json_result(
-            data=False, retmsg='Lack of "KB ID"', retcode=RetCode.ARGUMENT_ERROR)
+            data=False, message='Lack of "KB ID"', code=settings.RetCode.ARGUMENT_ERROR)
     if 'file' not in request.files:
         return get_json_result(
-            data=False, retmsg='No file part!', retcode=RetCode.ARGUMENT_ERROR)
+            data=False, message='No file part!', code=settings.RetCode.ARGUMENT_ERROR)
 
     file_objs = request.files.getlist('file')
     for file_obj in file_objs:
         if file_obj.filename == '':
             return get_json_result(
-                data=False, retmsg='No file selected!', retcode=RetCode.ARGUMENT_ERROR)
+                data=False, message='No file selected!', code=settings.RetCode.ARGUMENT_ERROR)
 
     e, kb = KnowledgebaseService.get_by_id(kb_id)
     if not e:
         raise LookupError("Can't find this knowledgebase!")
 
-    err, _ = FileService.upload_document(kb, file_objs, current_user.id)
+    err, files = FileService.upload_document(kb, file_objs, current_user.id)
+    files = [f[0] for f in files] # remove the blob
+    
     if err:
         return get_json_result(
-            data=False, retmsg="\n".join(err), retcode=RetCode.SERVER_ERROR)
-    return get_json_result(data=True)
+            data=files, message="\n".join(err), code=settings.RetCode.SERVER_ERROR)
+    return get_json_result(data=files)
 
 
-@manager.route('/web_crawl', methods=['POST'])
+@manager.route('/web_crawl', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("kb_id", "name", "url")
 def web_crawl():
     kb_id = request.form.get("kb_id")
     if not kb_id:
         return get_json_result(
-            data=False, retmsg='Lack of "KB ID"', retcode=RetCode.ARGUMENT_ERROR)
+            data=False, message='Lack of "KB ID"', code=settings.RetCode.ARGUMENT_ERROR)
     name = request.form.get("name")
     url = request.form.get("url")
     if not is_valid_url(url):
         return get_json_result(
-            data=False, retmsg='The URL format is invalid', retcode=RetCode.ARGUMENT_ERROR)
+            data=False, message='The URL format is invalid', code=settings.RetCode.ARGUMENT_ERROR)
     e, kb = KnowledgebaseService.get_by_id(kb_id)
     if not e:
         raise LookupError("Can't find this knowledgebase!")
 
     blob = html2pdf(url)
-    if not blob: return server_error_response(ValueError("Download failure."))
+    if not blob:
+        return server_error_response(ValueError("Download failure."))
 
     root_folder = FileService.get_root_folder(current_user.id)
     pf_id = root_folder["id"]
@@ -118,9 +117,9 @@ def web_crawl():
             raise RuntimeError("This type of file has not been supported yet!")
 
         location = filename
-        while MINIO.obj_exist(kb_id, location):
+        while STORAGE_IMPL.obj_exist(kb_id, location):
             location += "_"
-        MINIO.put(kb_id, location, blob)
+        STORAGE_IMPL.put(kb_id, location, blob)
         doc = {
             "id": get_uuid(),
             "kb_id": kb.id,
@@ -139,6 +138,8 @@ def web_crawl():
             doc["parser_id"] = ParserType.AUDIO.value
         if re.search(r"\.(ppt|pptx|pages)$", filename):
             doc["parser_id"] = ParserType.PRESENTATION.value
+        if re.search(r"\.(eml)$", filename):
+            doc["parser_id"] = ParserType.EMAIL.value
         DocumentService.insert(doc)
         FileService.add_file_from_kb(doc, kb_folder["id"], kb.tenant_id)
     except Exception as e:
@@ -146,7 +147,7 @@ def web_crawl():
     return get_json_result(data=True)
 
 
-@manager.route('/create', methods=['POST'])
+@manager.route('/create', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("name", "kb_id")
 def create():
@@ -154,17 +155,17 @@ def create():
     kb_id = req["kb_id"]
     if not kb_id:
         return get_json_result(
-            data=False, retmsg='Lack of "KB ID"', retcode=RetCode.ARGUMENT_ERROR)
+            data=False, message='Lack of "KB ID"', code=settings.RetCode.ARGUMENT_ERROR)
 
     try:
         e, kb = KnowledgebaseService.get_by_id(kb_id)
         if not e:
             return get_data_error_result(
-                retmsg="Can't find this knowledgebase!")
+                message="Can't find this knowledgebase!")
 
         if DocumentService.query(name=req["name"], kb_id=kb_id):
             return get_data_error_result(
-                retmsg="Duplicated document name in the same knowledgebase.")
+                message="Duplicated document name in the same knowledgebase.")
 
         doc = DocumentService.insert({
             "id": get_uuid(),
@@ -182,13 +183,22 @@ def create():
         return server_error_response(e)
 
 
-@manager.route('/list', methods=['GET'])
+@manager.route('/list', methods=['GET'])  # noqa: F821
 @login_required
 def list_docs():
     kb_id = request.args.get("kb_id")
     if not kb_id:
         return get_json_result(
-            data=False, retmsg='Lack of "KB ID"', retcode=RetCode.ARGUMENT_ERROR)
+            data=False, message='Lack of "KB ID"', code=settings.RetCode.ARGUMENT_ERROR)
+    tenants = UserTenantService.query(user_id=current_user.id)
+    for tenant in tenants:
+        if KnowledgebaseService.query(
+                tenant_id=tenant.tenant_id, id=kb_id):
+            break
+    else:
+        return get_json_result(
+            data=False, message='Only owner of knowledgebase authorized for this operation.',
+            code=settings.RetCode.OPERATING_ERROR)
     keywords = request.args.get("keywords", "")
 
     page_number = int(request.args.get("page", 1))
@@ -198,83 +208,108 @@ def list_docs():
     try:
         docs, tol = DocumentService.get_by_kb_id(
             kb_id, page_number, items_per_page, orderby, desc, keywords)
+
+        for doc_item in docs:
+            if doc_item['thumbnail'] and not doc_item['thumbnail'].startswith(IMG_BASE64_PREFIX):
+                doc_item['thumbnail'] = f"/v1/document/image/{kb_id}-{doc_item['thumbnail']}"
+
         return get_json_result(data={"total": tol, "docs": docs})
     except Exception as e:
         return server_error_response(e)
 
 
-@manager.route('/infos', methods=['POST'])
+@manager.route('/infos', methods=['POST'])  # noqa: F821
+@login_required
 def docinfos():
     req = request.json
     doc_ids = req["doc_ids"]
+    for doc_id in doc_ids:
+        if not DocumentService.accessible(doc_id, current_user.id):
+            return get_json_result(
+                data=False,
+                message='No authorization.',
+                code=settings.RetCode.AUTHENTICATION_ERROR
+            )
     docs = DocumentService.get_by_ids(doc_ids)
     return get_json_result(data=list(docs.dicts()))
 
 
-@manager.route('/thumbnails', methods=['GET'])
-#@login_required
+@manager.route('/thumbnails', methods=['GET'])  # noqa: F821
+# @login_required
 def thumbnails():
     doc_ids = request.args.get("doc_ids").split(",")
     if not doc_ids:
         return get_json_result(
-            data=False, retmsg='Lack of "Document ID"', retcode=RetCode.ARGUMENT_ERROR)
+            data=False, message='Lack of "Document ID"', code=settings.RetCode.ARGUMENT_ERROR)
 
     try:
         docs = DocumentService.get_thumbnails(doc_ids)
+
+        for doc_item in docs:
+            if doc_item['thumbnail'] and not doc_item['thumbnail'].startswith(IMG_BASE64_PREFIX):
+                doc_item['thumbnail'] = f"/v1/document/image/{doc_item['kb_id']}-{doc_item['thumbnail']}"
+
         return get_json_result(data={d["id"]: d["thumbnail"] for d in docs})
     except Exception as e:
         return server_error_response(e)
 
 
-@manager.route('/change_status', methods=['POST'])
+@manager.route('/change_status', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("doc_id", "status")
 def change_status():
     req = request.json
     if str(req["status"]) not in ["0", "1"]:
-        get_json_result(
+        return get_json_result(
             data=False,
-            retmsg='"Status" must be either 0 or 1!',
-            retcode=RetCode.ARGUMENT_ERROR)
+            message='"Status" must be either 0 or 1!',
+            code=settings.RetCode.ARGUMENT_ERROR)
+
+    if not DocumentService.accessible(req["doc_id"], current_user.id):
+        return get_json_result(
+            data=False,
+            message='No authorization.',
+            code=settings.RetCode.AUTHENTICATION_ERROR)
 
     try:
         e, doc = DocumentService.get_by_id(req["doc_id"])
         if not e:
-            return get_data_error_result(retmsg="Document not found!")
+            return get_data_error_result(message="Document not found!")
         e, kb = KnowledgebaseService.get_by_id(doc.kb_id)
         if not e:
             return get_data_error_result(
-                retmsg="Can't find this knowledgebase!")
+                message="Can't find this knowledgebase!")
 
         if not DocumentService.update_by_id(
                 req["doc_id"], {"status": str(req["status"])}):
             return get_data_error_result(
-                retmsg="Database error (Document update)!")
+                message="Database error (Document update)!")
 
-        if str(req["status"]) == "0":
-            ELASTICSEARCH.updateScriptByQuery(Q("term", doc_id=req["doc_id"]),
-                                              scripts="ctx._source.available_int=0;",
-                                              idxnm=search.index_name(
-                                                  kb.tenant_id)
-                                              )
-        else:
-            ELASTICSEARCH.updateScriptByQuery(Q("term", doc_id=req["doc_id"]),
-                                              scripts="ctx._source.available_int=1;",
-                                              idxnm=search.index_name(
-                                                  kb.tenant_id)
-                                              )
+        status = int(req["status"])
+        settings.docStoreConn.update({"doc_id": req["doc_id"]}, {"available_int": status},
+                                     search.index_name(kb.tenant_id), doc.kb_id)
         return get_json_result(data=True)
     except Exception as e:
         return server_error_response(e)
 
 
-@manager.route('/rm', methods=['POST'])
+@manager.route('/rm', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("doc_id")
 def rm():
     req = request.json
     doc_ids = req["doc_id"]
-    if isinstance(doc_ids, str): doc_ids = [doc_ids]
+    if isinstance(doc_ids, str):
+        doc_ids = [doc_ids]
+
+    for doc_id in doc_ids:
+        if not DocumentService.accessible4deletion(doc_id, current_user.id):
+            return get_json_result(
+                data=False,
+                message='No authorization.',
+                code=settings.RetCode.AUTHENTICATION_ERROR
+            )
+
     root_folder = FileService.get_root_folder(current_user.id)
     pf_id = root_folder["id"]
     FileService.init_knowledgebase_docs(pf_id, current_user.id)
@@ -283,88 +318,105 @@ def rm():
         try:
             e, doc = DocumentService.get_by_id(doc_id)
             if not e:
-                return get_data_error_result(retmsg="Document not found!")
+                return get_data_error_result(message="Document not found!")
             tenant_id = DocumentService.get_tenant_id(doc_id)
             if not tenant_id:
-                return get_data_error_result(retmsg="Tenant not found!")
+                return get_data_error_result(message="Tenant not found!")
 
-            b, n = File2DocumentService.get_minio_address(doc_id=doc_id)
+            b, n = File2DocumentService.get_storage_address(doc_id=doc_id)
 
+            TaskService.filter_delete([Task.doc_id == doc_id])
             if not DocumentService.remove_document(doc, tenant_id):
                 return get_data_error_result(
-                    retmsg="Database error (Document removal)!")
+                    message="Database error (Document removal)!")
 
             f2d = File2DocumentService.get_by_document_id(doc_id)
-            FileService.filter_delete([File.source_type == FileSource.KNOWLEDGEBASE, File.id == f2d[0].file_id])
+            deleted_file_count = FileService.filter_delete([File.source_type == FileSource.KNOWLEDGEBASE, File.id == f2d[0].file_id])
             File2DocumentService.delete_by_document_id(doc_id)
-
-            MINIO.rm(b, n)
+            if deleted_file_count > 0:
+                STORAGE_IMPL.rm(b, n)
         except Exception as e:
             errors += str(e)
 
     if errors:
-        return get_json_result(data=False, retmsg=errors, retcode=RetCode.SERVER_ERROR)
+        return get_json_result(data=False, message=errors, code=settings.RetCode.SERVER_ERROR)
 
     return get_json_result(data=True)
 
 
-@manager.route('/run', methods=['POST'])
+@manager.route('/run', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("doc_ids", "run")
-def run():
+def run(): 
     req = request.json
+    for doc_id in req["doc_ids"]:
+        if not DocumentService.accessible(doc_id, current_user.id):
+            return get_json_result(
+                data=False,
+                message='No authorization.',
+                code=settings.RetCode.AUTHENTICATION_ERROR
+            )
     try:
         for id in req["doc_ids"]:
             info = {"run": str(req["run"]), "progress": 0}
-            if str(req["run"]) == TaskStatus.RUNNING.value:
+            if str(req["run"]) == TaskStatus.RUNNING.value and req.get("delete", False):
                 info["progress_msg"] = ""
                 info["chunk_num"] = 0
                 info["token_num"] = 0
             DocumentService.update_by_id(id, info)
-            # if str(req["run"]) == TaskStatus.CANCEL.value:
             tenant_id = DocumentService.get_tenant_id(id)
             if not tenant_id:
-                return get_data_error_result(retmsg="Tenant not found!")
-            ELASTICSEARCH.deleteByQuery(
-                Q("match", doc_id=id), idxnm=search.index_name(tenant_id))
+                return get_data_error_result(message="Tenant not found!")
+            e, doc = DocumentService.get_by_id(id)
+            if not e:
+                return get_data_error_result(message="Document not found!")
+            if req.get("delete", False):
+                TaskService.filter_delete([Task.doc_id == id])
+                if settings.docStoreConn.indexExist(search.index_name(tenant_id), doc.kb_id):
+                    settings.docStoreConn.delete({"doc_id": id}, search.index_name(tenant_id), doc.kb_id)
 
             if str(req["run"]) == TaskStatus.RUNNING.value:
-                TaskService.filter_delete([Task.doc_id == id])
                 e, doc = DocumentService.get_by_id(id)
                 doc = doc.to_dict()
                 doc["tenant_id"] = tenant_id
-                bucket, name = File2DocumentService.get_minio_address(doc_id=doc["id"])
-                queue_tasks(doc, bucket, name)
+                bucket, name = File2DocumentService.get_storage_address(doc_id=doc["id"])
+                queue_tasks(doc, bucket, name, 0)
 
         return get_json_result(data=True)
     except Exception as e:
         return server_error_response(e)
 
 
-@manager.route('/rename', methods=['POST'])
+@manager.route('/rename', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("doc_id", "name")
 def rename():
     req = request.json
+    if not DocumentService.accessible(req["doc_id"], current_user.id):
+        return get_json_result(
+            data=False,
+            message='No authorization.',
+            code=settings.RetCode.AUTHENTICATION_ERROR
+        )
     try:
         e, doc = DocumentService.get_by_id(req["doc_id"])
         if not e:
-            return get_data_error_result(retmsg="Document not found!")
+            return get_data_error_result(message="Document not found!")
         if pathlib.Path(req["name"].lower()).suffix != pathlib.Path(
                 doc.name.lower()).suffix:
             return get_json_result(
                 data=False,
-                retmsg="The extension of file can't be changed",
-                retcode=RetCode.ARGUMENT_ERROR)
+                message="The extension of file can't be changed",
+                code=settings.RetCode.ARGUMENT_ERROR)
         for d in DocumentService.query(name=req["name"], kb_id=doc.kb_id):
             if d.name == req["name"]:
                 return get_data_error_result(
-                    retmsg="Duplicated document name in the same knowledgebase.")
+                    message="Duplicated document name in the same knowledgebase.")
 
         if not DocumentService.update_by_id(
                 req["doc_id"], {"name": req["name"]}):
             return get_data_error_result(
-                retmsg="Database error (Document rename)!")
+                message="Database error (Document rename)!")
 
         informs = File2DocumentService.get_by_document_id(req["doc_id"])
         if informs:
@@ -376,16 +428,16 @@ def rename():
         return server_error_response(e)
 
 
-@manager.route('/get/<doc_id>', methods=['GET'])
+@manager.route('/get/<doc_id>', methods=['GET'])  # noqa: F821
 # @login_required
 def get(doc_id):
     try:
         e, doc = DocumentService.get_by_id(doc_id)
         if not e:
-            return get_data_error_result(retmsg="Document not found!")
+            return get_data_error_result(message="Document not found!")
 
-        b, n = File2DocumentService.get_minio_address(doc_id=doc_id)
-        response = flask.make_response(MINIO.get(b, n))
+        b, n = File2DocumentService.get_storage_address(doc_id=doc_id)
+        response = flask.make_response(STORAGE_IMPL.get(b, n))
 
         ext = re.search(r"\.([^.]+)$", doc.name)
         if ext:
@@ -401,15 +453,22 @@ def get(doc_id):
         return server_error_response(e)
 
 
-@manager.route('/change_parser', methods=['POST'])
+@manager.route('/change_parser', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("doc_id", "parser_id")
 def change_parser():
     req = request.json
+
+    if not DocumentService.accessible(req["doc_id"], current_user.id):
+        return get_json_result(
+            data=False,
+            message='No authorization.',
+            code=settings.RetCode.AUTHENTICATION_ERROR
+        )
     try:
         e, doc = DocumentService.get_by_id(req["doc_id"])
         if not e:
-            return get_data_error_result(retmsg="Document not found!")
+            return get_data_error_result(message="Document not found!")
         if doc.parser_id.lower() == req["parser_id"].lower():
             if "parser_config" in req:
                 if req["parser_config"] == doc.parser_config:
@@ -417,59 +476,158 @@ def change_parser():
             else:
                 return get_json_result(data=True)
 
-        if doc.type == FileType.VISUAL or re.search(
-                r"\.(ppt|pptx|pages)$", doc.name):
-            return get_data_error_result(retmsg="Not supported yet!")
+        if ((doc.type == FileType.VISUAL and req["parser_id"] != "picture")
+                or (re.search(
+                    r"\.(ppt|pptx|pages)$", doc.name) and req["parser_id"] != "presentation")):
+            return get_data_error_result(message="Not supported yet!")
 
         e = DocumentService.update_by_id(doc.id,
                                          {"parser_id": req["parser_id"], "progress": 0, "progress_msg": "",
                                           "run": TaskStatus.UNSTART.value})
         if not e:
-            return get_data_error_result(retmsg="Document not found!")
+            return get_data_error_result(message="Document not found!")
         if "parser_config" in req:
             DocumentService.update_parser_config(doc.id, req["parser_config"])
         if doc.token_num > 0:
             e = DocumentService.increment_chunk_num(doc.id, doc.kb_id, doc.token_num * -1, doc.chunk_num * -1,
                                                     doc.process_duation * -1)
             if not e:
-                return get_data_error_result(retmsg="Document not found!")
+                return get_data_error_result(message="Document not found!")
             tenant_id = DocumentService.get_tenant_id(req["doc_id"])
             if not tenant_id:
-                return get_data_error_result(retmsg="Tenant not found!")
-            ELASTICSEARCH.deleteByQuery(
-                Q("match", doc_id=doc.id), idxnm=search.index_name(tenant_id))
+                return get_data_error_result(message="Tenant not found!")
+            if settings.docStoreConn.indexExist(search.index_name(tenant_id), doc.kb_id):
+                settings.docStoreConn.delete({"doc_id": doc.id}, search.index_name(tenant_id), doc.kb_id)
 
         return get_json_result(data=True)
     except Exception as e:
         return server_error_response(e)
 
 
-@manager.route('/image/<image_id>', methods=['GET'])
+@manager.route('/image/<image_id>', methods=['GET'])  # noqa: F821
 # @login_required
 def get_image(image_id):
     try:
+        arr = image_id.split("-")
+        if len(arr) != 2:
+            return get_data_error_result(message="Image not found.")
         bkt, nm = image_id.split("-")
-        response = flask.make_response(MINIO.get(bkt, nm))
+        response = flask.make_response(STORAGE_IMPL.get(bkt, nm))
         response.headers.set('Content-Type', 'image/JPEG')
         return response
     except Exception as e:
         return server_error_response(e)
 
 
-@manager.route('/upload_and_parse', methods=['POST'])
+@manager.route('/upload_and_parse', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("conversation_id")
 def upload_and_parse():
     if 'file' not in request.files:
         return get_json_result(
-            data=False, retmsg='No file part!', retcode=RetCode.ARGUMENT_ERROR)
+            data=False, message='No file part!', code=settings.RetCode.ARGUMENT_ERROR)
 
     file_objs = request.files.getlist('file')
     for file_obj in file_objs:
         if file_obj.filename == '':
             return get_json_result(
-                data=False, retmsg='No file selected!', retcode=RetCode.ARGUMENT_ERROR)
+                data=False, message='No file selected!', code=settings.RetCode.ARGUMENT_ERROR)
 
     doc_ids = doc_upload_and_parse(request.form.get("conversation_id"), file_objs, current_user.id)
 
     return get_json_result(data=doc_ids)
+
+
+@manager.route('/parse', methods=['POST'])  # noqa: F821
+@login_required
+def parse():
+    url = request.json.get("url") if request.json else ""
+    if url:
+        if not is_valid_url(url):
+            return get_json_result(
+                data=False, message='The URL format is invalid', code=settings.RetCode.ARGUMENT_ERROR)
+        download_path = os.path.join(get_project_base_directory(), "logs/downloads")
+        os.makedirs(download_path, exist_ok=True)
+        from seleniumwire.webdriver import Chrome, ChromeOptions
+        options = ChromeOptions()
+        options.add_argument('--headless')
+        options.add_argument('--disable-gpu')
+        options.add_argument('--no-sandbox')
+        options.add_argument('--disable-dev-shm-usage')
+        options.add_experimental_option('prefs', {
+            'download.default_directory': download_path,
+            'download.prompt_for_download': False,
+            'download.directory_upgrade': True,
+            'safebrowsing.enabled': True
+        })
+        driver = Chrome(options=options)
+        driver.get(url)
+        res_headers = [r.response.headers for r in driver.requests if r and r.response]
+        if len(res_headers) > 1:
+            sections = RAGFlowHtmlParser().parser_txt(driver.page_source)
+            driver.quit()
+            return get_json_result(data="\n".join(sections))
+
+        class File:
+            filename: str
+            filepath: str
+
+            def __init__(self, filename, filepath):
+                self.filename = filename
+                self.filepath = filepath
+
+            def read(self):
+                with open(self.filepath, "rb") as f:
+                    return f.read()
+
+        r = re.search(r"filename=\"([^\"]+)\"", str(res_headers))
+        if not r or not r.group(1):
+            return get_json_result(
+                data=False, message="Can't not identify downloaded file", code=settings.RetCode.ARGUMENT_ERROR)
+        f = File(r.group(1), os.path.join(download_path, r.group(1)))
+        txt = FileService.parse_docs([f], current_user.id)
+        return get_json_result(data=txt)
+
+    if 'file' not in request.files:
+        return get_json_result(
+            data=False, message='No file part!', code=settings.RetCode.ARGUMENT_ERROR)
+
+    file_objs = request.files.getlist('file')
+    txt = FileService.parse_docs(file_objs, current_user.id)
+
+    return get_json_result(data=txt)
+
+
+@manager.route('/set_meta', methods=['POST'])  # noqa: F821
+@login_required
+@validate_request("doc_id", "meta")
+def set_meta():
+    req = request.json
+    if not DocumentService.accessible(req["doc_id"], current_user.id):
+        return get_json_result(
+            data=False,
+            message='No authorization.',
+            code=settings.RetCode.AUTHENTICATION_ERROR
+        )
+    try:
+        meta = json.loads(req["meta"])
+    except Exception as e:
+        return get_json_result(
+            data=False, message=f'Json syntax error: {e}', code=settings.RetCode.ARGUMENT_ERROR)
+    if not isinstance(meta, dict):
+        return get_json_result(
+            data=False, message='Meta data should be in Json map format, like {"key": "value"}', code=settings.RetCode.ARGUMENT_ERROR)
+
+    try:
+        e, doc = DocumentService.get_by_id(req["doc_id"])
+        if not e:
+            return get_data_error_result(message="Document not found!")
+
+        if not DocumentService.update_by_id(
+                req["doc_id"], {"meta_fields": meta}):
+            return get_data_error_result(
+                message="Database error (meta updates)!")
+
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)

api/apps/file2document_app.py

@@ -13,9 +13,7 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License
 #
-from elasticsearch_dsl import Q
 
-from api.db.db_models import File2Document
 from api.db.services.file2document_service import File2DocumentService
 from api.db.services.file_service import FileService
 
@@ -26,13 +24,11 @@ from api.utils.api_utils import server_error_response, get_data_error_result, va
 from api.utils import get_uuid
 from api.db import FileType
 from api.db.services.document_service import DocumentService
-from api.settings import RetCode
+from api import settings
 from api.utils.api_utils import get_json_result
-from rag.nlp import search
-from rag.utils.es_conn import ELASTICSEARCH
 
 
-@manager.route('/convert', methods=['POST'])
+@manager.route('/convert', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("file_ids", "kb_ids")
 def convert():
@@ -42,8 +38,12 @@ def convert():
     file2documents = []
 
     try:
+        files = FileService.get_by_ids(file_ids)
+        files_set = dict({file.id: file for file in files})
         for file_id in file_ids:
-            e, file = FileService.get_by_id(file_id)
+            file = files_set[file_id]
+            if not file:
+                return get_data_error_result(message="File not found!")
             file_ids_list = [file_id]
             if file.type == FileType.FOLDER.value:
                 file_ids_list = FileService.get_all_innermost_file_ids(file_id, [])
@@ -54,13 +54,13 @@ def convert():
                     doc_id = inform.document_id
                     e, doc = DocumentService.get_by_id(doc_id)
                     if not e:
-                        return get_data_error_result(retmsg="Document not found!")
+                        return get_data_error_result(message="Document not found!")
                     tenant_id = DocumentService.get_tenant_id(doc_id)
                     if not tenant_id:
-                        return get_data_error_result(retmsg="Tenant not found!")
+                        return get_data_error_result(message="Tenant not found!")
                     if not DocumentService.remove_document(doc, tenant_id):
                         return get_data_error_result(
-                            retmsg="Database error (Document removal)!")
+                            message="Database error (Document removal)!")
                 File2DocumentService.delete_by_file_id(id)
 
                 # insert
@@ -68,16 +68,16 @@ def convert():
                     e, kb = KnowledgebaseService.get_by_id(kb_id)
                     if not e:
                         return get_data_error_result(
-                            retmsg="Can't find this knowledgebase!")
+                            message="Can't find this knowledgebase!")
                     e, file = FileService.get_by_id(id)
                     if not e:
                         return get_data_error_result(
-                            retmsg="Can't find this file!")
+                            message="Can't find this file!")
 
                     doc = DocumentService.insert({
                         "id": get_uuid(),
                         "kb_id": kb.id,
-                        "parser_id": kb.parser_id,
+                        "parser_id": FileService.get_parser(file.type, file.name, kb.parser_id),
                         "parser_config": kb.parser_config,
                         "created_by": current_user.id,
                         "type": file.type,
@@ -90,13 +90,14 @@ def convert():
                         "file_id": id,
                         "document_id": doc.id,
                     })
+
                     file2documents.append(file2document.to_json())
         return get_json_result(data=file2documents)
     except Exception as e:
         return server_error_response(e)
 
 
-@manager.route('/rm', methods=['POST'])
+@manager.route('/rm', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("file_ids")
 def rm():
@@ -104,26 +105,26 @@ def rm():
     file_ids = req["file_ids"]
     if not file_ids:
         return get_json_result(
-            data=False, retmsg='Lack of "Files ID"', retcode=RetCode.ARGUMENT_ERROR)
+            data=False, message='Lack of "Files ID"', code=settings.RetCode.ARGUMENT_ERROR)
     try:
         for file_id in file_ids:
             informs = File2DocumentService.get_by_file_id(file_id)
             if not informs:
-                return get_data_error_result(retmsg="Inform not found!")
+                return get_data_error_result(message="Inform not found!")
             for inform in informs:
                 if not inform:
-                    return get_data_error_result(retmsg="Inform not found!")
+                    return get_data_error_result(message="Inform not found!")
                 File2DocumentService.delete_by_file_id(file_id)
                 doc_id = inform.document_id
                 e, doc = DocumentService.get_by_id(doc_id)
                 if not e:
-                    return get_data_error_result(retmsg="Document not found!")
+                    return get_data_error_result(message="Document not found!")
                 tenant_id = DocumentService.get_tenant_id(doc_id)
                 if not tenant_id:
-                    return get_data_error_result(retmsg="Tenant not found!")
+                    return get_data_error_result(message="Tenant not found!")
                 if not DocumentService.remove_document(doc, tenant_id):
                     return get_data_error_result(
-                        retmsg="Database error (Document removal)!")
+                        message="Database error (Document removal)!")
         return get_json_result(data=True)
     except Exception as e:
         return server_error_response(e)

api/apps/file_app.py

@@ -18,7 +18,6 @@ import pathlib
 import re
 
 import flask
-from elasticsearch_dsl import Q
 from flask import request
 from flask_login import login_required, current_user
 
@@ -29,15 +28,13 @@ from api.utils import get_uuid
 from api.db import FileType, FileSource
 from api.db.services import duplicate_name
 from api.db.services.file_service import FileService
-from api.settings import RetCode
+from api import settings
 from api.utils.api_utils import get_json_result
 from api.utils.file_utils import filename_type
-from rag.nlp import search
-from rag.utils.es_conn import ELASTICSEARCH
-from rag.utils.minio_conn import MINIO
+from rag.utils.storage_factory import STORAGE_IMPL
 
 
-@manager.route('/upload', methods=['POST'])
+@manager.route('/upload', methods=['POST'])  # noqa: F821
 @login_required
 # @validate_request("parent_id")
 def upload():
@@ -49,29 +46,26 @@ def upload():
 
     if 'file' not in request.files:
         return get_json_result(
-            data=False, retmsg='No file part!', retcode=RetCode.ARGUMENT_ERROR)
+            data=False, message='No file part!', code=settings.RetCode.ARGUMENT_ERROR)
     file_objs = request.files.getlist('file')
 
     for file_obj in file_objs:
         if file_obj.filename == '':
             return get_json_result(
-                data=False, retmsg='No file selected!', retcode=RetCode.ARGUMENT_ERROR)
+                data=False, message='No file selected!', code=settings.RetCode.ARGUMENT_ERROR)
     file_res = []
     try:
+        e, pf_folder = FileService.get_by_id(pf_id)
+        if not e:
+            return get_data_error_result( message="Can't find this folder!")
         for file_obj in file_objs:
-            e, file = FileService.get_by_id(pf_id)
-            if not e:
-                return get_data_error_result(
-                    retmsg="Can't find this folder!")
             MAX_FILE_NUM_PER_USER = int(os.environ.get('MAX_FILE_NUM_PER_USER', 0))
             if MAX_FILE_NUM_PER_USER > 0 and DocumentService.get_doc_count(current_user.id) >= MAX_FILE_NUM_PER_USER:
-                return get_data_error_result(
-                    retmsg="Exceed the maximum file number of a free user!")
+                return get_data_error_result( message="Exceed the maximum file number of a free user!")
 
             # split file name path
             if not file_obj.filename:
-                e, file = FileService.get_by_id(pf_id)
-                file_obj_names = [file.name, file_obj.filename]
+                file_obj_names = [pf_folder.name, file_obj.filename]
             else:
                 full_path = '/' + file_obj.filename
                 file_obj_names = full_path.split('/')
@@ -85,20 +79,20 @@ def upload():
             if file_len != len_id_list:
                 e, file = FileService.get_by_id(file_id_list[len_id_list - 1])
                 if not e:
-                    return get_data_error_result(retmsg="Folder not found!")
+                    return get_data_error_result(message="Folder not found!")
                 last_folder = FileService.create_folder(file, file_id_list[len_id_list - 1], file_obj_names,
                                                         len_id_list)
             else:
                 e, file = FileService.get_by_id(file_id_list[len_id_list - 2])
                 if not e:
-                    return get_data_error_result(retmsg="Folder not found!")
+                    return get_data_error_result(message="Folder not found!")
                 last_folder = FileService.create_folder(file, file_id_list[len_id_list - 2], file_obj_names,
                                                         len_id_list)
 
             # file type
             filetype = filename_type(file_obj_names[file_len - 1])
             location = file_obj_names[file_len - 1]
-            while MINIO.obj_exist(last_folder.id, location):
+            while STORAGE_IMPL.obj_exist(last_folder.id, location):
                 location += "_"
             blob = file_obj.read()
             filename = duplicate_name(
@@ -116,14 +110,14 @@ def upload():
                 "size": len(blob),
             }
             file = FileService.insert(file)
-            MINIO.put(last_folder.id, location, blob)
+            STORAGE_IMPL.put(last_folder.id, location, blob)
             file_res.append(file.to_json())
         return get_json_result(data=file_res)
     except Exception as e:
         return server_error_response(e)
 
 
-@manager.route('/create', methods=['POST'])
+@manager.route('/create', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("name")
 def create():
@@ -137,10 +131,10 @@ def create():
     try:
         if not FileService.is_parent_folder_exist(pf_id):
             return get_json_result(
-                data=False, retmsg="Parent Folder Doesn't Exist!", retcode=RetCode.OPERATING_ERROR)
+                data=False, message="Parent Folder Doesn't Exist!", code=settings.RetCode.OPERATING_ERROR)
         if FileService.query(name=req["name"], parent_id=pf_id):
             return get_data_error_result(
-                retmsg="Duplicated folder name in the same folder.")
+                message="Duplicated folder name in the same folder.")
 
         if input_file_type == FileType.FOLDER.value:
             file_type = FileType.FOLDER.value
@@ -163,7 +157,7 @@ def create():
         return server_error_response(e)
 
 
-@manager.route('/list', methods=['GET'])
+@manager.route('/list', methods=['GET'])  # noqa: F821
 @login_required
 def list_files():
     pf_id = request.args.get("parent_id")
@@ -181,21 +175,21 @@ def list_files():
     try:
         e, file = FileService.get_by_id(pf_id)
         if not e:
-            return get_data_error_result(retmsg="Folder not found!")
+            return get_data_error_result(message="Folder not found!")
 
         files, total = FileService.get_by_pf_id(
             current_user.id, pf_id, page_number, items_per_page, orderby, desc, keywords)
 
         parent_folder = FileService.get_parent_folder(pf_id)
-        if not FileService.get_parent_folder(pf_id):
-            return get_json_result(retmsg="File not found!")
+        if not parent_folder:
+            return get_json_result(message="File not found!")
 
         return get_json_result(data={"total": total, "files": files, "parent_folder": parent_folder.to_json()})
     except Exception as e:
         return server_error_response(e)
 
 
-@manager.route('/root_folder', methods=['GET'])
+@manager.route('/root_folder', methods=['GET'])  # noqa: F821
 @login_required
 def get_root_folder():
     try:
@@ -205,14 +199,14 @@ def get_root_folder():
         return server_error_response(e)
 
 
-@manager.route('/parent_folder', methods=['GET'])
+@manager.route('/parent_folder', methods=['GET'])  # noqa: F821
 @login_required
 def get_parent_folder():
     file_id = request.args.get("file_id")
     try:
         e, file = FileService.get_by_id(file_id)
         if not e:
-            return get_data_error_result(retmsg="Folder not found!")
+            return get_data_error_result(message="Folder not found!")
 
         parent_folder = FileService.get_parent_folder(file_id)
         return get_json_result(data={"parent_folder": parent_folder.to_json()})
@@ -220,14 +214,14 @@ def get_parent_folder():
         return server_error_response(e)
 
 
-@manager.route('/all_parent_folder', methods=['GET'])
+@manager.route('/all_parent_folder', methods=['GET'])  # noqa: F821
 @login_required
 def get_all_parent_folders():
     file_id = request.args.get("file_id")
     try:
         e, file = FileService.get_by_id(file_id)
         if not e:
-            return get_data_error_result(retmsg="Folder not found!")
+            return get_data_error_result(message="Folder not found!")
 
         parent_folders = FileService.get_all_parent_folders(file_id)
         parent_folders_res = []
@@ -238,7 +232,7 @@ def get_all_parent_folders():
         return server_error_response(e)
 
 
-@manager.route('/rm', methods=['POST'])
+@manager.route('/rm', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("file_ids")
 def rm():
@@ -248,9 +242,9 @@ def rm():
         for file_id in file_ids:
             e, file = FileService.get_by_id(file_id)
             if not e:
-                return get_data_error_result(retmsg="File or Folder not found!")
+                return get_data_error_result(message="File or Folder not found!")
             if not file.tenant_id:
-                return get_data_error_result(retmsg="Tenant not found!")
+                return get_data_error_result(message="Tenant not found!")
             if file.source_type == FileSource.KNOWLEDGEBASE:
                 continue
 
@@ -259,13 +253,13 @@ def rm():
                 for inner_file_id in file_id_list:
                     e, file = FileService.get_by_id(inner_file_id)
                     if not e:
-                        return get_data_error_result(retmsg="File not found!")
-                    MINIO.rm(file.parent_id, file.location)
+                        return get_data_error_result(message="File not found!")
+                    STORAGE_IMPL.rm(file.parent_id, file.location)
                 FileService.delete_folder_by_pf_id(current_user.id, file_id)
             else:
                 if not FileService.delete(file):
                     return get_data_error_result(
-                        retmsg="Database error (File removal)!")
+                        message="Database error (File removal)!")
 
             # delete file2document
             informs = File2DocumentService.get_by_file_id(file_id)
@@ -273,13 +267,13 @@ def rm():
                 doc_id = inform.document_id
                 e, doc = DocumentService.get_by_id(doc_id)
                 if not e:
-                    return get_data_error_result(retmsg="Document not found!")
+                    return get_data_error_result(message="Document not found!")
                 tenant_id = DocumentService.get_tenant_id(doc_id)
                 if not tenant_id:
-                    return get_data_error_result(retmsg="Tenant not found!")
+                    return get_data_error_result(message="Tenant not found!")
                 if not DocumentService.remove_document(doc, tenant_id):
                     return get_data_error_result(
-                        retmsg="Database error (Document removal)!")
+                        message="Database error (Document removal)!")
             File2DocumentService.delete_by_file_id(file_id)
 
         return get_json_result(data=True)
@@ -287,7 +281,7 @@ def rm():
         return server_error_response(e)
 
 
-@manager.route('/rename', methods=['POST'])
+@manager.route('/rename', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("file_id", "name")
 def rename():
@@ -295,44 +289,50 @@ def rename():
     try:
         e, file = FileService.get_by_id(req["file_id"])
         if not e:
-            return get_data_error_result(retmsg="File not found!")
-        if pathlib.Path(req["name"].lower()).suffix != pathlib.Path(
+            return get_data_error_result(message="File not found!")
+        if file.type != FileType.FOLDER.value \
+            and pathlib.Path(req["name"].lower()).suffix != pathlib.Path(
                 file.name.lower()).suffix:
             return get_json_result(
                 data=False,
-                retmsg="The extension of file can't be changed",
-                retcode=RetCode.ARGUMENT_ERROR)
+                message="The extension of file can't be changed",
+                code=settings.RetCode.ARGUMENT_ERROR)
         for file in FileService.query(name=req["name"], pf_id=file.parent_id):
             if file.name == req["name"]:
                 return get_data_error_result(
-                    retmsg="Duplicated file name in the same folder.")
+                    message="Duplicated file name in the same folder.")
 
         if not FileService.update_by_id(
                 req["file_id"], {"name": req["name"]}):
             return get_data_error_result(
-                retmsg="Database error (File rename)!")
+                message="Database error (File rename)!")
 
         informs = File2DocumentService.get_by_file_id(req["file_id"])
         if informs:
             if not DocumentService.update_by_id(
                     informs[0].document_id, {"name": req["name"]}):
                 return get_data_error_result(
-                    retmsg="Database error (Document rename)!")
+                    message="Database error (Document rename)!")
 
         return get_json_result(data=True)
     except Exception as e:
         return server_error_response(e)
 
 
-@manager.route('/get/<file_id>', methods=['GET'])
-# @login_required
+@manager.route('/get/<file_id>', methods=['GET'])  # noqa: F821
+@login_required
 def get(file_id):
     try:
         e, file = FileService.get_by_id(file_id)
         if not e:
-            return get_data_error_result(retmsg="Document not found!")
-        b, n = File2DocumentService.get_minio_address(file_id=file_id)
-        response = flask.make_response(MINIO.get(b, n))
+            return get_data_error_result(message="Document not found!")
+
+        blob = STORAGE_IMPL.get(file.parent_id, file.location)
+        if not blob:
+            b, n = File2DocumentService.get_storage_address(file_id=file_id)
+            blob = STORAGE_IMPL.get(b, n)
+
+        response = flask.make_response(blob)
         ext = re.search(r"\.([^.]+)$", file.name)
         if ext:
             if file.type == FileType.VISUAL.value:
@@ -347,7 +347,7 @@ def get(file_id):
         return server_error_response(e)
 
 
-@manager.route('/mv', methods=['POST'])
+@manager.route('/mv', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("src_file_ids", "dest_file_id")
 def move():
@@ -355,15 +355,20 @@ def move():
     try:
         file_ids = req["src_file_ids"]
         parent_id = req["dest_file_id"]
+        files = FileService.get_by_ids(file_ids)
+        files_dict = {}
+        for file in files:
+            files_dict[file.id] = file
+
         for file_id in file_ids:
-            e, file = FileService.get_by_id(file_id)
-            if not e:
-                return get_data_error_result(retmsg="File or Folder not found!")
+            file = files_dict[file_id]
+            if not file:
+                return get_data_error_result(message="File or Folder not found!")
             if not file.tenant_id:
-                return get_data_error_result(retmsg="Tenant not found!")
+                return get_data_error_result(message="Tenant not found!")
         fe, _ = FileService.get_by_id(parent_id)
         if not fe:
-            return get_data_error_result(retmsg="Parent Folder not found!")
+            return get_data_error_result(message="Parent Folder not found!")
         FileService.move_file(file_ids, parent_id)
         return get_json_result(data=True)
     except Exception as e:

api/apps/kb_app.py

@@ -13,7 +13,9 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-from elasticsearch_dsl import Q
+import json
+import os
+
 from flask import request
 from flask_login import login_required, current_user
 
@@ -22,26 +24,36 @@ from api.db.services.document_service import DocumentService
 from api.db.services.file2document_service import File2DocumentService
 from api.db.services.file_service import FileService
 from api.db.services.user_service import TenantService, UserTenantService
-from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
-from api.utils import get_uuid, get_format_time
-from api.db import StatusEnum, UserTenantRole, FileSource
+from api.utils.api_utils import server_error_response, get_data_error_result, validate_request, not_allowed_parameters
+from api.utils import get_uuid
+from api.db import StatusEnum, FileSource
 from api.db.services.knowledgebase_service import KnowledgebaseService
-from api.db.db_models import Knowledgebase, File
-from api.settings import stat_logger, RetCode
+from api.db.db_models import File
 from api.utils.api_utils import get_json_result
+from api import settings
 from rag.nlp import search
-from rag.utils.es_conn import ELASTICSEARCH
+from api.constants import DATASET_NAME_LIMIT
+from rag.settings import PAGERANK_FLD
 
 
-@manager.route('/create', methods=['post'])
+@manager.route('/create', methods=['post'])  # noqa: F821
 @login_required
 @validate_request("name")
 def create():
     req = request.json
-    req["name"] = req["name"].strip()
-    req["name"] = duplicate_name(
+    dataset_name = req["name"]
+    if not isinstance(dataset_name, str):
+        return get_data_error_result(message="Dataset name must be string.")
+    if dataset_name == "":
+        return get_data_error_result(message="Dataset name can't be empty.")
+    if len(dataset_name) >= DATASET_NAME_LIMIT:
+        return get_data_error_result(
+            message=f"Dataset name length is {len(dataset_name)} which is large than {DATASET_NAME_LIMIT}")
+
+    dataset_name = dataset_name.strip()
+    dataset_name = duplicate_name(
         KnowledgebaseService.query,
-        name=req["name"],
+        name=dataset_name,
         tenant_id=current_user.id,
         status=StatusEnum.VALID.value)
     try:
@@ -50,7 +62,7 @@ def create():
         req["created_by"] = current_user.id
         e, t = TenantService.get_by_id(current_user.id)
         if not e:
-            return get_data_error_result(retmsg="Tenant not found.")
+            return get_data_error_result(message="Tenant not found.")
         req["embd_id"] = t.embd_id
         if not KnowledgebaseService.save(**req):
             return get_data_error_result()
@@ -59,95 +71,282 @@ def create():
         return server_error_response(e)
 
 
-@manager.route('/update', methods=['post'])
+@manager.route('/update', methods=['post'])  # noqa: F821
 @login_required
-@validate_request("kb_id", "name", "description", "permission", "parser_id")
+@validate_request("kb_id", "name", "description", "parser_id")
+@not_allowed_parameters("id", "tenant_id", "created_by", "create_time", "update_time", "create_date", "update_date", "created_by")
 def update():
     req = request.json
     req["name"] = req["name"].strip()
+    if not KnowledgebaseService.accessible4deletion(req["kb_id"], current_user.id):
+        return get_json_result(
+            data=False,
+            message='No authorization.',
+            code=settings.RetCode.AUTHENTICATION_ERROR
+        )
     try:
         if not KnowledgebaseService.query(
                 created_by=current_user.id, id=req["kb_id"]):
             return get_json_result(
-                data=False, retmsg=f'Only owner of knowledgebase authorized for this operation.', retcode=RetCode.OPERATING_ERROR)
+                data=False, message='Only owner of knowledgebase authorized for this operation.',
+                code=settings.RetCode.OPERATING_ERROR)
 
         e, kb = KnowledgebaseService.get_by_id(req["kb_id"])
         if not e:
             return get_data_error_result(
-                retmsg="Can't find this knowledgebase!")
+                message="Can't find this knowledgebase!")
+
+        if req.get("parser_id", "") == "tag" and os.environ.get('DOC_ENGINE', "elasticsearch") == "infinity":
+            return get_json_result(
+                data=False,
+                message='The chunk method Tag has not been supported by Infinity yet.',
+                code=settings.RetCode.OPERATING_ERROR
+            )
 
         if req["name"].lower() != kb.name.lower() \
-                and len(KnowledgebaseService.query(name=req["name"], tenant_id=current_user.id, status=StatusEnum.VALID.value)) > 1:
+                and len(
+            KnowledgebaseService.query(name=req["name"], tenant_id=current_user.id, status=StatusEnum.VALID.value)) > 1:
             return get_data_error_result(
-                retmsg="Duplicated knowledgebase name.")
+                message="Duplicated knowledgebase name.")
 
         del req["kb_id"]
         if not KnowledgebaseService.update_by_id(kb.id, req):
             return get_data_error_result()
 
+        if kb.pagerank != req.get("pagerank", 0):
+            if req.get("pagerank", 0) > 0:
+                settings.docStoreConn.update({"kb_id": kb.id}, {PAGERANK_FLD: req["pagerank"]},
+                                         search.index_name(kb.tenant_id), kb.id)
+            else:
+                # Elasticsearch requires PAGERANK_FLD be non-zero!
+                settings.docStoreConn.update({"exists": PAGERANK_FLD}, {"remove": PAGERANK_FLD},
+                                         search.index_name(kb.tenant_id), kb.id)
+
         e, kb = KnowledgebaseService.get_by_id(kb.id)
         if not e:
             return get_data_error_result(
-                retmsg="Database error (Knowledgebase rename)!")
+                message="Database error (Knowledgebase rename)!")
+        kb = kb.to_dict()
+        kb.update(req)
 
-        return get_json_result(data=kb.to_json())
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/detail', methods=['GET'])
-@login_required
-def detail():
-    kb_id = request.args["kb_id"]
-    try:
-        kb = KnowledgebaseService.get_detail(kb_id)
-        if not kb:
-            return get_data_error_result(
-                retmsg="Can't find this knowledgebase!")
         return get_json_result(data=kb)
     except Exception as e:
         return server_error_response(e)
 
 
-@manager.route('/list', methods=['GET'])
+@manager.route('/detail', methods=['GET'])  # noqa: F821
 @login_required
-def list_kbs():
-    page_number = request.args.get("page", 1)
-    items_per_page = request.args.get("page_size", 150)
-    orderby = request.args.get("orderby", "create_time")
-    desc = request.args.get("desc", True)
+def detail():
+    kb_id = request.args["kb_id"]
     try:
-        tenants = TenantService.get_joined_tenants_by_user_id(current_user.id)
-        kbs = KnowledgebaseService.get_by_tenant_ids(
-            [m["tenant_id"] for m in tenants], current_user.id, page_number, items_per_page, orderby, desc)
-        return get_json_result(data=kbs)
+        tenants = UserTenantService.query(user_id=current_user.id)
+        for tenant in tenants:
+            if KnowledgebaseService.query(
+                    tenant_id=tenant.tenant_id, id=kb_id):
+                break
+        else:
+            return get_json_result(
+                data=False, message='Only owner of knowledgebase authorized for this operation.',
+                code=settings.RetCode.OPERATING_ERROR)
+        kb = KnowledgebaseService.get_detail(kb_id)
+        if not kb:
+            return get_data_error_result(
+                message="Can't find this knowledgebase!")
+        return get_json_result(data=kb)
     except Exception as e:
         return server_error_response(e)
 
 
-@manager.route('/rm', methods=['post'])
+@manager.route('/list', methods=['POST'])  # noqa: F821
+@login_required
+def list_kbs():
+    keywords = request.args.get("keywords", "")
+    page_number = int(request.args.get("page", 0))
+    items_per_page = int(request.args.get("page_size", 0))
+    parser_id = request.args.get("parser_id")
+    orderby = request.args.get("orderby", "create_time")
+    desc = request.args.get("desc", True)
+
+    req = request.get_json()
+    owner_ids = req.get("owner_ids", [])
+    try:
+        if not owner_ids:
+            tenants = TenantService.get_joined_tenants_by_user_id(current_user.id)
+            tenants = [m["tenant_id"] for m in tenants]
+            kbs, total = KnowledgebaseService.get_by_tenant_ids(
+                tenants, current_user.id, page_number,
+                items_per_page, orderby, desc, keywords, parser_id)
+        else:
+            tenants = owner_ids
+            kbs, total = KnowledgebaseService.get_by_tenant_ids(
+                tenants, current_user.id, 0,
+                0, orderby, desc, keywords, parser_id)
+            kbs = [kb for kb in kbs if kb["tenant_id"] in tenants]
+            if page_number and items_per_page:
+                kbs = kbs[(page_number-1)*items_per_page:page_number*items_per_page]
+            total = len(kbs)
+        return get_json_result(data={"kbs": kbs, "total": total})
+    except Exception as e:
+        return server_error_response(e)
+
+@manager.route('/rm', methods=['post'])  # noqa: F821
 @login_required
 @validate_request("kb_id")
 def rm():
     req = request.json
+    if not KnowledgebaseService.accessible4deletion(req["kb_id"], current_user.id):
+        return get_json_result(
+            data=False,
+            message='No authorization.',
+            code=settings.RetCode.AUTHENTICATION_ERROR
+        )
     try:
         kbs = KnowledgebaseService.query(
-                created_by=current_user.id, id=req["kb_id"])
+            created_by=current_user.id, id=req["kb_id"])
         if not kbs:
             return get_json_result(
-                data=False, retmsg=f'Only owner of knowledgebase authorized for this operation.', retcode=RetCode.OPERATING_ERROR)
+                data=False, message='Only owner of knowledgebase authorized for this operation.',
+                code=settings.RetCode.OPERATING_ERROR)
 
         for doc in DocumentService.query(kb_id=req["kb_id"]):
             if not DocumentService.remove_document(doc, kbs[0].tenant_id):
                 return get_data_error_result(
-                    retmsg="Database error (Document removal)!")
+                    message="Database error (Document removal)!")
             f2d = File2DocumentService.get_by_document_id(doc.id)
-            FileService.filter_delete([File.source_type == FileSource.KNOWLEDGEBASE, File.id == f2d[0].file_id])
+            if f2d:
+                FileService.filter_delete([File.source_type == FileSource.KNOWLEDGEBASE, File.id == f2d[0].file_id])
             File2DocumentService.delete_by_document_id(doc.id)
-
+        FileService.filter_delete(
+            [File.source_type == FileSource.KNOWLEDGEBASE, File.type == "folder", File.name == kbs[0].name])
         if not KnowledgebaseService.delete_by_id(req["kb_id"]):
             return get_data_error_result(
-                retmsg="Database error (Knowledgebase removal)!")
+                message="Database error (Knowledgebase removal)!")
+        for kb in kbs:
+            settings.docStoreConn.delete({"kb_id": kb.id}, search.index_name(kb.tenant_id), kb.id)
+            settings.docStoreConn.deleteIdx(search.index_name(kb.tenant_id), kb.id)
         return get_json_result(data=True)
     except Exception as e:
         return server_error_response(e)
+
+
+@manager.route('/<kb_id>/tags', methods=['GET'])  # noqa: F821
+@login_required
+def list_tags(kb_id):
+    if not KnowledgebaseService.accessible(kb_id, current_user.id):
+        return get_json_result(
+            data=False,
+            message='No authorization.',
+            code=settings.RetCode.AUTHENTICATION_ERROR
+        )
+
+    tags = settings.retrievaler.all_tags(current_user.id, [kb_id])
+    return get_json_result(data=tags)
+
+
+@manager.route('/tags', methods=['GET'])  # noqa: F821
+@login_required
+def list_tags_from_kbs():
+    kb_ids = request.args.get("kb_ids", "").split(",")
+    for kb_id in kb_ids:
+        if not KnowledgebaseService.accessible(kb_id, current_user.id):
+            return get_json_result(
+                data=False,
+                message='No authorization.',
+                code=settings.RetCode.AUTHENTICATION_ERROR
+            )
+
+    tags = settings.retrievaler.all_tags(current_user.id, kb_ids)
+    return get_json_result(data=tags)
+
+
+@manager.route('/<kb_id>/rm_tags', methods=['POST'])  # noqa: F821
+@login_required
+def rm_tags(kb_id):
+    req = request.json
+    if not KnowledgebaseService.accessible(kb_id, current_user.id):
+        return get_json_result(
+            data=False,
+            message='No authorization.',
+            code=settings.RetCode.AUTHENTICATION_ERROR
+        )
+    e, kb = KnowledgebaseService.get_by_id(kb_id)
+
+    for t in req["tags"]:
+        settings.docStoreConn.update({"tag_kwd": t, "kb_id": [kb_id]},
+                                     {"remove": {"tag_kwd": t}},
+                                     search.index_name(kb.tenant_id),
+                                     kb_id)
+    return get_json_result(data=True)
+
+
+@manager.route('/<kb_id>/rename_tag', methods=['POST'])  # noqa: F821
+@login_required
+def rename_tags(kb_id):
+    req = request.json
+    if not KnowledgebaseService.accessible(kb_id, current_user.id):
+        return get_json_result(
+            data=False,
+            message='No authorization.',
+            code=settings.RetCode.AUTHENTICATION_ERROR
+        )
+    e, kb = KnowledgebaseService.get_by_id(kb_id)
+
+    settings.docStoreConn.update({"tag_kwd": req["from_tag"], "kb_id": [kb_id]},
+                                     {"remove": {"tag_kwd": req["from_tag"].strip()}, "add": {"tag_kwd": req["to_tag"]}},
+                                     search.index_name(kb.tenant_id),
+                                     kb_id)
+    return get_json_result(data=True)
+
+
+@manager.route('/<kb_id>/knowledge_graph', methods=['GET'])  # noqa: F821
+@login_required
+def knowledge_graph(kb_id):
+    if not KnowledgebaseService.accessible(kb_id, current_user.id):
+        return get_json_result(
+            data=False,
+            message='No authorization.',
+            code=settings.RetCode.AUTHENTICATION_ERROR
+        )
+    _, kb = KnowledgebaseService.get_by_id(kb_id)
+    req = {
+        "kb_id": [kb_id],
+        "knowledge_graph_kwd": ["graph"]
+    }
+
+    obj = {"graph": {}, "mind_map": {}}
+    if not settings.docStoreConn.indexExist(search.index_name(kb.tenant_id), kb_id):
+        return get_json_result(data=obj)
+    sres = settings.retrievaler.search(req, search.index_name(kb.tenant_id), [kb_id])
+    if not len(sres.ids):
+        return get_json_result(data=obj)
+
+    for id in sres.ids[:1]:
+        ty = sres.field[id]["knowledge_graph_kwd"]
+        try:
+            content_json = json.loads(sres.field[id]["content_with_weight"])
+        except Exception:
+            continue
+
+        obj[ty] = content_json
+
+    if "nodes" in obj["graph"]:
+        obj["graph"]["nodes"] = sorted(obj["graph"]["nodes"], key=lambda x: x.get("pagerank", 0), reverse=True)[:256]
+        if "edges" in obj["graph"]:
+            node_id_set = { o["id"] for o in obj["graph"]["nodes"] }
+            filtered_edges = [o for o in obj["graph"]["edges"] if o["source"] != o["target"] and o["source"] in node_id_set and o["target"] in node_id_set]
+            obj["graph"]["edges"] = sorted(filtered_edges, key=lambda x: x.get("weight", 0), reverse=True)[:128]
+    return get_json_result(data=obj)
+
+@manager.route('/<kb_id>/knowledge_graph', methods=['DELETE'])  # noqa: F821
+@login_required
+def delete_knowledge_graph(kb_id):
+    if not KnowledgebaseService.accessible(kb_id, current_user.id):
+        return get_json_result(
+            data=False,
+            message='No authorization.',
+            code=settings.RetCode.AUTHENTICATION_ERROR
+        )
+    _, kb = KnowledgebaseService.get_by_id(kb_id)
+    settings.docStoreConn.delete({"knowledge_graph_kwd": ["graph", "subgraph", "entity", "relation"]}, search.index_name(kb.tenant_id), kb_id)
+
+    return get_json_result(data=True)

api/apps/langfuse_app.py

@@ -0,0 +1,97 @@
+#
+#  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 request
+from flask_login import current_user, login_required
+from langfuse import Langfuse
+
+from api.db.db_models import DB
+from api.db.services.langfuse_service import TenantLangfuseService
+from api.utils.api_utils import get_error_data_result, get_json_result, server_error_response, validate_request
+
+
+@manager.route("/api_key", methods=["POST", "PUT"])  # noqa: F821
+@login_required
+@validate_request("secret_key", "public_key", "host")
+def set_api_key():
+    req = request.get_json()
+    secret_key = req.get("secret_key", "")
+    public_key = req.get("public_key", "")
+    host = req.get("host", "")
+    if not all([secret_key, public_key, host]):
+        return get_error_data_result(message="Missing required fields")
+
+    langfuse_keys = dict(
+        tenant_id=current_user.id,
+        secret_key=secret_key,
+        public_key=public_key,
+        host=host,
+    )
+
+    langfuse = Langfuse(public_key=langfuse_keys["public_key"], secret_key=langfuse_keys["secret_key"], host=langfuse_keys["host"])
+    if not langfuse.auth_check():
+        return get_error_data_result(message="Invalid Langfuse keys")
+
+    langfuse_entry = TenantLangfuseService.filter_by_tenant(tenant_id=current_user.id)
+    with DB.atomic():
+        try:
+            if not langfuse_entry:
+                TenantLangfuseService.save(**langfuse_keys)
+            else:
+                TenantLangfuseService.update_by_tenant(tenant_id=current_user.id, langfuse_keys=langfuse_keys)
+            return get_json_result(data=langfuse_keys)
+        except Exception as e:
+            server_error_response(e)
+
+
+@manager.route("/api_key", methods=["GET"])  # noqa: F821
+@login_required
+@validate_request()
+def get_api_key():
+    langfuse_entry = TenantLangfuseService.filter_by_tenant_with_info(tenant_id=current_user.id)
+    if not langfuse_entry:
+        return get_json_result(message="Have not record any Langfuse keys.")
+
+    langfuse = Langfuse(public_key=langfuse_entry["public_key"], secret_key=langfuse_entry["secret_key"], host=langfuse_entry["host"])
+    try:
+        if not langfuse.auth_check():
+            return get_error_data_result(message="Invalid Langfuse keys loaded")
+    except langfuse.api.core.api_error.ApiError as api_err:
+        return get_json_result(message=f"Error from Langfuse: {api_err}")
+    except Exception as e:
+        server_error_response(e)
+
+    langfuse_entry["project_id"] = langfuse.api.projects.get().dict()["data"][0]["id"]
+    langfuse_entry["project_name"] = langfuse.api.projects.get().dict()["data"][0]["name"]
+
+    return get_json_result(data=langfuse_entry)
+
+
+@manager.route("/api_key", methods=["DELETE"])  # noqa: F821
+@login_required
+@validate_request()
+def delete_api_key():
+    langfuse_entry = TenantLangfuseService.filter_by_tenant(tenant_id=current_user.id)
+    if not langfuse_entry:
+        return get_json_result(message="Have not record any Langfuse keys.")
+
+    with DB.atomic():
+        try:
+            TenantLangfuseService.delete_model(langfuse_entry)
+            return get_json_result(data=True)
+        except Exception as e:
+            server_error_response(e)

api/apps/llm_app.py

@@ -13,28 +13,44 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import logging
+import json
+import os
 from flask import request
 from flask_login import login_required, current_user
 from api.db.services.llm_service import LLMFactoriesService, TenantLLMService, LLMService
+from api import settings
 from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
 from api.db import StatusEnum, LLMType
 from api.db.db_models import TenantLLM
 from api.utils.api_utils import get_json_result
-from rag.llm import EmbeddingModel, ChatModel, RerankModel,CvModel
-import requests
-import ast
+from api.utils.file_utils import get_project_base_directory
+from rag.llm import EmbeddingModel, ChatModel, RerankModel, CvModel, TTSModel
 
-@manager.route('/factories', methods=['GET'])
+
+@manager.route('/factories', methods=['GET'])  # noqa: F821
 @login_required
 def factories():
     try:
         fac = LLMFactoriesService.get_all()
-        return get_json_result(data=[f.to_dict() for f in fac if f.name not in ["Youdao", "FastEmbed", "BAAI"]])
+        fac = [f.to_dict() for f in fac if f.name not in ["Youdao", "FastEmbed", "BAAI"]]
+        llms = LLMService.get_all()
+        mdl_types = {}
+        for m in llms:
+            if m.status != StatusEnum.VALID.value:
+                continue
+            if m.fid not in mdl_types:
+                mdl_types[m.fid] = set([])
+            mdl_types[m.fid].add(m.model_type)
+        for f in fac:
+            f["model_types"] = list(mdl_types.get(f["name"], [LLMType.CHAT, LLMType.EMBEDDING, LLMType.RERANK,
+                                                              LLMType.IMAGE2TEXT, LLMType.SPEECH2TEXT, LLMType.TTS]))
+        return get_json_result(data=fac)
     except Exception as e:
         return server_error_response(e)
 
 
-@manager.route('/set_api_key', methods=['POST'])
+@manager.route('/set_api_key', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("llm_factory", "api_key")
 def set_api_key():
@@ -45,6 +61,7 @@ def set_api_key():
     msg = ""
     for llm in LLMService.query(fid=factory):
         if not embd_passed and llm.model_type == LLMType.EMBEDDING.value:
+            assert factory in EmbeddingModel, f"Embedding model from {factory} is not supported yet."
             mdl = EmbeddingModel[factory](
                 req["api_key"], llm.llm_name, base_url=req.get("base_url"))
             try:
@@ -55,99 +72,126 @@ def set_api_key():
             except Exception as e:
                 msg += f"\nFail to access embedding model({llm.llm_name}) using this api key." + str(e)
         elif not chat_passed and llm.model_type == LLMType.CHAT.value:
+            assert factory in ChatModel, f"Chat model from {factory} is not supported yet."
             mdl = ChatModel[factory](
                 req["api_key"], llm.llm_name, base_url=req.get("base_url"))
             try:
-                m, tc = mdl.chat(None, [{"role": "user", "content": "Hello! How are you doing!"}], 
-                                 {"temperature": 0.9,'max_tokens':50})
-                if m.find("**ERROR**") >=0:
+                m, tc = mdl.chat(None, [{"role": "user", "content": "Hello! How are you doing!"}],
+                                 {"temperature": 0.9, 'max_tokens': 50})
+                if m.find("**ERROR**") >= 0:
                     raise Exception(m)
+                chat_passed = True
             except Exception as e:
                 msg += f"\nFail to access model({llm.llm_name}) using this api key." + str(
                     e)
-            chat_passed = True
         elif not rerank_passed and llm.model_type == LLMType.RERANK:
+            assert factory in RerankModel, f"Re-rank model from {factory} is not supported yet."
             mdl = RerankModel[factory](
                 req["api_key"], llm.llm_name, base_url=req.get("base_url"))
             try:
                 arr, tc = mdl.similarity("What's the weather?", ["Is it sunny today?"])
                 if len(arr) == 0 or tc == 0:
                     raise Exception("Fail")
+                rerank_passed = True
+                logging.debug(f'passed model rerank {llm.llm_name}')
             except Exception as e:
                 msg += f"\nFail to access model({llm.llm_name}) using this api key." + str(
                     e)
-            rerank_passed = True
+        if any([embd_passed, chat_passed, rerank_passed]):
+            msg = ''
+            break
 
     if msg:
-        return get_data_error_result(retmsg=msg)
+        return get_data_error_result(message=msg)
 
-    llm = {
+    llm_config = {
         "api_key": req["api_key"],
         "api_base": req.get("base_url", "")
     }
     for n in ["model_type", "llm_name"]:
         if n in req:
-            llm[n] = req[n]
+            llm_config[n] = req[n]
 
-    if not TenantLLMService.filter_update(
-            [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == factory], llm):
-        for llm in LLMService.query(fid=factory):
+    for llm in LLMService.query(fid=factory):
+        llm_config["max_tokens"]=llm.max_tokens
+        if not TenantLLMService.filter_update(
+                [TenantLLM.tenant_id == current_user.id,
+                 TenantLLM.llm_factory == factory,
+                 TenantLLM.llm_name == llm.llm_name],
+                llm_config):
             TenantLLMService.save(
                 tenant_id=current_user.id,
                 llm_factory=factory,
                 llm_name=llm.llm_name,
                 model_type=llm.model_type,
-                api_key=req["api_key"],
-                api_base=req.get("base_url", "")
+                api_key=llm_config["api_key"],
+                api_base=llm_config["api_base"],
+                max_tokens=llm_config["max_tokens"]
             )
 
     return get_json_result(data=True)
 
 
-@manager.route('/add_llm', methods=['POST'])
+@manager.route('/add_llm', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("llm_factory")
 def add_llm():
     req = request.json
     factory = req["llm_factory"]
+    api_key = req.get("api_key", "x")
+    llm_name = req.get("llm_name")
+
+    def apikey_json(keys):
+        nonlocal req
+        return json.dumps({k: req.get(k, "") for k in keys})
 
     if factory == "VolcEngine":
         # For VolcEngine, due to its special authentication method
-        # Assemble volc_ak, volc_sk, endpoint_id into api_key
-        temp = list(ast.literal_eval(req["llm_name"]).items())[0]
-        llm_name = temp[0]
-        endpoint_id = temp[1]
-        api_key = '{' + f'"volc_ak": "{req.get("volc_ak", "")}", ' \
-                        f'"volc_sk": "{req.get("volc_sk", "")}", ' \
-                        f'"ep_id": "{endpoint_id}", ' + '}'
+        # Assemble ark_api_key endpoint_id into api_key
+        api_key = apikey_json(["ark_api_key", "endpoint_id"])
+
     elif factory == "Tencent Hunyuan":
-        api_key = '{' + f'"hunyuan_sid": "{req.get("hunyuan_sid", "")}", ' \
-                        f'"hunyuan_sk": "{req.get("hunyuan_sk", "")}"' + '}'
-        req["api_key"] = api_key
+        req["api_key"] = apikey_json(["hunyuan_sid", "hunyuan_sk"])
         return set_api_key()
+
+    elif factory == "Tencent Cloud":
+        req["api_key"] = apikey_json(["tencent_cloud_sid", "tencent_cloud_sk"])
+        return set_api_key()
+
     elif factory == "Bedrock":
         # For Bedrock, due to its special authentication method
         # Assemble bedrock_ak, bedrock_sk, bedrock_region
-        llm_name = req["llm_name"]
-        api_key = '{' + f'"bedrock_ak": "{req.get("bedrock_ak", "")}", ' \
-                        f'"bedrock_sk": "{req.get("bedrock_sk", "")}", ' \
-                        f'"bedrock_region": "{req.get("bedrock_region", "")}", ' + '}'
+        api_key = apikey_json(["bedrock_ak", "bedrock_sk", "bedrock_region"])
+
     elif factory == "LocalAI":
-        llm_name = req["llm_name"]+"___LocalAI"
-        api_key = "xxxxxxxxxxxxxxx"
+        llm_name += "___LocalAI"
+
+    elif factory == "HuggingFace":
+        llm_name += "___HuggingFace"
+
     elif factory == "OpenAI-API-Compatible":
-        llm_name = req["llm_name"]+"___OpenAI-API"
-        api_key = req.get("api_key","xxxxxxxxxxxxxxx")
-    elif factory =="XunFei Spark":
-        llm_name = req["llm_name"]
-        api_key = req.get("spark_api_password","xxxxxxxxxxxxxxx") 
+        llm_name += "___OpenAI-API"
+
+    elif factory == "VLLM":
+        llm_name += "___VLLM"
+
+    elif factory == "XunFei Spark":
+        if req["model_type"] == "chat":
+            api_key = req.get("spark_api_password", "")
+        elif req["model_type"] == "tts":
+            api_key = apikey_json(["spark_app_id", "spark_api_secret", "spark_api_key"])
+
     elif factory == "BaiduYiyan":
-        llm_name = req["llm_name"]
-        api_key = '{' + f'"yiyan_ak": "{req.get("yiyan_ak", "")}", ' \
-                f'"yiyan_sk": "{req.get("yiyan_sk", "")}"' + '}'
-    else:
-        llm_name = req["llm_name"]
-        api_key = req.get("api_key","xxxxxxxxxxxxxxx") 
+        api_key = apikey_json(["yiyan_ak", "yiyan_sk"])
+
+    elif factory == "Fish Audio":
+        api_key = apikey_json(["fish_audio_ak", "fish_audio_refid"])
+
+    elif factory == "Google Cloud":
+        api_key = apikey_json(["google_project_id", "google_region", "google_service_account_key"])
+
+    elif factory == "Azure-OpenAI":
+        api_key = apikey_json(["api_key", "api_version"])
 
     llm = {
         "tenant_id": current_user.id,
@@ -155,94 +199,116 @@ def add_llm():
         "model_type": req["model_type"],
         "llm_name": llm_name,
         "api_base": req.get("api_base", ""),
-        "api_key": api_key
+        "api_key": api_key,
+        "max_tokens": req.get("max_tokens")
     }
 
     msg = ""
+    mdl_nm = llm["llm_name"].split("___")[0]
     if llm["model_type"] == LLMType.EMBEDDING.value:
+        assert factory in EmbeddingModel, f"Embedding model from {factory} is not supported yet."
         mdl = EmbeddingModel[factory](
             key=llm['api_key'],
-            model_name=llm["llm_name"], 
+            model_name=mdl_nm,
             base_url=llm["api_base"])
         try:
             arr, tc = mdl.encode(["Test if the api key is available"])
-            if len(arr[0]) == 0 or tc == 0:
+            if len(arr[0]) == 0:
                 raise Exception("Fail")
         except Exception as e:
-            msg += f"\nFail to access embedding model({llm['llm_name']})." + str(e)
+            msg += f"\nFail to access embedding model({mdl_nm})." + str(e)
     elif llm["model_type"] == LLMType.CHAT.value:
+        assert factory in ChatModel, f"Chat model from {factory} is not supported yet."
         mdl = ChatModel[factory](
             key=llm['api_key'],
-            model_name=llm["llm_name"],
+            model_name=mdl_nm,
             base_url=llm["api_base"]
         )
         try:
             m, tc = mdl.chat(None, [{"role": "user", "content": "Hello! How are you doing!"}], {
-                             "temperature": 0.9})
-            if not tc:
+                "temperature": 0.9})
+            if not tc and m.find("**ERROR**:") >= 0:
                 raise Exception(m)
         except Exception as e:
-            msg += f"\nFail to access model({llm['llm_name']})." + str(
+            msg += f"\nFail to access model({mdl_nm})." + str(
                 e)
     elif llm["model_type"] == LLMType.RERANK:
-        mdl = RerankModel[factory](
-            key=llm["api_key"], 
-            model_name=llm["llm_name"], 
-            base_url=llm["api_base"]
-        )
+        assert factory in RerankModel, f"RE-rank model from {factory} is not supported yet."
         try:
-            arr, tc = mdl.similarity("Hello~ Ragflower!", ["Hi, there!"])
-            if len(arr) == 0 or tc == 0:
+            mdl = RerankModel[factory](
+                key=llm["api_key"],
+                model_name=mdl_nm,
+                base_url=llm["api_base"]
+            )
+            arr, tc = mdl.similarity("Hello~ Ragflower!", ["Hi, there!", "Ohh, my friend!"])
+            if len(arr) == 0:
                 raise Exception("Not known.")
+        except KeyError:
+            msg += f"{factory} dose not support this model({mdl_nm})"
         except Exception as e:
-            msg += f"\nFail to access model({llm['llm_name']})." + str(
+            msg += f"\nFail to access model({mdl_nm})." + str(
                 e)
     elif llm["model_type"] == LLMType.IMAGE2TEXT.value:
+        assert factory in CvModel, f"Image to text model from {factory} is not supported yet."
         mdl = CvModel[factory](
-            key=llm["api_key"], 
-            model_name=llm["llm_name"], 
+            key=llm["api_key"],
+            model_name=mdl_nm,
             base_url=llm["api_base"]
         )
         try:
-            img_url = (
-                "https://upload.wikimedia.org/wikipedia/comm"
-                "ons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/256"
-                "0px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
-            )
-            res = requests.get(img_url)
-            if res.status_code == 200:
-                m, tc = mdl.describe(res.content)
-                if not tc:
+            with open(os.path.join(get_project_base_directory(), "web/src/assets/yay.jpg"), "rb") as f:
+                m, tc = mdl.describe(f.read())
+                if not m and not tc:
                     raise Exception(m)
-            else:
-                pass
         except Exception as e:
-            msg += f"\nFail to access model({llm['llm_name']})." + str(e)
+            msg += f"\nFail to access model({mdl_nm})." + str(e)
+    elif llm["model_type"] == LLMType.TTS:
+        assert factory in TTSModel, f"TTS model from {factory} is not supported yet."
+        mdl = TTSModel[factory](
+            key=llm["api_key"], model_name=mdl_nm, base_url=llm["api_base"]
+        )
+        try:
+            for resp in mdl.tts("Hello~ Ragflower!"):
+                pass
+        except RuntimeError as e:
+            msg += f"\nFail to access model({mdl_nm})." + str(e)
     else:
         # TODO: check other type of models
         pass
 
     if msg:
-        return get_data_error_result(retmsg=msg)
+        return get_data_error_result(message=msg)
 
     if not TenantLLMService.filter_update(
-            [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == factory, TenantLLM.llm_name == llm["llm_name"]], llm):
+            [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == factory,
+             TenantLLM.llm_name == llm["llm_name"]], llm):
         TenantLLMService.save(**llm)
 
     return get_json_result(data=True)
 
 
-@manager.route('/delete_llm', methods=['POST'])
+@manager.route('/delete_llm', methods=['POST'])  # noqa: F821
 @login_required
 @validate_request("llm_factory", "llm_name")
 def delete_llm():
     req = request.json
     TenantLLMService.filter_delete(
-            [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == req["llm_factory"], TenantLLM.llm_name == req["llm_name"]])
+        [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == req["llm_factory"],
+         TenantLLM.llm_name == req["llm_name"]])
     return get_json_result(data=True)
 
 
-@manager.route('/my_llms', methods=['GET'])
+@manager.route('/delete_factory', methods=['POST'])  # noqa: F821
+@login_required
+@validate_request("llm_factory")
+def delete_factory():
+    req = request.json
+    TenantLLMService.filter_delete(
+        [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == req["llm_factory"]])
+    return get_json_result(data=True)
+
+
+@manager.route('/my_llms', methods=['GET'])  # noqa: F821
 @login_required
 def my_llms():
     try:
@@ -263,28 +329,30 @@ def my_llms():
         return server_error_response(e)
 
 
-@manager.route('/list', methods=['GET'])
+@manager.route('/list', methods=['GET'])  # noqa: F821
 @login_required
 def list_app():
+    self_deployed = ["Youdao", "FastEmbed", "BAAI", "Ollama", "Xinference", "LocalAI", "LM-Studio", "GPUStack"]
+    weighted = ["Youdao", "FastEmbed", "BAAI"] if settings.LIGHTEN != 0 else []
     model_type = request.args.get("model_type")
     try:
         objs = TenantLLMService.query(tenant_id=current_user.id)
         facts = set([o.to_dict()["llm_factory"] for o in objs if o.api_key])
         llms = LLMService.get_all()
         llms = [m.to_dict()
-                for m in llms if m.status == StatusEnum.VALID.value]
+                for m in llms if m.status == StatusEnum.VALID.value and m.fid not in weighted]
         for m in llms:
-            m["available"] = m["fid"] in facts or m["llm_name"].lower() == "flag-embedding" or m["fid"] in ["Youdao","FastEmbed", "BAAI"]
+            m["available"] = m["fid"] in facts or m["llm_name"].lower() == "flag-embedding" or m["fid"] in self_deployed
 
-        llm_set = set([m["llm_name"] for m in llms])
+        llm_set = set([m["llm_name"] + "@" + m["fid"] for m in llms])
         for o in objs:
-            if not o.api_key:continue
-            if o.llm_name in llm_set:continue
+            if o.llm_name + "@" + o.llm_factory in llm_set:
+                continue
             llms.append({"llm_name": o.llm_name, "model_type": o.model_type, "fid": o.llm_factory, "available": True})
 
         res = {}
         for m in llms:
-            if model_type and m["model_type"].find(model_type)<0:
+            if model_type and m["model_type"].find(model_type) < 0:
                 continue
             if m["fid"] not in res:
                 res[m["fid"]] = []

api/apps/sdk/agent.py

@@ -0,0 +1,39 @@
+#
+#  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 api.db.services.canvas_service import UserCanvasService
+from api.utils.api_utils import get_error_data_result, token_required
+from api.utils.api_utils import get_result
+from flask import request
+
+@manager.route('/agents', methods=['GET'])  # noqa: F821
+@token_required
+def list_agents(tenant_id):
+    id = request.args.get("id")
+    title = request.args.get("title")
+    if id or title:
+        canvas = UserCanvasService.query(id=id, title=title, user_id=tenant_id)
+        if not canvas:
+            return get_error_data_result("The agent doesn't exist.")
+    page_number = int(request.args.get("page", 1))
+    items_per_page = int(request.args.get("page_size", 30))
+    orderby = request.args.get("orderby", "update_time")
+    if request.args.get("desc") == "False" or request.args.get("desc") == "false":
+        desc = False
+    else:
+        desc = True
+    canvas = UserCanvasService.get_list(tenant_id,page_number,items_per_page,orderby,desc,id,title)
+    return get_result(data=canvas)

api/apps/sdk/chat.py

@@ -0,0 +1,357 @@
+#
+#  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
+
+from flask import request
+from api import settings
+from api.db import StatusEnum
+from api.db.services.dialog_service import DialogService
+from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.db.services.llm_service import TenantLLMService
+from api.db.services.user_service import TenantService
+from api.utils import get_uuid
+from api.utils.api_utils import get_error_data_result, token_required, get_result, check_duplicate_ids
+
+
+
+@manager.route('/chats', methods=['POST'])  # noqa: F821
+@token_required
+def create(tenant_id):
+    req = request.json
+    ids = [i for i in req.get("dataset_ids", []) if i] 
+    for kb_id in ids:
+        kbs = KnowledgebaseService.accessible(kb_id=kb_id, user_id=tenant_id)
+        if not kbs:
+            return get_error_data_result(f"You don't own the dataset {kb_id}")
+        kbs = KnowledgebaseService.query(id=kb_id)
+        kb = kbs[0]
+        if kb.chunk_num == 0:
+            return get_error_data_result(f"The dataset {kb_id} doesn't own parsed file")
+    
+    kbs = KnowledgebaseService.get_by_ids(ids) if ids else []
+    embd_ids = [TenantLLMService.split_model_name_and_factory(kb.embd_id)[0] for kb in kbs]  # remove vendor suffix for comparison
+    embd_count = list(set(embd_ids))
+    if len(embd_count) > 1:
+        return get_result(message='Datasets use different embedding models."',
+                          code=settings.RetCode.AUTHENTICATION_ERROR)
+    req["kb_ids"] = ids
+    # llm
+    llm = req.get("llm")
+    if llm:
+        if "model_name" in llm:
+            req["llm_id"] = llm.pop("model_name")
+            if not TenantLLMService.query(tenant_id=tenant_id, llm_name=req["llm_id"], model_type="chat"):
+                return get_error_data_result(f"`model_name` {req.get('llm_id')} doesn't exist")
+        req["llm_setting"] = req.pop("llm")
+    e, tenant = TenantService.get_by_id(tenant_id)
+    if not e:
+        return get_error_data_result(message="Tenant not found!")
+    # prompt
+    prompt = req.get("prompt")
+    key_mapping = {"parameters": "variables",
+                   "prologue": "opener",
+                   "quote": "show_quote",
+                   "system": "prompt",
+                   "rerank_id": "rerank_model",
+                   "vector_similarity_weight": "keywords_similarity_weight"}
+    key_list = ["similarity_threshold", "vector_similarity_weight", "top_n", "rerank_id","top_k"]
+    if prompt:
+        for new_key, old_key in key_mapping.items():
+            if old_key in prompt:
+                prompt[new_key] = prompt.pop(old_key)
+        for key in key_list:
+            if key in prompt:
+                req[key] = prompt.pop(key)
+        req["prompt_config"] = req.pop("prompt")
+    # init
+    req["id"] = get_uuid()
+    req["description"] = req.get("description", "A helpful Assistant")
+    req["icon"] = req.get("avatar", "")
+    req["top_n"] = req.get("top_n", 6)
+    req["top_k"] = req.get("top_k", 1024)
+    req["rerank_id"] = req.get("rerank_id", "")
+    if req.get("rerank_id"):
+        value_rerank_model = ["BAAI/bge-reranker-v2-m3", "maidalun1020/bce-reranker-base_v1"]
+        if req["rerank_id"] not in value_rerank_model and not TenantLLMService.query(tenant_id=tenant_id,
+                                                                                     llm_name=req.get("rerank_id"),
+                                                                                     model_type="rerank"):
+            return get_error_data_result(f"`rerank_model` {req.get('rerank_id')} doesn't exist")
+    if not req.get("llm_id"):
+        req["llm_id"] = tenant.llm_id
+    if not req.get("name"):
+        return get_error_data_result(message="`name` is required.")
+    if DialogService.query(name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value):
+        return get_error_data_result(message="Duplicated chat name in creating chat.")
+    # tenant_id
+    if req.get("tenant_id"):
+        return get_error_data_result(message="`tenant_id` must not be provided.")
+    req["tenant_id"] = tenant_id
+    # prompt more parameter
+    default_prompt = {
+        "system": """You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, your answer must include the sentence "The answer you are looking for is not found in the knowledge base!" Answers need to consider chat history.
+      Here is the knowledge base:
+      {knowledge}
+      The above is the knowledge base.""",
+        "prologue": "Hi! I'm your assistant, what can I do for you?",
+        "parameters": [
+            {"key": "knowledge", "optional": False}
+        ],
+        "empty_response": "Sorry! No relevant content was found in the knowledge base!",
+        "quote": True,
+        "tts": False,
+        "refine_multiturn": True
+    }
+    key_list_2 = ["system", "prologue", "parameters", "empty_response", "quote", "tts", "refine_multiturn"]
+    if "prompt_config" not in req:
+        req['prompt_config'] = {}
+    for key in key_list_2:
+        temp = req['prompt_config'].get(key)
+        if (not temp and key == 'system') or (key not in req["prompt_config"]):
+            req['prompt_config'][key] = default_prompt[key]
+    for p in req['prompt_config']["parameters"]:
+        if p["optional"]:
+            continue
+        if req['prompt_config']["system"].find("{%s}" % p["key"]) < 0:
+            return get_error_data_result(
+                message="Parameter '{}' is not used".format(p["key"]))
+    # save
+    if not DialogService.save(**req):
+        return get_error_data_result(message="Fail to new a chat!")
+    # response
+    e, res = DialogService.get_by_id(req["id"])
+    if not e:
+        return get_error_data_result(message="Fail to new a chat!")
+    res = res.to_json()
+    renamed_dict = {}
+    for key, value in res["prompt_config"].items():
+        new_key = key_mapping.get(key, key)
+        renamed_dict[new_key] = value
+    res["prompt"] = renamed_dict
+    del res["prompt_config"]
+    new_dict = {"similarity_threshold": res["similarity_threshold"],
+                "keywords_similarity_weight": 1-res["vector_similarity_weight"],
+                "top_n": res["top_n"],
+                "rerank_model": res['rerank_id']}
+    res["prompt"].update(new_dict)
+    for key in key_list:
+        del res[key]
+    res["llm"] = res.pop("llm_setting")
+    res["llm"]["model_name"] = res.pop("llm_id")
+    del res["kb_ids"]
+    res["dataset_ids"] = req["dataset_ids"]
+    res["avatar"] = res.pop("icon")
+    return get_result(data=res)
+
+
+@manager.route('/chats/<chat_id>', methods=['PUT'])  # noqa: F821
+@token_required
+def update(tenant_id, chat_id):
+    if not DialogService.query(tenant_id=tenant_id, id=chat_id, status=StatusEnum.VALID.value):
+        return get_error_data_result(message='You do not own the chat')
+    req = request.json
+    ids = req.get("dataset_ids")
+    if "show_quotation" in req:
+        req["do_refer"] = req.pop("show_quotation")
+    if "dataset_ids" in req:
+        if not ids:
+            return get_error_data_result("`dataset_ids` can't be empty")
+        if ids:
+            for kb_id in ids:
+                kbs = KnowledgebaseService.accessible(kb_id=kb_id, user_id=tenant_id)
+                if not kbs:
+                    return get_error_data_result(f"You don't own the dataset {kb_id}")
+                kbs = KnowledgebaseService.query(id=kb_id)
+                kb = kbs[0]
+                if kb.chunk_num == 0:
+                    return get_error_data_result(f"The dataset {kb_id} doesn't own parsed file")
+                
+            kbs = KnowledgebaseService.get_by_ids(ids)
+            embd_ids = [TenantLLMService.split_model_name_and_factory(kb.embd_id)[0] for kb in kbs]  # remove vendor suffix for comparison
+            embd_count = list(set(embd_ids))
+            if len(embd_count) != 1:
+                return get_result(
+                    message='Datasets use different embedding models."',
+                    code=settings.RetCode.AUTHENTICATION_ERROR)
+            req["kb_ids"] = ids
+    llm = req.get("llm")
+    if llm:
+        if "model_name" in llm:
+            req["llm_id"] = llm.pop("model_name")
+            if not TenantLLMService.query(tenant_id=tenant_id, llm_name=req["llm_id"], model_type="chat"):
+                return get_error_data_result(f"`model_name` {req.get('llm_id')} doesn't exist")
+        req["llm_setting"] = req.pop("llm")
+    e, tenant = TenantService.get_by_id(tenant_id)
+    if not e:
+        return get_error_data_result(message="Tenant not found!")
+    # prompt
+    prompt = req.get("prompt")
+    key_mapping = {"parameters": "variables",
+                   "prologue": "opener",
+                   "quote": "show_quote",
+                   "system": "prompt",
+                   "rerank_id": "rerank_model",
+                   "vector_similarity_weight": "keywords_similarity_weight"}
+    key_list = ["similarity_threshold", "vector_similarity_weight", "top_n", "rerank_id","top_k"]
+    if prompt:
+        for new_key, old_key in key_mapping.items():
+            if old_key in prompt:
+                prompt[new_key] = prompt.pop(old_key)
+        for key in key_list:
+            if key in prompt:
+                req[key] = prompt.pop(key)
+        req["prompt_config"] = req.pop("prompt")
+    e, res = DialogService.get_by_id(chat_id)
+    res = res.to_json()
+    if req.get("rerank_id"):
+        value_rerank_model = ["BAAI/bge-reranker-v2-m3", "maidalun1020/bce-reranker-base_v1"]
+        if req["rerank_id"] not in value_rerank_model and not TenantLLMService.query(tenant_id=tenant_id,
+                                                                                     llm_name=req.get("rerank_id"),
+                                                                                     model_type="rerank"):
+            return get_error_data_result(f"`rerank_model` {req.get('rerank_id')} doesn't exist")
+    if "name" in req:
+        if not req.get("name"):
+            return get_error_data_result(message="`name` cannot be empty.")
+        if req["name"].lower() != res["name"].lower() \
+                and len(
+            DialogService.query(name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value)) > 0:
+            return get_error_data_result(message="Duplicated chat name in updating chat.")
+    if "prompt_config" in req:
+        res["prompt_config"].update(req["prompt_config"])
+        for p in res["prompt_config"]["parameters"]:
+            if p["optional"]:
+                continue
+            if res["prompt_config"]["system"].find("{%s}" % p["key"]) < 0:
+                return get_error_data_result(message="Parameter '{}' is not used".format(p["key"]))
+    if "llm_setting" in req:
+        res["llm_setting"].update(req["llm_setting"])
+    req["prompt_config"] = res["prompt_config"]
+    req["llm_setting"] = res["llm_setting"]
+    # avatar
+    if "avatar" in req:
+        req["icon"] = req.pop("avatar")
+    if "dataset_ids" in req:
+        req.pop("dataset_ids")
+    if not DialogService.update_by_id(chat_id, req):
+        return get_error_data_result(message="Chat not found!")
+    return get_result()
+
+
+@manager.route('/chats', methods=['DELETE'])  # noqa: F821
+@token_required
+def delete(tenant_id):
+    errors = []
+    success_count = 0
+    req = request.json
+    if not req:
+        ids = None
+    else:
+        ids = req.get("ids")
+    if not ids:
+        id_list = []
+        dias = DialogService.query(tenant_id=tenant_id, status=StatusEnum.VALID.value)
+        for dia in dias:
+            id_list.append(dia.id)
+    else:
+        id_list = ids
+
+    unique_id_list, duplicate_messages = check_duplicate_ids(id_list, "assistant")
+
+    for id in unique_id_list:
+        if not DialogService.query(tenant_id=tenant_id, id=id, status=StatusEnum.VALID.value):
+            errors.append(f"Assistant({id}) not found.")
+            continue
+        temp_dict = {"status": StatusEnum.INVALID.value}
+        DialogService.update_by_id(id, temp_dict)
+        success_count += 1
+        
+    if errors:
+        if success_count > 0:
+            return get_result(
+                data={"success_count": success_count, "errors": errors},
+                message=f"Partially deleted {success_count} chats with {len(errors)} errors"
+            )
+        else:
+            return get_error_data_result(message="; ".join(errors))
+    
+    if duplicate_messages:
+        if success_count > 0:
+            return get_result(
+                message=f"Partially deleted {success_count} chats with {len(duplicate_messages)} errors", 
+                data={"success_count": success_count, "errors": duplicate_messages}
+            )
+        else:
+            return get_error_data_result(message=";".join(duplicate_messages))
+    
+    return get_result()
+
+
+
+@manager.route('/chats', methods=['GET'])  # noqa: F821
+@token_required
+def list_chat(tenant_id):
+    id = request.args.get("id")
+    name = request.args.get("name")
+    if id or name:
+        chat = DialogService.query(id=id, name=name, status=StatusEnum.VALID.value, tenant_id=tenant_id)
+        if not chat:
+            return get_error_data_result(message="The chat doesn't exist")
+    page_number = int(request.args.get("page", 1))
+    items_per_page = int(request.args.get("page_size", 30))
+    orderby = request.args.get("orderby", "create_time")
+    if request.args.get("desc") == "False" or request.args.get("desc") == "false":
+        desc = False
+    else:
+        desc = True
+    chats = DialogService.get_list(tenant_id, page_number, items_per_page, orderby, desc, id, name)
+    if not chats:
+        return get_result(data=[])
+    list_assts = []
+    key_mapping = {"parameters": "variables",
+                   "prologue": "opener",
+                   "quote": "show_quote",
+                   "system": "prompt",
+                   "rerank_id": "rerank_model",
+                   "vector_similarity_weight": "keywords_similarity_weight",
+                   "do_refer": "show_quotation"}
+    key_list = ["similarity_threshold", "vector_similarity_weight", "top_n", "rerank_id"]
+    for res in chats:
+        renamed_dict = {}
+        for key, value in res["prompt_config"].items():
+            new_key = key_mapping.get(key, key)
+            renamed_dict[new_key] = value
+        res["prompt"] = renamed_dict
+        del res["prompt_config"]
+        new_dict = {"similarity_threshold": res["similarity_threshold"],
+                    "keywords_similarity_weight": 1-res["vector_similarity_weight"],
+                    "top_n": res["top_n"],
+                    "rerank_model": res['rerank_id']}
+        res["prompt"].update(new_dict)
+        for key in key_list:
+            del res[key]
+        res["llm"] = res.pop("llm_setting")
+        res["llm"]["model_name"] = res.pop("llm_id")
+        kb_list = []
+        for kb_id in res["kb_ids"]:
+            kb = KnowledgebaseService.query(id=kb_id)
+            if not kb:
+                logging.warning(f"The kb {kb_id} does not exist.")
+                continue
+            kb_list.append(kb[0].to_json())
+        del res["kb_ids"]
+        res["datasets"] = kb_list
+        res["avatar"] = res.pop("icon")
+        list_assts.append(res)
+    return get_result(data=list_assts)

api/apps/sdk/dataset.py

@@ -0,0 +1,554 @@
+#
+#  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 flask import request
+from api.db import StatusEnum, FileSource
+from api.db.db_models import File
+from api.db.services.document_service import DocumentService
+from api.db.services.file2document_service import File2DocumentService
+from api.db.services.file_service import FileService
+from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.db.services.llm_service import TenantLLMService, LLMService
+from api.db.services.user_service import TenantService
+from api import settings
+from api.utils import get_uuid
+from api.utils.api_utils import (
+    get_result,
+    token_required,
+    get_error_data_result,
+    valid,
+    get_parser_config, valid_parser_config, dataset_readonly_fields,check_duplicate_ids
+)
+
+
+@manager.route("/datasets", methods=["POST"])  # noqa: F821
+@token_required
+def create(tenant_id):
+    """
+    Create a new dataset.
+    ---
+    tags:
+      - Datasets
+    security:
+      - ApiKeyAuth: []
+    parameters:
+      - in: header
+        name: Authorization
+        type: string
+        required: true
+        description: Bearer token for authentication.
+      - in: body
+        name: body
+        description: Dataset creation parameters.
+        required: true
+        schema:
+          type: object
+          required:
+            - name
+          properties:
+            name:
+              type: string
+              description: Name of the dataset.
+            permission:
+              type: string
+              enum: ['me', 'team']
+              description: Dataset permission.
+            chunk_method:
+              type: string
+              enum: ["naive", "manual", "qa", "table", "paper", "book", "laws",
+                     "presentation", "picture", "one", "email", "tag"
+                     ]
+              description: Chunking method.
+            parser_config:
+              type: object
+              description: Parser configuration.
+    responses:
+      200:
+        description: Successful operation.
+        schema:
+          type: object
+          properties:
+            data:
+              type: object
+    """
+    req = request.json
+    for k in req.keys():
+        if dataset_readonly_fields(k):
+            return get_result(code=settings.RetCode.ARGUMENT_ERROR, message=f"'{k}' is readonly.")
+    e, t = TenantService.get_by_id(tenant_id)
+    permission = req.get("permission")
+    chunk_method = req.get("chunk_method")
+    parser_config = req.get("parser_config")
+    valid_parser_config(parser_config)
+    valid_permission = ["me", "team"]
+    valid_chunk_method = [
+        "naive",
+        "manual",
+        "qa",
+        "table",
+        "paper",
+        "book",
+        "laws",
+        "presentation",
+        "picture",
+        "one",
+        "email",
+        "tag"
+    ]
+    check_validation = valid(
+        permission,
+        valid_permission,
+        chunk_method,
+        valid_chunk_method,
+    )
+    if check_validation:
+        return check_validation
+    req["parser_config"] = get_parser_config(chunk_method, parser_config)
+    if "tenant_id" in req:
+        return get_error_data_result(message="`tenant_id` must not be provided")
+    if "chunk_count" in req or "document_count" in req:
+        return get_error_data_result(
+            message="`chunk_count` or `document_count` must not be provided"
+        )
+    if "name" not in req:
+        return get_error_data_result(message="`name` is not empty!")
+    req["id"] = get_uuid()
+    req["name"] = req["name"].strip()
+    if req["name"] == "":
+        return get_error_data_result(message="`name` is not empty string!")
+    if len(req["name"]) >= 128:
+        return get_error_data_result(
+            message="Dataset name should not be longer than 128 characters."
+        )
+    if KnowledgebaseService.query(
+        name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value
+    ):
+        return get_error_data_result(
+            message="Duplicated dataset name in creating dataset."
+        )
+    req["tenant_id"] = tenant_id
+    req["created_by"] = tenant_id
+    if not req.get("embedding_model"):
+        req["embedding_model"] = t.embd_id
+    else:
+        valid_embedding_models = [
+            "BAAI/bge-large-zh-v1.5",
+            "maidalun1020/bce-embedding-base_v1",
+        ]
+        embd_model = LLMService.query(
+            llm_name=req["embedding_model"], model_type="embedding"
+        )
+        if embd_model:
+            if req["embedding_model"] not in valid_embedding_models and not TenantLLMService.query(tenant_id=tenant_id,model_type="embedding",llm_name=req.get("embedding_model"),):
+                return get_error_data_result(f"`embedding_model` {req.get('embedding_model')} doesn't exist")
+        if not embd_model:
+            embd_model=TenantLLMService.query(tenant_id=tenant_id,model_type="embedding", llm_name=req.get("embedding_model"))
+        if not embd_model:
+            return get_error_data_result(
+                f"`embedding_model` {req.get('embedding_model')} doesn't exist"
+            )
+    key_mapping = {
+        "chunk_num": "chunk_count",
+        "doc_num": "document_count",
+        "parser_id": "chunk_method",
+        "embd_id": "embedding_model",
+    }
+    mapped_keys = {
+        new_key: req[old_key]
+        for new_key, old_key in key_mapping.items()
+        if old_key in req
+    }
+    req.update(mapped_keys)
+    flds = list(req.keys())
+    for f in flds:
+        if req[f] == "" and f in ["permission", "parser_id", "chunk_method"]:
+            del req[f]
+    if not KnowledgebaseService.save(**req):
+        return get_error_data_result(message="Create dataset error.(Database error)")
+    renamed_data = {}
+    e, k = KnowledgebaseService.get_by_id(req["id"])
+    for key, value in k.to_dict().items():
+        new_key = key_mapping.get(key, key)
+        renamed_data[new_key] = value
+    return get_result(data=renamed_data)
+
+
+@manager.route("/datasets", methods=["DELETE"])  # noqa: F821
+@token_required
+def delete(tenant_id):
+    """
+    Delete datasets.
+    ---
+    tags:
+      - Datasets
+    security:
+      - ApiKeyAuth: []
+    parameters:
+      - in: header
+        name: Authorization
+        type: string
+        required: true
+        description: Bearer token for authentication.
+      - in: body
+        name: body
+        description: Dataset deletion parameters.
+        required: true
+        schema:
+          type: object
+          properties:
+            ids:
+              type: array
+              items:
+                type: string
+              description: List of dataset IDs to delete.
+    responses:
+      200:
+        description: Successful operation.
+        schema:
+          type: object
+    """
+    errors = []
+    success_count = 0
+    req = request.json
+    if not req:
+        ids = None
+    else:
+        ids = req.get("ids")
+    if not ids:
+        id_list = []
+        kbs = KnowledgebaseService.query(tenant_id=tenant_id)
+        for kb in kbs:
+            id_list.append(kb.id)
+    else:
+        id_list = ids
+    unique_id_list, duplicate_messages = check_duplicate_ids(id_list, "dataset")
+    id_list = unique_id_list
+
+    for id in id_list:
+        kbs = KnowledgebaseService.query(id=id, tenant_id=tenant_id)
+        if not kbs:
+            errors.append(f"You don't own the dataset {id}")
+            continue
+        for doc in DocumentService.query(kb_id=id):
+            if not DocumentService.remove_document(doc, tenant_id):
+                errors.append(f"Remove document error for dataset {id}")
+                continue
+            f2d = File2DocumentService.get_by_document_id(doc.id)
+            FileService.filter_delete(
+                [
+                    File.source_type == FileSource.KNOWLEDGEBASE,
+                    File.id == f2d[0].file_id,
+                ]
+            )
+            File2DocumentService.delete_by_document_id(doc.id)
+        FileService.filter_delete(
+            [File.source_type == FileSource.KNOWLEDGEBASE, File.type == "folder", File.name == kbs[0].name])
+        if not KnowledgebaseService.delete_by_id(id):
+            errors.append(f"Delete dataset error for {id}")
+            continue
+        success_count += 1
+    if errors:
+        if success_count > 0:
+            return get_result(
+                data={"success_count": success_count, "errors": errors},
+                message=f"Partially deleted {success_count} datasets with {len(errors)} errors"
+            )
+        else:
+            return get_error_data_result(message="; ".join(errors))
+    if duplicate_messages:
+        if success_count > 0:
+            return get_result(message=f"Partially deleted {success_count} datasets with {len(duplicate_messages)} errors", data={"success_count": success_count, "errors": duplicate_messages},)
+        else:
+            return get_error_data_result(message=";".join(duplicate_messages))
+    return get_result(code=settings.RetCode.SUCCESS)
+
+
+@manager.route("/datasets/<dataset_id>", methods=["PUT"])  # noqa: F821  
+@token_required
+def update(tenant_id, dataset_id):
+    """
+    Update a dataset.
+    ---
+    tags:
+      - Datasets
+    security:
+      - ApiKeyAuth: []
+    parameters:
+      - in: path
+        name: dataset_id
+        type: string
+        required: true
+        description: ID of the dataset to update.
+      - in: header
+        name: Authorization
+        type: string
+        required: true
+        description: Bearer token for authentication.
+      - in: body
+        name: body
+        description: Dataset update parameters.
+        required: true
+        schema:
+          type: object
+          properties:
+            name:
+              type: string
+              description: New name of the dataset.
+            permission:
+              type: string
+              enum: ['me', 'team']
+              description: Updated permission.
+            chunk_method:
+              type: string
+              enum: ["naive", "manual", "qa", "table", "paper", "book", "laws",
+                     "presentation", "picture", "one", "email", "tag"
+                     ]
+              description: Updated chunking method.
+            parser_config:
+              type: object
+              description: Updated parser configuration.
+    responses:
+      200:
+        description: Successful operation.
+        schema:
+          type: object
+    """
+    if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id):
+        return get_error_data_result(message="You don't own the dataset")
+    req = request.json
+    for k in req.keys():
+        if dataset_readonly_fields(k):
+            return get_result(code=settings.RetCode.ARGUMENT_ERROR, message=f"'{k}' is readonly.")
+    e, t = TenantService.get_by_id(tenant_id)
+    invalid_keys = {"id", "embd_id", "chunk_num", "doc_num", "parser_id", "create_date", "create_time", "created_by", "status","token_num","update_date","update_time"}
+    if any(key in req for key in invalid_keys):
+        return get_error_data_result(message="The input parameters are invalid.")
+    permission = req.get("permission")
+    chunk_method = req.get("chunk_method")
+    parser_config = req.get("parser_config")
+    valid_parser_config(parser_config)
+    valid_permission = ["me", "team"]
+    valid_chunk_method = [
+        "naive",
+        "manual",
+        "qa",
+        "table",
+        "paper",
+        "book",
+        "laws",
+        "presentation",
+        "picture",
+        "one",
+        "email",
+        "tag"
+    ]
+    check_validation = valid(
+        permission,
+        valid_permission,
+        chunk_method,
+        valid_chunk_method,
+    )
+    if check_validation:
+        return check_validation
+    if "tenant_id" in req:
+        if req["tenant_id"] != tenant_id:
+            return get_error_data_result(message="Can't change `tenant_id`.")
+    e, kb = KnowledgebaseService.get_by_id(dataset_id)
+    if "parser_config" in req:
+        temp_dict = kb.parser_config
+        temp_dict.update(req["parser_config"])
+        req["parser_config"] = temp_dict
+    if "chunk_count" in req:
+        if req["chunk_count"] != kb.chunk_num:
+            return get_error_data_result(message="Can't change `chunk_count`.")
+        req.pop("chunk_count")
+    if "document_count" in req:
+        if req["document_count"] != kb.doc_num:
+            return get_error_data_result(message="Can't change `document_count`.")
+        req.pop("document_count")
+    if req.get("chunk_method"):
+        if kb.chunk_num != 0 and req["chunk_method"] != kb.parser_id:
+            return get_error_data_result(
+                message="If `chunk_count` is not 0, `chunk_method` is not changeable."
+            )
+        req["parser_id"] = req.pop("chunk_method")
+        if req["parser_id"] != kb.parser_id:
+            if not req.get("parser_config"):
+                req["parser_config"] = get_parser_config(chunk_method, parser_config)
+    if "embedding_model" in req:
+        if kb.chunk_num != 0 and req["embedding_model"] != kb.embd_id:
+            return get_error_data_result(
+                message="If `chunk_count` is not 0, `embedding_model` is not changeable."
+            )
+        if not req.get("embedding_model"):
+            return get_error_data_result("`embedding_model` can't be empty")
+        valid_embedding_models = [
+            "BAAI/bge-large-zh-v1.5",
+            "BAAI/bge-base-en-v1.5",
+            "BAAI/bge-large-en-v1.5",
+            "BAAI/bge-small-en-v1.5",
+            "BAAI/bge-small-zh-v1.5",
+            "jinaai/jina-embeddings-v2-base-en",
+            "jinaai/jina-embeddings-v2-small-en",
+            "nomic-ai/nomic-embed-text-v1.5",
+            "sentence-transformers/all-MiniLM-L6-v2",
+            "text-embedding-v2",
+            "text-embedding-v3",
+            "maidalun1020/bce-embedding-base_v1",
+        ]
+        embd_model = LLMService.query(
+            llm_name=req["embedding_model"], model_type="embedding"
+        )
+        if embd_model:
+            if req["embedding_model"] not in valid_embedding_models and not TenantLLMService.query(tenant_id=tenant_id,model_type="embedding",llm_name=req.get("embedding_model"),):
+                return get_error_data_result(f"`embedding_model` {req.get('embedding_model')} doesn't exist")
+        if not embd_model:
+            embd_model=TenantLLMService.query(tenant_id=tenant_id,model_type="embedding", llm_name=req.get("embedding_model"))
+
+        if not embd_model:
+            return get_error_data_result(
+                f"`embedding_model` {req.get('embedding_model')} doesn't exist"
+            )
+        req["embd_id"] = req.pop("embedding_model")
+    if "name" in req:
+        req["name"] = req["name"].strip()
+        if len(req["name"]) >= 128:
+            return get_error_data_result(
+                message="Dataset name should not be longer than 128 characters."
+            )
+        if (
+            req["name"].lower() != kb.name.lower()
+            and len(
+                KnowledgebaseService.query(
+                    name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value
+                )
+            )
+            > 0
+        ):
+            return get_error_data_result(
+                message="Duplicated dataset name in updating dataset."
+            )
+    flds = list(req.keys())
+    for f in flds:
+        if req[f] == "" and f in ["permission", "parser_id", "chunk_method"]:
+            del req[f]
+    if not KnowledgebaseService.update_by_id(kb.id, req):
+        return get_error_data_result(message="Update dataset error.(Database error)")
+    return get_result(code=settings.RetCode.SUCCESS)
+
+
+@manager.route("/datasets", methods=["GET"])  # noqa: F821
+@token_required
+def list_datasets(tenant_id):
+    """
+    List datasets.
+    ---
+    tags:
+      - Datasets
+    security:
+      - ApiKeyAuth: []
+    parameters:
+      - in: query
+        name: id
+        type: string
+        required: false
+        description: Dataset ID to filter.
+      - in: query
+        name: name
+        type: string
+        required: false
+        description: Dataset name to filter.
+      - in: query
+        name: page
+        type: integer
+        required: false
+        default: 1
+        description: Page number.
+      - in: query
+        name: page_size
+        type: integer
+        required: false
+        default: 1024
+        description: Number of items per page.
+      - in: query
+        name: orderby
+        type: string
+        required: false
+        default: "create_time"
+        description: Field to order by.
+      - in: query
+        name: desc
+        type: boolean
+        required: false
+        default: true
+        description: Order in descending.
+      - in: header
+        name: Authorization
+        type: string
+        required: true
+        description: Bearer token for authentication.
+    responses:
+      200:
+        description: Successful operation.
+        schema:
+          type: array
+          items:
+            type: object
+    """
+    id = request.args.get("id")
+    name = request.args.get("name")
+    if id:
+        kbs = KnowledgebaseService.get_kb_by_id(id,tenant_id)
+        if not kbs:
+            return get_error_data_result(f"You don't own the dataset {id}")
+    if name:
+        kbs = KnowledgebaseService.get_kb_by_name(name,tenant_id)
+        if not kbs:
+            return get_error_data_result(f"You don't own the dataset {name}")
+    page_number = int(request.args.get("page", 1))
+    items_per_page = int(request.args.get("page_size", 30))
+    orderby = request.args.get("orderby", "create_time")
+    if request.args.get("desc", "false").lower() not in ["true", "false"]:
+        return get_error_data_result("desc should be true or false")
+    if request.args.get("desc", "true").lower() == "false":
+        desc = False
+    else:
+        desc = True
+    tenants = TenantService.get_joined_tenants_by_user_id(tenant_id)
+    kbs = KnowledgebaseService.get_list(
+        [m["tenant_id"] for m in tenants],
+        tenant_id,
+        page_number,
+        items_per_page,
+        orderby,
+        desc,
+        id,
+        name,
+    )
+    renamed_list = []
+    for kb in kbs:
+        key_mapping = {
+            "chunk_num": "chunk_count",
+            "doc_num": "document_count",
+            "parser_id": "chunk_method",
+            "embd_id": "embedding_model",
+        }
+        renamed_data = {}
+        for key, value in kb.items():
+            new_key = key_mapping.get(key, key)
+            renamed_data[new_key] = value
+        renamed_list.append(renamed_data)
+    return get_result(data=renamed_list)

api/apps/sdk/dify_retrieval.py

@@ -0,0 +1,88 @@
+#
+#  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 flask import request, jsonify
+
+from api.db import LLMType
+from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.db.services.llm_service import LLMBundle
+from api import settings
+from api.utils.api_utils import validate_request, build_error_result, apikey_required
+from rag.app.tag import label_question
+
+
+@manager.route('/dify/retrieval', methods=['POST'])  # noqa: F821
+@apikey_required
+@validate_request("knowledge_id", "query")
+def retrieval(tenant_id):
+    req = request.json
+    question = req["query"]
+    kb_id = req["knowledge_id"]
+    use_kg = req.get("use_kg", False)
+    retrieval_setting = req.get("retrieval_setting", {})
+    similarity_threshold = float(retrieval_setting.get("score_threshold", 0.0))
+    top = int(retrieval_setting.get("top_k", 1024))
+
+    try:
+
+        e, kb = KnowledgebaseService.get_by_id(kb_id)
+        if not e:
+            return build_error_result(message="Knowledgebase not found!", code=settings.RetCode.NOT_FOUND)
+
+        if kb.tenant_id != tenant_id:
+            return build_error_result(message="Knowledgebase not found!", code=settings.RetCode.NOT_FOUND)
+
+        embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id)
+
+        ranks = settings.retrievaler.retrieval(
+            question,
+            embd_mdl,
+            kb.tenant_id,
+            [kb_id],
+            page=1,
+            page_size=top,
+            similarity_threshold=similarity_threshold,
+            vector_similarity_weight=0.3,
+            top=top,
+            rank_feature=label_question(question, [kb])
+        )
+
+        if use_kg:
+            ck = settings.kg_retrievaler.retrieval(question,
+                                                   [tenant_id],
+                                                   [kb_id],
+                                                   embd_mdl,
+                                                   LLMBundle(kb.tenant_id, LLMType.CHAT))
+            if ck["content_with_weight"]:
+                ranks["chunks"].insert(0, ck)
+
+        records = []
+        for c in ranks["chunks"]:
+            c.pop("vector", None)
+            records.append({
+                "content": c["content_with_weight"],
+                "score": c["similarity"],
+                "title": c["docnm_kwd"],
+                "metadata": {}
+            })
+
+        return jsonify({"records": records})
+    except Exception as e:
+        if str(e).find("not_found") > 0:
+            return build_error_result(
+                message='No chunk found! Check the chunk status please!',
+                code=settings.RetCode.NOT_FOUND
+            )
+        return build_error_result(message=str(e), code=settings.RetCode.SERVER_ERROR)