Update version number for poetry (#3637)

### What problem does this PR solve?



### Type of change

- [x] Refactoring

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -15,16 +15,16 @@ body:
     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/workflows/tests.yml

@@ -0,0 +1,130 @@
+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 PR labels
+        run: |
+          echo "Workflow triggered by ${{ github.event_name }}"
+          if [[ ${{ github.event_name }} == 'pull_request' ]]; then
+            echo "PR labels: ${{ join(github.event.pull_request.labels.*.name, ', ') }}"
+          fi
+
+      - name: Ensure workspace ownership
+        run: echo "chown -R $USER $GITHUB_WORKSPACE" && sudo chown -R $USER $GITHUB_WORKSPACE
+
+      # https://github.com/actions/checkout/issues/1781
+      - name: Check out code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          fetch-tags: true
+
+      - name: Build ragflow:dev-slim
+        run: |
+          RUNNER_WORKSPACE_PREFIX=${RUNNER_WORKSPACE_PREFIX:-$HOME}
+          cp -r ${RUNNER_WORKSPACE_PREFIX}/huggingface.co ${RUNNER_WORKSPACE_PREFIX}/nltk_data ${RUNNER_WORKSPACE_PREFIX}/libssl*.deb  ${RUNNER_WORKSPACE_PREFIX}/tika-server*.jar* ${RUNNER_WORKSPACE_PREFIX}/chrome* ${RUNNER_WORKSPACE_PREFIX}/cl100k_base.tiktoken .
+          sudo docker pull ubuntu:22.04
+          sudo docker build --progress=plain -f Dockerfile.slim -t infiniflow/ragflow:dev-slim .
+
+      - name: Build ragflow:dev
+        run: |
+          sudo docker build --progress=plain -f Dockerfile -t infiniflow/ragflow:dev .
+
+      - name: Start ragflow:dev-slim
+        run: |
+          sudo docker compose -f docker/docker-compose.yml up -d
+
+      - name: Stop ragflow:dev-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:dev
+        run: |
+          echo "RAGFLOW_IMAGE=infiniflow/ragflow:dev" >> 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 && poetry 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 && poetry install && source .venv/bin/activate && cd test/test_frontend_api && pytest -s --tb=short get_email.py test_dataset.py 
+          
+
+      - name: Stop ragflow:dev
+        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:dev
+        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 && poetry 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 && poetry install && source .venv/bin/activate && cd test/test_frontend_api && pytest -s --tb=short get_email.py test_dataset.py
+
+      - name: Stop ragflow:dev
+        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

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 guidlines 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.
@@ -42,6 +36,7 @@ The list below mentions some contributions you can make, but it is not a complet
 - 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
 
 - Ensure that your PR title is concise and clear, providing all the required information.
@@ -49,4 +44,5 @@ The list below mentions some contributions you can make, but it is not a complet
 - 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

@@ -1,22 +1,170 @@
-FROM infiniflow/ragflow-base:v2.0
+# base stage
+FROM ubuntu:22.04 AS base
+USER root
+SHELL ["/bin/bash", "-c"]
+
+ENV LIGHTEN=0
+
+WORKDIR /ragflow
+
+RUN rm -f /etc/apt/apt.conf.d/docker-clean \
+    && echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
+
+RUN --mount=type=cache,id=ragflow_base_apt,target=/var/cache/apt,sharing=locked \
+    apt update && apt-get --no-install-recommends install -y ca-certificates
+
+# Setup apt mirror site
+RUN sed -i 's|http://archive.ubuntu.com|https://mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list
+
+RUN --mount=type=cache,id=ragflow_base_apt,target=/var/cache/apt,sharing=locked \
+    apt update && DEBIAN_FRONTEND=noninteractive apt install -y curl libpython3-dev nginx libglib2.0-0 libglx-mesa0 pkg-config libicu-dev libgdiplus default-jdk python3-pip pipx \
+    libatk-bridge2.0-0 libgtk-4-1 libnss3 xdg-utils unzip libgbm-dev wget git \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN pip3 config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple && pip3 config set global.trusted-host "pypi.tuna.tsinghua.edu.cn mirrors.pku.edu.cn" && pip3 config set global.extra-index-url "https://mirrors.pku.edu.cn/pypi/web/simple" \
+    && pipx install poetry \
+    && /root/.local/bin/poetry self add poetry-plugin-pypi-mirror
+
+# 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,source=libssl1.1_1.1.1f-1ubuntu2_amd64.deb,target=/root/libssl1.1_1.1.1f-1ubuntu2_amd64.deb \
+    --mount=type=bind,source=libssl1.1_1.1.1f-1ubuntu2_arm64.deb,target=/root/libssl1.1_1.1.1f-1ubuntu2_arm64.deb \
+    if [ "$(uname -m)" = "x86_64" ]; then \
+        dpkg -i /root/libssl1.1_1.1.1f-1ubuntu2_amd64.deb; \
+    elif [ "$(uname -m)" = "aarch64" ]; then \
+        dpkg -i /root/libssl1.1_1.1.1f-1ubuntu2_arm64.deb; \
+    fi
+
+ENV PYTHONDONTWRITEBYTECODE=1 DOTNET_SYSTEM_GLOBALIZATION_INVARIANT=1
+ENV PATH=/root/.local/bin:$PATH
+# Configure Poetry
+ENV POETRY_NO_INTERACTION=1
+ENV POETRY_VIRTUALENVS_IN_PROJECT=true
+ENV POETRY_VIRTUALENVS_CREATE=true
+ENV POETRY_REQUESTS_TIMEOUT=15
+ENV POETRY_PYPI_MIRROR_URL=https://pypi.tuna.tsinghua.edu.cn/simple/
+
+# nodejs 12.22 on Ubuntu 22.04 is too old
+RUN --mount=type=cache,id=ragflow_base_apt,target=/var/cache/apt,sharing=locked \
+    curl -fsSL https://deb.nodesource.com/setup_20.x | bash - && \
+    apt purge -y nodejs npm && \
+    apt autoremove && \
+    apt update && \
+    apt install -y nodejs cargo && \
+    rm -rf /var/lib/apt/lists/*
+
+# builder stage
+FROM base AS builder
 USER root
 
 WORKDIR /ragflow
 
-ADD ./web ./web
-RUN cd ./web && npm i --force && npm run build
+COPY .git /ragflow/.git
 
-ADD ./api ./api
-ADD ./conf ./conf
-ADD ./deepdoc ./deepdoc
-ADD ./rag ./rag
-ADD ./graph ./graph
+RUN current_commit=$(git rev-parse --short HEAD); \
+    last_tag=$(git describe --tags --abbrev=0); \
+    commit_count=$(git rev-list --count "$last_tag..HEAD"); \
+    version_info=""; \
+    if [ "$commit_count" -eq 0 ]; then \
+        version_info=$last_tag; \
+    else \
+        version_info="$current_commit($last_tag~$commit_count)"; \
+    fi; \
+    if [ "$LIGHTEN" == "1" ]; then \
+        version_info="$version_info slim"; \
+    else \
+        version_info="$version_info full"; \
+    fi; \
+    echo "RAGFlow version: $version_info"; \
+    echo $version_info > /ragflow/VERSION
+
+COPY web web
+COPY docs docs
+RUN --mount=type=cache,id=ragflow_builder_npm,target=/root/.npm,sharing=locked \
+    cd web && npm install --force && npm run build
+
+# install dependencies from poetry.lock file
+COPY pyproject.toml poetry.toml poetry.lock ./
+
+RUN --mount=type=cache,id=ragflow_builder_poetry,target=/root/.cache/pypoetry,sharing=locked \
+    if [ "$LIGHTEN" == "1" ]; then \
+        poetry install --no-root; \
+    else \
+        poetry install --no-root --with=full; \
+    fi
+
+# production stage
+FROM base AS production
+USER root
+
+WORKDIR /ragflow
+
+COPY --from=builder /ragflow/VERSION /ragflow/VERSION
+
+# Install python packages' dependencies
+# cv2 requires libGL.so.1
+RUN --mount=type=cache,id=ragflow_production_apt,target=/var/cache/apt,sharing=locked \
+    apt update && apt install -y --no-install-recommends nginx libgl1 vim less && \
+    rm -rf /var/lib/apt/lists/*
+
+COPY web web
+COPY api api
+COPY conf conf
+COPY deepdoc deepdoc
+COPY rag rag
+COPY agent agent
+COPY graphrag graphrag
+COPY pyproject.toml poetry.toml poetry.lock ./
+
+# Copy models downloaded via download_deps.py
+RUN mkdir -p /ragflow/rag/res/deepdoc /root/.ragflow
+RUN --mount=type=bind,source=huggingface.co,target=/huggingface.co \
+    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,source=huggingface.co,target=/huggingface.co \
+    tar -cf - \
+        /huggingface.co/BAAI/bge-large-zh-v1.5 \
+        /huggingface.co/BAAI/bge-reranker-v2-m3 \
+        /huggingface.co/maidalun1020/bce-embedding-base_v1 \
+        /huggingface.co/maidalun1020/bce-reranker-base_v1 \
+        | tar -xf - --strip-components=2 -C /root/.ragflow
+
+# Copy nltk data downloaded via download_deps.py
+COPY nltk_data /root/nltk_data
+
+# 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.
+COPY tika-server-standard-3.0.0.jar /ragflow/tika-server-standard.jar
+COPY tika-server-standard-3.0.0.jar.md5 /ragflow/tika-server-standard.jar.md5
+ENV TIKA_SERVER_JAR="file:///ragflow/tika-server-standard.jar"
+
+# Copy cl100k_base
+COPY cl100k_base.tiktoken /ragflow/9b5ad71b2ce5302211f9c61530b329a4922fc6a4
+
+# Add dependencies of selenium
+RUN --mount=type=bind,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,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
+
+# Copy compiled web pages
+COPY --from=builder /ragflow/web/dist /ragflow/web/dist
+
+# 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 ./
+COPY docker/service_conf.yaml.template ./conf/service_conf.yaml.template
+COPY docker/entrypoint.sh ./entrypoint.sh
 RUN chmod +x ./entrypoint.sh
 
 ENTRYPOINT ["./entrypoint.sh"]

Dockerfile.arm

@@ -1,33 +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 ./graph ./graph
-
-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

@@ -1,25 +0,0 @@
-FROM infiniflow/ragflow-base:v2.0
-USER  root
-
-WORKDIR /ragflow
-
-## for cuda > 12.0
-RUN /root/miniconda3/envs/py11/bin/pip uninstall -y onnxruntime-gpu
-RUN /root/miniconda3/envs/py11/bin/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
-
-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

@@ -1,55 +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 ./graph ./graph
-
-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

@@ -26,17 +26,19 @@ RUN dnf install -y nginx
 
 ADD ./web ./web
 ADD ./api ./api
+ADD ./docs ./docs
 ADD ./conf ./conf
 ADD ./deepdoc ./deepdoc
 ADD ./rag ./rag
 ADD ./requirements.txt ./requirements.txt
-ADD ./graph ./graph
+ADD ./agent ./agent
+ADD ./graphrag ./graphrag
 
 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
 
@@ -51,6 +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
 

Dockerfile.slim

@@ -0,0 +1,163 @@
+# base stage
+FROM ubuntu:22.04 AS base
+USER root
+SHELL ["/bin/bash", "-c"]
+
+ENV LIGHTEN=1
+
+WORKDIR /ragflow
+
+RUN rm -f /etc/apt/apt.conf.d/docker-clean \
+    && echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
+
+RUN --mount=type=cache,id=ragflow_base_apt,target=/var/cache/apt,sharing=locked \
+    apt update && apt-get --no-install-recommends install -y ca-certificates
+
+# Setup apt mirror site
+RUN sed -i 's|http://archive.ubuntu.com|https://mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list
+
+RUN --mount=type=cache,id=ragflow_base_apt,target=/var/cache/apt,sharing=locked \
+    apt update && DEBIAN_FRONTEND=noninteractive apt install -y curl libpython3-dev nginx libglib2.0-0 libglx-mesa0 pkg-config libicu-dev libgdiplus default-jdk python3-pip pipx \
+    libatk-bridge2.0-0 libgtk-4-1 libnss3 xdg-utils unzip libgbm-dev wget git \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN pip3 config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple && pip3 config set global.trusted-host "pypi.tuna.tsinghua.edu.cn mirrors.pku.edu.cn" && pip3 config set global.extra-index-url "https://mirrors.pku.edu.cn/pypi/web/simple" \
+    && pipx install poetry \
+    && /root/.local/bin/poetry self add poetry-plugin-pypi-mirror
+
+# 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,source=libssl1.1_1.1.1f-1ubuntu2_amd64.deb,target=/root/libssl1.1_1.1.1f-1ubuntu2_amd64.deb \
+    --mount=type=bind,source=libssl1.1_1.1.1f-1ubuntu2_arm64.deb,target=/root/libssl1.1_1.1.1f-1ubuntu2_arm64.deb \
+    if [ "$(uname -m)" = "x86_64" ]; then \
+        dpkg -i /root/libssl1.1_1.1.1f-1ubuntu2_amd64.deb; \
+    elif [ "$(uname -m)" = "aarch64" ]; then \
+        dpkg -i /root/libssl1.1_1.1.1f-1ubuntu2_arm64.deb; \
+    fi
+
+ENV PYTHONDONTWRITEBYTECODE=1 DOTNET_SYSTEM_GLOBALIZATION_INVARIANT=1
+ENV PATH=/root/.local/bin:$PATH
+# Configure Poetry
+ENV POETRY_NO_INTERACTION=1
+ENV POETRY_VIRTUALENVS_IN_PROJECT=true
+ENV POETRY_VIRTUALENVS_CREATE=true
+ENV POETRY_REQUESTS_TIMEOUT=15
+ENV POETRY_PYPI_MIRROR_URL=https://pypi.tuna.tsinghua.edu.cn/simple/
+
+# nodejs 12.22 on Ubuntu 22.04 is too old
+RUN --mount=type=cache,id=ragflow_base_apt,target=/var/cache/apt,sharing=locked \
+    curl -fsSL https://deb.nodesource.com/setup_20.x | bash - && \
+    apt purge -y nodejs npm && \
+    apt autoremove && \
+    apt update && \
+    apt install -y nodejs cargo && \
+    rm -rf /var/lib/apt/lists/*
+
+# builder stage
+FROM base AS builder
+USER root
+
+WORKDIR /ragflow
+
+COPY .git /ragflow/.git
+
+RUN current_commit=$(git rev-parse --short HEAD); \
+    last_tag=$(git describe --tags --abbrev=0); \
+    commit_count=$(git rev-list --count "$last_tag..HEAD"); \
+    version_info=""; \
+    if [ "$commit_count" -eq 0 ]; then \
+        version_info=$last_tag; \
+    else \
+        version_info="$current_commit($last_tag~$commit_count)"; \
+    fi; \
+    if [ "$LIGHTEN" == "1" ]; then \
+        version_info="$version_info slim"; \
+    else \
+        version_info="$version_info full"; \
+    fi; \
+    echo "RAGFlow version: $version_info"; \
+    echo $version_info > /ragflow/VERSION
+
+COPY web web
+COPY docs docs
+RUN --mount=type=cache,id=ragflow_builder_npm,target=/root/.npm,sharing=locked \
+    cd web && npm install --force && npm run build
+
+# install dependencies from poetry.lock file
+COPY pyproject.toml poetry.toml poetry.lock ./
+
+RUN --mount=type=cache,id=ragflow_builder_poetry,target=/root/.cache/pypoetry,sharing=locked \
+    if [ "$LIGHTEN" == "1" ]; then \
+        poetry install --no-root; \
+    else \
+        poetry install --no-root --with=full; \
+    fi
+
+# production stage
+FROM base AS production
+USER root
+
+WORKDIR /ragflow
+
+COPY --from=builder /ragflow/VERSION /ragflow/VERSION
+
+# Install python packages' dependencies
+# cv2 requires libGL.so.1
+RUN --mount=type=cache,id=ragflow_production_apt,target=/var/cache/apt,sharing=locked \
+    apt update && apt install -y --no-install-recommends nginx libgl1 vim less && \
+    rm -rf /var/lib/apt/lists/*
+
+COPY web web
+COPY api api
+COPY conf conf
+COPY deepdoc deepdoc
+COPY rag rag
+COPY agent agent
+COPY graphrag graphrag
+COPY pyproject.toml poetry.toml poetry.lock ./
+
+# Copy models downloaded via download_deps.py
+RUN mkdir -p /ragflow/rag/res/deepdoc /root/.ragflow
+RUN --mount=type=bind,source=huggingface.co,target=/huggingface.co \
+    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
+
+# Copy nltk data downloaded via download_deps.py
+COPY nltk_data /root/nltk_data
+
+# 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.
+COPY tika-server-standard-3.0.0.jar /ragflow/tika-server-standard.jar
+COPY tika-server-standard-3.0.0.jar.md5 /ragflow/tika-server-standard.jar.md5
+ENV TIKA_SERVER_JAR="file:///ragflow/tika-server-standard.jar"
+
+# Copy cl100k_base
+COPY cl100k_base.tiktoken /ragflow/9b5ad71b2ce5302211f9c61530b329a4922fc6a4
+
+# Add dependencies of selenium
+RUN --mount=type=bind,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,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
+
+# Copy compiled web pages
+COPY --from=builder /ragflow/web/dist /ragflow/web/dist
+
+# 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/
+
+COPY docker/service_conf.yaml.template ./conf/service_conf.yaml.template
+COPY docker/entrypoint.sh ./entrypoint.sh
+RUN chmod +x ./entrypoint.sh
+
+ENTRYPOINT ["./entrypoint.sh"]

README.md

@@ -7,17 +7,24 @@
 <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_ja.md">日本語</a> |
+  <a href="./README_ko.md">한국어</a> |
+  <a href="./README_id.md">Bahasa Indonesia</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:v0.14.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.14.0">
+    </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:v0.8.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.8.0"></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>
@@ -41,8 +48,9 @@
 - 🔎 [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)
@@ -52,34 +60,41 @@
 
 ## 💡 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/user-attachments/assets/504bbbf1-c9f7-4d83-8cc5-e9cb63c26db6" width="1200"/>
 </div>
 
+## 🔥 Latest Updates
 
-## 📌 Latest Updates
+- 2024-11-22 Adds more variables to Agent.
+- 2024-11-01 Adds keyword extraction and related question generation to the parsed chunks to improve the accuracy of retrieval.
+- 2024-09-13 Adds search mode for knowledge base Q&A.
+- 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.
 
-- 2024-07-08 Supports [Graph](./graph/README.md).
+## 🎉 Stay Tuned
 
-- 2024-06-27 Supports Markdown and Docx in the Q&A parsing method. Supports extracting images from Docx files. Supports extracting tables from Markdown files.
-- 2024-06-14 Supports PDF in the Q&A parsing method.
-- 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.
-- 2024-05-28 Supports LLM Baichuan and VolcanoArk.
-- 2024-05-23 Supports [RAPTOR](https://arxiv.org/html/2401.18059v1) for better text retrieval.
-- 2024-05-21 Supports streaming output and text chunk retrieval API. 
-- 2024-05-15 Integrates OpenAI GPT-4o.
+⭐️ Star our repository to stay up-to-date with exciting new features and improvements! Get instant notifications for new
+releases! 🌟
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/user-attachments/assets/18c9707e-b8aa-4caf-a154-037089c105ba" width="1200"/>
+</div>
 
 ## 🌟 Key Features
 
 ### 🍭 **"Quality in, quality out"**
 
-- [Deep document understanding](./deepdoc/README.md)-based knowledge extraction from unstructured data with complicated formats.
+- [Deep document understanding](./deepdoc/README.md)-based knowledge extraction from unstructured data with complicated
+  formats.
 - Finds "needle in a data haystack" of literally unlimited tokens.
 
 ### 🍱 **Template-based chunking**
@@ -117,7 +132,8 @@ 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
 
@@ -136,7 +152,8 @@ 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
@@ -150,16 +167,27 @@ Try our demo at [https://demo.ragflow.io](https://demo.ragflow.io).
 
 3. Build the pre-built Docker images and start up the server:
 
-   > 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=v0.8.0`, before running the following commands.
+   > The command below downloads the dev version Docker image for RAGFlow slim (`dev-slim`). Note that RAGFlow slim
+   Docker images do not include embedding models or Python libraries and hence are approximately 1GB in size.
 
    ```bash
    $ cd ragflow/docker
-   $ chmod +x ./entrypoint.sh
-   $ docker compose up -d
+   $ docker compose -f docker-compose.yml up -d
    ```
 
+   > - To download a RAGFlow slim Docker image of a specific version, update the `RAGFLOW_IMAGE` variable in *
+       *docker/.env** to your desired version. For example, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0-slim`. After
+       making this change, rerun the command above to initiate the download.
+   > - To download the dev version of RAGFlow Docker image *including* embedding models and Python libraries, update the
+       `RAGFLOW_IMAGE` variable in **docker/.env** to `RAGFLOW_IMAGE=infiniflow/ragflow:dev`. After making this change,
+       rerun the command above to initiate the download.
+   > - To download a specific version of RAGFlow Docker image *including* embedding models and Python libraries, update
+       the `RAGFLOW_IMAGE` variable in **docker/.env** to your desired version. For example,
+       `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0`. After making this change, rerun the command above to initiate the
+       download.
 
-   > The core image is about 9 GB in size and may take a while to load.
+   > **NOTE:** A RAGFlow Docker image that includes embedding models and Python libraries is approximately 9GB in size
+   and may take significantly longer time to load.
 
 4. Check the server status after having the server up and running:
 
@@ -170,159 +198,155 @@ 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/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
-$ 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
+   $ docker compose -f docker/docker-compose.yml down -v
    ```
 
-## 🛠️ Launch service from source
+2. Set `DOC_ENGINE` in **docker/.env** to `infinity`.
 
-To launch the service from source:
-
-1. Clone the repository: 
+3. Start the containers:
 
    ```bash
-   $ git clone https://github.com/infiniflow/ragflow.git
-   $ cd ragflow/
+   $ docker compose -f docker/docker-compose.yml up -d
    ```
 
-2. Create a virtual environment, ensuring that Anaconda or Miniconda is installed:
+> [!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 1 GB in size and relies on external LLM and embedding services.
 
 ```bash
-   $ conda create -n ragflow python=3.11.0
-   $ conda activate ragflow
-   $ pip install -r requirements.txt
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+pip3 install huggingface-hub nltk
+python3 download_deps.py
+docker build -f Dockerfile.slim -t infiniflow/ragflow:dev-slim .
 ```
 
+## 🔧 Build a Docker image including embedding models
+
+This image is approximately 9 GB in size. As it includes embedding models, it relies on external LLM services only.
+
 ```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/
+pip3 install huggingface-hub nltk
+python3 download_deps.py
+docker build -f Dockerfile -t infiniflow/ragflow:dev .
 ```
 
-3. Copy the entry script and configure environment variables:
+## 🔨 Launch service from source for development
 
+1. Install Poetry, or skip this step if it is already installed:
    ```bash
-   # Get the Python path:
-   $ which python
-   # Get the ragflow project path:
-   $ pwd
+   curl -sSL https://install.python-poetry.org | python3 -
    ```
 
+2. Clone the source code and install Python dependencies:
    ```bash
-   $ cp docker/entrypoint.sh .
-   $ vi entrypoint.sh
+   git clone https://github.com/infiniflow/ragflow.git
+   cd ragflow/
+   export POETRY_VIRTUALENVS_CREATE=true POETRY_VIRTUALENVS_IN_PROJECT=true
+   ~/.local/bin/poetry install --sync --no-root --with=full # install RAGFlow dependent python modules
    ```
 
+3. Launch the dependent services (MinIO, Elasticsearch, Redis, and MySQL) using Docker Compose:
+   ```bash
+   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
+   ```  
+   In **docker/service_conf.yaml.template**, update mysql port to `5455` and es port to `1200`, as specified in **docker/.env**.
+
+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
-   $ chmod +x ./entrypoint.sh
-   $ bash ./entrypoint.sh
+   cd web
+   npm install --force
+   ```  
+7. Configure frontend to update `proxy.target` in **.umirc.ts** to `http://127.0.0.1:9380`:
+8. Launch frontend service:
+   ```bash
+   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)
+- [User guide](https://ragflow.io/docs/dev/category/guides)
 - [References](https://ragflow.io/docs/dev/category/references)
 - [FAQ](https://ragflow.io/docs/dev/faq)
 
@@ -338,4 +362,5 @@ See the [RAGFlow Roadmap 2024](https://github.com/infiniflow/ragflow/issues/162)
 
 ## 🙌 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 +1,341 @@
+<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_ja.md">日本語</a> |
+  <a href="./README_ko.md">한국어</a> |
+  <a href="./README_id.md">Bahasa Indonesia</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:v0.14.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.14.0">
+    </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/162">Peta Jalan</a> |
+  <a href="https://twitter.com/infiniflowai">Twitter</a> |
+  <a href="https://discord.gg/4XxujFgUN7">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
+
+- 22-11-2024 Peningkatan definisi dan penggunaan variabel di Agen.
+- 2024-11-01: Penambahan ekstraksi kata kunci dan pembuatan pertanyaan terkait untuk meningkatkan akurasi pengambilan.
+- 2024-09-13: Penambahan mode pencarian untuk Q&A basis pengetahuan.
+- 2024-08-22: Dukungan untuk teks ke pernyataan SQL melalui RAG.
+- 2024-08-02: Dukungan GraphRAG yang terinspirasi oleh [graphrag](https://github.com/microsoft/graphrag) dan mind map.
+
+## 🎉 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:
+
+   > Perintah di bawah ini akan mengunduh versi dev dari Docker image RAGFlow slim (`dev-slim`). Image RAGFlow slim
+   tidak termasuk model embedding atau library Python dan berukuran sekitar 1GB.
+
+   ```bash
+   $ cd ragflow/docker
+   $ docker compose -f docker-compose.yml up -d
+   ```
+
+   > - Untuk mengunduh versi tertentu dari image Docker RAGFlow slim, perbarui variabel `RAGFlow_IMAGE` di *
+       *docker/.env** sesuai dengan versi yang diinginkan. Misalnya, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0-slim`.
+       Setelah mengubah ini, jalankan ulang perintah di atas untuk memulai unduhan.
+   > - Untuk mengunduh versi dev dari image Docker RAGFlow *termasuk* model embedding dan library Python, perbarui
+       variabel `RAGFlow_IMAGE` di **docker/.env** menjadi `RAGFLOW_IMAGE=infiniflow/ragflow:dev`. Setelah mengubah ini,
+       jalankan ulang perintah di atas untuk memulai unduhan.
+   > - Untuk mengunduh versi tertentu dari image Docker RAGFlow *termasuk* model embedding dan library Python, perbarui
+       variabel `RAGFlow_IMAGE` di **docker/.env** sesuai dengan versi yang diinginkan. Misalnya,
+       `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0`. Setelah mengubah ini, jalankan ulang perintah di atas untuk memulai unduhan.
+
+   > **CATATAN:** Image Docker RAGFlow yang mencakup model embedding dan library Python berukuran sekitar 9GB
+   dan mungkin memerlukan waktu lebih lama untuk dimuat.
+
+4. 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)
+    * Running on http://127.0.0.1:9380
+    * Running on http://x.x.x.x:9380
+    INFO:werkzeug:Press CTRL+C to quit
+   ```
+   > Jika Anda melewatkan langkah ini dan langsung login ke RAGFlow, browser Anda mungkin menampilkan error `network anormal`
+   karena RAGFlow mungkin belum sepenuhnya siap.
+
+5. 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.
+6. Dalam [service_conf.yaml](./docker/service_conf.yaml), 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](./docker/service_conf.yaml): Mengonfigurasi aplikasi backend.
+- [docker-compose.yml](./docker/docker-compose.yml): Sistem ini bergantung pada [docker-compose.yml](./docker/docker-compose.yml) untuk memulai.
+
+Anda harus memastikan bahwa perubahan pada file [.env](./docker/.env) sesuai dengan yang ada di file [service_conf.yaml](./docker/service_conf.yaml).
+
+> File [./docker/README](./docker/README.md) menyediakan penjelasan detail tentang pengaturan lingkungan dan konfigurasi aplikasi,
+> dan Anda DIWAJIBKAN memastikan bahwa semua pengaturan lingkungan yang tercantum di
+> [./docker/README](./docker/README.md) selaras dengan konfigurasi yang sesuai di
+> [service_conf.yaml](./docker/service_conf.yaml).
+
+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/docker-compose.yml up -d
+> ```
+
+## 🔧 Membangun Docker Image tanpa Model Embedding
+
+Image ini berukuran sekitar 1 GB dan bergantung pada aplikasi LLM eksternal dan embedding.
+
+```bash
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+pip3 install huggingface-hub nltk
+python3 download_deps.py
+docker build -f Dockerfile.slim -t infiniflow/ragflow:dev-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/
+pip3 install huggingface-hub nltk
+python3 download_deps.py
+docker build -f Dockerfile -t infiniflow/ragflow:dev .
+```
+
+## 🔨 Menjalankan Aplikasi dari untuk Pengembangan
+
+1. Instal Poetry, atau lewati langkah ini jika sudah terinstal:
+   ```bash
+   curl -sSL https://install.python-poetry.org | python3 -
+   ```
+
+2. Clone kode sumber dan instal dependensi Python:
+   ```bash
+   git clone https://github.com/infiniflow/ragflow.git
+   cd ragflow/
+   export POETRY_VIRTUALENVS_CREATE=true POETRY_VIRTUALENVS_IN_PROJECT=true
+   ~/.local/bin/poetry install --sync --no-root # install modul python RAGFlow
+   ```
+
+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 **docker/service_conf.yaml** ke `127.0.0.1`:
+   ```
+   127.0.0.1       es01 infinity mysql minio redis
+   ```  
+   Di **docker/service_conf.yaml**, perbarui port mysql ke `5455` dan es ke `1200`, sesuai dengan yang ditentukan di **docker/.env**.
+
+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 --force
+   ```  
+7. Konfigurasikan frontend untuk memperbarui `proxy.target` di **.umirc.ts** menjadi `http://127.0.0.1:9380`:
+8. Jalankan aplikasi frontend:
+   ```bash
+   npm run dev 
+   ```  
+
+   _Output berikut menandakan bahwa sistem berhasil diluncurkan:_
+
+   ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
+
+## 📚 Dokumentasi
+
+- [Quickstart](https://ragflow.io/docs/dev/)
+- [Panduan Pengguna](https://ragflow.io/docs/dev/category/guides)
+- [Referensi](https://ragflow.io/docs/dev/category/references)
+- [FAQ](https://ragflow.io/docs/dev/faq)
+
+## 📜 Roadmap
+
+Lihat [Roadmap RAGFlow 2024](https://github.com/infiniflow/ragflow/issues/162)
+
+## 🏄 Komunitas
+
+- [Discord](https://discord.gg/4XxujFgUN7)
+- [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

@@ -7,23 +7,30 @@
 <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_ja.md">日本語</a> |
+  <a href="./README_ko.md">한국어</a> |
+  <a href="./README_id.md">Bahasa Indonesia</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:v0.14.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.14.0">
+    </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:v0.8.0-brightgreen"
-            alt="docker pull infiniflow/ragflow:v0.8.0"></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>
 </p>
 
+
 <h4 align="center">
   <a href="https://ragflow.io/docs/dev/">Document</a> |
   <a href="https://github.com/infiniflow/ragflow/issues/162">Roadmap</a> |
@@ -41,19 +48,23 @@
 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/user-attachments/assets/504bbbf1-c9f7-4d83-8cc5-e9cb63c26db6" width="1200"/>
 </div>
 
 
-## 📌 最新情報
-- 2024-07-08 [Graph](./graph/README.md) に対応しました。.
-- 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。Docxファイルからの画像の抽出をサポートします。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。
-- 2024-06-14 Q&A 解析メソッドは PDF ファイルをサポートしています。
-- 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。
-- 2024-05-28 LLM BaichuanとVolcanoArkを統合しました。
-- 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。
-- 2024-05-21 ストリーミング出力とテキストチャンク取得APIをサポート。
-- 2024-05-15 OpenAI GPT-4oを統合しました。
+## 🔥 最新情報
+
+- 2024-11-22 エージェントでの変数の定義と使用法を改善しました。
+- 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。
+- 2024-09-13 ナレッジベース Q&A の検索モードを追加しました。
+- 2024-08-22 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。
+
+## 🎉 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
 
@@ -130,15 +141,18 @@
 
 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:
 
+   > 以下のコマンドは、RAGFlow slim（`dev-slim`）の開発版Dockerイメージをダウンロードします。RAGFlow slimのDockerイメージには、埋め込みモデルやPythonライブラリが含まれていないため、サイズは約1GBです。
+
    ```bash
    $ cd ragflow/docker
-   $ chmod +x ./entrypoint.sh
-   $ docker compose up -d
+   $ docker compose -f docker-compose.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=v0.8.0として、上記のコマンドを実行してください。
+   > - 特定のバージョンのRAGFlow slim Dockerイメージをダウンロードするには、**docker/.env**内の`RAGFlow_IMAGE`変数を希望のバージョンに更新します。例えば、`RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0`とします。この変更を行った後、上記のコマンドを再実行してダウンロードを開始してください。
+   > - RAGFlowの埋め込みモデルとPythonライブラリを含む開発版Dockerイメージをダウンロードするには、**docker/.env**内の`RAGFlow_IMAGE`変数を`RAGFLOW_IMAGE=infiniflow/ragflow:dev`に更新します。この変更を行った後、上記のコマンドを再実行してダウンロードを開始してください。
+   > - 特定のバージョンのRAGFlow Dockerイメージ（埋め込みモデルとPythonライブラリを含む）をダウンロードするには、**docker/.env**内の`RAGFlow_IMAGE`変数を希望のバージョンに更新します。例えば、`RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0`とします。この変更を行った後、上記のコマンドを再実行してダウンロードを開始してください。  
    
-   > 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。
+   > **NOTE:** 埋め込みモデルとPythonライブラリを含むRAGFlow Dockerイメージのサイズは約9GBであり、読み込みにかなりの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:
 
@@ -149,12 +163,11 @@
    _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
@@ -188,86 +201,108 @@
 > 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/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
+   ```
+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/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:v0.8.0 .
-$ cd ragflow/docker
-$ chmod +x ./entrypoint.sh
-$ docker compose up -d
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+pip3 install huggingface-hub nltk
+python3 download_deps.py
+docker build -f Dockerfile.slim -t infiniflow/ragflow:dev-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:
+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。
 
-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/
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+pip3 install huggingface-hub nltk
+python3 download_deps.py
+docker build -f Dockerfile -t infiniflow/ragflow:dev .
 ```
 
-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）
+## 🔨 ソースコードからサービスを起動する方法
+
+1. Poetry 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:
    ```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/
+   curl -sSL https://install.python-poetry.org | python3 -
    ```
 
-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
+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
-$ 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
+   git clone https://github.com/infiniflow/ragflow.git
+   cd ragflow/
+   export POETRY_VIRTUALENVS_CREATE=true POETRY_VIRTUALENVS_IN_PROJECT=true
+   ~/.local/bin/poetry install --sync --no-root # install RAGFlow dependent python modules
    ```
 
-`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。
+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
+   ```
 
-`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。
+   `/etc/hosts` に以下の行を追加して、**docker/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
+   ```  
+   **docker/service_conf.yaml** で mysql のポートを `5455` に、es のポートを `1200` に更新します（**docker/.env** に指定された通り）.
+
+4. HuggingFace にアクセスできない場合は、`HF_ENDPOINT` 環境変数を設定してミラーサイトを使用してください:
  
    ```bash
-# 実際の状況に応じて設定を調整してください。以下の二つのexportは新たに追加された設定です
-PY=${PY}
-export PYTHONPATH=${PYTHONPATH}
-# オプション：Hugging Faceミラーを追加
    export HF_ENDPOINT=https://hf-mirror.com
    ```
 
-4. 基本サービスを起動します
+5. バックエンドサービスを起動する:
    ```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. 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。
-
-6. サービスを起動します
+6. フロントエンドの依存関係をインストールする:  
    ```bash
-$ chmod +x ./entrypoint.sh
-$ bash ./entrypoint.sh
+   cd web
+   npm install --force
    ```  
+7. フロントエンドを設定し、**.umirc.ts** の `proxy.target` を `http://127.0.0.1:9380` に更新します:
+8. フロントエンドサービスを起動する:  
+   ```bash
+   npm run dev 
+   ```
+
+   _以下の画面で、システムが正常に起動したことを示します:_  
+
+   ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
 
 ## 📚 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)
+- [User guide](https://ragflow.io/docs/dev/category/guides)
 - [References](https://ragflow.io/docs/dev/category/references)
 - [FAQ](https://ragflow.io/docs/dev/faq)
 
@@ -283,4 +318,4 @@ $ bash ./entrypoint.sh
 
 ## 🙌 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

@@ -0,0 +1,323 @@
+<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_ja.md">日本語</a> |
+  <a href="./README_ko.md">한국어</a> |
+  <a href="./README_id.md">Bahasa Indonesia</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:v0.14.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.14.0">
+    </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/162">Roadmap</a> |
+  <a href="https://twitter.com/infiniflowai">Twitter</a> |
+  <a href="https://discord.gg/4XxujFgUN7">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/user-attachments/assets/504bbbf1-c9f7-4d83-8cc5-e9cb63c26db6" width="1200"/>
+</div>
+
+
+## 🔥 업데이트
+
+- 2024-11-22 에이전트의 변수 정의 및 사용을 개선했습니다.
+  
+- 2024-11-01 파싱된 청크에 키워드 추출 및 관련 질문 생성을 추가하여 재현율을 향상시킵니다.
+  
+- 2024-09-13 지식베이스 Q&A 검색 모드를 추가합니다.
+  
+- 2024-08-22 RAG를 통해 SQL 문에 텍스트를 지원합니다.
+  
+- 2024-08-02: [graphrag](https://github.com/microsoft/graphrag)와 마인드맵에서 영감을 받은 GraphRAG를 지원합니다.
+
+
+## 🎉 계속 지켜봐 주세요
+⭐️우리의 저장소를 즐겨찾기에 등록하여 흥미로운 새로운 기능과 업데이트를 최신 상태로 유지하세요! 모든 새로운 릴리스에 대한 즉시 알림을 받으세요! 🌟
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/user-attachments/assets/18c9707e-b8aa-4caf-a154-037089c105ba" width="1200"/>
+</div>
+
+
+## 🌟 주요 기능
+
+### 🍭 **"Quality in, quality out"**
+- [심층 문서 이해](./deepdoc/README.md)를 기반으로 복잡한 형식의 비정형 데이터에서 지식을 추출합니다.
+- 문자 그대로 무한한 토큰에서 "데이터 속의 바늘"을 찾아냅니다.
+
+### 🍱 **템플릿 기반의 chunking**
+- 똑똑하고 설명 가능한 방식.
+- 다양한 템플릿 옵션을 제공합니다.
+
+
+### 🌱 **할루시네이션을 줄인 신뢰할 수 있는 인용**
+- 텍스트 청킹을 시각화하여 사용자가 개입할 수 있도록 합니다.
+- 중요한 참고 자료와 추적 가능한 인용을 빠르게 확인하여 신뢰할 수 있는 답변을 지원합니다.
+
+
+### 🍔 **다른 종류의 데이터 소스와의 호환성**
+- 워드, 슬라이드, 엑셀, 텍스트 파일, 이미지, 스캔본, 구조화된 데이터, 웹 페이지 등을 지원합니다.
+
+### 🛀 **자동화되고 손쉬운 RAG 워크플로우**
+- 개인 및 대규모 비즈니스에 맞춘 효율적인 RAG 오케스트레이션.
+- 구성 가능한 LLM 및 임베딩 모델.
+- 다중 검색과 결합된 re-ranking.
+- 비즈니스와 원활하게 통합할 수 있는 직관적인 API.
+
+
+## 🔎 시스템 아키텍처
+
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/infiniflow/ragflow/assets/12318111/d6ac5664-c237-4200-a7c2-a4a00691b485" width="1000"/>
+</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.
+
+
+### 🚀 서버 시작하기
+
+1. `vm.max_map_count`가 262144 이상인지 확인하세요:
+   > `vm.max_map_count`의 값을 아래 명령어를 통해 확인하세요:
+   >
+   > ```bash
+   > $ sysctl vm.max_map_count
+   > ```
+   >
+   > 만약 `vm.max_map_count` 이 262144 보다 작다면 값을 쟈설정하세요.
+   >
+   > ```bash
+   > # 이 경우에 262144로 설정했습니다.:
+   > $ sudo sysctl -w vm.max_map_count=262144
+   > ```
+   >
+   > 이 변경 사항은 시스템 재부팅 후에 초기화됩니다. 변경 사항을 영구적으로 적용하려면 /etc/sysctl.conf 파일에 vm.max_map_count 값을 추가하거나 업데이트하세요:
+   >
+   > ```bash
+   > vm.max_map_count=262144
+   > ```
+
+2. 레포지토리를 클론하세요:
+
+   ```bash
+   $ git clone https://github.com/infiniflow/ragflow.git
+   ```
+
+3. 미리 빌드된 Docker 이미지를 생성하고 서버를 시작하세요:
+
+   > 아래의 명령은 RAGFlow slim(dev-slim)의 개발 버전 Docker 이미지를 다운로드합니다. RAGFlow slim Docker 이미지에는 임베딩 모델이나 Python 라이브러리가 포함되어 있지 않으므로 크기는 약 1GB입니다.
+
+   ```bash
+   $ cd ragflow/docker
+   $ docker compose -f docker-compose.yml up -d
+   ```
+   
+   > - 특정 버전의 RAGFlow slim Docker 이미지를 다운로드하려면, **docker/.env**에서 `RAGFlow_IMAGE` 변수를 원하는 버전으로 업데이트하세요. 예를 들어, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0-slim`으로 설정합니다. 이 변경을 완료한 후, 위의 명령을 다시 실행하여 다운로드를 시작하세요.
+   > - RAGFlow의 임베딩 모델과 Python 라이브러리를 포함한 개발 버전 Docker 이미지를 다운로드하려면, **docker/.env**에서 `RAGFlow_IMAGE` 변수를 `RAGFLOW_IMAGE=infiniflow/ragflow:dev`로 업데이트하세요. 이 변경을 완료한 후, 위의 명령을 다시 실행하여 다운로드를 시작하세요.
+   > - 특정 버전의 RAGFlow Docker 이미지를 임베딩 모델과 Python 라이브러리를 포함하여 다운로드하려면, **docker/.env**에서 `RAGFlow_IMAGE` 변수를 원하는 버전으로 업데이트하세요. 예를 들어, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0` 로 설정합니다. 이 변경을 완료한 후, 위의 명령을 다시 실행하여 다운로드를 시작하세요.  
+   
+   > **NOTE:** 임베딩 모델과 Python 라이브러리를 포함한 RAGFlow Docker 이미지의 크기는 약 9GB이며, 로드하는 데 상당히 오랜 시간이 걸릴 수 있습니다.
+
+
+4. 서버가 시작된 후 서버 상태를 확인하세요:
+
+   ```bash
+   $ docker logs -f ragflow-server
+   ```
+
+   _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 anormal` 오류가 발생할 수 있습니다.
+
+5. 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.
+   > 자세한 내용은 [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup)를 참조하세요.
+
+   _이제 쇼가 시작됩니다!_
+
+## 🔧 설정
+
+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.
+- [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.
+
+> [./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.
+
+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 -f docker/docker-compose.yml up -d
+> ```
+
+### Elasticsearch 에서 Infinity 로 문서 엔진 전환
+
+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
+   ```
+2. **docker/.env**의 "DOC_ENGINE" 을 "infinity" 로 설정합니다.
+3. 컨테이너 부팅:
+   ```bash
+   $docker compose-f docker/docker-compose.yml up-d
+   ``` 
+> [!WARNING]
+> Linux/arm64 시스템에서 Infinity로 전환하는 것은 공식적으로 지원되지 않습니다.
+ 
+## 🔧 소스 코드로 Docker 이미지를 컴파일합니다(임베딩 모델 포함하지 않음)
+
+이 Docker 이미지의 크기는 약 1GB이며, 외부 대형 모델과 임베딩 서비스에 의존합니다.
+
+```bash
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+pip3 install huggingface-hub nltk
+python3 download_deps.py
+docker build -f Dockerfile.slim -t infiniflow/ragflow:dev-slim .
+```
+
+## 🔧 소스 코드로 Docker 이미지를 컴파일합니다(임베딩 모델 포함)
+
+이 Docker의 크기는 약 9GB이며, 이미 임베딩 모델을 포함하고 있으므로 외부 대형 모델 서비스에만 의존하면 됩니다.
+
+```bash
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+pip3 install huggingface-hub nltk
+python3 download_deps.py
+docker build -f Dockerfile -t infiniflow/ragflow:dev .
+```
+
+## 🔨 소스 코드로 서비스를 시작합니다.
+
+1. Poetry를 설치하거나 이미 설치된 경우 이 단계를 건너뜁니다:
+   ```bash
+   curl -sSL https://install.python-poetry.org | python3 -
+   ```
+
+2. 소스 코드를 클론하고 Python 의존성을 설치합니다:
+   ```bash
+   git clone https://github.com/infiniflow/ragflow.git
+   cd ragflow/
+   export POETRY_VIRTUALENVS_CREATE=true POETRY_VIRTUALENVS_IN_PROJECT=true
+   ~/.local/bin/poetry install --sync --no-root # install RAGFlow dependent python modules
+   ```
+
+3. Docker Compose를 사용하여 의존 서비스(MinIO, Elasticsearch, Redis 및 MySQL)를 시작합니다:
+   ```bash
+   docker compose -f docker/docker-compose-base.yml up -d
+   ```
+
+   `/etc/hosts` 에 다음 줄을 추가하여 **docker/service_conf.yaml** 에 지정된 모든 호스트를 `127.0.0.1` 로 해결합니다:  
+   ```
+   127.0.0.1       es01 infinity mysql minio redis
+   ```  
+   **docker/service_conf.yaml** 에서 mysql 포트를 `5455` 로, es 포트를 `1200` 으로 업데이트합니다( **docker/.env** 에 지정된 대로).
+
+4. HuggingFace에 접근할 수 없는 경우, `HF_ENDPOINT` 환경 변수를 설정하여 미러 사이트를 사용하세요:
+ 
+   ```bash
+   export HF_ENDPOINT=https://hf-mirror.com
+   ```
+
+5. 백엔드 서비스를 시작합니다:
+   ```bash
+   source .venv/bin/activate
+   export PYTHONPATH=$(pwd)
+   bash docker/launch_backend_service.sh
+   ```
+
+6. 프론트엔드 의존성을 설치합니다:  
+   ```bash
+   cd web
+   npm install --force
+   ```  
+7. **.umirc.ts** 에서 `proxy.target` 을 `http://127.0.0.1:9380` 으로 업데이트합니다:
+8. 프론트엔드 서비스를 시작합니다:  
+   ```bash
+   npm run dev 
+   ```
+
+   _다음 인터페이스는 시스템이 성공적으로 시작되었음을 나타냅니다:_  
+
+   ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
+
+## 📚 문서
+
+- [Quickstart](https://ragflow.io/docs/dev/)
+- [User guide](https://ragflow.io/docs/dev/category/guides)
+- [References](https://ragflow.io/docs/dev/category/references)
+- [FAQ](https://ragflow.io/docs/dev/faq)
+
+## 📜 로드맵
+
+[RAGFlow 로드맵 2024](https://github.com/infiniflow/ragflow/issues/162)을 확인하세요.
+
+## 🏄 커뮤니티
+
+- [Discord](https://discord.gg/4XxujFgUN7)
+- [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](./CONTRIBUTING.md)을 검토해 주세요.

README_zh.md

@@ -7,22 +7,30 @@
 <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_ja.md">日本語</a> |
+  <a href="./README_ko.md">한국어</a> |
+  <a href="./README_id.md">Bahasa Indonesia</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:v0.14.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.14.0">
+    </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:v0.8.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.8.0"></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>
 </p>
 
+
 <h4 align="center">
   <a href="https://ragflow.io/docs/dev/">Document</a> |
   <a href="https://github.com/infiniflow/ragflow/issues/162">Roadmap</a> |
@@ -40,20 +48,24 @@
 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/user-attachments/assets/504bbbf1-c9f7-4d83-8cc5-e9cb63c26db6" width="1200"/>
 </div>
 
 
-## 📌 近期更新
+## 🔥 近期更新
+
+- 2024-11-22 完善了 Agent 中的变量定义和使用。
+- 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。
+- 2024-09-13 增加知识库问答搜索模式。
+- 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。
+
+## 🎉 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>
 
-- 2024-07-08 支持 [Graph](./graph/README.md)。
-- 2024-06-27 Q&A 解析方式支持 Markdown 文件和 Docx 文件。支持提取出 Docx 文件中的图片。支持提取出 Markdown 文件中的表格。
-- 2024-06-14 Q&A 解析方式支持 PDF 文件。
-- 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。
-- 2024-05-28 集成大模型 Baichuan 和火山方舟。
-- 2024-05-23 实现 [RAPTOR](https://arxiv.org/html/2401.18059v1) 提供更好的文本检索。
-- 2024-05-21 支持流式结果输出和文本块获取API。
-- 2024-05-15 集成大模型 OpenAI GPT-4o。
 
 ## 🌟 主要功能
 
@@ -130,15 +142,17 @@
 
 3. 进入 **docker** 文件夹，利用提前编译好的 Docker 镜像启动服务器：
 
+   > 运行以下命令会自动下载 dev 版的 RAGFlow slim Docker 镜像（`dev-slim`），该镜像并不包含 embedding 模型以及一些 Python 库，因此镜像大小约 1GB。
+
    ```bash
    $ cd ragflow/docker
-   $ chmod +x ./entrypoint.sh
-   $ docker compose -f docker-compose-CN.yml up -d
+   $ docker compose -f docker-compose.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=v0.8.0，然后运行上述命令。
-
-   > 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。
+   > - 如果你想下载并运行特定版本的 RAGFlow slim Docker 镜像，请在 **docker/.env** 文件中找到 `RAGFLOW_IMAGE` 变量，将其改为对应版本。例如 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0-slim`，然后再运行上述命令。
+   > - 如果您想安装内置 embedding 模型和 Python 库的 dev 版本的 Docker 镜像，需要将 **docker/.env** 文件中的 `RAGFLOW_IMAGE` 变量修改为： `RAGFLOW_IMAGE=infiniflow/ragflow:dev`。
+   > - 如果您想安装内置 embedding 模型和 Python 库的指定版本的 RAGFlow Docker 镜像，需要将 **docker/.env** 文件中的 `RAGFLOW_IMAGE` 变量修改为： `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0`。修改后，再运行上面的命令。
+   > **注意：** 安装内置 embedding 模型和 Python 库的指定版本的 RAGFlow Docker 镜像大小约 9 GB，可能需要更长时间下载，请耐心等待。
    
 4. 服务器启动成功后再次确认服务器状态：
 
@@ -149,19 +163,18 @@
    _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 anormal` 或 `网络异常`，因为 RAGFlow 可能并未完全启动成功。
 
 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）。
@@ -177,118 +190,126 @@
 
 - [.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。
+- [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！
 
+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`。
+
 > [./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`。
+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
-$ git clone https://github.com/infiniflow/ragflow.git
-$ cd ragflow/
-$ docker build -t infiniflow/ragflow:v0.8.0 .
-$ cd ragflow/docker
-$ chmod +x ./entrypoint.sh
-$ docker compose up -d
+   $ docker compose -f docker/docker-compose.yml down -v
    ```
 
-## 🛠️ 源码启动服务
+2. 设置 **docker/.env** 目录中的 `DOC_ENGINE` 为 `infinity`.
 
-如需从源码启动服务，请参考以下步骤：
+3. 启动容器:
 
-1. 克隆仓库
    ```bash
-$ git clone https://github.com/infiniflow/ragflow.git
-$ cd ragflow/
+   $ docker compose -f docker/docker-compose.yml up -d
    ```
 
-2. 创建虚拟环境（确保已安装 Anaconda 或 Miniconda）
-```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/
-```
+> [!WARNING] 
+> Infinity 目前官方并未正式支持在 Linux/arm64 架构下的机器上运行.
 
-3. 拷贝入口脚本并配置环境变量
-```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
-```
 
-将上述`which python`的输出作为`PY`的值，将`pwd`的输出作为`PYTHONPATH`的值。
+## 🔧 源码编译 Docker 镜像（不含 embedding 模型）
 
-`LD_LIBRARY_PATH`如果环境已经配置好，可以注释掉。
+本 Docker 镜像大小约 1 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/
+pip3 install huggingface-hub nltk
+python3 download_deps.py
+docker build -f Dockerfile.slim -t infiniflow/ragflow:dev-slim .
+```
+
+## 🔧 源码编译 Docker 镜像（包含 embedding 模型）
+
+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/
+pip3 install huggingface-hub nltk
+python3 download_deps.py
+docker build -f Dockerfile -t infiniflow/ragflow:dev .
+```
+
+## 🔨 以源代码启动服务
+
+1. 安装 Poetry。如已经安装，可跳过本步骤：  
+   ```bash
+   curl -sSL https://install.python-poetry.org | python3 -
+   ```
+
+2. 下载源代码并安装 Python 依赖：  
+   ```bash
+   git clone https://github.com/infiniflow/ragflow.git
+   cd ragflow/
+   export POETRY_VIRTUALENVS_CREATE=true POETRY_VIRTUALENVS_IN_PROJECT=true
+   ~/.local/bin/poetry install --sync --no-root # install RAGFlow dependent python modules
+   ```
+
+3. 通过 Docker Compose 启动依赖的服务（MinIO, Elasticsearch, Redis, and MySQL）：  
+   ```bash
+   docker compose -f docker/docker-compose-base.yml up -d
+   ```
+
+   在 `/etc/hosts` 中添加以下代码，将 **docker/service_conf.yaml** 文件中的所有 host 地址都解析为 `127.0.0.1`：  
+   ```
+   127.0.0.1       es01 infinity mysql minio redis
+   ```  
+   在文件 **docker/service_conf.yaml** 中，对照 **docker/.env** 的配置将 mysql 端口更新为 `5455`，es 端口更新为 `1200`。
+
+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：  
  
    ```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
    ```
 
-4. 启动基础服务
+5. 启动后端服务：  
    ```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. 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。
-
-6. 启动服务
+6. 安装前端依赖：  
    ```bash
-$ chmod +x ./entrypoint.sh
-$ bash ./entrypoint.sh
+   cd web
+   npm install --force
    ```  
-7. 启动WebUI服务
+7. 配置前端，将 **.umirc.ts** 的 `proxy.target` 更新为 `http://127.0.0.1:9380`：  
+8. 启动前端服务：  
    ```bash
-$ cd web
-$ npm install --registry=https://registry.npmmirror.com --force
-$ vim .umirc.ts
-# 修改proxy.target为http://127.0.0.1:9380
-$ npm run dev 
+   npm run dev 
    ```  
 
-8. 部署WebUI服务
-```bash
-$ cd web
-$ npm install --registry=https://registry.npmmirror.com --force
-$ umi build
-$ mkdir -p /ragflow/web
-$ cp -r dist /ragflow/web
-$ apt install nginx -y
-$ cp ../docker/nginx/proxy.conf /etc/nginx
-$ cp ../docker/nginx/nginx.conf /etc/nginx
-$ cp ../docker/nginx/ragflow.conf /etc/nginx/conf.d
-$ systemctl start nginx
-```
+   _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)
+
 ## 📚 技术文档
 
 - [Quickstart](https://ragflow.io/docs/dev/)
-- [User guide](https://ragflow.io/docs/dev/category/user-guides)
+- [User guide](https://ragflow.io/docs/dev/category/guides)
 - [References](https://ragflow.io/docs/dev/category/references)
 - [FAQ](https://ragflow.io/docs/dev/faq)
 
@@ -304,7 +325,11 @@ $ systemctl start nginx
 
 ## 🙌 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
+
+- [预约咨询](https://aao615odquw.feishu.cn/share/base/form/shrcnjw7QleretCLqh1nuPo1xxh)
 
 ## 👥 加入社区
 

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/canvas.py

@@ -13,19 +13,13 @@
 #  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
-
-import pandas as pd
-
-from graph.component import component_class
-from graph.component.base import ComponentBase
-from graph.settings import flow_logger, DEBUG
-
+from agent.component import component_class
+from agent.component.base import ComponentBase
 
 class Canvas(ABC):
     """
@@ -162,8 +156,12 @@ class Canvas(ABC):
             self.components[k]["obj"].reset()
         self._embed_id = ""
 
+    def get_compnent_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)
@@ -173,10 +171,10 @@ 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)
@@ -184,22 +182,33 @@ class Canvas(ABC):
 
         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
                 cpn = self.components[c]["obj"]
                 if cpn.component_name == "Answer":
                     self.answer.append(c)
                 else:
-                    if DEBUG: print("RUN: ", c)
+                    logging.debug(f"Canvas.prepare2run: {c}")
+                    if c not in without_dependent_checking:
+                        cpids = cpn.get_dependent_components()
+                        if any([cc not in self.path[-1] for cc in cpids]):
+                            if c not in waiting: waiting.append(c)
+                            continue
+                    yield "*'{}'* is running...🕞".format(self.get_compnent_name(c))
                     ans = cpn.run(self.history, **kwargs)
                     self.path[-1].append(c)
             ran += 1
 
-        prepare2run(self.components[self.path[-2][-1]]["downstream"])
+        for m in prepare2run(self.components[self.path[-2][-1]]["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
@@ -212,25 +221,26 @@ class Canvas(ABC):
                 assert switch_out in self.components, \
                     "{}'s output: {} not valid.".format(cpn_id, switch_out)
                 try:
-                    prepare2run([switch_out])
+                    for m in prepare2run([switch_out]):
+                        yield {"content": m, "running_status": True}
                 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()
+                    yield {"content": "*Exception*: {}".format(e), "running_status": True}
+                    logging.exception("Canvas.run got exception")
                 continue
 
             try:
-                prepare2run(cpn["downstream"])
+                for m in prepare2run(cpn["downstream"]):
+                    yield {"content": m, "running_status": True}
             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()
+                yield {"content": "*Exception*: {}".format(e), "running_status": True}
+                logging.exception("Canvas.run got exception")
+
+            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}
+                ran -= 1
 
         if self.answer:
             cpn_id = self.answer[0]
@@ -239,11 +249,13 @@ 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]
@@ -253,9 +265,11 @@ 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):
@@ -267,7 +281,7 @@ class Canvas(ABC):
     def get_embedding_model(self):
         return self._embed_id
 
-    def _find_loop(self, max_loops=2):
+    def _find_loop(self, max_loops=6):
         path = self.path[-1][::-1]
         if len(path) < 2: return False
 
@@ -293,3 +307,6 @@ class Canvas(ABC):
                 return pat + " => " + pat
 
         return False
+
+    def get_prologue(self):
+        return self.components["begin"]["obj"]._param.prologue

agent/component/__init__.py

@@ -0,0 +1,39 @@
+import importlib
+from .begin import Begin, BeginParam
+from .generate import Generate, GenerateParam
+from .retrieval import Retrieval, RetrievalParam
+from .answer import Answer, AnswerParam
+from .categorize import Categorize, CategorizeParam
+from .switch import Switch, SwitchParam
+from .relevant import Relevant, RelevantParam
+from .message import Message, MessageParam
+from .rewrite import RewriteQuestion, RewriteQuestionParam
+from .keyword import KeywordExtract, KeywordExtractParam
+from .concentrator import Concentrator, ConcentratorParam
+from .baidu import Baidu, BaiduParam
+from .duckduckgo import DuckDuckGo, DuckDuckGoParam
+from .wikipedia import Wikipedia, WikipediaParam
+from .pubmed import PubMed, PubMedParam
+from .arxiv import ArXiv, ArXivParam
+from .google import Google, GoogleParam
+from .bing import Bing, BingParam
+from .googlescholar import GoogleScholar, GoogleScholarParam
+from .deepl import DeepL, DeepLParam
+from .github import GitHub, GitHubParam
+from .baidufanyi import BaiduFanyi, BaiduFanyiParam
+from .qweather import QWeather, QWeatherParam
+from .exesql import ExeSQL, ExeSQLParam
+from .yahoofinance import YahooFinance, YahooFinanceParam
+from .wencai import WenCai, WenCaiParam
+from .jin10 import Jin10, Jin10Param
+from .tushare import TuShare, TuShareParam
+from .akshare import AkShare, AkShareParam
+from .crawler import Crawler, CrawlerParam
+from .invoke import Invoke, InvokeParam
+from .template import Template, TemplateParam
+
+
+def component_class(class_name):
+    m = importlib.import_module("agent.component")
+    c = getattr(m, class_name)
+    return c

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
+import akshare as ak
+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):
+        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

@@ -19,7 +19,7 @@ from functools import partial
 
 import pandas as pd
 
-from graph.component.base import ComponentBase, ComponentParamBase
+from agent.component.base import ComponentBase, ComponentParamBase
 
 
 class AnswerParam(ComponentParamBase):
@@ -59,8 +59,10 @@ class Answer(ComponentBase, ABC):
         stream = self.get_stream_input()
         if isinstance(stream, pd.DataFrame):
             res = stream
+            answer = ""
             for ii, row in stream.iterrows():
-                yield row.to_dict()
+                answer += row.to_dict()["content"]
+                yield {"content": answer}
         else:
             for st in stream():
                 res = st

agent/component/arxiv.py

@@ -0,0 +1,68 @@
+#
+#  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 arxiv
+import pandas as pd
+from agent.component.base import ComponentBase, ComponentParamBase
+
+class ArXivParam(ComponentParamBase):
+    """
+    Define the ArXiv component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 6
+        self.sort_by = 'submittedDate'
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+        self.check_valid_value(self.sort_by, "ArXiv Search Sort_by",
+                               ['submittedDate', 'lastUpdatedDate', 'relevance'])
+
+
+class ArXiv(ComponentBase, ABC):
+    component_name = "ArXiv"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return ArXiv.be_output("")
+
+        try:
+            sort_choices = {"relevance": arxiv.SortCriterion.Relevance,
+                            "lastUpdatedDate": arxiv.SortCriterion.LastUpdatedDate,
+                            'submittedDate': arxiv.SortCriterion.SubmittedDate}
+            arxiv_client = arxiv.Client()
+            search = arxiv.Search(
+                query=ans,
+                max_results=self._param.top_n,
+                sort_by=sort_choices[self._param.sort_by]
+            )
+            arxiv_res = [
+                {"content": 'Title: ' + i.title + '\nPdf_Url: <a href="' + i.pdf_url + '"></a> \nSummary: ' + i.summary} for
+                i in list(arxiv_client.results(search))]
+        except Exception as e:
+            return ArXiv.be_output("**ERROR**: " + str(e))
+
+        if not arxiv_res:
+            return ArXiv.be_output("")
+
+        df = pd.DataFrame(arxiv_res)
+        logging.debug(f"df: {str(df)}")
+        return df

agent/component/baidu.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.
+#
+import logging
+from abc import ABC
+import pandas as pd
+import requests
+import re
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class BaiduParam(ComponentParamBase):
+    """
+    Define the Baidu component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 10
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+
+
+class Baidu(ComponentBase, ABC):
+    component_name = "Baidu"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return Baidu.be_output("")
+
+        try:
+            url = 'https://www.baidu.com/s?wd=' + ans + '&rn=' + str(self._param.top_n)
+            headers = {
+                'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.104 Safari/537.36'}
+            response = requests.get(url=url, headers=headers)
+
+            url_res = re.findall(r"'url': \\\"(.*?)\\\"}", response.text)
+            title_res = re.findall(r"'title': \\\"(.*?)\\\",\\n", response.text)
+            body_res = re.findall(r"\"contentText\":\"(.*?)\"", response.text)
+            baidu_res = [{"content": re.sub('<em>|</em>', '', '<a href="' + url + '">' + title + '</a>    ' + body)} for
+                         url, title, body in zip(url_res, title_res, body_res)]
+            del body_res, url_res, title_res
+        except Exception as e:
+            return Baidu.be_output("**ERROR**: " + str(e))
+
+        if not baidu_res:
+            return Baidu.be_output("")
+
+        df = pd.DataFrame(baidu_res)
+        logging.debug(f"df: {str(df)}")
+        return df
+

agent/component/baidufanyi.py

@@ -0,0 +1,98 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import random
+from abc import ABC
+import requests
+from agent.component.base import ComponentBase, ComponentParamBase
+from hashlib import md5
+
+
+class BaiduFanyiParam(ComponentParamBase):
+    """
+    Define the BaiduFanyi component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.appid = "xxx"
+        self.secret_key = "xxx"
+        self.trans_type = 'translate'
+        self.parameters = []
+        self.source_lang = 'auto'
+        self.target_lang = 'auto'
+        self.domain = 'finance'
+
+    def check(self):
+        self.check_empty(self.appid, "BaiduFanyi APPID")
+        self.check_empty(self.secret_key, "BaiduFanyi Secret Key")
+        self.check_valid_value(self.trans_type, "Translate type", ['translate', 'fieldtranslate'])
+        self.check_valid_value(self.trans_type, "Translate domain",
+                               ['it', 'finance', 'machinery', 'senimed', 'novel', 'academic', 'aerospace', 'wiki',
+                                'news', 'law', 'contract'])
+        self.check_valid_value(self.source_lang, "Source language",
+                               ['auto', 'zh', 'en', 'yue', 'wyw', 'jp', 'kor', 'fra', 'spa', 'th', 'ara', 'ru', 'pt',
+                                'de', 'it', 'el', 'nl', 'pl', 'bul', 'est', 'dan', 'fin', 'cs', 'rom', 'slo', 'swe',
+                                'hu', 'cht', 'vie'])
+        self.check_valid_value(self.target_lang, "Target language",
+                               ['auto', 'zh', 'en', 'yue', 'wyw', 'jp', 'kor', 'fra', 'spa', 'th', 'ara', 'ru', 'pt',
+                                'de', 'it', 'el', 'nl', 'pl', 'bul', 'est', 'dan', 'fin', 'cs', 'rom', 'slo', 'swe',
+                                'hu', 'cht', 'vie'])
+        self.check_valid_value(self.domain, "Translate field",
+                               ['it', 'finance', 'machinery', 'senimed', 'novel', 'academic', 'aerospace', 'wiki',
+                                'news', 'law', 'contract'])
+
+
+class BaiduFanyi(ComponentBase, ABC):
+    component_name = "BaiduFanyi"
+
+    def _run(self, history, **kwargs):
+
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return BaiduFanyi.be_output("")
+
+        try:
+            source_lang = self._param.source_lang
+            target_lang = self._param.target_lang
+            appid = self._param.appid
+            salt = random.randint(32768, 65536)
+            secret_key = self._param.secret_key
+
+            if self._param.trans_type == 'translate':
+                sign = md5((appid + ans + salt + secret_key).encode('utf-8')).hexdigest()
+                url = 'http://api.fanyi.baidu.com/api/trans/vip/translate?' + 'q=' + ans + '&from=' + source_lang + '&to=' + target_lang + '&appid=' + appid + '&salt=' + salt + '&sign=' + sign
+                headers = {"Content-Type": "application/x-www-form-urlencoded"}
+                response = requests.post(url=url, headers=headers).json()
+
+                if response.get('error_code'):
+                    BaiduFanyi.be_output("**Error**:" + response['error_msg'])
+
+                return BaiduFanyi.be_output(response['trans_result'][0]['dst'])
+            elif self._param.trans_type == 'fieldtranslate':
+                domain = self._param.domain
+                sign = md5((appid + ans + salt + domain + secret_key).encode('utf-8')).hexdigest()
+                url = 'http://api.fanyi.baidu.com/api/trans/vip/fieldtranslate?' + 'q=' + ans + '&from=' + source_lang + '&to=' + target_lang + '&appid=' + appid + '&salt=' + salt + '&domain=' + domain + '&sign=' + sign
+                headers = {"Content-Type": "application/x-www-form-urlencoded"}
+                response = requests.post(url=url, headers=headers).json()
+
+                if response.get('error_code'):
+                    BaiduFanyi.be_output("**Error**:" + response['error_msg'])
+
+                return BaiduFanyi.be_output(response['trans_result'][0]['dst'])
+
+        except Exception as e:
+            BaiduFanyi.be_output("**Error**:" + str(e))

agent/component/base.py

@@ -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 graph import settings
-from graph.settings import flow_logger, DEBUG
+from agent import settings
 
 _FEEDED_DEPRECATED_PARAMS = "_feeded_deprecated_params"
 _DEPRECATED_PARAMS = "_deprecated_params"
@@ -35,7 +34,9 @@ _IS_RAW_CONF = "_is_raw_conf"
 class ComponentParamBase(ABC):
     def __init__(self):
         self.output_var_name = "output"
-        self.message_history_window_size = 4
+        self.message_history_window_size = 22
+        self.query = []
+        self.inputs = []
 
     def set_name(self, name: str):
         self._name = name
@@ -81,7 +82,6 @@ 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 +359,13 @@ 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."
             )
@@ -385,9 +385,13 @@ class ComponentBase(ABC):
         """
         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):
@@ -396,8 +400,15 @@ class ComponentBase(ABC):
         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)))
         try:
             res = self._run(history, **kwargs)
@@ -431,36 +442,85 @@ class ComponentBase(ABC):
 
     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: partial | pd.DataFrame):
         setattr(self._param, self._param.output_var_name, v)
 
     def get_input(self):
-        upstream_outs = []
         reversed_cpnts = []
         if len(self._canvas.path) > 1:
             reversed_cpnts.extend(self._canvas.path[-2])
         reversed_cpnts.extend(self._canvas.path[-1])
 
-        if DEBUG: print(self.component_name, reversed_cpnts[::-1])
+        if self._param.query:
+            self._param.inputs = []
+            outs = []
+            for q in self._param.query:
+                if q["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
+
+                    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["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_cpnts[::-1]:
-            if self.get_component_name(u) in ["switch"]: continue
+            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 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:
-                if self.get_component_name(u) in ["relevant"]: continue
-
-            else: upstream_outs.append(self._canvas.get_component(u)["obj"].output(allow_partial=False)[1])
+            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
 
-        return pd.concat(upstream_outs, ignore_index=False)
+        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_stream_input(self):
         reversed_cpnts = []

agent/component/begin.py

@@ -13,11 +13,10 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-import json
 from functools import partial
-
 import pandas as pd
-from graph.component.base import ComponentBase, ComponentParamBase
+from agent.component.base import ComponentBase, ComponentParamBase
+
 
 class BeginParam(ComponentParamBase):
 

agent/component/bing.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 requests
+import pandas as pd
+from agent.component.base import ComponentBase, ComponentParamBase
+
+class BingParam(ComponentParamBase):
+    """
+    Define the Bing component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 10
+        self.channel = "Webpages"
+        self.api_key = "YOUR_ACCESS_KEY"
+        self.country = "CN"
+        self.language = "en"
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+        self.check_valid_value(self.channel, "Bing Web Search or Bing News", ["Webpages", "News"])
+        self.check_empty(self.api_key, "Bing subscription key")
+        self.check_valid_value(self.country, "Bing Country",
+                               ['AR', 'AU', 'AT', 'BE', 'BR', 'CA', 'CL', 'DK', 'FI', 'FR', 'DE', 'HK', 'IN', 'ID',
+                                'IT', 'JP', 'KR', 'MY', 'MX', 'NL', 'NZ', 'NO', 'CN', 'PL', 'PT', 'PH', 'RU', 'SA',
+                                'ZA', 'ES', 'SE', 'CH', 'TW', 'TR', 'GB', 'US'])
+        self.check_valid_value(self.language, "Bing Languages",
+                               ['ar', 'eu', 'bn', 'bg', 'ca', 'ns', 'nt', 'hr', 'cs', 'da', 'nl', 'en', 'gb', 'et',
+                                'fi', 'fr', 'gl', 'de', 'gu', 'he', 'hi', 'hu', 'is', 'it', 'jp', 'kn', 'ko', 'lv',
+                                'lt', 'ms', 'ml', 'mr', 'nb', 'pl', 'br', 'pt', 'pa', 'ro', 'ru', 'sr', 'sk', 'sl',
+                                'es', 'sv', 'ta', 'te', 'th', 'tr', 'uk', 'vi'])
+
+
+class Bing(ComponentBase, ABC):
+    component_name = "Bing"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return Bing.be_output("")
+
+        try:
+            headers = {"Ocp-Apim-Subscription-Key": self._param.api_key, 'Accept-Language': self._param.language}
+            params = {"q": ans, "textDecorations": True, "textFormat": "HTML", "cc": self._param.country,
+                      "answerCount": 1, "promote": self._param.channel}
+            if self._param.channel == "Webpages":
+                response = requests.get("https://api.bing.microsoft.com/v7.0/search", headers=headers, params=params)
+                response.raise_for_status()
+                search_results = response.json()
+                bing_res = [{"content": '<a href="' + i["url"] + '">' + i["name"] + '</a>    ' + i["snippet"]} for i in
+                            search_results["webPages"]["value"]]
+            elif self._param.channel == "News":
+                response = requests.get("https://api.bing.microsoft.com/v7.0/news/search", headers=headers,
+                                        params=params)
+                response.raise_for_status()
+                search_results = response.json()
+                bing_res = [{"content": '<a href="' + i["url"] + '">' + i["name"] + '</a>    ' + i["description"]} for i
+                            in search_results['news']['value']]
+        except Exception as e:
+            return Bing.be_output("**ERROR**: " + str(e))
+
+        if not bing_res:
+            return Bing.be_output("")
+
+        df = pd.DataFrame(bing_res)
+        logging.debug(f"df: {str(df)}")
+        return df

agent/component/categorize.py

@@ -13,14 +13,11 @@
 #  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 api.db import LLMType
 from api.db.services.llm_service import LLMBundle
-from graph.component import GenerateParam, Generate
-from graph.settings import DEBUG
+from agent.component import GenerateParam, Generate
 
 
 class CategorizeParam(GenerateParam):
@@ -37,7 +34,7 @@ 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 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):
@@ -76,15 +73,15 @@ 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 = "Question: " + (list(input["content"])[-1] if "content" in input else "") + "\tCategory: "
         chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id)
         ans = chat_mdl.chat(self._param.get_prompt(), [{"role": "user", "content": input}],
                             self._param.gen_conf())
-        if DEBUG: print(ans, ":::::::::::::::::::::::::::::::::", input)
+        logging.debug(f"input: {input}, answer: {str(ans)}")
         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"])
 
-        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"])
 
 

agent/component/concentrator.py

@@ -1,5 +1,5 @@
 #
-#  Copyright 2019 The FATE Authors. All Rights Reserved.
+#  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.
@@ -13,22 +13,24 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-# Logger
-import os
+from abc import ABC
+from agent.component.base import ComponentBase, ComponentParamBase
 
-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
+class ConcentratorParam(ComponentParamBase):
+    """
+    Define the Concentrator component parameters.
+    """
 
-flow_logger = getLogger("flow")
-database_logger = getLogger("database")
-FLOAT_ZERO = 1e-8
-PARAM_MAXDEPTH = 5
+    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 +1,70 @@
+#
+#  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
+
+
+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 ans:
+            return Crawler.be_output("")
+        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

@@ -0,0 +1,62 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from abc import ABC
+import re
+from agent.component.base import ComponentBase, ComponentParamBase
+import deepl
+
+
+class DeepLParam(ComponentParamBase):
+    """
+    Define the DeepL component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.auth_key = "xxx"
+        self.parameters = []
+        self.source_lang = 'ZH'
+        self.target_lang = 'EN-GB'
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+        self.check_valid_value(self.source_lang, "Source language",
+                               ['AR', 'BG', 'CS', 'DA', 'DE', 'EL', 'EN', 'ES', 'ET', 'FI', 'FR', 'HU', 'ID', 'IT',
+                                'JA', 'KO', 'LT', 'LV', 'NB', 'NL', 'PL', 'PT', 'RO', 'RU', 'SK', 'SL', 'SV', 'TR',
+                                'UK', 'ZH'])
+        self.check_valid_value(self.target_lang, "Target language",
+                               ['AR', 'BG', 'CS', 'DA', 'DE', 'EL', 'EN-GB', 'EN-US', 'ES', 'ET', 'FI', 'FR', 'HU',
+                                'ID', 'IT', 'JA', 'KO', 'LT', 'LV', 'NB', 'NL', 'PL', 'PT-BR', 'PT-PT', 'RO', 'RU',
+                                'SK', 'SL', 'SV', 'TR', 'UK', 'ZH'])
+
+
+class DeepL(ComponentBase, ABC):
+    component_name = "GitHub"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return DeepL.be_output("")
+
+        try:
+            translator = deepl.Translator(self._param.auth_key)
+            result = translator.translate_text(ans, source_lang=self._param.source_lang,
+                                               target_lang=self._param.target_lang)
+
+            return DeepL.be_output(result.text)
+        except Exception as e:
+            DeepL.be_output("**Error**:" + str(e))

agent/component/duckduckgo.py

@@ -0,0 +1,66 @@
+#
+#  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
+from duckduckgo_search import DDGS
+import pandas as pd
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class DuckDuckGoParam(ComponentParamBase):
+    """
+    Define the DuckDuckGo component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 10
+        self.channel = "text"
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+        self.check_valid_value(self.channel, "Web Search or News", ["text", "news"])
+
+
+class DuckDuckGo(ComponentBase, ABC):
+    component_name = "DuckDuckGo"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return DuckDuckGo.be_output("")
+
+        try:
+            if self._param.channel == "text":
+                with DDGS() as ddgs:
+                    # {'title': '', 'href': '', 'body': ''}
+                    duck_res = [{"content": '<a href="' + i["href"] + '">' + i["title"] + '</a>    ' + i["body"]} for i
+                                in ddgs.text(ans, max_results=self._param.top_n)]
+            elif self._param.channel == "news":
+                with DDGS() as ddgs:
+                    # {'date': '', 'title': '', 'body': '', 'url': '', 'image': '', 'source': ''}
+                    duck_res = [{"content": '<a href="' + i["url"] + '">' + i["title"] + '</a>    ' + i["body"]} for i
+                                in ddgs.news(ans, max_results=self._param.top_n)]
+        except Exception as e:
+            return DuckDuckGo.be_output("**ERROR**: " + str(e))
+
+        if not duck_res:
+            return DuckDuckGo.be_output("")
+
+        df = pd.DataFrame(duck_res)
+        logging.debug("df: {df}")
+        return df

agent/component/exesql.py

@@ -0,0 +1,103 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from abc import ABC
+import re
+import pandas as pd
+import pymysql
+import psycopg2
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class ExeSQLParam(ComponentParamBase):
+    """
+    Define the ExeSQL component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.db_type = "mysql"
+        self.database = ""
+        self.username = ""
+        self.host = ""
+        self.port = 3306
+        self.password = ""
+        self.loop = 3
+        self.top_n = 30
+
+    def check(self):
+        self.check_valid_value(self.db_type, "Choose DB type", ['mysql', 'postgresql', 'mariadb'])
+        self.check_empty(self.database, "Database name")
+        self.check_empty(self.username, "database username")
+        self.check_empty(self.host, "IP Address")
+        self.check_positive_integer(self.port, "IP Port")
+        self.check_empty(self.password, "Database password")
+        self.check_positive_integer(self.top_n, "Number of records")
+        if self.database == "rag_flow":
+            if self.host == "ragflow-mysql": raise ValueError("The host is not accessible.")
+            if self.password == "infini_rag_flow": raise ValueError("The host is not accessible.")
+
+
+class ExeSQL(ComponentBase, ABC):
+    component_name = "ExeSQL"
+
+    def _run(self, history, **kwargs):
+        if not hasattr(self, "_loop"):
+            setattr(self, "_loop", 0)
+        if self._loop >= self._param.loop:
+            self._loop = 0
+            raise Exception("Maximum loop time exceeds. Can't query the correct data via SQL statement.")
+        self._loop += 1
+
+        ans = self.get_input()
+        ans = "".join(ans["content"]) if "content" in ans else ""
+        ans = re.sub(r'^.*?SELECT ', 'SELECT ', repr(ans), flags=re.IGNORECASE)
+        ans = re.sub(r';.*?SELECT ', '; SELECT ', ans, flags=re.IGNORECASE)
+        ans = re.sub(r';[^;]*$', r';', ans)
+        if not ans:
+            raise Exception("SQL statement not found!")
+
+        if self._param.db_type in ["mysql", "mariadb"]:
+            db = 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 = psycopg2.connect(dbname=self._param.database, user=self._param.username, host=self._param.host,
+                                  port=self._param.port, password=self._param.password)
+
+        try:
+            cursor = db.cursor()
+        except Exception as e:
+            raise Exception("Database Connection Failed! \n" + str(e))
+        sql_res = []
+        for single_sql in re.split(r';', ans.replace(r"\n", " ")):
+            if not single_sql:
+                continue
+            try:
+                cursor.execute(single_sql)
+                if cursor.rowcount == 0:
+                    sql_res.append({"content": "\nTotal: 0\n No record in the database!"})
+                    continue
+                single_res = pd.DataFrame([i for i in cursor.fetchmany(size=self._param.top_n)])
+                single_res.columns = [i[0] for i in cursor.description]
+                sql_res.append({"content": "\nTotal: " + str(cursor.rowcount) + "\n" + single_res.to_markdown()})
+            except Exception as e:
+                sql_res.append({"content": "**Error**:" + str(e) + "\nError SQL Statement:" + single_sql})
+                pass
+        db.close()
+
+        if not sql_res:
+            return ExeSQL.be_output("")
+
+        return pd.DataFrame(sql_res)

agent/component/generate.py

@@ -0,0 +1,201 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import re
+from functools import partial
+import pandas as pd
+from api.db import LLMType
+from api.db.services.dialog_service import message_fit_in
+from api.db.services.llm_service import LLMBundle
+from api import settings
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class GenerateParam(ComponentParamBase):
+    """
+    Define the Generate component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.llm_id = ""
+        self.prompt = ""
+        self.max_tokens = 0
+        self.temperature = 0
+        self.top_p = 0
+        self.presence_penalty = 0
+        self.frequency_penalty = 0
+        self.cite = True
+        self.parameters = []
+
+    def check(self):
+        self.check_decimal_float(self.temperature, "[Generate] Temperature")
+        self.check_decimal_float(self.presence_penalty, "[Generate] Presence penalty")
+        self.check_decimal_float(self.frequency_penalty, "[Generate] Frequency penalty")
+        self.check_nonnegative_number(self.max_tokens, "[Generate] Max tokens")
+        self.check_decimal_float(self.top_p, "[Generate] Top P")
+        self.check_empty(self.llm_id, "[Generate] LLM")
+        # self.check_defined_type(self.parameters, "Parameters", ["list"])
+
+    def gen_conf(self):
+        conf = {}
+        if self.max_tokens > 0: conf["max_tokens"] = self.max_tokens
+        if self.temperature > 0: conf["temperature"] = self.temperature
+        if self.top_p > 0: conf["top_p"] = self.top_p
+        if self.presence_penalty > 0: conf["presence_penalty"] = self.presence_penalty
+        if self.frequency_penalty > 0: conf["frequency_penalty"] = self.frequency_penalty
+        return conf
+
+
+class Generate(ComponentBase):
+    component_name = "Generate"
+
+    def get_dependent_components(self):
+        cpnts = set([para["component_id"].split("@")[0] for para in self._param.parameters \
+                     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 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 = settings.retrievaler.insert_citations(answer,
+                                                            [ck["content_ltks"] for _, ck in retrieval_res.iterrows()],
+                                                            [ck["vector"] for _, ck in retrieval_res.iterrows()],
+                                                            LLMBundle(self._canvas.get_tenant_id(), LLMType.EMBEDDING,
+                                                                      self._canvas.get_embedding_model()), tkweight=0.7,
+                                                            vtweight=0.3)
+        doc_ids = set([])
+        recall_docs = []
+        for i in idx:
+            did = retrieval_res.loc[int(i), "doc_id"]
+            if did in doc_ids: continue
+            doc_ids.add(did)
+            recall_docs.append({"doc_id": did, "doc_name": retrieval_res.loc[int(i), "docnm_kwd"]})
+
+        del retrieval_res["vector"]
+        del retrieval_res["content_ltks"]
+
+        reference = {
+            "chunks": [ck.to_dict() for _, ck in retrieval_res.iterrows()],
+            "doc_aggs": recall_docs
+        }
+
+        if answer.lower().find("invalid key") >= 0 or answer.lower().find("invalid api") >= 0:
+            answer += " Please set LLM API-Key in 'User Setting -> Model Providers -> API-Key'"
+        res = {"content": answer, "reference": reference}
+
+        return res
+
+    def _run(self, history, **kwargs):
+        chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id)
+        prompt = self._param.prompt
+
+        retrieval_res = []
+        self._param.inputs = []
+        for para in self._param.parameters:
+            if not para.get("component_id"): continue
+            component_id = para["component_id"].split("@")[0]
+            if para["component_id"].lower().find("@") >= 0:
+                cpn_id, key = para["component_id"].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["component_id"], "content": kwargs[para["key"]]})
+                        break
+                else:
+                    assert False, f"Can't find parameter '{key}' for {cpn_id}"
+                continue
+
+            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"]] = ""
+            else:
+                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["component_id"], "content": kwargs[para["key"]]})
+
+        if retrieval_res:
+            retrieval_res = pd.concat(retrieval_res, ignore_index=True)
+        else: retrieval_res = pd.DataFrame([])
+
+        for n, v in kwargs.items():
+            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])[
+            "obj"].component_name.lower() == "answer":
+            return partial(self.stream_output, chat_mdl, prompt, retrieval_res)
+
+        if "empty_response" in retrieval_res.columns and not "".join(retrieval_res["content"]):
+            res = {"content": "\n- ".join(retrieval_res["empty_response"]) if "\n- ".join(
+                retrieval_res["empty_response"]) else "Nothing found in knowledgebase!", "reference": []}
+            return pd.DataFrame([res])
+
+        msg = self._canvas.get_history(self._param.message_history_window_size)
+        if len(msg) < 1: msg.append({"role": "user", "content": ""})
+        _, 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": ""})
+        ans = chat_mdl.chat(msg[0]["content"], msg[1:], self._param.gen_conf())
+
+        if self._param.cite and "content_ltks" in retrieval_res.columns and "vector" 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": []}
+            yield res
+            self.set_output(res)
+            return
+
+        msg = self._canvas.get_history(self._param.message_history_window_size)
+        if len(msg) < 1: msg.append({"role": "user", "content": ""})
+        _, 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": ""})
+        answer = ""
+        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:
+            res = self.set_cite(retrieval_res, answer)
+            yield res
+
+        self.set_output(res)

agent/component/github.py

@@ -0,0 +1,61 @@
+#
+#  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
+import requests
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class GitHubParam(ComponentParamBase):
+    """
+    Define the GitHub component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 10
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+
+
+class GitHub(ComponentBase, ABC):
+    component_name = "GitHub"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return GitHub.be_output("")
+
+        try:
+            url = 'https://api.github.com/search/repositories?q=' + ans + '&sort=stars&order=desc&per_page=' + str(
+                self._param.top_n)
+            headers = {"Content-Type": "application/vnd.github+json", "X-GitHub-Api-Version": '2022-11-28'}
+            response = requests.get(url=url, headers=headers).json()
+
+            github_res = [{"content": '<a href="' + i["html_url"] + '">' + i["name"] + '</a>' + str(
+                i["description"]) + '\n stars:' + str(i['watchers'])} for i in response['items']]
+        except Exception as e:
+            return GitHub.be_output("**ERROR**: " + str(e))
+
+        if not github_res:
+            return GitHub.be_output("")
+
+        df = pd.DataFrame(github_res)
+        logging.debug(f"df: {df}")
+        return df

agent/component/google.py

@@ -0,0 +1,96 @@
+#
+#  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
+from serpapi import GoogleSearch
+import pandas as pd
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class GoogleParam(ComponentParamBase):
+    """
+    Define the Google component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 10
+        self.api_key = "xxx"
+        self.country = "cn"
+        self.language = "en"
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+        self.check_empty(self.api_key, "SerpApi API key")
+        self.check_valid_value(self.country, "Google Country",
+                               ['af', 'al', 'dz', 'as', 'ad', 'ao', 'ai', 'aq', 'ag', 'ar', 'am', 'aw', 'au', 'at',
+                                'az', 'bs', 'bh', 'bd', 'bb', 'by', 'be', 'bz', 'bj', 'bm', 'bt', 'bo', 'ba', 'bw',
+                                'bv', 'br', 'io', 'bn', 'bg', 'bf', 'bi', 'kh', 'cm', 'ca', 'cv', 'ky', 'cf', 'td',
+                                'cl', 'cn', 'cx', 'cc', 'co', 'km', 'cg', 'cd', 'ck', 'cr', 'ci', 'hr', 'cu', 'cy',
+                                'cz', 'dk', 'dj', 'dm', 'do', 'ec', 'eg', 'sv', 'gq', 'er', 'ee', 'et', 'fk', 'fo',
+                                'fj', 'fi', 'fr', 'gf', 'pf', 'tf', 'ga', 'gm', 'ge', 'de', 'gh', 'gi', 'gr', 'gl',
+                                'gd', 'gp', 'gu', 'gt', 'gn', 'gw', 'gy', 'ht', 'hm', 'va', 'hn', 'hk', 'hu', 'is',
+                                'in', 'id', 'ir', 'iq', 'ie', 'il', 'it', 'jm', 'jp', 'jo', 'kz', 'ke', 'ki', 'kp',
+                                'kr', 'kw', 'kg', 'la', 'lv', 'lb', 'ls', 'lr', 'ly', 'li', 'lt', 'lu', 'mo', 'mk',
+                                'mg', 'mw', 'my', 'mv', 'ml', 'mt', 'mh', 'mq', 'mr', 'mu', 'yt', 'mx', 'fm', 'md',
+                                'mc', 'mn', 'ms', 'ma', 'mz', 'mm', 'na', 'nr', 'np', 'nl', 'an', 'nc', 'nz', 'ni',
+                                'ne', 'ng', 'nu', 'nf', 'mp', 'no', 'om', 'pk', 'pw', 'ps', 'pa', 'pg', 'py', 'pe',
+                                'ph', 'pn', 'pl', 'pt', 'pr', 'qa', 're', 'ro', 'ru', 'rw', 'sh', 'kn', 'lc', 'pm',
+                                'vc', 'ws', 'sm', 'st', 'sa', 'sn', 'rs', 'sc', 'sl', 'sg', 'sk', 'si', 'sb', 'so',
+                                'za', 'gs', 'es', 'lk', 'sd', 'sr', 'sj', 'sz', 'se', 'ch', 'sy', 'tw', 'tj', 'tz',
+                                'th', 'tl', 'tg', 'tk', 'to', 'tt', 'tn', 'tr', 'tm', 'tc', 'tv', 'ug', 'ua', 'ae',
+                                'uk', 'gb', 'us', 'um', 'uy', 'uz', 'vu', 've', 'vn', 'vg', 'vi', 'wf', 'eh', 'ye',
+                                'zm', 'zw'])
+        self.check_valid_value(self.language, "Google languages",
+                               ['af', 'ak', 'sq', 'ws', 'am', 'ar', 'hy', 'az', 'eu', 'be', 'bem', 'bn', 'bh',
+                                'xx-bork', 'bs', 'br', 'bg', 'bt', 'km', 'ca', 'chr', 'ny', 'zh-cn', 'zh-tw', 'co',
+                                'hr', 'cs', 'da', 'nl', 'xx-elmer', 'en', 'eo', 'et', 'ee', 'fo', 'tl', 'fi', 'fr',
+                                'fy', 'gaa', 'gl', 'ka', 'de', 'el', 'kl', 'gn', 'gu', 'xx-hacker', 'ht', 'ha', 'haw',
+                                'iw', 'hi', 'hu', 'is', 'ig', 'id', 'ia', 'ga', 'it', 'ja', 'jw', 'kn', 'kk', 'rw',
+                                'rn', 'xx-klingon', 'kg', 'ko', 'kri', 'ku', 'ckb', 'ky', 'lo', 'la', 'lv', 'ln', 'lt',
+                                'loz', 'lg', 'ach', 'mk', 'mg', 'ms', 'ml', 'mt', 'mv', 'mi', 'mr', 'mfe', 'mo', 'mn',
+                                'sr-me', 'my', 'ne', 'pcm', 'nso', 'no', 'nn', 'oc', 'or', 'om', 'ps', 'fa',
+                                'xx-pirate', 'pl', 'pt', 'pt-br', 'pt-pt', 'pa', 'qu', 'ro', 'rm', 'nyn', 'ru', 'gd',
+                                'sr', 'sh', 'st', 'tn', 'crs', 'sn', 'sd', 'si', 'sk', 'sl', 'so', 'es', 'es-419', 'su',
+                                'sw', 'sv', 'tg', 'ta', 'tt', 'te', 'th', 'ti', 'to', 'lua', 'tum', 'tr', 'tk', 'tw',
+                                'ug', 'uk', 'ur', 'uz', 'vu', 'vi', 'cy', 'wo', 'xh', 'yi', 'yo', 'zu']
+                               )
+
+
+class Google(ComponentBase, ABC):
+    component_name = "Google"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return Google.be_output("")
+
+        try:
+            client = GoogleSearch(
+                {"engine": "google", "q": ans, "api_key": self._param.api_key, "gl": self._param.country,
+                 "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:
+            return Google.be_output("**ERROR**: Existing Unavailable Parameters!")
+
+        if not google_res:
+            return Google.be_output("")
+
+        df = pd.DataFrame(google_res)
+        logging.debug(f"df: {df}")
+        return df

agent/component/googlescholar.py

@@ -0,0 +1,70 @@
+#
+#  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
+from scholarly import scholarly
+
+
+class GoogleScholarParam(ComponentParamBase):
+    """
+    Define the GoogleScholar component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 6
+        self.sort_by = 'relevance'
+        self.year_low = None
+        self.year_high = None
+        self.patents = True
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+        self.check_valid_value(self.sort_by, "GoogleScholar Sort_by", ['date', 'relevance'])
+        self.check_boolean(self.patents, "Whether or not to include patents, defaults to True")
+
+
+class GoogleScholar(ComponentBase, ABC):
+    component_name = "GoogleScholar"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return GoogleScholar.be_output("")
+
+        scholar_client = scholarly.search_pubs(ans, patents=self._param.patents, year_low=self._param.year_low,
+                                               year_high=self._param.year_high, sort_by=self._param.sort_by)
+        scholar_res = []
+        for i in range(self._param.top_n):
+            try:
+                pub = next(scholar_client)
+                scholar_res.append({"content": 'Title: ' + pub['bib']['title'] + '\n_Url: <a href="' + pub[
+                    'pub_url'] + '"></a> ' + "\n author: " + ",".join(pub['bib']['author']) + '\n Abstract: ' + pub[
+                                                   'bib'].get('abstract', 'no abstract')})
+
+            except StopIteration or Exception:
+                logging.exception("GoogleScholar")
+                break
+
+        if not scholar_res:
+            return GoogleScholar.be_output("")
+
+        df = pd.DataFrame(scholar_res)
+        logging.debug(f"df: {df}")
+        return df

agent/component/invoke.py

@@ -0,0 +1,106 @@
+#
+#  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
+
+    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")
+
+
+class Invoke(ComponentBase, ABC):
+    component_name = "Invoke"
+
+    def _run(self, history, **kwargs):
+        args = {}
+        for para in self._param.variables:
+            if para.get("component_id"):
+                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)
+                args[para["key"]] = "\n".join(out["content"])
+            else:
+                args[para["key"]] = "\n".join(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':
+            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':
+            response = requests.post(url=url,
+                                    json=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/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 graph.component import GenerateParam, Generate
-from graph.settings import DEBUG
+from agent.component import GenerateParam, Generate
 
 
 class KeywordExtractParam(GenerateParam):
@@ -50,16 +50,13 @@ 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".*keyword:", "", ans).strip()
-        if DEBUG: print(ans, ":::::::::::::::::::::::::::::::::")
+        logging.debug(f"ans: {ans}")
         return KeywordExtract.be_output(ans)

agent/component/message.py

@@ -16,10 +16,7 @@
 import random
 from abc import ABC
 from functools import partial
-
-import pandas as pd
-
-from graph.component.base import ComponentBase, ComponentParamBase
+from agent.component.base import ComponentBase, ComponentParamBase
 
 
 class MessageParam(ComponentParamBase):
@@ -46,7 +43,11 @@ class Message(ComponentBase, ABC):
         return Message.be_output(random.choice(self._param.messages))
 
     def stream_output(self):
+        res = None
         if self._param.messages:
-            yield {"content": random.choice(self._param.messages)}
+            res = {"content": random.choice(self._param.messages)}
+            yield res
+
+        self.set_output(res)
 
 

agent/component/pubmed.py

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

agent/component/qweather.py

@@ -0,0 +1,111 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from abc import ABC
+import pandas as pd
+import requests
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class QWeatherParam(ComponentParamBase):
+    """
+    Define the QWeather component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.web_apikey = "xxx"
+        self.lang = "zh"
+        self.type = "weather"
+        self.user_type = 'free'
+        self.error_code = {
+            "204": "The request was successful, but the region you are querying does not have the data you need at this time.",
+            "400": "Request error, may contain incorrect request parameters or missing mandatory request parameters.",
+            "401": "Authentication fails, possibly using the wrong KEY, wrong digital signature, wrong type of KEY (e.g. using the SDK's KEY to access the Web API).",
+            "402": "Exceeded the number of accesses or the balance is not enough to support continued access to the service, you can recharge, upgrade the accesses or wait for the accesses to be reset.",
+            "403": "No access, may be the binding PackageName, BundleID, domain IP address is inconsistent, or the data that requires additional payment.",
+            "404": "The queried data or region does not exist.",
+            "429": "Exceeded the limited QPM (number of accesses per minute), please refer to the QPM description",
+            "500": "No response or timeout, interface service abnormality please contact us"
+            }
+        # Weather
+        self.time_period = 'now'
+
+    def check(self):
+        self.check_empty(self.web_apikey, "BaiduFanyi APPID")
+        self.check_valid_value(self.type, "Type", ["weather", "indices", "airquality"])
+        self.check_valid_value(self.user_type, "Free subscription or paid subscription", ["free", "paid"])
+        self.check_valid_value(self.lang, "Use language",
+                               ['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_valid_value(self.time_period, "Time period", ['now', '3d', '7d', '10d', '15d', '30d'])
+
+
+class QWeather(ComponentBase, ABC):
+    component_name = "QWeather"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = "".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return QWeather.be_output("")
+
+        try:
+            response = requests.get(
+                url="https://geoapi.qweather.com/v2/city/lookup?location=" + ans + "&key=" + self._param.web_apikey).json()
+            if response["code"] == "200":
+                location_id = response["location"][0]["id"]
+            else:
+                return QWeather.be_output("**Error**" + self._param.error_code[response["code"]])
+
+            base_url = "https://api.qweather.com/v7/" if self._param.user_type == 'paid' else "https://devapi.qweather.com/v7/"
+
+            if self._param.type == "weather":
+                url = base_url + "weather/" + self._param.time_period + "?location=" + location_id + "&key=" + self._param.web_apikey + "&lang=" + self._param.lang
+                response = requests.get(url=url).json()
+                if response["code"] == "200":
+                    if self._param.time_period == "now":
+                        return QWeather.be_output(str(response["now"]))
+                    else:
+                        qweather_res = [{"content": str(i) + "\n"} for i in response["daily"]]
+                        if not qweather_res:
+                            return QWeather.be_output("")
+
+                        df = pd.DataFrame(qweather_res)
+                        return df
+                else:
+                    return QWeather.be_output("**Error**" + self._param.error_code[response["code"]])
+
+            elif self._param.type == "indices":
+                url = base_url + "indices/1d?type=0&location=" + location_id + "&key=" + self._param.web_apikey + "&lang=" + self._param.lang
+                response = requests.get(url=url).json()
+                if response["code"] == "200":
+                    indices_res = response["daily"][0]["date"] + "\n" + "\n".join(
+                        [i["name"] + ": " + i["category"] + ", " + i["text"] for i in response["daily"]])
+                    return QWeather.be_output(indices_res)
+
+                else:
+                    return QWeather.be_output("**Error**" + self._param.error_code[response["code"]])
+
+            elif self._param.type == "airquality":
+                url = base_url + "air/now?location=" + location_id + "&key=" + self._param.web_apikey + "&lang=" + self._param.lang
+                response = requests.get(url=url).json()
+                if response["code"] == "200":
+                    return QWeather.be_output(str(response["now"]))
+                else:
+                    return QWeather.be_output("**Error**" + self._param.error_code[response["code"]])
+        except Exception as e:
+            return QWeather.be_output("**Error**" + str(e))

agent/component/relevant.py

@@ -13,10 +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 graph.component import GenerateParam, Generate
+from agent.component import GenerateParam, Generate
 from rag.utils import num_tokens_from_string, encoder
 
 
@@ -70,7 +71,7 @@ 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:

agent/component/retrieval.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
 
 import pandas as pd
@@ -20,8 +21,8 @@ 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 graph.component.base import ComponentBase, ComponentParamBase
+from api import settings
+from agent.component.base import ComponentBase, ComponentParamBase
 
 
 class RetrievalParam(ComponentParamBase):
@@ -43,22 +44,19 @@ class RetrievalParam(ComponentParamBase):
         self.check_decimal_float(self.similarity_threshold, "[Retrieval] Similarity threshold")
         self.check_decimal_float(self.keywords_similarity_weight, "[Retrieval] Keywords similarity weight")
         self.check_positive_number(self.top_n, "[Retrieval] Top N")
-        self.check_empty(self.kb_ids, "[Retrieval] Knowledge bases")
 
 
 class Retrieval(ComponentBase, ABC):
     component_name = "Retrieval"
 
     def _run(self, history, **kwargs):
-        query = []
-        for role, cnt in history[::-1][:self._param.message_history_window_size]:
-            if role != "user":continue
-            query.append(cnt)
-        query = "\n".join(query)
+        query = 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."
 
@@ -69,20 +67,21 @@ class Retrieval(ComponentBase, ABC):
         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,
+        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)
 
         if not kbinfos["chunks"]:
-            df = Retrieval.be_output(self._param.empty_response)
-            df["empty_response"] = True
+            df = Retrieval.be_output("")
+            if self._param.empty_response and self._param.empty_response.strip():
+                df["empty_response"] = self._param.empty_response
             return df
 
         df = pd.DataFrame(kbinfos["chunks"])
         df["content"] = df["content_with_weight"]
         del df["content_with_weight"]
-        print(">>>>>>>>>>>>>>>>>>>>>>>>>>\n", query, df)
+        logging.debug("{} {}".format(query, df))
         return df
 
 

agent/component/rewrite.py

@@ -13,10 +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 graph.component import GenerateParam, Generate
+from agent.component import GenerateParam, Generate
 
 
 class RewriteQuestionParam(GenerateParam):
@@ -33,7 +34,7 @@ class RewriteQuestionParam(GenerateParam):
     def check(self):
         super().check()
 
-    def get_prompt(self):
+    def get_prompt(self, conv):
         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.     
@@ -42,6 +43,40 @@ class RewriteQuestionParam(GenerateParam):
         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 f"""
+Role: A helpful assistant
+Task: Generate a full user question that would follow the conversation.
+Requirements & Restrictions:
+  - Text generated MUST be in the same language of the original user's question.
+  - If the user's latest question is completely, don't do anything, just return the original question.
+  - DON'T generate anything except a refined question.
+
+######################
+-Examples-
+######################
+# Example 1
+## Conversation
+USER: What is the name of Donald Trump's father?
+ASSISTANT:  Fred Trump.
+USER: And his mother?
+###############
+Output: What's the name of Donald Trump's mother?
+------------
+# Example 2
+## Conversation
+USER: What is the name of Donald Trump's father?
+ASSISTANT:  Fred Trump.
+USER: And his mother?
+ASSISTANT:  Mary Trump.
+User: What's her full name?
+###############
+Output: What's the full name of Donald Trump's mother Mary Trump?
+######################
+# Real Data
+## Conversation
+{conv}
+###############
     """
         return self.prompt
 
@@ -54,19 +89,23 @@ class RewriteQuestion(Generate, ABC):
             setattr(self, "_loop", 0)
         if self._loop >= self._param.loop:
             self._loop = 0
-            raise Exception("Maximum loop time exceeds. Can't find relevant information.")
+            raise Exception("Sorry! Nothing relevant found.")
         self._loop += 1
-        q = "Question: "
-        for r, c in self._canvas.history[::-1]:
-            if r == "user":
-                q += c
-                break
+
+        hist = self._canvas.get_history(4)
+        conv = []
+        for m in hist:
+            if m["role"] not in ["user", "assistant"]: continue
+            conv.append("{}: {}".format(m["role"].upper(), m["content"]))
+        conv = "\n".join(conv)
 
         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(conv), [{"role": "user", "content": "Output: "}],
                             self._param.gen_conf())
+        self._canvas.history.pop()
+        self._canvas.history.append(("user", ans))
 
-        print(ans, ":::::::::::::::::::::::::::::::::")
+        logging.debug(ans)
         return RewriteQuestion.be_output(ans)
 
 

agent/component/switch.py

@@ -0,0 +1,128 @@
+#
+#  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 SwitchParam(ComponentParamBase):
+    """
+    Define the Switch component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        """
+        {
+            "logical_operator" : "and | or"
+            "items" : [
+                            {"cpn_id": "categorize:0", "operator": "contains", "value": ""},
+                            {"cpn_id": "categorize:0", "operator": "contains", "value": ""},...],
+            "to": ""
+        }
+        """
+        self.conditions = []
+        self.end_cpn_id = "answer:0"
+        self.operators = ['contains', 'not contains', 'start with', 'end with', 'empty', 'not empty', '=', '≠', '>',
+                          '<', '≥', '≤']
+
+    def check(self):
+        self.check_empty(self.conditions, "[Switch] conditions")
+        for cond in self.conditions:
+            if not cond["to"]: raise ValueError(f"[Switch] 'To' can not be empty!")
+
+
+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"].find("begin") >= 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:
+            res = []
+            for item in cond["items"]:
+                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()[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)
+
+    def process_operator(self, input: str, operator: str, value: str) -> bool:
+        if not isinstance(input, str) or not isinstance(value, str):
+            raise ValueError('Invalid input or value type: string')
+
+        if operator == "contains":
+            return True if value.lower() in input.lower() else False
+        elif operator == "not contains":
+            return True if value.lower() not in input.lower() else False
+        elif operator == "start with":
+            return True if input.lower().startswith(value.lower()) else False
+        elif operator == "end with":
+            return True if input.lower().endswith(value.lower()) else False
+        elif operator == "empty":
+            return True if not input else False
+        elif operator == "not empty":
+            return True if input else False
+        elif operator == "=":
+            return True if input == value else False
+        elif operator == "≠":
+            return True if input != value else False
+        elif operator == ">":
+            try:
+                return True if float(input) > float(value) else False
+            except Exception as e:
+                return True if input > value else False
+        elif operator == "<":
+            try:
+                return True if float(input) < float(value) else False
+            except Exception as e:
+                return True if input < value else False
+        elif operator == "≥":
+            try:
+                return True if float(input) >= float(value) else False
+            except Exception as e:
+                return True if input >= value else False
+        elif operator == "≤":
+            try:
+                return True if float(input) <= float(value) else False
+            except Exception as e:
+                return True if input <= value else False
+
+        raise ValueError('Not supported operator' + operator)

agent/component/template.py

@@ -0,0 +1,85 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import re
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+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):
+        cpnts = set([para["component_id"].split("@")[0] for para in self._param.parameters \
+                     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):
+        content = self._param.content
+
+        self._param.inputs = []
+        for para in self._param.parameters:
+            if not para.get("component_id"): continue
+            component_id = para["component_id"].split("@")[0]
+            if para["component_id"].lower().find("@") >= 0:
+                cpn_id, key = para["component_id"].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["component_id"], "content": kwargs[para["key"]]})
+                        break
+                else:
+                    assert False, f"Can't find parameter '{key}' for {cpn_id}"
+                continue
+
+            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"]] = ""
+            else:
+                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["component_id"], "content": kwargs[para["key"]]})
+
+        for n, v in kwargs.items():
+            content = re.sub(r"\{%s\}" % re.escape(n), str(v).replace("\\", " "), content)
+
+        return Template.be_output(content)
+

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

@@ -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.
+#
+import logging
+from abc import ABC
+import wikipedia
+import pandas as pd
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class WikipediaParam(ComponentParamBase):
+    """
+    Define the Wikipedia component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 10
+        self.language = "en"
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+        self.check_valid_value(self.language, "Wikipedia languages",
+                               ['af', 'pl', 'ar', 'ast', 'az', 'bg', 'nan', 'bn', 'be', 'ca', 'cs', 'cy', 'da', 'de',
+                                'et', 'el', 'en', 'es', 'eo', 'eu', 'fa', 'fr', 'gl', 'ko', 'hy', 'hi', 'hr', 'id',
+                                'it', 'he', 'ka', 'lld', 'la', 'lv', 'lt', 'hu', 'mk', 'arz', 'ms', 'min', 'my', 'nl',
+                                'ja', 'nb', 'nn', 'ce', 'uz', 'pt', 'kk', 'ro', 'ru', 'ceb', 'sk', 'sl', 'sr', 'sh',
+                                'fi', 'sv', 'ta', 'tt', 'th', 'tg', 'azb', 'tr', 'uk', 'ur', 'vi', 'war', 'zh', 'yue'])
+
+
+class Wikipedia(ComponentBase, ABC):
+    component_name = "Wikipedia"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return Wikipedia.be_output("")
+
+        try:
+            wiki_res = []
+            wikipedia.set_lang(self._param.language)
+            wiki_engine = wikipedia
+            for wiki_key in wiki_engine.search(ans, results=self._param.top_n):
+                page = wiki_engine.page(title=wiki_key, auto_suggest=False)
+                wiki_res.append({"content": '<a href="' + page.url + '">' + page.title + '</a> ' + page.summary})
+        except Exception as e:
+            return Wikipedia.be_output("**ERROR**: " + str(e))
+
+        if not wiki_res:
+            return Wikipedia.be_output("")
+
+        df = pd.DataFrame(wiki_res)
+        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 2024 The InfiniFlow Authors. All Rights Reserved.
+#  Copyright 2019 The FATE 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.
@@ -12,5 +12,7 @@
 #  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.
+#
 
-NAME_LENGTH_LIMIT = 2 ** 10
+FLOAT_ZERO = 1e-8
+PARAM_MAXDEPTH = 5

agent/templates/interpreter.json

@@ -1,73 +1,239 @@
 {
-  "id": 3,
+  "id": 4,
   "title": "Interpreter",
-  "description": "An interpreter. Type the content you want to translate and the object language like: Hi there => Spanish. Hava a try!",
+  "description": "Translation Agent: Agentic translation using reflection workflow.\n\nThis is inspired by Andrew NG's project: https://github.com/andrewyng/translation-agent\n\n",
   "canvas_type": "chatbot",
   "dsl": {
             "answer": [],
             "components": {
-        "answer:0": {
-          "downstream": ["generate:0"],
+                "Answer:TinyGamesGuess": {
+                    "downstream": [],
                     "obj": {
                         "component_name": "Answer",
-            "params": {}
-          },
-          "upstream": ["begin", "generate:0"]
-        },
-        "begin": {
-          "downstream": ["answer:0"],
-          "obj": {
-            "component_name": "Begin",
                         "params": {
-              "prologue": "Hi there! Please enter the text you want to translate in format like: 'text you want to translate' => target language. For an example: 您好！ => English"
+                            "inputs": [],
+                            "message_history_window_size": 22,
+                            "output_var_name": "output",
+                            "post_answers": [],
+                            "query": []
                         }
                     },
-          "upstream": []
+                    "upstream": [
+                        "Generate:ChubbyCougarsRush"
+                    ]
                 },
-        "generate:0": {
-          "downstream": ["answer:0"],
+                "Generate:ChubbyCougarsRush": {
+                    "downstream": [
+                        "Answer:TinyGamesGuess"
+                    ],
                     "obj": {
                         "component_name": "Generate",
                         "params": {
-              "llm_id": "deepseek-chat",
-              "prompt": "You are an professional interpreter.\n- Role: an professional interpreter.\n- Input format: content need to be translated => target language. \n- Answer format: => translated content in target language. \n- Examples:\n  - user: 您好！ => English. assistant: => How are you doing!\n  - user: You look good today. => Japanese. assistant: => 今日は調子がいいですね 。\n"
+                            "cite": false,
+                            "frequency_penalty": 0.7,
+                            "inputs": [],
+                            "llm_id": "deepseek-chat@DeepSeek",
+                            "max_tokens": 0,
+                            "message_history_window_size": 12,
+                            "output_var_name": "output",
+                            "parameters": [
+                                {
+                                    "component_id": "begin@lang",
+                                    "id": "73f48a67-b78f-4bcd-8326-a83c31073ab9",
+                                    "key": "target_lang"
+                                },
+                                {
+                                    "component_id": "begin@file",
+                                    "id": "c9142975-25b3-4199-8fce-aa0bc29a31f2",
+                                    "key": "source_text"
+                                },
+                                {
+                                    "component_id": "Generate:RichWordsDeny",
+                                    "id": "6c824b2a-fe3b-4336-95b5-e85f676bef39",
+                                    "key": "translation_1"
+                                },
+                                {
+                                    "component_id": "Generate:SlimyFrogsArgue",
+                                    "id": "f3bd4569-4852-43fa-b80a-e0dd27dd9e1c",
+                                    "key": "reflection"
+                                }
+                            ],
+                            "presence_penalty": 0.4,
+                            "prompt": "Your task is to carefully read, then edit, a translation to {target_lang}, taking into\naccount a list of expert suggestions and constructive criticisms.\n\nThe source text, the initial translation, and the expert linguist suggestions are delimited by XML tags <SOURCE_TEXT></SOURCE_TEXT>, <TRANSLATION></TRANSLATION> and <EXPERT_SUGGESTIONS></EXPERT_SUGGESTIONS> \\\nas follows:\n\n<SOURCE_TEXT>\n{source_text}\n</SOURCE_TEXT>\n\n<TRANSLATION>\n{translation_1}\n</TRANSLATION>\n\n<EXPERT_SUGGESTIONS>\n{reflection}\n</EXPERT_SUGGESTIONS>\n\nPlease take into account the expert suggestions when editing the translation. Edit the translation by ensuring:\n\n(i) accuracy (by correcting errors of addition, mistranslation, omission, or untranslated text),\n(ii) fluency (by applying {target_lang} grammar, spelling and punctuation rules and ensuring there are no unnecessary repetitions), \n(iii) style (by ensuring the translations reflect the style of the source text)\n(iv) terminology (inappropriate for context, inconsistent use), or\n(v) other errors.\n\nOutput only the new translation and nothing else.",
+                            "query": [],
+                            "temperature": 0.1,
+                            "top_p": 0.3
                         }
                     },
-          "upstream": ["answer:0"]
+                    "upstream": [
+                        "Generate:SlimyFrogsArgue"
+                    ]
+                },
+                "Generate:RichWordsDeny": {
+                    "downstream": [
+                        "Generate:SlimyFrogsArgue"
+                    ],
+                    "obj": {
+                        "component_name": "Generate",
+                        "params": {
+                            "cite": false,
+                            "frequency_penalty": 0.7,
+                            "inputs": [],
+                            "llm_id": "deepseek-chat@DeepSeek",
+                            "max_tokens": 0,
+                            "message_history_window_size": 12,
+                            "output_var_name": "output",
+                            "parameters": [
+                                {
+                                    "component_id": "begin@lang",
+                                    "id": "a36e78fb-b431-4ae6-afa8-77839587fcf8",
+                                    "key": "lang"
+                                },
+                                {
+                                    "component_id": "begin@file",
+                                    "id": "f8a704b7-693b-4480-aa9a-da4a83250059",
+                                    "key": "file"
+                                }
+                            ],
+                            "presence_penalty": 0.4,
+                            "prompt": "Role: You are a professional translator proficient in {lang}, with an exceptional ability to convert specialized academic papers into accessible popular science articles. Please assist me in translating the following paragraph into {lang}, ensuring that its style resembles that of popular science articles in {lang}.\n\nRequirements & Restrictions:\n - Use Markdown format to output.\n - DO NOT overlook any details.\n\n\n<ORIGINAL_TEXT>\n{file}\n\n<TRANSLATED_TEXT>",
+                            "query": [],
+                            "temperature": 0.1,
+                            "top_p": 0.3
                         }
                     },
+                    "upstream": [
+                        "begin"
+                    ]
+                },
+                "Generate:SlimyFrogsArgue": {
+                    "downstream": [
+                        "Generate:ChubbyCougarsRush"
+                    ],
+                    "obj": {
+                        "component_name": "Generate",
+                        "params": {
+                            "cite": false,
+                            "frequency_penalty": 0.7,
+                            "inputs": [],
+                            "llm_id": "deepseek-chat@DeepSeek",
+                            "max_tokens": 0,
+                            "message_history_window_size": 12,
+                            "output_var_name": "output",
+                            "parameters": [
+                                {
+                                    "component_id": "begin@lang",
+                                    "id": "b2f5e7ec-7f77-485f-af15-461d0f1ca913",
+                                    "key": "target_lang"
+                                },
+                                {
+                                    "component_id": "begin@file",
+                                    "id": "fbc44092-9f9e-4e85-b5b1-dbd808239d3d",
+                                    "key": "source_text"
+                                },
+                                {
+                                    "component_id": "Generate:RichWordsDeny",
+                                    "id": "c253af54-61d4-40f3-9990-604e2212506f",
+                                    "key": "translation_1"
+                                }
+                            ],
+                            "presence_penalty": 0.4,
+                            "prompt": "Your task is to carefully read a source text and a translation to {target_lang}, and then give constructive criticisms and helpful suggestions to improve the translation. \n\nThe source text and initial translation, delimited by XML tags <SOURCE_TEXT></SOURCE_TEXT> and <TRANSLATION></TRANSLATION>, are as follows:\n\n<SOURCE_TEXT>\n{source_text}\n</SOURCE_TEXT>\n\n<TRANSLATION>\n{translation_1}\n</TRANSLATION>\n\nWhen writing suggestions, pay attention to whether there are ways to improve the translation's \n(i) accuracy (by correcting errors of addition, mistranslation, omission, or untranslated text),\n(ii) fluency (by applying {target_lang} grammar, spelling and punctuation rules, and ensuring there are no unnecessary repetitions),\n(iii) style (by ensuring the translations reflect the style of the source text and take into account any cultural context),\n(iv) terminology (by ensuring terminology use is consistent and reflects the source text domain; and by only ensuring you use equivalent idioms {target_lang}).\n\nWrite a list of specific, helpful and constructive suggestions for improving the translation.\nEach suggestion should address one specific part of the translation.\nOutput only the suggestions and nothing else.",
+                            "query": [],
+                            "temperature": 0.1,
+                            "top_p": 0.3
+                        }
+                    },
+                    "upstream": [
+                        "Generate:RichWordsDeny"
+                    ]
+                },
+                "begin": {
+                    "downstream": [
+                        "Generate:RichWordsDeny"
+                    ],
+                    "obj": {
+                        "component_name": "Begin",
+                        "params": {
+                            "inputs": [],
+                            "message_history_window_size": 22,
+                            "output": {},
+                            "output_var_name": "output",
+                            "prologue": "",
+                            "query": [
+                                {
+                                    "key": "lang",
+                                    "name": "Target Language",
+                                    "optional": false,
+                                    "type": "line",
+                                    "value": ""
+                                },
+                                {
+                                    "key": "file",
+                                    "name": "Files",
+                                    "optional": false,
+                                    "type": "file",
+                                    "value": ""
+                                }
+                            ]
+                        }
+                    },
+                    "upstream": []
+                }
+            },
+            "embed_id": "",
             "graph": {
                 "edges": [
                     {
-            "id": "c87c7805-8cf0-4cd4-b45b-152031811020",
-            "label": "",
+                        "id": "reactflow__edge-begin-Generate:RichWordsDenyc",
+                        "markerEnd": "logo",
                         "source": "begin",
-            "target": "answer:0"
+                        "sourceHandle": null,
+                        "style": {
+                            "stroke": "rgb(202 197 245)",
+                            "strokeWidth": 2
+                        },
+                        "target": "Generate:RichWordsDeny",
+                        "targetHandle": "c",
+                        "type": "buttonEdge"
                     },
                     {
-            "id": "reactflow__edge-answer:0b-generate:0d",
+                        "id": "reactflow__edge-Generate:RichWordsDenyb-Generate:SlimyFrogsArguec",
                         "markerEnd": "logo",
-            "source": "answer:0",
+                        "source": "Generate:RichWordsDeny",
                         "sourceHandle": "b",
                         "style": {
                             "stroke": "rgb(202 197 245)",
                             "strokeWidth": 2
                         },
-            "target": "generate:0",
-            "targetHandle": "d",
+                        "target": "Generate:SlimyFrogsArgue",
+                        "targetHandle": "c",
                         "type": "buttonEdge"
                     },
                     {
-            "id": "reactflow__edge-generate:0c-answer:0a",
+                        "id": "reactflow__edge-Generate:SlimyFrogsArgueb-Generate:ChubbyCougarsRushc",
                         "markerEnd": "logo",
-            "source": "generate:0",
-            "sourceHandle": "c",
+                        "source": "Generate:SlimyFrogsArgue",
+                        "sourceHandle": "b",
                         "style": {
                             "stroke": "rgb(202 197 245)",
                             "strokeWidth": 2
                         },
-            "target": "answer:0",
-            "targetHandle": "a",
+                        "target": "Generate:ChubbyCougarsRush",
+                        "targetHandle": "c",
+                        "type": "buttonEdge"
+                    },
+                    {
+                        "id": "reactflow__edge-Generate:ChubbyCougarsRushb-Answer:TinyGamesGuessc",
+                        "markerEnd": "logo",
+                        "source": "Generate:ChubbyCougarsRush",
+                        "sourceHandle": "b",
+                        "style": {
+                            "stroke": "rgb(202 197 245)",
+                            "strokeWidth": 2
+                        },
+                        "target": "Answer:TinyGamesGuess",
+                        "targetHandle": "c",
                         "type": "buttonEdge"
                     }
                 ],
@@ -75,77 +241,296 @@
                     {
                         "data": {
                             "form": {
-                "prologue": "Hi there! Please enter the text you want to translate in format like: 'text you want to translate' => target language. For an example: 您好！ => English"
+                                "prologue": "",
+                                "query": [
+                                    {
+                                        "key": "lang",
+                                        "name": "Target Language",
+                                        "optional": false,
+                                        "type": "line",
+                                        "value": ""
+                                    },
+                                    {
+                                        "key": "file",
+                                        "name": "Files",
+                                        "optional": false,
+                                        "type": "file",
+                                        "value": ""
+                                    }
+                                ]
                             },
                             "label": "Begin",
-              "name": "Instruction"
+                            "name": "begin"
                         },
                         "dragging": false,
-            "height": 50,
+                        "height": 128,
                         "id": "begin",
                         "position": {
-              "x": -175.31950791077287,
-              "y": 32.340246044613565
+                            "x": -383.5,
+                            "y": 143.5
                         },
                         "positionAbsolute": {
-              "x": -175.31950791077287,
-              "y": 32.340246044613565
+                            "x": -383.5,
+                            "y": 143.5
                         },
-            "selected": true,
+                        "selected": false,
                         "sourcePosition": "left",
                         "targetPosition": "right",
                         "type": "beginNode",
-            "width": 50
+                        "width": 200
+                    },
+                    {
+                        "data": {
+                            "form": {
+                                "cite": false,
+                                "frequencyPenaltyEnabled": true,
+                                "frequency_penalty": 0.7,
+                                "llm_id": "deepseek-chat@DeepSeek",
+                                "maxTokensEnabled": false,
+                                "max_tokens": 256,
+                                "message_history_window_size": 12,
+                                "parameter": "Precise",
+                                "parameters": [
+                                    {
+                                        "component_id": "begin@lang",
+                                        "id": "a36e78fb-b431-4ae6-afa8-77839587fcf8",
+                                        "key": "lang"
+                                    },
+                                    {
+                                        "component_id": "begin@file",
+                                        "id": "f8a704b7-693b-4480-aa9a-da4a83250059",
+                                        "key": "file"
+                                    }
+                                ],
+                                "presencePenaltyEnabled": true,
+                                "presence_penalty": 0.4,
+                                "prompt": "Role: You are a professional translator proficient in {lang}, with an exceptional ability to convert specialized academic papers into accessible popular science articles. Please assist me in translating the following paragraph into {lang}, ensuring that its style resembles that of popular science articles in {lang}.\n\nRequirements & Restrictions:\n - Use Markdown format to output.\n - DO NOT overlook any details.\n\n\n<ORIGINAL_TEXT>\n{file}\n\n<TRANSLATED_TEXT>",
+                                "temperature": 0.1,
+                                "temperatureEnabled": true,
+                                "topPEnabled": true,
+                                "top_p": 0.3
+                            },
+                            "label": "Generate",
+                            "name": "Translate directly"
+                        },
+                        "dragging": false,
+                        "height": 190,
+                        "id": "Generate:RichWordsDeny",
+                        "position": {
+                            "x": -98,
+                            "y": 113.359375
+                        },
+                        "positionAbsolute": {
+                            "x": -98,
+                            "y": 113.359375
+                        },
+                        "selected": false,
+                        "sourcePosition": "right",
+                        "targetPosition": "left",
+                        "type": "generateNode",
+                        "width": 200
+                    },
+                    {
+                        "data": {
+                            "form": {
+                                "cite": false,
+                                "frequencyPenaltyEnabled": true,
+                                "frequency_penalty": 0.7,
+                                "llm_id": "deepseek-chat@DeepSeek",
+                                "maxTokensEnabled": false,
+                                "max_tokens": 512,
+                                "message_history_window_size": 12,
+                                "parameter": "Precise",
+                                "parameters": [
+                                    {
+                                        "component_id": "begin@lang",
+                                        "id": "b2f5e7ec-7f77-485f-af15-461d0f1ca913",
+                                        "key": "target_lang"
+                                    },
+                                    {
+                                        "component_id": "begin@file",
+                                        "id": "fbc44092-9f9e-4e85-b5b1-dbd808239d3d",
+                                        "key": "source_text"
+                                    },
+                                    {
+                                        "component_id": "Generate:RichWordsDeny",
+                                        "id": "c253af54-61d4-40f3-9990-604e2212506f",
+                                        "key": "translation_1"
+                                    }
+                                ],
+                                "presencePenaltyEnabled": true,
+                                "presence_penalty": 0.4,
+                                "prompt": "Your task is to carefully read a source text and a translation to {target_lang}, and then give constructive criticisms and helpful suggestions to improve the translation. \n\nThe source text and initial translation, delimited by XML tags <SOURCE_TEXT></SOURCE_TEXT> and <TRANSLATION></TRANSLATION>, are as follows:\n\n<SOURCE_TEXT>\n{source_text}\n</SOURCE_TEXT>\n\n<TRANSLATION>\n{translation_1}\n</TRANSLATION>\n\nWhen writing suggestions, pay attention to whether there are ways to improve the translation's \n(i) accuracy (by correcting errors of addition, mistranslation, omission, or untranslated text),\n(ii) fluency (by applying {target_lang} grammar, spelling and punctuation rules, and ensuring there are no unnecessary repetitions),\n(iii) style (by ensuring the translations reflect the style of the source text and take into account any cultural context),\n(iv) terminology (by ensuring terminology use is consistent and reflects the source text domain; and by only ensuring you use equivalent idioms {target_lang}).\n\nWrite a list of specific, helpful and constructive suggestions for improving the translation.\nEach suggestion should address one specific part of the translation.\nOutput only the suggestions and nothing else.",
+                                "temperature": 0.1,
+                                "temperatureEnabled": true,
+                                "topPEnabled": true,
+                                "top_p": 0.3
+                            },
+                            "label": "Generate",
+                            "name": "Reflect"
+                        },
+                        "dragging": false,
+                        "height": 232,
+                        "id": "Generate:SlimyFrogsArgue",
+                        "position": {
+                            "x": 178.5,
+                            "y": 91.859375
+                        },
+                        "positionAbsolute": {
+                            "x": 178.5,
+                            "y": 91.859375
+                        },
+                        "selected": false,
+                        "sourcePosition": "right",
+                        "targetPosition": "left",
+                        "type": "generateNode",
+                        "width": 200
+                    },
+                    {
+                        "data": {
+                            "form": {
+                                "cite": false,
+                                "frequencyPenaltyEnabled": true,
+                                "frequency_penalty": 0.7,
+                                "llm_id": "deepseek-chat@DeepSeek",
+                                "maxTokensEnabled": false,
+                                "max_tokens": 512,
+                                "message_history_window_size": 12,
+                                "parameter": "Precise",
+                                "parameters": [
+                                    {
+                                        "component_id": "begin@lang",
+                                        "id": "73f48a67-b78f-4bcd-8326-a83c31073ab9",
+                                        "key": "target_lang"
+                                    },
+                                    {
+                                        "component_id": "begin@file",
+                                        "id": "c9142975-25b3-4199-8fce-aa0bc29a31f2",
+                                        "key": "source_text"
+                                    },
+                                    {
+                                        "component_id": "Generate:RichWordsDeny",
+                                        "id": "6c824b2a-fe3b-4336-95b5-e85f676bef39",
+                                        "key": "translation_1"
+                                    },
+                                    {
+                                        "component_id": "Generate:SlimyFrogsArgue",
+                                        "id": "f3bd4569-4852-43fa-b80a-e0dd27dd9e1c",
+                                        "key": "reflection"
+                                    }
+                                ],
+                                "presencePenaltyEnabled": true,
+                                "presence_penalty": 0.4,
+                                "prompt": "Your task is to carefully read, then edit, a translation to {target_lang}, taking into\naccount a list of expert suggestions and constructive criticisms.\n\nThe source text, the initial translation, and the expert linguist suggestions are delimited by XML tags <SOURCE_TEXT></SOURCE_TEXT>, <TRANSLATION></TRANSLATION> and <EXPERT_SUGGESTIONS></EXPERT_SUGGESTIONS> \\\nas follows:\n\n<SOURCE_TEXT>\n{source_text}\n</SOURCE_TEXT>\n\n<TRANSLATION>\n{translation_1}\n</TRANSLATION>\n\n<EXPERT_SUGGESTIONS>\n{reflection}\n</EXPERT_SUGGESTIONS>\n\nPlease take into account the expert suggestions when editing the translation. Edit the translation by ensuring:\n\n(i) accuracy (by correcting errors of addition, mistranslation, omission, or untranslated text),\n(ii) fluency (by applying {target_lang} grammar, spelling and punctuation rules and ensuring there are no unnecessary repetitions), \n(iii) style (by ensuring the translations reflect the style of the source text)\n(iv) terminology (inappropriate for context, inconsistent use), or\n(v) other errors.\n\nOutput only the new translation and nothing else.",
+                                "temperature": 0.1,
+                                "temperatureEnabled": true,
+                                "topPEnabled": true,
+                                "top_p": 0.3
+                            },
+                            "label": "Generate",
+                            "name": "Improve"
+                        },
+                        "dragging": false,
+                        "height": 274,
+                        "id": "Generate:ChubbyCougarsRush",
+                        "position": {
+                            "x": 437,
+                            "y": 70.859375
+                        },
+                        "positionAbsolute": {
+                            "x": 437,
+                            "y": 70.859375
+                        },
+                        "selected": false,
+                        "sourcePosition": "right",
+                        "targetPosition": "left",
+                        "type": "generateNode",
+                        "width": 200
                     },
                     {
                         "data": {
                             "form": {},
                             "label": "Answer",
-              "name": "Interface"
+                            "name": "Interact_0"
                         },
                         "dragging": false,
-            "height": 100,
-            "id": "answer:0",
+                        "height": 44,
+                        "id": "Answer:TinyGamesGuess",
                         "position": {
-              "x": 0,
-              "y": 6
+                            "x": 688.5,
+                            "y": 183.859375
                         },
                         "positionAbsolute": {
-              "x": 0,
-              "y": 6
+                            "x": 688.5,
+                            "y": 183.859375
                         },
                         "selected": false,
-            "sourcePosition": "left",
-            "targetPosition": "right",
-            "type": "ragNode",
-            "width": 100
+                        "sourcePosition": "right",
+                        "targetPosition": "left",
+                        "type": "logicNode",
+                        "width": 200
                     },
                     {
                         "data": {
                             "form": {
-                "llm_id": "deepseek-chat",
-                "prompt": "You are an professional interpreter.\n- Role: an professional interpreter.\n- Input format: content need to be translated => target language. \n- Answer format: => translated content in target language. \n- Examples:\n  - user: 您好！ => English. assistant: => How are you doing!\n  - user: You look good today. => Japanese. assistant: => 今日は調子がいいですね 。\n",
-                "temperature": 0.5
+                                "text": "Translation Agent: Agentic translation using reflection workflow\n\nThis is inspired by Andrew NG's project: https://github.com/andrewyng/translation-agent\n\n1. Prompt an LLM to translate a text to target_language;\n2. Have the LLM reflect on the translation to come up with constructive suggestions for improving it;\n3. Use the suggestions to improve the translation."
                             },
-              "label": "Generate",
-              "name": "Translate"
+                            "label": "Note",
+                            "name": "Breif"
                         },
+                        "dragHandle": ".note-drag-handle",
                         "dragging": false,
-            "height": 150,
-            "id": "generate:0",
+                        "height": 227,
+                        "id": "Note:MoodyKnivesCheat",
                         "position": {
-              "x": 214.89015821545786,
-              "y": 135.10439391733706
+                            "x": 46.02198421645994,
+                            "y": -267.69527832581736
                         },
                         "positionAbsolute": {
-              "x": 214.89015821545786,
-              "y": 135.10439391733706
+                            "x": 46.02198421645994,
+                            "y": -267.69527832581736
                         },
+                        "resizing": false,
                         "selected": false,
-            "sourcePosition": "left",
-            "targetPosition": "right",
-            "type": "ragNode",
-            "width": 150
+                        "sourcePosition": "right",
+                        "style": {
+                            "height": 227,
+                            "width": 703
+                        },
+                        "targetPosition": "left",
+                        "type": "noteNode",
+                        "width": 703
+                    },
+                    {
+                        "data": {
+                            "form": {
+                                "text": "Many businesses use specialized terms that are not widely used on the internet and that LLMs thus don\u2019t know about, and there are also many terms that can be translated in multiple ways. For example, \u201dopen source\u201d in Spanish can be \u201cC\u00f3digo abierto\u201d or \u201cFuente abierta\u201d; both are fine, but it\u2019d better to pick one and stick with it for a single document.\n\nYou can add those glossary translation into prompt to any of `Translate directly` or 'Reflect'."
+                            },
+                            "label": "Note",
+                            "name": "Tip: Add glossary "
+                        },
+                        "dragHandle": ".note-drag-handle",
+                        "dragging": false,
+                        "height": 181,
+                        "id": "Note:SourCarrotsAct",
+                        "position": {
+                            "x": 65.0676250238289,
+                            "y": 397.6323270065299
+                        },
+                        "positionAbsolute": {
+                            "x": 65.0676250238289,
+                            "y": 397.6323270065299
+                        },
+                        "resizing": false,
+                        "selected": false,
+                        "sourcePosition": "right",
+                        "style": {
+                            "height": 181,
+                            "width": 832
+                        },
+                        "targetPosition": "left",
+                        "type": "noteNode",
+                        "width": 832
                     }
                 ]
             },

agent/test/client.py

@@ -16,9 +16,8 @@
 import argparse
 import os
 from functools import partial
-import readline
-from graph.canvas import Canvas
-from graph.settings import DEBUG
+from agent.canvas import Canvas
+from agent.settings import DEBUG
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()

agent/test/dsl_examples/baidu_generate_and_switch.json

@@ -0,0 +1,129 @@
+{
+  "components": {
+            "begin": {
+                "obj":{
+                    "component_name": "Begin",
+                    "params": {
+                      "prologue": "Hi there!"
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": []
+            },
+            "answer:0": {
+                "obj": {
+                    "component_name": "Answer",
+                    "params": {}
+                },
+                "downstream": ["baidu:0"],
+                "upstream": ["begin", "message:0","message:1"]
+            },
+            "baidu:0": {
+                "obj": {
+                    "component_name": "Baidu",
+                    "params": {}
+                },
+                "downstream": ["generate:0"],
+                "upstream": ["answer:0"]
+            },
+            "generate:0": {
+                "obj": {
+                    "component_name": "Generate",
+                    "params": {
+                      "llm_id": "deepseek-chat",
+		              "prompt": "You are an intelligent assistant. Please answer the user's question based on what Baidu searched. First, please output the user's question and the content searched by Baidu, and then answer yes, no, or i don't know.Here is the user's question:{user_input}The above is the user's question.Here is what Baidu searched for:{baidu}The above is the content searched by Baidu.",
+                      "temperature": 0.2
+                    },
+                    "parameters": [
+                                      {
+                                          "component_id": "answer:0",
+                                          "id": "69415446-49bf-4d4b-8ec9-ac86066f7709",
+                                          "key": "user_input"
+                                      },
+                                      {
+                                          "component_id": "baidu:0",
+                                          "id": "83363c2a-00a8-402f-a45c-ddc4097d7d8b",
+                                          "key": "baidu"
+                                      }
+                                  ]
+                        },
+                "downstream": ["switch:0"],
+                "upstream": ["baidu:0"]
+            },
+            "switch:0": {
+                "obj": {
+                    "component_name": "Switch",
+                    "params": {
+                      "conditions": [
+                                        {
+                                            "logical_operator" : "or",
+                                            "items" : [
+                                                          {"cpn_id": "generate:0", "operator": "contains", "value": "yes"},
+                                                          {"cpn_id": "generate:0", "operator": "contains", "value": "yeah"}
+                                                      ],
+                                             "to": "message:0"
+                                        },
+                                        {
+                                            "logical_operator" : "and",
+                                            "items" : [
+                                                          {"cpn_id": "generate:0", "operator": "contains", "value": "no"},
+                                                          {"cpn_id": "generate:0", "operator": "not contains", "value": "yes"},
+							  {"cpn_id": "generate:0", "operator": "not contains", "value": "know"}
+                                                      ],
+                                            "to": "message:1"
+                                        },
+                                        {
+                                            "logical_operator" : "",
+                                            "items" : [
+                                                          {"cpn_id": "generate:0", "operator": "contains", "value": "know"}
+                                                      ],
+                                             "to": "message:2"
+                                        }
+                                    ],
+                      "end_cpn_id": "answer:0"
+
+                    }
+                },
+                "downstream": ["message:0","message:1"],
+                "upstream": ["generate:0"]
+            },
+            "message:0": {
+                "obj": {
+                    "component_name": "Message",
+                    "params": {
+                        "messages": ["YES YES YES YES YES YES YES YES YES YES YES YES"]
+                    }
+                },
+
+                "upstream": ["switch:0"],
+                "downstream": ["answer:0"]
+            },
+            "message:1": {
+                "obj": {
+                    "component_name": "Message",
+                    "params": {
+                        "messages": ["NO NO NO NO NO NO NO NO NO NO NO NO NO NO"]
+                    }
+                },
+
+                "upstream": ["switch:0"],
+                "downstream": ["answer:0"]
+            },
+            "message:2": {
+                "obj": {
+                    "component_name": "Message",
+                    "params": {
+                        "messages": ["I DON'T KNOW---------------------------"]
+                    }
+                },
+
+                "upstream": ["switch:0"],
+                "downstream": ["answer:0"]
+            }
+  },
+  "history": [],
+  "messages": [],
+  "reference": {},
+  "path": [],
+  "answer": []
+}

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": []
+}

agent/test/dsl_examples/exesql.json

@@ -0,0 +1,43 @@
+{
+  "components": {
+            "begin": {
+                "obj":{
+                    "component_name": "Begin",
+                    "params": {
+                      "prologue": "Hi there!"
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": []
+            },
+            "answer:0": {
+                "obj": {
+                    "component_name": "Answer",
+                    "params": {}
+                },
+                "downstream": ["exesql:0"],
+                "upstream": ["begin", "exesql:0"]
+            },
+            "exesql:0": {
+                "obj": {
+                    "component_name": "ExeSQL",
+                    "params": {
+                                    "database": "rag_flow",
+                                    "username": "root",
+                                    "host": "mysql",
+                                    "port": 3306,
+                                    "password": "infini_rag_flow",
+				    "top_n": 3
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": ["answer:0"]
+            }
+  },
+  "history": [],
+  "messages": [],
+  "reference": {},
+  "path": [],
+  "answer": []
+}
+

agent/test/dsl_examples/keyword_wikipedia_and_generate.json

@@ -0,0 +1,62 @@
+{
+  "components": {
+            "begin": {
+                "obj":{
+                    "component_name": "Begin",
+                    "params": {
+                      "prologue": "Hi there!"
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": []
+            },
+            "answer:0": {
+                "obj": {
+                    "component_name": "Answer",
+                    "params": {}
+                },
+                "downstream": ["keyword:0"],
+                "upstream": ["begin"]
+            },
+            "keyword:0": {
+                "obj": {
+                    "component_name": "KeywordExtract",
+                    "params": {
+                      "llm_id": "deepseek-chat",
+                      "prompt": "- Role: You're a question analyzer.\n    - Requirements:\n     - Summarize user's question, and give top %s important keyword/phrase.\n    - Use comma as a delimiter to separate keywords/phrases.\n    - Answer format: (in language of user's question)\n    - keyword: ",
+                      "temperature": 0.2,
+                      "top_n": 1
+                    }
+                },
+                "downstream": ["wikipedia:0"],
+                "upstream": ["answer:0"]
+            },
+            "wikipedia:0": {
+                "obj":{
+                    "component_name": "Wikipedia",
+                    "params": {
+                      "top_n": 10
+                    }
+                },
+                "downstream": ["generate:0"],
+                "upstream": ["keyword:0"]
+            },
+            "generate:1": {
+                "obj": {
+                    "component_name": "Generate",
+                    "params": {
+                      "llm_id": "deepseek-chat",
+                      "prompt": "You are an intelligent assistant. Please answer the question based on content from Wikipedia. When the answer from Wikipedia is incomplete, you need to output the URL link of the corresponding content as well. When all the content searched from Wikipedia is irrelevant to the question, your answer must include the sentence, \"The answer you are looking for is not found in the Wikipedia!\". Answers need to consider chat history.\n       The content of Wikipedia is as follows:\n    {input}\n     The above is the content of Wikipedia.",
+                      "temperature": 0.2
+                    }
+                },
+                "downstream": ["answer:0"],
+                "upstream": ["wikipedia:0"]
+            }
+  },
+  "history": [],
+  "path": [],
+  "messages": [],
+  "reference": {},
+  "answer": []
+}

api/apps/__init__.py

@@ -13,67 +13,104 @@
 #  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
 from api.db.services import UserService
-from api.utils import CustomJSONEncoder
+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__)
+
+# 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["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", 128 * 1024 * 1024)
+)
 
 Session(app)
 login_manager = LoginManager()
 login_manager.init_app(app)
 
+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)
@@ -81,8 +118,10 @@ 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)
+    url_prefix = (
+        f"/api/{API_VERSION}" if "/sdk/" in path else f"/{API_VERSION}/{page_name}"
+    )
 
     app.register_blueprint(page.manager, url_prefix=url_prefix)
     return url_prefix
@@ -90,30 +129,31 @@ 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)
+        except Exception:
+            logging.exception("load_user got exception")
             return None
     else:
         return None

api/apps/api_app.py

@@ -18,53 +18,57 @@ import os
 import re
 from datetime import datetime, timedelta
 from flask import request, Response
+from api.db.services.llm_service import TenantLLMService
 from flask_login import login_required, current_user
 
-from api.db import FileType, ParserType, FileSource
-from api.db.db_models import APIToken, API4Conversation, Task, File
+from api.db import FileType, LLMType, ParserType, FileSource
+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
-from api.db.services.document_service import DocumentService
+from api.db.services.dialog_service import DialogService, chat, keyword_extraction
+from api.db.services.document_service import DocumentService, doc_upload_and_parse
 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.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.utils.minio_conn import MINIO
+from rag.utils.storage_factory import STORAGE_IMPL
 
-
-def generate_confirmation_token(tenent_id):
-    serializer = URLSafeTimedSerializer(tenent_id)
-    return "ragflow-" + serializer.dumps(get_uuid(), salt=tenent_id)[2:34]
+from api.db.services.canvas_service import UserCanvasService
+from agent.canvas import Canvas
+from functools import partial
 
 
 @manager.route('/new_token', methods=['POST'])
-@validate_request("dialog_id")
 @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),
-               "dialog_id": req["dialog_id"],
                "create_time": current_timestamp(),
                "create_date": datetime_format(datetime.now()),
                "update_time": None,
                "update_date": None
                }
+        if req.get("canvas_id"):
+            obj["dialog_id"] = req["canvas_id"]
+            obj["source"] = "agent"
+        else:
+            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:
@@ -77,9 +81,10 @@ 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!")
 
-        objs = APITokenService.query(tenant_id=tenants[0].tenant_id, dialog_id=request.args["dialog_id"])
+        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)
         return get_json_result(data=[o.to_dict() for o in objs])
     except Exception as e:
         return server_error_response(e)
@@ -105,17 +110,18 @@ 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(
                 "from_date",
                 (datetime.now() -
                  timedelta(
-                    days=7)).strftime("%Y-%m-%d 24:00:00")),
+                     days=7)).strftime("%Y-%m-%d 00:00:00")),
             request.args.get(
                 "to_date",
-                datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
+                datetime.now().strftime("%Y-%m-%d %H:%M:%S")),
+            "agent" if "canvas_id" in request.args else None)
         res = {
             "pv": [(o["dt"], o["pv"]) for o in objs],
             "uv": [(o["dt"], o["uv"]) for o in objs],
@@ -135,12 +141,29 @@ def set_conversation():
     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='Token is not valid!"', code=settings.RetCode.AUTHENTICATION_ERROR)
     req = request.json
     try:
+        if objs[0].source == "agent":
+            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": cvs.id,
+                "user_id": request.args.get("user_id", ""),
+                "message": [{"role": "assistant", "content": canvas.get_prologue()}],
+                "source": "agent"
+            }
+            API4ConversationService.save(**conv)
+            return get_json_result(data=conv)
+        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,
@@ -148,10 +171,6 @@ def set_conversation():
                 "message": [{"role": "assistant", "content": dia.prompt_config["prologue"]}]
             }
             API4ConversationService.save(**conv)
-        e, conv = API4ConversationService.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)
@@ -161,13 +180,14 @@ def set_conversation():
 @validate_request("conversation_id", "messages")
 def completion():
     token = request.headers.get('Authorization').split()[1]
-    if not APIToken.query(token=token):
+    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='Token is not valid!"', 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!")
+        return get_data_error_result(message="Conversation not found!")
     if "quote" not in req: req["quote"] = False
 
     msg = []
@@ -176,46 +196,128 @@ 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, 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
+
+    def rename_field(ans):
+        reference = ans['reference']
+        if not isinstance(reference, dict):
+            return
+        for chunk_i in reference.get('chunks', []):
+            if 'docnm_kwd' in chunk_i:
+                chunk_i['doc_name'] = chunk_i['docnm_kwd']
+                chunk_i.pop('docnm_kwd')
 
     try:
+        if conv.source == "agent":
+            stream = req.get("stream", True)
+            conv.message.append(msg[-1])
+            e, cvs = UserCanvasService.get_by_id(conv.dialog_id)
+            if not e:
+                return server_error_response("canvas not found.")
+            del req["conversation_id"]
+            del req["messages"]
+
+            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": [], "content": ""}
+            canvas = Canvas(cvs.dsl, objs[0].tenant_id)
+
+            canvas.messages.append(msg[-1])
+            canvas.add_user_input(msg[-1]["content"])
+            answer = canvas.run(stream=stream)
+
+            assert answer is not None, "Nothing. Is it over?"
+
+            if stream:
+                assert isinstance(answer, partial), "Nothing. Is it over?"
+
+                def sse():
+                    nonlocal answer, cvs, conv
+                    try:
+                        for ans in answer():
+                            for k in ans.keys():
+                                final_ans[k] = ans[k]
+                            ans = {"answer": ans["content"], "reference": ans.get("reference", [])}
+                            fillin_conv(ans)
+                            rename_field(ans)
+                            yield "data:" + json.dumps({"code": 0, "message": "", "data": ans},
+                                                       ensure_ascii=False) + "\n\n"
+
+                        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({"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(sse(), 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
+
+            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))
+
+            result = {"answer": final_ans["content"], "reference": final_ans.get("reference", [])}
+            fillin_conv(result)
+            API4ConversationService.append_message(conv.id, conv.to_dict())
+            rename_field(result)
+            return get_json_result(data=result)
+
+        # ******************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 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 rename_field(ans):
-            for chunk_i in ans['reference'].get('chunks', []):
-                chunk_i['doc_name'] = chunk_i['docnm_kwd']
-                chunk_i.pop('docnm_kwd')
-
         def stream():
             nonlocal dia, msg, req, conv
             try:
                 for ans in chat(dia, msg, True, **req):
                     fillin_conv(ans)
                     rename_field(ans)
-                    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"
                 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")
@@ -224,18 +326,14 @@ def completion():
             resp.headers.add_header("X-Accel-Buffering", "no")
             resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8")
             return resp
-        else:
+
         answer = None
         for ans in chat(dia, msg, **req):
             answer = ans
             fillin_conv(ans)
             API4ConversationService.append_message(conv.id, conv.to_dict())
             break
-
-            for chunk_i in answer['reference'].get('chunks',[]):
-                chunk_i['doc_name'] = chunk_i['docnm_kwd']
-                chunk_i.pop('docnm_kwd')
-
+        rename_field(answer)
         return get_json_result(data=answer)
 
     except Exception as e:
@@ -245,13 +343,25 @@ def completion():
 @manager.route('/conversation/<conversation_id>', methods=['GET'])
 # @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='Token is not valid!"', 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='Token is not valid 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
             for chunk_i in referenct_i['chunks']:
                 if 'docnm_kwd' in chunk_i.keys():
                     chunk_i['doc_name'] = chunk_i['docnm_kwd']
@@ -268,7 +378,7 @@ def upload():
     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='Token is not valid!"', code=settings.RetCode.AUTHENTICATION_ERROR)
 
     kb_name = request.form.get("kb_name").strip()
     tenant_id = objs[0].tenant_id
@@ -277,19 +387,19 @@ 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"]
@@ -300,7 +410,7 @@ 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,
@@ -309,13 +419,13 @@ 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,
@@ -335,8 +445,12 @@ def upload():
                 doc["parser_id"] = request.form.get("parser_id").strip()
         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)$", filename):
             doc["parser_id"] = ParserType.PRESENTATION.value
+        if re.search(r"\.(eml)$", filename):
+            doc["parser_id"] = ParserType.EMAIL.value
 
         doc_result = DocumentService.insert(doc)
         FileService.add_file_from_kb(doc, kb_folder["id"], kb.tenant_id)
@@ -354,14 +468,14 @@ 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"])
+                bucket, name = File2DocumentService.get_storage_address(doc_id=doc["id"])
                 queue_tasks(doc, bucket, name)
             except Exception as e:
                 return server_error_response(e)
@@ -369,6 +483,29 @@ def upload():
     return get_json_result(data=doc_result.to_json())
 
 
+@manager.route('/document/upload_and_parse', methods=['POST'])
+@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, message='Token is not valid!"', code=settings.RetCode.AUTHENTICATION_ERROR)
+
+    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')
+    for file_obj in file_objs:
+        if file_obj.filename == '':
+            return get_json_result(
+                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'])
 # @login_required
 def list_chunks():
@@ -376,7 +513,7 @@ def list_chunks():
     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='Token is not valid!"', code=settings.RetCode.AUTHENTICATION_ERROR)
 
     req = request.json
 
@@ -390,15 +527,16 @@ 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
         ]
 
@@ -415,7 +553,7 @@ def list_kb_docs():
     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='Token is not valid!"', code=settings.RetCode.AUTHENTICATION_ERROR)
 
     req = request.json
     tenant_id = objs[0].tenant_id
@@ -425,7 +563,7 @@ 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:
@@ -448,6 +586,20 @@ def list_kb_docs():
         return server_error_response(e)
 
 
+@manager.route('/document/infos', methods=['POST'])
+@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, message='Token is not valid!"', 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'])
 # @login_required
 def document_rm():
@@ -455,11 +607,10 @@ def document_rm():
     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='Token is not valid!"', code=settings.RetCode.AUTHENTICATION_ERROR)
 
     tenant_id = objs[0].tenant_id
     req = request.json
-    doc_ids = []
     try:
         doc_ids = [DocumentService.get_doc_id_by_doc_name(doc_name) for doc_name in req.get("doc_names", [])]
         for doc_id in req.get("doc_ids", []):
@@ -468,7 +619,7 @@ 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:
@@ -483,27 +634,27 @@ 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)
 
@@ -518,35 +669,99 @@ 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='Token is not valid!"', 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!")
+        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"
@@ -570,9 +785,10 @@ 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
                 except Exception as e:
                     return server_error_response(e)
 
@@ -581,3 +797,52 @@ def completion_faq():
 
     except Exception as e:
         return server_error_response(e)
+
+
+@manager.route('/retrieval', methods=['POST'])
+@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, message='Token is not valid!"', code=settings.RetCode.AUTHENTICATION_ERROR)
+
+    req = request.json
+    kb_ids = req.get("kb_id", [])
+    doc_ids = req.get("doc_ids", [])
+    question = req.get("question")
+    page = int(req.get("page", 1))
+    size = int(req.get("size", 30))
+    similarity_threshold = float(req.get("similarity_threshold", 0.2))
+    vector_similarity_weight = float(req.get("vector_similarity_weight", 0.3))
+    top = int(req.get("top_k", 1024))
+
+    try:
+        kbs = KnowledgebaseService.get_by_ids(kb_ids)
+        embd_nms = list(set([kb.embd_id for kb in kbs]))
+        if len(embd_nms) != 1:
+            return get_json_result(
+                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"])
+        if req.get("keyword", False):
+            chat_mdl = TenantLLMService.model_instance(kbs[0].tenant_id, LLMType.CHAT)
+            question += keyword_extraction(chat_mdl, question)
+        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)
+        for c in ranks["chunks"]:
+            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, message='No chunk found! Check the chunk status please!',
+                                   code=settings.RetCode.DATA_ERROR)
+        return server_error_response(e)

api/apps/canvas_app.py

@@ -13,17 +13,18 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import logging
 import json
+import traceback
 from functools import partial
-
 from flask import request, Response
 from flask_login import login_required, current_user
-
-from api.db.db_models import UserCanvas
 from api.db.services.canvas_service import CanvasTemplateService, UserCanvasService
+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 graph.canvas import Canvas
+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
 
 
 @manager.route('/templates', methods=['GET'])
@@ -45,6 +46,10 @@ def canvas_list():
 @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)
 
@@ -63,10 +68,13 @@ def save():
             return server_error_response(ValueError("Duplicated title."))
         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)
-
     return get_json_result(data=req)
 
 
@@ -75,7 +83,7 @@ def save():
 def get(canvas_id):
     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())
 
 
@@ -87,46 +95,55 @@ 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, "Nothing. Is it over?"
-
     if stream:
-        assert isinstance(answer, partial)
-
         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 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))
+                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")
@@ -135,12 +152,15 @@ def run():
         resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8")
         return resp
 
-    canvas.messages.append({"role": "assistant", "content": final_ans["content"]})
+    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=req["dsl"])
+        return get_json_result(data={"answer": final_ans["content"], "reference": final_ans.get("reference", [])})
 
 
 @manager.route('/reset', methods=['POST'])
@@ -151,7 +171,11 @@ 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()
@@ -160,3 +184,22 @@ def reset():
         return get_json_result(data=req["dsl"])
     except Exception as e:
         return server_error_response(e)
+
+
+@manager.route('/test_db_connect', methods=['POST'])
+@validate_request("db_type", "database", "username", "host", "port", "password")
+@login_required
+def test_db_connect():
+    req = request.json
+    try:
+        if req["db_type"] in ["mysql", "mariadb"]:
+            db = MySQLDatabase(req["database"], user=req["username"], host=req["host"], port=req["port"],
+                               password=req["password"])
+        elif req["db_type"] == 'postgresql':
+            db = PostgresqlDatabase(req["database"], user=req["username"], host=req["host"], port=req["port"],
+                                    password=req["password"])
+        db.connect()
+        db.close()
+        return get_json_result(data="Database Connection Successful!")
+    except Exception as e:
+        return server_error_response(e)

api/apps/chunk_app.py

@@ -14,22 +14,22 @@
 #  limitations under the License.
 #
 import datetime
+import json
 
 from flask import request
 from flask_login import login_required, current_user
-from elasticsearch_dsl import Q
 
+from api.db.services.dialog_service import keyword_extraction
 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.nlp import search, rag_tokenizer
 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
+from api import settings
 from api.utils.api_utils import get_json_result
 import hashlib
 import re
@@ -47,41 +47,39 @@ 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 = {
                 "chunk_id": id,
-                "content_with_weight": rmSpace(sres.highlight[id]) if question and id in  sres.highlight else sres.field[id].get(
+                "content_with_weight": rmSpace(sres.highlight[id]) if question and id in sres.highlight else sres.field[
+                    id].get(
                     "content_with_weight", ""),
                 "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", ""),
+                "image_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")
+                "positions": json.loads(sres.field[id].get("position_list", "[]")),
             }
-            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)
 
 
@@ -92,27 +90,25 @@ def get():
     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 get_data_error_result(message="Tenant not found!")
+        tenant_id = tenants[0].tenant_id
+
+        kb_ids = KnowledgebaseService.get_kb_ids(tenant_id)
+        chunk = settings.docStoreConn.get(chunk_id, search.index_name(tenant_id), kb_ids)
+        if chunk is None:
             return server_error_response("Chunk not found")
-        id = res["_id"]
-        res = res["_source"]
-        res["chunk_id"] = id
         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)
 
 
@@ -135,15 +131,14 @@ def set():
     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 = [
@@ -152,7 +147,7 @@ def set():
                     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.")
+                    message="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(
                 [rag_tokenizer.is_chinese(t) for t in q + a]))
@@ -160,7 +155,7 @@ def set():
         v, c = embd_mdl.encode([doc.name, req["content_with_weight"]])
         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.insert([d], search.index_name(tenant_id), doc.kb_id)
         return get_json_result(data=True)
     except Exception as e:
         return server_error_response(e)
@@ -172,12 +167,15 @@ def set():
 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)
@@ -185,13 +183,18 @@ def switch():
 
 @manager.route('/rm', methods=['POST'])
 @login_required
-@validate_request("chunk_ids")
+@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(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)
         return get_json_result(data=True)
     except Exception as e:
         return server_error_response(e)
@@ -216,24 +219,22 @@ def create():
     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["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!")
 
         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"]])
-        DocumentService.increment_chunk_num(req["doc_id"], doc.kb_id, c, 1, 0)
         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)
@@ -250,38 +251,93 @@ 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))
     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)
 
-        ranks = retrievaler.retrieval(question, embd_mdl, kb.tenant_id, [kb_id], page, size,
+        retr = settings.retrievaler if kb.parser_id != ParserType.KG else settings.kg_retrievaler
+        ranks = retr.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"))
         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 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'])
+@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],
+        "knowledge_graph_kwd": ["graph", "mind_map"]
+    }
+    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:
+            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

@@ -13,14 +13,22 @@
 #  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
+from api.db.services.user_service import UserTenantService
+from flask import request, Response
+from flask_login import login_required, current_user
+
+from api.db import LLMType
+from api.db.services.dialog_service import DialogService, ConversationService, chat, ask
+from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.db.services.llm_service import LLMBundle, TenantService, TenantLLMService
+from api import settings
+from api.utils.api_utils import get_json_result
+from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
+from graphrag.mind_map_extractor import MindMapExtractor
 
 
 @manager.route('/set', methods=['POST'])
@@ -28,15 +36,17 @@ import json
 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!")
+                    message="Fail to update a conversation!")
             conv = conv.to_dict()
             return get_json_result(data=conv)
         except Exception as e:
@@ -45,9 +55,9 @@ 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")
+            return get_data_error_result(message="Dialog not found")
         conv = {
-            "id": get_uuid(),
+            "id": conv_id,
             "dialog_id": req["dialog_id"],
             "name": req.get("name", "New conversation"),
             "message": [{"role": "assistant", "content": dia.prompt_config["prologue"]}]
@@ -55,7 +65,7 @@ def set_conversation():
         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!")
+            return get_data_error_result(message="Fail to new a conversation!")
         conv = conv.to_dict()
         return get_json_result(data=conv)
     except Exception as e:
@@ -69,7 +79,15 @@ def get():
     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)
+        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)
         conv = conv.to_dict()
         return get_json_result(data=conv)
     except Exception as e:
@@ -82,6 +100,17 @@ 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:
@@ -93,6 +122,10 @@ def rm():
 def list_convsersation():
     dialog_id = request.args["dialog_id"]
     try:
+        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,
@@ -105,54 +138,56 @@ def list_convsersation():
 
 @manager.route('/completion', methods=['POST'])
 @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"]})
+        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.message.append({"role": "assistant", "content": "", "id": message_id})
         conv.reference.append({"chunks": [], "doc_aggs": []})
 
         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"]}
+            else:
+                conv.reference[-1] = ans["reference"]
+            conv.message[-1] = {"role": "assistant", "content": ans["answer"],
+                                "id": message_id, "prompt": ans.get("prompt", "")}
+            ans["id"] = message_id
 
         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"
+                    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),
+                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"
 
         if req.get("stream", True):
             resp = Response(stream(), mimetype="text/event-stream")
@@ -173,3 +208,170 @@ def completion():
     except Exception as e:
         return server_error_response(e)
 
+
+@manager.route('/tts', methods=['POST'])
+@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'])
+@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'])
+@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("set")
+    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'])
+@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'])
+@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 = TenantLLMService.model_instance(
+        kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id)
+    chat_mdl = LLMBundle(current_user.id, LLMType.CHAT)
+    ranks = settings.retrievaler.retrieval(req["question"], embd_mdl, kb.tenant_id, kb_ids, 1, 12,
+                                           0.3, 0.3, aggs=False)
+    mindmap = MindMapExtractor(chat_mdl)
+    mind_map = mindmap([c["content_with_weight"] for c in ranks["chunks"]]).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'])
+@login_required
+@validate_request("question")
+def related_questions():
+    req = request.json
+    question = req["question"]
+    chat_mdl = LLMBundle(current_user.id, LLMType.CHAT)
+    prompt = """
+Objective: To generate search terms related to the user's search keywords, helping users find more valuable information.
+Instructions:
+ - Based on the keywords provided by the user, generate 5-10 related search terms.
+ - Each search term should be directly or indirectly related to the keyword, guiding the user to find more valuable information.
+ - Use common, general terms as much as possible, avoiding obscure words or technical jargon.
+ - Keep the term length between 2-4 words, concise and clear.
+ - DO NOT translate, use the language of the original keywords.
+
+### Example:
+Keywords: Chinese football
+Related search terms:
+1. Current status of Chinese football
+2. Reform of Chinese football
+3. Youth training of Chinese football
+4. Chinese football in the Asian Cup
+5. Chinese football in the World Cup
+
+Reason:
+ - When searching, users often only use one or two keywords, making it difficult to fully express their information needs.
+ - Generating related search terms can help users dig deeper into relevant information and improve search efficiency. 
+ - At the same time, related terms can also help search engines better understand user needs and return more accurate search results.
+ 
+"""
+    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

@@ -1,615 +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 io import BytesIO
-
-from flask import request, send_file
-from flask_login import login_required, current_user
-from httpx import HTTPError
-from minio import S3Error
-
-from api.contants import NAME_LENGTH_LIMIT
-from api.db import FileType, ParserType, FileSource
-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.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:
-                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 req.get("chunk_method"):
-            if chunk_num == 0:
-                dataset_updating_data['parser_id'] = req["chunk_method"]
-            else:
-                construct_json_result(code=RetCode.DATA_ERROR, message="You have already parsed the document "
-                                                                       "in this dataset, so you cannot "
-                                                                       "change the chunk method.")
-        # 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 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 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-----------------------------------------------------
-
-# ----------------------------stop parsing-----------------------------------------------------
-
-# ----------------------------show the status of the file-----------------------------------------------------
-
-# ----------------------------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

@@ -19,7 +19,8 @@ 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.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
@@ -67,17 +68,17 @@ def set_dialog():
             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!")
         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!")
+                    message="Fail! Please select knowledgebase!")
             dia = {
                 "id": get_uuid(),
                 "tenant_id": current_user.id,
@@ -95,20 +96,20 @@ def set_dialog():
                 "icon": icon
             }
             if not DialogService.save(**dia):
-                return get_data_error_result(retmsg="Fail to new a dialog!")
+                return get_data_error_result(message="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_data_error_result(message="Fail to new a dialog!")
             return get_json_result(data=dia.to_json())
         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["kb_ids"], dia["kb_names"] = get_kb_names(dia["kb_ids"])
             return get_json_result(data=dia)
@@ -123,7 +124,7 @@ def get():
     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)
@@ -164,9 +165,19 @@ def list_dialogs():
 @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

@@ -13,13 +13,12 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License
 #
-
-import os
+import json
+import os.path
 import pathlib
 import re
 
 import flask
-from elasticsearch_dsl import Q
 from flask import request
 from flask_login import login_required, current_user
 
@@ -27,20 +26,21 @@ from api.db.db_models import Task, File
 from api.db.services.file2document_service import File2DocumentService
 from api.db.services.file_service import FileService
 from api.db.services.task_service import TaskService, queue_tasks
+from api.db.services.user_service import UserTenantService
+from deepdoc.parser.html_parser import RAGFlowHtmlParser
 from rag.nlp import search
-from rag.utils.es_conn import ELASTICSEARCH
 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
-from api.db.services.document_service import DocumentService
-from api.settings import RetCode
+from api.db.services.document_service import DocumentService, doc_upload_and_parse
+from api import settings
 from api.utils.api_utils import get_json_result
-from rag.utils.minio_conn import MINIO
-from api.utils.file_utils import filename_type, thumbnail
-from api.utils.web_utils import html2pdf, is_valid_url
+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'])
@@ -50,71 +50,25 @@ 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!")
 
-    root_folder = FileService.get_root_folder(current_user.id)
-    pf_id = root_folder["id"]
-    FileService.init_knowledgebase_docs(pf_id, current_user.id)
-    kb_root_folder = FileService.get_kb_folder(current_user.id)
-    kb_folder = FileService.new_a_file_from_kb(kb.tenant_id, kb.name, kb_root_folder["id"])
-
-    err = []
-    for file in file_objs:
-        try:
-            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(kb.tenant_id) >= MAX_FILE_NUM_PER_USER:
-                raise RuntimeError("Exceed the maximum file number of a free user!")
-
-            filename = duplicate_name(
-                DocumentService.query,
-                name=file.filename,
-                kb_id=kb.id)
-            filetype = filename_type(filename)
-            if filetype == FileType.OTHER.value:
-                raise RuntimeError("This type of file has not been supported yet!")
-
-            location = filename
-            while MINIO.obj_exist(kb_id, location):
-                location += "_"
-            blob = file.read()
-            MINIO.put(kb_id, location, blob)
-            doc = {
-                "id": get_uuid(),
-                "kb_id": kb.id,
-                "parser_id": kb.parser_id,
-                "parser_config": kb.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 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"], kb.tenant_id)
-        except Exception as e:
-            err.append(file.filename + ": " + str(e))
+    err, _ = FileService.upload_document(kb, file_objs, current_user.id)
     if err:
         return get_json_result(
-            data=False, retmsg="\n".join(err), retcode=RetCode.SERVER_ERROR)
+            data=False, message="\n".join(err), code=settings.RetCode.SERVER_ERROR)
     return get_json_result(data=True)
 
 
@@ -125,12 +79,12 @@ 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!")
@@ -154,9 +108,9 @@ 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,
@@ -171,8 +125,12 @@ def web_crawl():
         }
         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)$", filename):
             doc["parser_id"] = ParserType.PRESENTATION.value
+        if re.search(r"\.(eml)$", 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:
@@ -188,17 +146,17 @@ 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(),
@@ -222,7 +180,16 @@ 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))
@@ -232,21 +199,47 @@ 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('/thumbnails', methods=['GET'])
+@manager.route('/infos', methods=['POST'])
 @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
 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)
@@ -258,37 +251,34 @@ def thumbnails():
 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)
@@ -301,6 +291,15 @@ def rm():
     req = request.json
     doc_ids = req["doc_id"]
     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)
@@ -309,27 +308,27 @@ 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)
 
             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)
 
@@ -339,6 +338,13 @@ def rm():
 @validate_request("doc_ids", "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}
@@ -350,16 +356,19 @@ def run():
             # 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 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"])
+                bucket, name = File2DocumentService.get_storage_address(doc_id=doc["id"])
                 queue_tasks(doc, bucket, name)
 
         return get_json_result(data=True)
@@ -372,25 +381,31 @@ def run():
 @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:
@@ -408,10 +423,10 @@ 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:
@@ -432,10 +447,17 @@ def get(doc_id):
 @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:
@@ -443,27 +465,28 @@ 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:
@@ -475,8 +498,87 @@ def change_parser():
 def get_image(image_id):
     try:
         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'])
+@login_required
+@validate_request("conversation_id")
+def upload_and_parse():
+    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')
+    for file_obj in file_objs:
+        if file_obj.filename == '':
+            return get_json_result(
+                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'])
+@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 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)

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
 
@@ -26,10 +24,8 @@ 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'])
@@ -54,13 +50,13 @@ 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 +64,16 @@ 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,
@@ -104,26 +100,26 @@ 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
 
@@ -29,12 +28,10 @@ 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'])
@@ -49,24 +46,24 @@ 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:
         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!")
+                    message="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!")
+                    message="Exceed the maximum file number of a free user!")
 
             # split file name path
             if not file_obj.filename:
@@ -85,20 +82,20 @@ 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(
@@ -116,7 +113,7 @@ 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:
@@ -137,10 +134,10 @@ 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
@@ -181,14 +178,14 @@ 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!")
+            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:
@@ -212,7 +209,7 @@ def get_parent_folder():
     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()})
@@ -227,7 +224,7 @@ def get_all_parent_folders():
     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 = []
@@ -248,9 +245,9 @@ 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 +256,13 @@ 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 +270,13 @@ 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)
@@ -295,29 +292,30 @@ 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:
@@ -330,9 +328,9 @@ 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!")
+        b, n = File2DocumentService.get_storage_address(file_id=file_id)
+        response = flask.make_response(STORAGE_IMPL.get(b, n))
         ext = re.search(r"\.([^.]+)$", file.name)
         if ext:
             if file.type == FileType.VISUAL.value:
@@ -358,12 +356,12 @@ def move():
         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!")
         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 +13,6 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-from elasticsearch_dsl import Q
 from flask import request
 from flask_login import login_required, current_user
 
@@ -23,14 +22,13 @@ 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 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
 
 
 @manager.route('/create', methods=['post'])
@@ -50,7 +48,7 @@ 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()
@@ -65,21 +63,27 @@ def create():
 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["name"].lower() != kb.name.lower() \
                 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):
@@ -88,7 +92,7 @@ def update():
         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)!")
 
         return get_json_result(data=kb.to_json())
     except Exception as e:
@@ -100,10 +104,19 @@ def update():
 def detail():
     kb_id = request.args["kb_id"]
     try:
+        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(
-                retmsg="Can't find this knowledgebase!")
+                message="Can't find this knowledgebase!")
         return get_json_result(data=kb)
     except Exception as e:
         return server_error_response(e)
@@ -130,24 +143,32 @@ def list_kbs():
 @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"])
         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])
+            FileService.filter_delete([File.source_type == FileSource.KNOWLEDGEBASE, File.type == "folder", File.name == kbs[0].name])
             File2DocumentService.delete_by_document_id(doc.id)
 
         if not KnowledgebaseService.delete_by_id(req["kb_id"]):
             return get_data_error_result(
-                retmsg="Database error (Knowledgebase removal)!")
+                message="Database error (Knowledgebase removal)!")
+        settings.docStoreConn.delete({"kb_id": req["kb_id"]}, search.index_name(kbs[0].tenant_id), req["kb_id"])
         return get_json_result(data=True)
     except Exception as e:
         return server_error_response(e)

api/apps/llm_app.py

@@ -13,14 +13,19 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import logging
+import json
+
 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
+from rag.llm import EmbeddingModel, ChatModel, RerankModel, CvModel, TTSModel
+import requests
 
 
 @manager.route('/factories', methods=['GET'])
@@ -28,7 +33,19 @@ from rag.llm import EmbeddingModel, ChatModel, RerankModel
 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)
 
@@ -48,7 +65,7 @@ def set_api_key():
                 req["api_key"], llm.llm_name, base_url=req.get("base_url"))
             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")
                 embd_passed = True
             except Exception as e:
@@ -57,14 +74,14 @@ def set_api_key():
             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})
-                if not tc:
+                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:
             mdl = RerankModel[factory](
                 req["api_key"], llm.llm_name, base_url=req.get("base_url"))
@@ -72,32 +89,41 @@ def set_api_key():
                 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):
+        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)
@@ -105,30 +131,72 @@ def set_api_key():
 
 @manager.route('/add_llm', methods=['POST'])
 @login_required
-@validate_request("llm_factory", "llm_name", "model_type")
+@validate_request("llm_factory")
 def add_llm():
     req = request.json
     factory = req["llm_factory"]
 
+    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(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
+        llm_name = req["llm_name"]
+        api_key = apikey_json(["ark_api_key", "endpoint_id"])
+
+    elif factory == "Tencent Hunyuan":
+        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"])
+
     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"
+
+    elif factory == "HuggingFace":
+        llm_name = req["llm_name"] + "___HuggingFace"
+        api_key = "xxxxxxxxxxxxxxx"
+
+    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"]
+        if req["model_type"] == "chat":
+            api_key = req.get("spark_api_password", "xxxxxxxxxxxxxxx")
+        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 = apikey_json(["yiyan_ak", "yiyan_sk"])
+
+    elif factory == "Fish Audio":
+        llm_name = req["llm_name"]
+        api_key = apikey_json(["fish_audio_ak", "fish_audio_refid"])
+
+    elif factory == "Google Cloud":
+        llm_name = req["llm_name"]
+        api_key = apikey_json(["google_project_id", "google_region", "google_service_account_key"])
+
+    elif factory == "Azure-OpenAI":
+        llm_name = req["llm_name"]
+        api_key = apikey_json(["api_key", "api_version"])
+
     else:
         llm_name = req["llm_name"]
-        api_key = "xxxxxxxxxxxxxxx"
+        api_key = req.get("api_key", "xxxxxxxxxxxxxxx")
 
     llm = {
         "tenant_id": current_user.id,
@@ -136,13 +204,14 @@ 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 = ""
     if llm["model_type"] == LLMType.EMBEDDING.value:
         mdl = EmbeddingModel[factory](
-            key=llm['api_key'] if factory in ["VolcEngine", "Bedrock"] else None,
+            key=llm['api_key'],
             model_name=llm["llm_name"],
             base_url=llm["api_base"])
         try:
@@ -153,7 +222,7 @@ def add_llm():
             msg += f"\nFail to access embedding model({llm['llm_name']})." + str(e)
     elif llm["model_type"] == LLMType.CHAT.value:
         mdl = ChatModel[factory](
-            key=llm['api_key'] if factory in ["VolcEngine", "Bedrock"] else None,
+            key=llm['api_key'],
             model_name=llm["llm_name"],
             base_url=llm["api_base"]
         )
@@ -165,15 +234,59 @@ def add_llm():
         except Exception as e:
             msg += f"\nFail to access model({llm['llm_name']})." + 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"]
+        )
+        try:
+            arr, tc = mdl.similarity("Hello~ Ragflower!", ["Hi, there!", "Ohh, my friend!"])
+            if len(arr) == 0 or tc == 0:
+                raise Exception("Not known.")
+        except Exception as e:
+            msg += f"\nFail to access model({llm['llm_name']})." + str(
+                e)
+    elif llm["model_type"] == LLMType.IMAGE2TEXT.value:
+        mdl = CvModel[factory](
+            key=llm["api_key"],
+            model_name=llm["llm_name"],
+            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:
+                    raise Exception(m)
+            else:
+                pass
+        except Exception as e:
+            msg += f"\nFail to access model({llm['llm_name']})." + str(e)
+    elif llm["model_type"] == LLMType.TTS:
+        mdl = TTSModel[factory](
+            key=llm["api_key"], model_name=llm["llm_name"], base_url=llm["api_base"]
+        )
+        try:
+            for resp in mdl.tts("Hello~ Ragflower!"):
+                pass
+        except RuntimeError as e:
+            msg += f"\nFail to access model({llm['llm_name']})." + 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)
@@ -185,7 +298,18 @@ def add_llm():
 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('/delete_factory', methods=['POST'])
+@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)
 
 
@@ -213,20 +337,22 @@ def my_llms():
 @manager.route('/list', methods=['GET'])
 @login_required
 def list_app():
+    self_deploied = ["Youdao", "FastEmbed", "BAAI", "Ollama", "Xinference", "LocalAI", "LM-Studio"]
+    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_deploied
 
-        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 = {}

api/apps/sdk/chat.py

@@ -0,0 +1,313 @@
+#
+#  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 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
+from api.utils.api_utils import get_result
+
+
+
+@manager.route('/chats', methods=['POST'])
+@token_required
+def create(tenant_id):
+    req=request.json
+    ids= req.get("dataset_ids")
+    if not ids:
+        return get_error_data_result(message="`dataset_ids` is required")
+    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_count = list(set([kb.embd_id for kb in kbs]))
+    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"]
+    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"):
+        if 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!"
+    }
+    key_list_2 = ["system", "prologue", "parameters", "empty_response"]
+    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": 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'])
+@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("`datasets` can't be empty")
+        if ids:
+            for kb_id in ids:
+                kbs = KnowledgebaseService.accessible(kb_id=chat_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_count=list(set([kb.embd_id for kb in kbs]))
+            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!")
+    if req.get("rerank_model"):
+        if not TenantLLMService.query(tenant_id=tenant_id,llm_name=req.get("rerank_model"),model_type="rerank"):
+            return get_error_data_result(f"`rerank_model` {req.get('rerank_model')} doesn't exist")
+    # 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"]
+    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 "name" in req:
+        if not req.get("name"):
+            return get_error_data_result(message="`name` is not 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 dataset.")
+    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'])
+@token_required
+def delete(tenant_id):
+    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
+    for id in id_list:
+        if not DialogService.query(tenant_id=tenant_id, id=id, status=StatusEnum.VALID.value):
+            return get_error_data_result(message=f"You don't own the chat {id}")
+        temp_dict = {"status": StatusEnum.INVALID.value}
+        DialogService.update_by_id(id, temp_dict)
+    return get_result()
+
+@manager.route('/chats', methods=['GET'])
+@token_required
+def list_chat(tenant_id):
+    id = request.args.get("id")
+    name = request.args.get("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 = []
+    renamed_dict = {}
+    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:
+        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": 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 :
+                return get_error_data_result(message=f"Don't exist the kb {kb_id}")
+            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 +1,531 @@
+#
+#  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,
+)
+
+
+@manager.route("/datasets", methods=["POST"])
+@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.
+            language:
+              type: string
+              enum: ['Chinese', 'English']
+              description: Language of the dataset.
+            chunk_method:
+              type: string
+              enum: ["naive", "manual", "qa", "table", "paper", "book", "laws",
+                     "presentation", "picture", "one", "knowledge_graph", "email"]
+              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
+    e, t = TenantService.get_by_id(tenant_id)
+    permission = req.get("permission")
+    language = req.get("language")
+    chunk_method = req.get("chunk_method")
+    parser_config = req.get("parser_config")
+    valid_permission = ["me", "team"]
+    valid_language = ["Chinese", "English"]
+    valid_chunk_method = [
+        "naive",
+        "manual",
+        "qa",
+        "table",
+        "paper",
+        "book",
+        "laws",
+        "presentation",
+        "picture",
+        "one",
+        "knowledge_graph",
+        "email",
+    ]
+    check_validation = valid(
+        permission,
+        valid_permission,
+        language,
+        valid_language,
+        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 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"] = 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",
+            "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"
+            )
+    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)
+    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"])
+@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
+    """
+    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
+    for id in id_list:
+        kbs = KnowledgebaseService.query(id=id, tenant_id=tenant_id)
+        if not kbs:
+            return get_error_data_result(message=f"You don't own the dataset {id}")
+        for doc in DocumentService.query(kb_id=id):
+            if not DocumentService.remove_document(doc, tenant_id):
+                return get_error_data_result(
+                    message="Remove document error.(Database error)"
+                )
+            f2d = File2DocumentService.get_by_document_id(doc.id)
+            FileService.filter_delete(
+                [
+                    File.source_type == FileSource.KNOWLEDGEBASE,
+                    File.id == f2d[0].file_id,
+                ]
+            )
+            FileService.filter_delete(
+                [File.source_type == FileSource.KNOWLEDGEBASE, File.type == "folder", File.name == kbs[0].name])
+            File2DocumentService.delete_by_document_id(doc.id)
+        if not KnowledgebaseService.delete_by_id(id):
+            return get_error_data_result(message="Delete dataset error.(Database error)")
+    return get_result(code=settings.RetCode.SUCCESS)
+
+
+@manager.route("/datasets/<dataset_id>", methods=["PUT"])
+@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.
+            language:
+              type: string
+              enum: ['Chinese', 'English']
+              description: Updated language.
+            chunk_method:
+              type: string
+              enum: ["naive", "manual", "qa", "table", "paper", "book", "laws",
+                     "presentation", "picture", "one", "knowledge_graph", "email"]
+              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
+    e, t = TenantService.get_by_id(tenant_id)
+    invalid_keys = {"id", "embd_id", "chunk_num", "doc_num", "parser_id"}
+    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")
+    language = req.get("language")
+    chunk_method = req.get("chunk_method")
+    parser_config = req.get("parser_config")
+    valid_permission = ["me", "team"]
+    valid_language = ["Chinese", "English"]
+    valid_chunk_method = [
+        "naive",
+        "manual",
+        "qa",
+        "table",
+        "paper",
+        "book",
+        "laws",
+        "presentation",
+        "picture",
+        "one",
+        "knowledge_graph",
+        "email",
+    ]
+    check_validation = valid(
+        permission,
+        valid_permission,
+        language,
+        valid_language,
+        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 "chunk_method" in req:
+        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 (
+            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."
+            )
+    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"])
+@token_required
+def list(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" or request.args.get("desc") == "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 +1,76 @@
+#
+#  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, ParserType
+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
+
+
+@manager.route('/dify/retrieval', methods=['POST'])
+@apikey_required
+@validate_request("knowledge_id", "query")
+def retrieval(tenant_id):
+    req = request.json
+    question = req["query"]
+    kb_id = req["knowledge_id"]
+    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)
+
+        retr = settings.retrievaler if kb.parser_id != ParserType.KG else settings.kg_retrievaler
+        ranks = retr.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
+        )
+        records = []
+        for c in ranks["chunks"]:
+            c.pop("vector", None)
+            records.append({
+                "content": c["content_ltks"],
+                "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)

api/apps/sdk/session.py

@@ -0,0 +1,531 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import re
+import json
+from copy import deepcopy
+from uuid import uuid4
+from api.db import LLMType
+from flask import request, Response
+from api.db.services.dialog_service import ask
+from agent.canvas import Canvas
+from api.db import StatusEnum
+from api.db.db_models import API4Conversation
+from api.db.services.api_service import API4ConversationService
+from api.db.services.canvas_service import UserCanvasService
+from api.db.services.dialog_service import DialogService, ConversationService, chat
+from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.utils import get_uuid
+from api.utils.api_utils import get_error_data_result
+from api.utils.api_utils import get_result, token_required
+from api.db.services.llm_service import LLMBundle
+
+
+@manager.route('/chats/<chat_id>/sessions', methods=['POST'])
+@token_required
+def create(tenant_id,chat_id):
+    req = request.json
+    req["dialog_id"] = chat_id
+    dia = DialogService.query(tenant_id=tenant_id, id=req["dialog_id"], status=StatusEnum.VALID.value)
+    if not dia:
+        return get_error_data_result(message="You do not own the assistant.")
+    conv = {
+        "id": get_uuid(),
+        "dialog_id": req["dialog_id"],
+        "name": req.get("name", "New session"),
+        "message": [{"role": "assistant", "content": "Hi! I am your assistant，can I help you?"}]
+    }
+    if not conv.get("name"):
+        return get_error_data_result(message="`name` can not be empty.")
+    ConversationService.save(**conv)
+    e, conv = ConversationService.get_by_id(conv["id"])
+    if not e:
+        return get_error_data_result(message="Fail to create a session!")
+    conv = conv.to_dict()
+    conv['messages'] = conv.pop("message")
+    conv["chat_id"] = conv.pop("dialog_id")
+    del conv["reference"]
+    return get_result(data=conv)
+
+
+@manager.route('/agents/<agent_id>/sessions', methods=['POST'])
+@token_required
+def create_agent_session(tenant_id, agent_id):
+    req = request.json
+    e, cvs = UserCanvasService.get_by_id(agent_id)
+    if not e:
+        return get_error_data_result("Agent not found.")
+    if cvs.user_id != tenant_id:
+        return get_error_data_result(message="You do not own the agent.")
+
+    if not isinstance(cvs.dsl, str):
+        cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False)
+
+    canvas = Canvas(cvs.dsl, tenant_id)
+    conv = {
+        "id": get_uuid(),
+        "dialog_id": cvs.id,
+        "user_id": req.get("usr_id","") if isinstance(req, dict) else "",
+        "message": [{"role": "assistant", "content": canvas.get_prologue()}],
+        "source": "agent"
+    }
+    API4ConversationService.save(**conv)
+    conv["agent_id"] = conv.pop("dialog_id")
+    return get_result(data=conv)
+
+
+@manager.route('/chats/<chat_id>/sessions/<session_id>', methods=['PUT'])
+@token_required
+def update(tenant_id,chat_id,session_id):
+    req = request.json
+    req["dialog_id"] = chat_id
+    conv_id = session_id
+    conv = ConversationService.query(id=conv_id,dialog_id=chat_id)
+    if not conv:
+        return get_error_data_result(message="Session does not exist")
+    if not DialogService.query(id=chat_id, tenant_id=tenant_id, status=StatusEnum.VALID.value):
+        return get_error_data_result(message="You do not own the session")
+    if "message" in req or "messages" in req:
+        return get_error_data_result(message="`message` can not be change")
+    if "reference" in req:
+        return get_error_data_result(message="`reference` can not be change")
+    if "name" in req and not req.get("name"):
+        return get_error_data_result(message="`name` can not be empty.")
+    if not ConversationService.update_by_id(conv_id, req):
+        return get_error_data_result(message="Session updates error")
+    return get_result()
+
+
+@manager.route('/chats/<chat_id>/completions', methods=['POST'])
+@token_required
+def completion(tenant_id, chat_id):
+    req = request.json
+    if not req.get("session_id"):
+        conv = {
+            "id": get_uuid(),
+            "dialog_id": chat_id,
+            "name": req.get("name", "New session"),
+            "message": [{"role": "assistant", "content": "Hi! I am your assistant，can I help you?"}]
+        }
+        if not conv.get("name"):
+            return get_error_data_result(message="`name` can not be empty.")
+        ConversationService.save(**conv)
+        e, conv = ConversationService.get_by_id(conv["id"])
+        session_id=conv.id
+    else:
+        session_id = req.get("session_id")
+    if not req.get("question"):
+        return get_error_data_result(message="Please input your question.")
+    conv = ConversationService.query(id=session_id,dialog_id=chat_id)
+    if not conv:
+        return get_error_data_result(message="Session does not exist")
+    conv = conv[0]
+    if not DialogService.query(id=chat_id, tenant_id=tenant_id, status=StatusEnum.VALID.value):
+        return get_error_data_result(message="You do not own the chat")
+    msg = []
+    question = {
+        "content": req.get("question"),
+        "role": "user",
+        "id": str(uuid4())
+    }
+    conv.message.append(question)
+    for m in conv.message:
+        if m["role"] == "system": continue
+        if m["role"] == "assistant" and not msg: continue
+        msg.append(m)
+    message_id = msg[-1].get("id")
+    e, dia = DialogService.get_by_id(conv.dialog_id)
+
+    if not conv.reference:
+        conv.reference = []
+    conv.message.append({"role": "assistant", "content": "", "id": message_id})
+    conv.reference.append({"chunks": [], "doc_aggs": []})
+
+    def fillin_conv(ans):
+        reference = ans["reference"]
+        temp_reference = deepcopy(ans["reference"])
+        nonlocal conv, message_id
+        if not conv.reference:
+            conv.reference.append(temp_reference)
+        else:
+            conv.reference[-1] = temp_reference
+        conv.message[-1] = {"role": "assistant", "content": ans["answer"],
+                            "id": message_id, "prompt": ans.get("prompt", "")}
+        if "chunks" in reference:
+            chunks = reference.get("chunks")
+            chunk_list = []
+            for chunk in chunks:
+                new_chunk = {
+                    "id": chunk["chunk_id"],
+                    "content": chunk["content_with_weight"],
+                    "document_id": chunk["doc_id"],
+                    "document_name": chunk["docnm_kwd"],
+                    "dataset_id": chunk["kb_id"],
+                    "image_id": chunk.get("image_id", ""),
+                    "similarity": chunk["similarity"],
+                    "vector_similarity": chunk["vector_similarity"],
+                    "term_similarity": chunk["term_similarity"],
+                    "positions": chunk.get("positions", []),
+                }
+                chunk_list.append(new_chunk)
+            reference["chunks"] = chunk_list
+        ans["id"] = message_id
+        ans["session_id"]=session_id
+
+    def stream():
+        nonlocal dia, msg, req, conv
+        try:
+            for ans in chat(dia, msg, **req):
+                fillin_conv(ans)
+                yield "data:" + json.dumps({"code": 0,  "data": ans}, ensure_ascii=False) + "\n\n"
+            ConversationService.update_by_id(conv.id, conv.to_dict())
+        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, "data": True}, ensure_ascii=False) + "\n\n"
+
+    if req.get("stream", True):
+        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
+
+    else:
+        answer = None
+        for ans in chat(dia, msg, **req):
+            answer = ans
+            fillin_conv(ans)
+            ConversationService.update_by_id(conv.id, conv.to_dict())
+            break
+        return get_result(data=answer)
+
+
+@manager.route('/agents/<agent_id>/completions', methods=['POST'])
+@token_required
+def agent_completion(tenant_id, agent_id):
+    req = request.json
+
+    e, cvs = UserCanvasService.get_by_id(agent_id)
+    if not e:
+        return get_error_data_result("Agent not found.")
+    if cvs.user_id != tenant_id:
+        return get_error_data_result(message="You do not own the agent.")
+    if not isinstance(cvs.dsl, str):
+        cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False)
+    canvas = Canvas(cvs.dsl, tenant_id)
+
+    if not req.get("session_id"):
+        session_id = get_uuid()
+        conv = {
+            "id": session_id,
+            "dialog_id": cvs.id,
+            "user_id": req.get("user_id",""),
+            "message": [{"role": "assistant", "content": canvas.get_prologue()}],
+            "source": "agent"
+        }
+        API4ConversationService.save(**conv)
+        conv = API4Conversation(**conv)
+    else:
+        session_id = req.get("session_id")
+        e, conv = API4ConversationService.get_by_id(req["session_id"])
+        if not e:
+            return get_error_data_result(message="Session not found!")
+
+    messages = conv.message
+    question = req.get("question")
+    if not question:
+        return get_error_data_result("`question` is required.")
+    question={
+        "role":"user",
+        "content":question,
+        "id": str(uuid4())
+    }
+    messages.append(question)
+    msg = []
+    for m in messages:
+        if m["role"] == "system":
+            continue
+        if m["role"] == "assistant" and not msg:
+            continue
+        msg.append(m)
+    if not msg[-1].get("id"): msg[-1]["id"] = get_uuid()
+    message_id = msg[-1]["id"]
+
+    if "quote" not in req: req["quote"] = False
+    stream = req.get("stream", True)
+
+    def fillin_conv(ans):
+        reference = ans["reference"]
+        temp_reference = deepcopy(ans["reference"])
+        nonlocal conv, message_id
+        if not conv.reference:
+            conv.reference.append(temp_reference)
+        else:
+            conv.reference[-1] = temp_reference
+        conv.message[-1] = {"role": "assistant", "content": ans["answer"], "id": message_id}
+        if "chunks" in reference:
+            chunks = reference.get("chunks")
+            chunk_list = []
+            for chunk in chunks:
+                new_chunk = {
+                    "id": chunk["chunk_id"],
+                    "content": chunk["content_with_weight"],
+                    "document_id": chunk["doc_id"],
+                    "document_name": chunk["docnm_kwd"],
+                    "dataset_id": chunk["kb_id"],
+                    "image_id": chunk["image_id"],
+                    "similarity": chunk["similarity"],
+                    "vector_similarity": chunk["vector_similarity"],
+                    "term_similarity": chunk["term_similarity"],
+                    "positions": chunk["positions"],
+                }
+                chunk_list.append(new_chunk)
+            reference["chunks"] = chunk_list
+        ans["id"] = message_id
+        ans["session_id"] = session_id
+
+    def rename_field(ans):
+        reference = ans['reference']
+        if not isinstance(reference, dict):
+            return
+        for chunk_i in reference.get('chunks', []):
+            if 'docnm_kwd' in chunk_i:
+                chunk_i['doc_name'] = chunk_i['docnm_kwd']
+                chunk_i.pop('docnm_kwd')
+    conv.message.append(msg[-1])
+
+    if not conv.reference:
+        conv.reference = []
+    conv.message.append({"role": "assistant", "content": "", "id": message_id})
+    conv.reference.append({"chunks": [], "doc_aggs": []})
+
+    final_ans = {"reference": [], "content": ""}
+
+    canvas.messages.append(msg[-1])
+    canvas.add_user_input(msg[-1]["content"])
+
+    if stream:
+        def sse():
+            nonlocal answer, cvs
+            try:
+                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", [])}
+                    fillin_conv(ans)
+                    rename_field(ans)
+                    yield "data:" + json.dumps({"code": 0, "message": "", "data": ans},
+                                               ensure_ascii=False) + "\n\n"
+
+                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:
+                cvs.dsl = json.loads(str(canvas))
+                API4ConversationService.append_message(conv.id, conv.to_dict())
+                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(sse(), 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
+
+    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))
+
+        result = {"answer": final_ans["content"], "reference": final_ans.get("reference", [])}
+        fillin_conv(result)
+        API4ConversationService.append_message(conv.id, conv.to_dict())
+        rename_field(result)
+        return get_result(data=result)
+
+
+@manager.route('/chats/<chat_id>/sessions', methods=['GET'])
+@token_required
+def list_session(chat_id,tenant_id):
+    if not DialogService.query(tenant_id=tenant_id, id=chat_id, status=StatusEnum.VALID.value):
+        return get_error_data_result(message=f"You don't own the assistant {chat_id}.")
+    id = request.args.get("id")
+    name = request.args.get("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" or request.args.get("desc") == "false":
+        desc = False
+    else:
+        desc = True
+    convs = ConversationService.get_list(chat_id,page_number,items_per_page,orderby,desc,id,name)
+    if not convs:
+        return get_result(data=[])
+    for conv in convs:
+        conv['messages'] = conv.pop("message")
+        infos = conv["messages"]
+        for info in infos:
+            if "prompt" in info:
+                info.pop("prompt")
+        conv["chat_id"] = conv.pop("dialog_id")
+        if conv["reference"]:
+            messages = conv["messages"]
+            message_num = 0
+            chunk_num = 0
+            while message_num < len(messages):
+                if message_num != 0 and messages[message_num]["role"] != "user":
+                    chunk_list = []
+                    if "chunks" in conv["reference"][chunk_num]:
+                        chunks = conv["reference"][chunk_num]["chunks"]
+                        for chunk in chunks:
+                            new_chunk = {
+                                "id": chunk["chunk_id"],
+                                "content": chunk["content_with_weight"],
+                                "document_id": chunk["doc_id"],
+                                "document_name": chunk["docnm_kwd"],
+                                "dataset_id": chunk["kb_id"],
+                                "image_id": chunk["image_id"],
+                                "similarity": chunk["similarity"],
+                                "vector_similarity": chunk["vector_similarity"],
+                                "term_similarity": chunk["term_similarity"],
+                                "positions": chunk["positions"],
+                            }
+                            chunk_list.append(new_chunk)
+                    chunk_num += 1
+                    messages[message_num]["reference"] = chunk_list
+                message_num += 1
+        del conv["reference"]
+    return get_result(data=convs)
+
+
+@manager.route('/chats/<chat_id>/sessions', methods=["DELETE"])
+@token_required
+def delete(tenant_id,chat_id):
+    if not DialogService.query(id=chat_id, tenant_id=tenant_id, status=StatusEnum.VALID.value):
+        return get_error_data_result(message="You don't own the chat")
+    req = request.json
+    convs = ConversationService.query(dialog_id=chat_id)
+    if not req:
+        ids = None
+    else:
+        ids=req.get("ids")
+
+    if not ids:
+        conv_list = []
+        for conv in convs:
+            conv_list.append(conv.id)
+    else:
+        conv_list=ids
+    for id in conv_list:
+        conv = ConversationService.query(id=id,dialog_id=chat_id)
+        if not conv:
+            return get_error_data_result(message="The chat doesn't own the session")
+        ConversationService.delete_by_id(id)
+    return get_result()
+
+@manager.route('/sessions/ask', methods=['POST'])
+@token_required
+def ask_about(tenant_id):
+    req = request.json
+    if not req.get("question"):
+        return get_error_data_result("`question` is required.")
+    if not req.get("dataset_ids"):
+        return get_error_data_result("`dataset_ids` is required.")
+    if not isinstance(req.get("dataset_ids"),list):
+        return get_error_data_result("`dataset_ids` should be a list.")
+    req["kb_ids"]=req.pop("dataset_ids")
+    for kb_id in req["kb_ids"]:
+        if not KnowledgebaseService.accessible(kb_id,tenant_id):
+            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")
+    uid = tenant_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('/sessions/related_questions', methods=['POST'])
+@token_required
+def related_questions(tenant_id):
+    req = request.json
+    if not req.get("question"):
+        return get_error_data_result("`question` is required.")
+    question = req["question"]
+    chat_mdl = LLMBundle(tenant_id, LLMType.CHAT)
+    prompt = """
+Objective: To generate search terms related to the user's search keywords, helping users find more valuable information.
+Instructions:
+ - Based on the keywords provided by the user, generate 5-10 related search terms.
+ - Each search term should be directly or indirectly related to the keyword, guiding the user to find more valuable information.
+ - Use common, general terms as much as possible, avoiding obscure words or technical jargon.
+ - Keep the term length between 2-4 words, concise and clear.
+ - DO NOT translate, use the language of the original keywords.
+
+### Example:
+Keywords: Chinese football
+Related search terms:
+1. Current status of Chinese football
+2. Reform of Chinese football
+3. Youth training of Chinese football
+4. Chinese football in the Asian Cup
+5. Chinese football in the World Cup
+
+Reason:
+ - When searching, users often only use one or two keywords, making it difficult to fully express their information needs.
+ - Generating related search terms can help users dig deeper into relevant information and improve search efficiency. 
+ - At the same time, related terms can also help search engines better understand user needs and return more accurate search results.
+
+"""
+    ans = chat_mdl.chat(prompt, [{"role": "user", "content": f"""
+Keywords: {question}
+Related search terms:
+    """}], {"temperature": 0.9})
+    return get_result(data=[re.sub(r"^[0-9]\. ", "", a) for a in ans.split("\n") if re.match(r"^[0-9]\. ", a)])

api/apps/system_app.py

@@ -13,56 +13,281 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License
 #
-from flask_login import login_required
+import logging
+from datetime import datetime
+import json
 
+from flask_login import login_required, current_user
+
+from api.db.db_models import APIToken
+from api.db.services.api_service import APITokenService
 from api.db.services.knowledgebase_service import KnowledgebaseService
-from api.utils.api_utils import get_json_result
-from api.versions import get_rag_version
-from rag.settings import SVR_QUEUE_NAME
-from rag.utils.es_conn import ELASTICSEARCH
-from rag.utils.minio_conn import MINIO
+from api.db.services.user_service import UserTenantService
+from api import settings
+from api.utils import current_timestamp, datetime_format
+from api.utils.api_utils import (
+    get_json_result,
+    get_data_error_result,
+    server_error_response,
+    generate_confirmation_token,
+)
+from api.versions import get_ragflow_version
+from rag.utils.storage_factory import STORAGE_IMPL, STORAGE_IMPL_TYPE
 from timeit import default_timer as timer
 
 from rag.utils.redis_conn import REDIS_CONN
 
 
-@manager.route('/version', methods=['GET'])
+@manager.route("/version", methods=["GET"])
 @login_required
 def version():
-    return get_json_result(data=get_rag_version())
+    """
+    Get the current version of the application.
+    ---
+    tags:
+      - System
+    security:
+      - ApiKeyAuth: []
+    responses:
+      200:
+        description: Version retrieved successfully.
+        schema:
+          type: object
+          properties:
+            version:
+              type: string
+              description: Version number.
+    """
+    return get_json_result(data=get_ragflow_version())
 
 
-@manager.route('/status', methods=['GET'])
+@manager.route("/status", methods=["GET"])
 @login_required
 def status():
+    """
+    Get the system status.
+    ---
+    tags:
+      - System
+    security:
+      - ApiKeyAuth: []
+    responses:
+      200:
+        description: System is operational.
+        schema:
+          type: object
+          properties:
+            es:
+              type: object
+              description: Elasticsearch status.
+            storage:
+              type: object
+              description: Storage status.
+            database:
+              type: object
+              description: Database status.
+      503:
+        description: Service unavailable.
+        schema:
+          type: object
+          properties:
+            error:
+              type: string
+              description: Error message.
+    """
     res = {}
     st = timer()
     try:
-        res["es"] = ELASTICSEARCH.health()
-        res["es"]["elapsed"] = "{:.1f}".format((timer() - st)*1000.)
+        res["doc_engine"] = settings.docStoreConn.health()
+        res["doc_engine"]["elapsed"] = "{:.1f}".format((timer() - st) * 1000.0)
     except Exception as e:
-        res["es"] = {"status": "red", "elapsed": "{:.1f}".format((timer() - st)*1000.), "error": str(e)}
+        res["doc_engine"] = {
+            "type": "unknown",
+            "status": "red",
+            "elapsed": "{:.1f}".format((timer() - st) * 1000.0),
+            "error": str(e),
+        }
 
     st = timer()
     try:
-        MINIO.health()
-        res["minio"] = {"status": "green", "elapsed": "{:.1f}".format((timer() - st)*1000.)}
+        STORAGE_IMPL.health()
+        res["storage"] = {
+            "storage": STORAGE_IMPL_TYPE.lower(),
+            "status": "green",
+            "elapsed": "{:.1f}".format((timer() - st) * 1000.0),
+        }
     except Exception as e:
-        res["minio"] = {"status": "red", "elapsed": "{:.1f}".format((timer() - st)*1000.), "error": str(e)}
+        res["storage"] = {
+            "storage": STORAGE_IMPL_TYPE.lower(),
+            "status": "red",
+            "elapsed": "{:.1f}".format((timer() - st) * 1000.0),
+            "error": str(e),
+        }
 
     st = timer()
     try:
         KnowledgebaseService.get_by_id("x")
-        res["mysql"] = {"status": "green", "elapsed": "{:.1f}".format((timer() - st)*1000.)}
+        res["database"] = {
+            "database": settings.DATABASE_TYPE.lower(),
+            "status": "green",
+            "elapsed": "{:.1f}".format((timer() - st) * 1000.0),
+        }
     except Exception as e:
-        res["mysql"] = {"status": "red", "elapsed": "{:.1f}".format((timer() - st)*1000.), "error": str(e)}
+        res["database"] = {
+            "database": settings.DATABASE_TYPE.lower(),
+            "status": "red",
+            "elapsed": "{:.1f}".format((timer() - st) * 1000.0),
+            "error": str(e),
+        }
 
     st = timer()
     try:
-        qinfo = REDIS_CONN.health(SVR_QUEUE_NAME)
-        res["redis"] = {"status": "green", "elapsed": "{:.1f}".format((timer() - st)*1000.),
-                        "pending": qinfo.get("pending", 0)}
+        if not REDIS_CONN.health():
+            raise Exception("Lost connection!")
+        res["redis"] = {
+            "status": "green",
+            "elapsed": "{:.1f}".format((timer() - st) * 1000.0),
+        }
     except Exception as e:
-        res["redis"] = {"status": "red", "elapsed": "{:.1f}".format((timer() - st)*1000.), "error": str(e)}
+        res["redis"] = {
+            "status": "red",
+            "elapsed": "{:.1f}".format((timer() - st) * 1000.0),
+            "error": str(e),
+        }
+
+    task_executor_heartbeats = {}
+    try:
+        task_executors = REDIS_CONN.smembers("TASKEXE")
+        now = datetime.now().timestamp()
+        for task_executor_id in task_executors:
+            heartbeats = REDIS_CONN.zrangebyscore(task_executor_id, now - 60*30, now)
+            heartbeats = [json.loads(heartbeat) for heartbeat in heartbeats]
+            task_executor_heartbeats[task_executor_id] = heartbeats
+    except Exception:
+        logging.exception("get task executor heartbeats failed!")
+    res["task_executor_heartbeats"] = task_executor_heartbeats
 
     return get_json_result(data=res)
+
+
+@manager.route("/new_token", methods=["POST"])
+@login_required
+def new_token():
+    """
+    Generate a new API token.
+    ---
+    tags:
+      - API Tokens
+    security:
+      - ApiKeyAuth: []
+    parameters:
+      - in: query
+        name: name
+        type: string
+        required: false
+        description: Name of the token.
+    responses:
+      200:
+        description: Token generated successfully.
+        schema:
+          type: object
+          properties:
+            token:
+              type: string
+              description: The generated API token.
+    """
+    try:
+        tenants = UserTenantService.query(user_id=current_user.id)
+        if not tenants:
+            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),
+            "create_time": current_timestamp(),
+            "create_date": datetime_format(datetime.now()),
+            "update_time": None,
+            "update_date": None,
+        }
+
+        if not APITokenService.save(**obj):
+            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"])
+@login_required
+def token_list():
+    """
+    List all API tokens for the current user.
+    ---
+    tags:
+      - API Tokens
+    security:
+      - ApiKeyAuth: []
+    responses:
+      200:
+        description: List of API tokens.
+        schema:
+          type: object
+          properties:
+            tokens:
+              type: array
+              items:
+                type: object
+                properties:
+                  token:
+                    type: string
+                    description: The API token.
+                  name:
+                    type: string
+                    description: Name of the token.
+                  create_time:
+                    type: string
+                    description: Token creation time.
+    """
+    try:
+        tenants = UserTenantService.query(user_id=current_user.id)
+        if not tenants:
+            return get_data_error_result(message="Tenant not found!")
+
+        objs = APITokenService.query(tenant_id=tenants[0].tenant_id)
+        return get_json_result(data=[o.to_dict() for o in objs])
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route("/token/<token>", methods=["DELETE"])
+@login_required
+def rm(token):
+    """
+    Remove an API token.
+    ---
+    tags:
+      - API Tokens
+    security:
+      - ApiKeyAuth: []
+    parameters:
+      - in: path
+        name: token
+        type: string
+        required: true
+        description: The API token to remove.
+    responses:
+      200:
+        description: Token removed successfully.
+        schema:
+          type: object
+          properties:
+            success:
+              type: boolean
+              description: Deletion status.
+    """
+    APITokenService.filter_delete(
+        [APIToken.tenant_id == current_user.id, APIToken.token == token]
+    )
+    return get_json_result(data=True)

api/apps/tenant_app.py

@@ -0,0 +1,118 @@
+#
+#  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 flask_login import login_required, current_user
+
+from api import settings
+from api.db import UserTenantRole, StatusEnum
+from api.db.db_models import UserTenant
+from api.db.services.user_service import UserTenantService, UserService
+
+from api.utils import get_uuid, delta_seconds
+from api.utils.api_utils import get_json_result, validate_request, server_error_response, get_data_error_result
+
+
+@manager.route("/<tenant_id>/user/list", methods=["GET"])
+@login_required
+def user_list(tenant_id):
+    if current_user.id != tenant_id:
+        return get_json_result(
+            data=False,
+            message='No authorization.',
+            code=settings.RetCode.AUTHENTICATION_ERROR)
+
+    try:
+        users = UserTenantService.get_by_tenant_id(tenant_id)
+        for u in users:
+            u["delta_seconds"] = delta_seconds(str(u["update_date"]))
+        return get_json_result(data=users)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/<tenant_id>/user', methods=['POST'])
+@login_required
+@validate_request("email")
+def create(tenant_id):
+    if current_user.id != tenant_id:
+        return get_json_result(
+            data=False,
+            message='No authorization.',
+            code=settings.RetCode.AUTHENTICATION_ERROR)
+
+    req = request.json
+    usrs = UserService.query(email=req["email"])
+    if not usrs:
+        return get_data_error_result(message="User not found.")
+
+    user_id = usrs[0].id
+    user_tenants = UserTenantService.query(user_id=user_id, tenant_id=tenant_id)
+    if user_tenants:
+        if user_tenants[0].status == UserTenantRole.NORMAL.value:
+            return get_data_error_result(message="This user is in the team already.")
+        return get_data_error_result(message="Invitation notification is sent.")
+
+    UserTenantService.save(
+        id=get_uuid(),
+        user_id=user_id,
+        tenant_id=tenant_id,
+        invited_by=current_user.id,
+        role=UserTenantRole.INVITE,
+        status=StatusEnum.VALID.value)
+
+    usr = usrs[0].to_dict()
+    usr = {k: v for k, v in usr.items() if k in ["id", "avatar", "email", "nickname"]}
+
+    return get_json_result(data=usr)
+
+
+@manager.route('/<tenant_id>/user/<user_id>', methods=['DELETE'])
+@login_required
+def rm(tenant_id, user_id):
+    if current_user.id != tenant_id and current_user.id != user_id:
+        return get_json_result(
+            data=False,
+            message='No authorization.',
+            code=settings.RetCode.AUTHENTICATION_ERROR)
+
+    try:
+        UserTenantService.filter_delete([UserTenant.tenant_id == tenant_id, UserTenant.user_id == user_id])
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route("/list", methods=["GET"])
+@login_required
+def tenant_list():
+    try:
+        users = UserTenantService.get_tenants_by_user_id(current_user.id)
+        for u in users:
+            u["delta_seconds"] = delta_seconds(str(u["update_date"]))
+        return get_json_result(data=users)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route("/agree/<tenant_id>", methods=["PUT"])
+@login_required
+def agree(tenant_id):
+    try:
+        UserTenantService.filter_update([UserTenant.tenant_id == tenant_id, UserTenant.user_id == current_user.id], {"role": UserTenantRole.NORMAL})
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)

api/apps/user_app.py

@@ -13,6 +13,7 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import logging
 import json
 import re
 from datetime import datetime
@@ -23,61 +24,127 @@ from flask_login import login_required, current_user, login_user, logout_user
 
 from api.db.db_models import TenantLLM
 from api.db.services.llm_service import TenantLLMService, LLMService
-from api.utils.api_utils import server_error_response, validate_request
-from api.utils import get_uuid, get_format_time, decrypt, download_img, current_timestamp, datetime_format
-from api.db import UserTenantRole, LLMType, FileType
-from api.settings import RetCode, GITHUB_OAUTH, FEISHU_OAUTH, CHAT_MDL, EMBEDDING_MDL, ASR_MDL, IMAGE2TEXT_MDL, PARSERS, \
-    API_KEY, \
-    LLM_FACTORY, LLM_BASE_URL, RERANK_MDL
+from api.utils.api_utils import (
+    server_error_response,
+    validate_request,
+    get_data_error_result,
+)
+from api.utils import (
+    get_uuid,
+    get_format_time,
+    decrypt,
+    download_img,
+    current_timestamp,
+    datetime_format,
+)
+from api.db import UserTenantRole, FileType
+from api import settings
 from api.db.services.user_service import UserService, TenantService, UserTenantService
 from api.db.services.file_service import FileService
-from api.settings import stat_logger
-from api.utils.api_utils import get_json_result, cors_reponse
+from api.utils.api_utils import get_json_result, construct_response
 
 
-@manager.route('/login', methods=['POST', 'GET'])
+@manager.route("/login", methods=["POST", "GET"])
 def login():
-    login_channel = "password"
+    """
+    User login endpoint.
+    ---
+    tags:
+      - User
+    parameters:
+      - in: body
+        name: body
+        description: Login credentials.
+        required: true
+        schema:
+          type: object
+          properties:
+            email:
+              type: string
+              description: User email.
+            password:
+              type: string
+              description: User password.
+    responses:
+      200:
+        description: Login successful.
+        schema:
+          type: object
+      401:
+        description: Authentication failed.
+        schema:
+          type: object
+    """
     if not request.json:
-        return get_json_result(data=False, retcode=RetCode.AUTHENTICATION_ERROR,
-                               retmsg='Unautherized!')
+        return get_json_result(
+            data=False, code=settings.RetCode.AUTHENTICATION_ERROR, message="Unauthorized!"
+        )
 
-    email = request.json.get('email', "")
+    email = request.json.get("email", "")
     users = UserService.query(email=email)
     if not users:
         return get_json_result(
-            data=False, retcode=RetCode.AUTHENTICATION_ERROR, retmsg=f'This Email is not registered!')
+            data=False,
+            code=settings.RetCode.AUTHENTICATION_ERROR,
+            message=f"Email: {email} is not registered!",
+        )
 
-    password = request.json.get('password')
+    password = request.json.get("password")
     try:
         password = decrypt(password)
     except BaseException:
         return get_json_result(
-            data=False, retcode=RetCode.SERVER_ERROR, retmsg='Fail to crypt password')
+            data=False, code=settings.RetCode.SERVER_ERROR, message="Fail to crypt password"
+        )
 
     user = UserService.query_user(email, password)
     if user:
         response_data = user.to_json()
         user.access_token = get_uuid()
         login_user(user)
-        user.update_time = current_timestamp(),
-        user.update_date = datetime_format(datetime.now()),
+        user.update_time = (current_timestamp(),)
+        user.update_date = (datetime_format(datetime.now()),)
         user.save()
         msg = "Welcome back!"
-        return cors_reponse(data=response_data, auth=user.get_id(), retmsg=msg)
+        return construct_response(data=response_data, auth=user.get_id(), message=msg)
     else:
-        return get_json_result(data=False, retcode=RetCode.AUTHENTICATION_ERROR,
-                               retmsg='Email and Password do not match!')
+        return get_json_result(
+            data=False,
+            code=settings.RetCode.AUTHENTICATION_ERROR,
+            message="Email and password do not match!",
+        )
 
 
-@manager.route('/github_callback', methods=['GET'])
+@manager.route("/github_callback", methods=["GET"])
 def github_callback():
+    """
+    GitHub OAuth callback endpoint.
+    ---
+    tags:
+      - OAuth
+    parameters:
+      - in: query
+        name: code
+        type: string
+        required: true
+        description: Authorization code from GitHub.
+    responses:
+      200:
+        description: Authentication successful.
+        schema:
+          type: object
+    """
     import requests
-    res = requests.post(GITHUB_OAUTH.get("url"), data={
-        "client_id": GITHUB_OAUTH.get("client_id"),
-        "client_secret": GITHUB_OAUTH.get("secret_key"),
-        "code": request.args.get('code')
-    }, headers={"Accept": "application/json"})
+
+    res = requests.post(
+        settings.GITHUB_OAUTH.get("url"),
+        data={
+            "client_id": settings.GITHUB_OAUTH.get("client_id"),
+            "client_secret": settings.GITHUB_OAUTH.get("secret_key"),
+            "code": request.args.get("code"),
+        },
+        headers={"Accept": "application/json"},
+    )
     res = res.json()
     if "error" in res:
         return redirect("/?error=%s" % res["error_description"])
@@ -87,36 +154,45 @@ def github_callback():
 
     session["access_token"] = res["access_token"]
     session["access_token_from"] = "github"
-    userinfo = user_info_from_github(session["access_token"])
-    users = UserService.query(email=userinfo["email"])
+    user_info = user_info_from_github(session["access_token"])
+    email_address = user_info["email"]
+    users = UserService.query(email=email_address)
     user_id = get_uuid()
     if not users:
+        # User isn't try to register
         try:
             try:
-                avatar = download_img(userinfo["avatar_url"])
+                avatar = download_img(user_info["avatar_url"])
             except Exception as e:
-                stat_logger.exception(e)
+                logging.exception(e)
                 avatar = ""
-            users = user_register(user_id, {
+            users = user_register(
+                user_id,
+                {
                     "access_token": session["access_token"],
-                "email": userinfo["email"],
+                    "email": email_address,
                     "avatar": avatar,
-                "nickname": userinfo["login"],
+                    "nickname": user_info["login"],
                     "login_channel": "github",
                     "last_login_time": get_format_time(),
                     "is_superuser": False,
-            })
+                },
+            )
             if not users:
-                raise Exception('Register user failure.')
+                raise Exception(f"Fail to register {email_address}.")
             if len(users) > 1:
-                raise Exception('Same E-mail exist!')
+                raise Exception(f"Same email: {email_address} exists!")
+
+            # Try to log in
             user = users[0]
             login_user(user)
             return redirect("/?auth=%s" % user.get_id())
         except Exception as e:
             rollback_user_registration(user_id)
-            stat_logger.exception(e)
+            logging.exception(e)
             return redirect("/?error=%s" % str(e))
+
+    # User has already registered, try to log in
     user = users[0]
     user.access_token = get_uuid()
     login_user(user)
@@ -124,60 +200,101 @@ def github_callback():
     return redirect("/?auth=%s" % user.get_id())
 
 
-@manager.route('/feishu_callback', methods=['GET'])
+@manager.route("/feishu_callback", methods=["GET"])
 def feishu_callback():
+    """
+    Feishu OAuth callback endpoint.
+    ---
+    tags:
+      - OAuth
+    parameters:
+      - in: query
+        name: code
+        type: string
+        required: true
+        description: Authorization code from Feishu.
+    responses:
+      200:
+        description: Authentication successful.
+        schema:
+          type: object
+    """
     import requests
-    app_access_token_res = requests.post(FEISHU_OAUTH.get("app_access_token_url"), data=json.dumps({
-        "app_id": FEISHU_OAUTH.get("app_id"),
-        "app_secret": FEISHU_OAUTH.get("app_secret")
-    }), headers={"Content-Type": "application/json; charset=utf-8"})
+
+    app_access_token_res = requests.post(
+        settings.FEISHU_OAUTH.get("app_access_token_url"),
+        data=json.dumps(
+            {
+                "app_id": settings.FEISHU_OAUTH.get("app_id"),
+                "app_secret": settings.FEISHU_OAUTH.get("app_secret"),
+            }
+        ),
+        headers={"Content-Type": "application/json; charset=utf-8"},
+    )
     app_access_token_res = app_access_token_res.json()
-    if app_access_token_res['code'] != 0:
+    if app_access_token_res["code"] != 0:
         return redirect("/?error=%s" % app_access_token_res)
 
-    res = requests.post(FEISHU_OAUTH.get("user_access_token_url"), data=json.dumps({
-        "grant_type": FEISHU_OAUTH.get("grant_type"),
-        "code": request.args.get('code')
-    }), headers={"Content-Type": "application/json; charset=utf-8",
-                 'Authorization': f"Bearer {app_access_token_res['app_access_token']}"})
+    res = requests.post(
+        settings.FEISHU_OAUTH.get("user_access_token_url"),
+        data=json.dumps(
+            {
+                "grant_type": settings.FEISHU_OAUTH.get("grant_type"),
+                "code": request.args.get("code"),
+            }
+        ),
+        headers={
+            "Content-Type": "application/json; charset=utf-8",
+            "Authorization": f"Bearer {app_access_token_res['app_access_token']}",
+        },
+    )
     res = res.json()
-    if res['code'] != 0:
+    if res["code"] != 0:
         return redirect("/?error=%s" % res["message"])
 
     if "contact:user.email:readonly" not in res["data"]["scope"].split(" "):
         return redirect("/?error=contact:user.email:readonly not in scope")
     session["access_token"] = res["data"]["access_token"]
     session["access_token_from"] = "feishu"
-    userinfo = user_info_from_feishu(session["access_token"])
-    users = UserService.query(email=userinfo["email"])
+    user_info = user_info_from_feishu(session["access_token"])
+    email_address = user_info["email"]
+    users = UserService.query(email=email_address)
     user_id = get_uuid()
     if not users:
+        # User isn't try to register
         try:
             try:
-                avatar = download_img(userinfo["avatar_url"])
+                avatar = download_img(user_info["avatar_url"])
             except Exception as e:
-                stat_logger.exception(e)
+                logging.exception(e)
                 avatar = ""
-            users = user_register(user_id, {
+            users = user_register(
+                user_id,
+                {
                     "access_token": session["access_token"],
-                "email": userinfo["email"],
+                    "email": email_address,
                     "avatar": avatar,
-                "nickname": userinfo["en_name"],
+                    "nickname": user_info["en_name"],
                     "login_channel": "feishu",
                     "last_login_time": get_format_time(),
                     "is_superuser": False,
-            })
+                },
+            )
             if not users:
-                raise Exception('Register user failure.')
+                raise Exception(f"Fail to register {email_address}.")
             if len(users) > 1:
-                raise Exception('Same E-mail exist!')
+                raise Exception(f"Same email: {email_address} exists!")
+
+            # Try to log in
             user = users[0]
             login_user(user)
             return redirect("/?auth=%s" % user.get_id())
         except Exception as e:
             rollback_user_registration(user_id)
-            stat_logger.exception(e)
+            logging.exception(e)
             return redirect("/?error=%s" % str(e))
+
+    # User has already registered, try to log in
     user = users[0]
     user.access_token = get_uuid()
     login_user(user)
@@ -187,11 +304,14 @@ def feishu_callback():
 
 def user_info_from_feishu(access_token):
     import requests
-    headers = {"Content-Type": "application/json; charset=utf-8",
-               'Authorization': f"Bearer {access_token}"}
+
+    headers = {
+        "Content-Type": "application/json; charset=utf-8",
+        "Authorization": f"Bearer {access_token}",
+    }
     res = requests.get(
-        f"https://open.feishu.cn/open-apis/authen/v1/user_info",
-        headers=headers)
+        "https://open.feishu.cn/open-apis/authen/v1/user_info", headers=headers
+    )
     user_info = res.json()["data"]
     user_info["email"] = None if user_info.get("email") == "" else user_info["email"]
     return user_info
@@ -199,24 +319,38 @@ def user_info_from_feishu(access_token):
 
 def user_info_from_github(access_token):
     import requests
-    headers = {"Accept": "application/json",
-               'Authorization': f"token {access_token}"}
+
+    headers = {"Accept": "application/json", "Authorization": f"token {access_token}"}
     res = requests.get(
-        f"https://api.github.com/user?access_token={access_token}",
-        headers=headers)
+        f"https://api.github.com/user?access_token={access_token}", headers=headers
+    )
     user_info = res.json()
     email_info = requests.get(
         f"https://api.github.com/user/emails?access_token={access_token}",
-        headers=headers).json()
+        headers=headers,
+    ).json()
     user_info["email"] = next(
-        (email for email in email_info if email['primary'] == True),
-        None)["email"]
+        (email for email in email_info if email["primary"] == True), None
+    )["email"]
     return user_info
 
 
-@manager.route("/logout", methods=['GET'])
+@manager.route("/logout", methods=["GET"])
 @login_required
 def log_out():
+    """
+    User logout endpoint.
+    ---
+    tags:
+      - User
+    security:
+      - ApiKeyAuth: []
+    responses:
+      200:
+        description: Logout successful.
+        schema:
+          type: object
+    """
     current_user.access_token = ""
     current_user.save()
     logout_user()
@@ -226,21 +360,62 @@ def log_out():
 @manager.route("/setting", methods=["POST"])
 @login_required
 def setting_user():
+    """
+    Update user settings.
+    ---
+    tags:
+      - User
+    security:
+      - ApiKeyAuth: []
+    parameters:
+      - in: body
+        name: body
+        description: User settings to update.
+        required: true
+        schema:
+          type: object
+          properties:
+            nickname:
+              type: string
+              description: New nickname.
+            email:
+              type: string
+              description: New email.
+    responses:
+      200:
+        description: Settings updated successfully.
+        schema:
+          type: object
+    """
     update_dict = {}
     request_data = request.json
     if request_data.get("password"):
         new_password = request_data.get("new_password")
         if not check_password_hash(
-                current_user.password, decrypt(request_data["password"])):
+                current_user.password, decrypt(request_data["password"])
+        ):
             return get_json_result(
-                data=False, retcode=RetCode.AUTHENTICATION_ERROR, retmsg='Password error!')
+                data=False,
+                code=settings.RetCode.AUTHENTICATION_ERROR,
+                message="Password error!",
+            )
 
         if new_password:
-            update_dict["password"] = generate_password_hash(
-                decrypt(new_password))
+            update_dict["password"] = generate_password_hash(decrypt(new_password))
 
     for k in request_data.keys():
-        if k in ["password", "new_password"]:
+        if k in [
+            "password",
+            "new_password",
+            "email",
+            "status",
+            "is_superuser",
+            "login_channel",
+            "is_anonymous",
+            "is_active",
+            "is_authenticated",
+            "last_login_time",
+        ]:
             continue
         update_dict[k] = request_data[k]
 
@@ -248,35 +423,59 @@ def setting_user():
         UserService.update_by_id(current_user.id, update_dict)
         return get_json_result(data=True)
     except Exception as e:
-        stat_logger.exception(e)
+        logging.exception(e)
         return get_json_result(
-            data=False, retmsg='Update failure!', retcode=RetCode.EXCEPTION_ERROR)
+            data=False, message="Update failure!", code=settings.RetCode.EXCEPTION_ERROR
+        )
 
 
 @manager.route("/info", methods=["GET"])
 @login_required
-def user_info():
+def user_profile():
+    """
+    Get user profile information.
+    ---
+    tags:
+      - User
+    security:
+      - ApiKeyAuth: []
+    responses:
+      200:
+        description: User profile retrieved successfully.
+        schema:
+          type: object
+          properties:
+            id:
+              type: string
+              description: User ID.
+            nickname:
+              type: string
+              description: User nickname.
+            email:
+              type: string
+              description: User email.
+    """
     return get_json_result(data=current_user.to_dict())
 
 
 def rollback_user_registration(user_id):
     try:
         UserService.delete_by_id(user_id)
-    except Exception as e:
+    except Exception:
         pass
     try:
         TenantService.delete_by_id(user_id)
-    except Exception as e:
+    except Exception:
         pass
     try:
         u = UserTenantService.query(tenant_id=user_id)
         if u:
             UserTenantService.delete_by_id(u[0].id)
-    except Exception as e:
+    except Exception:
         pass
     try:
         TenantLLM.delete().where(TenantLLM.tenant_id == user_id).execute()
-    except Exception as e:
+    except Exception:
         pass
 
 
@@ -285,18 +484,18 @@ def user_register(user_id, user):
     tenant = {
         "id": user_id,
         "name": user["nickname"] + "‘s Kingdom",
-        "llm_id": CHAT_MDL,
-        "embd_id": EMBEDDING_MDL,
-        "asr_id": ASR_MDL,
-        "parser_ids": PARSERS,
-        "img2txt_id": IMAGE2TEXT_MDL,
-        "rerank_id": RERANK_MDL
+        "llm_id": settings.CHAT_MDL,
+        "embd_id": settings.EMBEDDING_MDL,
+        "asr_id": settings.ASR_MDL,
+        "parser_ids": settings.PARSERS,
+        "img2txt_id": settings.IMAGE2TEXT_MDL,
+        "rerank_id": settings.RERANK_MDL,
     }
     usr_tenant = {
         "tenant_id": user_id,
         "user_id": user_id,
         "invited_by": user_id,
-        "role": UserTenantRole.OWNER
+        "role": UserTenantRole.OWNER,
     }
     file_id = get_uuid()
     file = {
@@ -310,14 +509,18 @@ def user_register(user_id, user):
         "location": "",
     }
     tenant_llm = []
-    for llm in LLMService.query(fid=LLM_FACTORY):
-        tenant_llm.append({"tenant_id": user_id,
-                           "llm_factory": LLM_FACTORY,
+    for llm in LLMService.query(fid=settings.LLM_FACTORY):
+        tenant_llm.append(
+            {
+                "tenant_id": user_id,
+                "llm_factory": settings.LLM_FACTORY,
                 "llm_name": llm.llm_name,
                 "model_type": llm.model_type,
-                           "api_key": API_KEY,
-                           "api_base": LLM_BASE_URL
-                           })
+                "api_key": settings.API_KEY,
+                "api_base": settings.LLM_BASE_URL,
+                "max_tokens": llm.max_tokens if llm.max_tokens else 8192
+            }
+        )
 
     if not UserService.save(**user):
         return
@@ -331,18 +534,59 @@ def user_register(user_id, user):
 @manager.route("/register", methods=["POST"])
 @validate_request("nickname", "email", "password")
 def user_add():
+    """
+    Register a new user.
+    ---
+    tags:
+      - User
+    parameters:
+      - in: body
+        name: body
+        description: Registration details.
+        required: true
+        schema:
+          type: object
+          properties:
+            nickname:
+              type: string
+              description: User nickname.
+            email:
+              type: string
+              description: User email.
+            password:
+              type: string
+              description: User password.
+    responses:
+      200:
+        description: Registration successful.
+        schema:
+          type: object
+    """
     req = request.json
-    if UserService.query(email=req["email"]):
-        return get_json_result(
-            data=False, retmsg=f'Email: {req["email"]} has already registered!', retcode=RetCode.OPERATING_ERROR)
-    if not re.match(r"^[\w\._-]+@([\w_-]+\.)+[\w-]{2,4}$", req["email"]):
-        return get_json_result(data=False, retmsg=f'Invaliad e-mail: {req["email"]}!',
-                               retcode=RetCode.OPERATING_ERROR)
+    email_address = req["email"]
 
+    # Validate the email address
+    if not re.match(r"^[\w\._-]+@([\w_-]+\.)+[\w-]{2,5}$", email_address):
+        return get_json_result(
+            data=False,
+            message=f"Invalid email address: {email_address}!",
+            code=settings.RetCode.OPERATING_ERROR,
+        )
+
+    # Check if the email address is already used
+    if UserService.query(email=email_address):
+        return get_json_result(
+            data=False,
+            message=f"Email: {email_address} has already registered!",
+            code=settings.RetCode.OPERATING_ERROR,
+        )
+
+    # Construct user info data
+    nickname = req["nickname"]
     user_dict = {
         "access_token": get_uuid(),
-        "email": req["email"],
-        "nickname": req["nickname"],
+        "email": email_address,
+        "nickname": nickname,
         "password": decrypt(req["password"]),
         "login_channel": "password",
         "last_login_time": get_format_time(),
@@ -353,26 +597,60 @@ def user_add():
     try:
         users = user_register(user_id, user_dict)
         if not users:
-            raise Exception('Register user failure.')
+            raise Exception(f"Fail to register {email_address}.")
         if len(users) > 1:
-            raise Exception('Same E-mail exist!')
+            raise Exception(f"Same email: {email_address} exists!")
         user = users[0]
         login_user(user)
-        return cors_reponse(data=user.to_json(),
-                            auth=user.get_id(), retmsg="Welcome aboard!")
+        return construct_response(
+            data=user.to_json(),
+            auth=user.get_id(),
+            message=f"{nickname}, welcome aboard!",
+        )
     except Exception as e:
         rollback_user_registration(user_id)
-        stat_logger.exception(e)
+        logging.exception(e)
         return get_json_result(
-            data=False, retmsg='User registration failure!', retcode=RetCode.EXCEPTION_ERROR)
+            data=False,
+            message=f"User registration failure, error: {str(e)}",
+            code=settings.RetCode.EXCEPTION_ERROR,
+        )
 
 
 @manager.route("/tenant_info", methods=["GET"])
 @login_required
 def tenant_info():
+    """
+    Get tenant information.
+    ---
+    tags:
+      - Tenant
+    security:
+      - ApiKeyAuth: []
+    responses:
+      200:
+        description: Tenant information retrieved successfully.
+        schema:
+          type: object
+          properties:
+            tenant_id:
+              type: string
+              description: Tenant ID.
+            name:
+              type: string
+              description: Tenant name.
+            llm_id:
+              type: string
+              description: LLM ID.
+            embd_id:
+              type: string
+              description: Embedding model ID.
+    """
     try:
-        tenants = TenantService.get_by_user_id(current_user.id)[0]
-        return get_json_result(data=tenants)
+        tenants = TenantService.get_info_by(current_user.id)
+        if not tenants:
+            return get_data_error_result(message="Tenant not found!")
+        return get_json_result(data=tenants[0])
     except Exception as e:
         return server_error_response(e)
 
@@ -381,10 +659,45 @@ def tenant_info():
 @login_required
 @validate_request("tenant_id", "asr_id", "embd_id", "img2txt_id", "llm_id")
 def set_tenant_info():
+    """
+    Update tenant information.
+    ---
+    tags:
+      - Tenant
+    security:
+      - ApiKeyAuth: []
+    parameters:
+      - in: body
+        name: body
+        description: Tenant information to update.
+        required: true
+        schema:
+          type: object
+          properties:
+            tenant_id:
+              type: string
+              description: Tenant ID.
+            llm_id:
+              type: string
+              description: LLM ID.
+            embd_id:
+              type: string
+              description: Embedding model ID.
+            asr_id:
+              type: string
+              description: ASR model ID.
+            img2txt_id:
+              type: string
+              description: Image to Text model ID.
+    responses:
+      200:
+        description: Tenant information updated successfully.
+        schema:
+          type: object
+    """
     req = request.json
     try:
-        tid = req["tenant_id"]
-        del req["tenant_id"]
+        tid = req.pop("tenant_id")
         TenantService.update_by_id(tid, req)
         return get_json_result(data=True)
     except Exception as e:

api/constants.py

@@ -12,10 +12,14 @@
 #  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 operator
-import time
-import typing
-from api.utils.log_utils import sql_logger
-import peewee
+NAME_LENGTH_LIMIT = 2 ** 10
+
+IMG_BASE64_PREFIX = 'data:image/png;base64,'
+
+SERVICE_CONF = "service_conf.yaml"
+
+API_VERSION = "v1"
+RAG_FLOW_SERVICE_NAME = "ragflow"
+REQUEST_WAIT_SEC = 2
+REQUEST_MAX_WAIT_SEC = 300