minor (#3137)

### What problem does this PR solve?

_Briefly describe what this PR aims to solve. Include background context
that will help reviewers understand the purpose of the PR._

### Type of change


- [x] Documentation Update

.github/workflows/tests.yml

@@ -0,0 +1,86 @@
+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 $GITHUB_WORKSPACE
+
+      - name: Check out code
+        uses: actions/checkout@v4
+
+      - name: Build ragflow:dev-slim
+        run: |
+          RUNNER_WORKSPACE_PREFIX=${RUNNER_WORKSPACE_PREFIX:-$HOME}
+          cp -r ${RUNNER_WORKSPACE_PREFIX}/huggingface.co ${RUNNER_WORKSPACE_PREFIX}/nltk_data ${RUNNER_WORKSPACE_PREFIX}/libssl*.deb .
+          sudo docker pull ubuntu:24.04
+          sudo docker build -f Dockerfile.slim -t infiniflow/ragflow:dev-slim .
+
+      - name: Build ragflow:dev
+        run: |
+          sudo docker build -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 tests
+        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 && pytest t_dataset.py t_chat.py t_session.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

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.
@@ -27,7 +21,7 @@ The list below mentions some contributions you can make, but it is not a complet
 ### General workflow
 
 1. Fork our GitHub repository.
-2. Clone your fork to your local machine: 
+2. Clone your fork to your local machine:
 `git clone git@github.com:<yourname>/ragflow.git`
 3. Create a local branch: 
 `git checkout -b my-branch`
@@ -39,14 +33,16 @@ The list below mentions some contributions you can make, but it is not a complet
 
 ### Before filing a PR
 
-- Consider splitting a large PR into multiple smaller, standalone PRs to keep a traceable development history. 
+- Consider splitting a large PR into multiple smaller, standalone PRs to keep a traceable development history.
 - Ensure that your PR addresses just one issue, or keep any unrelated changes small.
 - Add test cases when contributing new features. They demonstrate that your code functions correctly and protect against potential issues from future changes.
-### Describing your PR 
+
+### Describing your PR
 
 - Ensure that your PR title is concise and clear, providing all the required information.
-- Refer to a corresponding GitHub issue in your PR description if applicable. 
+- Refer to a corresponding GitHub issue in your PR description if applicable.
 - Include sufficient design details for *breaking changes* or *API changes* in your description.
 
 ### Reviewing & merging a PR
-- Ensure that your PR passes all Continuous Integration (CI) tests before merging it.
+
+Ensure that your PR passes all Continuous Integration (CI) tests before merging it.

Dockerfile

@@ -1,22 +1,117 @@
-FROM infiniflow/ragflow-base:v2.0
-USER  root
+# base stage
+FROM ubuntu:24.04 AS base
+USER root
+
+ARG ARCH=amd64
+ENV LIGHTEN=0
 
 WORKDIR /ragflow
 
-ADD ./web ./web
-RUN cd ./web && npm i --force && npm run build
+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
 
-ADD ./api ./api
-ADD ./conf ./conf
-ADD ./deepdoc ./deepdoc
-ADD ./rag ./rag
-ADD ./graph ./graph
+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
+
+# If you download Python modules too slow, you can use a pip mirror site to speed up apt and poetry
+RUN sed -i 's|http://archive.ubuntu.com|https://mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list.d/ubuntu.sources
+ENV POETRY_PYPI_MIRROR_URL=https://pypi.tuna.tsinghua.edu.cn/simple/
+
+RUN --mount=type=cache,id=ragflow_base_apt,target=/var/cache/apt,sharing=locked \
+    apt update && apt install -y curl libpython3-dev nginx libglib2.0-0 libglx-mesa0 pkg-config libicu-dev libgdiplus python3-pip python3-poetry \
+    && pip3 install --user --break-system-packages poetry-plugin-pypi-mirror --index-url https://pypi.tuna.tsinghua.edu.cn/simple/ \
+    && rm -rf /var/lib/apt/lists/*
+
+# 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 \
+    if [ "${ARCH}" = "amd64" ]; then \
+        dpkg -i /root/libssl1.1_1.1.1f-1ubuntu2_amd64.deb; \
+    fi
+
+ENV PYTHONDONTWRITEBYTECODE=1 DOTNET_SYSTEM_GLOBALIZATION_INVARIANT=1
+
+# Configure Poetry
+ENV POETRY_NO_INTERACTION=1
+ENV POETRY_VIRTUALENVS_IN_PROJECT=true
+ENV POETRY_VIRTUALENVS_CREATE=true
+ENV POETRY_REQUESTS_TIMEOUT=15
+
+# builder stage
+FROM base AS builder
+USER root
+
+WORKDIR /ragflow
+
+RUN --mount=type=cache,id=ragflow_builder_apt,target=/var/cache/apt,sharing=locked \
+    apt update && apt install -y nodejs npm cargo && \
+    rm -rf /var/lib/apt/lists/*
+
+COPY web web
+COPY docs docs
+RUN --mount=type=cache,id=ragflow_builder_npm,target=/root/.npm,sharing=locked \
+    cd web && npm i --force && npm run build
+
+# install dependencies from poetry.lock file
+COPY pyproject.toml poetry.toml poetry.lock ./
+
+RUN --mount=type=cache,id=ragflow_builder_poetry,target=/root/.cache/pypoetry,sharing=locked \
+    if [ "$LIGHTEN" -eq 0 ]; then \
+        poetry install --sync --no-root --with=full; \
+    else \
+        poetry install --sync --no-root; \
+    fi
+
+# production stage
+FROM base AS production
+USER root
+
+WORKDIR /ragflow
+
+# 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
+
+# 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/entrypoint.sh ./entrypoint.sh
 RUN chmod +x ./entrypoint.sh
 
-ENTRYPOINT ["./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

@@ -1,57 +1,59 @@
-FROM opencloudos/opencloudos:9.0
-USER root
-
-WORKDIR /ragflow
-
-RUN dnf update -y && dnf install -y wget curl gcc-c++ openmpi-devel
-
-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://rpm.nodesource.com/setup_14.x | bash -
-RUN dnf install -y nodejs
-
-RUN dnf 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 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 conda run -n py11 pip install $(grep -ivE "mpi4py" ./requirements.txt) # without mpi4py==3.1.5
-RUN conda run -n py11 pip install redis
-
-RUN dnf update -y && \
-    dnf install -y glib2 mesa-libGL && \
-    dnf clean all
-
-RUN conda run -n py11 pip install ollama
-RUN conda run -n py11 python -m nltk.downloader punkt
-RUN conda run -n py11 python -m nltk.downloader wordnet
-
-ENV PYTHONPATH=/ragflow/
-ENV HF_ENDPOINT=https://hf-mirror.com
-
-ADD docker/entrypoint.sh ./entrypoint.sh
-RUN chmod +x ./entrypoint.sh
-
-ENTRYPOINT ["./entrypoint.sh"]
+FROM opencloudos/opencloudos:9.0
+USER root
+
+WORKDIR /ragflow
+
+RUN dnf update -y && dnf install -y wget curl gcc-c++ openmpi-devel
+
+RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh && \
+    bash ~/miniconda.sh -b -p /root/miniconda3 && \
+    rm ~/miniconda.sh && ln -s /root/miniconda3/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \
+    echo ". /root/miniconda3/etc/profile.d/conda.sh" >> ~/.bashrc && \
+    echo "conda activate base" >> ~/.bashrc
+
+ENV PATH /root/miniconda3/bin:$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://rpm.nodesource.com/setup_14.x | bash -
+RUN dnf install -y nodejs
+
+RUN dnf install -y nginx
+
+ADD ./web ./web
+ADD ./api ./api
+ADD ./docs ./docs
+ADD ./conf ./conf
+ADD ./deepdoc ./deepdoc
+ADD ./rag ./rag
+ADD ./requirements.txt ./requirements.txt
+ADD ./agent ./agent
+ADD ./graphrag ./graphrag
+
+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 && 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
+
+RUN dnf update -y && \
+    dnf install -y glib2 mesa-libGL && \
+    dnf clean all
+
+RUN conda run -n py11 pip install 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.slim

@@ -0,0 +1,109 @@
+# base stage
+FROM ubuntu:24.04 AS base
+USER root
+
+ARG ARCH=amd64
+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
+
+# If you download Python modules too slow, you can use a pip mirror site to speed up apt and poetry
+RUN sed -i 's|http://archive.ubuntu.com|https://mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list.d/ubuntu.sources
+ENV POETRY_PYPI_MIRROR_URL=https://pypi.tuna.tsinghua.edu.cn/simple/
+
+RUN --mount=type=cache,id=ragflow_base_apt,target=/var/cache/apt,sharing=locked \
+    apt update && apt install -y curl libpython3-dev nginx libglib2.0-0 libglx-mesa0 pkg-config libicu-dev libgdiplus python3-pip python3-poetry \
+    && pip3 install --user --break-system-packages poetry-plugin-pypi-mirror --index-url https://pypi.tuna.tsinghua.edu.cn/simple/ \
+    && rm -rf /var/lib/apt/lists/*
+
+# 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 if [ "${ARCH}" = "amd64" ]; then \
+        curl -o libssl1.deb http://archive.ubuntu.com/ubuntu/pool/main/o/openssl/libssl1.1_1.1.1f-1ubuntu2_amd64.deb && dpkg -i libssl1.deb && rm -f libssl1.deb; \
+    fi
+
+ENV PYTHONDONTWRITEBYTECODE=1 DOTNET_SYSTEM_GLOBALIZATION_INVARIANT=1
+
+# Configure Poetry
+ENV POETRY_NO_INTERACTION=1
+ENV POETRY_VIRTUALENVS_IN_PROJECT=true
+ENV POETRY_VIRTUALENVS_CREATE=true
+ENV POETRY_REQUESTS_TIMEOUT=15
+
+# builder stage
+FROM base AS builder
+USER root
+
+WORKDIR /ragflow
+
+RUN --mount=type=cache,id=ragflow_builder_apt,target=/var/cache/apt,sharing=locked \
+    apt update && apt install -y nodejs npm cargo && \
+    rm -rf /var/lib/apt/lists/*
+
+COPY web web
+COPY docs docs
+RUN --mount=type=cache,id=ragflow_builder_npm,target=/root/.npm,sharing=locked \
+    cd web && npm i && npm run build
+
+# install dependencies from poetry.lock file
+COPY pyproject.toml poetry.toml poetry.lock ./
+
+RUN --mount=type=cache,id=ragflow_builder_poetry,target=/root/.cache/pypoetry,sharing=locked \
+    if [ "$LIGHTEN" -eq 0 ]; then \
+        poetry install --sync --no-root --with=full; \
+    else \
+        poetry install --sync --no-root; \
+    fi
+
+# production stage
+FROM base AS production
+USER root
+
+WORKDIR /ragflow
+
+# 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
+
+# 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/entrypoint.sh ./entrypoint.sh
+RUN chmod +x ./entrypoint.sh
+
+ENTRYPOINT ["./entrypoint.sh"]

README.md

@@ -1,341 +1,350 @@
-<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>
-</p>
-
-<p align="center">
-    <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> |
-  <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>📕 Table of Contents</b></summary>
-  
-- 💡 [What is RAGFlow?](#-what-is-ragflow)
-- 🎮 [Demo](#-demo)
-- 📌 [Latest Updates](#-latest-updates)
-- 🌟 [Key Features](#-key-features)
-- 🔎 [System Architecture](#-system-architecture)
-- 🎬 [Get Started](#-get-started)
-- 🔧 [Configurations](#-configurations)
-- 🛠️ [Build from source](#-build-from-source)
-- 🛠️ [Launch service from source](#-launch-service-from-source)
-- 📚 [Documentation](#-documentation)
-- 📜 [Roadmap](#-roadmap)
-- 🏄 [Community](#-community)
-- 🙌 [Contributing](#-contributing)
-
-</details>
-
-## 💡 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.
-
-## 🎮 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"/>
-</div>
-
-
-## 📌 Latest Updates
-
-- 2024-07-08 Supports [Graph](./graph/README.md).
-
-- 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.
-
-## 🌟 Key Features
-
-### 🍭 **"Quality in, quality out"**
-
-- [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**
-
-- Intelligent and explainable.
-- Plenty of template options to choose from.
-
-### 🌱 **Grounded citations with reduced hallucinations**
-
-- Visualization of text chunking to allow human intervention.
-- Quick view of the key references and traceable citations to support grounded answers.
-
-### 🍔 **Compatibility with heterogeneous data sources**
-
-- Supports Word, slides, excel, txt, images, scanned copies, structured data, web pages, and more.
-
-### 🛀 **Automated and effortless RAG workflow**
-
-- Streamlined RAG orchestration catered to both personal and large businesses.
-- Configurable LLMs as well as embedding models.
-- Multiple recall paired with fused re-ranking.
-- Intuitive APIs for seamless integration with business.
-
-## 🔎 System Architecture
-
-<div align="center" style="margin-top:20px;margin-bottom:20px;">
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/d6ac5664-c237-4200-a7c2-a4a00691b485" width="1000"/>
-</div>
-
-## 🎬 Get Started
-
-### 📝 Prerequisites
-
-- CPU >= 4 cores
-- 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/).
-
-### 🚀 Start up the server
-
-1. Ensure `vm.max_map_count` >= 262144:
-
-   > To check the value of `vm.max_map_count`:
-   >
-   > ```bash
-   > $ sysctl vm.max_map_count
-   > ```
-   >
-   > Reset `vm.max_map_count` to a value at least 262144 if it is not.
-   >
-   > ```bash
-   > # In this case, we set it to 262144:
-   > $ 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:
-   >
-   > ```bash
-   > vm.max_map_count=262144
-   > ```
-
-2. Clone the repo:
-
-   ```bash
-   $ git clone https://github.com/infiniflow/ragflow.git
-   ```
-
-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.
-
-   ```bash
-   $ cd ragflow/docker
-   $ chmod +x ./entrypoint.sh
-   $ docker compose up -d
-   ```
-   
-
-   > The core image is about 9 GB in size and may take a while to load.
-
-4. Check the server status after having the server up and running:
-
-   ```bash
-   $ docker logs -f ragflow-server
-   ```
-
-   _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.  
-
-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.
-
-   > See [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) for more information.
-
-   _The show is now 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.
-- [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, 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`.
-
-> Updates to all system configurations require a system reboot to take effect:
->
-> ```bash
-> $ docker-compose up -d
-> ```
-
-## 🛠️ Build from source
-
-To build the Docker images from source:
-
-```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
-```
-
-## 🛠️ Launch service from source
-
-To launch the service from source:
-
-1. Clone the repository: 
-
-   ```bash
-   $ git clone https://github.com/infiniflow/ragflow.git
-   $ cd ragflow/
-   ```
-
-2. Create a virtual environment, ensuring that Anaconda or Miniconda is installed:
-
-   ```bash
-   $ conda create -n ragflow python=3.11.0
-   $ conda activate ragflow
-   $ pip install -r requirements.txt
-   ```
-   
-   ```bash
-   # If your CUDA version is higher than 12.0, run the following additional commands:
-   $ pip uninstall -y onnxruntime-gpu
-   $ pip install onnxruntime-gpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/
-   ```
-
-3. Copy the entry script and configure environment variables:
-
-   ```bash
-   # Get the Python path:
-   $ which python
-   # Get the ragflow project path:
-   $ pwd
-   ```
-   
-   ```bash
-   $ cp docker/entrypoint.sh .
-   $ vi entrypoint.sh
-   ```
-
-   ```bash
-   # Adjust configurations according to your actual situation (the following two export commands are newly added):
-   # - Assign the result of `which python` to `PY`.
-   # - Assign the result of `pwd` to `PYTHONPATH`.
-   # - Comment out `LD_LIBRARY_PATH`, if it is configured.
-   # - Optional: Add Hugging Face mirror.
-   PY=${PY}
-   export PYTHONPATH=${PYTHONPATH}
-   export HF_ENDPOINT=https://hf-mirror.com
-   ```
-
-4. Launch the third-party services (MinIO, Elasticsearch, Redis, and MySQL):
-
-   ```bash
-   $ cd docker
-   $ docker compose -f docker-compose-base.yml up -d 
-   ```
-
-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:
-
-   ```bash
-   $ chmod +x ./entrypoint.sh
-   $ bash ./entrypoint.sh
-   ```
-
-7. Launch the frontend service:
-
-   ```bash
-   $ cd web
-   $ npm install --registry=https://registry.npmmirror.com --force
-   $ vim .umirc.ts
-   # Update proxy.target to http://127.0.0.1:9380
-   $ npm run dev 
-   ```
-
-8. Deploy the frontend service:
-
-   ```bash
-   $ cd web
-   $ npm install --registry=https://registry.npmmirror.com --force
-   $ umi build
-   $ mkdir -p /ragflow/web
-   $ cp -r dist /ragflow/web
-   $ apt install nginx -y
-   $ cp ../docker/nginx/proxy.conf /etc/nginx
-   $ cp ../docker/nginx/nginx.conf /etc/nginx
-   $ cp ../docker/nginx/ragflow.conf /etc/nginx/conf.d
-   $ systemctl start nginx
-   ```
-
-## 📚 Documentation
-
-- [Quickstart](https://ragflow.io/docs/dev/)
-- [User guide](https://ragflow.io/docs/dev/category/user-guides)
-- [References](https://ragflow.io/docs/dev/category/references)
-- [FAQ](https://ragflow.io/docs/dev/faq)
-
-## 📜 Roadmap
-
-See the [RAGFlow Roadmap 2024](https://github.com/infiniflow/ragflow/issues/162)
-
-## 🏄 Community
-
-- [Discord](https://discord.gg/4XxujFgUN7)
-- [Twitter](https://twitter.com/infiniflowai)
-- [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
-
-## 🙌 Contributing
-
-RAGFlow flourishes via open-source collaboration. In this spirit, we embrace diverse contributions from the community. If you would like to be a part, review our [Contribution Guidelines](./docs/references/CONTRIBUTING.md) first.
+<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>
+</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:v0.13.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.13.0">
+    </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>
+
+<details open>
+<summary></b>📕 Table of Contents</b></summary>
+
+- 💡 [What is RAGFlow?](#-what-is-ragflow)
+- 🎮 [Demo](#-demo)
+- 📌 [Latest Updates](#-latest-updates)
+- 🌟 [Key Features](#-key-features)
+- 🔎 [System Architecture](#-system-architecture)
+- 🎬 [Get Started](#-get-started)
+- 🔧 [Configurations](#-configurations)
+- 🔧 [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)
+- 🙌 [Contributing](#-contributing)
+
+</details>
+
+## 💡 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.
+
+## 🎮 Demo
+
+Try our demo at [https://demo.ragflow.io](https://demo.ragflow.io).
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/infiniflow/ragflow/assets/7248/2f6baa3e-1092-4f11-866d-36f6a9d075e5" width="1200"/>
+<img src="https://github.com/infiniflow/ragflow/assets/12318111/b083d173-dadc-4ea9-bdeb-180d7df514eb" width="1200"/>
+</div>
+
+## 🔥 Latest Updates
+
+- 2024-09-29 Optimizes multi-round conversations.
+- 2024-09-13 Adds search mode for knowledge base Q&A.
+- 2024-09-09 Adds a medical consultant agent template.
+- 2024-08-22 Support text to SQL statements through RAG.
+- 2024-08-02 Supports GraphRAG inspired by [graphrag](https://github.com/microsoft/graphrag) and mind map.
+
+## 🎉 Stay Tuned
+
+⭐️ Star our repository to stay up-to-date with exciting new features and improvements! Get instant notifications for new
+releases! 🌟
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/user-attachments/assets/18c9707e-b8aa-4caf-a154-037089c105ba" width="1200"/>
+</div>
+
+## 🌟 Key Features
+
+### 🍭 **"Quality in, quality out"**
+
+- [Deep document understanding](./deepdoc/README.md)-based knowledge extraction from unstructured data with complicated
+  formats.
+- Finds "needle in a data haystack" of literally unlimited tokens.
+
+### 🍱 **Template-based chunking**
+
+- Intelligent and explainable.
+- Plenty of template options to choose from.
+
+### 🌱 **Grounded citations with reduced hallucinations**
+
+- Visualization of text chunking to allow human intervention.
+- Quick view of the key references and traceable citations to support grounded answers.
+
+### 🍔 **Compatibility with heterogeneous data sources**
+
+- Supports Word, slides, excel, txt, images, scanned copies, structured data, web pages, and more.
+
+### 🛀 **Automated and effortless RAG workflow**
+
+- Streamlined RAG orchestration catered to both personal and large businesses.
+- Configurable LLMs as well as embedding models.
+- Multiple recall paired with fused re-ranking.
+- Intuitive APIs for seamless integration with business.
+
+## 🔎 System Architecture
+
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/infiniflow/ragflow/assets/12318111/d6ac5664-c237-4200-a7c2-a4a00691b485" width="1000"/>
+</div>
+
+## 🎬 Get Started
+
+### 📝 Prerequisites
+
+- CPU >= 4 cores
+- 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/).
+
+### 🚀 Start up the server
+
+1. Ensure `vm.max_map_count` >= 262144:
+
+   > To check the value of `vm.max_map_count`:
+   >
+   > ```bash
+   > $ sysctl vm.max_map_count
+   > ```
+   >
+   > Reset `vm.max_map_count` to a value at least 262144 if it is not.
+   >
+   > ```bash
+   > # In this case, we set it to 262144:
+   > $ 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:
+   >
+   > ```bash
+   > vm.max_map_count=262144
+   > ```
+
+2. Clone the repo:
+
+   ```bash
+   $ git clone https://github.com/infiniflow/ragflow.git
+   ```
+
+3. Build the pre-built Docker images and start up the server:
+
+   > 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
+   $ 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:v0.13.0-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:v0.13.0`. After making this change, rerun the command above to initiate the
+       download.
+
+   > **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:
+
+   ```bash
+   $ docker logs -f ragflow-server
+   ```
+
+   _The following output confirms a successful launch of the system:_
+
+   ```bash
+
+         ____   ___    ______ ______ __               
+        / __ \ /   |  / ____// ____// /____  _      __
+       / /_/ // /| | / / __ / /_   / // __ \| | /| / /
+      / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ / 
+     /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/ 
+
+    * Running on all addresses (0.0.0.0)
+    * Running on http://127.0.0.1:9380
+    * Running on http://x.x.x.x:9380
+    INFO:werkzeug:Press CTRL+C to quit
+   ```
+   > If you skip this confirmation step and directly log in to RAGFlow, your browser may prompt a `network abnormal`
+   error because, at that moment, your RAGFlow may not be fully initialized.
+
+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.
+
+   > See [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) for more information.
+
+   _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.
+- [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, 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`.
+
+Updates to the above configurations require a reboot of all containers to take effect:
+
+> ```bash
+> $ docker compose -f docker/docker-compose.yml up -d
+> ```
+
+## 🔧 Build a Docker image without embedding models
+
+This image is approximately 1 GB in size and relies on external LLM and embedding services.
+
+```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 .
+```
+
+## 🔧 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
+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 .
+```
+
+## 🔨 Launch service from source for development
+
+1. Install Poetry, or skip this step if it is already installed:
+   ```bash
+   curl -sSL https://install.python-poetry.org | python3 -
+   ```
+
+2. Clone the source code and install Python dependencies:
+   ```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. 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/service_conf.yaml** to `127.0.0.1`:
+   ```
+   127.0.0.1       es01 mysql minio redis
+   ```  
+   In **docker/service_conf.yaml**, 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
+   export HF_ENDPOINT=https://hf-mirror.com
+   ```
+
+5. Launch backend service:
+   ```bash
+   source .venv/bin/activate
+   export PYTHONPATH=$(pwd)
+   bash docker/launch_backend_service.sh
+   ```
+
+6. Install frontend dependencies:
+   ```bash
+   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 
+   ```  
+
+   _The following output confirms a successful launch of the system:_
+
+   ![](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/guides)
+- [References](https://ragflow.io/docs/dev/category/references)
+- [FAQ](https://ragflow.io/docs/dev/faq)
+
+## 📜 Roadmap
+
+See the [RAGFlow Roadmap 2024](https://github.com/infiniflow/ragflow/issues/162)
+
+## 🏄 Community
+
+- [Discord](https://discord.gg/4XxujFgUN7)
+- [Twitter](https://twitter.com/infiniflowai)
+- [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
+
+## 🙌 Contributing
+
+RAGFlow flourishes via open-source collaboration. In this spirit, we embrace diverse contributions from the community.
+If you would like to be a part, review our [Contribution Guidelines](./CONTRIBUTING.md) first.

README_ja.md

@@ -1,286 +1,303 @@
-<div align="center">
-<a href="https://demo.ragflow.io/">
-<img src="web/src/assets/logo-with-text.png" width="350" alt="ragflow logo">
-</a>
-</div>
-
-<p align="center">
-  <a href="./README.md">English</a> |
-  <a href="./README_zh.md">简体中文</a> |
-  <a href="./README_ja.md">日本語</a>
-</p>
-
-<p align="center">
-    <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> |
-  <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>
-
-## 💡 RAGFlow とは？
-
-[RAGFlow](https://ragflow.io/) は、深い文書理解に基づいたオープンソースの RAG (Retrieval-Augmented Generation) エンジンである。LLM（大規模言語モデル）を組み合わせることで、様々な複雑なフォーマットのデータから根拠のある引用に裏打ちされた、信頼できる質問応答機能を実現し、あらゆる規模のビジネスに適した RAG ワークフローを提供します。
-
-## 🎮 Demo
-
-デモをお試しください：[https://demo.ragflow.io](https://demo.ragflow.io)。
-<div align="center" style="margin-top:20px;margin-bottom:20px;">
-<img src="https://github.com/infiniflow/ragflow/assets/7248/2f6baa3e-1092-4f11-866d-36f6a9d075e5" width="1200"/>
-</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。
-
-## 🌟 主な特徴
-
-### 🍭 **"Quality in, quality out"**
-
-- 複雑な形式の非構造化データからの[深い文書理解](./deepdoc/README.md)ベースの知識抽出。
-- 無限のトークンから"干し草の山の中の針"を見つける。
-
-### 🍱 **テンプレートベースのチャンク化**
-
-- 知的で解釈しやすい。
-- テンプレートオプションが豊富。
-
-### 🌱 **ハルシネーションが軽減された根拠のある引用**
-
-- 可視化されたテキストチャンキング（text chunking）で人間の介入を可能にする。
-- 重要な参考文献のクイックビューと、追跡可能な引用によって根拠ある答えをサポートする。
-
-### 🍔 **多様なデータソースとの互換性**
-
-- Word、スライド、Excel、txt、画像、スキャンコピー、構造化データ、Web ページなどをサポート。
-
-### 🛀 **自動化された楽な RAG ワークフロー**
-
-- 個人から大企業まで対応できる RAG オーケストレーション（orchestration）。
-- カスタマイズ可能な LLM とエンベッディングモデル。
-- 複数の想起と融合された再ランク付け。
-- 直感的な API によってビジネスとの統合がシームレスに。
-
-## 🔎 システム構成
-
-<div align="center" style="margin-top:20px;margin-bottom:20px;">
-<img src="https://github.com/infiniflow/ragflow/assets/12318111/d6ac5664-c237-4200-a7c2-a4a00691b485" width="1000"/>
-</div>
-
-## 🎬 初期設定
-
-### 📝 必要条件
-
-- CPU >= 4 cores
-- RAM >= 16 GB
-- Disk >= 50 GB
-- Docker >= 24.0.0 & Docker Compose >= v2.26.1
-  > ローカルマシン（Windows、Mac、または Linux）に Docker をインストールしていない場合は、[Docker Engine のインストール](https://docs.docker.com/engine/install/) を参照してください。
-
-### 🚀 サーバーを起動
-
-1. `vm.max_map_count` >= 262144 であることを確認する:
-
-   > `vm.max_map_count` の値をチェックするには:
-   >
-   > ```bash
-   > $ sysctl vm.max_map_count
-   > ```
-   >
-   > `vm.max_map_count` が 262144 より大きい値でなければリセットする。
-   >
-   > ```bash
-   > # In this case, we set it to 262144:
-   > $ sudo sysctl -w vm.max_map_count=262144
-   > ```
-   >
-   > この変更はシステム再起動後にリセットされる。変更を恒久的なものにするには、**/etc/sysctl.conf** の `vm.max_map_count` 値を適宜追加または更新する:
-   >
-   > ```bash
-   > vm.max_map_count=262144
-   > ```
-
-2. リポジトリをクローンする:
-
-   ```bash
-   $ git clone https://github.com/infiniflow/ragflow.git
-   ```
-
-3. ビルド済みの Docker イメージをビルドし、サーバーを起動する:
-
-   ```bash
-   $ cd ragflow/docker
-   $ chmod +x ./entrypoint.sh
-   $ docker compose up -d
-   ```
-
-   > 上記のコマンドを実行すると、RAGFlowの開発版dockerイメージが自動的にダウンロードされます。 特定のバージョンのDockerイメージをダウンロードして実行したい場合は、docker/.envファイルのRAGFLOW_VERSION変数を見つけて、対応するバージョンに変更してください。 例えば、RAGFLOW_VERSION=v0.8.0として、上記のコマンドを実行してください。
-
-   > 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。
-
-4. サーバーを立ち上げた後、サーバーの状態を確認する:
-
-   ```bash
-   $ docker logs -f ragflow-server
-   ```
-
-   _以下の出力は、システムが正常に起動したことを確認するものです:_
-
-   ```bash
-       ____                 ______ __
-      / __ \ ____ _ ____ _ / ____// /____  _      __
-     / /_/ // __ `// __ `// /_   / // __ \| | /| / /
-    / _, _// /_/ // /_/ // __/  / // /_/ /| |/ |/ /
-   /_/ |_| \__,_/ \__, //_/    /_/ \____/ |__/|__/
-                 /____/
-
-    * Running on all addresses (0.0.0.0)
-    * Running on http://127.0.0.1:9380
-    * Running on http://x.x.x.x:9380
-    INFO:werkzeug:Press CTRL+C to quit
-   ```
-   > もし確認ステップをスキップして直接 RAGFlow にログインした場合、その時点で RAGFlow が完全に初期化されていない可能性があるため、ブラウザーがネットワーク異常エラーを表示するかもしれません。
-
-5. ウェブブラウザで、プロンプトに従ってサーバーの IP アドレスを入力し、RAGFlow にログインします。
-   > デフォルトの設定を使用する場合、デフォルトの HTTP サービングポート `80` は省略できるので、与えられたシナリオでは、`http://IP_OF_YOUR_MACHINE`（ポート番号は省略）だけを入力すればよい。
-6. [service_conf.yaml](./docker/service_conf.yaml) で、`user_default_llm` で希望の LLM ファクトリを選択し、`API_KEY` フィールドを対応する API キーで更新する。
-
-   > 詳しくは [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) を参照してください。
-
-   _これで初期設定完了！ショーの開幕です！_
-
-## 🔧 コンフィグ
-
-システムコンフィグに関しては、以下のファイルを管理する必要がある:
-
-- [.env](./docker/.env): `SVR_HTTP_PORT`、`MYSQL_PASSWORD`、`MINIO_PASSWORD` などのシステムの基本設定を保持する。
-- [service_conf.yaml](./docker/service_conf.yaml): バックエンドのサービスを設定します。
-- [docker-compose.yml](./docker/docker-compose.yml): システムの起動は [docker-compose.yml](./docker/docker-compose.yml) に依存している。
-
-[.env](./docker/.env) ファイルの変更が [service_conf.yaml](./docker/service_conf.yaml) ファイルの内容と一致していることを確認する必要があります。
-
-> [./docker/README](./docker/README.md) ファイルは環境設定とサービスコンフィグの詳細な説明を提供し、[./docker/README](./docker/README.md) ファイルに記載されている全ての環境設定が [service_conf.yaml](./docker/service_conf.yaml) ファイルの対応するコンフィグと一致していることを確認することが義務付けられています。
-
-デフォルトの HTTP サービングポート(80)を更新するには、[docker-compose.yml](./docker/docker-compose.yml) にアクセスして、`80:80` を `<YOUR_SERVING_PORT>:80` に変更します。
-
-> すべてのシステム設定のアップデートを有効にするには、システムの再起動が必要です:
->
-> ```bash
-> $ docker-compose up -d
-> ```
-
-## 🛠️ ソースからビルドする
-
-ソースからDockerイメージをビルドするには:
-
-```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
-```
-
-## 🛠️ ソースコードからサービスを起動する方法
-
-ソースコードからサービスを起動する場合は、以下の手順に従ってください:
-
-1. リポジトリをクローンします
-```bash
-$ git clone https://github.com/infiniflow/ragflow.git
-$ cd ragflow/
-```
-
-2. 仮想環境を作成します（AnacondaまたはMinicondaがインストールされていることを確認してください）
-```bash
-$ conda create -n ragflow python=3.11.0
-$ conda activate ragflow
-$ pip install -r requirements.txt
-```
-CUDAのバージョンが12.0以上の場合、以下の追加コマンドを実行してください：
-```bash
-$ pip uninstall -y onnxruntime-gpu
-$ pip install onnxruntime-gpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/
-```
-
-3. エントリースクリプトをコピーし、環境変数を設定します
-```bash
-$ cp docker/entrypoint.sh .
-$ vi entrypoint.sh
-```
-以下のコマンドでPythonのパスとragflowプロジェクトのパスを取得します：
-```bash
-$ which python
-$ pwd
-```
-
-`which python`の出力を`PY`の値として、`pwd`の出力を`PYTHONPATH`の値として設定します。
-
-`LD_LIBRARY_PATH`が既に設定されている場合は、コメントアウトできます。
-
-```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
-```bash
-$ cd docker
-$ docker compose -f docker-compose-base.yml up -d 
-```
-
-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
-```bash
-$ chmod +x ./entrypoint.sh
-$ bash ./entrypoint.sh
-```
-
-## 📚 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)
-- [References](https://ragflow.io/docs/dev/category/references)
-- [FAQ](https://ragflow.io/docs/dev/faq)
-
-## 📜 ロードマップ
-
-[RAGFlow ロードマップ 2024](https://github.com/infiniflow/ragflow/issues/162) を参照
-
-## 🏄 コミュニティ
-
-- [Discord](https://discord.gg/4XxujFgUN7)
-- [Twitter](https://twitter.com/infiniflowai)
-- [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
-
-## 🙌 コントリビュート
-
-RAGFlow はオープンソースのコラボレーションによって発展してきました。この精神に基づき、私たちはコミュニティからの多様なコントリビュートを受け入れています。 参加を希望される方は、まず[コントリビューションガイド](./docs/references/CONTRIBUTING.md)をご覧ください。
+<div align="center">
+<a href="https://demo.ragflow.io/">
+<img src="web/src/assets/logo-with-text.png" width="350" alt="ragflow logo">
+</a>
+</div>
+
+<p align="center">
+  <a href="./README.md">English</a> |
+  <a href="./README_zh.md">简体中文</a> |
+  <a href="./README_ja.md">日本語</a> |
+  <a href="./README_ko.md">한국어</a>
+</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:v0.13.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.13.0">
+    </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>
+
+## 💡 RAGFlow とは？
+
+[RAGFlow](https://ragflow.io/) は、深い文書理解に基づいたオープンソースの RAG (Retrieval-Augmented Generation) エンジンである。LLM（大規模言語モデル）を組み合わせることで、様々な複雑なフォーマットのデータから根拠のある引用に裏打ちされた、信頼できる質問応答機能を実現し、あらゆる規模のビジネスに適した RAG ワークフローを提供します。
+
+## 🎮 Demo
+
+デモをお試しください：[https://demo.ragflow.io](https://demo.ragflow.io)。
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/infiniflow/ragflow/assets/7248/2f6baa3e-1092-4f11-866d-36f6a9d075e5" width="1200"/>
+<img src="https://github.com/infiniflow/ragflow/assets/12318111/b083d173-dadc-4ea9-bdeb-180d7df514eb" width="1200"/>
+</div>
+
+
+## 🔥 最新情報
+
+- 2024-09-29 マルチラウンドダイアログを最適化。
+- 2024-09-13 ナレッジベース Q&A の検索モードを追加しました。
+- 2024-09-09 エージェントに医療相談テンプレートを追加しました。
+- 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
+
+### 🍭 **"Quality in, quality out"**
+
+- 複雑な形式の非構造化データからの[深い文書理解](./deepdoc/README.md)ベースの知識抽出。
+- 無限のトークンから"干し草の山の中の針"を見つける。
+
+### 🍱 **テンプレートベースのチャンク化**
+
+- 知的で解釈しやすい。
+- テンプレートオプションが豊富。
+
+### 🌱 **ハルシネーションが軽減された根拠のある引用**
+
+- 可視化されたテキストチャンキング（text chunking）で人間の介入を可能にする。
+- 重要な参考文献のクイックビューと、追跡可能な引用によって根拠ある答えをサポートする。
+
+### 🍔 **多様なデータソースとの互換性**
+
+- Word、スライド、Excel、txt、画像、スキャンコピー、構造化データ、Web ページなどをサポート。
+
+### 🛀 **自動化された楽な RAG ワークフロー**
+
+- 個人から大企業まで対応できる RAG オーケストレーション（orchestration）。
+- カスタマイズ可能な LLM とエンベッディングモデル。
+- 複数の想起と融合された再ランク付け。
+- 直感的な API によってビジネスとの統合がシームレスに。
+
+## 🔎 システム構成
+
+<div align="center" style="margin-top:20px;margin-bottom:20px;">
+<img src="https://github.com/infiniflow/ragflow/assets/12318111/d6ac5664-c237-4200-a7c2-a4a00691b485" width="1000"/>
+</div>
+
+## 🎬 初期設定
+
+### 📝 必要条件
+
+- CPU >= 4 cores
+- RAM >= 16 GB
+- Disk >= 50 GB
+- Docker >= 24.0.0 & Docker Compose >= v2.26.1
+  > ローカルマシン（Windows、Mac、または Linux）に Docker をインストールしていない場合は、[Docker Engine のインストール](https://docs.docker.com/engine/install/) を参照してください。
+
+### 🚀 サーバーを起動
+
+1. `vm.max_map_count` >= 262144 であることを確認する:
+
+   > `vm.max_map_count` の値をチェックするには:
+   >
+   > ```bash
+   > $ sysctl vm.max_map_count
+   > ```
+   >
+   > `vm.max_map_count` が 262144 より大きい値でなければリセットする。
+   >
+   > ```bash
+   > # In this case, we set it to 262144:
+   > $ sudo sysctl -w vm.max_map_count=262144
+   > ```
+   >
+   > この変更はシステム再起動後にリセットされる。変更を恒久的なものにするには、**/etc/sysctl.conf** の `vm.max_map_count` 値を適宜追加または更新する:
+   >
+   > ```bash
+   > vm.max_map_count=262144
+   > ```
+
+2. リポジトリをクローンする:
+
+   ```bash
+   $ git clone https://github.com/infiniflow/ragflow.git
+   ```
+
+3. ビルド済みの Docker イメージをビルドし、サーバーを起動する:
+
+   > 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
+   $ docker compose -f docker-compose.yml up -d
+   ```
+
+   > - 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:v0.13.0`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:v0.13.0`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。  
+   
+   > **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:
+
+   ```bash
+   $ docker logs -f ragflow-server
+   ```
+
+   _以下の出力は、システムが正常に起動したことを確認するものです:_
+
+   ```bash
+        ____   ___    ______ ______ __               
+       / __ \ /   |  / ____// ____// /____  _      __
+      / /_/ // /| | / / __ / /_   / // __ \| | /| / /
+     / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ / 
+    /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/  
+
+    * Running on all addresses (0.0.0.0)
+    * Running on http://127.0.0.1:9380
+    * Running on http://x.x.x.x:9380
+    INFO:werkzeug:Press CTRL+C to quit
+   ```
+   > もし確認ステップをスキップして直接 RAGFlow にログインした場合、その時点で RAGFlow が完全に初期化されていない可能性があるため、ブラウザーがネットワーク異常エラーを表示するかもしれません。
+
+5. ウェブブラウザで、プロンプトに従ってサーバーの IP アドレスを入力し、RAGFlow にログインします。
+   > デフォルトの設定を使用する場合、デフォルトの HTTP サービングポート `80` は省略できるので、与えられたシナリオでは、`http://IP_OF_YOUR_MACHINE`（ポート番号は省略）だけを入力すればよい。
+6. [service_conf.yaml](./docker/service_conf.yaml) で、`user_default_llm` で希望の LLM ファクトリを選択し、`API_KEY` フィールドを対応する API キーで更新する。
+
+   > 詳しくは [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) を参照してください。
+
+   _これで初期設定完了！ショーの開幕です！_
+
+## 🔧 コンフィグ
+
+システムコンフィグに関しては、以下のファイルを管理する必要がある:
+
+- [.env](./docker/.env): `SVR_HTTP_PORT`、`MYSQL_PASSWORD`、`MINIO_PASSWORD` などのシステムの基本設定を保持する。
+- [service_conf.yaml](./docker/service_conf.yaml): バックエンドのサービスを設定します。
+- [docker-compose.yml](./docker/docker-compose.yml): システムの起動は [docker-compose.yml](./docker/docker-compose.yml) に依存している。
+
+[.env](./docker/.env) ファイルの変更が [service_conf.yaml](./docker/service_conf.yaml) ファイルの内容と一致していることを確認する必要があります。
+
+> [./docker/README](./docker/README.md) ファイルは環境設定とサービスコンフィグの詳細な説明を提供し、[./docker/README](./docker/README.md) ファイルに記載されている全ての環境設定が [service_conf.yaml](./docker/service_conf.yaml) ファイルの対応するコンフィグと一致していることを確認することが義務付けられています。
+
+デフォルトの HTTP サービングポート(80)を更新するには、[docker-compose.yml](./docker/docker-compose.yml) にアクセスして、`80:80` を `<YOUR_SERVING_PORT>:80` に変更します。
+
+> すべてのシステム設定のアップデートを有効にするには、システムの再起動が必要です:
+>
+> ```bash
+> $ docker compose -f docker/docker-compose.yml up -d
+> ```
+
+## 🔧 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/
+pip3 install huggingface-hub nltk
+python3 download_deps.py
+docker build -f Dockerfile.slim -t infiniflow/ragflow:dev-slim .
+```
+
+## 🔧 ソースコードをコンパイルしたDockerイメージ（埋め込みモデルを含む）
+
+この Docker のサイズは約 9GB で、埋め込みモデルを含むため、外部の大モデルサービスのみが必要です。
+
+```bash
+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 .
+```
+
+## 🔨 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
+   curl -sSL https://install.python-poetry.org | python3 -
+   ```
+
+2. ソースコードをクローンし、Python の依存関係をインストールする:
+   ```bash
+   git clone https://github.com/infiniflow/ragflow.git
+   cd ragflow/
+   export POETRY_VIRTUALENVS_CREATE=true POETRY_VIRTUALENVS_IN_PROJECT=true
+   ~/.local/bin/poetry install --sync --no-root # install RAGFlow dependent python modules
+   ```
+
+3. Docker Compose を使用して依存サービス（MinIO、Elasticsearch、Redis、MySQL）を起動する:
+   ```bash
+   docker compose -f docker/docker-compose-base.yml up -d
+   ```
+
+   `/etc/hosts` に以下の行を追加して、**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 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
+   export HF_ENDPOINT=https://hf-mirror.com
+   ```
+
+5. バックエンドサービスを起動する:
+   ```bash
+   source .venv/bin/activate
+   export PYTHONPATH=$(pwd)
+   bash docker/launch_backend_service.sh
+   ```
+
+6. フロントエンドの依存関係をインストールする:  
+   ```bash
+   cd web
+   npm install --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/guides)
+- [References](https://ragflow.io/docs/dev/category/references)
+- [FAQ](https://ragflow.io/docs/dev/faq)
+
+## 📜 ロードマップ
+
+[RAGFlow ロードマップ 2024](https://github.com/infiniflow/ragflow/issues/162) を参照
+
+## 🏄 コミュニティ
+
+- [Discord](https://discord.gg/4XxujFgUN7)
+- [Twitter](https://twitter.com/infiniflowai)
+- [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
+
+## 🙌 コントリビュート
+
+RAGFlow はオープンソースのコラボレーションによって発展してきました。この精神に基づき、私たちはコミュニティからの多様なコントリビュートを受け入れています。 参加を希望される方は、まず [コントリビューションガイド](./CONTRIBUTING.md)をご覧ください。

README_ko.md

@@ -0,0 +1,307 @@
+<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> |
+</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:v0.13.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.13.0">
+    </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/infiniflow/ragflow/assets/12318111/b083d173-dadc-4ea9-bdeb-180d7df514eb" width="1200"/>
+</div>
+
+
+## 🔥 업데이트
+
+- 2024-09-29 다단계 대화를 최적화합니다.
+  
+- 2024-09-13 지식베이스 Q&A 검색 모드를 추가합니다.
+  
+- 2024-09-09 Agent에 의료상담 템플릿을 추가하였습니다.
+  
+- 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:v0.13.0-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:v0.13.0` 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 abnormal` 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
+> ```
+
+## 🔧 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 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 +7,29 @@
 <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>
 </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:v0.13.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.13.0">
+    </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>
+        <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 +47,24 @@
 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/infiniflow/ragflow/assets/12318111/b083d173-dadc-4ea9-bdeb-180d7df514eb" width="1200"/>
 </div>
 
 
-## 📌 近期更新
+## 🔥 近期更新
+
+- 2024-09-29 优化多轮对话.
+- 2024-09-13 增加知识库问答搜索模式。
+- 2024-09-09 在 Agent 中加入医疗问诊模板。
+- 2024-08-22 支持用 RAG 技术实现从自然语言到 SQL 语句的转换。
+- 2024-08-02 支持 GraphRAG 启发于 [graphrag](https://github.com/microsoft/graphrag) 和思维导图。
+
+## 🎉 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
 
@@ -130,16 +141,18 @@
 
 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:v0.13.0-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:v0.13.0`。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：
 
    ```bash
@@ -149,19 +162,18 @@
    _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 abnormal` 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 +189,104 @@
 
 - [.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
+## 🔧 源码编译 Docker 镜像（不含 embedding 模型）
 
-如需从源码安装 Docker 镜像：
+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/
-$ 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 .
 ```
 
-## 🛠️ 源码启动服务
+## 🔧 源码编译 Docker 镜像（包含 embedding 模型）
 
-如需从源码启动服务，请参考以下步骤：
-
-1. 克隆仓库
-```bash
-$ git clone https://github.com/infiniflow/ragflow.git
-$ cd ragflow/
-```
-
-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/
-```
-
-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。
-
-`LD_LIBRARY_PATH`如果环境已经配置好，可以注释掉。
+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
-# 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
+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 .
 ```
 
-4. 启动基础服务
-```bash
-$ cd docker
-$ docker compose -f docker-compose-base.yml up -d 
-```
+## 🔨 以源代码启动服务
 
-5. 检查配置文件
-确保**docker/.env**中的配置与**conf/service_conf.yaml**中配置一致， **service_conf.yaml**中相关服务的IP地址与端口应该改成本机IP地址及容器映射出来的端口。
+1. 安装 Poetry。如已经安装，可跳过本步骤：  
+   ```bash
+   curl -sSL https://install.python-poetry.org | python3 -
+   ```
 
-6. 启动服务
-```bash
-$ chmod +x ./entrypoint.sh
-$ bash ./entrypoint.sh
-```
-7. 启动WebUI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 
-```
+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 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
+   export HF_ENDPOINT=https://hf-mirror.com
+   ```
+
+5. 启动后端服务：  
+   ```bash
+   source .venv/bin/activate
+   export PYTHONPATH=$(pwd)
+   bash docker/launch_backend_service.sh
+   ```
+
+6. 安装前端依赖：  
+   ```bash
+   cd web
+   npm install --force
+   ```  
+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
+   npm run dev 
+   ```  
+
+   _以下界面说明系统已经成功启动：_  
+
+   ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
 
-8. 部署WebUI服务
-```bash
-$ cd web
-$ npm install --registry=https://registry.npmmirror.com --force
-$ umi build
-$ mkdir -p /ragflow/web
-$ cp -r dist /ragflow/web
-$ apt install nginx -y
-$ cp ../docker/nginx/proxy.conf /etc/nginx
-$ cp ../docker/nginx/nginx.conf /etc/nginx
-$ cp ../docker/nginx/ragflow.conf /etc/nginx/conf.d
-$ systemctl start nginx
-```
 ## 📚 技术文档
 
 - [Quickstart](https://ragflow.io/docs/dev/)
-- [User guide](https://ragflow.io/docs/dev/category/user-guides)
+- [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 +302,11 @@ $ 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

@@ -22,9 +22,9 @@ 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
+from agent.settings import flow_logger, DEBUG
 
 
 class Canvas(ABC):
@@ -188,14 +188,19 @@ class Canvas(ABC):
         def prepare2run(cpns):
             nonlocal ran, ans
             for c in cpns:
+                if self.path[-1] and c == self.path[-1][-1]: continue
                 cpn = self.components[c]["obj"]
                 if cpn.component_name == "Answer":
                     self.answer.append(c)
                 else:
                     if DEBUG: print("RUN: ", c)
+                    if cpn.component_name == "Generate":
+                        cpids = cpn.get_dependent_components()
+                        if any([c not in self.path[-1] for c in cpids]):
+                            continue
                     ans = cpn.run(self.history, **kwargs)
                     self.path[-1].append(c)
-                ran += 1
+            ran += 1
 
         prepare2run(self.components[self.path[-2][-1]]["downstream"])
         while 0 <= ran < len(self.path[-1]):
@@ -220,6 +225,7 @@ class Canvas(ABC):
                             prepare2run([p])
                             break
                     traceback.print_exc()
+                    break
                 continue
 
             try:
@@ -231,6 +237,7 @@ class Canvas(ABC):
                         prepare2run([p])
                         break
                 traceback.print_exc()
+                break
 
         if self.answer:
             cpn_id = self.answer[0]
@@ -253,9 +260,9 @@ class Canvas(ABC):
 
     def get_history(self, window_size):
         convs = []
-        for role, obj in self.history[window_size * -2:]:
+        for role, obj in self.history[window_size * -1:]:
             convs.append({"role": role, "content": (obj if role == "user" else
-                                                    '\n'.join(pd.DataFrame(obj)['content']))})
+                    '\n'.join([str(s) for s in pd.DataFrame(obj)['content']]))})
         return convs
 
     def add_user_input(self, question):
@@ -267,7 +274,7 @@ 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 +300,6 @@ 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 +1,38 @@
+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
+
+
+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 +1,69 @@
+#
+#  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.
+#
+from abc import ABC
+import arxiv
+import pandas as pd
+from agent.settings import DEBUG
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class ArXivParam(ComponentParamBase):
+    """
+    Define the ArXiv component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 6
+        self.sort_by = 'submittedDate'
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+        self.check_valid_value(self.sort_by, "ArXiv Search Sort_by",
+                               ['submittedDate', 'lastUpdatedDate', 'relevance'])
+
+
+class ArXiv(ComponentBase, ABC):
+    component_name = "ArXiv"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return ArXiv.be_output("")
+
+        try:
+            sort_choices = {"relevance": arxiv.SortCriterion.Relevance,
+                            "lastUpdatedDate": arxiv.SortCriterion.LastUpdatedDate,
+                            'submittedDate': arxiv.SortCriterion.SubmittedDate}
+            arxiv_client = arxiv.Client()
+            search = arxiv.Search(
+                query=ans,
+                max_results=self._param.top_n,
+                sort_by=sort_choices[self._param.sort_by]
+            )
+            arxiv_res = [
+                {"content": 'Title: ' + i.title + '\nPdf_Url: <a href="' + i.pdf_url + '"></a> \nSummary: ' + i.summary} for
+                i in list(arxiv_client.results(search))]
+        except Exception as e:
+            return ArXiv.be_output("**ERROR**: " + str(e))
+
+        if not arxiv_res:
+            return ArXiv.be_output("")
+
+        df = pd.DataFrame(arxiv_res)
+        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
+        return df

agent/component/baidu.py

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

agent/component/baidufanyi.py

@@ -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

@@ -23,8 +23,8 @@ from typing import List, Dict, Tuple, Union
 
 import pandas as pd
 
-from graph import settings
-from graph.settings import flow_logger, DEBUG
+from agent import settings
+from agent.settings import flow_logger, DEBUG
 
 _FEEDED_DEPRECATED_PARAMS = "_feeded_deprecated_params"
 _DEPRECATED_PARAMS = "_deprecated_params"
@@ -35,7 +35,7 @@ _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
 
     def set_name(self, name: str):
         self._name = name
@@ -444,7 +444,13 @@ class ComponentBase(ABC):
 
         if DEBUG: print(self.component_name, reversed_cpnts[::-1])
         for u in reversed_cpnts[::-1]:
-            if self.get_component_name(u) in ["switch"]: continue
+            if self.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:
+                    upstream_outs.append(o)
+                    continue
+            if u not in self._canvas.get_component(self._id)["upstream"]: continue
             if self.component_name.lower().find("switch") < 0 \
                     and self.get_component_name(u) in ["relevant", "categorize"]:
                 continue
@@ -454,13 +460,19 @@ class ComponentBase(ABC):
                         upstream_outs.append(pd.DataFrame([{"content": c}]))
                         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:
+                upstream_outs.append(o)
             break
 
-        return pd.concat(upstream_outs, ignore_index=False)
+        if upstream_outs:
+            df = pd.concat(upstream_outs, ignore_index=True)
+            if "content" in df:
+                df = df.drop_duplicates(subset=['content']).reset_index(drop=True)
+            return df
+        return pd.DataFrame(self._canvas.get_history(3)[-1:])
 
     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 +1,85 @@
+#
+#  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.
+#
+from abc import ABC
+import requests
+import pandas as pd
+from agent.settings import DEBUG
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class BingParam(ComponentParamBase):
+    """
+    Define the Bing component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 10
+        self.channel = "Webpages"
+        self.api_key = "YOUR_ACCESS_KEY"
+        self.country = "CN"
+        self.language = "en"
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+        self.check_valid_value(self.channel, "Bing Web Search or Bing News", ["Webpages", "News"])
+        self.check_empty(self.api_key, "Bing subscription key")
+        self.check_valid_value(self.country, "Bing Country",
+                               ['AR', 'AU', 'AT', 'BE', 'BR', 'CA', 'CL', 'DK', 'FI', 'FR', 'DE', 'HK', 'IN', 'ID',
+                                'IT', 'JP', 'KR', 'MY', 'MX', 'NL', 'NZ', 'NO', 'CN', 'PL', 'PT', 'PH', 'RU', 'SA',
+                                'ZA', 'ES', 'SE', 'CH', 'TW', 'TR', 'GB', 'US'])
+        self.check_valid_value(self.language, "Bing Languages",
+                               ['ar', 'eu', 'bn', 'bg', 'ca', 'ns', 'nt', 'hr', 'cs', 'da', 'nl', 'en', 'gb', 'et',
+                                'fi', 'fr', 'gl', 'de', 'gu', 'he', 'hi', 'hu', 'is', 'it', 'jp', 'kn', 'ko', 'lv',
+                                'lt', 'ms', 'ml', 'mr', 'nb', 'pl', 'br', 'pt', 'pa', 'ro', 'ru', 'sr', 'sk', 'sl',
+                                'es', 'sv', 'ta', 'te', 'th', 'tr', 'uk', 'vi'])
+
+
+class Bing(ComponentBase, ABC):
+    component_name = "Bing"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return Bing.be_output("")
+
+        try:
+            headers = {"Ocp-Apim-Subscription-Key": self._param.api_key, 'Accept-Language': self._param.language}
+            params = {"q": ans, "textDecorations": True, "textFormat": "HTML", "cc": self._param.country,
+                      "answerCount": 1, "promote": self._param.channel}
+            if self._param.channel == "Webpages":
+                response = requests.get("https://api.bing.microsoft.com/v7.0/search", headers=headers, params=params)
+                response.raise_for_status()
+                search_results = response.json()
+                bing_res = [{"content": '<a href="' + i["url"] + '">' + i["name"] + '</a>    ' + i["snippet"]} for i in
+                            search_results["webPages"]["value"]]
+            elif self._param.channel == "News":
+                response = requests.get("https://api.bing.microsoft.com/v7.0/news/search", headers=headers,
+                                        params=params)
+                response.raise_for_status()
+                search_results = response.json()
+                bing_res = [{"content": '<a href="' + i["url"] + '">' + i["name"] + '</a>    ' + i["description"]} for i
+                            in search_results['news']['value']]
+        except Exception as e:
+            return Bing.be_output("**ERROR**: " + str(e))
+
+        if not bing_res:
+            return Bing.be_output("")
+
+        df = pd.DataFrame(bing_res)
+        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
+        return df

agent/component/categorize.py

@@ -14,13 +14,10 @@
 #  limitations under the License.
 #
 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
+from agent.settings import DEBUG
 
 
 class CategorizeParam(GenerateParam):
@@ -76,7 +73,7 @@ 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())
@@ -85,6 +82,6 @@ class Categorize(Generate, ABC):
             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/cite.py

@@ -21,7 +21,7 @@ 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 agent.component.base import ComponentBase, ComponentParamBase
 
 
 class CiteParam(ComponentParamBase):

agent/component/concentrator.py

@@ -0,0 +1,36 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from abc import ABC
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class ConcentratorParam(ComponentParamBase):
+    """
+    Define the Concentrator component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+
+    def check(self):
+        return True
+
+
+class Concentrator(ComponentBase, ABC):
+    component_name = "Concentrator"
+
+    def _run(self, history, **kwargs):
+        return Concentrator.be_output("")

agent/component/crawler.py

@@ -0,0 +1,70 @@
+#
+#  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.
+#
+from abc import ABC
+from duckduckgo_search import DDGS
+import pandas as pd
+from agent.settings import DEBUG
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class DuckDuckGoParam(ComponentParamBase):
+    """
+    Define the DuckDuckGo component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 10
+        self.channel = "text"
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+        self.check_valid_value(self.channel, "Web Search or News", ["text", "news"])
+
+
+class DuckDuckGo(ComponentBase, ABC):
+    component_name = "DuckDuckGo"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return DuckDuckGo.be_output("")
+
+        try:
+            if self._param.channel == "text":
+                with DDGS() as ddgs:
+                    # {'title': '', 'href': '', 'body': ''}
+                    duck_res = [{"content": '<a href="' + i["href"] + '">' + i["title"] + '</a>    ' + i["body"]} for i
+                                in ddgs.text(ans, max_results=self._param.top_n)]
+            elif self._param.channel == "news":
+                with DDGS() as ddgs:
+                    # {'date': '', 'title': '', 'body': '', 'url': '', 'image': '', 'source': ''}
+                    duck_res = [{"content": '<a href="' + i["url"] + '">' + i["title"] + '</a>    ' + i["body"]} for i
+                                in ddgs.news(ans, max_results=self._param.top_n)]
+        except Exception as e:
+            return DuckDuckGo.be_output("**ERROR**: " + str(e))
+
+        if not duck_res:
+            return DuckDuckGo.be_output("")
+
+        df = pd.DataFrame(duck_res)
+        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
+        return df

agent/component/exesql.py

@@ -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 +1,162 @@
+#
+#  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.settings import retrievaler
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class GenerateParam(ComponentParamBase):
+    """
+    Define the Generate component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.llm_id = ""
+        self.prompt = ""
+        self.max_tokens = 0
+        self.temperature = 0
+        self.top_p = 0
+        self.presence_penalty = 0
+        self.frequency_penalty = 0
+        self.cite = True
+        self.parameters = []
+
+    def check(self):
+        self.check_decimal_float(self.temperature, "[Generate] Temperature")
+        self.check_decimal_float(self.presence_penalty, "[Generate] Presence penalty")
+        self.check_decimal_float(self.frequency_penalty, "[Generate] Frequency penalty")
+        self.check_nonnegative_number(self.max_tokens, "[Generate] Max tokens")
+        self.check_decimal_float(self.top_p, "[Generate] Top P")
+        self.check_empty(self.llm_id, "[Generate] LLM")
+        # self.check_defined_type(self.parameters, "Parameters", ["list"])
+
+    def gen_conf(self):
+        conf = {}
+        if self.max_tokens > 0: conf["max_tokens"] = self.max_tokens
+        if self.temperature > 0: conf["temperature"] = self.temperature
+        if self.top_p > 0: conf["top_p"] = self.top_p
+        if self.presence_penalty > 0: conf["presence_penalty"] = self.presence_penalty
+        if self.frequency_penalty > 0: conf["frequency_penalty"] = self.frequency_penalty
+        return conf
+
+
+class Generate(ComponentBase):
+    component_name = "Generate"
+
+    def get_dependent_components(self):
+        cpnts = [para["component_id"] for para in self._param.parameters]
+        return cpnts
+
+    def set_cite(self, retrieval_res, answer):
+        retrieval_res = retrieval_res.dropna(subset=["vector", "content_ltks"]).reset_index(drop=True)
+        if "empty_response" in retrieval_res.columns:
+            retrieval_res["empty_response"].fillna("", inplace=True)
+        answer, idx = retrievaler.insert_citations(answer, [ck["content_ltks"] for _, ck in retrieval_res.iterrows()],
+                                                   [ck["vector"] for _, ck in retrieval_res.iterrows()],
+                                                   LLMBundle(self._canvas.get_tenant_id(), LLMType.EMBEDDING,
+                                                             self._canvas.get_embedding_model()), tkweight=0.7,
+                                                   vtweight=0.3)
+        doc_ids = set([])
+        recall_docs = []
+        for i in idx:
+            did = retrieval_res.loc[int(i), "doc_id"]
+            if did in doc_ids: continue
+            doc_ids.add(did)
+            recall_docs.append({"doc_id": did, "doc_name": retrieval_res.loc[int(i), "docnm_kwd"]})
+
+        del retrieval_res["vector"]
+        del retrieval_res["content_ltks"]
+
+        reference = {
+            "chunks": [ck.to_dict() for _, ck in retrieval_res.iterrows()],
+            "doc_aggs": recall_docs
+        }
+
+        if answer.lower().find("invalid key") >= 0 or answer.lower().find("invalid api") >= 0:
+            answer += " Please set LLM API-Key in 'User Setting -> Model Providers -> API-Key'"
+        res = {"content": answer, "reference": reference}
+
+        return res
+
+    def _run(self, history, **kwargs):
+        chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id)
+        prompt = self._param.prompt
+
+        retrieval_res = self.get_input()
+        input = ("  - "+"\n  - ".join([c for c in retrieval_res["content"] if isinstance(c, str)])) if "content" in retrieval_res else ""
+        for para in self._param.parameters:
+            cpn = self._canvas.get_component(para["component_id"])["obj"]
+            if cpn.component_name.lower() == "answer":
+                kwargs[para["key"]] = self._canvas.get_history(1)[0]["content"]
+                continue
+            _, out = cpn.output(allow_partial=False)
+            if "content" not in out.columns:
+                kwargs[para["key"]] = "Nothing"
+            else:
+                kwargs[para["key"]] = "  - "+"\n - ".join([o if isinstance(o, str) else str(o) for o in out["content"]])
+
+        kwargs["input"] = input
+        for n, v in kwargs.items():
+            prompt = re.sub(r"\{%s\}" % re.escape(n), re.escape(str(v)), prompt)
+
+        downstreams = self._canvas.get_component(self._id)["downstream"]
+        if kwargs.get("stream") and len(downstreams) == 1 and self._canvas.get_component(downstreams[0])[
+            "obj"].component_name.lower() == "answer":
+            return partial(self.stream_output, chat_mdl, prompt, retrieval_res)
+
+        if "empty_response" in retrieval_res.columns and not "".join(retrieval_res["content"]):
+            res = {"content": "\n- ".join(retrieval_res["empty_response"]) if "\n- ".join(
+                retrieval_res["empty_response"]) else "Nothing found in knowledgebase!", "reference": []}
+            return pd.DataFrame([res])
+
+        msg = self._canvas.get_history(self._param.message_history_window_size)
+        _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(chat_mdl.max_length * 0.97))
+        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)
+        _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(chat_mdl.max_length * 0.97))
+        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.
+#
+from abc import ABC
+import pandas as pd
+import requests
+from agent.settings import DEBUG
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class GitHubParam(ComponentParamBase):
+    """
+    Define the GitHub component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 10
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+
+
+class GitHub(ComponentBase, ABC):
+    component_name = "GitHub"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return GitHub.be_output("")
+
+        try:
+            url = 'https://api.github.com/search/repositories?q=' + ans + '&sort=stars&order=desc&per_page=' + str(
+                self._param.top_n)
+            headers = {"Content-Type": "application/vnd.github+json", "X-GitHub-Api-Version": '2022-11-28'}
+            response = requests.get(url=url, headers=headers).json()
+
+            github_res = [{"content": '<a href="' + i["html_url"] + '">' + i["name"] + '</a>' + str(
+                i["description"]) + '\n stars:' + str(i['watchers'])} for i in response['items']]
+        except Exception as e:
+            return GitHub.be_output("**ERROR**: " + str(e))
+
+        if not github_res:
+            return GitHub.be_output("")
+
+        df = pd.DataFrame(github_res)
+        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
+        return df

agent/component/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.
+#
+from abc import ABC
+from serpapi import GoogleSearch
+import pandas as pd
+from agent.settings import DEBUG
+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 as e:
+            return Google.be_output("**ERROR**: Existing Unavailable Parameters!")
+
+        if not google_res:
+            return Google.be_output("")
+
+        df = pd.DataFrame(google_res)
+        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
+        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.
+#
+from abc import ABC
+import pandas as pd
+from agent.settings import DEBUG
+from agent.component.base import ComponentBase, ComponentParamBase
+from scholarly import scholarly
+
+
+class GoogleScholarParam(ComponentParamBase):
+    """
+    Define the GoogleScholar component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 6
+        self.sort_by = 'relevance'
+        self.year_low = None
+        self.year_high = None
+        self.patents = True
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+        self.check_valid_value(self.sort_by, "GoogleScholar Sort_by", ['date', 'relevance'])
+        self.check_boolean(self.patents, "Whether or not to include patents, defaults to True")
+
+
+class GoogleScholar(ComponentBase, ABC):
+    component_name = "GoogleScholar"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return GoogleScholar.be_output("")
+
+        scholar_client = scholarly.search_pubs(ans, patents=self._param.patents, year_low=self._param.year_low,
+                                               year_high=self._param.year_high, sort_by=self._param.sort_by)
+        scholar_res = []
+        for i in range(self._param.top_n):
+            try:
+                pub = next(scholar_client)
+                scholar_res.append({"content": 'Title: ' + pub['bib']['title'] + '\n_Url: <a href="' + pub[
+                    'pub_url'] + '"></a> ' + "\n author: " + ",".join(pub['bib']['author']) + '\n Abstract: ' + pub[
+                                                   'bib'].get('abstract', 'no abstract')})
+
+            except StopIteration or Exception as e:
+                print("**ERROR** " + str(e))
+                break
+
+        if not scholar_res:
+            return GoogleScholar.be_output("")
+
+        df = pd.DataFrame(scholar_res)
+        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
+        return df

agent/component/invoke.py

@@ -0,0 +1,103 @@
+#
+#  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.
+#
+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"]
+                _, 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

@@ -17,8 +17,8 @@ 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
+from agent.settings import DEBUG
 
 
 class KeywordExtractParam(GenerateParam):

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.
+#
+from abc import ABC
+from Bio import Entrez
+import re
+import pandas as pd
+import xml.etree.ElementTree as ET
+from agent.settings import DEBUG
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class PubMedParam(ComponentParamBase):
+    """
+    Define the PubMed component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 5
+        self.email = "A.N.Other@example.com"
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+
+
+class PubMed(ComponentBase, ABC):
+    component_name = "PubMed"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return PubMed.be_output("")
+
+        try:
+            Entrez.email = self._param.email
+            pubmedids = Entrez.read(Entrez.esearch(db='pubmed', retmax=self._param.top_n, term=ans))['IdList']
+            pubmedcnt = ET.fromstring(re.sub(r'<(/?)b>|<(/?)i>', '', Entrez.efetch(db='pubmed', id=",".join(pubmedids),
+                                                                                   retmode="xml").read().decode(
+                "utf-8")))
+            pubmed_res = [{"content": 'Title:' + child.find("MedlineCitation").find("Article").find(
+                "ArticleTitle").text + '\nUrl:<a href=" https://pubmed.ncbi.nlm.nih.gov/' + child.find(
+                "MedlineCitation").find("PMID").text + '">' + '</a>\n' + 'Abstract:' + (
+                                          child.find("MedlineCitation").find("Article").find("Abstract").find(
+                                              "AbstractText").text if child.find("MedlineCitation").find(
+                                              "Article").find("Abstract") else "No abstract available")} for child in
+                          pubmedcnt.findall("PubmedArticle")]
+        except Exception as e:
+            return PubMed.be_output("**ERROR**: " + str(e))
+
+        if not pubmed_res:
+            return PubMed.be_output("")
+
+        df = pd.DataFrame(pubmed_res)
+        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
+        return df

agent/component/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

@@ -16,7 +16,7 @@
 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
 
 

agent/component/retrieval.py

@@ -21,7 +21,7 @@ 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 agent.component.base import ComponentBase, ComponentParamBase
 
 
 class RetrievalParam(ComponentParamBase):
@@ -43,22 +43,19 @@ 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."
 
@@ -75,8 +72,9 @@ class Retrieval(ComponentBase, ABC):
                                         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"])

agent/component/rewrite.py

@@ -16,7 +16,7 @@
 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 +33,7 @@ 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.     
@@ -43,6 +43,40 @@ class RewriteQuestionParam(GenerateParam):
         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
 
 
@@ -54,17 +88,21 @@ 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, ":::::::::::::::::::::::::::::::::")
         return RewriteQuestion.be_output(ans)

agent/component/switch.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.
+#
+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 _run(self, history, **kwargs):
+        for cond in self._param.conditions:
+            res = []
+            for item in cond["items"]:
+                out = self._canvas.get_component(item["cpn_id"])["obj"].output()[1]
+                cpn_input = "" if "content" not in out.columns else " ".join(out["content"])
+                res.append(self.process_operator(cpn_input, item["operator"], item["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/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 +1,69 @@
+#
+#  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 random
+from abc import ABC
+from functools import partial
+import wikipedia
+import pandas as pd
+from agent.settings import DEBUG
+from agent.component.base import ComponentBase, ComponentParamBase
+
+
+class WikipediaParam(ComponentParamBase):
+    """
+    Define the Wikipedia component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 10
+        self.language = "en"
+
+    def check(self):
+        self.check_positive_integer(self.top_n, "Top N")
+        self.check_valid_value(self.language, "Wikipedia languages",
+                               ['af', 'pl', 'ar', 'ast', 'az', 'bg', 'nan', 'bn', 'be', 'ca', 'cs', 'cy', 'da', 'de',
+                                'et', 'el', 'en', 'es', 'eo', 'eu', 'fa', 'fr', 'gl', 'ko', 'hy', 'hi', 'hr', 'id',
+                                'it', 'he', 'ka', 'lld', 'la', 'lv', 'lt', 'hu', 'mk', 'arz', 'ms', 'min', 'my', 'nl',
+                                'ja', 'nb', 'nn', 'ce', 'uz', 'pt', 'kk', 'ro', 'ru', 'ceb', 'sk', 'sl', 'sr', 'sh',
+                                'fi', 'sv', 'ta', 'tt', 'th', 'tg', 'azb', 'tr', 'uk', 'ur', 'vi', 'war', 'zh', 'yue'])
+
+
+class Wikipedia(ComponentBase, ABC):
+    component_name = "Wikipedia"
+
+    def _run(self, history, **kwargs):
+        ans = self.get_input()
+        ans = " - ".join(ans["content"]) if "content" in ans else ""
+        if not ans:
+            return Wikipedia.be_output("")
+
+        try:
+            wiki_res = []
+            wikipedia.set_lang(self._param.language)
+            wiki_engine = wikipedia
+            for wiki_key in wiki_engine.search(ans, results=self._param.top_n):
+                page = wiki_engine.page(title=wiki_key, auto_suggest=False)
+                wiki_res.append({"content": '<a href="' + page.url + '">' + page.title + '</a> ' + page.summary})
+        except Exception as e:
+            return Wikipedia.be_output("**ERROR**: " + str(e))
+
+        if not wiki_res:
+            return Wikipedia.be_output("")
+
+        df = pd.DataFrame(wiki_res)
+        if DEBUG: print(df, ":::::::::::::::::::::::::::::::::")
+        return df

agent/component/yahoofinance.py

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

agent/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

@@ -1,124 +1,126 @@
-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import logging
-import os
-import sys
-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 api.db import StatusEnum
-from api.db.db_models import close_connection
-from api.db.services import UserService
-from api.utils import CustomJSONEncoder
-
-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.utils.api_utils import server_error_response
-from itsdangerous.url_safe import URLSafeTimedSerializer as Serializer
-
-__all__ = ['app']
-
-
-logger = logging.getLogger('flask.app')
-for h in access_logger.handlers:
-    logger.addHandler(h)
-
-Request.json = property(lambda self: self.get_json(force=True, silent=True))
-
-app = Flask(__name__)
-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))
-
-Session(app)
-login_manager = LoginManager()
-login_manager.init_app(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.extend(api_path_list)
-    return app_path_list
-
-
-def register_page(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,))
-
-    spec = spec_from_file_location(module_name, page_path)
-    page = module_from_spec(spec)
-    page.app = app
-    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}'
-
-    app.register_blueprint(page.manager, url_prefix=url_prefix)
-    return url_prefix
-
-
-pages_dir = [
-    Path(__file__).parent,
-    Path(__file__).parent.parent / 'api' / 'apps', # FIXME: ragflow/api/api/apps, can be remove?
-]
-
-client_urls_prefix = [
-    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)
-    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)
-            if user:
-                return user[0]
-            else:
-                return None
-        except Exception as e:
-            stat_logger.exception(e)
-            return None
-    else:
-        return None
-
-
-@app.teardown_request
-def _db_close(exc):
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+import logging
+import os
+import sys
+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 api.db import StatusEnum
+from api.db.db_models import close_connection
+from api.db.services import UserService
+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.utils.api_utils import server_error_response
+from itsdangerous.url_safe import URLSafeTimedSerializer as Serializer
+
+__all__ = ['app']
+
+
+logger = logging.getLogger('flask.app')
+for h in access_logger.handlers:
+    logger.addHandler(h)
+
+Request.json = property(lambda self: self.get_json(force=True, silent=True))
+
+app = Flask(__name__)
+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))
+
+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('*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}'
+
+    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)
+    page.app = app
+    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}' if "/sdk/" in path else f'/{API_VERSION}/{page_name}'
+
+    app.register_blueprint(page.manager, url_prefix=url_prefix)
+    return url_prefix
+
+
+pages_dir = [
+    Path(__file__).parent,
+    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)
+]
+
+
+@login_manager.request_loader
+def load_user(web_request):
+    jwt = Serializer(secret_key=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)
+            if user:
+                return user[0]
+            else:
+                return None
+        except Exception as e:
+            stat_logger.exception(e)
+            return None
+    else:
+        return None
+
+
+@app.teardown_request
+def _db_close(exc):
     close_connection()

api/apps/canvas_app.py

@@ -15,15 +15,14 @@
 #
 import json
 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 +44,10 @@ 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, retmsg=f'Only owner of canvas authorized for this operation.',
+                retcode=RetCode.OPERATING_ERROR)
         UserCanvasService.delete_by_id(i)
     return get_json_result(data=True)
 
@@ -63,10 +66,13 @@ 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(retmsg="Fail to save canvas.")
     else:
+        if not UserCanvasService.query(user_id=current_user.id, id=req["id"]):
+            return get_json_result(
+                data=False, retmsg=f'Only owner of canvas authorized for this operation.',
+                retcode=RetCode.OPERATING_ERROR)
         UserCanvasService.update_by_id(req["id"], req)
-
     return get_json_result(data=req)
 
 
@@ -75,7 +81,7 @@ 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(retmsg="canvas not found.")
     return get_json_result(data=c.to_dict())
 
 
@@ -87,26 +93,35 @@ 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(retmsg="canvas not found.")
+    if not UserCanvasService.query(user_id=current_user.id, id=req["id"]):
+        return get_json_result(
+            data=False, retmsg=f'Only owner of canvas authorized for this operation.',
+            retcode=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})
+            if len([m for m in canvas.messages if m["role"] == "user"]) > 1:
+                #ten = TenantService.get_info_by(current_user.id)[0]
+                #req["message"] = full_question(ten["tenant_id"], ten["llm_id"], canvas.messages)
+                pass
             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?"
+    assert answer is not None, "Nothing. Is it over?"
 
     if stream:
-        assert isinstance(answer, partial)
+        assert isinstance(answer, partial), "Nothing. Is it over?"
 
         def sse():
             nonlocal answer, cvs
@@ -117,7 +132,7 @@ def run():
                     ans = {"answer": ans["content"], "reference": ans.get("reference", [])}
                     yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "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})
                 if final_ans.get("reference"):
                     canvas.reference.append(final_ans["reference"])
                 cvs.dsl = json.loads(str(canvas))
@@ -135,12 +150,13 @@ 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"]})
+    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 +167,11 @@ 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(retmsg="canvas not found.")
+        if not UserCanvasService.query(user_id=current_user.id, id=req["id"]):
+            return get_json_result(
+                data=False, retmsg=f'Only owner of canvas authorized for this operation.',
+                retcode=RetCode.OPERATING_ERROR)
 
         canvas = Canvas(json.dumps(user_canvas.dsl), current_user.id)
         canvas.reset()
@@ -160,3 +180,22 @@ 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

@@ -1,287 +1,347 @@
-#
-#  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 datetime
-
-from flask import request
-from flask_login import login_required, current_user
-from elasticsearch_dsl import Q
-
-from rag.app.qa import rmPrefix, beAdoc
-from rag.nlp import search, rag_tokenizer, keyword_extraction
-from rag.utils.es_conn import ELASTICSEARCH
-from rag.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.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.utils.api_utils import get_json_result
-import hashlib
-import re
-
-
-@manager.route('/list', methods=['POST'])
-@login_required
-@validate_request("doc_id")
-def list_chunk():
-    req = request.json
-    doc_id = req["doc_id"]
-    page = int(req.get("page", 1))
-    size = int(req.get("size", 30))
-    question = req.get("keywords", "")
-    try:
-        tenant_id = DocumentService.get_tenant_id(req["doc_id"])
-        if not tenant_id:
-            return get_data_error_result(retmsg="Tenant not found!")
-        e, doc = DocumentService.get_by_id(doc_id)
-        if not e:
-            return get_data_error_result(retmsg="Document not found!")
-        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))
-        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", ""),
-                "doc_id": sres.field[id]["doc_id"],
-                "docnm_kwd": sres.field[id]["docnm_kwd"],
-                "important_kwd": sres.field[id].get("important_kwd", []),
-                "img_id": sres.field[id].get("img_id", ""),
-                "available_int": sres.field[id].get("available_int", 1),
-                "positions": sres.field[id].get("position_int", "").split("\t")
-            }
-            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
-            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 server_error_response(e)
-
-
-@manager.route('/get', methods=['GET'])
-@login_required
-def get():
-    chunk_id = request.args["chunk_id"]
-    try:
-        tenants = UserTenantService.query(user_id=current_user.id)
-        if not tenants:
-            return get_data_error_result(retmsg="Tenant not found!")
-        res = ELASTICSEARCH.get(
-            chunk_id, search.index_name(
-                tenants[0].tenant_id))
-        if not res.get("found"):
-            return server_error_response("Chunk not found")
-        id = res["_id"]
-        res = res["_source"]
-        res["chunk_id"] = id
-        k = []
-        for n in res.keys():
-            if re.search(r"(_vec$|_sm_|_tks|_ltks)", n):
-                k.append(n)
-        for n in k:
-            del res[n]
-
-        return get_json_result(data=res)
-    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 server_error_response(e)
-
-
-@manager.route('/set', methods=['POST'])
-@login_required
-@validate_request("doc_id", "chunk_id", "content_with_weight",
-                  "important_kwd")
-def set():
-    req = request.json
-    d = {
-        "id": req["chunk_id"],
-        "content_with_weight": req["content_with_weight"]}
-    d["content_ltks"] = rag_tokenizer.tokenize(req["content_with_weight"])
-    d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
-    d["important_kwd"] = req["important_kwd"]
-    d["important_tks"] = rag_tokenizer.tokenize(" ".join(req["important_kwd"]))
-    if "available_int" in req:
-        d["available_int"] = req["available_int"]
-
-    try:
-        tenant_id = DocumentService.get_tenant_id(req["doc_id"])
-        if not tenant_id:
-            return get_data_error_result(retmsg="Tenant not found!")
-        
-        embd_id = DocumentService.get_embd_id(req["doc_id"])
-        embd_mdl = TenantLLMService.model_instance(
-            tenant_id, LLMType.EMBEDDING.value, embd_id)
-        
-        e, doc = DocumentService.get_by_id(req["doc_id"])
-        if not e:
-            return get_data_error_result(retmsg="Document not found!")
-
-        if doc.parser_id == ParserType.QA:
-            arr = [
-                t for t in re.split(
-                    r"[\n\t]",
-                    req["content_with_weight"]) if len(t) > 1]
-            if len(arr) != 2:
-                return get_data_error_result(
-                    retmsg="Q&A must be separated by TAB/ENTER key.")
-            q, a = rmPrefix(arr[0]), rmPrefix(arr[1])
-            d = beAdoc(d, arr[0], arr[1], not any(
-                [rag_tokenizer.is_chinese(t) for t in q + a]))
-
-        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))
-        return get_json_result(data=True)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/switch', methods=['POST'])
-@login_required
-@validate_request("chunk_ids", "available_int", "doc_id")
-def switch():
-    req = request.json
-    try:
-        tenant_id = DocumentService.get_tenant_id(req["doc_id"])
-        if not tenant_id:
-            return get_data_error_result(retmsg="Tenant not found!")
-        if not ELASTICSEARCH.upsert([{"id": i, "available_int": int(req["available_int"])} for i in req["chunk_ids"]],
-                                    search.index_name(tenant_id)):
-            return get_data_error_result(retmsg="Index updating failure")
-        return get_json_result(data=True)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/rm', methods=['POST'])
-@login_required
-@validate_request("chunk_ids")
-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")
-        return get_json_result(data=True)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/create', methods=['POST'])
-@login_required
-@validate_request("doc_id", "content_with_weight")
-def create():
-    req = request.json
-    md5 = hashlib.md5()
-    md5.update((req["content_with_weight"] + req["doc_id"]).encode("utf-8"))
-    chunck_id = md5.hexdigest()
-    d = {"id": chunck_id, "content_ltks": rag_tokenizer.tokenize(req["content_with_weight"]),
-         "content_with_weight": req["content_with_weight"]}
-    d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
-    d["important_kwd"] = req.get("important_kwd", [])
-    d["important_tks"] = rag_tokenizer.tokenize(" ".join(req.get("important_kwd", [])))
-    d["create_time"] = str(datetime.datetime.now()).replace("T", " ")[:19]
-    d["create_timestamp_flt"] = datetime.datetime.now().timestamp()
-
-    try:
-        e, doc = DocumentService.get_by_id(req["doc_id"])
-        if not e:
-            return get_data_error_result(retmsg="Document not found!")
-        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!")
-        
-        embd_id = DocumentService.get_embd_id(req["doc_id"])
-        embd_mdl = TenantLLMService.model_instance(
-            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))
-
-        DocumentService.increment_chunk_num(
-            doc.id, doc.kb_id, c, 1, 0)
-        return get_json_result(data={"chunk_id": chunck_id})
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/retrieval_test', methods=['POST'])
-@login_required
-@validate_request("kb_id", "question")
-def retrieval_test():
-    req = request.json
-    page = int(req.get("page", 1))
-    size = int(req.get("size", 30))
-    question = req["question"]
-    kb_id = req["kb_id"]
-    doc_ids = req.get("doc_ids", [])
-    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:
-        e, kb = KnowledgebaseService.get_by_id(kb_id)
-        if not e:
-            return get_data_error_result(retmsg="Knowledgebase not found!")
-
-        embd_mdl = TenantLLMService.model_instance(
-            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"])
-
-        if req.get("keyword", False):
-            chat_mdl = TenantLLMService.model_instance(kb.tenant_id, LLMType.CHAT)
-            question += keyword_extraction(chat_mdl, question)
-
-        ranks = retrievaler.retrieval(question, embd_mdl, kb.tenant_id, [kb_id], page, size,
-                                      similarity_threshold, vector_similarity_weight, top,
-                                      doc_ids, rerank_mdl=rerank_mdl)
-        for c in ranks["chunks"]:
-            if "vector" in c:
-                del c["vector"]
-
-        return get_json_result(data=ranks)
-    except Exception as e:
-        if str(e).find("not_found") > 0:
-            return get_json_result(data=False, retmsg=f'No chunk found! Check the chunk status please!',
-                                   retcode=RetCode.DATA_ERROR)
-        return server_error_response(e)
+#
+#  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 datetime
+import json
+import traceback
+
+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
+from rag.utils.es_conn import ELASTICSEARCH
+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 LLMBundle
+from api.db.services.user_service import UserTenantService
+from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
+from api.db.services.document_service import DocumentService
+from api.settings import RetCode, retrievaler, kg_retrievaler
+from api.utils.api_utils import get_json_result
+import hashlib
+import re
+
+
+@manager.route('/list', methods=['POST'])
+@login_required
+@validate_request("doc_id")
+def list_chunk():
+    req = request.json
+    doc_id = req["doc_id"]
+    page = int(req.get("page", 1))
+    size = int(req.get("size", 30))
+    question = req.get("keywords", "")
+    try:
+        tenant_id = DocumentService.get_tenant_id(req["doc_id"])
+        if not tenant_id:
+            return get_data_error_result(retmsg="Tenant not found!")
+        e, doc = DocumentService.get_by_id(doc_id)
+        if not e:
+            return get_data_error_result(retmsg="Document not found!")
+        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), 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", ""),
+                "doc_id": sres.field[id]["doc_id"],
+                "docnm_kwd": sres.field[id]["docnm_kwd"],
+                "important_kwd": sres.field[id].get("important_kwd", []),
+                "img_id": sres.field[id].get("img_id", ""),
+                "available_int": sres.field[id].get("available_int", 1),
+                "positions": sres.field[id].get("position_int", "").split("\t")
+            }
+            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
+            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 server_error_response(e)
+
+
+@manager.route('/get', methods=['GET'])
+@login_required
+def get():
+    chunk_id = request.args["chunk_id"]
+    try:
+        tenants = UserTenantService.query(user_id=current_user.id)
+        if not tenants:
+            return get_data_error_result(retmsg="Tenant not found!")
+        res = ELASTICSEARCH.get(
+            chunk_id, search.index_name(
+                tenants[0].tenant_id))
+        if not res.get("found"):
+            return server_error_response("Chunk not found")
+        id = res["_id"]
+        res = res["_source"]
+        res["chunk_id"] = id
+        k = []
+        for n in res.keys():
+            if re.search(r"(_vec$|_sm_|_tks|_ltks)", n):
+                k.append(n)
+        for n in k:
+            del res[n]
+
+        return get_json_result(data=res)
+    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 server_error_response(e)
+
+
+@manager.route('/set', methods=['POST'])
+@login_required
+@validate_request("doc_id", "chunk_id", "content_with_weight",
+                  "important_kwd")
+def set():
+    req = request.json
+    d = {
+        "id": req["chunk_id"],
+        "content_with_weight": req["content_with_weight"]}
+    d["content_ltks"] = rag_tokenizer.tokenize(req["content_with_weight"])
+    d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
+    d["important_kwd"] = req["important_kwd"]
+    d["important_tks"] = rag_tokenizer.tokenize(" ".join(req["important_kwd"]))
+    if "available_int" in req:
+        d["available_int"] = req["available_int"]
+
+    try:
+        tenant_id = DocumentService.get_tenant_id(req["doc_id"])
+        if not tenant_id:
+            return get_data_error_result(retmsg="Tenant not found!")
+
+        embd_id = DocumentService.get_embd_id(req["doc_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!")
+
+        if doc.parser_id == ParserType.QA:
+            arr = [
+                t for t in re.split(
+                    r"[\n\t]",
+                    req["content_with_weight"]) if len(t) > 1]
+            if len(arr) != 2:
+                return get_data_error_result(
+                    retmsg="Q&A must be separated by TAB/ENTER key.")
+            q, a = rmPrefix(arr[0]), rmPrefix(arr[1])
+            d = beAdoc(d, arr[0], arr[1], not any(
+                [rag_tokenizer.is_chinese(t) for t in q + a]))
+
+        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))
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/switch', methods=['POST'])
+@login_required
+@validate_request("chunk_ids", "available_int", "doc_id")
+def switch():
+    req = request.json
+    try:
+        tenant_id = DocumentService.get_tenant_id(req["doc_id"])
+        if not tenant_id:
+            return get_data_error_result(retmsg="Tenant not found!")
+        if not ELASTICSEARCH.upsert([{"id": i, "available_int": int(req["available_int"])} for i in req["chunk_ids"]],
+                                    search.index_name(tenant_id)):
+            return get_data_error_result(retmsg="Index updating failure")
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/rm', methods=['POST'])
+@login_required
+@validate_request("chunk_ids", "doc_id")
+def rm():
+    req = request.json
+    try:
+        if not ELASTICSEARCH.deleteByQuery(
+                Q("ids", values=req["chunk_ids"]), search.index_name(current_user.id)):
+            return get_data_error_result(retmsg="Index updating failure")
+        e, doc = DocumentService.get_by_id(req["doc_id"])
+        if not e:
+            return get_data_error_result(retmsg="Document not found!")
+        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)
+
+
+@manager.route('/create', methods=['POST'])
+@login_required
+@validate_request("doc_id", "content_with_weight")
+def create():
+    req = request.json
+    md5 = hashlib.md5()
+    md5.update((req["content_with_weight"] + req["doc_id"]).encode("utf-8"))
+    chunck_id = md5.hexdigest()
+    d = {"id": chunck_id, "content_ltks": rag_tokenizer.tokenize(req["content_with_weight"]),
+         "content_with_weight": req["content_with_weight"]}
+    d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
+    d["important_kwd"] = req.get("important_kwd", [])
+    d["important_tks"] = rag_tokenizer.tokenize(" ".join(req.get("important_kwd", [])))
+    d["create_time"] = str(datetime.datetime.now()).replace("T", " ")[:19]
+    d["create_timestamp_flt"] = datetime.datetime.now().timestamp()
+
+    try:
+        e, doc = DocumentService.get_by_id(req["doc_id"])
+        if not e:
+            return get_data_error_result(retmsg="Document not found!")
+        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!")
+
+        embd_id = DocumentService.get_embd_id(req["doc_id"])
+        embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING.value, embd_id)
+
+        v, c = embd_mdl.encode([doc.name, req["content_with_weight"]])
+        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))
+
+        DocumentService.increment_chunk_num(
+            doc.id, doc.kb_id, c, 1, 0)
+        return get_json_result(data={"chunk_id": chunck_id})
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/retrieval_test', methods=['POST'])
+@login_required
+@validate_request("kb_id", "question")
+def retrieval_test():
+    req = request.json
+    page = int(req.get("page", 1))
+    size = int(req.get("size", 30))
+    question = req["question"]
+    kb_id = req["kb_id"]
+    if isinstance(kb_id, str): kb_id = [kb_id]
+    doc_ids = req.get("doc_ids", [])
+    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:
+        tenants = UserTenantService.query(user_id=current_user.id)
+        for kid in kb_id:
+            for tenant in tenants:
+                if KnowledgebaseService.query(
+                        tenant_id=tenant.tenant_id, id=kid):
+                    break
+            else:
+                return get_json_result(
+                    data=False, retmsg=f'Only owner of knowledgebase authorized for this operation.',
+                    retcode=RetCode.OPERATING_ERROR)
+
+        e, kb = KnowledgebaseService.get_by_id(kb_id[0])
+        if not e:
+            return get_data_error_result(retmsg="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 = LLMBundle(kb.tenant_id, LLMType.RERANK.value, llm_name=req["rerank_id"])
+
+        if req.get("keyword", False):
+            chat_mdl = LLMBundle(kb.tenant_id, LLMType.CHAT)
+            question += keyword_extraction(chat_mdl, question)
+
+        retr = retrievaler if kb.parser_id != ParserType.KG else kg_retrievaler
+        ranks = retr.retrieval(question, embd_mdl, kb.tenant_id, kb_id, page, size,
+                               similarity_threshold, vector_similarity_weight, top,
+                               doc_ids, rerank_mdl=rerank_mdl, highlight=req.get("highlight"))
+        for c in ranks["chunks"]:
+            if "vector" in c:
+                del c["vector"]
+
+        return get_json_result(data=ranks)
+    except Exception as e:
+        if str(e).find("not_found") > 0:
+            return get_json_result(data=False, retmsg=f'No chunk found! Check the chunk status please!',
+                                   retcode=RetCode.DATA_ERROR)
+        return server_error_response(e)
+
+
+@manager.route('/knowledge_graph', methods=['GET'])
+@login_required
+def knowledge_graph():
+    doc_id = request.args["doc_id"]
+    req = {
+        "doc_ids":[doc_id],
+        "knowledge_graph_kwd": ["graph", "mind_map"]
+    }
+    tenant_id = DocumentService.get_tenant_id(doc_id)
+    sres = retrievaler.search(req, search.index_name(tenant_id))
+    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 as e:
+            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

@@ -1,175 +1,376 @@
-#
-#  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 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
-
-
-@manager.route('/set', methods=['POST'])
-@login_required
-def set_conversation():
-    req = request.json
-    conv_id = req.get("conversation_id")
-    if conv_id:
-        del req["conversation_id"]
-        try:
-            if not ConversationService.update_by_id(conv_id, req):
-                return get_data_error_result(retmsg="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!")
-            conv = conv.to_dict()
-            return get_json_result(data=conv)
-        except Exception as e:
-            return server_error_response(e)
-
-    try:
-        e, dia = DialogService.get_by_id(req["dialog_id"])
-        if not e:
-            return get_data_error_result(retmsg="Dialog not found")
-        conv = {
-            "id": get_uuid(),
-            "dialog_id": req["dialog_id"],
-            "name": req.get("name", "New conversation"),
-            "message": [{"role": "assistant", "content": dia.prompt_config["prologue"]}]
-        }
-        ConversationService.save(**conv)
-        e, conv = ConversationService.get_by_id(conv["id"])
-        if not e:
-            return get_data_error_result(retmsg="Fail to new a conversation!")
-        conv = conv.to_dict()
-        return get_json_result(data=conv)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/get', methods=['GET'])
-@login_required
-def get():
-    conv_id = request.args["conversation_id"]
-    try:
-        e, conv = ConversationService.get_by_id(conv_id)
-        if not e:
-            return get_data_error_result(retmsg="Conversation not found!")
-        conv = conv.to_dict()
-        return get_json_result(data=conv)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/rm', methods=['POST'])
-@login_required
-def rm():
-    conv_ids = request.json["conversation_ids"]
-    try:
-        for cid in conv_ids:
-            ConversationService.delete_by_id(cid)
-        return get_json_result(data=True)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/list', methods=['GET'])
-@login_required
-def list_convsersation():
-    dialog_id = request.args["dialog_id"]
-    try:
-        convs = ConversationService.query(
-            dialog_id=dialog_id,
-            order_by=ConversationService.model.create_time,
-            reverse=True)
-        convs = [d.to_dict() for d in convs]
-        return get_json_result(data=convs)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/completion', methods=['POST'])
-@login_required
-#@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"]})
-    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]))
-        e, dia = DialogService.get_by_id(conv.dialog_id)
-        if not e:
-            return get_data_error_result(retmsg="Dialog not found!")
-        del req["conversation_id"]
-        del req["messages"]
-
-        if not conv.reference:
-            conv.reference = []
-        conv.message.append({"role": "assistant", "content": ""})
-        conv.reference.append({"chunks": [], "doc_aggs": []})
-
-        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 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"
-                ConversationService.update_by_id(conv.id, conv.to_dict())
-            except Exception as e:
-                yield "data:" + json.dumps({"retcode": 500, "retmsg": str(e),
-                                            "data": {"answer": "**ERROR**: "+str(e), "reference": []}},
-                                           ensure_ascii=False) + "\n\n"
-            yield "data:"+json.dumps({"retcode": 0, "retmsg": "", "data": True}, ensure_ascii=False) + "\n\n"
-
-        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_json_result(data=answer)
-    except Exception as e:
-        return server_error_response(e)
-
+#
+#  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
+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.settings import RetCode, retrievaler
+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'])
+@login_required
+def set_conversation():
+    req = request.json
+    conv_id = req.get("conversation_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!")
+            e, conv = ConversationService.get_by_id(conv_id)
+            if not e:
+                return get_data_error_result(
+                    retmsg="Fail to update a conversation!")
+            conv = conv.to_dict()
+            return get_json_result(data=conv)
+        except Exception as e:
+            return server_error_response(e)
+
+    try:
+        e, dia = DialogService.get_by_id(req["dialog_id"])
+        if not e:
+            return get_data_error_result(retmsg="Dialog not found")
+        conv = {
+            "id": conv_id,
+            "dialog_id": req["dialog_id"],
+            "name": req.get("name", "New conversation"),
+            "message": [{"role": "assistant", "content": dia.prompt_config["prologue"]}]
+        }
+        ConversationService.save(**conv)
+        e, conv = ConversationService.get_by_id(conv["id"])
+        if not e:
+            return get_data_error_result(retmsg="Fail to new a conversation!")
+        conv = conv.to_dict()
+        return get_json_result(data=conv)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/get', methods=['GET'])
+@login_required
+def get():
+    conv_id = request.args["conversation_id"]
+    try:
+        e, conv = ConversationService.get_by_id(conv_id)
+        if not e:
+            return get_data_error_result(retmsg="Conversation not found!")
+        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, retmsg=f'Only owner of conversation authorized for this operation.',
+                retcode=RetCode.OPERATING_ERROR)
+        conv = conv.to_dict()
+        return get_json_result(data=conv)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/rm', methods=['POST'])
+@login_required
+def rm():
+    conv_ids = request.json["conversation_ids"]
+    try:
+        for cid in conv_ids:
+            exist, conv = ConversationService.get_by_id(cid)
+            if not exist:
+                return get_data_error_result(retmsg="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, retmsg=f'Only owner of conversation authorized for this operation.',
+                    retcode=RetCode.OPERATING_ERROR)
+            ConversationService.delete_by_id(cid)
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/list', methods=['GET'])
+@login_required
+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, retmsg=f'Only owner of dialog authorized for this operation.',
+                retcode=RetCode.OPERATING_ERROR)
+        convs = ConversationService.query(
+            dialog_id=dialog_id,
+            order_by=ConversationService.model.create_time,
+            reverse=True)
+        convs = [d.to_dict() for d in convs]
+        return get_json_result(data=convs)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/completion', methods=['POST'])
+@login_required
+@validate_request("conversation_id", "messages")
+def completion():
+    req = request.json
+    msg = []
+    for m in req["messages"]:
+        if m["role"] == "system":
+            continue
+        if m["role"] == "assistant" and not msg:
+            continue
+        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 = deepcopy(req["messages"])
+        e, dia = DialogService.get_by_id(conv.dialog_id)
+        if not e:
+            return get_data_error_result(retmsg="Dialog not found!")
+        del req["conversation_id"]
+        del req["messages"]
+
+        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):
+            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, "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"
+                ConversationService.update_by_id(conv.id, conv.to_dict())
+            except Exception as e:
+                traceback.print_exc()
+                yield "data:" + json.dumps({"retcode": 500, "retmsg": str(e),
+                                            "data": {"answer": "**ERROR**: " + str(e), "reference": []}},
+                                           ensure_ascii=False) + "\n\n"
+            yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": True}, ensure_ascii=False) + "\n\n"
+
+        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_json_result(data=answer)
+    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(retmsg="Tenant not found!")
+
+    tts_id = tenants[0]["tts_id"]
+    if not tts_id:
+        return get_data_error_result(retmsg="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({"retcode": 500, "retmsg": 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(retmsg="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(retmsg="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({"retcode": 0, "retmsg": "", "data": ans}, ensure_ascii=False) + "\n\n"
+        except Exception as e:
+            yield "data:" + json.dumps({"retcode": 500, "retmsg": str(e),
+                                        "data": {"answer": "**ERROR**: " + str(e), "reference": []}},
+                                       ensure_ascii=False) + "\n\n"
+        yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": True}, ensure_ascii=False) + "\n\n"
+
+    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(retmsg="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 = 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

@@ -1,172 +1,183 @@
-#
-#  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.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.utils.api_utils import server_error_response, get_data_error_result, validate_request
-from api.utils import get_uuid
-from api.utils.api_utils import get_json_result
-
-
-@manager.route('/set', methods=['POST'])
-@login_required
-def set_dialog():
-    req = request.json
-    dialog_id = req.get("dialog_id")
-    name = req.get("name", "New Dialog")
-    description = req.get("description", "A helpful Dialog")
-    icon = req.get("icon", "")
-    top_n = req.get("top_n", 6)
-    top_k = req.get("top_k", 1024)
-    rerank_id = req.get("rerank_id", "")
-    if not rerank_id: req["rerank_id"] = ""
-    similarity_threshold = req.get("similarity_threshold", 0.1)
-    vector_similarity_weight = req.get("vector_similarity_weight", 0.3)
-    if vector_similarity_weight is None: vector_similarity_weight = 0.3
-    llm_setting = req.get("llm_setting", {})
-    default_prompt = {
-        "system": """你是一个智能助手，请总结知识库的内容来回答问题，请列举知识库中的数据详细回答。当所有知识库内容都与问题无关时，你的回答必须包括“知识库中未找到您要的答案！”这句话。回答需要考虑聊天历史。
-以下是知识库：
-{knowledge}
-以上是知识库。""",
-        "prologue": "您好，我是您的助手小樱，长得可爱又善良，can I help you?",
-        "parameters": [
-            {"key": "knowledge", "optional": False}
-        ],
-        "empty_response": "Sorry! 知识库中未找到相关内容！"
-    }
-    prompt_config = req.get("prompt_config", default_prompt)
-
-    if not prompt_config["system"]:
-        prompt_config["system"] = default_prompt["system"]
-    # if len(prompt_config["parameters"]) < 1:
-    #     prompt_config["parameters"] = default_prompt["parameters"]
-    # for p in prompt_config["parameters"]:
-    #     if p["key"] == "knowledge":break
-    # else: prompt_config["parameters"].append(default_prompt["parameters"][0])
-
-    for p in prompt_config["parameters"]:
-        if p["optional"]:
-            continue
-        if prompt_config["system"].find("{%s}" % p["key"]) < 0:
-            return get_data_error_result(
-                retmsg="Parameter '{}' is not used".format(p["key"]))
-
-    try:
-        e, tenant = TenantService.get_by_id(current_user.id)
-        if not e:
-            return get_data_error_result(retmsg="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!")
-            dia = {
-                "id": get_uuid(),
-                "tenant_id": current_user.id,
-                "name": name,
-                "kb_ids": req["kb_ids"],
-                "description": description,
-                "llm_id": llm_id,
-                "llm_setting": llm_setting,
-                "prompt_config": prompt_config,
-                "top_n": top_n,
-                "top_k": top_k,
-                "rerank_id": rerank_id,
-                "similarity_threshold": similarity_threshold,
-                "vector_similarity_weight": vector_similarity_weight,
-                "icon": icon
-            }
-            if not DialogService.save(**dia):
-                return get_data_error_result(retmsg="Fail to new a dialog!")
-            e, dia = DialogService.get_by_id(dia["id"])
-            if not e:
-                return get_data_error_result(retmsg="Fail to new a dialog!")
-            return get_json_result(data=dia.to_json())
-        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!")
-            e, dia = DialogService.get_by_id(dialog_id)
-            if not e:
-                return get_data_error_result(retmsg="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)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/get', methods=['GET'])
-@login_required
-def get():
-    dialog_id = request.args["dialog_id"]
-    try:
-        e, dia = DialogService.get_by_id(dialog_id)
-        if not e:
-            return get_data_error_result(retmsg="Dialog not found!")
-        dia = dia.to_dict()
-        dia["kb_ids"], dia["kb_names"] = get_kb_names(dia["kb_ids"])
-        return get_json_result(data=dia)
-    except Exception as e:
-        return server_error_response(e)
-
-
-def get_kb_names(kb_ids):
-    ids, nms = [], []
-    for kid in kb_ids:
-        e, kb = KnowledgebaseService.get_by_id(kid)
-        if not e or kb.status != StatusEnum.VALID.value:
-            continue
-        ids.append(kid)
-        nms.append(kb.name)
-    return ids, nms
-
-
-@manager.route('/list', methods=['GET'])
-@login_required
-def list_dialogs():
-    try:
-        diags = DialogService.query(
-            tenant_id=current_user.id,
-            status=StatusEnum.VALID.value,
-            reverse=True,
-            order_by=DialogService.model.create_time)
-        diags = [d.to_dict() for d in diags]
-        for d in diags:
-            d["kb_ids"], d["kb_names"] = get_kb_names(d["kb_ids"])
-        return get_json_result(data=diags)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/rm', methods=['POST'])
-@login_required
-@validate_request("dialog_ids")
-def rm():
-    req = request.json
-    try:
-        DialogService.update_many_by_id(
-            [{"id": id, "status": StatusEnum.INVALID.value} for id in req["dialog_ids"]])
-        return get_json_result(data=True)
-    except Exception as e:
-        return server_error_response(e)
+#
+#  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.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, UserTenantService
+from api.settings import RetCode
+from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
+from api.utils import get_uuid
+from api.utils.api_utils import get_json_result
+
+
+@manager.route('/set', methods=['POST'])
+@login_required
+def set_dialog():
+    req = request.json
+    dialog_id = req.get("dialog_id")
+    name = req.get("name", "New Dialog")
+    description = req.get("description", "A helpful Dialog")
+    icon = req.get("icon", "")
+    top_n = req.get("top_n", 6)
+    top_k = req.get("top_k", 1024)
+    rerank_id = req.get("rerank_id", "")
+    if not rerank_id: req["rerank_id"] = ""
+    similarity_threshold = req.get("similarity_threshold", 0.1)
+    vector_similarity_weight = req.get("vector_similarity_weight", 0.3)
+    if vector_similarity_weight is None: vector_similarity_weight = 0.3
+    llm_setting = req.get("llm_setting", {})
+    default_prompt = {
+        "system": """你是一个智能助手，请总结知识库的内容来回答问题，请列举知识库中的数据详细回答。当所有知识库内容都与问题无关时，你的回答必须包括“知识库中未找到您要的答案！”这句话。回答需要考虑聊天历史。
+以下是知识库：
+{knowledge}
+以上是知识库。""",
+        "prologue": "您好，我是您的助手小樱，长得可爱又善良，can I help you?",
+        "parameters": [
+            {"key": "knowledge", "optional": False}
+        ],
+        "empty_response": "Sorry! 知识库中未找到相关内容！"
+    }
+    prompt_config = req.get("prompt_config", default_prompt)
+
+    if not prompt_config["system"]:
+        prompt_config["system"] = default_prompt["system"]
+    # if len(prompt_config["parameters"]) < 1:
+    #     prompt_config["parameters"] = default_prompt["parameters"]
+    # for p in prompt_config["parameters"]:
+    #     if p["key"] == "knowledge":break
+    # else: prompt_config["parameters"].append(default_prompt["parameters"][0])
+
+    for p in prompt_config["parameters"]:
+        if p["optional"]:
+            continue
+        if prompt_config["system"].find("{%s}" % p["key"]) < 0:
+            return get_data_error_result(
+                retmsg="Parameter '{}' is not used".format(p["key"]))
+
+    try:
+        e, tenant = TenantService.get_by_id(current_user.id)
+        if not e:
+            return get_data_error_result(retmsg="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!")
+            dia = {
+                "id": get_uuid(),
+                "tenant_id": current_user.id,
+                "name": name,
+                "kb_ids": req["kb_ids"],
+                "description": description,
+                "llm_id": llm_id,
+                "llm_setting": llm_setting,
+                "prompt_config": prompt_config,
+                "top_n": top_n,
+                "top_k": top_k,
+                "rerank_id": rerank_id,
+                "similarity_threshold": similarity_threshold,
+                "vector_similarity_weight": vector_similarity_weight,
+                "icon": icon
+            }
+            if not DialogService.save(**dia):
+                return get_data_error_result(retmsg="Fail to new a dialog!")
+            e, dia = DialogService.get_by_id(dia["id"])
+            if not e:
+                return get_data_error_result(retmsg="Fail to new a dialog!")
+            return get_json_result(data=dia.to_json())
+        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!")
+            e, dia = DialogService.get_by_id(dialog_id)
+            if not e:
+                return get_data_error_result(retmsg="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)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/get', methods=['GET'])
+@login_required
+def get():
+    dialog_id = request.args["dialog_id"]
+    try:
+        e, dia = DialogService.get_by_id(dialog_id)
+        if not e:
+            return get_data_error_result(retmsg="Dialog not found!")
+        dia = dia.to_dict()
+        dia["kb_ids"], dia["kb_names"] = get_kb_names(dia["kb_ids"])
+        return get_json_result(data=dia)
+    except Exception as e:
+        return server_error_response(e)
+
+
+def get_kb_names(kb_ids):
+    ids, nms = [], []
+    for kid in kb_ids:
+        e, kb = KnowledgebaseService.get_by_id(kid)
+        if not e or kb.status != StatusEnum.VALID.value:
+            continue
+        ids.append(kid)
+        nms.append(kb.name)
+    return ids, nms
+
+
+@manager.route('/list', methods=['GET'])
+@login_required
+def list_dialogs():
+    try:
+        diags = DialogService.query(
+            tenant_id=current_user.id,
+            status=StatusEnum.VALID.value,
+            reverse=True,
+            order_by=DialogService.model.create_time)
+        diags = [d.to_dict() for d in diags]
+        for d in diags:
+            d["kb_ids"], d["kb_names"] = get_kb_names(d["kb_ids"])
+        return get_json_result(data=diags)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/rm', methods=['POST'])
+@login_required
+@validate_request("dialog_ids")
+def rm():
+    req = request.json
+    dialog_list=[]
+    tenants = UserTenantService.query(user_id=current_user.id)
+    try:
+        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, retmsg=f'Only owner of dialog authorized for this operation.',
+                    retcode=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/file2document_app.py

@@ -77,7 +77,7 @@ def convert():
                     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,

api/apps/file_app.py

@@ -34,7 +34,7 @@ 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'])
@@ -98,7 +98,7 @@ def upload():
             # 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 +116,7 @@ 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:
@@ -260,7 +260,7 @@ def rm():
                     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)
+                    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):
@@ -296,7 +296,8 @@ def rename():
         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(
+        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,
@@ -331,8 +332,8 @@ def get(file_id):
         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))
+        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:

api/apps/kb_app.py

@@ -1,153 +1,171 @@
-#
-#  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 elasticsearch_dsl import Q
-from flask import request
-from flask_login import login_required, current_user
-
-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.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.db.services.knowledgebase_service import KnowledgebaseService
-from api.db.db_models import Knowledgebase, File
-from api.settings import stat_logger, RetCode
-from api.utils.api_utils import get_json_result
-from rag.nlp import search
-from rag.utils.es_conn import ELASTICSEARCH
-
-
-@manager.route('/create', methods=['post'])
-@login_required
-@validate_request("name")
-def create():
-    req = request.json
-    req["name"] = req["name"].strip()
-    req["name"] = duplicate_name(
-        KnowledgebaseService.query,
-        name=req["name"],
-        tenant_id=current_user.id,
-        status=StatusEnum.VALID.value)
-    try:
-        req["id"] = get_uuid()
-        req["tenant_id"] = current_user.id
-        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.")
-        req["embd_id"] = t.embd_id
-        if not KnowledgebaseService.save(**req):
-            return get_data_error_result()
-        return get_json_result(data={"kb_id": req["id"]})
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/update', methods=['post'])
-@login_required
-@validate_request("kb_id", "name", "description", "permission", "parser_id")
-def update():
-    req = request.json
-    req["name"] = req["name"].strip()
-    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)
-
-        e, kb = KnowledgebaseService.get_by_id(req["kb_id"])
-        if not e:
-            return get_data_error_result(
-                retmsg="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.")
-
-        del req["kb_id"]
-        if not KnowledgebaseService.update_by_id(kb.id, req):
-            return get_data_error_result()
-
-        e, kb = KnowledgebaseService.get_by_id(kb.id)
-        if not e:
-            return get_data_error_result(
-                retmsg="Database error (Knowledgebase rename)!")
-
-        return get_json_result(data=kb.to_json())
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/detail', methods=['GET'])
-@login_required
-def detail():
-    kb_id = request.args["kb_id"]
-    try:
-        kb = KnowledgebaseService.get_detail(kb_id)
-        if not kb:
-            return get_data_error_result(
-                retmsg="Can't find this knowledgebase!")
-        return get_json_result(data=kb)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/list', methods=['GET'])
-@login_required
-def list_kbs():
-    page_number = request.args.get("page", 1)
-    items_per_page = request.args.get("page_size", 150)
-    orderby = request.args.get("orderby", "create_time")
-    desc = request.args.get("desc", True)
-    try:
-        tenants = TenantService.get_joined_tenants_by_user_id(current_user.id)
-        kbs = KnowledgebaseService.get_by_tenant_ids(
-            [m["tenant_id"] for m in tenants], current_user.id, page_number, items_per_page, orderby, desc)
-        return get_json_result(data=kbs)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/rm', methods=['post'])
-@login_required
-@validate_request("kb_id")
-def rm():
-    req = request.json
-    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)
-
-        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)!")
-            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)
-
-        if not KnowledgebaseService.delete_by_id(req["kb_id"]):
-            return get_data_error_result(
-                retmsg="Database error (Knowledgebase removal)!")
-        return get_json_result(data=True)
-    except Exception as e:
-        return server_error_response(e)
+#
+#  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.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.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
+from api.db import StatusEnum, FileSource
+from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.db.db_models import File
+from api.settings import RetCode
+from api.utils.api_utils import get_json_result
+
+
+@manager.route('/create', methods=['post'])
+@login_required
+@validate_request("name")
+def create():
+    req = request.json
+    req["name"] = req["name"].strip()
+    req["name"] = duplicate_name(
+        KnowledgebaseService.query,
+        name=req["name"],
+        tenant_id=current_user.id,
+        status=StatusEnum.VALID.value)
+    try:
+        req["id"] = get_uuid()
+        req["tenant_id"] = current_user.id
+        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.")
+        req["embd_id"] = t.embd_id
+        if not KnowledgebaseService.save(**req):
+            return get_data_error_result()
+        return get_json_result(data={"kb_id": req["id"]})
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/update', methods=['post'])
+@login_required
+@validate_request("kb_id", "name", "description", "permission", "parser_id")
+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,
+            retmsg='No authorization.',
+            retcode=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)
+
+        e, kb = KnowledgebaseService.get_by_id(req["kb_id"])
+        if not e:
+            return get_data_error_result(
+                retmsg="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.")
+
+        del req["kb_id"]
+        if not KnowledgebaseService.update_by_id(kb.id, req):
+            return get_data_error_result()
+
+        e, kb = KnowledgebaseService.get_by_id(kb.id)
+        if not e:
+            return get_data_error_result(
+                retmsg="Database error (Knowledgebase rename)!")
+
+        return get_json_result(data=kb.to_json())
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/detail', methods=['GET'])
+@login_required
+def detail():
+    kb_id = request.args["kb_id"]
+    try:
+        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, retmsg=f'Only owner of knowledgebase authorized for this operation.',
+                retcode=RetCode.OPERATING_ERROR)
+        kb = KnowledgebaseService.get_detail(kb_id)
+        if not kb:
+            return get_data_error_result(
+                retmsg="Can't find this knowledgebase!")
+        return get_json_result(data=kb)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/list', methods=['GET'])
+@login_required
+def list_kbs():
+    page_number = request.args.get("page", 1)
+    items_per_page = request.args.get("page_size", 150)
+    orderby = request.args.get("orderby", "create_time")
+    desc = request.args.get("desc", True)
+    try:
+        tenants = TenantService.get_joined_tenants_by_user_id(current_user.id)
+        kbs = KnowledgebaseService.get_by_tenant_ids(
+            [m["tenant_id"] for m in tenants], current_user.id, page_number, items_per_page, orderby, desc)
+        return get_json_result(data=kbs)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/rm', methods=['post'])
+@login_required
+@validate_request("kb_id")
+def rm():
+    req = request.json
+    if not KnowledgebaseService.accessible4deletion(req["kb_id"], current_user.id):
+        return get_json_result(
+            data=False,
+            retmsg='No authorization.',
+            retcode=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)
+
+        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)!")
+            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)
+
+        if not KnowledgebaseService.delete_by_id(req["kb_id"]):
+            return get_data_error_result(
+                retmsg="Database error (Knowledgebase removal)!")
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)

api/apps/llm_app.py

@@ -1,242 +1,362 @@
-#
-#  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.db.services.llm_service import LLMFactoriesService, TenantLLMService, LLMService
-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
-
-
-@manager.route('/factories', methods=['GET'])
-@login_required
-def factories():
-    try:
-        fac = LLMFactoriesService.get_all()
-        return get_json_result(data=[f.to_dict() for f in fac if f.name not in ["Youdao", "FastEmbed", "BAAI"]])
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/set_api_key', methods=['POST'])
-@login_required
-@validate_request("llm_factory", "api_key")
-def set_api_key():
-    req = request.json
-    # test if api key works
-    chat_passed, embd_passed, rerank_passed = False, False, False
-    factory = req["llm_factory"]
-    msg = ""
-    for llm in LLMService.query(fid=factory):
-        if not embd_passed and llm.model_type == LLMType.EMBEDDING.value:
-            mdl = EmbeddingModel[factory](
-                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:
-                    raise Exception("Fail")
-                embd_passed = True
-            except Exception as e:
-                msg += f"\nFail to access embedding model({llm.llm_name}) using this api key." + str(e)
-        elif not chat_passed and llm.model_type == LLMType.CHAT.value:
-            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:
-                    raise Exception(m)
-            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"))
-            try:
-                arr, tc = mdl.similarity("What's the weather?", ["Is it sunny today?"])
-                if len(arr) == 0 or tc == 0:
-                    raise Exception("Fail")
-            except Exception as e:
-                msg += f"\nFail to access model({llm.llm_name}) using this api key." + str(
-                    e)
-            rerank_passed = True
-
-    if msg:
-        return get_data_error_result(retmsg=msg)
-
-    llm = {
-        "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]
-
-    if not TenantLLMService.filter_update(
-            [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == factory], llm):
-        for llm in LLMService.query(fid=factory):
-            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", "")
-            )
-
-    return get_json_result(data=True)
-
-
-@manager.route('/add_llm', methods=['POST'])
-@login_required
-@validate_request("llm_factory", "llm_name", "model_type")
-def add_llm():
-    req = request.json
-    factory = req["llm_factory"]
-
-    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}", ' + '}'
-    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", "")}", ' + '}'
-    else:
-        llm_name = req["llm_name"]
-        api_key = "xxxxxxxxxxxxxxx"
-
-    llm = {
-        "tenant_id": current_user.id,
-        "llm_factory": factory,
-        "model_type": req["model_type"],
-        "llm_name": llm_name,
-        "api_base": req.get("api_base", ""),
-        "api_key": api_key
-    }
-
-    msg = ""
-    if llm["model_type"] == LLMType.EMBEDDING.value:
-        mdl = EmbeddingModel[factory](
-            key=llm['api_key'] if factory in ["VolcEngine", "Bedrock"] else None,
-            model_name=llm["llm_name"], 
-            base_url=llm["api_base"])
-        try:
-            arr, tc = mdl.encode(["Test if the api key is available"])
-            if len(arr[0]) == 0 or tc == 0:
-                raise Exception("Fail")
-        except Exception as e:
-            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,
-            model_name=llm["llm_name"],
-            base_url=llm["api_base"]
-        )
-        try:
-            m, tc = mdl.chat(None, [{"role": "user", "content": "Hello! How are you doing!"}], {
-                             "temperature": 0.9})
-            if not tc:
-                raise Exception(m)
-        except Exception 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)
-
-    if not TenantLLMService.filter_update(
-            [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == factory, TenantLLM.llm_name == llm["llm_name"]], llm):
-        TenantLLMService.save(**llm)
-
-    return get_json_result(data=True)
-
-
-@manager.route('/delete_llm', methods=['POST'])
-@login_required
-@validate_request("llm_factory", "llm_name")
-def delete_llm():
-    req = request.json
-    TenantLLMService.filter_delete(
-            [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == req["llm_factory"], TenantLLM.llm_name == req["llm_name"]])
-    return get_json_result(data=True)
-
-
-@manager.route('/my_llms', methods=['GET'])
-@login_required
-def my_llms():
-    try:
-        res = {}
-        for o in TenantLLMService.get_my_llms(current_user.id):
-            if o["llm_factory"] not in res:
-                res[o["llm_factory"]] = {
-                    "tags": o["tags"],
-                    "llm": []
-                }
-            res[o["llm_factory"]]["llm"].append({
-                "type": o["model_type"],
-                "name": o["llm_name"],
-                "used_token": o["used_tokens"]
-            })
-        return get_json_result(data=res)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route('/list', methods=['GET'])
-@login_required
-def list_app():
-    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:
-            m["available"] = m["fid"] in facts or m["llm_name"].lower() == "flag-embedding" or m["fid"] in ["Youdao","FastEmbed", "BAAI"]
-
-        llm_set = set([m["llm_name"] for m in llms])
-        for o in objs:
-            if not o.api_key:continue
-            if o.llm_name in llm_set:continue
-            llms.append({"llm_name": o.llm_name, "model_type": o.model_type, "fid": o.llm_factory, "available": True})
-
-        res = {}
-        for m in llms:
-            if model_type and m["model_type"].find(model_type)<0:
-                continue
-            if m["fid"] not in res:
-                res[m["fid"]] = []
-            res[m["fid"]].append(m)
-
-        return get_json_result(data=res)
-    except Exception as e:
-        return server_error_response(e)
+#
+#  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 flask import request
+from flask_login import login_required, current_user
+from api.db.services.llm_service import LLMFactoriesService, TenantLLMService, LLMService
+from api.settings import LIGHTEN
+from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
+from api.db import StatusEnum, LLMType
+from api.db.db_models import TenantLLM
+from api.utils.api_utils import get_json_result
+from rag.llm import EmbeddingModel, ChatModel, RerankModel, CvModel, TTSModel
+import requests
+
+
+@manager.route('/factories', methods=['GET'])
+@login_required
+def factories():
+    try:
+        fac = LLMFactoriesService.get_all()
+        fac = [f.to_dict() for f in fac if f.name not in ["Youdao", "FastEmbed", "BAAI"]]
+        llms = LLMService.get_all()
+        mdl_types = {}
+        for m in llms:
+            if m.status != StatusEnum.VALID.value:
+                continue
+            if m.fid not in mdl_types:
+                mdl_types[m.fid] = set([])
+            mdl_types[m.fid].add(m.model_type)
+        for f in fac:
+            f["model_types"] = list(mdl_types.get(f["name"], [LLMType.CHAT, LLMType.EMBEDDING, LLMType.RERANK,
+                                                              LLMType.IMAGE2TEXT, LLMType.SPEECH2TEXT, LLMType.TTS]))
+        return get_json_result(data=fac)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route('/set_api_key', methods=['POST'])
+@login_required
+@validate_request("llm_factory", "api_key")
+def set_api_key():
+    req = request.json
+    # test if api key works
+    chat_passed, embd_passed, rerank_passed = False, False, False
+    factory = req["llm_factory"]
+    msg = ""
+    for llm in LLMService.query(fid=factory):
+        if not embd_passed and llm.model_type == LLMType.EMBEDDING.value:
+            mdl = EmbeddingModel[factory](
+                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:
+                    raise Exception("Fail")
+                embd_passed = True
+            except Exception as e:
+                msg += f"\nFail to access embedding model({llm.llm_name}) using this api key." + str(e)
+        elif not chat_passed and llm.model_type == LLMType.CHAT.value:
+            mdl = ChatModel[factory](
+                req["api_key"], llm.llm_name, base_url=req.get("base_url"))
+            try:
+                m, tc = mdl.chat(None, [{"role": "user", "content": "Hello! How are you doing!"}], 
+                                 {"temperature": 0.9,'max_tokens':50})
+                if m.find("**ERROR**") >=0:
+                    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)
+        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"))
+            try:
+                arr, tc = mdl.similarity("What's the weather?", ["Is it sunny today?"])
+                if len(arr) == 0 or tc == 0:
+                    raise Exception("Fail")
+                rerank_passed = True
+                print(f'passed model rerank{llm.llm_name}',flush=True)
+            except Exception as e:
+                msg += f"\nFail to access model({llm.llm_name}) using this api key." + str(
+                    e)
+        if any([embd_passed, chat_passed, rerank_passed]):
+            msg = ''
+            break
+
+    if msg:
+        return get_data_error_result(retmsg=msg)
+
+    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_config[n] = req[n]
+
+    for llm in LLMService.query(fid=factory):
+        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=llm_config["api_key"],
+                api_base=llm_config["api_base"]
+            )
+
+    return get_json_result(data=True)
+
+
+@manager.route('/add_llm', methods=['POST'])
+@login_required
+@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 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 = 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 = req.get("api_key", "xxxxxxxxxxxxxxx")
+
+    llm = {
+        "tenant_id": current_user.id,
+        "llm_factory": factory,
+        "model_type": req["model_type"],
+        "llm_name": llm_name,
+        "api_base": req.get("api_base", ""),
+        "api_key": api_key
+    }
+
+    msg = ""
+    if llm["model_type"] == LLMType.EMBEDDING.value:
+        mdl = EmbeddingModel[factory](
+            key=llm['api_key'],
+            model_name=llm["llm_name"], 
+            base_url=llm["api_base"])
+        try:
+            arr, tc = mdl.encode(["Test if the api key is available"])
+            if len(arr[0]) == 0 or tc == 0:
+                raise Exception("Fail")
+        except Exception as e:
+            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'],
+            model_name=llm["llm_name"],
+            base_url=llm["api_base"]
+        )
+        try:
+            m, tc = mdl.chat(None, [{"role": "user", "content": "Hello! How are you doing!"}], {
+                             "temperature": 0.9})
+            if not tc:
+                raise Exception(m)
+        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!"])
+            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)
+
+    if not TenantLLMService.filter_update(
+            [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == factory, TenantLLM.llm_name == llm["llm_name"]], llm):
+        TenantLLMService.save(**llm)
+
+    return get_json_result(data=True)
+
+
+@manager.route('/delete_llm', methods=['POST'])
+@login_required
+@validate_request("llm_factory", "llm_name")
+def delete_llm():
+    req = request.json
+    TenantLLMService.filter_delete(
+            [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == req["llm_factory"], TenantLLM.llm_name == req["llm_name"]])
+    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)
+
+
+@manager.route('/my_llms', methods=['GET'])
+@login_required
+def my_llms():
+    try:
+        res = {}
+        for o in TenantLLMService.get_my_llms(current_user.id):
+            if o["llm_factory"] not in res:
+                res[o["llm_factory"]] = {
+                    "tags": o["tags"],
+                    "llm": []
+                }
+            res[o["llm_factory"]]["llm"].append({
+                "type": o["model_type"],
+                "name": o["llm_name"],
+                "used_token": o["used_tokens"]
+            })
+        return get_json_result(data=res)
+    except Exception as e:
+        return server_error_response(e)
+
+
+@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 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 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 self_deploied
+
+        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+"@"+o.llm_factory in llm_set:continue
+            llms.append({"llm_name": o.llm_name, "model_type": o.model_type, "fid": o.llm_factory, "available": True})
+
+        res = {}
+        for m in llms:
+            if model_type and m["model_type"].find(model_type)<0:
+                continue
+            if m["fid"] not in res:
+                res[m["fid"]] = []
+            res[m["fid"]].append(m)
+
+        return get_json_result(data=res)
+    except Exception as e:
+        return server_error_response(e)

api/apps/sdk/chat.py

@@ -0,0 +1,311 @@
+#
+#  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.settings import RetCode
+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(retmsg="`dataset_ids` is required")
+    for kb_id in ids:
+        kbs = KnowledgebaseService.query(id=kb_id,tenant_id=tenant_id)
+        if not kbs:
+            return get_error_data_result(f"You don't own the dataset {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(retmsg='Datasets use different embedding models."',retcode=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(retmsg="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(retmsg="`name` is required.")
+    if DialogService.query(name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value):
+        return get_error_data_result(retmsg="Duplicated chat name in creating chat.")
+    # tenant_id
+    if req.get("tenant_id"):
+        return get_error_data_result(retmsg="`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:
+            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(
+                retmsg="Parameter '{}' is not used".format(p["key"]))
+    # save
+    if not DialogService.save(**req):
+        return get_error_data_result(retmsg="Fail to new a chat!")
+    # response
+    e, res = DialogService.get_by_id(req["id"])
+    if not e:
+        return get_error_data_result(retmsg="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(retmsg='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.query(id=kb_id, tenant_id=tenant_id)
+                if not kbs:
+                    return get_error_data_result(f"You don't own the dataset {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(
+                    retmsg='Datasets use different embedding models."',
+                    retcode=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(retmsg="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(retmsg="`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(retmsg="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(retmsg="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(retmsg="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(retmsg=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)
+    if not chat:
+        return get_error_data_result(retmsg="The chat doesn't exist")
+    page_number = int(request.args.get("page", 1))
+    items_per_page = int(request.args.get("page_size", 1024))
+    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(retmsg=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 +1,232 @@
+#
+#  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.settings import RetCode
+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):
+    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(
+            retmsg="`tenant_id` must not be provided")
+    if "chunk_count" in req or "document_count" in req:
+        return get_error_data_result(retmsg="`chunk_count` or `document_count` must not be provided")
+    if "name" not in req:
+        return get_error_data_result(
+            retmsg="`name` is not empty!")
+    req['id'] = get_uuid()
+    req["name"] = req["name"].strip()
+    if req["name"] == "":
+        return get_error_data_result(
+            retmsg="`name` is not empty string!")
+    if KnowledgebaseService.query(name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value):
+        return get_error_data_result(
+            retmsg="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 not embd_model:
+            return get_error_data_result(f"`embedding_model` {req.get('embedding_model')} doesn't exist")
+        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")
+    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(retmsg="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):
+    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(retmsg=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(
+                    retmsg="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])
+            File2DocumentService.delete_by_document_id(doc.id)
+        if not KnowledgebaseService.delete_by_id(id):
+            return get_error_data_result(
+                retmsg="Delete dataset error.(Database error)")
+    return get_result(retcode=RetCode.SUCCESS)
+
+@manager.route('/datasets/<dataset_id>', methods=['PUT'])
+@token_required
+def update(tenant_id,dataset_id):
+    if not KnowledgebaseService.query(id=dataset_id,tenant_id=tenant_id):
+        return get_error_data_result(retmsg="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(retmsg="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(
+                retmsg="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(
+                retmsg="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(
+                retmsg="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(
+                retmsg="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(
+                retmsg="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 not embd_model:
+            return get_error_data_result(f"`embedding_model` {req.get('embedding_model')} doesn't exist")
+        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")
+        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(
+                retmsg="Duplicated dataset name in updating dataset.")
+    if not KnowledgebaseService.update_by_id(kb.id, req):
+        return get_error_data_result(retmsg="Update dataset error.(Database error)")
+    return get_result(retcode=RetCode.SUCCESS)
+
+@manager.route('/datasets', methods=['GET'])
+@token_required
+def list(tenant_id):
+    id = request.args.get("id")
+    name = request.args.get("name")
+    kbs = KnowledgebaseService.query(id=id,name=name,status=1)
+    if not kbs:
+        return get_error_data_result(retmsg="The dataset doesn't exist")
+    page_number = int(request.args.get("page", 1))
+    items_per_page = int(request.args.get("page_size", 1024))
+    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 +1,77 @@
+#
+#  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.settings import retrievaler, kg_retrievaler, RetCode
+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(error_msg="Knowledgebase not found!", retcode=RetCode.NOT_FOUND)
+
+        if kb.tenant_id != tenant_id:
+            return build_error_result(error_msg="Knowledgebase not found!", retcode=RetCode.NOT_FOUND)
+
+        embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id)
+
+        retr = retrievaler if kb.parser_id != ParserType.KG else 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"]:
+            if "vector" in c:
+                del c["vector"]
+            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(
+                error_msg=f'No chunk found! Check the chunk status please!',
+                retcode=RetCode.NOT_FOUND
+            )
+        return build_error_result(error_msg=str(e), retcode=RetCode.SERVER_ERROR)

api/apps/sdk/doc.py

@@ -0,0 +1,661 @@
+#
+#  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 pathlib
+import datetime
+
+from api.db.services.dialog_service import keyword_extraction
+from rag.app.qa import rmPrefix, beAdoc
+from rag.nlp import rag_tokenizer
+from api.db import LLMType, ParserType
+from api.db.services.llm_service import TenantLLMService
+from api.settings import kg_retrievaler
+import hashlib
+import re
+from api.utils.api_utils import token_required
+from api.db.db_models import Task
+from api.db.services.task_service import TaskService, queue_tasks
+from api.utils.api_utils import server_error_response
+from api.utils.api_utils import get_result, get_error_data_result
+from io import BytesIO
+from elasticsearch_dsl import Q
+from flask import request, send_file
+from api.db import FileSource, TaskStatus, FileType
+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.settings import RetCode, retrievaler
+from api.utils.api_utils import construct_json_result,get_parser_config
+from rag.nlp import search
+from rag.utils import rmSpace
+from rag.utils.es_conn import ELASTICSEARCH
+from rag.utils.storage_factory import STORAGE_IMPL
+import os
+
+MAXIMUM_OF_UPLOADING_FILES = 256
+
+
+
+@manager.route('/datasets/<dataset_id>/documents', methods=['POST'])
+@token_required
+def upload(dataset_id, tenant_id):
+    if 'file' not in request.files:
+        return get_error_data_result(
+            retmsg='No file part!', retcode=RetCode.ARGUMENT_ERROR)
+    file_objs = request.files.getlist('file')
+    for file_obj in file_objs:
+        if file_obj.filename == '':
+            return get_result(
+                retmsg='No file selected!', retcode=RetCode.ARGUMENT_ERROR)
+    # total size
+    total_size = 0
+    for file_obj in file_objs:
+        file_obj.seek(0, os.SEEK_END)
+        total_size += file_obj.tell()
+        file_obj.seek(0)
+    MAX_TOTAL_FILE_SIZE=10*1024*1024
+    if total_size > MAX_TOTAL_FILE_SIZE:
+        return get_result(
+            retmsg=f'Total file size exceeds 10MB limit! ({total_size / (1024 * 1024):.2f} MB)',
+            retcode=RetCode.ARGUMENT_ERROR)
+    e, kb = KnowledgebaseService.get_by_id(dataset_id)
+    if not e:
+        raise LookupError(f"Can't find the dataset with ID {dataset_id}!")
+    err, files= FileService.upload_document(kb, file_objs, tenant_id)
+    if err:
+        return get_result(
+            retmsg="\n".join(err), retcode=RetCode.SERVER_ERROR)
+    # rename key's name
+    renamed_doc_list = []
+    for file in files:
+        doc = file[0]
+        key_mapping = {
+            "chunk_num": "chunk_count",
+            "kb_id": "dataset_id",
+            "token_num": "token_count",
+            "parser_id": "chunk_method"
+        }
+        renamed_doc = {}
+        for key, value in doc.items():
+            new_key = key_mapping.get(key, key)
+            renamed_doc[new_key] = value
+        renamed_doc["run"] = "UNSTART"
+        renamed_doc_list.append(renamed_doc)
+    return get_result(data=renamed_doc_list)
+
+
+@manager.route('/datasets/<dataset_id>/documents/<document_id>', methods=['PUT'])
+@token_required
+def update_doc(tenant_id, dataset_id, document_id):
+    req = request.json
+    if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id):
+        return get_error_data_result(retmsg="You don't own the dataset.")
+    doc = DocumentService.query(kb_id=dataset_id, id=document_id)
+    if not doc:
+        return get_error_data_result(retmsg="The dataset doesn't own the document.")
+    doc = doc[0]
+    if "chunk_count" in req:
+        if req["chunk_count"] != doc.chunk_num:
+            return get_error_data_result(retmsg="Can't change `chunk_count`.")
+    if "token_count" in req:
+        if req["token_count"] != doc.token_num:
+            return get_error_data_result(retmsg="Can't change `token_count`.")
+    if "progress" in req:
+        if req['progress'] != doc.progress:
+            return get_error_data_result(retmsg="Can't change `progress`.")
+
+    if "name" in req and req["name"] != doc.name:
+        if pathlib.Path(req["name"].lower()).suffix != pathlib.Path(doc.name.lower()).suffix:
+            return get_result(retmsg="The extension of file can't be changed", retcode=RetCode.ARGUMENT_ERROR)
+        for d in DocumentService.query(name=req["name"], kb_id=doc.kb_id):
+            if d.name == req["name"]:
+                return get_error_data_result(
+                    retmsg="Duplicated document name in the same dataset.")
+        if not DocumentService.update_by_id(
+                document_id, {"name": req["name"]}):
+            return get_error_data_result(
+                retmsg="Database error (Document rename)!")
+
+        informs = File2DocumentService.get_by_document_id(document_id)
+        if informs:
+            e, file = FileService.get_by_id(informs[0].file_id)
+            FileService.update_by_id(file.id, {"name": req["name"]})
+    if "parser_config" in req:
+        DocumentService.update_parser_config(doc.id, req["parser_config"])
+    if "chunk_method" in req:
+        valid_chunk_method = {"naive","manual","qa","table","paper","book","laws","presentation","picture","one","knowledge_graph","email"}
+        if req.get("chunk_method") not in valid_chunk_method:
+            return get_error_data_result(f"`chunk_method` {req['chunk_method']} doesn't exist")
+        if doc.parser_id.lower() == req["chunk_method"].lower():
+                return get_result()
+
+        if doc.type == FileType.VISUAL or re.search(
+                r"\.(ppt|pptx|pages)$", doc.name):
+            return get_error_data_result(retmsg="Not supported yet!")
+
+        e = DocumentService.update_by_id(doc.id,
+                                         {"parser_id": req["chunk_method"], "progress": 0, "progress_msg": "",
+                                          "run": TaskStatus.UNSTART.value})
+        if not e:
+            return get_error_data_result(retmsg="Document not found!")
+        req["parser_config"] = get_parser_config(req["chunk_method"], req.get("parser_config"))
+        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_error_data_result(retmsg="Document not found!")
+            ELASTICSEARCH.deleteByQuery(
+                Q("match", doc_id=doc.id), idxnm=search.index_name(tenant_id))
+
+    return get_result()
+
+
+@manager.route('/datasets/<dataset_id>/documents/<document_id>', methods=['GET'])
+@token_required
+def download(tenant_id, dataset_id, document_id):
+    if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id):
+        return get_error_data_result(retmsg=f'You do not own the dataset {dataset_id}.')
+    doc = DocumentService.query(kb_id=dataset_id, id=document_id)
+    if not doc:
+        return get_error_data_result(retmsg=f'The dataset not own the document {document_id}.')
+    # The process of downloading
+    doc_id, doc_location = File2DocumentService.get_storage_address(doc_id=document_id)  # minio address
+    file_stream = STORAGE_IMPL.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=doc[0].name,
+        mimetype='application/octet-stream'  # Set a default MIME type
+    )
+
+
+@manager.route('/datasets/<dataset_id>/documents', methods=['GET'])
+@token_required
+def list_docs(dataset_id, tenant_id):
+    if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id):
+        return get_error_data_result(retmsg=f"You don't own the dataset {dataset_id}. ")
+    id = request.args.get("id")
+    if not DocumentService.query(id=id,kb_id=dataset_id):
+        return get_error_data_result(retmsg=f"You don't own the document {id}.")
+    offset = int(request.args.get("offset", 1))
+    keywords = request.args.get("keywords","")
+    limit = int(request.args.get("limit", 1024))
+    orderby = request.args.get("orderby", "create_time")
+    if request.args.get("desc") == "False":
+        desc = False
+    else:
+        desc = True
+    docs, tol = DocumentService.get_list(dataset_id, offset, limit, orderby, desc, keywords, id)
+
+    # rename key's name
+    renamed_doc_list = []
+    for doc in docs:
+        key_mapping = {
+            "chunk_num": "chunk_count",
+            "kb_id": "dataset_id",
+            "token_num": "token_count",
+            "parser_id": "chunk_method"
+        }
+        run_mapping = {
+         "0" :"UNSTART",
+         "1":"RUNNING",
+         "2":"CANCEL",
+         "3":"DONE",
+         "4":"FAIL"
+        }
+        renamed_doc = {}
+        for key, value in doc.items():
+            new_key = key_mapping.get(key, key)
+            renamed_doc[new_key] = value
+            if key =="run":
+                renamed_doc["run"]=run_mapping.get(value)
+        renamed_doc_list.append(renamed_doc)
+    return get_result(data={"total": tol, "docs": renamed_doc_list})
+
+
+@manager.route('/datasets/<dataset_id>/documents', methods=['DELETE'])
+@token_required
+def delete(tenant_id,dataset_id):
+    if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id):
+        return get_error_data_result(retmsg=f"You don't own the dataset {dataset_id}. ")
+    req = request.json
+    if not req:
+        doc_ids=None
+    else:
+        doc_ids=req.get("ids")
+    if not doc_ids:
+        doc_list = []
+        docs=DocumentService.query(kb_id=dataset_id)
+        for doc in docs:
+            doc_list.append(doc.id)
+    else:
+        doc_list=doc_ids
+    root_folder = FileService.get_root_folder(tenant_id)
+    pf_id = root_folder["id"]
+    FileService.init_knowledgebase_docs(pf_id, tenant_id)
+    errors = ""
+    for doc_id in doc_list:
+        try:
+            e, doc = DocumentService.get_by_id(doc_id)
+            if not e:
+                return get_error_data_result(retmsg="Document not found!")
+            tenant_id = DocumentService.get_tenant_id(doc_id)
+            if not tenant_id:
+                return get_error_data_result(retmsg="Tenant not found!")
+
+            b, n = File2DocumentService.get_storage_address(doc_id=doc_id)
+
+            if not DocumentService.remove_document(doc, tenant_id):
+                return get_error_data_result(
+                    retmsg="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)
+
+            STORAGE_IMPL.rm(b, n)
+        except Exception as e:
+            errors += str(e)
+
+    if errors:
+        return get_result(retmsg=errors, retcode=RetCode.SERVER_ERROR)
+
+    return get_result()
+
+
+@manager.route('/datasets/<dataset_id>/chunks', methods=['POST'])
+@token_required
+def parse(tenant_id,dataset_id):
+    if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id):
+        return get_error_data_result(retmsg=f"You don't own the dataset {dataset_id}.")
+    req = request.json
+    if not req.get("document_ids"):
+        return get_error_data_result("`document_ids` is required")
+    for id in req["document_ids"]:
+        doc = DocumentService.query(id=id,kb_id=dataset_id)
+        if not doc:
+            return get_error_data_result(retmsg=f"You don't own the document {id}.")
+        info = {"run": "1", "progress": 0}
+        info["progress_msg"] = ""
+        info["chunk_num"] = 0
+        info["token_num"] = 0
+        DocumentService.update_by_id(id, info)
+        ELASTICSEARCH.deleteByQuery(
+            Q("match", doc_id=id), idxnm=search.index_name(tenant_id))
+        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_storage_address(doc_id=doc["id"])
+        queue_tasks(doc, bucket, name)
+    return get_result()
+
+@manager.route('/datasets/<dataset_id>/chunks', methods=['DELETE'])
+@token_required
+def stop_parsing(tenant_id,dataset_id):
+    if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id):
+        return get_error_data_result(retmsg=f"You don't own the dataset {dataset_id}.")
+    req = request.json
+    if not req.get("document_ids"):
+        return get_error_data_result("`document_ids` is required")
+    for id in req["document_ids"]:
+        doc = DocumentService.query(id=id, kb_id=dataset_id)
+        if not doc:
+            return get_error_data_result(retmsg=f"You don't own the document {id}.")
+        if doc[0].progress == 100.0 or doc[0].progress == 0.0:
+            return get_error_data_result("Can't stop parsing document with progress at 0 or 100")
+        info = {"run": "2", "progress": 0,"chunk_num":0}
+        DocumentService.update_by_id(id, info)
+        ELASTICSEARCH.deleteByQuery(
+            Q("match", doc_id=id), idxnm=search.index_name(tenant_id))
+    return get_result()
+
+
+@manager.route('/datasets/<dataset_id>/documents/<document_id>/chunks', methods=['GET'])
+@token_required
+def list_chunks(tenant_id,dataset_id,document_id):
+    if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id):
+        return get_error_data_result(retmsg=f"You don't own the dataset {dataset_id}.")
+    doc=DocumentService.query(id=document_id, kb_id=dataset_id)
+    if not doc:
+        return get_error_data_result(retmsg=f"You don't own the document {document_id}.")
+    doc=doc[0]
+    req = request.args
+    doc_id = document_id
+    page = int(req.get("offset", 1))
+    size = int(req.get("limit", 30))
+    question = req.get("keywords", "")
+    query = {
+        "doc_ids": [doc_id], "page": page, "size": size, "question": question, "sort": True
+    }
+    sres = retrievaler.search(query, search.index_name(tenant_id), highlight=True)
+    key_mapping = {
+        "chunk_num": "chunk_count",
+        "kb_id": "dataset_id",
+        "token_num": "token_count",
+        "parser_id": "chunk_method"
+    }
+    run_mapping = {
+        "0": "UNSTART",
+        "1": "RUNNING",
+        "2": "CANCEL",
+        "3": "DONE",
+        "4": "FAIL"
+    }
+    doc=doc.to_dict()
+    renamed_doc = {}
+    for key, value in doc.items():
+        new_key = key_mapping.get(key, key)
+        renamed_doc[new_key] = value
+        if key == "run":
+            renamed_doc["run"] = run_mapping.get(str(value))
+    res = {"total": sres.total, "chunks": [], "doc": renamed_doc}
+    origin_chunks = []
+    sign = 0
+    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", ""),
+            "doc_id": sres.field[id]["doc_id"],
+            "docnm_kwd": sres.field[id]["docnm_kwd"],
+            "important_kwd": sres.field[id].get("important_kwd", []),
+            "img_id": sres.field[id].get("img_id", ""),
+            "available_int": sres.field[id].get("available_int", 1),
+            "positions": sres.field[id].get("position_int", "").split("\t")
+        }
+        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
+
+        origin_chunks.append(d)
+        if req.get("id"):
+            if req.get("id") == id:
+                origin_chunks.clear()
+                origin_chunks.append(d)
+                sign = 1
+                break
+    if req.get("id"):
+        if sign == 0:
+            return get_error_data_result(f"Can't find this chunk {req.get('id')}")
+    for chunk in origin_chunks:
+        key_mapping = {
+            "chunk_id": "id",
+            "content_with_weight": "content",
+            "doc_id": "document_id",
+            "important_kwd": "important_keywords",
+            "img_id": "image_id",
+            "available_int":"available"
+        }
+        renamed_chunk = {}
+        for key, value in chunk.items():
+            new_key = key_mapping.get(key, key)
+            renamed_chunk[new_key] = value
+        if renamed_chunk["available"] == "0":
+            renamed_chunk["available"] = False
+        if renamed_chunk["available"] == "1":
+            renamed_chunk["available"] = True
+        res["chunks"].append(renamed_chunk)
+    return get_result(data=res)
+
+
+
+@manager.route('/datasets/<dataset_id>/documents/<document_id>/chunks', methods=['POST'])
+@token_required
+def add_chunk(tenant_id,dataset_id,document_id):
+    if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id):
+        return get_error_data_result(retmsg=f"You don't own the dataset {dataset_id}.")
+    doc = DocumentService.query(id=document_id, kb_id=dataset_id)
+    if not doc:
+        return get_error_data_result(retmsg=f"You don't own the document {document_id}.")
+    doc = doc[0]
+    req = request.json
+    if not req.get("content"):
+        return get_error_data_result(retmsg="`content` is required")
+    if "important_keywords" in req:
+        if type(req["important_keywords"]) != list:
+            return get_error_data_result("`important_keywords` is required to be a list")
+    md5 = hashlib.md5()
+    md5.update((req["content"] + document_id).encode("utf-8"))
+
+    chunk_id = md5.hexdigest()
+    d = {"id": chunk_id, "content_ltks": rag_tokenizer.tokenize(req["content"]),
+         "content_with_weight": req["content"]}
+    d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
+    d["important_kwd"] = req.get("important_keywords", [])
+    d["important_tks"] = rag_tokenizer.tokenize(" ".join(req.get("important_keywords", [])))
+    d["create_time"] = str(datetime.datetime.now()).replace("T", " ")[:19]
+    d["create_timestamp_flt"] = datetime.datetime.now().timestamp()
+    d["kb_id"] = [doc.kb_id]
+    d["docnm_kwd"] = doc.name
+    d["doc_id"] = doc.id
+    embd_id = DocumentService.get_embd_id(document_id)
+    embd_mdl = TenantLLMService.model_instance(
+        tenant_id, LLMType.EMBEDDING.value, embd_id)
+    print(embd_mdl,flush=True)
+    v, c = embd_mdl.encode([doc.name, req["content"]])
+    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))
+
+    DocumentService.increment_chunk_num(
+        doc.id, doc.kb_id, c, 1, 0)
+    d["chunk_id"] = chunk_id
+    # rename keys
+    key_mapping = {
+        "chunk_id": "id",
+        "content_with_weight": "content",
+        "doc_id": "document_id",
+        "important_kwd": "important_keywords",
+        "kb_id": "dataset_id",
+        "create_timestamp_flt": "create_timestamp",
+        "create_time": "create_time",
+        "document_keyword": "document"
+    }
+    renamed_chunk = {}
+    for key, value in d.items():
+        if key in key_mapping:
+            new_key = key_mapping.get(key, key)
+            renamed_chunk[new_key] = value
+    return get_result(data={"chunk": renamed_chunk})
+    # return get_result(data={"chunk_id": chunk_id})
+
+
+@manager.route('datasets/<dataset_id>/documents/<document_id>/chunks', methods=['DELETE'])
+@token_required
+def rm_chunk(tenant_id,dataset_id,document_id):
+    if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id):
+        return get_error_data_result(retmsg=f"You don't own the dataset {dataset_id}.")
+    doc = DocumentService.query(id=document_id, kb_id=dataset_id)
+    if not doc:
+        return get_error_data_result(retmsg=f"You don't own the document {document_id}.")
+    doc = doc[0]
+    req = request.json
+    query = {
+        "doc_ids": [doc.id], "page": 1, "size": 1024, "question": "", "sort": True}
+    sres = retrievaler.search(query, search.index_name(tenant_id), highlight=True)
+    if not req:
+        chunk_ids=None
+    else:
+        chunk_ids=req.get("chunk_ids")
+    if not chunk_ids:
+        chunk_list=sres.ids
+    else:
+        chunk_list=chunk_ids
+    for chunk_id in chunk_list:
+        if chunk_id not in sres.ids:
+            return get_error_data_result(f"Chunk {chunk_id} not found")
+    if not ELASTICSEARCH.deleteByQuery(
+            Q("ids", values=chunk_list), search.index_name(tenant_id)):
+        return get_error_data_result(retmsg="Index updating failure")
+    deleted_chunk_ids = chunk_list
+    chunk_number = len(deleted_chunk_ids)
+    DocumentService.decrement_chunk_num(doc.id, doc.kb_id, 1, chunk_number, 0)
+    return get_result()
+
+
+
+@manager.route('/datasets/<dataset_id>/documents/<document_id>/chunks/<chunk_id>', methods=['PUT'])
+@token_required
+def update_chunk(tenant_id,dataset_id,document_id,chunk_id):
+    try:
+        res = ELASTICSEARCH.get(
+        chunk_id, search.index_name(
+            tenant_id))
+    except Exception as e:
+        return get_error_data_result(f"Can't find this chunk {chunk_id}")
+    if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id):
+        return get_error_data_result(retmsg=f"You don't own the dataset {dataset_id}.")
+    doc = DocumentService.query(id=document_id, kb_id=dataset_id)
+    if not doc:
+        return get_error_data_result(retmsg=f"You don't own the document {document_id}.")
+    doc = doc[0]
+    query = {
+        "doc_ids": [document_id], "page": 1, "size": 1024, "question": "", "sort": True
+    }
+    sres = retrievaler.search(query, search.index_name(tenant_id), highlight=True)
+    if chunk_id not in sres.ids:
+        return get_error_data_result(f"You don't own the chunk {chunk_id}")
+    req = request.json
+    content=res["_source"].get("content_with_weight")
+    d = {
+        "id": chunk_id,
+        "content_with_weight": req.get("content",content)}
+    d["content_ltks"] = rag_tokenizer.tokenize(d["content_with_weight"])
+    d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
+    if "important_keywords" in req:
+        if not isinstance(req["important_keywords"],list):
+            return get_error_data_result("`important_keywords` should be a list")
+        d["important_kwd"] = req.get("important_keywords")
+        d["important_tks"] = rag_tokenizer.tokenize(" ".join(req["important_keywords"]))
+    if "available" in req:
+        d["available_int"] = int(req["available"])
+    embd_id = DocumentService.get_embd_id(document_id)
+    embd_mdl = TenantLLMService.model_instance(
+        tenant_id, LLMType.EMBEDDING.value, embd_id)
+    if doc.parser_id == ParserType.QA:
+        arr = [
+            t for t in re.split(
+                r"[\n\t]",
+                d["content_with_weight"]) if len(t) > 1]
+        if len(arr) != 2:
+            return get_error_data_result(
+                retmsg="Q&A must be separated by TAB/ENTER key.")
+        q, a = rmPrefix(arr[0]), rmPrefix(arr[1])
+        d = beAdoc(d, arr[0], arr[1], not any(
+            [rag_tokenizer.is_chinese(t) for t in q + a]))
+
+    v, c = embd_mdl.encode([doc.name, d["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))
+    return get_result()
+
+
+
+@manager.route('/retrieval', methods=['POST'])
+@token_required
+def retrieval_test(tenant_id):
+    req = request.json
+    if not req.get("dataset_ids"):
+        return get_error_data_result("`datasets` is required.")
+    kb_ids = req["dataset_ids"]
+    if not isinstance(kb_ids,list):
+        return get_error_data_result("`datasets` should be a list")
+    kbs = KnowledgebaseService.get_by_ids(kb_ids)
+    for id in kb_ids:
+        if not KnowledgebaseService.query(id=id,tenant_id=tenant_id):
+            return get_error_data_result(f"You don't own the dataset {id}.")
+    embd_nms = list(set([kb.embd_id for kb in kbs]))
+    if len(embd_nms) != 1:
+        return get_result(
+            retmsg='Datasets use different embedding models."',
+            retcode=RetCode.AUTHENTICATION_ERROR)
+    if "question" not in req:
+        return get_error_data_result("`question` is required.")
+    page = int(req.get("offset", 1))
+    size = int(req.get("limit", 1024))
+    question = req["question"]
+    doc_ids = req.get("document_ids", [])
+    if not isinstance(doc_ids,list):
+        return get_error_data_result("`documents` should be a list")
+    doc_ids_list=KnowledgebaseService.list_documents_by_ids(kb_ids)
+    for doc_id in doc_ids:
+        if doc_id not in doc_ids_list:
+            return get_error_data_result(f"The datasets don't own the document {doc_id}")
+    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))
+    if req.get("highlight")=="False" or  req.get("highlight")=="false":
+        highlight = False
+    else:
+        highlight = True
+    try:
+        e, kb = KnowledgebaseService.get_by_id(kb_ids[0])
+        if not e:
+            return get_error_data_result(retmsg="Dataset not found!")
+        embd_mdl = TenantLLMService.model_instance(
+            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"])
+
+        if req.get("keyword", False):
+            chat_mdl = TenantLLMService.model_instance(kb.tenant_id, LLMType.CHAT)
+            question += keyword_extraction(chat_mdl, question)
+
+        retr = retrievaler if kb.parser_id != ParserType.KG else kg_retrievaler
+        ranks = retr.retrieval(question, embd_mdl, kb.tenant_id, kb_ids, page, size,
+                               similarity_threshold, vector_similarity_weight, top,
+                               doc_ids, rerank_mdl=rerank_mdl, highlight=highlight)
+        for c in ranks["chunks"]:
+            if "vector" in c:
+                del c["vector"]
+
+        ##rename keys
+        renamed_chunks = []
+        for chunk in ranks["chunks"]:
+            key_mapping = {
+                "chunk_id": "id",
+                "content_with_weight": "content",
+                "doc_id": "document_id",
+                "important_kwd": "important_keywords",
+                "docnm_kwd": "document_keyword"
+            }
+            rename_chunk = {}
+            for key, value in chunk.items():
+                new_key = key_mapping.get(key, key)
+                rename_chunk[new_key] = value
+            renamed_chunks.append(rename_chunk)
+        ranks["chunks"] = renamed_chunks
+        return get_result(data=ranks)
+    except Exception as e:
+        if str(e).find("not_found") > 0:
+            return get_result(retmsg=f'No chunk found! Check the chunk status please!',
+                                   retcode=RetCode.DATA_ERROR)
+        return server_error_response(e)

api/apps/sdk/session.py

@@ -0,0 +1,237 @@
+#
+#  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 uuid import uuid4
+
+from flask import request, Response
+
+from api.db import StatusEnum
+from api.db.services.dialog_service import DialogService, ConversationService, chat
+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
+
+@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(retmsg="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(retmsg="`name` can not be empty.")
+    ConversationService.save(**conv)
+    e, conv = ConversationService.get_by_id(conv["id"])
+    if not e:
+        return get_error_data_result(retmsg="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('/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(retmsg="Session does not exist")
+    if not DialogService.query(id=chat_id, tenant_id=tenant_id, status=StatusEnum.VALID.value):
+        return get_error_data_result(retmsg="You do not own the session")
+    if "message" in req or "messages" in req:
+        return get_error_data_result(retmsg="`message` can not be change")
+    if "reference" in req:
+        return get_error_data_result(retmsg="`reference` can not be change")
+    if "name" in req and not req.get("name"):
+        return get_error_data_result(retmsg="`name` can not be empty.")
+    if not ConversationService.update_by_id(conv_id, req):
+        return get_error_data_result(retmsg="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(retmsg="`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(retmsg="Please input your question.")
+    conv = ConversationService.query(id=session_id,dialog_id=chat_id)
+    if not conv:
+        return get_error_data_result(retmsg="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(retmsg="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):
+        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, "prompt": ans.get("prompt", "")}
+        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('/chats/<chat_id>/sessions', methods=['GET'])
+@token_required
+def list(chat_id,tenant_id):
+    if not DialogService.query(tenant_id=tenant_id, id=chat_id, status=StatusEnum.VALID.value):
+        return get_error_data_result(retmsg=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", 1024))
+    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"] = 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["img_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(retmsg="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(retmsg="The chat doesn't own the session")
+        ConversationService.delete_by_id(id)
+    return get_result()

api/apps/system_app.py

@@ -13,14 +13,22 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License
 #
-from flask_login import login_required
+import json
+from datetime import datetime
 
+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.db.services.user_service import UserTenantService
+from api.settings import DATABASE_TYPE
+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, request, validate_request
 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 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
@@ -45,24 +53,89 @@ def status():
 
     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.)}
     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.), "error": str(e)}
 
     st = timer()
     try:
         KnowledgebaseService.get_by_id("x")
-        res["mysql"] = {"status": "green", "elapsed": "{:.1f}".format((timer() - st)*1000.)}
+        res["database"] = {"database": DATABASE_TYPE.lower(), "status": "green", "elapsed": "{:.1f}".format((timer() - st)*1000.)}
     except Exception as e:
-        res["mysql"] = {"status": "red", "elapsed": "{:.1f}".format((timer() - st)*1000.), "error": str(e)}
+        res["database"] = {"database": DATABASE_TYPE.lower(), "status": "red", "elapsed": "{:.1f}".format((timer() - st)*1000.), "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.)}
     except Exception as e:
         res["redis"] = {"status": "red", "elapsed": "{:.1f}".format((timer() - st)*1000.), "error": str(e)}
 
+    try:
+        v = REDIS_CONN.get("TASKEXE")
+        if not v:
+            raise Exception("No task executor running!")
+        obj = json.loads(v)
+        color = "green"
+        for id in obj.keys():
+            arr = obj[id]
+            if len(arr) == 1:
+                obj[id] = [0]
+            else:
+                obj[id] = [arr[i+1]-arr[i] for i in range(len(arr)-1)]
+            elapsed = max(obj[id])
+            if elapsed > 50: color = "yellow"
+            if elapsed > 120: color = "red"
+        res["task_executor"] = {"status": color, "elapsed": obj}
+    except Exception as e:
+        res["task_executor"] = {"status": "red", "error": str(e)}
+
     return get_json_result(data=res)
+
+
+@manager.route('/new_token', methods=['POST'])
+@login_required
+def new_token():
+    try:
+        tenants = UserTenantService.query(user_id=current_user.id)
+        if not tenants:
+            return get_data_error_result(retmsg="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(retmsg="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():
+    try:
+        tenants = UserTenantService.query(user_id=current_user.id)
+        if not tenants:
+            return get_data_error_result(retmsg="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):
+    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 +1,99 @@
+#
+#  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.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):
+    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):
+    req = request.json
+    usrs = UserService.query(email=req["email"])
+    if not usrs:
+        return get_data_error_result(retmsg="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(retmsg="This user is in the team already.")
+        return get_data_error_result(retmsg="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):
+    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

@@ -1,391 +1,425 @@
-#
-#  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 datetime import datetime
-
-from flask import request, session, redirect
-from werkzeug.security import generate_password_hash, check_password_hash
-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.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
-
-
-@manager.route('/login', methods=['POST', 'GET'])
-def login():
-    login_channel = "password"
-    if not request.json:
-        return get_json_result(data=False, retcode=RetCode.AUTHENTICATION_ERROR,
-                               retmsg='Unautherized!')
-
-    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!')
-
-    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')
-
-    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.save()
-        msg = "Welcome back!"
-        return cors_reponse(data=response_data, auth=user.get_id(), retmsg=msg)
-    else:
-        return get_json_result(data=False, retcode=RetCode.AUTHENTICATION_ERROR,
-                               retmsg='Email and Password do not match!')
-
-
-@manager.route('/github_callback', methods=['GET'])
-def github_callback():
-    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 = res.json()
-    if "error" in res:
-        return redirect("/?error=%s" % res["error_description"])
-
-    if "user:email" not in res["scope"].split(","):
-        return redirect("/?error=user:email not in scope")
-
-    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_id = get_uuid()
-    if not users:
-        try:
-            try:
-                avatar = download_img(userinfo["avatar_url"])
-            except Exception as e:
-                stat_logger.exception(e)
-                avatar = ""
-            users = user_register(user_id, {
-                "access_token": session["access_token"],
-                "email": userinfo["email"],
-                "avatar": avatar,
-                "nickname": userinfo["login"],
-                "login_channel": "github",
-                "last_login_time": get_format_time(),
-                "is_superuser": False,
-            })
-            if not users:
-                raise Exception('Register user failure.')
-            if len(users) > 1:
-                raise Exception('Same E-mail exist!')
-            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)
-            return redirect("/?error=%s" % str(e))
-    user = users[0]
-    user.access_token = get_uuid()
-    login_user(user)
-    user.save()
-    return redirect("/?auth=%s" % user.get_id())
-
-
-@manager.route('/feishu_callback', methods=['GET'])
-def feishu_callback():
-    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 = app_access_token_res.json()
-    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 = res.json()
-    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_id = get_uuid()
-    if not users:
-        try:
-            try:
-                avatar = download_img(userinfo["avatar_url"])
-            except Exception as e:
-                stat_logger.exception(e)
-                avatar = ""
-            users = user_register(user_id, {
-                "access_token": session["access_token"],
-                "email": userinfo["email"],
-                "avatar": avatar,
-                "nickname": userinfo["en_name"],
-                "login_channel": "feishu",
-                "last_login_time": get_format_time(),
-                "is_superuser": False,
-            })
-            if not users:
-                raise Exception('Register user failure.')
-            if len(users) > 1:
-                raise Exception('Same E-mail exist!')
-            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)
-            return redirect("/?error=%s" % str(e))
-    user = users[0]
-    user.access_token = get_uuid()
-    login_user(user)
-    user.save()
-    return redirect("/?auth=%s" % user.get_id())
-
-
-def user_info_from_feishu(access_token):
-    import requests
-    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)
-    user_info = res.json()["data"]
-    user_info["email"] = None if user_info.get("email") == "" else user_info["email"]
-    return user_info
-
-
-def user_info_from_github(access_token):
-    import requests
-    headers = {"Accept": "application/json",
-               'Authorization': f"token {access_token}"}
-    res = requests.get(
-        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()
-    user_info["email"] = next(
-        (email for email in email_info if email['primary'] == True),
-        None)["email"]
-    return user_info
-
-
-@manager.route("/logout", methods=['GET'])
-@login_required
-def log_out():
-    current_user.access_token = ""
-    current_user.save()
-    logout_user()
-    return get_json_result(data=True)
-
-
-@manager.route("/setting", methods=["POST"])
-@login_required
-def setting_user():
-    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"])):
-            return get_json_result(
-                data=False, retcode=RetCode.AUTHENTICATION_ERROR, retmsg='Password error!')
-
-        if new_password:
-            update_dict["password"] = generate_password_hash(
-                decrypt(new_password))
-
-    for k in request_data.keys():
-        if k in ["password", "new_password"]:
-            continue
-        update_dict[k] = request_data[k]
-
-    try:
-        UserService.update_by_id(current_user.id, update_dict)
-        return get_json_result(data=True)
-    except Exception as e:
-        stat_logger.exception(e)
-        return get_json_result(
-            data=False, retmsg='Update failure!', retcode=RetCode.EXCEPTION_ERROR)
-
-
-@manager.route("/info", methods=["GET"])
-@login_required
-def user_info():
-    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:
-        pass
-    try:
-        TenantService.delete_by_id(user_id)
-    except Exception as e:
-        pass
-    try:
-        u = UserTenantService.query(tenant_id=user_id)
-        if u:
-            UserTenantService.delete_by_id(u[0].id)
-    except Exception as e:
-        pass
-    try:
-        TenantLLM.delete().where(TenantLLM.tenant_id == user_id).execute()
-    except Exception as e:
-        pass
-
-
-def user_register(user_id, user):
-    user["id"] = user_id
-    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
-    }
-    usr_tenant = {
-        "tenant_id": user_id,
-        "user_id": user_id,
-        "invited_by": user_id,
-        "role": UserTenantRole.OWNER
-    }
-    file_id = get_uuid()
-    file = {
-        "id": file_id,
-        "parent_id": file_id,
-        "tenant_id": user_id,
-        "created_by": user_id,
-        "name": "/",
-        "type": FileType.FOLDER.value,
-        "size": 0,
-        "location": "",
-    }
-    tenant_llm = []
-    for llm in LLMService.query(fid=LLM_FACTORY):
-        tenant_llm.append({"tenant_id": user_id,
-                           "llm_factory": LLM_FACTORY,
-                           "llm_name": llm.llm_name,
-                           "model_type": llm.model_type,
-                           "api_key": API_KEY,
-                           "api_base": LLM_BASE_URL
-                           })
-
-    if not UserService.save(**user):
-        return
-    TenantService.insert(**tenant)
-    UserTenantService.insert(**usr_tenant)
-    TenantLLMService.insert_many(tenant_llm)
-    FileService.insert(file)
-    return UserService.query(email=user["email"])
-
-
-@manager.route("/register", methods=["POST"])
-@validate_request("nickname", "email", "password")
-def user_add():
-    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)
-
-    user_dict = {
-        "access_token": get_uuid(),
-        "email": req["email"],
-        "nickname": req["nickname"],
-        "password": decrypt(req["password"]),
-        "login_channel": "password",
-        "last_login_time": get_format_time(),
-        "is_superuser": False,
-    }
-
-    user_id = get_uuid()
-    try:
-        users = user_register(user_id, user_dict)
-        if not users:
-            raise Exception('Register user failure.')
-        if len(users) > 1:
-            raise Exception('Same E-mail exist!')
-        user = users[0]
-        login_user(user)
-        return cors_reponse(data=user.to_json(),
-                            auth=user.get_id(), retmsg="Welcome aboard!")
-    except Exception as e:
-        rollback_user_registration(user_id)
-        stat_logger.exception(e)
-        return get_json_result(
-            data=False, retmsg='User registration failure!', retcode=RetCode.EXCEPTION_ERROR)
-
-
-@manager.route("/tenant_info", methods=["GET"])
-@login_required
-def tenant_info():
-    try:
-        tenants = TenantService.get_by_user_id(current_user.id)[0]
-        return get_json_result(data=tenants)
-    except Exception as e:
-        return server_error_response(e)
-
-
-@manager.route("/set_tenant_info", methods=["POST"])
-@login_required
-@validate_request("tenant_id", "asr_id", "embd_id", "img2txt_id", "llm_id")
-def set_tenant_info():
-    req = request.json
-    try:
-        tid = req["tenant_id"]
-        del req["tenant_id"]
-        TenantService.update_by_id(tid, req)
-        return get_json_result(data=True)
-    except Exception as e:
-        return server_error_response(e)
+#
+#  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 datetime import datetime
+
+from flask import request, session, redirect
+from werkzeug.security import generate_password_hash, check_password_hash
+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, 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, 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.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, construct_response
+
+
+@manager.route('/login', methods=['POST', 'GET'])
+def login():
+    if not request.json:
+        return get_json_result(data=False,
+                               retcode=RetCode.AUTHENTICATION_ERROR,
+                               retmsg='Unauthorized!')
+
+    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'Email: {email} is not registered!')
+
+    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')
+
+    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.save()
+        msg = "Welcome back!"
+        return construct_response(data=response_data, auth=user.get_id(), retmsg=msg)
+    else:
+        return get_json_result(data=False,
+                               retcode=RetCode.AUTHENTICATION_ERROR,
+                               retmsg='Email and password do not match!')
+
+
+@manager.route('/github_callback', methods=['GET'])
+def github_callback():
+    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 = res.json()
+    if "error" in res:
+        return redirect("/?error=%s" % res["error_description"])
+
+    if "user:email" not in res["scope"].split(","):
+        return redirect("/?error=user:email not in scope")
+
+    session["access_token"] = res["access_token"]
+    session["access_token_from"] = "github"
+    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(user_info["avatar_url"])
+            except Exception as e:
+                stat_logger.exception(e)
+                avatar = ""
+            users = user_register(user_id, {
+                "access_token": session["access_token"],
+                "email": email_address,
+                "avatar": avatar,
+                "nickname": user_info["login"],
+                "login_channel": "github",
+                "last_login_time": get_format_time(),
+                "is_superuser": False,
+            })
+            if not users:
+                raise Exception(f'Fail to register {email_address}.')
+            if len(users) > 1:
+                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)
+            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)
+    user.save()
+    return redirect("/?auth=%s" % user.get_id())
+
+
+@manager.route('/feishu_callback', methods=['GET'])
+def feishu_callback():
+    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 = app_access_token_res.json()
+    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 = res.json()
+    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"
+    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(user_info["avatar_url"])
+            except Exception as e:
+                stat_logger.exception(e)
+                avatar = ""
+            users = user_register(user_id, {
+                "access_token": session["access_token"],
+                "email": email_address,
+                "avatar": avatar,
+                "nickname": user_info["en_name"],
+                "login_channel": "feishu",
+                "last_login_time": get_format_time(),
+                "is_superuser": False,
+            })
+            if not users:
+                raise Exception(f'Fail to register {email_address}.')
+            if len(users) > 1:
+                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)
+            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)
+    user.save()
+    return redirect("/?auth=%s" % user.get_id())
+
+
+def user_info_from_feishu(access_token):
+    import requests
+    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)
+    user_info = res.json()["data"]
+    user_info["email"] = None if user_info.get("email") == "" else user_info["email"]
+    return user_info
+
+
+def user_info_from_github(access_token):
+    import requests
+    headers = {"Accept": "application/json",
+               'Authorization': f"token {access_token}"}
+    res = requests.get(
+        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()
+    user_info["email"] = next(
+        (email for email in email_info if email['primary'] == True),
+        None)["email"]
+    return user_info
+
+
+@manager.route("/logout", methods=['GET'])
+@login_required
+def log_out():
+    current_user.access_token = ""
+    current_user.save()
+    logout_user()
+    return get_json_result(data=True)
+
+
+@manager.route("/setting", methods=["POST"])
+@login_required
+def setting_user():
+    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"])):
+            return get_json_result(data=False, retcode=RetCode.AUTHENTICATION_ERROR, retmsg='Password error!')
+
+        if new_password:
+            update_dict["password"] = generate_password_hash(decrypt(new_password))
+
+    for k in request_data.keys():
+        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]
+
+    try:
+        UserService.update_by_id(current_user.id, update_dict)
+        return get_json_result(data=True)
+    except Exception as e:
+        stat_logger.exception(e)
+        return get_json_result(data=False, retmsg='Update failure!', retcode=RetCode.EXCEPTION_ERROR)
+
+
+@manager.route("/info", methods=["GET"])
+@login_required
+def user_profile():
+    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:
+        pass
+    try:
+        TenantService.delete_by_id(user_id)
+    except Exception as e:
+        pass
+    try:
+        u = UserTenantService.query(tenant_id=user_id)
+        if u:
+            UserTenantService.delete_by_id(u[0].id)
+    except Exception as e:
+        pass
+    try:
+        TenantLLM.delete().where(TenantLLM.tenant_id == user_id).execute()
+    except Exception as e:
+        pass
+
+
+def user_register(user_id, user):
+    user["id"] = user_id
+    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
+    }
+    usr_tenant = {
+        "tenant_id": user_id,
+        "user_id": user_id,
+        "invited_by": user_id,
+        "role": UserTenantRole.OWNER
+    }
+    file_id = get_uuid()
+    file = {
+        "id": file_id,
+        "parent_id": file_id,
+        "tenant_id": user_id,
+        "created_by": user_id,
+        "name": "/",
+        "type": FileType.FOLDER.value,
+        "size": 0,
+        "location": "",
+    }
+    tenant_llm = []
+    for llm in LLMService.query(fid=LLM_FACTORY):
+        tenant_llm.append({"tenant_id": user_id,
+                           "llm_factory": LLM_FACTORY,
+                           "llm_name": llm.llm_name,
+                           "model_type": llm.model_type,
+                           "api_key": API_KEY,
+                           "api_base": LLM_BASE_URL
+                           })
+
+    if not UserService.save(**user):
+        return
+    TenantService.insert(**tenant)
+    UserTenantService.insert(**usr_tenant)
+    TenantLLMService.insert_many(tenant_llm)
+    FileService.insert(file)
+    return UserService.query(email=user["email"])
+
+
+@manager.route("/register", methods=["POST"])
+@validate_request("nickname", "email", "password")
+def user_add():
+    req = request.json
+    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,
+                               retmsg=f'Invalid email address: {email_address}!',
+                               retcode=RetCode.OPERATING_ERROR)
+
+    # Check if the email address is already used
+    if UserService.query(email=email_address):
+        return get_json_result(
+            data=False,
+            retmsg=f'Email: {email_address} has already registered!',
+            retcode=RetCode.OPERATING_ERROR)
+
+    # Construct user info data
+    nickname = req["nickname"]
+    user_dict = {
+        "access_token": get_uuid(),
+        "email": email_address,
+        "nickname": nickname,
+        "password": decrypt(req["password"]),
+        "login_channel": "password",
+        "last_login_time": get_format_time(),
+        "is_superuser": False,
+    }
+
+    user_id = get_uuid()
+    try:
+        users = user_register(user_id, user_dict)
+        if not users:
+            raise Exception(f'Fail to register {email_address}.')
+        if len(users) > 1:
+            raise Exception(f'Same email: {email_address} exists!')
+        user = users[0]
+        login_user(user)
+        return construct_response(data=user.to_json(),
+                                  auth=user.get_id(),
+                                  retmsg=f"{nickname}, welcome aboard!")
+    except Exception as e:
+        rollback_user_registration(user_id)
+        stat_logger.exception(e)
+        return get_json_result(data=False,
+                               retmsg=f'User registration failure, error: {str(e)}',
+                               retcode=RetCode.EXCEPTION_ERROR)
+
+
+@manager.route("/tenant_info", methods=["GET"])
+@login_required
+def tenant_info():
+    try:
+        tenants = TenantService.get_info_by(current_user.id)
+        if not tenants:
+            return get_data_error_result(retmsg="Tenant not found!")
+        return get_json_result(data=tenants[0])
+    except Exception as e:
+        return server_error_response(e)
+
+
+@manager.route("/set_tenant_info", methods=["POST"])
+@login_required
+@validate_request("tenant_id", "asr_id", "embd_id", "img2txt_id", "llm_id")
+def set_tenant_info():
+    req = request.json
+    try:
+        tid = req["tenant_id"]
+        del req["tenant_id"]
+        TenantService.update_by_id(tid, req)
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)

api/contants.py

@@ -13,4 +13,6 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-NAME_LENGTH_LIMIT = 2 ** 10
+NAME_LENGTH_LIMIT = 2 ** 10
+
+IMG_BASE64_PREFIX = 'data:image/png;base64,'

api/db/__init__.py

@@ -1,99 +1,104 @@
-#
-#  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 enum import Enum
-from enum import IntEnum
-from strenum import StrEnum
-
-
-class StatusEnum(Enum):
-    VALID = "1"
-    INVALID = "0"
-
-
-class UserTenantRole(StrEnum):
-    OWNER = 'owner'
-    ADMIN = 'admin'
-    NORMAL = 'normal'
-
-
-class TenantPermission(StrEnum):
-    ME = 'me'
-    TEAM = 'team'
-
-
-class SerializedType(IntEnum):
-    PICKLE = 1
-    JSON = 2
-
-
-class FileType(StrEnum):
-    PDF = 'pdf'
-    DOC = 'doc'
-    VISUAL = 'visual'
-    AURAL = 'aural'
-    VIRTUAL = 'virtual'
-    FOLDER = 'folder'
-    OTHER = "other"
-
-
-class LLMType(StrEnum):
-    CHAT = 'chat'
-    EMBEDDING = 'embedding'
-    SPEECH2TEXT = 'speech2text'
-    IMAGE2TEXT = 'image2text'
-    RERANK = 'rerank'
-
-
-class ChatStyle(StrEnum):
-    CREATIVE = 'Creative'
-    PRECISE = 'Precise'
-    EVENLY = 'Evenly'
-    CUSTOM = 'Custom'
-
-
-class TaskStatus(StrEnum):
-    UNSTART = "0"
-    RUNNING = "1"
-    CANCEL = "2"
-    DONE = "3"
-    FAIL = "4"
-
-
-class ParserType(StrEnum):
-    PRESENTATION = "presentation"
-    LAWS = "laws"
-    MANUAL = "manual"
-    PAPER = "paper"
-    RESUME = "resume"
-    BOOK = "book"
-    QA = "qa"
-    TABLE = "table"
-    NAIVE = "naive"
-    PICTURE = "picture"
-    ONE = "one"
-
-
-class FileSource(StrEnum):
-    LOCAL = ""
-    KNOWLEDGEBASE = "knowledgebase"
-    S3 = "s3"
-
-
-class CanvasType(StrEnum):
-    ChatBot = "chatbot"
-    DocBot = "docbot"
-
-KNOWLEDGEBASE_FOLDER_NAME=".knowledgebase"
+#
+#  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 enum import Enum
+from enum import IntEnum
+from strenum import StrEnum
+
+
+class StatusEnum(Enum):
+    VALID = "1"
+    INVALID = "0"
+
+
+class UserTenantRole(StrEnum):
+    OWNER = 'owner'
+    ADMIN = 'admin'
+    NORMAL = 'normal'
+    INVITE = 'invite'
+
+
+class TenantPermission(StrEnum):
+    ME = 'me'
+    TEAM = 'team'
+
+
+class SerializedType(IntEnum):
+    PICKLE = 1
+    JSON = 2
+
+
+class FileType(StrEnum):
+    PDF = 'pdf'
+    DOC = 'doc'
+    VISUAL = 'visual'
+    AURAL = 'aural'
+    VIRTUAL = 'virtual'
+    FOLDER = 'folder'
+    OTHER = "other"
+
+
+class LLMType(StrEnum):
+    CHAT = 'chat'
+    EMBEDDING = 'embedding'
+    SPEECH2TEXT = 'speech2text'
+    IMAGE2TEXT = 'image2text'
+    RERANK = 'rerank'
+    TTS    = 'tts'
+
+
+class ChatStyle(StrEnum):
+    CREATIVE = 'Creative'
+    PRECISE = 'Precise'
+    EVENLY = 'Evenly'
+    CUSTOM = 'Custom'
+
+
+class TaskStatus(StrEnum):
+    UNSTART = "0"
+    RUNNING = "1"
+    CANCEL = "2"
+    DONE = "3"
+    FAIL = "4"
+
+
+class ParserType(StrEnum):
+    PRESENTATION = "presentation"
+    LAWS = "laws"
+    MANUAL = "manual"
+    PAPER = "paper"
+    RESUME = "resume"
+    BOOK = "book"
+    QA = "qa"
+    TABLE = "table"
+    NAIVE = "naive"
+    PICTURE = "picture"
+    ONE = "one"
+    AUDIO = "audio"
+    EMAIL = "email"
+    KG = "knowledge_graph"
+
+
+class FileSource(StrEnum):
+    LOCAL = ""
+    KNOWLEDGEBASE = "knowledgebase"
+    S3 = "s3"
+
+
+class CanvasType(StrEnum):
+    ChatBot = "chatbot"
+    DocBot = "docbot"
+
+KNOWLEDGEBASE_FOLDER_NAME=".knowledgebase"

api/db/db_utils.py

@@ -1,130 +1,135 @@
-#
-#  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 operator
-from functools import reduce
-from typing import Dict, Type, Union
-
-from api.utils import current_timestamp, timestamp_to_date
-
-from api.db.db_models import DB, DataBaseModel
-from api.db.runtime_config import RuntimeConfig
-from api.utils.log_utils import getLogger
-from enum import Enum
-
-
-LOGGER = getLogger()
-
-
-@DB.connection_context()
-def bulk_insert_into_db(model, data_source, replace_on_conflict=False):
-    DB.create_tables([model])
-
-    for i, data in enumerate(data_source):
-        current_time = current_timestamp() + i
-        current_date = timestamp_to_date(current_time)
-        if 'create_time' not in data:
-            data['create_time'] = current_time
-        data['create_date'] = timestamp_to_date(data['create_time'])
-        data['update_time'] = current_time
-        data['update_date'] = current_date
-
-    preserve = tuple(data_source[0].keys() - {'create_time', 'create_date'})
-
-    batch_size = 1000
-
-    for i in range(0, len(data_source), batch_size):
-        with DB.atomic():
-            query = model.insert_many(data_source[i:i + batch_size])
-            if replace_on_conflict:
-                query = query.on_conflict(preserve=preserve)
-            query.execute()
-
-
-def get_dynamic_db_model(base, job_id):
-    return type(base.model(
-        table_index=get_dynamic_tracking_table_index(job_id=job_id)))
-
-
-def get_dynamic_tracking_table_index(job_id):
-    return job_id[:8]
-
-
-def fill_db_model_object(model_object, human_model_dict):
-    for k, v in human_model_dict.items():
-        attr_name = 'f_%s' % k
-        if hasattr(model_object.__class__, attr_name):
-            setattr(model_object, attr_name, v)
-    return model_object
-
-
-# https://docs.peewee-orm.com/en/latest/peewee/query_operators.html
-supported_operators = {
-    '==': operator.eq,
-    '<': operator.lt,
-    '<=': operator.le,
-    '>': operator.gt,
-    '>=': operator.ge,
-    '!=': operator.ne,
-    '<<': operator.lshift,
-    '>>': operator.rshift,
-    '%': operator.mod,
-    '**': operator.pow,
-    '^': operator.xor,
-    '~': operator.inv,
-}
-
-
-def query_dict2expression(
-        model: Type[DataBaseModel], query: Dict[str, Union[bool, int, str, list, tuple]]):
-    expression = []
-
-    for field, value in query.items():
-        if not isinstance(value, (list, tuple)):
-            value = ('==', value)
-        op, *val = value
-
-        field = getattr(model, f'f_{field}')
-        value = supported_operators[op](
-            field, val[0]) if op in supported_operators else getattr(
-            field, op)(
-            *val)
-        expression.append(value)
-
-    return reduce(operator.iand, expression)
-
-
-def query_db(model: Type[DataBaseModel], limit: int = 0, offset: int = 0,
-             query: dict = None, order_by: Union[str, list, tuple] = None):
-    data = model.select()
-    if query:
-        data = data.where(query_dict2expression(model, query))
-    count = data.count()
-
-    if not order_by:
-        order_by = 'create_time'
-    if not isinstance(order_by, (list, tuple)):
-        order_by = (order_by, 'asc')
-    order_by, order = order_by
-    order_by = getattr(model, f'f_{order_by}')
-    order_by = getattr(order_by, order)()
-    data = data.order_by(order_by)
-
-    if limit > 0:
-        data = data.limit(limit)
-    if offset > 0:
-        data = data.offset(offset)
-
-    return list(data), count
+#
+#  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 operator
+from functools import reduce
+from typing import Dict, Type, Union
+
+from playhouse.pool import PooledMySQLDatabase
+
+from api.utils import current_timestamp, timestamp_to_date
+
+from api.db.db_models import DB, DataBaseModel
+from api.db.runtime_config import RuntimeConfig
+from api.utils.log_utils import getLogger
+from enum import Enum
+
+
+LOGGER = getLogger()
+
+
+@DB.connection_context()
+def bulk_insert_into_db(model, data_source, replace_on_conflict=False):
+    DB.create_tables([model])
+
+    for i, data in enumerate(data_source):
+        current_time = current_timestamp() + i
+        current_date = timestamp_to_date(current_time)
+        if 'create_time' not in data:
+            data['create_time'] = current_time
+        data['create_date'] = timestamp_to_date(data['create_time'])
+        data['update_time'] = current_time
+        data['update_date'] = current_date
+
+    preserve = tuple(data_source[0].keys() - {'create_time', 'create_date'})
+
+    batch_size = 1000
+
+    for i in range(0, len(data_source), batch_size):
+        with DB.atomic():
+            query = model.insert_many(data_source[i:i + batch_size])
+            if replace_on_conflict:
+                if isinstance(DB, PooledMySQLDatabase):
+                    query = query.on_conflict(preserve=preserve)
+                else:
+                    query = query.on_conflict(conflict_target="id", preserve=preserve)
+            query.execute()
+
+
+def get_dynamic_db_model(base, job_id):
+    return type(base.model(
+        table_index=get_dynamic_tracking_table_index(job_id=job_id)))
+
+
+def get_dynamic_tracking_table_index(job_id):
+    return job_id[:8]
+
+
+def fill_db_model_object(model_object, human_model_dict):
+    for k, v in human_model_dict.items():
+        attr_name = 'f_%s' % k
+        if hasattr(model_object.__class__, attr_name):
+            setattr(model_object, attr_name, v)
+    return model_object
+
+
+# https://docs.peewee-orm.com/en/latest/peewee/query_operators.html
+supported_operators = {
+    '==': operator.eq,
+    '<': operator.lt,
+    '<=': operator.le,
+    '>': operator.gt,
+    '>=': operator.ge,
+    '!=': operator.ne,
+    '<<': operator.lshift,
+    '>>': operator.rshift,
+    '%': operator.mod,
+    '**': operator.pow,
+    '^': operator.xor,
+    '~': operator.inv,
+}
+
+
+def query_dict2expression(
+        model: Type[DataBaseModel], query: Dict[str, Union[bool, int, str, list, tuple]]):
+    expression = []
+
+    for field, value in query.items():
+        if not isinstance(value, (list, tuple)):
+            value = ('==', value)
+        op, *val = value
+
+        field = getattr(model, f'f_{field}')
+        value = supported_operators[op](
+            field, val[0]) if op in supported_operators else getattr(
+            field, op)(
+            *val)
+        expression.append(value)
+
+    return reduce(operator.iand, expression)
+
+
+def query_db(model: Type[DataBaseModel], limit: int = 0, offset: int = 0,
+             query: dict = None, order_by: Union[str, list, tuple] = None):
+    data = model.select()
+    if query:
+        data = data.where(query_dict2expression(model, query))
+    count = data.count()
+
+    if not order_by:
+        order_by = 'create_time'
+    if not isinstance(order_by, (list, tuple)):
+        order_by = (order_by, 'asc')
+    order_by, order = order_by
+    order_by = getattr(model, f'f_{order_by}')
+    order_by = getattr(order_by, order)()
+    data = data.order_by(order_by)
+
+    if limit > 0:
+        data = data.limit(limit)
+    if offset > 0:
+        data = data.offset(offset)
+
+    return list(data), count