Refa: knowledge_graph chunk method is deprecated (#7220)

### What problem does this PR solve?

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

### Type of change

- [x] Refactoring

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -1,15 +1,21 @@
-name: Bug Report
+name: "🐞 Bug Report"
 description: Create a bug issue for RAGFlow
 title: "[Bug]: "
-labels: [bug]
+labels: ["🐞 bug"]
 body:
 - type: checkboxes
   attributes:
-    label: Is there an existing issue for the same bug?
-    description: Please check if an issue already exists for the bug you encountered.
+    label: Self Checks
+    description: "Please check the following in order to be responded in time :)"
     options:
-    - label: I have checked the existing issues.
-      required: true
+      - label: I have searched for existing issues [search for existing issues](https://github.com/infiniflow/ragflow/issues), including closed ones.
+        required: true
+      - label: I confirm that I am using English to submit this report ([Language Policy](https://github.com/infiniflow/ragflow/issues/5910)).
+        required: true
+      - label: Non-english title submitions will be closed directly ( 非英文标题的提交将会被直接关闭 ) ([Language Policy](https://github.com/infiniflow/ragflow/issues/5910)).
+        required: true
+      - label: "Please do not modify this template :) and fill in all the required fields."
+        required: true
 - type: markdown
   attributes:
     value: "Please provide the following information to help us understand the issue."

.github/ISSUE_TEMPLATE/feature_request.md

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

.github/ISSUE_TEMPLATE/feature_request.yml

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

.github/ISSUE_TEMPLATE/question.yml

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

.github/workflows/tests.yml

@@ -51,7 +51,7 @@ jobs:
         uses: astral-sh/ruff-action@v2
         with:
           version: ">=0.8.2"
-          args: "check --ignore E402"
+          args: "check"
 
       - name: Build ragflow:nightly-slim
         run: |
@@ -98,6 +98,15 @@ jobs:
           done
           cd sdk/python && uv sync --python 3.10 --frozen && uv pip install . && source .venv/bin/activate && cd test/test_frontend_api && pytest -s --tb=short get_email.py test_dataset.py
           
+      - name: Run http api tests against Elasticsearch
+        run: |
+          export http_proxy=""; export https_proxy=""; export no_proxy=""; export HTTP_PROXY=""; export HTTPS_PROXY=""; export NO_PROXY=""
+          export HOST_ADDRESS=http://host.docker.internal:9380
+          until sudo docker exec ragflow-server curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
+            echo "Waiting for service to be available..."
+            sleep 5
+          done
+          cd sdk/python && uv sync --python 3.10 --frozen && uv pip install . && source .venv/bin/activate && cd test/test_http_api && pytest -s --tb=short -m "not slow"
 
       - name: Stop ragflow:nightly
         if: always()  # always run this step even if previous steps failed
@@ -128,6 +137,16 @@ jobs:
           done
           cd sdk/python && uv sync --python 3.10 --frozen && uv pip install . && source .venv/bin/activate && cd test/test_frontend_api && pytest -s --tb=short get_email.py test_dataset.py
 
+      - name: Run http api tests against Infinity
+        run: |
+          export http_proxy=""; export https_proxy=""; export no_proxy=""; export HTTP_PROXY=""; export HTTPS_PROXY=""; export NO_PROXY=""
+          export HOST_ADDRESS=http://host.docker.internal:9380
+          until sudo docker exec ragflow-server curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
+            echo "Waiting for service to be available..."
+            sleep 5
+          done
+          cd sdk/python && uv sync --python 3.10 --frozen && uv pip install . && source .venv/bin/activate && cd test/test_http_api && DOC_ENGINE=infinity pytest -s --tb=short -m "not slow"
+
       - name: Stop ragflow:nightly
         if: always()  # always run this step even if previous steps failed
         run: |

.gitignore

@@ -41,3 +41,4 @@ nltk_data/
 
 # Exclude hash-like temporary files like 9b5ad71b2ce5302211f9c61530b329a4922fc6a4
 *[0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f]*
+.lh/

Dockerfile

@@ -21,9 +21,7 @@ RUN --mount=type=bind,from=infiniflow/ragflow_deps:latest,source=/huggingface.co
     if [ "$LIGHTEN" != "1" ]; then \
         (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) \
     fi
 
@@ -46,7 +44,8 @@ ENV DEBIAN_FRONTEND=noninteractive
 # Building C extensions: libpython3-dev libgtk-4-1 libnss3 xdg-utils libgbm-dev
 RUN --mount=type=cache,id=ragflow_apt,target=/var/cache/apt,sharing=locked \
     if [ "$NEED_MIRROR" == "1" ]; then \
-        sed -i 's|http://archive.ubuntu.com|https://mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list; \
+        sed -i 's|http://ports.ubuntu.com|http://mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list; \
+        sed -i 's|http://archive.ubuntu.com|http://mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list; \
     fi; \
     rm -f /etc/apt/apt.conf.d/docker-clean && \
     echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache && \
@@ -59,6 +58,7 @@ RUN --mount=type=cache,id=ragflow_apt,target=/var/cache/apt,sharing=locked \
     apt install -y default-jdk && \
     apt install -y libatk-bridge2.0-0 && \
     apt install -y libpython3-dev libgtk-4-1 libnss3 xdg-utils libgbm-dev && \
+    apt install -y libjemalloc-dev && \
     apt install -y python3-pip pipx nginx unzip curl wget git vim less
 
 RUN if [ "$NEED_MIRROR" == "1" ]; then \
@@ -198,9 +198,10 @@ COPY agent agent
 COPY graphrag graphrag
 COPY agentic_reasoning agentic_reasoning
 COPY pyproject.toml uv.lock ./
+COPY mcp mcp
 
 COPY docker/service_conf.yaml.template ./conf/service_conf.yaml.template
-COPY docker/entrypoint.sh docker/entrypoint-parser.sh ./
+COPY docker/entrypoint.sh ./
 RUN chmod +x ./entrypoint*.sh
 
 # Copy compiled web pages

README.md

@@ -22,7 +22,7 @@
         <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:v0.17.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.17.0">
+        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.18.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.18.0">
     </a>
     <a href="https://github.com/infiniflow/ragflow/releases/latest">
         <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Latest%20Release" alt="Latest Release">
@@ -36,7 +36,7 @@
   <a href="https://ragflow.io/docs/dev/">Document</a> |
   <a href="https://github.com/infiniflow/ragflow/issues/4214">Roadmap</a> |
   <a href="https://twitter.com/infiniflowai">Twitter</a> |
-  <a href="https://discord.gg/4XxujFgUN7">Discord</a> |
+  <a href="https://discord.gg/NjYzJD3GM3">Discord</a> |
   <a href="https://demo.ragflow.io">Demo</a>
 </h4>
 
@@ -78,11 +78,10 @@ Try our demo at [https://demo.ragflow.io](https://demo.ragflow.io).
 
 ## 🔥 Latest Updates
 
-- 2025-02-05 Updates the model list of 'SILICONFLOW' and adds support for Deepseek-R1/DeepSeek-V3.
+- 2025-03-19 Supports using a multi-modal model to make sense of images within PDF or DOCX files.
+- 2025-02-28 Combined with Internet search (Tavily), supports reasoning like Deep Research for any LLMs.
 - 2025-01-26 Optimizes knowledge graph extraction and application, offering various configuration options.
 - 2024-12-18 Upgrades Document Layout Analysis model in DeepDoc.
-- 2024-12-04 Adds support for pagerank score in knowledge base.
-- 2024-11-22 Adds more variables to Agent.
 - 2024-11-01 Adds keyword extraction and related question generation to the parsed chunks to improve the accuracy of retrieval.
 - 2024-08-22 Support text to SQL statements through RAG.
 
@@ -173,19 +172,27 @@ releases! 🌟
 
 3. Start up the server using the pre-built Docker images:
 
-   > The command below downloads the `v0.17.0-slim` edition of the RAGFlow Docker image. Refer to the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.17.0-slim`, update the `RAGFLOW_IMAGE` variable accordingly in **docker/.env** before using `docker compose` to start the server. For example: set `RAGFLOW_IMAGE=infiniflow/ragflow:v0.17.0` for the full edition `v0.17.0`.
+> [!CAUTION]
+> All Docker images are built for x86 platforms. We don't currently offer Docker images for ARM64.
+> If you are on an ARM64 platform, follow [this guide](https://ragflow.io/docs/dev/build_docker_image) to build a Docker image compatible with your system.
+
+   > The command below downloads the `v0.18.0-slim` edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.18.0-slim`, update the `RAGFLOW_IMAGE` variable accordingly in **docker/.env** before using `docker compose` to start the server. For example: set `RAGFLOW_IMAGE=infiniflow/ragflow:v0.18.0` for the full edition `v0.18.0`.
 
    ```bash
    $ cd ragflow/docker
+   # Use CPU for embedding and DeepDoc tasks:
    $ docker compose -f docker-compose.yml up -d
+
+   # To use GPU to accelerate embedding and DeepDoc tasks:
+   # docker compose -f docker-compose-gpu.yml up -d
    ```
 
    | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
    |-------------------|-----------------|-----------------------|--------------------------|
-   | v0.17.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
-   | v0.17.0-slim      | &approx;2       | ❌                     | Stable release           |
+   | v0.18.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
+   | v0.18.0-slim      | &approx;2       | ❌                   | Stable release            |
    | nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
-   | nightly-slim      | &approx;2       | ❌                     | _Unstable_ nightly build |
+   | nightly-slim      | &approx;2       | ❌                   | _Unstable_ nightly build  |
 
 4. Check the server status after having the server up and running:
 
@@ -271,7 +278,7 @@ This image is approximately 2 GB in size and relies on external LLM and embeddin
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
+docker build --platform linux/amd64 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
 ```
 
 ## 🔧 Build a Docker image including embedding models
@@ -281,7 +288,7 @@ This image is approximately 9 GB in size. As it includes embedding models, it re
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build -f Dockerfile -t infiniflow/ragflow:nightly .
+docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .
 ```
 
 ## 🔨 Launch service from source for development
@@ -344,9 +351,12 @@ docker build -f Dockerfile -t infiniflow/ragflow:nightly .
 ## 📚 Documentation
 
 - [Quickstart](https://ragflow.io/docs/dev/)
-- [User guide](https://ragflow.io/docs/dev/category/guides)
+- [Configuration](https://ragflow.io/docs/dev/configurations)
+- [Release notes](https://ragflow.io/docs/dev/release_notes)
+- [User guides](https://ragflow.io/docs/dev/category/guides)
+- [Developer guides](https://ragflow.io/docs/dev/category/developers)
 - [References](https://ragflow.io/docs/dev/category/references)
-- [FAQ](https://ragflow.io/docs/dev/faq)
+- [FAQs](https://ragflow.io/docs/dev/faq)
 
 ## 📜 Roadmap
 
@@ -354,7 +364,7 @@ See the [RAGFlow Roadmap 2025](https://github.com/infiniflow/ragflow/issues/4214
 
 ## 🏄 Community
 
-- [Discord](https://discord.gg/4XxujFgUN7)
+- [Discord](https://discord.gg/NjYzJD3GM3)
 - [Twitter](https://twitter.com/infiniflowai)
 - [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
 

README_id.md

@@ -22,7 +22,7 @@
         <img alt="Lencana Daring" src="https://img.shields.io/badge/Online-Demo-4e6b99">
     </a>
     <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
-        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.17.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.17.0">
+        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.18.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.18.0">
     </a>
     <a href="https://github.com/infiniflow/ragflow/releases/latest">
         <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Rilis%20Terbaru" alt="Rilis Terbaru">
@@ -36,7 +36,7 @@
   <a href="https://ragflow.io/docs/dev/">Dokumentasi</a> |
   <a href="https://github.com/infiniflow/ragflow/issues/4214">Peta Jalan</a> |
   <a href="https://twitter.com/infiniflowai">Twitter</a> |
-  <a href="https://discord.gg/4XxujFgUN7">Discord</a> |
+  <a href="https://discord.gg/NjYzJD3GM3">Discord</a> |
   <a href="https://demo.ragflow.io">Demo</a>
 </h4>
 
@@ -75,11 +75,10 @@ Coba demo kami di [https://demo.ragflow.io](https://demo.ragflow.io).
 
 ## 🔥 Pembaruan Terbaru
 
-- 2025-02-05 Memperbarui daftar model 'SILICONFLOW' dan menambahkan dukungan untuk Deepseek-R1/DeepSeek-V3.
+- 2025-03-19 Mendukung penggunaan model multi-modal untuk memahami gambar di dalam file PDF atau DOCX.
+- 2025-02-28 dikombinasikan dengan pencarian Internet (TAVILY), mendukung penelitian mendalam untuk LLM apa pun.
 - 2025-01-26 Optimalkan ekstraksi dan penerapan grafik pengetahuan dan sediakan berbagai opsi konfigurasi.
 - 2024-12-18 Meningkatkan model Analisis Tata Letak Dokumen di DeepDoc.
-- 2024-12-04 Mendukung skor pagerank ke basis pengetahuan.
-- 2024-11-22 Peningkatan definisi dan penggunaan variabel di Agen.
 - 2024-11-01 Penambahan ekstraksi kata kunci dan pembuatan pertanyaan terkait untuk meningkatkan akurasi pengambilan.
 - 2024-08-22 Dukungan untuk teks ke pernyataan SQL melalui RAG.
 
@@ -166,21 +165,29 @@ Coba demo kami di [https://demo.ragflow.io](https://demo.ragflow.io).
 
 3. Bangun image Docker pre-built dan jalankan server:
 
-   > Perintah di bawah ini mengunduh edisi v0.17.0-slim dari gambar Docker RAGFlow. Silakan merujuk ke tabel berikut untuk deskripsi berbagai edisi RAGFlow. Untuk mengunduh edisi RAGFlow yang berbeda dari v0.17.0-slim, perbarui variabel RAGFLOW_IMAGE di docker/.env sebelum menggunakan docker compose untuk memulai server. Misalnya, atur RAGFLOW_IMAGE=infiniflow/ragflow:v0.17.0 untuk edisi lengkap v0.17.0.
+> [!CAUTION]
+> Semua gambar Docker dibangun untuk platform x86. Saat ini, kami tidak menawarkan gambar Docker untuk ARM64.
+> Jika Anda menggunakan platform ARM64, [silakan gunakan panduan ini untuk membangun gambar Docker yang kompatibel dengan sistem Anda](https://ragflow.io/docs/dev/build_docker_image).
 
-   ```bash
-   $ cd ragflow/docker
-   $ docker compose -f docker-compose.yml up -d
-   ```
+> Perintah di bawah ini mengunduh edisi v0.18.0-slim dari gambar Docker RAGFlow. Silakan merujuk ke tabel berikut untuk deskripsi berbagai edisi RAGFlow. Untuk mengunduh edisi RAGFlow yang berbeda dari v0.18.0-slim, perbarui variabel RAGFLOW_IMAGE di docker/.env sebelum menggunakan docker compose untuk memulai server. Misalnya, atur RAGFLOW_IMAGE=infiniflow/ragflow:v0.18.0 untuk edisi lengkap v0.18.0.
 
-   | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
-   | ----------------- | --------------- | --------------------- | ------------------------ |
-   | v0.17.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
-   | v0.17.0-slim      | &approx;2       | ❌                    | Stable release           |
-   | nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
-   | nightly-slim      | &approx;2       | ❌                     | _Unstable_ nightly build |
+```bash
+$ cd ragflow/docker
+# Use CPU for embedding and DeepDoc tasks:
+$ docker compose -f docker-compose.yml up -d
 
-4. Periksa status server setelah server aktif dan berjalan:
+# To use GPU to accelerate embedding and DeepDoc tasks:
+# docker compose -f docker-compose-gpu.yml up -d
+```
+
+| RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
+| ----------------- | --------------- | --------------------- | ------------------------ |
+| v0.18.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
+| v0.18.0-slim      | &approx;2       | ❌                    | Stable release           |
+| nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
+| nightly-slim      | &approx;2       | ❌                    | _Unstable_ nightly build |
+
+1. Periksa status server setelah server aktif dan berjalan:
 
    ```bash
    $ docker logs -f ragflow-server
@@ -202,10 +209,10 @@ Coba demo kami di [https://demo.ragflow.io](https://demo.ragflow.io).
    > Jika Anda melewatkan langkah ini dan langsung login ke RAGFlow, browser Anda mungkin menampilkan error `network anormal`
    > karena RAGFlow mungkin belum sepenuhnya siap.
 
-5. Buka browser web Anda, masukkan alamat IP server Anda, dan login ke RAGFlow.
+2. Buka browser web Anda, masukkan alamat IP server Anda, dan login ke RAGFlow.
    > Dengan pengaturan default, Anda hanya perlu memasukkan `http://IP_DEVICE_ANDA` (**tanpa** nomor port) karena
    > port HTTP default `80` bisa dihilangkan saat menggunakan konfigurasi default.
-6. Dalam [service_conf.yaml.template](./docker/service_conf.yaml.template), pilih LLM factory yang diinginkan di `user_default_llm` dan perbarui
+3. Dalam [service_conf.yaml.template](./docker/service_conf.yaml.template), pilih LLM factory yang diinginkan di `user_default_llm` dan perbarui
    bidang `API_KEY` dengan kunci API yang sesuai.
 
    > Lihat [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) untuk informasi lebih lanjut.
@@ -237,7 +244,7 @@ Image ini berukuran sekitar 2 GB dan bergantung pada aplikasi LLM eksternal dan
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
+docker build --platform linux/amd64 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
 ```
 
 ## 🔧 Membangun Docker Image Termasuk Model Embedding
@@ -247,7 +254,7 @@ Image ini berukuran sekitar 9 GB. Karena sudah termasuk model embedding, ia hany
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build -f Dockerfile -t infiniflow/ragflow:nightly .
+docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .
 ```
 
 ## 🔨 Menjalankan Aplikasi dari untuk Pengembangan
@@ -310,9 +317,12 @@ docker build -f Dockerfile -t infiniflow/ragflow:nightly .
 ## 📚 Dokumentasi
 
 - [Quickstart](https://ragflow.io/docs/dev/)
-- [Panduan Pengguna](https://ragflow.io/docs/dev/category/guides)
-- [Referensi](https://ragflow.io/docs/dev/category/references)
-- [FAQ](https://ragflow.io/docs/dev/faq)
+- [Configuration](https://ragflow.io/docs/dev/configurations)
+- [Release notes](https://ragflow.io/docs/dev/release_notes)
+- [User guides](https://ragflow.io/docs/dev/category/guides)
+- [Developer guides](https://ragflow.io/docs/dev/category/developers)
+- [References](https://ragflow.io/docs/dev/category/references)
+- [FAQs](https://ragflow.io/docs/dev/faq)
 
 ## 📜 Roadmap
 
@@ -320,7 +330,7 @@ Lihat [Roadmap RAGFlow 2025](https://github.com/infiniflow/ragflow/issues/4214)
 
 ## 🏄 Komunitas
 
-- [Discord](https://discord.gg/4XxujFgUN7)
+- [Discord](https://discord.gg/NjYzJD3GM3)
 - [Twitter](https://twitter.com/infiniflowai)
 - [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
 

README_ja.md

@@ -22,7 +22,7 @@
         <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:v0.17.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.17.0">
+        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.18.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.18.0">
     </a>
     <a href="https://github.com/infiniflow/ragflow/releases/latest">
         <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Latest%20Release" alt="Latest Release">
@@ -36,7 +36,7 @@
   <a href="https://ragflow.io/docs/dev/">Document</a> |
   <a href="https://github.com/infiniflow/ragflow/issues/4214">Roadmap</a> |
   <a href="https://twitter.com/infiniflowai">Twitter</a> |
-  <a href="https://discord.gg/4XxujFgUN7">Discord</a> |
+  <a href="https://discord.gg/NjYzJD3GM3">Discord</a> |
   <a href="https://demo.ragflow.io">Demo</a>
 </h4>
 
@@ -55,11 +55,10 @@
 
 ## 🔥 最新情報
 
-- 2025-02-05 シリコン フローの St およびモデル リストを更新し、Deep Seek-R1/Deep Seek-V3 のサポートを追加しました。
+- 2025-03-19 PDFまたはDOCXファイル内の画像を理解するために、多モーダルモデルを使用することをサポートします。
+- 2025-02-28 インターネット検索 (TAVILY) と組み合わせて、あらゆる LLM の詳細な調査をサポートします。
 - 2025-01-26 ナレッジ グラフの抽出と適用を最適化し、さまざまな構成オプションを提供します。
 - 2024-12-18 DeepDoc のドキュメント レイアウト分析モデルをアップグレードします。
-- 2024-12-04 ナレッジ ベースへのページランク スコアをサポートしました。
-- 2024-11-22 エージェントでの変数の定義と使用法を改善しました。
 - 2024-11-01 再現の精度を向上させるために、解析されたチャンクにキーワード抽出と関連質問の生成を追加しました。
 - 2024-08-22 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。
 
@@ -146,21 +145,29 @@
 
 3. ビルド済みの Docker イメージをビルドし、サーバーを起動する:
 
-   > 以下のコマンドは、RAGFlow Docker イメージの v0.17.0-slim エディションをダウンロードします。異なる RAGFlow エディションの説明については、以下の表を参照してください。v0.17.0-slim とは異なるエディションをダウンロードするには、docker/.env ファイルの RAGFLOW_IMAGE 変数を適宜更新し、docker compose を使用してサーバーを起動してください。例えば、完全版 v0.17.0 をダウンロードするには、RAGFLOW_IMAGE=infiniflow/ragflow:v0.17.0 と設定します。
+> [!CAUTION]
+> 現在、公式に提供されているすべての Docker イメージは x86 アーキテクチャ向けにビルドされており、ARM64 用の Docker イメージは提供されていません。
+> ARM64 アーキテクチャのオペレーティングシステムを使用している場合は、[このドキュメント](https://ragflow.io/docs/dev/build_docker_image)を参照して Docker イメージを自分でビルドしてください。
+
+   > 以下のコマンドは、RAGFlow Docker イメージの v0.18.0-slim エディションをダウンロードします。異なる RAGFlow エディションの説明については、以下の表を参照してください。v0.18.0-slim とは異なるエディションをダウンロードするには、docker/.env ファイルの RAGFLOW_IMAGE 変数を適宜更新し、docker compose を使用してサーバーを起動してください。例えば、完全版 v0.18.0 をダウンロードするには、RAGFLOW_IMAGE=infiniflow/ragflow:v0.18.0 と設定します。
 
    ```bash
    $ cd ragflow/docker
+   # Use CPU for embedding and DeepDoc tasks:
    $ docker compose -f docker-compose.yml up -d
+
+   # To use GPU to accelerate embedding and DeepDoc tasks:
+   # docker compose -f docker-compose-gpu.yml up -d
    ```
 
    | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
    | ----------------- | --------------- | --------------------- | ------------------------ |
-   | v0.17.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
-   | v0.17.0-slim      | &approx;2       | ❌                    | Stable release           |
+   | v0.18.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
+   | v0.18.0-slim      | &approx;2       | ❌                    | Stable release           |
    | nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
    | nightly-slim      | &approx;2       | ❌                     | _Unstable_ nightly build |
 
-4. サーバーを立ち上げた後、サーバーの状態を確認する:
+1. サーバーを立ち上げた後、サーバーの状態を確認する:
 
    ```bash
    $ docker logs -f ragflow-server
@@ -180,9 +187,9 @@
 
    > もし確認ステップをスキップして直接 RAGFlow にログインした場合、その時点で RAGFlow が完全に初期化されていない可能性があるため、ブラウザーがネットワーク異常エラーを表示するかもしれません。
 
-5. ウェブブラウザで、プロンプトに従ってサーバーの IP アドレスを入力し、RAGFlow にログインします。
+2. ウェブブラウザで、プロンプトに従ってサーバーの IP アドレスを入力し、RAGFlow にログインします。
    > デフォルトの設定を使用する場合、デフォルトの HTTP サービングポート `80` は省略できるので、与えられたシナリオでは、`http://IP_OF_YOUR_MACHINE`（ポート番号は省略）だけを入力すればよい。
-6. [service_conf.yaml.template](./docker/service_conf.yaml.template) で、`user_default_llm` で希望の LLM ファクトリを選択し、`API_KEY` フィールドを対応する API キーで更新する。
+3. [service_conf.yaml.template](./docker/service_conf.yaml.template) で、`user_default_llm` で希望の LLM ファクトリを選択し、`API_KEY` フィールドを対応する API キーで更新する。
 
    > 詳しくは [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) を参照してください。
 
@@ -233,7 +240,7 @@ RAGFlow はデフォルトで Elasticsearch を使用して全文とベクトル
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
+docker build --platform linux/amd64 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-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）
@@ -243,7 +250,7 @@ docker build --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-s
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build -f Dockerfile -t infiniflow/ragflow:nightly .
+docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .
 ```
 
 ## 🔨 ソースコードからサービスを起動する方法
@@ -306,9 +313,12 @@ docker build -f Dockerfile -t infiniflow/ragflow:nightly .
 ## 📚 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)
+- [Configuration](https://ragflow.io/docs/dev/configurations)
+- [Release notes](https://ragflow.io/docs/dev/release_notes)
+- [User guides](https://ragflow.io/docs/dev/category/guides)
+- [Developer guides](https://ragflow.io/docs/dev/category/developers)
 - [References](https://ragflow.io/docs/dev/category/references)
-- [FAQ](https://ragflow.io/docs/dev/faq)
+- [FAQs](https://ragflow.io/docs/dev/faq)
 
 ## 📜 ロードマップ
 
@@ -316,7 +326,7 @@ docker build -f Dockerfile -t infiniflow/ragflow:nightly .
 
 ## 🏄 コミュニティ
 
-- [Discord](https://discord.gg/4XxujFgUN7)
+- [Discord](https://discord.gg/NjYzJD3GM3)
 - [Twitter](https://twitter.com/infiniflowai)
 - [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
 

README_ko.md

@@ -22,7 +22,7 @@
         <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:v0.17.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.17.0">
+        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.18.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.18.0">
     </a>
     <a href="https://github.com/infiniflow/ragflow/releases/latest">
         <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Latest%20Release" alt="Latest Release">
@@ -36,7 +36,7 @@
   <a href="https://ragflow.io/docs/dev/">Document</a> |
   <a href="https://github.com/infiniflow/ragflow/issues/4214">Roadmap</a> |
   <a href="https://twitter.com/infiniflowai">Twitter</a> |
-  <a href="https://discord.gg/4XxujFgUN7">Discord</a> |
+  <a href="https://discord.gg/NjYzJD3GM3">Discord</a> |
   <a href="https://demo.ragflow.io">Demo</a>
 </h4>
 
@@ -55,12 +55,10 @@
 
 ## 🔥 업데이트
 
-- 2025-02-05 'SILICONFLOW' 모델 목록을 업데이트하고 Deepseek-R1/DeepSeek-V3에 대한 지원을 추가합니다.
+- 2025-03-19 PDF 또는 DOCX 파일 내의 이미지를 이해하기 위해 다중 모드 모델을 사용하는 것을 지원합니다.
+- 2025-02-28 인터넷 검색(TAVILY)과 결합되어 모든 LLM에 대한 심층 연구를 지원합니다.
 - 2025-01-26 지식 그래프 추출 및 적용을 최적화하고 다양한 구성 옵션을 제공합니다.
 - 2024-12-18 DeepDoc의 문서 레이아웃 분석 모델 업그레이드.
-- 2024-12-04 지식베이스에 대한 페이지랭크 점수를 지원합니다.
-
-- 2024-11-22 에이전트의 변수 정의 및 사용을 개선했습니다.
 - 2024-11-01 파싱된 청크에 키워드 추출 및 관련 질문 생성을 추가하여 재현율을 향상시킵니다.
 - 2024-08-22 RAG를 통해 SQL 문에 텍스트를 지원합니다.
 
@@ -147,21 +145,29 @@
 
 3. 미리 빌드된 Docker 이미지를 생성하고 서버를 시작하세요:
 
-   > 아래 명령어는 RAGFlow Docker 이미지의 v0.17.0-slim 버전을 다운로드합니다. 다양한 RAGFlow 버전에 대한 설명은 다음 표를 참조하십시오. v0.17.0-slim과 다른 RAGFlow 버전을 다운로드하려면, docker/.env 파일에서 RAGFLOW_IMAGE 변수를 적절히 업데이트한 후 docker compose를 사용하여 서버를 시작하십시오. 예를 들어, 전체 버전인 v0.17.0을 다운로드하려면 RAGFLOW_IMAGE=infiniflow/ragflow:v0.17.0로 설정합니다.
+> [!CAUTION]
+> 모든 Docker 이미지는 x86 플랫폼을 위해 빌드되었습니다. 우리는 현재 ARM64 플랫폼을 위한 Docker 이미지를 제공하지 않습니다.
+> ARM64 플랫폼을 사용 중이라면, [시스템과 호환되는 Docker 이미지를 빌드하려면 이 가이드를 사용해 주세요](https://ragflow.io/docs/dev/build_docker_image).
+
+   > 아래 명령어는 RAGFlow Docker 이미지의 v0.18.0-slim 버전을 다운로드합니다. 다양한 RAGFlow 버전에 대한 설명은 다음 표를 참조하십시오. v0.18.0-slim과 다른 RAGFlow 버전을 다운로드하려면, docker/.env 파일에서 RAGFLOW_IMAGE 변수를 적절히 업데이트한 후 docker compose를 사용하여 서버를 시작하십시오. 예를 들어, 전체 버전인 v0.18.0을 다운로드하려면 RAGFLOW_IMAGE=infiniflow/ragflow:v0.18.0로 설정합니다.
 
    ```bash
    $ cd ragflow/docker
+   # Use CPU for embedding and DeepDoc tasks:
    $ docker compose -f docker-compose.yml up -d
+
+   # To use GPU to accelerate embedding and DeepDoc tasks:
+   # docker compose -f docker-compose-gpu.yml up -d
    ```
 
    | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
    | ----------------- | --------------- | --------------------- | ------------------------ |
-   | v0.17.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
-   | v0.17.0-slim      | &approx;2       | ❌                    | Stable release           |
+   | v0.18.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
+   | v0.18.0-slim      | &approx;2       | ❌                    | Stable release           |
    | nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
    | nightly-slim      | &approx;2       | ❌                     | _Unstable_ nightly build |
 
-4. 서버가 시작된 후 서버 상태를 확인하세요:
+1. 서버가 시작된 후 서버 상태를 확인하세요:
 
    ```bash
    $ docker logs -f ragflow-server
@@ -181,9 +187,9 @@
 
    > 만약 확인 단계를 건너뛰고 바로 RAGFlow에 로그인하면, RAGFlow가 완전히 초기화되지 않았기 때문에 브라우저에서 `network anormal` 오류가 발생할 수 있습니다.
 
-5. 웹 브라우저에 서버의 IP 주소를 입력하고 RAGFlow에 로그인하세요.
+2. 웹 브라우저에 서버의 IP 주소를 입력하고 RAGFlow에 로그인하세요.
    > 기본 설정을 사용할 경우, `http://IP_OF_YOUR_MACHINE`만 입력하면 됩니다 (포트 번호는 제외). 기본 HTTP 서비스 포트 `80`은 기본 구성으로 사용할 때 생략할 수 있습니다.
-6. [service_conf.yaml.template](./docker/service_conf.yaml.template) 파일에서 원하는 LLM 팩토리를 `user_default_llm`에 선택하고, `API_KEY` 필드를 해당 API 키로 업데이트하세요.
+3. [service_conf.yaml.template](./docker/service_conf.yaml.template) 파일에서 원하는 LLM 팩토리를 `user_default_llm`에 선택하고, `API_KEY` 필드를 해당 API 키로 업데이트하세요.
 
    > 자세한 내용은 [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup)를 참조하세요.
 
@@ -233,7 +239,7 @@ RAGFlow 는 기본적으로 Elasticsearch 를 사용하여 전체 텍스트 및
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
+docker build --platform linux/amd64 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
 ```
 
 ## 🔧 소스 코드로 Docker 이미지를 컴파일합니다(임베딩 모델 포함)
@@ -243,7 +249,7 @@ docker build --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-s
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build -f Dockerfile -t infiniflow/ragflow:nightly .
+docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .
 ```
 
 ## 🔨 소스 코드로 서비스를 시작합니다.
@@ -306,9 +312,12 @@ docker build -f Dockerfile -t infiniflow/ragflow:nightly .
 ## 📚 문서
 
 - [Quickstart](https://ragflow.io/docs/dev/)
-- [User guide](https://ragflow.io/docs/dev/category/guides)
+- [Configuration](https://ragflow.io/docs/dev/configurations)
+- [Release notes](https://ragflow.io/docs/dev/release_notes)
+- [User guides](https://ragflow.io/docs/dev/category/guides)
+- [Developer guides](https://ragflow.io/docs/dev/category/developers)
 - [References](https://ragflow.io/docs/dev/category/references)
-- [FAQ](https://ragflow.io/docs/dev/faq)
+- [FAQs](https://ragflow.io/docs/dev/faq)
 
 ## 📜 로드맵
 
@@ -316,7 +325,7 @@ docker build -f Dockerfile -t infiniflow/ragflow:nightly .
 
 ## 🏄 커뮤니티
 
-- [Discord](https://discord.gg/4XxujFgUN7)
+- [Discord](https://discord.gg/NjYzJD3GM3)
 - [Twitter](https://twitter.com/infiniflowai)
 - [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
 

README_pt_br.md

@@ -22,7 +22,7 @@
         <img alt="Badge Estático" src="https://img.shields.io/badge/Online-Demo-4e6b99">
     </a>
     <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
-        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.17.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.17.0">
+        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.18.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.18.0">
     </a>
     <a href="https://github.com/infiniflow/ragflow/releases/latest">
         <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Última%20Relese" alt="Última Versão">
@@ -36,7 +36,7 @@
   <a href="https://ragflow.io/docs/dev/">Documentação</a> |
   <a href="https://github.com/infiniflow/ragflow/issues/4214">Roadmap</a> |
   <a href="https://twitter.com/infiniflowai">Twitter</a> |
-  <a href="https://discord.gg/4XxujFgUN7">Discord</a> |
+  <a href="https://discord.gg/NjYzJD3GM3">Discord</a> |
   <a href="https://demo.ragflow.io">Demo</a>
 </h4>
 
@@ -75,11 +75,10 @@ Experimente nossa demo em [https://demo.ragflow.io](https://demo.ragflow.io).
 
 ## 🔥 Últimas Atualizações
 
-- 05-02-2025 Atualiza a lista de modelos de 'SILICONFLOW' e adiciona suporte para Deepseek-R1/DeepSeek-V3.
+- 19-03-2025 Suporta o uso de um modelo multi-modal para entender imagens dentro de arquivos PDF ou DOCX.
+- 28-02-2025 combinado com a pesquisa na Internet (T AVI LY), suporta pesquisas profundas para qualquer LLM.
 - 26-01-2025 Otimize a extração e aplicação de gráficos de conhecimento e forneça uma variedade de opções de configuração.
 - 18-12-2024 Atualiza o modelo de Análise de Layout de Documentos no DeepDoc.
-- 04-12-2024 Adiciona suporte para pontuação de pagerank na base de conhecimento.
-- 22-11-2024 Adiciona mais variáveis para o Agente.
 - 01-11-2024 Adiciona extração de palavras-chave e geração de perguntas relacionadas aos blocos analisados para melhorar a precisão da recuperação.
 - 22-08-2024 Suporta conversão de texto para comandos SQL via RAG.
 
@@ -166,17 +165,25 @@ Experimente nossa demo em [https://demo.ragflow.io](https://demo.ragflow.io).
 
 3.  Inicie o servidor usando as imagens Docker pré-compiladas:
 
-    > O comando abaixo baixa a edição `v0.17.0-slim` da imagem Docker do RAGFlow. Consulte a tabela a seguir para descrições de diferentes edições do RAGFlow. Para baixar uma edição do RAGFlow diferente da `v0.17.0-slim`, atualize a variável `RAGFLOW_IMAGE` conforme necessário no **docker/.env** antes de usar `docker compose` para iniciar o servidor. Por exemplo: defina `RAGFLOW_IMAGE=infiniflow/ragflow:v0.17.0` para a edição completa `v0.17.0`.
+> [!CAUTION]
+> Todas as imagens Docker são construídas para plataformas x86. Atualmente, não oferecemos imagens Docker para ARM64.
+> Se você estiver usando uma plataforma ARM64, por favor, utilize [este guia](https://ragflow.io/docs/dev/build_docker_image) para construir uma imagem Docker compatível com o seu sistema.
+
+    > O comando abaixo baixa a edição `v0.18.0-slim` da imagem Docker do RAGFlow. Consulte a tabela a seguir para descrições de diferentes edições do RAGFlow. Para baixar uma edição do RAGFlow diferente da `v0.18.0-slim`, atualize a variável `RAGFLOW_IMAGE` conforme necessário no **docker/.env** antes de usar `docker compose` para iniciar o servidor. Por exemplo: defina `RAGFLOW_IMAGE=infiniflow/ragflow:v0.18.0` para a edição completa `v0.18.0`.
 
     ```bash
     $ cd ragflow/docker
+    # Use CPU for embedding and DeepDoc tasks:
     $ docker compose -f docker-compose.yml up -d
+
+    # To use GPU to accelerate embedding and DeepDoc tasks:
+    # docker compose -f docker-compose-gpu.yml up -d
     ```
 
     | Tag da imagem RAGFlow | Tamanho da imagem (GB) | Possui modelos de incorporação? | Estável?                 |
     | --------------------- | ---------------------- | ------------------------------- | ------------------------ |
-    | v0.17.0               | ~9                     | :heavy_check_mark:              | Lançamento estável       |
-    | v0.17.0-slim          | ~2                     | ❌                              | Lançamento estável       |
+    | v0.18.0               | ~9                     | :heavy_check_mark:              | Lançamento estável       |
+    | v0.18.0-slim          | ~2                     | ❌                              | Lançamento estável       |
     | nightly               | ~9                     | :heavy_check_mark:              | _Instável_ build noturno |
     | nightly-slim          | ~2                     | ❌                               | _Instável_ build noturno |
 
@@ -256,7 +263,7 @@ Esta imagem tem cerca de 2 GB de tamanho e depende de serviços externos de LLM
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
+docker build --platform linux/amd64 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
 ```
 
 ## 🔧 Criar uma imagem Docker incluindo modelos de incorporação
@@ -266,7 +273,7 @@ Esta imagem tem cerca de 9 GB de tamanho. Como inclui modelos de incorporação,
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build -f Dockerfile -t infiniflow/ragflow:nightly .
+docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .
 ```
 
 ## 🔨 Lançar o serviço a partir do código-fonte para desenvolvimento
@@ -330,10 +337,13 @@ docker build -f Dockerfile -t infiniflow/ragflow:nightly .
 
 ## 📚 Documentação
 
-- [Início rápido](https://ragflow.io/docs/dev/)
-- [Guia do usuário](https://ragflow.io/docs/dev/category/guides)
-- [Referências](https://ragflow.io/docs/dev/category/references)
-- [FAQ](https://ragflow.io/docs/dev/faq)
+- [Quickstart](https://ragflow.io/docs/dev/)
+- [Configuration](https://ragflow.io/docs/dev/configurations)
+- [Release notes](https://ragflow.io/docs/dev/release_notes)
+- [User guides](https://ragflow.io/docs/dev/category/guides)
+- [Developer guides](https://ragflow.io/docs/dev/category/developers)
+- [References](https://ragflow.io/docs/dev/category/references)
+- [FAQs](https://ragflow.io/docs/dev/faq)
 
 ## 📜 Roadmap
 
@@ -341,7 +351,7 @@ Veja o [RAGFlow Roadmap 2025](https://github.com/infiniflow/ragflow/issues/4214)
 
 ## 🏄 Comunidade
 
-- [Discord](https://discord.gg/4XxujFgUN7)
+- [Discord](https://discord.gg/NjYzJD3GM3)
 - [Twitter](https://twitter.com/infiniflowai)
 - [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
 

README_tzh.md

@@ -21,7 +21,7 @@
         <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:v0.17.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.17.0">
+        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.18.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.18.0">
     </a>
     <a href="https://github.com/infiniflow/ragflow/releases/latest">
         <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Latest%20Release" alt="Latest Release">
@@ -35,7 +35,7 @@
   <a href="https://ragflow.io/docs/dev/">Document</a> |
   <a href="https://github.com/infiniflow/ragflow/issues/4214">Roadmap</a> |
   <a href="https://twitter.com/infiniflowai">Twitter</a> |
-  <a href="https://discord.gg/4XxujFgUN7">Discord</a> |
+  <a href="https://discord.gg/NjYzJD3GM3">Discord</a> |
   <a href="https://demo.ragflow.io">Demo</a>
 </h4>
 
@@ -54,11 +54,10 @@
 
 ## 🔥 近期更新
 
-- 2025-02-05 更新「SILICONFLOW」的型號清單並新增 Deepseek-R1/DeepSeek-V3 的支援。
+- 2025-03-19 PDF和DOCX中的圖支持用多模態大模型去解析得到描述.
+- 2025-02-28 結合網路搜尋（Tavily），對於任意大模型實現類似 Deep Research 的推理功能.
 - 2025-01-26 最佳化知識圖譜的擷取與應用，提供了多種配置選擇。
 - 2024-12-18 升級了 DeepDoc 的文檔佈局分析模型。
-- 2024-12-04 支援知識庫的 Pagerank 分數。
-- 2024-11-22 完善了 Agent 中的變數定義和使用。
 - 2024-11-01 對解析後的 chunk 加入關鍵字抽取和相關問題產生以提高回想的準確度。
 - 2024-08-22 支援用 RAG 技術實現從自然語言到 SQL 語句的轉換。
 
@@ -145,17 +144,25 @@
 
 3. 進入 **docker** 資料夾，利用事先編譯好的 Docker 映像啟動伺服器：
 
-   > 執行以下指令會自動下載 RAGFlow slim Docker 映像 `v0.17.0-slim`。請參考下表查看不同 Docker 發行版的說明。如需下載不同於 `v0.17.0-slim` 的 Docker 映像，請在執行 `docker compose` 啟動服務之前先更新 **docker/.env** 檔案內的 `RAGFLOW_IMAGE` 變數。例如，你可以透過設定 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.17.0` 來下載 RAGFlow 鏡像的 `v0.17.0` 完整發行版。
+> [!CAUTION]
+> 所有 Docker 映像檔都是為 x86 平台建置的。目前，我們不提供 ARM64 平台的 Docker 映像檔。
+> 如果您使用的是 ARM64 平台，請使用 [這份指南](https://ragflow.io/docs/dev/build_docker_image) 來建置適合您系統的 Docker 映像檔。
+
+   > 執行以下指令會自動下載 RAGFlow slim Docker 映像 `v0.18.0-slim`。請參考下表查看不同 Docker 發行版的說明。如需下載不同於 `v0.18.0-slim` 的 Docker 映像，請在執行 `docker compose` 啟動服務之前先更新 **docker/.env** 檔案內的 `RAGFLOW_IMAGE` 變數。例如，你可以透過設定 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.18.0` 來下載 RAGFlow 鏡像的 `v0.18.0` 完整發行版。
 
    ```bash
    $ cd ragflow/docker
+   # Use CPU for embedding and DeepDoc tasks:
    $ docker compose -f docker-compose.yml up -d
+
+   # To use GPU to accelerate embedding and DeepDoc tasks:
+   # docker compose -f docker-compose-gpu.yml up -d
    ```
 
    | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
    | ----------------- | --------------- | --------------------- | ------------------------ |
-   | v0.17.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
-   | v0.17.0-slim      | &approx;2       | ❌                    | Stable release           |
+   | v0.18.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
+   | v0.18.0-slim      | &approx;2       | ❌                    | Stable release           |
    | nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
    | nightly-slim      | &approx;2       | ❌                     | _Unstable_ nightly build |
 
@@ -244,7 +251,7 @@ RAGFlow 預設使用 Elasticsearch 儲存文字和向量資料. 如果要切換
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build --build-arg LIGHTEN=1 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
+docker build --platform linux/amd64 --build-arg LIGHTEN=1 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
 ```
 
 ## 🔧 原始碼編譯 Docker 映像（包含 embedding 模型）
@@ -254,7 +261,7 @@ docker build --build-arg LIGHTEN=1 --build-arg NEED_MIRROR=1 -f Dockerfile -t in
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly .
+docker build --platform linux/amd64 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly .
 ```
 
 ## 🔨 以原始碼啟動服務
@@ -320,9 +327,12 @@ npm install
 ## 📚 技術文檔
 
 - [Quickstart](https://ragflow.io/docs/dev/)
-- [User guide](https://ragflow.io/docs/dev/category/guides)
+- [Configuration](https://ragflow.io/docs/dev/configurations)
+- [Release notes](https://ragflow.io/docs/dev/release_notes)
+- [User guides](https://ragflow.io/docs/dev/category/guides)
+- [Developer guides](https://ragflow.io/docs/dev/category/developers)
 - [References](https://ragflow.io/docs/dev/category/references)
-- [FAQ](https://ragflow.io/docs/dev/faq)
+- [FAQs](https://ragflow.io/docs/dev/faq)
 
 ## 📜 路線圖
 
@@ -330,7 +340,7 @@ npm install
 
 ## 🏄 開源社群
 
-- [Discord](https://discord.gg/4XxujFgUN7)
+- [Discord](https://discord.gg/zd4qPW6t)
 - [Twitter](https://twitter.com/infiniflowai)
 - [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
 

README_zh.md

@@ -22,7 +22,7 @@
         <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:v0.17.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.17.0">
+        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.18.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.18.0">
     </a>
     <a href="https://github.com/infiniflow/ragflow/releases/latest">
         <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Latest%20Release" alt="Latest Release">
@@ -36,7 +36,7 @@
   <a href="https://ragflow.io/docs/dev/">Document</a> |
   <a href="https://github.com/infiniflow/ragflow/issues/4214">Roadmap</a> |
   <a href="https://twitter.com/infiniflowai">Twitter</a> |
-  <a href="https://discord.gg/4XxujFgUN7">Discord</a> |
+  <a href="https://discord.gg/NjYzJD3GM3">Discord</a> |
   <a href="https://demo.ragflow.io">Demo</a>
 </h4>
 
@@ -55,11 +55,10 @@
 
 ## 🔥 近期更新
 
-- 2025-02-05 更新硅基流动的模型列表，增加了对 Deepseek-R1/DeepSeek-V3 的支持。
+- 2025-03-19 PDF和DOCX中的图支持用多模态大模型去解析得到描述.
+- 2025-02-28 结合互联网搜索（Tavily），对于任意大模型实现类似 Deep Research 的推理功能.
 - 2025-01-26 优化知识图谱的提取和应用，提供了多种配置选择。
 - 2024-12-18 升级了 DeepDoc 的文档布局分析模型。
-- 2024-12-04 支持知识库的 Pagerank 分数。
-- 2024-11-22 完善了 Agent 中的变量定义和使用。
 - 2024-11-01 对解析后的 chunk 加入关键词抽取和相关问题生成以提高召回的准确度。
 - 2024-08-22 支持用 RAG 技术实现从自然语言到 SQL 语句的转换。
 
@@ -146,17 +145,25 @@
 
 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 RAGFlow slim Docker 镜像 `v0.17.0-slim`。请参考下表查看不同 Docker 发行版的描述。如需下载不同于 `v0.17.0-slim` 的 Docker 镜像，请在运行 `docker compose` 启动服务之前先更新 **docker/.env** 文件内的 `RAGFLOW_IMAGE` 变量。比如，你可以通过设置 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.17.0` 来下载 RAGFlow 镜像的 `v0.17.0` 完整发行版。
+> [!CAUTION]
+> 请注意，目前官方提供的所有 Docker 镜像均基于 x86 架构构建，并不提供基于 ARM64 的 Docker 镜像。
+> 如果你的操作系统是 ARM64 架构，请参考[这篇文档](https://ragflow.io/docs/dev/build_docker_image)自行构建 Docker 镜像。
+
+   > 运行以下命令会自动下载 RAGFlow slim Docker 镜像 `v0.18.0-slim`。请参考下表查看不同 Docker 发行版的描述。如需下载不同于 `v0.18.0-slim` 的 Docker 镜像，请在运行 `docker compose` 启动服务之前先更新 **docker/.env** 文件内的 `RAGFLOW_IMAGE` 变量。比如，你可以通过设置 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.18.0` 来下载 RAGFlow 镜像的 `v0.18.0` 完整发行版。
 
    ```bash
    $ cd ragflow/docker
+   # Use CPU for embedding and DeepDoc tasks:
    $ docker compose -f docker-compose.yml up -d
+
+   # To use GPU to accelerate embedding and DeepDoc tasks:
+   # docker compose -f docker-compose-gpu.yml up -d
    ```
 
    | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
    | ----------------- | --------------- | --------------------- | ------------------------ |
-   | v0.17.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
-   | v0.17.0-slim      | &approx;2       | ❌                    | Stable release           |
+   | v0.18.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
+   | v0.18.0-slim      | &approx;2       | ❌                    | Stable release           |
    | nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
    | nightly-slim      | &approx;2       | ❌                     | _Unstable_ nightly build |
 
@@ -245,7 +252,7 @@ RAGFlow 默认使用 Elasticsearch 存储文本和向量数据. 如果要切换
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build --build-arg LIGHTEN=1 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
+docker build --platform linux/amd64 --build-arg LIGHTEN=1 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
 ```
 
 ## 🔧 源码编译 Docker 镜像（包含 embedding 模型）
@@ -255,7 +262,7 @@ docker build --build-arg LIGHTEN=1 --build-arg NEED_MIRROR=1 -f Dockerfile -t in
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly .
+docker build --platform linux/amd64 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly .
 ```
 
 ## 🔨 以源代码启动服务
@@ -281,12 +288,11 @@ docker build --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:night
    docker compose -f docker/docker-compose-base.yml up -d
    ```
 
-   在 `/etc/hosts` 中添加以下代码，将 **conf/service_conf.yaml** 文件中的所有 host 地址都解析为 `127.0.0.1`：
+   在 `/etc/hosts` 中添加以下代码，目的是将 **conf/service_conf.yaml** 文件中的所有 host 地址都解析为 `127.0.0.1`：
 
    ```
    127.0.0.1       es01 infinity mysql minio redis
    ```
-
 4. 如果无法访问 HuggingFace，可以把环境变量 `HF_ENDPOINT` 设成相应的镜像站点：
 
    ```bash
@@ -315,13 +321,21 @@ docker build --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:night
    _以下界面说明系统已经成功启动：_
 
    ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
+8. 开发完成后停止 RAGFlow 服务
+   停止 RAGFlow 前端和后端服务：
+   ```bash
+   pkill -f "ragflow_server.py|task_executor.py"
+   ```
 
 ## 📚 技术文档
 
 - [Quickstart](https://ragflow.io/docs/dev/)
-- [User guide](https://ragflow.io/docs/dev/category/guides)
+- [Configuration](https://ragflow.io/docs/dev/configurations)
+- [Release notes](https://ragflow.io/docs/dev/release_notes)
+- [User guides](https://ragflow.io/docs/dev/category/guides)
+- [Developer guides](https://ragflow.io/docs/dev/category/developers)
 - [References](https://ragflow.io/docs/dev/category/references)
-- [FAQ](https://ragflow.io/docs/dev/faq)
+- [FAQs](https://ragflow.io/docs/dev/faq)
 
 ## 📜 路线图
 
@@ -329,7 +343,7 @@ docker build --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:night
 
 ## 🏄 开源社区
 
-- [Discord](https://discord.gg/4XxujFgUN7)
+- [Discord](https://discord.gg/zd4qPW6t)
 - [Twitter](https://twitter.com/infiniflowai)
 - [GitHub Discussions](https://github.com/orgs/infiniflow/discussions)
 

agent/canvas.py

@@ -235,7 +235,7 @@ class Canvas:
             pid = self.components[cid]["parent_id"]
             o, _ = self.components[cid]["obj"].output(allow_partial=False)
             oo, _ = self.components[pid]["obj"].output(allow_partial=False)
-            self.components[pid]["obj"].set(pd.concat([oo, o], ignore_index=True))
+            self.components[pid]["obj"].set_output(pd.concat([oo, o], ignore_index=True).dropna())
             downstream = [pid]
 
         for m in prepare2run(downstream):
@@ -252,20 +252,20 @@ class Canvas:
             if loop:
                 raise OverflowError(f"Too much loops: {loop}")
 
+            downstream = []
             if cpn["obj"].component_name.lower() in ["switch", "categorize", "relevant"]:
                 switch_out = cpn["obj"].output()[1].iloc[0, 0]
                 assert switch_out in self.components, \
                     "{}'s output: {} not valid.".format(cpn_id, switch_out)
-                for m in prepare2run([switch_out]):
-                    yield {"content": m, "running_status": True}
-                continue
+                downstream = [switch_out]
+            else:
+                downstream = cpn["downstream"]
 
-            downstream = cpn["downstream"]
             if not downstream and cpn.get("parent_id"):
                 pid = cpn["parent_id"]
                 _, o = cpn["obj"].output(allow_partial=False)
                 _, oo = self.components[pid]["obj"].output(allow_partial=False)
-                self.components[pid]["obj"].set_output(pd.concat([oo.dropna(axis=1), o.dropna(axis=1)], ignore_index=True))
+                self.components[pid]["obj"].set_output(pd.concat([oo.dropna(axis=1), o.dropna(axis=1)], ignore_index=True).dropna())
                 downstream = [pid]
 
             for m in prepare2run(downstream):

agent/component/base.py

@@ -384,6 +384,11 @@ class ComponentBase(ABC):
             "params": {}
         }
         """
+        out = getattr(self._param, self._param.output_var_name)
+        if isinstance(out, pd.DataFrame) and "chunks" in out:
+            del out["chunks"]
+            setattr(self._param, self._param.output_var_name, out)
+
         return """{{
             "component_name": "{}",
             "params": {},
@@ -396,6 +401,8 @@ class ComponentBase(ABC):
         )
 
     def __init__(self, canvas, id, param: ComponentParamBase):
+        from agent.canvas import Canvas  # Local import to avoid cyclic dependency
+        assert isinstance(canvas, Canvas), "canvas must be an instance of Canvas"
         self._canvas = canvas
         self._id = id
         self._param = param
@@ -429,7 +436,7 @@ class ComponentBase(ABC):
         if not isinstance(o, partial):
             if not isinstance(o, pd.DataFrame):
                 if isinstance(o, list):
-                    return self._param.output_var_name, pd.DataFrame(o)
+                    return self._param.output_var_name, pd.DataFrame(o).dropna()
                 if o is None:
                     return self._param.output_var_name, pd.DataFrame()
                 return self._param.output_var_name, pd.DataFrame([{"content": str(o)}])
@@ -437,15 +444,15 @@ class ComponentBase(ABC):
 
         if allow_partial or not isinstance(o, partial):
             if not isinstance(o, partial) and not isinstance(o, pd.DataFrame):
-                return pd.DataFrame(o if isinstance(o, list) else [o])
+                return pd.DataFrame(o if isinstance(o, list) else [o]).dropna()
             return self._param.output_var_name, o
 
         outs = None
         for oo in o():
             if not isinstance(oo, pd.DataFrame):
-                outs = pd.DataFrame(oo if isinstance(oo, list) else [oo])
+                outs = pd.DataFrame(oo if isinstance(oo, list) else [oo]).dropna()
             else:
-                outs = oo
+                outs = oo.dropna()
         return self._param.output_var_name, outs
 
     def reset(self):
@@ -463,6 +470,8 @@ class ComponentBase(ABC):
         if len(self._canvas.path) > 1:
             reversed_cpnts.extend(self._canvas.path[-2])
         reversed_cpnts.extend(self._canvas.path[-1])
+        up_cpns = self.get_upstream()
+        reversed_up_cpnts = [cpn for cpn in reversed_cpnts if cpn in up_cpns]
 
         if self._param.query:
             self._param.inputs = []
@@ -484,7 +493,7 @@ class ComponentBase(ABC):
                     if q["component_id"].lower().find("answer") == 0:
                         txt = []
                         for r, c in self._canvas.history[::-1][:self._param.message_history_window_size][::-1]:
-                            txt.append(f"{r.upper()}: {c}")
+                            txt.append(f"{r.upper()}:{c}")
                         txt = "\n".join(txt)
                         self._param.inputs.append({"content": txt, "component_id": q["component_id"]})
                         outs.append(pd.DataFrame([{"content": txt}]))
@@ -505,7 +514,7 @@ class ComponentBase(ABC):
 
         upstream_outs = []
 
-        for u in reversed_cpnts[::-1]:
+        for u in reversed_up_cpnts[::-1]:
             if self.get_component_name(u) in ["switch", "concentrator"]:
                 continue
             if self.component_name.lower() == "generate" and self.get_component_name(u) == "retrieval":
@@ -545,7 +554,7 @@ class ComponentBase(ABC):
         return df
 
     def get_input_elements(self):
-        assert self._param.query, "Please identify input parameters firstly."
+        assert self._param.query, "Please verify the input parameters first."
         eles = []
         for q in self._param.query:
             if q.get("component_id"):
@@ -565,8 +574,10 @@ class ComponentBase(ABC):
         if len(self._canvas.path) > 1:
             reversed_cpnts.extend(self._canvas.path[-2])
         reversed_cpnts.extend(self._canvas.path[-1])
+        up_cpns = self.get_upstream()
+        reversed_up_cpnts = [cpn for cpn in reversed_cpnts if cpn in up_cpns]
 
-        for u in reversed_cpnts[::-1]:
+        for u in reversed_up_cpnts[::-1]:
             if self.get_component_name(u) in ["switch", "answer"]:
                 continue
             return self._canvas.get_component(u)["obj"].output()[1]
@@ -584,3 +595,7 @@ class ComponentBase(ABC):
     def get_parent(self):
         pid = self._canvas.get_component(self._id)["parent_id"]
         return self._canvas.get_component(pid)["obj"]
+
+    def get_upstream(self):
+        cpn_nms = self._canvas.get_component(self._id)['upstream']
+        return cpn_nms

agent/component/categorize.py

@@ -50,26 +50,29 @@ class CategorizeParam(GenerateParam):
         for c, desc in self.category_description.items():
             if desc.get("description"):
                 descriptions.append(
-                    "--------------------\nCategory: {}\nDescription: {}\n".format(c, desc["description"]))
+                    "\nCategory: {}\nDescription: {}".format(c, desc["description"]))
 
         self.prompt = """
-        You're a text classifier. You need to categorize the user’s questions into {} categories, 
-        namely: {}
-        Here's description of each category:
-        {}
+Role: You're a text classifier. 
+Task: You need to categorize the user’s questions into {} categories, namely: {}
 
-        You could learn from the following examples:
-        {}
-        You could learn from the above examples.
-        Just mention the category names, no need for any additional words.
+Here's description of each category:
+{}
 
-        ---- Real Data ----
-        {}
+You could learn from the following examples:
+{}
+You could learn from the above examples.
+
+Requirements:
+- Just mention the category names, no need for any additional words.
+
+---- Real Data ----
+USER: {}\n
         """.format(
             len(self.category_description.keys()),
             "/".join(list(self.category_description.keys())),
             "\n".join(descriptions),
-            "- ".join(cate_lines),
+            "\n\n- ".join(cate_lines),
             chat_hist
         )
         return self.prompt
@@ -85,9 +88,16 @@ class Categorize(Generate, ABC):
         ans = chat_mdl.chat(self._param.get_prompt(input), [{"role": "user", "content": "\nCategory: "}],
                             self._param.gen_conf())
         logging.debug(f"input: {input}, answer: {str(ans)}")    
+        # Count the number of times each category appears in the answer.
+        category_counts = {}
         for c in self._param.category_description.keys():
-            if ans.lower().find(c.lower()) >= 0:
-                return Categorize.be_output(self._param.category_description[c]["to"])
+            count = ans.lower().count(c.lower())
+            category_counts[c] = count
+            
+        # If a category is found, return the category with the highest count.
+        if any(category_counts.values()):
+            max_category = max(category_counts.items(), key=lambda x: x[1])
+            return Categorize.be_output(self._param.category_description[max_category[0]]["to"])
 
         return Categorize.be_output(list(self._param.category_description.items())[-1][1]["to"])
 

agent/component/email.py

@@ -82,7 +82,10 @@ class Email(ComponentBase, ABC):
             logging.info(f"Connecting to SMTP server {self._param.smtp_server}:{self._param.smtp_port}")
             
             context = smtplib.ssl.create_default_context()
-            with smtplib.SMTP_SSL(self._param.smtp_server, self._param.smtp_port, context=context) as server:
+            with smtplib.SMTP(self._param.smtp_server, self._param.smtp_port) as server:
+                server.ehlo()
+                server.starttls(context=context)
+                server.ehlo()
                 # Login
                 logging.info(f"Attempting to login with email: {self._param.email}")
                 server.login(self._param.email, self._param.password)

agent/component/generate.py

@@ -13,6 +13,7 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import json
 import re
 from functools import partial
 import pandas as pd
@@ -74,29 +75,30 @@ class Generate(ComponentBase):
         return list(cpnts)
 
     def set_cite(self, retrieval_res, answer):
-        retrieval_res = retrieval_res.dropna(subset=["vector", "content_ltks"]).reset_index(drop=True)
         if "empty_response" in retrieval_res.columns:
             retrieval_res["empty_response"].fillna("", inplace=True)
+        chunks = json.loads(retrieval_res["chunks"][0])
         answer, idx = settings.retrievaler.insert_citations(answer,
-                                                            [ck["content_ltks"] for _, ck in retrieval_res.iterrows()],
-                                                            [ck["vector"] for _, ck in retrieval_res.iterrows()],
+                                                            [ck["content_ltks"] for ck in chunks],
+                                                            [ck["vector"] for ck in chunks],
                                                             LLMBundle(self._canvas.get_tenant_id(), LLMType.EMBEDDING,
                                                                       self._canvas.get_embedding_model()), tkweight=0.7,
                                                             vtweight=0.3)
         doc_ids = set([])
         recall_docs = []
         for i in idx:
-            did = retrieval_res.loc[int(i), "doc_id"]
+            did = chunks[int(i)]["doc_id"]
             if did in doc_ids:
                 continue
             doc_ids.add(did)
-            recall_docs.append({"doc_id": did, "doc_name": retrieval_res.loc[int(i), "docnm_kwd"]})
+            recall_docs.append({"doc_id": did, "doc_name": chunks[int(i)]["docnm_kwd"]})
 
-        del retrieval_res["vector"]
-        del retrieval_res["content_ltks"]
+        for c in chunks:
+            del c["vector"]
+            del c["content_ltks"]
 
         reference = {
-            "chunks": [ck.to_dict() for _, ck in retrieval_res.iterrows()],
+            "chunks": chunks,
             "doc_aggs": recall_docs
         }
 
@@ -200,7 +202,7 @@ class Generate(ComponentBase):
         ans = chat_mdl.chat(msg[0]["content"], msg[1:], self._param.gen_conf())
         ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
 
-        if self._param.cite and "content_ltks" in retrieval_res.columns and "vector" in retrieval_res.columns:
+        if self._param.cite and "chunks" in retrieval_res.columns:
             res = self.set_cite(retrieval_res, ans)
             return pd.DataFrame([res])
 
@@ -216,6 +218,8 @@ class Generate(ComponentBase):
             return
 
         msg = self._canvas.get_history(self._param.message_history_window_size)
+        if msg and msg[0]['role'] == 'assistant':
+            msg.pop(0)
         if len(msg) < 1:
             msg.append({"role": "user", "content": "Output: "})
         _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(chat_mdl.max_length * 0.97))
@@ -227,7 +231,7 @@ class Generate(ComponentBase):
             answer = ans
             yield res
 
-        if self._param.cite and "content_ltks" in retrieval_res.columns and "vector" in retrieval_res.columns:
+        if self._param.cite and "chunks" in retrieval_res.columns:
             res = self.set_cite(retrieval_res, answer)
             yield res
 

agent/component/iterationitem.py

@@ -38,6 +38,10 @@ class IterationItem(ComponentBase, ABC):
         ans = parent.get_input()
         ans = parent._param.delimiter.join(ans["content"]) if "content" in ans else ""
         ans = [a.strip() for a in ans.split(parent._param.delimiter)]
+        if not ans:
+            self._idx = -1
+            return pd.DataFrame()
+
         df = pd.DataFrame([{"content": ans[self._idx]}])
         self._idx += 1
         if self._idx >= len(ans):

agent/component/keyword.py

@@ -57,6 +57,7 @@ class KeywordExtract(Generate, ABC):
         ans = chat_mdl.chat(self._param.get_prompt(), [{"role": "user", "content": query}],
                             self._param.gen_conf())
 
+        ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
         ans = re.sub(r".*keyword:", "", ans).strip()
         logging.debug(f"ans: {ans}")
         return KeywordExtract.be_output(ans)

agent/component/retrieval.py

@@ -13,6 +13,7 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import json
 import logging
 from abc import ABC
 
@@ -24,6 +25,8 @@ from api.db.services.llm_service import LLMBundle
 from api import settings
 from agent.component.base import ComponentBase, ComponentParamBase
 from rag.app.tag import label_question
+from rag.prompts import kb_prompt
+from rag.utils.tavily_conn import Tavily
 
 
 class RetrievalParam(ComponentParamBase):
@@ -40,6 +43,8 @@ class RetrievalParam(ComponentParamBase):
         self.kb_ids = []
         self.rerank_id = ""
         self.empty_response = ""
+        self.tavily_api_key = ""
+        self.use_kg = False
 
     def check(self):
         self.check_decimal_float(self.similarity_threshold, "[Retrieval] Similarity threshold")
@@ -53,7 +58,6 @@ class Retrieval(ComponentBase, ABC):
     def _run(self, history, **kwargs):
         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:
             return Retrieval.be_output("")
@@ -61,18 +65,38 @@ class Retrieval(ComponentBase, ABC):
         embd_nms = list(set([kb.embd_id for kb in kbs]))
         assert len(embd_nms) == 1, "Knowledge bases use different embedding models."
 
-        embd_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.EMBEDDING, embd_nms[0])
-        self._canvas.set_embedding_model(embd_nms[0])
+        embd_mdl = None
+        if embd_nms:
+            embd_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.EMBEDDING, embd_nms[0])
+            self._canvas.set_embedding_model(embd_nms[0])
 
         rerank_mdl = None
         if self._param.rerank_id:
             rerank_mdl = LLMBundle(kbs[0].tenant_id, LLMType.RERANK, self._param.rerank_id)
 
-        kbinfos = settings.retrievaler.retrieval(query, embd_mdl, kbs[0].tenant_id, self._param.kb_ids,
+        if kbs:
+            kbinfos = settings.retrievaler.retrieval(query, embd_mdl, kbs[0].tenant_id, self._param.kb_ids,
                                         1, self._param.top_n,
                                         self._param.similarity_threshold, 1 - self._param.keywords_similarity_weight,
                                         aggs=False, rerank_mdl=rerank_mdl,
                                         rank_feature=label_question(query, kbs))
+        else:
+            kbinfos = {"chunks": [], "doc_aggs": []}
+
+        if self._param.use_kg and kbs:
+            ck = settings.kg_retrievaler.retrieval(query,
+                                                   [kbs[0].tenant_id],
+                                                   self._param.kb_ids,
+                                                   embd_mdl,
+                                                   LLMBundle(kbs[0].tenant_id, LLMType.CHAT))
+            if ck["content_with_weight"]:
+                kbinfos["chunks"].insert(0, ck)
+
+        if self._param.tavily_api_key:
+            tav = Tavily(self._param.tavily_api_key)
+            tav_res = tav.retrieve_chunks(query)
+            kbinfos["chunks"].extend(tav_res["chunks"])
+            kbinfos["doc_aggs"].extend(tav_res["doc_aggs"])
 
         if not kbinfos["chunks"]:
             df = Retrieval.be_output("")
@@ -80,10 +104,8 @@ class Retrieval(ComponentBase, ABC):
                 df["empty_response"] = self._param.empty_response
             return df
 
-        df = pd.DataFrame(kbinfos["chunks"])
-        df["content"] = df["content_with_weight"]
-        del df["content_with_weight"]
+        df = pd.DataFrame({"content": kb_prompt(kbinfos, 200000), "chunks": json.dumps(kbinfos["chunks"])})
         logging.debug("{} {}".format(query, df))
-        return df
+        return df.dropna()
 
 

agent/component/rewrite.py

@@ -14,9 +14,8 @@
 #  limitations under the License.
 #
 from abc import ABC
-from api.db import LLMType
-from api.db.services.llm_service import LLMBundle
 from agent.component import GenerateParam, Generate
+from rag.prompts import full_question
 
 
 class RewriteQuestionParam(GenerateParam):
@@ -33,48 +32,6 @@ class RewriteQuestionParam(GenerateParam):
     def check(self):
         super().check()
 
-    def get_prompt(self, conv, language, query):
-        prompt = """
-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."""
-
-        if language:
-            prompt += f"""
-  - Text generated MUST be in {language}"""
-
-        prompt += f"""
-######################
--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 prompt
-
 
 class RewriteQuestion(Generate, ABC):
     component_name = "RewriteQuestion"
@@ -83,15 +40,10 @@ class RewriteQuestion(Generate, ABC):
         hist = self._canvas.get_history(self._param.message_history_window_size)
         query = self.get_input()
         query = str(query["content"][0]) if "content" in query else ""
-        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(conv, self.gen_lang(self._param.language), query),
-                            [{"role": "user", "content": "Output: "}], self._param.gen_conf())
+        messages = [h for h in hist if h["role"]!="system"]
+        if messages[-1]["role"] != "user":
+            messages.append({"role": "user", "content": query})
+        ans = full_question(self._canvas.get_tenant_id(), self._param.llm_id, messages, self.gen_lang(self._param.language))
         self._canvas.history.pop()
         self._canvas.history.append(("user", ans))
         return RewriteQuestion.be_output(ans)

agent/component/switch.py

@@ -54,7 +54,7 @@ class Switch(ComponentBase, ABC):
             for item in cond["items"]:
                 if not item["cpn_id"]:
                     continue
-                if item["cpn_id"].find("begin") >= 0:
+                if item["cpn_id"].lower().find("begin") >= 0 or item["cpn_id"].lower().find("answer") >= 0:
                     continue
                 cid = item["cpn_id"].split("@")[0]
                 res.append(cid)
@@ -75,7 +75,7 @@ class Switch(ComponentBase, ABC):
                             res.append(self.process_operator(p.get("value",""), item["operator"], item.get("value", "")))
                             break
                 else:
-                    out = self._canvas.get_component(cid)["obj"].output()[1]
+                    out = self._canvas.get_component(cid)["obj"].output(allow_partial=False)[1]
                     cpn_input = "" if "content" not in out.columns else " ".join([str(s) for s in out["content"]])
                     res.append(self.process_operator(cpn_input, item["operator"], item.get("value", "")))
 

agent/component/template.py

@@ -109,16 +109,14 @@ class Template(ComponentBase):
             pass
 
         for n, v in kwargs.items():
-            try:
-                v = json.dumps(v, ensure_ascii=False)
-            except Exception:
-                pass
+            if not isinstance(v, str):
+                try:
+                    v = json.dumps(v, ensure_ascii=False)
+                except Exception:
+                    pass
             content = re.sub(
                 r"\{%s\}" % re.escape(n), v, content
             )
-            content = re.sub(
-                r"(\\\"|\")", "", content
-            )
             content = re.sub(
                 r"(#+)", r" \1 ", content
             )

agentic_reasoning/deep_research.py

@@ -36,132 +36,188 @@ class DeepResearcher:
         self._kb_retrieve = kb_retrieve
         self._kg_retrieve = kg_retrieve
 
+    @staticmethod
+    def _remove_query_tags(text):
+        """Remove query tags from text"""
+        pattern = re.escape(BEGIN_SEARCH_QUERY) + r"(.*?)" + re.escape(END_SEARCH_QUERY)
+        return re.sub(pattern, "", text)
+
+    @staticmethod
+    def _remove_result_tags(text):
+        """Remove result tags from text"""
+        pattern = re.escape(BEGIN_SEARCH_RESULT) + r"(.*?)" + re.escape(END_SEARCH_RESULT)
+        return re.sub(pattern, "", text)
+
+    def _generate_reasoning(self, msg_history):
+        """Generate reasoning steps"""
+        query_think = ""
+        if msg_history[-1]["role"] != "user":
+            msg_history.append({"role": "user", "content": "Continues reasoning with the new information.\n"})
+        else:
+            msg_history[-1]["content"] += "\n\nContinues reasoning with the new information.\n"
+            
+        for ans in self.chat_mdl.chat_streamly(REASON_PROMPT, msg_history, {"temperature": 0.7}):
+            ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
+            if not ans:
+                continue
+            query_think = ans
+            yield query_think
+        return query_think
+
+    def _extract_search_queries(self, query_think, question, step_index):
+        """Extract search queries from thinking"""
+        queries = extract_between(query_think, BEGIN_SEARCH_QUERY, END_SEARCH_QUERY)
+        if not queries and step_index == 0:
+            # If this is the first step and no queries are found, use the original question as the query
+            queries = [question]
+        return queries
+
+    def _truncate_previous_reasoning(self, all_reasoning_steps):
+        """Truncate previous reasoning steps to maintain a reasonable length"""
+        truncated_prev_reasoning = ""
+        for i, step in enumerate(all_reasoning_steps):
+            truncated_prev_reasoning += f"Step {i + 1}: {step}\n\n"
+
+        prev_steps = truncated_prev_reasoning.split('\n\n')
+        if len(prev_steps) <= 5:
+            truncated_prev_reasoning = '\n\n'.join(prev_steps)
+        else:
+            truncated_prev_reasoning = ''
+            for i, step in enumerate(prev_steps):
+                if i == 0 or i >= len(prev_steps) - 4 or BEGIN_SEARCH_QUERY in step or BEGIN_SEARCH_RESULT in step:
+                    truncated_prev_reasoning += step + '\n\n'
+                else:
+                    if truncated_prev_reasoning[-len('\n\n...\n\n'):] != '\n\n...\n\n':
+                        truncated_prev_reasoning += '...\n\n'
+        
+        return truncated_prev_reasoning.strip('\n')
+
+    def _retrieve_information(self, search_query):
+        """Retrieve information from different sources"""
+        # 1. Knowledge base retrieval
+        kbinfos = self._kb_retrieve(question=search_query) if self._kb_retrieve else {"chunks": [], "doc_aggs": []}
+        
+        # 2. Web retrieval (if Tavily API is configured)
+        if self.prompt_config.get("tavily_api_key"):
+            tav = Tavily(self.prompt_config["tavily_api_key"])
+            tav_res = tav.retrieve_chunks(search_query)
+            kbinfos["chunks"].extend(tav_res["chunks"])
+            kbinfos["doc_aggs"].extend(tav_res["doc_aggs"])
+        
+        # 3. Knowledge graph retrieval (if configured)
+        if self.prompt_config.get("use_kg") and self._kg_retrieve:
+            ck = self._kg_retrieve(question=search_query)
+            if ck["content_with_weight"]:
+                kbinfos["chunks"].insert(0, ck)
+                
+        return kbinfos
+
+    def _update_chunk_info(self, chunk_info, kbinfos):
+        """Update chunk information for citations"""
+        if not chunk_info["chunks"]:
+            # If this is the first retrieval, use the retrieval results directly
+            for k in chunk_info.keys():
+                chunk_info[k] = kbinfos[k]
+        else:
+            # Merge newly retrieved information, avoiding duplicates
+            cids = [c["chunk_id"] for c in chunk_info["chunks"]]
+            for c in kbinfos["chunks"]:
+                if c["chunk_id"] not in cids:
+                    chunk_info["chunks"].append(c)
+                    
+            dids = [d["doc_id"] for d in chunk_info["doc_aggs"]]
+            for d in kbinfos["doc_aggs"]:
+                if d["doc_id"] not in dids:
+                    chunk_info["doc_aggs"].append(d)
+
+    def _extract_relevant_info(self, truncated_prev_reasoning, search_query, kbinfos):
+        """Extract and summarize relevant information"""
+        summary_think = ""
+        for ans in self.chat_mdl.chat_streamly(
+                RELEVANT_EXTRACTION_PROMPT.format(
+                    prev_reasoning=truncated_prev_reasoning,
+                    search_query=search_query,
+                    document="\n".join(kb_prompt(kbinfos, 4096))
+                ),
+                [{"role": "user",
+                  "content": f'Now you should analyze each web page and find helpful information based on the current search query "{search_query}" and previous reasoning steps.'}],
+                {"temperature": 0.7}):
+            ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
+            if not ans:
+                continue
+            summary_think = ans
+            yield summary_think
+        
+        return summary_think
+
     def thinking(self, chunk_info: dict, question: str):
-        def rm_query_tags(line):
-            pattern = re.escape(BEGIN_SEARCH_QUERY) + r"(.*?)" + re.escape(END_SEARCH_QUERY)
-            return re.sub(pattern, "", line)
-
-        def rm_result_tags(line):
-            pattern = re.escape(BEGIN_SEARCH_RESULT) + r"(.*?)" + re.escape(END_SEARCH_RESULT)
-            return re.sub(pattern, "", line)
-
         executed_search_queries = []
-        msg_hisotry = [{"role": "user", "content": f'Question:\"{question}\"\n'}]
+        msg_history = [{"role": "user", "content": f'Question:\"{question}\"\n'}]
         all_reasoning_steps = []
         think = "<think>"
-        for ii in range(MAX_SEARCH_LIMIT + 1):
-            if ii == MAX_SEARCH_LIMIT - 1:
+        
+        for step_index in range(MAX_SEARCH_LIMIT + 1):
+            # Check if the maximum search limit has been reached
+            if step_index == MAX_SEARCH_LIMIT - 1:
                 summary_think = f"\n{BEGIN_SEARCH_RESULT}\nThe maximum search limit is exceeded. You are not allowed to search.\n{END_SEARCH_RESULT}\n"
                 yield {"answer": think + summary_think + "</think>", "reference": {}, "audio_binary": None}
                 all_reasoning_steps.append(summary_think)
-                msg_hisotry.append({"role": "assistant", "content": summary_think})
+                msg_history.append({"role": "assistant", "content": summary_think})
                 break
 
+            # Step 1: Generate reasoning
             query_think = ""
-            if msg_hisotry[-1]["role"] != "user":
-                msg_hisotry.append({"role": "user", "content": "Continues reasoning with the new information.\n"})
-            else:
-                msg_hisotry[-1]["content"] += "\n\nContinues reasoning with the new information.\n"
-            for ans in self.chat_mdl.chat_streamly(REASON_PROMPT, msg_hisotry, {"temperature": 0.7}):
-                ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
-                if not ans:
-                    continue
+            for ans in self._generate_reasoning(msg_history):
                 query_think = ans
-                yield {"answer": think + rm_query_tags(query_think) + "</think>", "reference": {}, "audio_binary": None}
+                yield {"answer": think + self._remove_query_tags(query_think) + "</think>", "reference": {}, "audio_binary": None}
 
-            think += rm_query_tags(query_think)
+            think += self._remove_query_tags(query_think)
             all_reasoning_steps.append(query_think)
-            queries = extract_between(query_think, BEGIN_SEARCH_QUERY, END_SEARCH_QUERY)
-            if not queries:
-                if ii > 0:
-                    break
-                queries = [question]
             
+            # Step 2: Extract search queries
+            queries = self._extract_search_queries(query_think, question, step_index)
+            if not queries and step_index > 0:
+                # If not the first step and no queries, end the search process
+                break
+
+            # Process each search query
             for search_query in queries:
-                logging.info(f"[THINK]Query: {ii}. {search_query}")
-                msg_hisotry.append({"role": "assistant", "content": search_query})
-                think += f"\n\n> {ii +1}. {search_query}\n\n"
+                logging.info(f"[THINK]Query: {step_index}. {search_query}")
+                msg_history.append({"role": "assistant", "content": search_query})
+                think += f"\n\n> {step_index + 1}. {search_query}\n\n"
                 yield {"answer": think + "</think>", "reference": {}, "audio_binary": None}
 
-                summary_think = ""
-                # The search query has been searched in previous steps.
+                # Check if the query has already been executed
                 if search_query in executed_search_queries:
                     summary_think = f"\n{BEGIN_SEARCH_RESULT}\nYou have searched this query. Please refer to previous results.\n{END_SEARCH_RESULT}\n"
                     yield {"answer": think + summary_think + "</think>", "reference": {}, "audio_binary": None}
                     all_reasoning_steps.append(summary_think)
-                    msg_hisotry.append({"role": "user", "content": summary_think})
+                    msg_history.append({"role": "user", "content": summary_think})
                     think += summary_think
                     continue
                 
-                truncated_prev_reasoning = ""
-                for i, step in enumerate(all_reasoning_steps):
-                    truncated_prev_reasoning += f"Step {i + 1}: {step}\n\n"
+                executed_search_queries.append(search_query)
                 
-                prev_steps = truncated_prev_reasoning.split('\n\n')
-                if len(prev_steps) <= 5:
-                    truncated_prev_reasoning = '\n\n'.join(prev_steps)
-                else:
-                    truncated_prev_reasoning = ''
-                    for i, step in enumerate(prev_steps):
-                        if i == 0 or i >= len(prev_steps) - 4 or BEGIN_SEARCH_QUERY in step or BEGIN_SEARCH_RESULT in step:
-                            truncated_prev_reasoning += step + '\n\n'
-                        else:
-                            if truncated_prev_reasoning[-len('\n\n...\n\n'):] != '\n\n...\n\n':
-                                truncated_prev_reasoning += '...\n\n'
-                truncated_prev_reasoning = truncated_prev_reasoning.strip('\n')
+                # Step 3: Truncate previous reasoning steps
+                truncated_prev_reasoning = self._truncate_previous_reasoning(all_reasoning_steps)
                 
-                # Retrieval procedure:
-                # 1. KB search
-                # 2. Web search (optional)
-                # 3. KG search (optional)
-                kbinfos = self._kb_retrieve(question=search_query) if self._kb_retrieve else {"chunks": [], "doc_aggs": []}
+                # Step 4: Retrieve information
+                kbinfos = self._retrieve_information(search_query)
                 
-                if self.prompt_config.get("tavily_api_key"):
-                    tav = Tavily(self.prompt_config["tavily_api_key"])
-                    tav_res = tav.retrieve_chunks(" ".join(search_query))
-                    kbinfos["chunks"].extend(tav_res["chunks"])
-                    kbinfos["doc_aggs"].extend(tav_res["doc_aggs"])
-                if self.prompt_config.get("use_kg") and self._kg_retrieve:
-                    ck = self._kg_retrieve(question=search_query)
-                    if ck["content_with_weight"]:
-                        kbinfos["chunks"].insert(0, ck)
-
-                # Merge chunk info for citations
-                if not chunk_info["chunks"]:
-                    for k in chunk_info.keys():
-                        chunk_info[k] = kbinfos[k]
-                else:
-                    cids = [c["chunk_id"] for c in chunk_info["chunks"]]
-                    for c in kbinfos["chunks"]:
-                        if c["chunk_id"] in cids:
-                            continue
-                        chunk_info["chunks"].append(c)
-                    dids = [d["doc_id"] for d in chunk_info["doc_aggs"]]
-                    for d in kbinfos["doc_aggs"]:
-                        if d["doc_id"] in dids:
-                            continue
-                        chunk_info["doc_aggs"].append(d)
+                # Step 5: Update chunk information
+                self._update_chunk_info(chunk_info, kbinfos)
                 
+                # Step 6: Extract relevant information
                 think += "\n\n"
-                for ans in self.chat_mdl.chat_streamly(
-                        RELEVANT_EXTRACTION_PROMPT.format(
-                            prev_reasoning=truncated_prev_reasoning,
-                            search_query=search_query,
-                            document="\n".join(kb_prompt(kbinfos, 4096))
-                        ),
-                        [{"role": "user",
-                          "content": f'Now you should analyze each web page and find helpful information based on the current search query "{search_query}" and previous reasoning steps.'}],
-                        {"temperature": 0.7}):
-                    ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
-                    if not ans:
-                        continue
+                summary_think = ""
+                for ans in self._extract_relevant_info(truncated_prev_reasoning, search_query, kbinfos):
                     summary_think = ans
-                    yield {"answer": think + rm_result_tags(summary_think) + "</think>", "reference": {}, "audio_binary": None}
+                    yield {"answer": think + self._remove_result_tags(summary_think) + "</think>", "reference": {}, "audio_binary": None}
 
                 all_reasoning_steps.append(summary_think)
-                msg_hisotry.append(
+                msg_history.append(
                     {"role": "user", "content": f"\n\n{BEGIN_SEARCH_RESULT}{summary_think}{END_SEARCH_RESULT}\n\n"})
-                think += rm_result_tags(summary_think)
-                logging.info(f"[THINK]Summary: {ii}. {summary_think}")
+                think += self._remove_result_tags(summary_think)
+                logging.info(f"[THINK]Summary: {step_index}. {summary_think}")
 
         yield think + "</think>"

agentic_reasoning/prompts.py

@@ -68,6 +68,7 @@ REASON_PROMPT = (
         f"- You have a dataset to search, so you just provide a proper search query.\n"
         f"- Use {BEGIN_SEARCH_QUERY} to request a dataset search and end with {END_SEARCH_QUERY}.\n"
         "- The language of query MUST be as the same as 'Question' or 'search result'.\n"
+        "- If no helpful information can be found, rewrite the search query to be less and precise keywords.\n"
         "- When done searching, continue your reasoning.\n\n"
         'Please answer the following question. You should think step by step to solve it.\n\n'
     )

api/apps/__init__.py

@@ -83,7 +83,7 @@ app.errorhandler(Exception)(server_error_response)
 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)
+    os.environ.get("MAX_CONTENT_LENGTH", 1024 * 1024 * 1024)
 )
 
 Session(app)

api/apps/api_app.py

@@ -479,7 +479,7 @@ def upload():
                 doc = doc.to_dict()
                 doc["tenant_id"] = tenant_id
                 bucket, name = File2DocumentService.get_storage_address(doc_id=doc["id"])
-                queue_tasks(doc, bucket, name)
+                queue_tasks(doc, bucket, name, 0)
             except Exception as e:
                 return server_error_response(e)
 

api/apps/canvas_app.py

@@ -18,13 +18,16 @@ import traceback
 from flask import request, Response
 from flask_login import login_required, current_user
 from api.db.services.canvas_service import CanvasTemplateService, UserCanvasService
+from api.db.services.user_service import TenantService
+from api.db.services.user_canvas_version import UserCanvasVersionService
 from api.settings import RetCode
 from api.utils import get_uuid
 from api.utils.api_utils import get_json_result, server_error_response, validate_request, get_data_error_result
 from agent.canvas import Canvas
 from peewee import MySQLDatabase, PostgresqlDatabase
 from api.db.db_models import APIToken
-
+import logging
+import time
 
 @manager.route('/templates', methods=['GET'])  # noqa: F821
 @login_required
@@ -61,7 +64,6 @@ def save():
     req["user_id"] = current_user.id
     if not isinstance(req["dsl"], str):
         req["dsl"] = json.dumps(req["dsl"], ensure_ascii=False)
-
     req["dsl"] = json.loads(req["dsl"])
     if "id" not in req:
         if UserCanvasService.query(user_id=current_user.id, title=req["title"].strip()):
@@ -75,16 +77,22 @@ def save():
                 data=False, message='Only owner of canvas authorized for this operation.',
                 code=RetCode.OPERATING_ERROR)
         UserCanvasService.update_by_id(req["id"], req)
+    # save version    
+    UserCanvasVersionService.insert( user_canvas_id=req["id"], dsl=req["dsl"], title="{0}_{1}".format(req["title"], time.strftime("%Y_%m_%d_%H_%M_%S")))
+    UserCanvasVersionService.delete_all_versions(req["id"])
     return get_json_result(data=req)
 
  
+
+
 @manager.route('/get/<canvas_id>', methods=['GET'])  # noqa: F821
 @login_required
 def get(canvas_id):
-    e, c = UserCanvasService.get_by_id(canvas_id)
+    e, c = UserCanvasService.get_by_tenant_id(canvas_id)
+    logging.info(f"get canvas_id: {canvas_id} c: {c}")
     if not e:
         return get_data_error_result(message="canvas not found.")
-    return get_json_result(data=c.to_dict())
+    return get_json_result(data=c)
 
 @manager.route('/getsse/<canvas_id>', methods=['GET'])  # type: ignore # noqa: F821
 def getsse(canvas_id):
@@ -283,4 +291,62 @@ def test_db_connect():
         return get_json_result(data="Database Connection Successful!")
     except Exception as e:
         return server_error_response(e)
+#api get list version dsl of canvas
+@manager.route('/getlistversion/<canvas_id>', methods=['GET'])  # noqa: F821
+@login_required
+def getlistversion(canvas_id):
+    try:
+        list =sorted([c.to_dict() for c in UserCanvasVersionService.list_by_canvas_id(canvas_id)], key=lambda x: x["update_time"]*-1)
+        return get_json_result(data=list)
+    except Exception as e:
+        return get_data_error_result(message=f"Error getting history files: {e}")
+#api get version dsl of canvas
+@manager.route('/getversion/<version_id>', methods=['GET'])  # noqa: F821
+@login_required
+def getversion( version_id):
+    try:
       
+        e, version = UserCanvasVersionService.get_by_id(version_id)
+        if version:
+            return get_json_result(data=version.to_dict())
+    except Exception as e:
+        return get_json_result(data=f"Error getting history file: {e}")
+@manager.route('/listteam', methods=['GET'])  # noqa: F821
+@login_required
+def list_kbs():
+    keywords = request.args.get("keywords", "")
+    page_number = int(request.args.get("page", 1))
+    items_per_page = int(request.args.get("page_size", 150))
+    orderby = request.args.get("orderby", "create_time")
+    desc = request.args.get("desc", True)
+    try:
+        tenants = TenantService.get_joined_tenants_by_user_id(current_user.id)
+        kbs, total = UserCanvasService.get_by_tenant_ids(
+            [m["tenant_id"] for m in tenants], current_user.id, page_number,
+            items_per_page, orderby, desc, keywords)
+        return get_json_result(data={"kbs": kbs, "total": total})
+    except Exception as e:
+        return server_error_response(e)
+@manager.route('/setting', methods=['POST'])  # noqa: F821
+@validate_request("id", "title", "permission")
+@login_required
+def setting():
+    req = request.json
+    req["user_id"] = current_user.id
+    e,flow = UserCanvasService.get_by_id(req["id"])
+    if not e:
+        return get_data_error_result(message="canvas not found.")
+    flow = flow.to_dict()
+    flow["title"] = req["title"]
+    if req["description"]:
+        flow["description"] = req["description"]
+    if req["permission"]:
+        flow["permission"] = req["permission"]
+    if req["avatar"]:
+        flow["avatar"] = req["avatar"]
+    if not UserCanvasService.query(user_id=current_user.id, id=req["id"]):
+        return get_json_result(
+            data=False, message='Only owner of canvas authorized for this operation.',
+            code=RetCode.OPERATING_ERROR)
+    num= UserCanvasService.update_by_id(req["id"], flow)
+    return get_json_result(data=num)

api/apps/conversation_app.py

@@ -17,25 +17,25 @@ import json
 import re
 import traceback
 from copy import deepcopy
-from api.db.db_models import APIToken
 
-from api.db.services.conversation_service import ConversationService, structure_answer
-from api.db.services.user_service import UserTenantService
-from flask import request, Response
-from flask_login import login_required, current_user
+import trio
+from flask import Response, request
+from flask_login import current_user, login_required
 
+from api import settings
 from api.db import LLMType
-from api.db.services.dialog_service import DialogService, chat, ask
+from api.db.db_models import APIToken
+from api.db.services.conversation_service import ConversationService, structure_answer
+from api.db.services.dialog_service import DialogService, ask, chat
 from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.db.services.llm_service import LLMBundle, TenantService
-from api import settings
-from api.utils.api_utils import get_json_result
-from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
+from api.db.services.user_service import UserTenantService
+from api.utils.api_utils import get_data_error_result, get_json_result, server_error_response, validate_request
 from graphrag.general.mind_map_extractor import MindMapExtractor
 from rag.app.tag import label_question
 
 
-@manager.route('/set', methods=['POST'])  # noqa: F821
+@manager.route("/set", methods=["POST"])  # noqa: F821
 @login_required
 def set_conversation():
     req = request.json
@@ -49,8 +49,7 @@ def set_conversation():
                 return get_data_error_result(message="Conversation not found!")
             e, conv = ConversationService.get_by_id(conv_id)
             if not e:
-                return get_data_error_result(
-                    message="Fail to update a conversation!")
+                return get_data_error_result(message="Fail to update a conversation!")
             conv = conv.to_dict()
             return get_json_result(data=conv)
         except Exception as e:
@@ -60,38 +59,30 @@ def set_conversation():
         e, dia = DialogService.get_by_id(req["dialog_id"])
         if not e:
             return get_data_error_result(message="Dialog not found")
-        conv = {
-            "id": conv_id,
-            "dialog_id": req["dialog_id"],
-            "name": req.get("name", "New conversation"),
-            "message": [{"role": "assistant", "content": dia.prompt_config["prologue"]}]
-        }
+        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)
         return get_json_result(data=conv)
     except Exception as e:
         return server_error_response(e)
 
 
-@manager.route('/get', methods=['GET'])  # noqa: F821
+@manager.route("/get", methods=["GET"])  # noqa: F821
 @login_required
 def get():
     conv_id = request.args["conversation_id"]
     try:
-        
         e, conv = ConversationService.get_by_id(conv_id)
         if not e:
             return get_data_error_result(message="Conversation not found!")
         tenants = UserTenantService.query(user_id=current_user.id)
-        avatar =None
+        avatar = None
         for tenant in tenants:
             dialog = DialogService.query(tenant_id=tenant.tenant_id, id=conv.dialog_id)
-            if dialog and len(dialog)>0:
+            if dialog and len(dialog) > 0:
                 avatar = dialog[0].icon
                 break
         else:
-            return get_json_result(
-                data=False, message='Only owner of conversation authorized for this operation.',
-                code=settings.RetCode.OPERATING_ERROR)
+            return get_json_result(data=False, message="Only owner of conversation authorized for this operation.", code=settings.RetCode.OPERATING_ERROR)
 
         def get_value(d, k1, k2):
             return d.get(k1, d.get(k2))
@@ -99,26 +90,29 @@ def get():
         for ref in conv.reference:
             if isinstance(ref, list):
                 continue
-            ref["chunks"] = [{
-                "id": get_value(ck, "chunk_id", "id"),
-                "content": get_value(ck, "content", "content_with_weight"),
-                "document_id": get_value(ck, "doc_id", "document_id"),
-                "document_name": get_value(ck, "docnm_kwd", "document_name"),
-                "dataset_id": get_value(ck, "kb_id", "dataset_id"),
-                "image_id": get_value(ck, "image_id", "img_id"),
-                "positions": get_value(ck, "positions", "position_int"),
-            } for ck in ref.get("chunks", [])]
+            ref["chunks"] = [
+                {
+                    "id": get_value(ck, "chunk_id", "id"),
+                    "content": get_value(ck, "content", "content_with_weight"),
+                    "document_id": get_value(ck, "doc_id", "document_id"),
+                    "document_name": get_value(ck, "docnm_kwd", "document_name"),
+                    "dataset_id": get_value(ck, "kb_id", "dataset_id"),
+                    "image_id": get_value(ck, "image_id", "img_id"),
+                    "positions": get_value(ck, "positions", "position_int"),
+                }
+                for ck in ref.get("chunks", [])
+            ]
 
         conv = conv.to_dict()
-        conv["avatar"]=avatar
+        conv["avatar"] = avatar
         return get_json_result(data=conv)
     except Exception as e:
         return server_error_response(e)
 
-@manager.route('/getsse/<dialog_id>', methods=['GET'])  # type: ignore # noqa: F821
-def getsse(dialog_id):
 
-    token = request.headers.get('Authorization').split()
+@manager.route("/getsse/<dialog_id>", methods=["GET"])  # type: ignore # noqa: F821
+def getsse(dialog_id):
+    token = request.headers.get("Authorization").split()
     if len(token) != 2:
         return get_data_error_result(message='Authorization is not valid!"')
     token = token[1]
@@ -130,13 +124,14 @@ def getsse(dialog_id):
         if not e:
             return get_data_error_result(message="Dialog not found!")
         conv = conv.to_dict()
-        conv["avatar"]= conv["icon"]
+        conv["avatar"] = conv["icon"]
         del conv["icon"]
         return get_json_result(data=conv)
     except Exception as e:
         return server_error_response(e)
 
-@manager.route('/rm', methods=['POST'])  # noqa: F821
+
+@manager.route("/rm", methods=["POST"])  # noqa: F821
 @login_required
 def rm():
     conv_ids = request.json["conversation_ids"]
@@ -150,28 +145,21 @@ def rm():
                 if DialogService.query(tenant_id=tenant.tenant_id, id=conv.dialog_id):
                     break
             else:
-                return get_json_result(
-                    data=False, message='Only owner of conversation authorized for this operation.',
-                    code=settings.RetCode.OPERATING_ERROR)
+                return get_json_result(data=False, message="Only owner of conversation authorized for this operation.", code=settings.RetCode.OPERATING_ERROR)
             ConversationService.delete_by_id(cid)
         return get_json_result(data=True)
     except Exception as e:
         return server_error_response(e)
 
 
-@manager.route('/list', methods=['GET'])  # noqa: F821
+@manager.route("/list", methods=["GET"])  # noqa: F821
 @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, message='Only owner of dialog authorized for this operation.',
-                code=settings.RetCode.OPERATING_ERROR)
-        convs = ConversationService.query(
-            dialog_id=dialog_id,
-            order_by=ConversationService.model.create_time,
-            reverse=True)
+            return get_json_result(data=False, message="Only owner of dialog authorized for this operation.", code=settings.RetCode.OPERATING_ERROR)
+        convs = ConversationService.query(dialog_id=dialog_id, order_by=ConversationService.model.create_time, reverse=True)
 
         convs = [d.to_dict() for d in convs]
         return get_json_result(data=convs)
@@ -179,7 +167,7 @@ def list_convsersation():
         return server_error_response(e)
 
 
-@manager.route('/completion', methods=['POST'])  # noqa: F821
+@manager.route("/completion", methods=["POST"])  # noqa: F821
 @login_required
 @validate_request("conversation_id", "messages")
 def completion():
@@ -206,25 +194,30 @@ def completion():
         if not conv.reference:
             conv.reference = []
         else:
+
             def get_value(d, k1, k2):
                 return d.get(k1, d.get(k2))
 
             for ref in conv.reference:
                 if isinstance(ref, list):
                     continue
-                ref["chunks"] = [{
-                    "id": get_value(ck, "chunk_id", "id"),
-                    "content": get_value(ck, "content", "content_with_weight"),
-                    "document_id": get_value(ck, "doc_id", "document_id"),
-                    "document_name": get_value(ck, "docnm_kwd", "document_name"),
-                    "dataset_id": get_value(ck, "kb_id", "dataset_id"),
-                    "image_id": get_value(ck, "image_id", "img_id"),
-                    "positions": get_value(ck, "positions", "position_int"),
-                } for ck in ref.get("chunks", [])]
+                ref["chunks"] = [
+                    {
+                        "id": get_value(ck, "chunk_id", "id"),
+                        "content": get_value(ck, "content", "content_with_weight"),
+                        "document_id": get_value(ck, "doc_id", "document_id"),
+                        "document_name": get_value(ck, "docnm_kwd", "document_name"),
+                        "dataset_id": get_value(ck, "kb_id", "dataset_id"),
+                        "image_id": get_value(ck, "image_id", "img_id"),
+                        "positions": get_value(ck, "positions", "position_int"),
+                    }
+                    for ck in ref.get("chunks", [])
+                ]
 
         if not conv.reference:
             conv.reference = []
         conv.reference.append({"chunks": [], "doc_aggs": []})
+
         def stream():
             nonlocal dia, msg, req, conv
             try:
@@ -234,9 +227,7 @@ def completion():
                 ConversationService.update_by_id(conv.id, conv.to_dict())
             except Exception as e:
                 traceback.print_exc()
-                yield "data:" + json.dumps({"code": 500, "message": str(e),
-                                            "data": {"answer": "**ERROR**: " + str(e), "reference": []}},
-                                           ensure_ascii=False) + "\n\n"
+                yield "data:" + json.dumps({"code": 500, "message": str(e), "data": {"answer": "**ERROR**: " + str(e), "reference": []}}, ensure_ascii=False) + "\n\n"
             yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n"
 
         if req.get("stream", True):
@@ -258,7 +249,7 @@ def completion():
         return server_error_response(e)
 
 
-@manager.route('/tts', methods=['POST'])  # noqa: F821
+@manager.route("/tts", methods=["POST"])  # noqa: F821
 @login_required
 def tts():
     req = request.json
@@ -280,9 +271,7 @@ def tts():
                 for chunk in tts_mdl.tts(txt):
                     yield chunk
         except Exception as e:
-            yield ("data:" + json.dumps({"code": 500, "message": str(e),
-                                         "data": {"answer": "**ERROR**: " + str(e)}},
-                                        ensure_ascii=False)).encode('utf-8')
+            yield ("data:" + json.dumps({"code": 500, "message": str(e), "data": {"answer": "**ERROR**: " + str(e)}}, ensure_ascii=False)).encode("utf-8")
 
     resp = Response(stream_audio(), mimetype="audio/mpeg")
     resp.headers.add_header("Cache-Control", "no-cache")
@@ -292,7 +281,7 @@ def tts():
     return resp
 
 
-@manager.route('/delete_msg', methods=['POST'])  # noqa: F821
+@manager.route("/delete_msg", methods=["POST"])  # noqa: F821
 @login_required
 @validate_request("conversation_id", "message_id")
 def delete_msg():
@@ -315,7 +304,7 @@ def delete_msg():
     return get_json_result(data=conv)
 
 
-@manager.route('/thumbup', methods=['POST'])  # noqa: F821
+@manager.route("/thumbup", methods=["POST"])  # noqa: F821
 @login_required
 @validate_request("conversation_id", "message_id")
 def thumbup():
@@ -323,7 +312,7 @@ def thumbup():
     e, conv = ConversationService.get_by_id(req["conversation_id"])
     if not e:
         return get_data_error_result(message="Conversation not found!")
-    up_down = req.get("set")
+    up_down = req.get("thumbup")
     feedback = req.get("feedback", "")
     conv = conv.to_dict()
     for i, msg in enumerate(conv["message"]):
@@ -342,7 +331,7 @@ def thumbup():
     return get_json_result(data=conv)
 
 
-@manager.route('/ask', methods=['POST'])  # noqa: F821
+@manager.route("/ask", methods=["POST"])  # noqa: F821
 @login_required
 @validate_request("question", "kb_ids")
 def ask_about():
@@ -355,9 +344,7 @@ def ask_about():
             for ans in ask(req["question"], req["kb_ids"], uid):
                 yield "data:" + json.dumps({"code": 0, "message": "", "data": ans}, ensure_ascii=False) + "\n\n"
         except Exception as e:
-            yield "data:" + json.dumps({"code": 500, "message": str(e),
-                                        "data": {"answer": "**ERROR**: " + str(e), "reference": []}},
-                                       ensure_ascii=False) + "\n\n"
+            yield "data:" + json.dumps({"code": 500, "message": str(e), "data": {"answer": "**ERROR**: " + str(e), "reference": []}}, ensure_ascii=False) + "\n\n"
         yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n"
 
     resp = Response(stream(), mimetype="text/event-stream")
@@ -368,7 +355,7 @@ def ask_about():
     return resp
 
 
-@manager.route('/mindmap', methods=['POST'])  # noqa: F821
+@manager.route("/mindmap", methods=["POST"])  # noqa: F821
 @login_required
 @validate_request("question", "kb_ids")
 def mindmap():
@@ -381,18 +368,16 @@ def mindmap():
     embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING, llm_name=kb.embd_id)
     chat_mdl = LLMBundle(current_user.id, LLMType.CHAT)
     question = req["question"]
-    ranks = settings.retrievaler.retrieval(question, embd_mdl, kb.tenant_id, kb_ids, 1, 12,
-                                           0.3, 0.3, aggs=False,
-                                           rank_feature=label_question(question, [kb])
-                                           )
+    ranks = settings.retrievaler.retrieval(question, embd_mdl, kb.tenant_id, kb_ids, 1, 12, 0.3, 0.3, aggs=False, rank_feature=label_question(question, [kb]))
     mindmap = MindMapExtractor(chat_mdl)
-    mind_map = mindmap([c["content_with_weight"] for c in ranks["chunks"]]).output
+    mind_map = trio.run(mindmap, [c["content_with_weight"] for c in ranks["chunks"]])
+    mind_map = mind_map.output
     if "error" in mind_map:
         return server_error_response(Exception(mind_map["error"]))
     return get_json_result(data=mind_map)
 
 
-@manager.route('/related_questions', methods=['POST'])  # noqa: F821
+@manager.route("/related_questions", methods=["POST"])  # noqa: F821
 @login_required
 @validate_request("question")
 def related_questions():
@@ -400,31 +385,49 @@ def related_questions():
     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.
+Role: You are an AI language model assistant tasked with generating 5-10 related questions based on a user’s original query. These questions should help expand the search query scope and improve search relevance.
 
-### 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
+Instructions:
+	Input: You are provided with a user’s question.
+	Output: Generate 5-10 alternative questions that are related to the original user question. These alternatives should help retrieve a broader range of relevant documents from a vector database.
+	Context: Focus on rephrasing the original question in different ways, making sure the alternative questions are diverse but still connected to the topic of the original query. Do not create overly obscure, irrelevant, or unrelated questions.
+	Fallback: If you cannot generate any relevant alternatives, do not return any questions.
+	Guidance:
+	1. Each alternative should be unique but still relevant to the original query.
+	2. Keep the phrasing clear, concise, and easy to understand.
+	3. Avoid overly technical jargon or specialized terms unless directly relevant.
+	4. Ensure that each question contributes towards improving search results by broadening the search angle, not narrowing it.
+
+Example:
+Original Question: What are the benefits of electric vehicles?
+
+Alternative Questions:
+	1. How do electric vehicles impact the environment?
+	2. What are the advantages of owning an electric car?
+	3. What is the cost-effectiveness of electric vehicles?
+	4. How do electric vehicles compare to traditional cars in terms of fuel efficiency?
+	5. What are the environmental benefits of switching to electric cars?
+	6. How do electric vehicles help reduce carbon emissions?
+	7. Why are electric vehicles becoming more popular?
+	8. What are the long-term savings of using electric vehicles?
+	9. How do electric vehicles contribute to sustainability?
+	10. What are the key benefits of electric vehicles for consumers?
 
 Reason:
- - 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.
- 
+	Rephrasing the original query into multiple alternative questions helps the user explore different aspects of their search topic, improving the quality of search results.
+	These questions guide the search engine to provide a more comprehensive set of relevant documents.
 """
-    ans = chat_mdl.chat(prompt, [{"role": "user", "content": f"""
+    ans = chat_mdl.chat(
+        prompt,
+        [
+            {
+                "role": "user",
+                "content": f"""
 Keywords: {question}
 Related search terms:
-    """}], {"temperature": 0.9})
+    """,
+            }
+        ],
+        {"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/document_app.py

@@ -71,11 +71,13 @@ def upload():
     if not e:
         raise LookupError("Can't find this knowledgebase!")
 
-    err, _ = FileService.upload_document(kb, file_objs, current_user.id)
+    err, files = FileService.upload_document(kb, file_objs, current_user.id)
+    files = [f[0] for f in files] # remove the blob
+    
     if err:
         return get_json_result(
-            data=False, message="\n".join(err), code=settings.RetCode.SERVER_ERROR)
-    return get_json_result(data=True)
+            data=files, message="\n".join(err), code=settings.RetCode.SERVER_ERROR)
+    return get_json_result(data=files)
 
 
 @manager.route('/web_crawl', methods=['POST'])  # noqa: F821
@@ -329,10 +331,10 @@ def rm():
                     message="Database error (Document removal)!")
 
             f2d = File2DocumentService.get_by_document_id(doc_id)
-            FileService.filter_delete([File.source_type == FileSource.KNOWLEDGEBASE, File.id == f2d[0].file_id])
+            deleted_file_count = FileService.filter_delete([File.source_type == FileSource.KNOWLEDGEBASE, File.id == f2d[0].file_id])
             File2DocumentService.delete_by_document_id(doc_id)
-
-            STORAGE_IMPL.rm(b, n)
+            if deleted_file_count > 0:
+                STORAGE_IMPL.rm(b, n)
         except Exception as e:
             errors += str(e)
 
@@ -378,7 +380,7 @@ def run():
                 doc = doc.to_dict()
                 doc["tenant_id"] = tenant_id
                 bucket, name = File2DocumentService.get_storage_address(doc_id=doc["id"])
-                queue_tasks(doc, bucket, name)
+                queue_tasks(doc, bucket, name, 0)
 
         return get_json_result(data=True)
     except Exception as e:

api/apps/file2document_app.py

@@ -38,8 +38,12 @@ def convert():
     file2documents = []
 
     try:
+        files = FileService.get_by_ids(file_ids)
+        files_set = dict({file.id: file for file in files})
         for file_id in file_ids:
-            e, file = FileService.get_by_id(file_id)
+            file = files_set[file_id]
+            if not file:
+                return get_data_error_result(message="File not found!")
             file_ids_list = [file_id]
             if file.type == FileType.FOLDER.value:
                 file_ids_list = FileService.get_all_innermost_file_ids(file_id, [])
@@ -86,6 +90,7 @@ def convert():
                         "file_id": id,
                         "document_id": doc.id,
                     })
+
                     file2documents.append(file2document.to_json())
         return get_json_result(data=file2documents)
     except Exception as e:

api/apps/file_app.py

@@ -55,20 +55,17 @@ def upload():
                 data=False, message='No file selected!', code=settings.RetCode.ARGUMENT_ERROR)
     file_res = []
     try:
+        e, pf_folder = FileService.get_by_id(pf_id)
+        if not e:
+            return get_data_error_result( message="Can't find this folder!")
         for file_obj in file_objs:
-            e, file = FileService.get_by_id(pf_id)
-            if not e:
-                return get_data_error_result(
-                    message="Can't find this folder!")
             MAX_FILE_NUM_PER_USER = int(os.environ.get('MAX_FILE_NUM_PER_USER', 0))
             if MAX_FILE_NUM_PER_USER > 0 and DocumentService.get_doc_count(current_user.id) >= MAX_FILE_NUM_PER_USER:
-                return get_data_error_result(
-                    message="Exceed the maximum file number of a free user!")
+                return get_data_error_result( message="Exceed the maximum file number of a free user!")
 
             # split file name path
             if not file_obj.filename:
-                e, file = FileService.get_by_id(pf_id)
-                file_obj_names = [file.name, file_obj.filename]
+                file_obj_names = [pf_folder.name, file_obj.filename]
             else:
                 full_path = '/' + file_obj.filename
                 file_obj_names = full_path.split('/')
@@ -184,7 +181,7 @@ def list_files():
             current_user.id, pf_id, page_number, items_per_page, orderby, desc, keywords)
 
         parent_folder = FileService.get_parent_folder(pf_id)
-        if not FileService.get_parent_folder(pf_id):
+        if not parent_folder:
             return get_json_result(message="File not found!")
 
         return get_json_result(data={"total": total, "files": files, "parent_folder": parent_folder.to_json()})
@@ -358,9 +355,14 @@ def move():
     try:
         file_ids = req["src_file_ids"]
         parent_id = req["dest_file_id"]
+        files = FileService.get_by_ids(file_ids)
+        files_dict = {}
+        for file in files:
+            files_dict[file.id] = file
+
         for file_id in file_ids:
-            e, file = FileService.get_by_id(file_id)
-            if not e:
+            file = files_dict[file_id]
+            if not file:
                 return get_data_error_result(message="File or Folder not found!")
             if not file.tenant_id:
                 return get_data_error_result(message="Tenant not found!")

api/apps/kb_app.py

@@ -73,7 +73,7 @@ def create():
 
 @manager.route('/update', methods=['post'])  # noqa: F821
 @login_required
-@validate_request("kb_id", "name", "description", "permission", "parser_id")
+@validate_request("kb_id", "name", "description", "parser_id")
 @not_allowed_parameters("id", "tenant_id", "created_by", "create_time", "update_time", "create_date", "update_date", "created_by")
 def update():
     req = request.json
@@ -157,25 +157,38 @@ def detail():
         return server_error_response(e)
 
 
-@manager.route('/list', methods=['GET'])  # noqa: F821
+@manager.route('/list', methods=['POST'])  # noqa: F821
 @login_required
 def list_kbs():
     keywords = request.args.get("keywords", "")
-    page_number = int(request.args.get("page", 1))
-    items_per_page = int(request.args.get("page_size", 150))
+    page_number = int(request.args.get("page", 0))
+    items_per_page = int(request.args.get("page_size", 0))
     parser_id = request.args.get("parser_id")
     orderby = request.args.get("orderby", "create_time")
     desc = request.args.get("desc", True)
+
+    req = request.get_json()
+    owner_ids = req.get("owner_ids", [])
     try:
-        tenants = TenantService.get_joined_tenants_by_user_id(current_user.id)
-        kbs, total = KnowledgebaseService.get_by_tenant_ids(
-            [m["tenant_id"] for m in tenants], current_user.id, page_number,
-            items_per_page, orderby, desc, keywords, parser_id)
+        if not owner_ids:
+            tenants = TenantService.get_joined_tenants_by_user_id(current_user.id)
+            tenants = [m["tenant_id"] for m in tenants]
+            kbs, total = KnowledgebaseService.get_by_tenant_ids(
+                tenants, current_user.id, page_number,
+                items_per_page, orderby, desc, keywords, parser_id)
+        else:
+            tenants = owner_ids
+            kbs, total = KnowledgebaseService.get_by_tenant_ids(
+                tenants, current_user.id, 0,
+                0, orderby, desc, keywords, parser_id)
+            kbs = [kb for kb in kbs if kb["tenant_id"] in tenants]
+            if page_number and items_per_page:
+                kbs = kbs[(page_number-1)*items_per_page:page_number*items_per_page]
+            total = len(kbs)
         return get_json_result(data={"kbs": kbs, "total": total})
     except Exception as e:
         return server_error_response(e)
 
-
 @manager.route('/rm', methods=['post'])  # noqa: F821
 @login_required
 @validate_request("kb_id")
@@ -323,3 +336,17 @@ def knowledge_graph(kb_id):
             filtered_edges = [o for o in obj["graph"]["edges"] if o["source"] != o["target"] and o["source"] in node_id_set and o["target"] in node_id_set]
             obj["graph"]["edges"] = sorted(filtered_edges, key=lambda x: x.get("weight", 0), reverse=True)[:128]
     return get_json_result(data=obj)
+
+@manager.route('/<kb_id>/knowledge_graph', methods=['DELETE'])  # noqa: F821
+@login_required
+def delete_knowledge_graph(kb_id):
+    if not KnowledgebaseService.accessible(kb_id, current_user.id):
+        return get_json_result(
+            data=False,
+            message='No authorization.',
+            code=settings.RetCode.AUTHENTICATION_ERROR
+        )
+    _, kb = KnowledgebaseService.get_by_id(kb_id)
+    settings.docStoreConn.delete({"knowledge_graph_kwd": ["graph", "subgraph", "entity", "relation"]}, search.index_name(kb.tenant_id), kb_id)
+
+    return get_json_result(data=True)

api/apps/langfuse_app.py

@@ -0,0 +1,97 @@
+#
+#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+
+
+from flask import request
+from flask_login import current_user, login_required
+from langfuse import Langfuse
+
+from api.db.db_models import DB
+from api.db.services.langfuse_service import TenantLangfuseService
+from api.utils.api_utils import get_error_data_result, get_json_result, server_error_response, validate_request
+
+
+@manager.route("/api_key", methods=["POST", "PUT"])  # noqa: F821
+@login_required
+@validate_request("secret_key", "public_key", "host")
+def set_api_key():
+    req = request.get_json()
+    secret_key = req.get("secret_key", "")
+    public_key = req.get("public_key", "")
+    host = req.get("host", "")
+    if not all([secret_key, public_key, host]):
+        return get_error_data_result(message="Missing required fields")
+
+    langfuse_keys = dict(
+        tenant_id=current_user.id,
+        secret_key=secret_key,
+        public_key=public_key,
+        host=host,
+    )
+
+    langfuse = Langfuse(public_key=langfuse_keys["public_key"], secret_key=langfuse_keys["secret_key"], host=langfuse_keys["host"])
+    if not langfuse.auth_check():
+        return get_error_data_result(message="Invalid Langfuse keys")
+
+    langfuse_entry = TenantLangfuseService.filter_by_tenant(tenant_id=current_user.id)
+    with DB.atomic():
+        try:
+            if not langfuse_entry:
+                TenantLangfuseService.save(**langfuse_keys)
+            else:
+                TenantLangfuseService.update_by_tenant(tenant_id=current_user.id, langfuse_keys=langfuse_keys)
+            return get_json_result(data=langfuse_keys)
+        except Exception as e:
+            server_error_response(e)
+
+
+@manager.route("/api_key", methods=["GET"])  # noqa: F821
+@login_required
+@validate_request()
+def get_api_key():
+    langfuse_entry = TenantLangfuseService.filter_by_tenant_with_info(tenant_id=current_user.id)
+    if not langfuse_entry:
+        return get_json_result(message="Have not record any Langfuse keys.")
+
+    langfuse = Langfuse(public_key=langfuse_entry["public_key"], secret_key=langfuse_entry["secret_key"], host=langfuse_entry["host"])
+    try:
+        if not langfuse.auth_check():
+            return get_error_data_result(message="Invalid Langfuse keys loaded")
+    except langfuse.api.core.api_error.ApiError as api_err:
+        return get_json_result(message=f"Error from Langfuse: {api_err}")
+    except Exception as e:
+        server_error_response(e)
+
+    langfuse_entry["project_id"] = langfuse.api.projects.get().dict()["data"][0]["id"]
+    langfuse_entry["project_name"] = langfuse.api.projects.get().dict()["data"][0]["name"]
+
+    return get_json_result(data=langfuse_entry)
+
+
+@manager.route("/api_key", methods=["DELETE"])  # noqa: F821
+@login_required
+@validate_request()
+def delete_api_key():
+    langfuse_entry = TenantLangfuseService.filter_by_tenant(tenant_id=current_user.id)
+    if not langfuse_entry:
+        return get_json_result(message="Have not record any Langfuse keys.")
+
+    with DB.atomic():
+        try:
+            TenantLangfuseService.delete_model(langfuse_entry)
+            return get_json_result(data=True)
+        except Exception as e:
+            server_error_response(e)

api/apps/llm_app.py

@@ -61,6 +61,7 @@ def set_api_key():
     msg = ""
     for llm in LLMService.query(fid=factory):
         if not embd_passed and llm.model_type == LLMType.EMBEDDING.value:
+            assert factory in EmbeddingModel, f"Embedding model from {factory} is not supported yet."
             mdl = EmbeddingModel[factory](
                 req["api_key"], llm.llm_name, base_url=req.get("base_url"))
             try:
@@ -71,6 +72,7 @@ def set_api_key():
             except Exception as e:
                 msg += f"\nFail to access embedding model({llm.llm_name}) using this api key." + str(e)
         elif not chat_passed and llm.model_type == LLMType.CHAT.value:
+            assert factory in ChatModel, f"Chat model from {factory} is not supported yet."
             mdl = ChatModel[factory](
                 req["api_key"], llm.llm_name, base_url=req.get("base_url"))
             try:
@@ -83,6 +85,7 @@ def set_api_key():
                 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:
+            assert factory in RerankModel, f"Re-rank model from {factory} is not supported yet."
             mdl = RerankModel[factory](
                 req["api_key"], llm.llm_name, base_url=req.get("base_url"))
             try:
@@ -135,6 +138,8 @@ def set_api_key():
 def add_llm():
     req = request.json
     factory = req["llm_factory"]
+    api_key = req.get("api_key", "x")
+    llm_name = req.get("llm_name")
 
     def apikey_json(keys):
         nonlocal req
@@ -143,7 +148,6 @@ def add_llm():
     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":
@@ -157,52 +161,38 @@ def add_llm():
     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"
+        llm_name += "___LocalAI"
 
     elif factory == "HuggingFace":
-        llm_name = req["llm_name"] + "___HuggingFace"
-        api_key = "xxxxxxxxxxxxxxx"
+        llm_name += "___HuggingFace"
 
     elif factory == "OpenAI-API-Compatible":
-        llm_name = req["llm_name"] + "___OpenAI-API"
-        api_key = req.get("api_key", "xxxxxxxxxxxxxxx")
+        llm_name += "___OpenAI-API"
 
     elif factory == "VLLM":
-        llm_name = req["llm_name"] + "___VLLM"
-        api_key = req.get("api_key", "xxxxxxxxxxxxxxx")
+        llm_name += "___VLLM"
 
     elif factory == "XunFei Spark":
-        llm_name = req["llm_name"]
         if req["model_type"] == "chat":
-            api_key = req.get("spark_api_password", "xxxxxxxxxxxxxxx")
+            api_key = req.get("spark_api_password", "")
         elif req["model_type"] == "tts":
             api_key = apikey_json(["spark_app_id", "spark_api_secret", "spark_api_key"])
 
     elif factory == "BaiduYiyan":
-        llm_name = req["llm_name"]
         api_key = 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,
@@ -216,6 +206,7 @@ def add_llm():
     msg = ""
     mdl_nm = llm["llm_name"].split("___")[0]
     if llm["model_type"] == LLMType.EMBEDDING.value:
+        assert factory in EmbeddingModel, f"Embedding model from {factory} is not supported yet."
         mdl = EmbeddingModel[factory](
             key=llm['api_key'],
             model_name=mdl_nm,
@@ -227,6 +218,7 @@ def add_llm():
         except Exception as e:
             msg += f"\nFail to access embedding model({mdl_nm})." + str(e)
     elif llm["model_type"] == LLMType.CHAT.value:
+        assert factory in ChatModel, f"Chat model from {factory} is not supported yet."
         mdl = ChatModel[factory](
             key=llm['api_key'],
             model_name=mdl_nm,
@@ -241,6 +233,7 @@ def add_llm():
             msg += f"\nFail to access model({mdl_nm})." + str(
                 e)
     elif llm["model_type"] == LLMType.RERANK:
+        assert factory in RerankModel, f"RE-rank model from {factory} is not supported yet."
         try:
             mdl = RerankModel[factory](
                 key=llm["api_key"],
@@ -256,6 +249,7 @@ def add_llm():
             msg += f"\nFail to access model({mdl_nm})." + str(
                 e)
     elif llm["model_type"] == LLMType.IMAGE2TEXT.value:
+        assert factory in CvModel, f"Image to text model from {factory} is not supported yet."
         mdl = CvModel[factory](
             key=llm["api_key"],
             model_name=mdl_nm,
@@ -269,6 +263,7 @@ def add_llm():
         except Exception as e:
             msg += f"\nFail to access model({mdl_nm})." + str(e)
     elif llm["model_type"] == LLMType.TTS:
+        assert factory in TTSModel, f"TTS model from {factory} is not supported yet."
         mdl = TTSModel[factory](
             key=llm["api_key"], model_name=mdl_nm, base_url=llm["api_base"]
         )
@@ -351,8 +346,6 @@ def list_app():
 
         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})

api/apps/sdk/chat.py

@@ -23,17 +23,15 @@ 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
+from api.utils.api_utils import get_error_data_result, token_required, get_result, check_duplicate_ids
+
 
 
 @manager.route('/chats', methods=['POST'])  # noqa: F821
 @token_required
 def create(tenant_id):
     req = request.json
-    ids = req.get("dataset_ids")
-    if not ids:
-        return get_error_data_result(message="`dataset_ids` is required")
+    ids = [i for i in req.get("dataset_ids", []) if i] 
     for kb_id in ids:
         kbs = KnowledgebaseService.accessible(kb_id=kb_id, user_id=tenant_id)
         if not kbs:
@@ -42,10 +40,11 @@ def create(tenant_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)
+    
+    kbs = KnowledgebaseService.get_by_ids(ids) if ids else []
     embd_ids = [TenantLLMService.split_model_name_and_factory(kb.embd_id)[0] for kb in kbs]  # remove vendor suffix for comparison
     embd_count = list(set(embd_ids))
-    if len(embd_count) != 1:
+    if len(embd_count) > 1:
         return get_result(message='Datasets use different embedding models."',
                           code=settings.RetCode.AUTHENTICATION_ERROR)
     req["kb_ids"] = ids
@@ -178,6 +177,7 @@ def update(tenant_id, chat_id):
                 kb = kbs[0]
                 if kb.chunk_num == 0:
                     return get_error_data_result(f"The dataset {kb_id} doesn't own parsed file")
+                
             kbs = KnowledgebaseService.get_by_ids(ids)
             embd_ids = [TenantLLMService.split_model_name_and_factory(kb.embd_id)[0] for kb in kbs]  # remove vendor suffix for comparison
             embd_count = list(set(embd_ids))
@@ -223,11 +223,11 @@ def update(tenant_id, chat_id):
             return get_error_data_result(f"`rerank_model` {req.get('rerank_id')} doesn't exist")
     if "name" in req:
         if not req.get("name"):
-            return get_error_data_result(message="`name` is not empty.")
+            return get_error_data_result(message="`name` cannot be empty.")
         if req["name"].lower() != res["name"].lower() \
                 and len(
             DialogService.query(name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value)) > 0:
-            return get_error_data_result(message="Duplicated chat name in updating dataset.")
+            return get_error_data_result(message="Duplicated chat name in updating chat.")
     if "prompt_config" in req:
         res["prompt_config"].update(req["prompt_config"])
         for p in res["prompt_config"]["parameters"]:
@@ -252,6 +252,8 @@ def update(tenant_id, chat_id):
 @manager.route('/chats', methods=['DELETE'])  # noqa: F821
 @token_required
 def delete(tenant_id):
+    errors = []
+    success_count = 0
     req = request.json
     if not req:
         ids = None
@@ -264,14 +266,39 @@ def delete(tenant_id):
             id_list.append(dia.id)
     else:
         id_list = ids
-    for id in id_list:
+
+    unique_id_list, duplicate_messages = check_duplicate_ids(id_list, "assistant")
+
+    for id in unique_id_list:
         if not DialogService.query(tenant_id=tenant_id, id=id, status=StatusEnum.VALID.value):
-            return get_error_data_result(message=f"You don't own the chat {id}")
+            errors.append(f"Assistant({id}) not found.")
+            continue
         temp_dict = {"status": StatusEnum.INVALID.value}
         DialogService.update_by_id(id, temp_dict)
+        success_count += 1
+        
+    if errors:
+        if success_count > 0:
+            return get_result(
+                data={"success_count": success_count, "errors": errors},
+                message=f"Partially deleted {success_count} chats with {len(errors)} errors"
+            )
+        else:
+            return get_error_data_result(message="; ".join(errors))
+    
+    if duplicate_messages:
+        if success_count > 0:
+            return get_result(
+                message=f"Partially deleted {success_count} chats with {len(duplicate_messages)} errors", 
+                data={"success_count": success_count, "errors": duplicate_messages}
+            )
+        else:
+            return get_error_data_result(message=";".join(duplicate_messages))
+    
     return get_result()
 
 
+
 @manager.route('/chats', methods=['GET'])  # noqa: F821
 @token_required
 def list_chat(tenant_id):
@@ -320,7 +347,7 @@ def list_chat(tenant_id):
         for kb_id in res["kb_ids"]:
             kb = KnowledgebaseService.query(id=kb_id)
             if not kb:
-                logging.WARN(f"Don't exist the kb {kb_id}")
+                logging.warning(f"The kb {kb_id} does not exist.")
                 continue
             kb_list.append(kb[0].to_json())
         del res["kb_ids"]

api/apps/sdk/dataset.py

@@ -30,7 +30,7 @@ from api.utils.api_utils import (
     token_required,
     get_error_data_result,
     valid,
-    get_parser_config,
+    get_parser_config, valid_parser_config, dataset_readonly_fields,check_duplicate_ids
 )
 
 
@@ -66,14 +66,10 @@ def create(tenant_id):
               type: string
               enum: ['me', 'team']
               description: Dataset permission.
-            language:
-              type: string
-              enum: ['Chinese', 'English']
-              description: Language of the dataset.
             chunk_method:
               type: string
               enum: ["naive", "manual", "qa", "table", "paper", "book", "laws",
-                     "presentation", "picture", "one", "knowledge_graph", "email", "tag"
+                     "presentation", "picture", "one", "email", "tag"
                      ]
               description: Chunking method.
             parser_config:
@@ -89,13 +85,15 @@ def create(tenant_id):
               type: object
     """
     req = request.json
+    for k in req.keys():
+        if dataset_readonly_fields(k):
+            return get_result(code=settings.RetCode.ARGUMENT_ERROR, message=f"'{k}' is readonly.")
     e, t = TenantService.get_by_id(tenant_id)
     permission = req.get("permission")
-    language = req.get("language")
     chunk_method = req.get("chunk_method")
     parser_config = req.get("parser_config")
+    valid_parser_config(parser_config)
     valid_permission = ["me", "team"]
-    valid_language = ["Chinese", "English"]
     valid_chunk_method = [
         "naive",
         "manual",
@@ -107,15 +105,12 @@ def create(tenant_id):
         "presentation",
         "picture",
         "one",
-        "knowledge_graph",
         "email",
         "tag"
     ]
     check_validation = valid(
         permission,
         valid_permission,
-        language,
-        valid_language,
         chunk_method,
         valid_chunk_method,
     )
@@ -134,28 +129,23 @@ def create(tenant_id):
     req["name"] = req["name"].strip()
     if req["name"] == "":
         return get_error_data_result(message="`name` is not empty string!")
+    if len(req["name"]) >= 128:
+        return get_error_data_result(
+            message="Dataset name should not be longer than 128 characters."
+        )
     if KnowledgebaseService.query(
         name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value
     ):
         return get_error_data_result(
             message="Duplicated dataset name in creating dataset."
         )
-    req["tenant_id"] = req["created_by"] = tenant_id
+    req["tenant_id"] = tenant_id
+    req["created_by"] = tenant_id
     if not req.get("embedding_model"):
         req["embedding_model"] = t.embd_id
     else:
         valid_embedding_models = [
             "BAAI/bge-large-zh-v1.5",
-            "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(
@@ -182,6 +172,10 @@ def create(tenant_id):
         if old_key in req
     }
     req.update(mapped_keys)
+    flds = list(req.keys())
+    for f in flds:
+        if req[f] == "" and f in ["permission", "parser_id", "chunk_method"]:
+            del req[f]
     if not KnowledgebaseService.save(**req):
         return get_error_data_result(message="Create dataset error.(Database error)")
     renamed_data = {}
@@ -226,6 +220,8 @@ def delete(tenant_id):
         schema:
           type: object
     """
+    errors = []
+    success_count = 0
     req = request.json
     if not req:
         ids = None
@@ -238,15 +234,18 @@ def delete(tenant_id):
             id_list.append(kb.id)
     else:
         id_list = ids
+    unique_id_list, duplicate_messages = check_duplicate_ids(id_list, "dataset")
+    id_list = unique_id_list
+
     for id in id_list:
         kbs = KnowledgebaseService.query(id=id, tenant_id=tenant_id)
         if not kbs:
-            return get_error_data_result(message=f"You don't own the dataset {id}")
+            errors.append(f"You don't own the dataset {id}")
+            continue
         for doc in DocumentService.query(kb_id=id):
             if not DocumentService.remove_document(doc, tenant_id):
-                return get_error_data_result(
-                    message="Remove document error.(Database error)"
-                )
+                errors.append(f"Remove document error for dataset {id}")
+                continue
             f2d = File2DocumentService.get_by_document_id(doc.id)
             FileService.filter_delete(
                 [
@@ -258,7 +257,22 @@ def delete(tenant_id):
         FileService.filter_delete(
             [File.source_type == FileSource.KNOWLEDGEBASE, File.type == "folder", File.name == kbs[0].name])
         if not KnowledgebaseService.delete_by_id(id):
-            return get_error_data_result(message="Delete dataset error.(Database error)")
+            errors.append(f"Delete dataset error for {id}")
+            continue
+        success_count += 1
+    if errors:
+        if success_count > 0:
+            return get_result(
+                data={"success_count": success_count, "errors": errors},
+                message=f"Partially deleted {success_count} datasets with {len(errors)} errors"
+            )
+        else:
+            return get_error_data_result(message="; ".join(errors))
+    if duplicate_messages:
+        if success_count > 0:
+            return get_result(message=f"Partially deleted {success_count} datasets with {len(duplicate_messages)} errors", data={"success_count": success_count, "errors": duplicate_messages},)
+        else:
+            return get_error_data_result(message=";".join(duplicate_messages))
     return get_result(code=settings.RetCode.SUCCESS)
 
 
@@ -297,14 +311,10 @@ def update(tenant_id, dataset_id):
               type: string
               enum: ['me', 'team']
               description: Updated permission.
-            language:
-              type: string
-              enum: ['Chinese', 'English']
-              description: Updated language.
             chunk_method:
               type: string
               enum: ["naive", "manual", "qa", "table", "paper", "book", "laws",
-                     "presentation", "picture", "one", "knowledge_graph", "email", "tag"
+                     "presentation", "picture", "one", "email", "tag"
                      ]
               description: Updated chunking method.
             parser_config:
@@ -319,16 +329,18 @@ def update(tenant_id, dataset_id):
     if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id):
         return get_error_data_result(message="You don't own the dataset")
     req = request.json
+    for k in req.keys():
+        if dataset_readonly_fields(k):
+            return get_result(code=settings.RetCode.ARGUMENT_ERROR, message=f"'{k}' is readonly.")
     e, t = TenantService.get_by_id(tenant_id)
-    invalid_keys = {"id", "embd_id", "chunk_num", "doc_num", "parser_id"}
+    invalid_keys = {"id", "embd_id", "chunk_num", "doc_num", "parser_id", "create_date", "create_time", "created_by", "status","token_num","update_date","update_time"}
     if any(key in req for key in invalid_keys):
         return get_error_data_result(message="The input parameters are invalid.")
     permission = req.get("permission")
-    language = req.get("language")
     chunk_method = req.get("chunk_method")
     parser_config = req.get("parser_config")
+    valid_parser_config(parser_config)
     valid_permission = ["me", "team"]
-    valid_language = ["Chinese", "English"]
     valid_chunk_method = [
         "naive",
         "manual",
@@ -340,15 +352,12 @@ def update(tenant_id, dataset_id):
         "presentation",
         "picture",
         "one",
-        "knowledge_graph",
         "email",
         "tag"
     ]
     check_validation = valid(
         permission,
         valid_permission,
-        language,
-        valid_language,
         chunk_method,
         valid_chunk_method,
     )
@@ -370,7 +379,7 @@ def update(tenant_id, dataset_id):
         if req["document_count"] != kb.doc_num:
             return get_error_data_result(message="Can't change `document_count`.")
         req.pop("document_count")
-    if "chunk_method" in req:
+    if req.get("chunk_method"):
         if kb.chunk_num != 0 and req["chunk_method"] != kb.parser_id:
             return get_error_data_result(
                 message="If `chunk_count` is not 0, `chunk_method` is not changeable."
@@ -416,6 +425,10 @@ def update(tenant_id, dataset_id):
         req["embd_id"] = req.pop("embedding_model")
     if "name" in req:
         req["name"] = req["name"].strip()
+        if len(req["name"]) >= 128:
+            return get_error_data_result(
+                message="Dataset name should not be longer than 128 characters."
+            )
         if (
             req["name"].lower() != kb.name.lower()
             and len(
@@ -428,6 +441,10 @@ def update(tenant_id, dataset_id):
             return get_error_data_result(
                 message="Duplicated dataset name in updating dataset."
             )
+    flds = list(req.keys())
+    for f in flds:
+        if req[f] == "" and f in ["permission", "parser_id", "chunk_method"]:
+            del req[f]
     if not KnowledgebaseService.update_by_id(kb.id, req):
         return get_error_data_result(message="Update dataset error.(Database error)")
     return get_result(code=settings.RetCode.SUCCESS)
@@ -435,7 +452,7 @@ def update(tenant_id, dataset_id):
 
 @manager.route("/datasets", methods=["GET"])  # noqa: F821
 @token_required
-def list(tenant_id):
+def list_datasets(tenant_id):
     """
     List datasets.
     ---
@@ -504,7 +521,9 @@ def list(tenant_id):
     page_number = int(request.args.get("page", 1))
     items_per_page = int(request.args.get("page_size", 30))
     orderby = request.args.get("orderby", "create_time")
-    if request.args.get("desc") == "False" or request.args.get("desc") == "false":
+    if request.args.get("desc", "false").lower() not in ["true", "false"]:
+        return get_error_data_result("desc should be true or false")
+    if request.args.get("desc", "true").lower() == "false":
         desc = False
     else:
         desc = True

api/apps/sdk/doc.py

@@ -36,7 +36,7 @@ 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.utils.api_utils import construct_json_result, get_parser_config
+from api.utils.api_utils import construct_json_result, get_parser_config, check_duplicate_ids
 from rag.nlp import search
 from rag.prompts import keyword_extraction
 from rag.app.tag import label_question
@@ -67,6 +67,7 @@ class Chunk(BaseModel):
                 raise ValueError("Each sublist in positions must have a length of 5")
         return value
 
+
 @manager.route("/datasets/<dataset_id>/documents", methods=["POST"])  # noqa: F821
 @token_required
 def upload(dataset_id, tenant_id):
@@ -136,6 +137,10 @@ def upload(dataset_id, tenant_id):
             return get_result(
                 message="No file selected!", code=settings.RetCode.ARGUMENT_ERROR
             )
+        if len(file_obj.filename.encode("utf-8")) >= 128:
+            return get_result(
+                message="File name should be less than 128 bytes.", code=settings.RetCode.ARGUMENT_ERROR
+            )
     '''
     # total size
     total_size = 0
@@ -240,7 +245,17 @@ def update_doc(tenant_id, dataset_id, document_id):
         if req["progress"] != doc.progress:
             return get_error_data_result(message="Can't change `progress`.")
 
+    if "meta_fields" in req:
+        if not isinstance(req["meta_fields"], dict):
+            return get_error_data_result(message="meta_fields must be a dictionary")
+        DocumentService.update_meta_fields(document_id, req["meta_fields"])
+
     if "name" in req and req["name"] != doc.name:
+        if len(req["name"].encode("utf-8")) >= 128:
+            return get_result(
+                message="The name should be less than 128 bytes.",
+                code=settings.RetCode.ARGUMENT_ERROR,
+            )
         if (
                 pathlib.Path(req["name"].lower()).suffix
                 != pathlib.Path(doc.name.lower()).suffix
@@ -256,15 +271,12 @@ def update_doc(tenant_id, dataset_id, document_id):
                 )
         if not DocumentService.update_by_id(document_id, {"name": req["name"]}):
             return get_error_data_result(message="Database error (Document rename)!")
-        if "meta_fields" in req:
-            if not isinstance(req["meta_fields"], dict):
-                return get_error_data_result(message="meta_fields must be a dictionary")
-            DocumentService.update_meta_fields(document_id, req["meta_fields"])
 
         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:
@@ -361,6 +373,10 @@ def download(tenant_id, dataset_id, document_id):
         schema:
           type: object
     """
+    if not document_id:
+        return get_error_data_result(
+            message="Specify document_id please."
+        )
     if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id):
         return get_error_data_result(message=f"You do not own the dataset {dataset_id}.")
     doc = DocumentService.query(kb_id=dataset_id, id=document_id)
@@ -576,15 +592,22 @@ def delete(tenant_id, dataset_id):
             doc_list.append(doc.id)
     else:
         doc_list = doc_ids
+
+    unique_doc_ids, duplicate_messages = check_duplicate_ids(doc_list, "document")
+    doc_list = unique_doc_ids
+
     root_folder = FileService.get_root_folder(tenant_id)
     pf_id = root_folder["id"]
     FileService.init_knowledgebase_docs(pf_id, tenant_id)
     errors = ""
+    not_found = []
+    success_count = 0
     for doc_id in doc_list:
         try:
             e, doc = DocumentService.get_by_id(doc_id)
             if not e:
-                return get_error_data_result(message="Document not found!")
+                not_found.append(doc_id)
+                continue
             tenant_id = DocumentService.get_tenant_id(doc_id)
             if not tenant_id:
                 return get_error_data_result(message="Tenant not found!")
@@ -606,12 +629,22 @@ def delete(tenant_id, dataset_id):
             File2DocumentService.delete_by_document_id(doc_id)
 
             STORAGE_IMPL.rm(b, n)
+            success_count += 1
         except Exception as e:
             errors += str(e)
 
+    if not_found:
+        return get_result(message=f"Documents not found: {not_found}", code=settings.RetCode.DATA_ERROR)
+
     if errors:
         return get_result(message=errors, code=settings.RetCode.SERVER_ERROR)
 
+    if duplicate_messages:
+        if success_count > 0:
+            return get_result(message=f"Partially deleted {success_count} datasets with {len(duplicate_messages)} errors", data={"success_count": success_count, "errors": duplicate_messages},)
+        else:
+            return get_error_data_result(message=";".join(duplicate_messages))
+
     return get_result()
 
 
@@ -659,18 +692,24 @@ def parse(tenant_id, 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_list = req.get("document_ids")
+    unique_doc_ids, duplicate_messages = check_duplicate_ids(doc_list, "document")
+    doc_list = unique_doc_ids
+
+    not_found = []
+    success_count = 0
+    for id in doc_list:
         doc = DocumentService.query(id=id, kb_id=dataset_id)
+        if not doc:
+            not_found.append(id)
+            continue
         if not doc:
             return get_error_data_result(message=f"You don't own the document {id}.")
-        if doc[0].progress != 0.0:
+        if 0.0 < doc[0].progress < 1.0:
             return get_error_data_result(
-                "Can't stop parsing document with progress at 0 or 100"
+                "Can't parse document that is currently being processed"
             )
-        info = {"run": "1", "progress": 0}
-        info["progress_msg"] = ""
-        info["chunk_num"] = 0
-        info["token_num"] = 0
+        info = {"run": "1", "progress": 0, "progress_msg": "", "chunk_num": 0, "token_num": 0}
         DocumentService.update_by_id(id, info)
         settings.docStoreConn.delete({"doc_id": id}, search.index_name(tenant_id), dataset_id)
         TaskService.filter_delete([Task.doc_id == id])
@@ -678,7 +717,16 @@ def parse(tenant_id, dataset_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)
+        queue_tasks(doc, bucket, name, 0)
+        success_count += 1
+    if not_found:
+        return get_result(message=f"Documents not found: {not_found}", code=settings.RetCode.DATA_ERROR)
+    if duplicate_messages:
+        if success_count > 0:
+            return get_result(message=f"Partially parsed {success_count} documents with {len(duplicate_messages)} errors", data={"success_count": success_count, "errors": duplicate_messages},)
+        else:
+            return get_error_data_result(message=";".join(duplicate_messages))
+
     return get_result()
 
 
@@ -724,9 +772,15 @@ def stop_parsing(tenant_id, dataset_id):
     if not KnowledgebaseService.accessible(kb_id=dataset_id, user_id=tenant_id):
         return get_error_data_result(message=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_list = req.get("document_ids")
+    unique_doc_ids, duplicate_messages = check_duplicate_ids(doc_list, "document")
+    doc_list = unique_doc_ids
+
+    success_count = 0
+    for id in doc_list:
         doc = DocumentService.query(id=id, kb_id=dataset_id)
         if not doc:
             return get_error_data_result(message=f"You don't own the document {id}.")
@@ -737,6 +791,12 @@ def stop_parsing(tenant_id, dataset_id):
         info = {"run": "2", "progress": 0, "chunk_num": 0}
         DocumentService.update_by_id(id, info)
         settings.docStoreConn.delete({"doc_id": doc[0].id}, search.index_name(tenant_id), dataset_id)
+        success_count += 1
+    if duplicate_messages:
+        if success_count > 0:
+            return get_result(message=f"Partially stopped {success_count} documents with {len(duplicate_messages)} errors", data={"success_count": success_count, "errors": duplicate_messages},)
+        else:
+            return get_error_data_result(message=";".join(duplicate_messages))
     return get_result()
 
 
@@ -857,6 +917,8 @@ def list_chunks(tenant_id, dataset_id, document_id):
     res = {"total": 0, "chunks": [], "doc": renamed_doc}
     if req.get("id"):
         chunk = settings.docStoreConn.get(req.get("id"), search.index_name(tenant_id), [dataset_id])
+        if not chunk:
+            return get_result(message=f"Chunk not found: {dataset_id}/{req.get('id')}", code=settings.RetCode.NOT_FOUND)
         k = []
         for n in chunk.keys():
             if re.search(r"(_vec$|_sm_|_tks|_ltks)", n):
@@ -874,7 +936,7 @@ def list_chunks(tenant_id, dataset_id, document_id):
             "important_keywords":chunk.get("important_kwd",[]),
             "questions":chunk.get("question_kwd",[]),
             "dataset_id":chunk.get("kb_id",chunk.get("dataset_id")),
-            "image_id":chunk["img_id"],
+            "image_id":chunk.get("img_id", ""),
             "available":bool(chunk.get("available_int",1)),
             "positions":chunk.get("position_int",[]),
         }
@@ -899,7 +961,7 @@ def list_chunks(tenant_id, dataset_id, document_id):
                 "questions": sres.field[id].get("question_kwd", []),
                 "dataset_id": sres.field[id].get("kb_id", sres.field[id].get("dataset_id")),
                 "image_id": sres.field[id].get("img_id", ""),
-                "available": bool(sres.field[id].get("available_int", 1)),
+                "available": bool(int(sres.field[id].get("available_int", "1"))),
                 "positions": sres.field[id].get("position_int",[]),
             }
             res["chunks"].append(d)
@@ -984,7 +1046,7 @@ def add_chunk(tenant_id, dataset_id, document_id):
         )
     doc = doc[0]
     req = request.json
-    if not req.get("content"):
+    if not str(req.get("content", "")).strip():
         return get_error_data_result(message="`content` is required")
     if "important_keywords" in req:
         if not isinstance(req["important_keywords"], list):
@@ -1007,7 +1069,7 @@ def add_chunk(tenant_id, dataset_id, document_id):
     d["important_tks"] = rag_tokenizer.tokenize(
         " ".join(req.get("important_keywords", []))
     )
-    d["question_kwd"] = req.get("questions", [])
+    d["question_kwd"] = [str(q).strip() for q in req.get("questions", []) if str(q).strip()]
     d["question_tks"] = rag_tokenizer.tokenize(
         "\n".join(req.get("questions", []))
     )
@@ -1096,15 +1158,23 @@ def rm_chunk(tenant_id, dataset_id, document_id):
     """
     if not KnowledgebaseService.accessible(kb_id=dataset_id, user_id=tenant_id):
         return get_error_data_result(message=f"You don't own the dataset {dataset_id}.")
+    docs = DocumentService.get_by_ids([document_id])
+    if not docs:
+        raise LookupError(f"Can't find the document with ID {document_id}!")
     req = request.json
     condition = {"doc_id": document_id}
     if "chunk_ids" in req:
-        condition["id"] = req["chunk_ids"]
+        unique_chunk_ids, duplicate_messages = check_duplicate_ids(req["chunk_ids"], "chunk")
+        condition["id"] = unique_chunk_ids
     chunk_number = settings.docStoreConn.delete(condition, search.index_name(tenant_id), dataset_id)
     if chunk_number != 0:
         DocumentService.decrement_chunk_num(document_id, dataset_id, 1, chunk_number, 0)
-    if "chunk_ids" in req and chunk_number != len(req["chunk_ids"]):
-        return get_error_data_result(message=f"rm_chunk deleted chunks {chunk_number}, expect {len(req['chunk_ids'])}")
+    if "chunk_ids" in req and chunk_number != len(unique_chunk_ids):
+        if len(unique_chunk_ids) == 0:
+            return get_result(message=f"deleted {chunk_number} chunks")
+        return get_error_data_result(message=f"rm_chunk deleted chunks {chunk_number}, expect {len(unique_chunk_ids)}")
+    if duplicate_messages:
+        return get_result(message=f"Partially deleted {chunk_number} chunks with {len(duplicate_messages)} errors", data={"success_count": chunk_number, "errors": duplicate_messages},)
     return get_result(message=f"deleted {chunk_number} chunks")
 
 
@@ -1192,7 +1262,7 @@ def update_chunk(tenant_id, dataset_id, document_id, chunk_id):
     if "questions" in req:
         if not isinstance(req["questions"], list):
             return get_error_data_result("`questions` should be a list")
-        d["question_kwd"] = req.get("questions")
+        d["question_kwd"] = [str(q).strip() for q in req.get("questions", []) if str(q).strip()]
         d["question_tks"] = rag_tokenizer.tokenize("\n".join(req["questions"]))
     if "available" in req:
         d["available_int"] = int(req["available"])

api/apps/sdk/session.py

@@ -13,31 +13,30 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-import re
 import json
+import re
 import time
 
-from api.db import LLMType
+import tiktoken
+from flask import Response, jsonify, request
 from api.db.services.conversation_service import ConversationService, iframe_completion
 from api.db.services.conversation_service import completion as rag_completion
-from api.db.services.canvas_service import completion as agent_completion
-from api.db.services.dialog_service import ask, chat
+from api.db.services.canvas_service import completion as agent_completion, completionOpenAI
 from agent.canvas import Canvas
-from api.db import StatusEnum
+from api.db import LLMType, StatusEnum
 from api.db.db_models import APIToken
 from api.db.services.api_service import API4ConversationService
 from api.db.services.canvas_service import UserCanvasService
-from api.db.services.dialog_service import DialogService
+from api.db.services.dialog_service import DialogService, ask, chat
+from api.db.services.file_service import FileService
 from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.utils import get_uuid
-from api.utils.api_utils import get_error_data_result, validate_request
-from api.utils.api_utils import get_result, token_required
+from api.utils.api_utils import get_result, token_required, get_data_openai, get_error_data_result, validate_request, check_duplicate_ids
 from api.db.services.llm_service import LLMBundle
-from api.db.services.file_service import FileService
 
-from flask import jsonify, request, Response
 
-@manager.route('/chats/<chat_id>/sessions', methods=['POST'])  # noqa: F821
+
+@manager.route("/chats/<chat_id>/sessions", methods=["POST"])  # noqa: F821
 @token_required
 def create(tenant_id, chat_id):
     req = request.json
@@ -50,7 +49,7 @@ def create(tenant_id, chat_id):
         "dialog_id": req["dialog_id"],
         "name": req.get("name", "New session"),
         "message": [{"role": "assistant", "content": dia[0].prompt_config.get("prologue")}],
-        "user_id": req.get("user_id", "")
+        "user_id": req.get("user_id", ""),
     }
     if not conv.get("name"):
         return get_error_data_result(message="`name` can not be empty.")
@@ -59,28 +58,25 @@ def create(tenant_id, chat_id):
     if not e:
         return get_error_data_result(message="Fail to create a session!")
     conv = conv.to_dict()
-    conv['messages'] = conv.pop("message")
+    conv["messages"] = conv.pop("message")
     conv["chat_id"] = conv.pop("dialog_id")
     del conv["reference"]
     return get_result(data=conv)
 
 
-@manager.route('/agents/<agent_id>/sessions', methods=['POST'])  # noqa: F821
+@manager.route("/agents/<agent_id>/sessions", methods=["POST"])  # noqa: F821
 @token_required
 def create_agent_session(tenant_id, agent_id):
     req = request.json
     if not request.is_json:
         req = request.form
     files = request.files
-    user_id = request.args.get('user_id', '')
-
+    user_id = request.args.get("user_id", "")
     e, cvs = UserCanvasService.get_by_id(agent_id)
     if not e:
         return get_error_data_result("Agent not found.")
-
     if not UserCanvasService.query(user_id=tenant_id, id=agent_id):
         return get_error_data_result("You cannot access the agent.")
-
     if not isinstance(cvs.dsl, str):
         cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False)
 
@@ -113,28 +109,22 @@ def create_agent_session(tenant_id, agent_id):
                             ele.pop("value")
                 else:
                     if req is not None and req.get(ele["key"]):
-                        ele["value"] = req[ele['key']]
+                        ele["value"] = req[ele["key"]]
                     else:
                         if "value" in ele:
                             ele.pop("value")
-    else:
-        for ans in canvas.run(stream=False):
-            pass
+
+    for ans in canvas.run(stream=False):
+        pass
+
     cvs.dsl = json.loads(str(canvas))
-    conv = {
-        "id": get_uuid(),
-        "dialog_id": cvs.id,
-        "user_id": user_id,
-        "message": [{"role": "assistant", "content": canvas.get_prologue()}],
-        "source": "agent",
-        "dsl": cvs.dsl
-    }
+    conv = {"id": get_uuid(), "dialog_id": cvs.id, "user_id": user_id, "message": [{"role": "assistant", "content": canvas.get_prologue()}], "source": "agent", "dsl": cvs.dsl}
     API4ConversationService.save(**conv)
     conv["agent_id"] = conv.pop("dialog_id")
     return get_result(data=conv)
 
 
-@manager.route('/chats/<chat_id>/sessions/<session_id>', methods=['PUT'])  # noqa: F821
+@manager.route("/chats/<chat_id>/sessions/<session_id>", methods=["PUT"])  # noqa: F821
 @token_required
 def update(tenant_id, chat_id, session_id):
     req = request.json
@@ -156,14 +146,14 @@ def update(tenant_id, chat_id, session_id):
     return get_result()
 
 
-@manager.route('/chats/<chat_id>/completions', methods=['POST'])  # noqa: F821
+@manager.route("/chats/<chat_id>/completions", methods=["POST"])  # noqa: F821
 @token_required
 def chat_completion(tenant_id, chat_id):
     req = request.json
     if not req:
         req = {"question": ""}
     if not req.get("session_id"):
-        req["question"]=""
+        req["question"] = ""
     if not DialogService.query(tenant_id=tenant_id, id=chat_id, status=StatusEnum.VALID.value):
         return get_error_data_result(f"You don't own the chat {chat_id}")
     if req.get("session_id"):
@@ -185,7 +175,7 @@ def chat_completion(tenant_id, chat_id):
         return get_result(data=answer)
 
 
-@manager.route('chats_openai/<chat_id>/chat/completions', methods=['POST'])  # noqa: F821
+@manager.route("/chats_openai/<chat_id>/chat/completions", methods=["POST"])  # noqa: F821
 @validate_request("model", "messages")  # noqa: F821
 @token_required
 def chat_completion_openai_like(tenant_id, chat_id):
@@ -250,8 +240,18 @@ def chat_completion_openai_like(tenant_id, chat_id):
     dia = dia[0]
 
     # Filter system and non-sense assistant messages
-    msg = None
-    msg = [m for m in messages if m["role"] != "system" and (m["role"] != "assistant" or msg)]
+    msg = []
+    for m in messages:
+        if m["role"] == "system":
+            continue
+        if m["role"] == "assistant" and not msg:
+            continue
+        msg.append(m)
+
+    # tools = get_tools()
+    # toolcall_session = SimpleFunctionCallServer()
+    tools = None
+    toolcall_session = None
 
     if req.get("stream", True):
         # The value for the usage field on all chunks except for the last one will be null.
@@ -259,34 +259,61 @@ def chat_completion_openai_like(tenant_id, chat_id):
         # The choices field on the last chunk will always be an empty array [].
         def streamed_response_generator(chat_id, dia, msg):
             token_used = 0
+            answer_cache = ""
+            reasoning_cache = ""
             response = {
                 "id": f"chatcmpl-{chat_id}",
-                "choices": [
-                    {
-                        "delta": {
-                            "content": "",
-                            "role": "assistant",
-                            "function_call": None,
-                            "tool_calls": None
-                        },
-                        "finish_reason": None,
-                        "index": 0,
-                        "logprobs": None
-                    }
-                ],
+                "choices": [{"delta": {"content": "", "role": "assistant", "function_call": None, "tool_calls": None, "reasoning_content": ""}, "finish_reason": None, "index": 0, "logprobs": None}],
                 "created": int(time.time()),
                 "model": "model",
                 "object": "chat.completion.chunk",
                 "system_fingerprint": "",
-                "usage": None
+                "usage": None,
             }
 
             try:
-                for ans in chat(dia, msg, True):
+                for ans in chat(dia, msg, True, toolcall_session=toolcall_session, tools=tools):
                     answer = ans["answer"]
-                    incremental = answer[token_used:]
-                    token_used += len(incremental)
-                    response["choices"][0]["delta"]["content"] = incremental
+
+                    reasoning_match = re.search(r"<think>(.*?)</think>", answer, flags=re.DOTALL)
+                    if reasoning_match:
+                        reasoning_part = reasoning_match.group(1)
+                        content_part = answer[reasoning_match.end() :]
+                    else:
+                        reasoning_part = ""
+                        content_part = answer
+
+                    reasoning_incremental = ""
+                    if reasoning_part:
+                        if reasoning_part.startswith(reasoning_cache):
+                            reasoning_incremental = reasoning_part.replace(reasoning_cache, "", 1)
+                        else:
+                            reasoning_incremental = reasoning_part
+                        reasoning_cache = reasoning_part
+
+                    content_incremental = ""
+                    if content_part:
+                        if content_part.startswith(answer_cache):
+                            content_incremental = content_part.replace(answer_cache, "", 1)
+                    else:
+                        content_incremental = content_part
+                    answer_cache = content_part
+
+                    token_used += len(reasoning_incremental) + len(content_incremental)
+
+                    if not any([reasoning_incremental, content_incremental]):
+                        continue
+
+                    if reasoning_incremental:
+                        response["choices"][0]["delta"]["reasoning_content"] = reasoning_incremental
+                    else:
+                        response["choices"][0]["delta"]["reasoning_content"] = None
+
+                    if content_incremental:
+                        response["choices"][0]["delta"]["content"] = content_incremental
+                    else:
+                        response["choices"][0]["delta"]["content"] = None
+
                     yield f"data:{json.dumps(response, ensure_ascii=False)}\n\n"
             except Exception as e:
                 response["choices"][0]["delta"]["content"] = "**ERROR**: " + str(e)
@@ -294,16 +321,12 @@ def chat_completion_openai_like(tenant_id, chat_id):
 
             # The last chunk
             response["choices"][0]["delta"]["content"] = None
+            response["choices"][0]["delta"]["reasoning_content"] = None
             response["choices"][0]["finish_reason"] = "stop"
-            response["usage"] = {
-                "prompt_tokens": len(prompt),
-                "completion_tokens": token_used,
-                "total_tokens": len(prompt) + token_used
-            }
+            response["usage"] = {"prompt_tokens": len(prompt), "completion_tokens": token_used, "total_tokens": len(prompt) + token_used}
             yield f"data:{json.dumps(response, ensure_ascii=False)}\n\n"
             yield "data:[DONE]\n\n"
 
-
         resp = Response(streamed_response_generator(chat_id, dia, msg), mimetype="text/event-stream")
         resp.headers.add_header("Cache-control", "no-cache")
         resp.headers.add_header("Connection", "keep-alive")
@@ -312,13 +335,13 @@ def chat_completion_openai_like(tenant_id, chat_id):
         return resp
     else:
         answer = None
-        for ans in chat(dia, msg, False):
+        for ans in chat(dia, msg, False, toolcall_session=toolcall_session, tools=tools):
             # focus answer content only
             answer = ans
             break
         content = answer["answer"]
 
-        response  = {
+        response = {
             "id": f"chatcmpl-{chat_id}",
             "object": "chat.completion",
             "created": int(time.time()),
@@ -330,25 +353,49 @@ def chat_completion_openai_like(tenant_id, chat_id):
                 "completion_tokens_details": {
                     "reasoning_tokens": context_token_used,
                     "accepted_prediction_tokens": len(content),
-                    "rejected_prediction_tokens": 0 # 0 for simplicity
-                }
+                    "rejected_prediction_tokens": 0,  # 0 for simplicity
+                },
             },
-            "choices": [
-                {
-                    "message": {
-                        "role": "assistant",
-                        "content": content
-                    },
-                    "logprobs": None,
-                    "finish_reason": "stop",
-                    "index": 0
-                }
-            ]
+            "choices": [{"message": {"role": "assistant", "content": content}, "logprobs": None, "finish_reason": "stop", "index": 0}],
         }
         return jsonify(response)
 
+@manager.route('/agents_openai/<agent_id>/chat/completions', methods=['POST'])  # noqa: F821
+@validate_request("model", "messages")  # noqa: F821
+@token_required
+def agents_completion_openai_compatibility (tenant_id, agent_id):
+    req = request.json
+    tiktokenenc = tiktoken.get_encoding("cl100k_base")
+    messages = req.get("messages", [])
+    if not messages:
+        return get_error_data_result("You must provide at least one message.")
+    if not UserCanvasService.query(user_id=tenant_id, id=agent_id):
+        return get_error_data_result(f"You don't own the agent {agent_id}")
   
-@manager.route('/agents/<agent_id>/completions', methods=['POST'])  # noqa: F821
+    filtered_messages = [m for m in messages if m["role"] in ["user", "assistant"]]
+    prompt_tokens = sum(len(tiktokenenc.encode(m["content"])) for m in filtered_messages)
+    if not filtered_messages:
+        return jsonify(get_data_openai( 
+            id=agent_id,
+            content="No valid messages found (user or assistant).",
+            finish_reason="stop",
+            model=req.get("model", ""), 
+            completion_tokens=len(tiktokenenc.encode("No valid messages found (user or assistant).")),
+            prompt_tokens=prompt_tokens, 
+        ))
+    
+    # Get the last user message as the question
+    question = next((m["content"] for m in reversed(messages) if m["role"] == "user"), "")
+    
+    if req.get("stream", True):
+        return Response(completionOpenAI(tenant_id, agent_id, question, session_id=req.get("id", ""), stream=True), mimetype="text/event-stream")
+    else:
+        # For non-streaming, just return the response directly
+        response = next(completionOpenAI(tenant_id, agent_id, question, session_id=req.get("id", ""), stream=False))
+        return jsonify(response)
+    
+
+@manager.route("/agents/<agent_id>/completions", methods=["POST"])  # noqa: F821
 @token_required
 def agent_completions(tenant_id, agent_id):
     req = request.json
@@ -359,12 +406,20 @@ def agent_completions(tenant_id, agent_id):
         dsl = cvs[0].dsl
         if not isinstance(dsl, str):
             dsl = json.dumps(dsl)
-        #canvas = Canvas(dsl, tenant_id)
-        #if canvas.get_preset_param():
-        #    req["question"] = ""
+
         conv = API4ConversationService.query(id=req["session_id"], dialog_id=agent_id)
         if not conv:
             return get_error_data_result(f"You don't own the session {req['session_id']}")
+        # If an update to UserCanvas is detected, update the API4Conversation.dsl
+        sync_dsl = req.get("sync_dsl", False)
+        if sync_dsl is True and cvs[0].update_time > conv[0].update_time:
+            current_dsl = conv[0].dsl
+            new_dsl = json.loads(dsl)
+            state_fields = ["history", "messages", "path", "reference"]
+            states = {field: current_dsl.get(field, []) for field in state_fields}
+            current_dsl.update(new_dsl)
+            current_dsl.update(states)
+            API4ConversationService.update_by_id(req["session_id"], {"dsl": current_dsl})
     else:
         req["question"] = ""
     if req.get("stream", True):
@@ -381,7 +436,7 @@ def agent_completions(tenant_id, agent_id):
         return get_error_data_result(str(e))
 
 
-@manager.route('/chats/<chat_id>/sessions', methods=['GET'])  # noqa: F821
+@manager.route("/chats/<chat_id>/sessions", methods=["GET"])  # noqa: F821
 @token_required
 def list_session(tenant_id, chat_id):
     if not DialogService.query(tenant_id=tenant_id, id=chat_id, status=StatusEnum.VALID.value):
@@ -400,7 +455,7 @@ def list_session(tenant_id, chat_id):
     if not convs:
         return get_result(data=[])
     for conv in convs:
-        conv['messages'] = conv.pop("message")
+        conv["messages"] = conv.pop("message")
         infos = conv["messages"]
         for info in infos:
             if "prompt" in info:
@@ -434,7 +489,7 @@ def list_session(tenant_id, chat_id):
     return get_result(data=convs)
 
 
-@manager.route('/agents/<agent_id>/sessions', methods=['GET'])  # noqa: F821
+@manager.route("/agents/<agent_id>/sessions", methods=["GET"])  # noqa: F821
 @token_required
 def list_agent_session(tenant_id, agent_id):
     if not UserCanvasService.query(user_id=tenant_id, id=agent_id):
@@ -448,11 +503,13 @@ def list_agent_session(tenant_id, agent_id):
         desc = False
     else:
         desc = True
-    convs = API4ConversationService.get_list(agent_id, tenant_id, page_number, items_per_page, orderby, desc, id, user_id)
+    # dsl defaults to True in all cases except for False and false
+    include_dsl = request.args.get("dsl") != "False" and request.args.get("dsl") != "false"
+    convs = API4ConversationService.get_list(agent_id, tenant_id, page_number, items_per_page, orderby, desc, id, user_id, include_dsl)
     if not convs:
         return get_result(data=[])
     for conv in convs:
-        conv['messages'] = conv.pop("message")
+        conv["messages"] = conv.pop("message")
         infos = conv["messages"]
         for info in infos:
             if "prompt" in info:
@@ -485,11 +542,14 @@ def list_agent_session(tenant_id, agent_id):
     return get_result(data=convs)
 
 
-@manager.route('/chats/<chat_id>/sessions', methods=["DELETE"])  # noqa: F821
+@manager.route("/chats/<chat_id>/sessions", methods=["DELETE"])  # noqa: F821
 @token_required
 def delete(tenant_id, chat_id):
     if not DialogService.query(id=chat_id, tenant_id=tenant_id, status=StatusEnum.VALID.value):
         return get_error_data_result(message="You don't own the chat")
+    
+    errors = []
+    success_count = 0
     req = request.json
     convs = ConversationService.query(dialog_id=chat_id)
     if not req:
@@ -503,15 +563,98 @@ def delete(tenant_id, chat_id):
             conv_list.append(conv.id)
     else:
         conv_list = ids
+    
+    unique_conv_ids, duplicate_messages = check_duplicate_ids(conv_list, "session")
+    conv_list = unique_conv_ids
+    
     for id in conv_list:
         conv = ConversationService.query(id=id, dialog_id=chat_id)
         if not conv:
-            return get_error_data_result(message="The chat doesn't own the session")
+            errors.append(f"The chat doesn't own the session {id}")
+            continue
         ConversationService.delete_by_id(id)
+        success_count += 1
+    
+    if errors:
+        if success_count > 0:
+            return get_result(
+                data={"success_count": success_count, "errors": errors},
+                message=f"Partially deleted {success_count} sessions with {len(errors)} errors"
+            )
+        else:
+            return get_error_data_result(message="; ".join(errors))
+    
+    if duplicate_messages:
+        if success_count > 0:
+            return get_result(
+                message=f"Partially deleted {success_count} sessions with {len(duplicate_messages)} errors", 
+                data={"success_count": success_count, "errors": duplicate_messages}
+            )
+        else:
+            return get_error_data_result(message=";".join(duplicate_messages))
+    
     return get_result()
 
 
-@manager.route('/sessions/ask', methods=['POST'])  # noqa: F821
+@manager.route("/agents/<agent_id>/sessions", methods=["DELETE"])  # noqa: F821
+@token_required
+def delete_agent_session(tenant_id, agent_id):
+    errors = []
+    success_count = 0
+    req = request.json
+    cvs = UserCanvasService.query(user_id=tenant_id, id=agent_id)
+    if not cvs:
+        return get_error_data_result(f"You don't own the agent {agent_id}")
+
+    convs = API4ConversationService.query(dialog_id=agent_id)
+    if not convs:
+        return get_error_data_result(f"Agent {agent_id} has no sessions")
+
+    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
+
+    unique_conv_ids, duplicate_messages = check_duplicate_ids(conv_list, "session")
+    conv_list = unique_conv_ids
+
+    for session_id in conv_list:
+        conv = API4ConversationService.query(id=session_id, dialog_id=agent_id)
+        if not conv:
+            errors.append(f"The agent doesn't own the session {session_id}")
+            continue
+        API4ConversationService.delete_by_id(session_id)
+        success_count += 1
+    
+    if errors:
+        if success_count > 0:
+            return get_result(
+                data={"success_count": success_count, "errors": errors},
+                message=f"Partially deleted {success_count} sessions with {len(errors)} errors"
+            )
+        else:
+            return get_error_data_result(message="; ".join(errors))
+    
+    if duplicate_messages:
+        if success_count > 0:
+            return get_result(
+                message=f"Partially deleted {success_count} sessions with {len(duplicate_messages)} errors", 
+                data={"success_count": success_count, "errors": duplicate_messages}
+            )
+        else:
+            return get_error_data_result(message=";".join(duplicate_messages))
+    
+    return get_result()
+
+
+@manager.route("/sessions/ask", methods=["POST"])  # noqa: F821
 @token_required
 def ask_about(tenant_id):
     req = request.json
@@ -537,9 +680,7 @@ def ask_about(tenant_id):
             for ans in ask(req["question"], req["kb_ids"], uid):
                 yield "data:" + json.dumps({"code": 0, "message": "", "data": ans}, ensure_ascii=False) + "\n\n"
         except Exception as e:
-            yield "data:" + json.dumps({"code": 500, "message": str(e),
-                                        "data": {"answer": "**ERROR**: " + str(e), "reference": []}},
-                                       ensure_ascii=False) + "\n\n"
+            yield "data:" + json.dumps({"code": 500, "message": str(e), "data": {"answer": "**ERROR**: " + str(e), "reference": []}}, ensure_ascii=False) + "\n\n"
         yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n"
 
     resp = Response(stream(), mimetype="text/event-stream")
@@ -550,7 +691,7 @@ def ask_about(tenant_id):
     return resp
 
 
-@manager.route('/sessions/related_questions', methods=['POST'])  # noqa: F821
+@manager.route("/sessions/related_questions", methods=["POST"])  # noqa: F821
 @token_required
 def related_questions(tenant_id):
     req = request.json
@@ -582,18 +723,27 @@ Reason:
  - 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"""
+    ans = chat_mdl.chat(
+        prompt,
+        [
+            {
+                "role": "user",
+                "content": f"""
 Keywords: {question}
 Related search terms:
-    """}], {"temperature": 0.9})
+    """,
+            }
+        ],
+        {"temperature": 0.9},
+    )
     return get_result(data=[re.sub(r"^[0-9]\. ", "", a) for a in ans.split("\n") if re.match(r"^[0-9]\. ", a)])
 
 
-@manager.route('/chatbots/<dialog_id>/completions', methods=['POST'])  # noqa: F821
+@manager.route("/chatbots/<dialog_id>/completions", methods=["POST"])  # noqa: F821
 def chatbot_completions(dialog_id):
     req = request.json
 
-    token = request.headers.get('Authorization').split()
+    token = request.headers.get("Authorization").split()
     if len(token) != 2:
         return get_error_data_result(message='Authorization is not valid!"')
     token = token[1]
@@ -616,11 +766,11 @@ def chatbot_completions(dialog_id):
         return get_result(data=answer)
 
 
-@manager.route('/agentbots/<agent_id>/completions', methods=['POST'])  # noqa: F821
+@manager.route("/agentbots/<agent_id>/completions", methods=["POST"])  # noqa: F821
 def agent_bot_completions(agent_id):
     req = request.json
 
-    token = request.headers.get('Authorization').split()
+    token = request.headers.get("Authorization").split()
     if len(token) != 2:
         return get_error_data_result(message='Authorization is not valid!"')
     token = token[1]

api/apps/system_app.py

@@ -37,7 +37,6 @@ from timeit import default_timer as timer
 
 from rag.utils.redis_conn import REDIS_CONN
 
-
 @manager.route("/version", methods=["GET"])  # noqa: F821
 @login_required
 def version():
@@ -201,7 +200,7 @@ def new_token():
         if not tenants:
             return get_data_error_result(message="Tenant not found!")
 
-        tenant_id = tenants[0].tenant_id
+        tenant_id = [tenant for tenant in tenants if tenant.role == 'owner'][0].tenant_id
         obj = {
             "tenant_id": tenant_id,
             "token": generate_confirmation_token(tenant_id),
@@ -256,7 +255,7 @@ def token_list():
         if not tenants:
             return get_data_error_result(message="Tenant not found!")
 
-        tenant_id = tenants[0].tenant_id
+        tenant_id = [tenant for tenant in tenants if tenant.role == 'owner'][0].tenant_id
         objs = APITokenService.query(tenant_id=tenant_id)
         objs = [o.to_dict() for o in objs]
         for o in objs:
@@ -298,3 +297,25 @@ def rm(token):
         [APIToken.tenant_id == current_user.id, APIToken.token == token]
     )
     return get_json_result(data=True)
+
+
+@manager.route('/config', methods=['GET'])  # noqa: F821
+def get_config():
+    """
+    Get system configuration.
+    ---
+    tags:
+        - System
+    responses:
+        200:
+            description: Return system configuration
+            schema:
+                type: object
+                properties:
+                    registerEnable:
+                        type: integer 0 means disabled, 1 means enabled
+                        description: Whether user registration is enabled
+    """
+    return get_json_result(data={
+        "registerEnabled": settings.REGISTER_ENABLED
+    })

api/apps/user_app.py

@@ -562,11 +562,19 @@ def user_add():
         schema:
           type: object
     """
+
+    if not settings.REGISTER_ENABLED:
+        return get_json_result(
+            data=False,
+            message="User registration is disabled!",
+            code=settings.RetCode.OPERATING_ERROR,
+        )
+
     req = request.json
     email_address = req["email"]
 
     # Validate the email address
-    if not re.match(r"^[\w\._-]+@([\w_-]+\.)+[\w-]{2,5}$", email_address):
+    if not re.match(r"^[\w\._-]+@([\w_-]+\.)+[\w-]{2,}$", email_address):
         return get_json_result(
             data=False,
             message=f"Invalid email address: {email_address}!",

api/db/init_data.py

@@ -103,16 +103,12 @@ def init_llm_factory():
     except Exception:
         pass
 
-    factory_llm_infos = json.load(
-        open(
-            os.path.join(get_project_base_directory(), "conf", "llm_factories.json"),
-            "r",
-        )
-    )
-    for factory_llm_info in factory_llm_infos["factory_llm_infos"]:
-        llm_infos = factory_llm_info.pop("llm")
+    factory_llm_infos = settings.FACTORY_LLM_INFOS    
+    for factory_llm_info in factory_llm_infos:
+        info = deepcopy(factory_llm_info)
+        llm_infos = info.pop("llm")
         try:
-            LLMFactoriesService.save(**factory_llm_info)
+            LLMFactoriesService.save(**info)
         except Exception:
             pass
         LLMService.filter_delete([LLM.fid == factory_llm_info["name"]])
@@ -152,7 +148,7 @@ def init_llm_factory():
                 pass
             break
     for kb_id in KnowledgebaseService.get_all_ids():
-        KnowledgebaseService.update_by_id(kb_id, {"doc_num": DocumentService.get_kb_doc_count(kb_id)})
+        KnowledgebaseService.update_document_number_in_init(kb_id=kb_id, doc_num=DocumentService.get_kb_doc_count(kb_id))
 
 
 
@@ -160,7 +156,7 @@ def add_graph_templates():
     dir = os.path.join(get_project_base_directory(), "agent", "templates")
     for fnm in os.listdir(dir):
         try:
-            cnvs = json.load(open(os.path.join(dir, fnm), "r"))
+            cnvs = json.load(open(os.path.join(dir, fnm), "r",encoding="utf-8"))
             try:
                 CanvasTemplateService.save(**cnvs)
             except Exception:

api/db/services/api_service.py

@@ -43,8 +43,12 @@ class API4ConversationService(CommonService):
     @DB.connection_context()
     def get_list(cls, dialog_id, tenant_id,
                  page_number, items_per_page,
-                 orderby, desc, id, user_id=None):
-        sessions = cls.model.select().where(cls.model.dialog_id == dialog_id)
+                 orderby, desc, id, user_id=None, include_dsl=True):
+        if include_dsl:
+            sessions = cls.model.select().where(cls.model.dialog_id == dialog_id)
+        else:
+            fields = [field for field in cls.model._meta.fields.values() if field.name != 'dsl']
+            sessions = cls.model.select(*fields).where(cls.model.dialog_id == dialog_id)
         if id:
             sessions = sessions.where(cls.model.id == id)
         if user_id:

api/db/services/canvas_service.py

@@ -18,13 +18,15 @@ import time
 import traceback
 from uuid import uuid4
 from agent.canvas import Canvas
-from api.db.db_models import DB, CanvasTemplate, UserCanvas, API4Conversation
+from api.db import TenantPermission
+from api.db.db_models import DB, CanvasTemplate, User, UserCanvas, API4Conversation
 from api.db.services.api_service import API4ConversationService
 from api.db.services.common_service import CommonService
 from api.db.services.conversation_service import structure_answer
 from api.utils import get_uuid
-
-
+from api.utils.api_utils import get_data_openai
+import tiktoken
+from peewee import fn
 class CanvasTemplateService(CommonService):
     model = CanvasTemplate
 
@@ -51,6 +53,73 @@ class UserCanvasService(CommonService):
 
         return list(agents.dicts())
    
+    @classmethod
+    @DB.connection_context()
+    def get_by_tenant_id(cls, pid):
+        try:
+            
+            fields = [
+                cls.model.id,
+                cls.model.avatar,
+                cls.model.title,
+                cls.model.dsl,
+                cls.model.description,
+                cls.model.permission,
+                cls.model.update_time,
+                cls.model.user_id,
+                cls.model.create_time,
+                cls.model.create_date,
+                cls.model.update_date,
+                User.nickname,
+                User.avatar.alias('tenant_avatar'),
+            ]
+            angents = cls.model.select(*fields) \
+            .join(User, on=(cls.model.user_id == User.id)) \
+            .where(cls.model.id == pid)
+            # obj = cls.model.query(id=pid)[0]
+            return True, angents.dicts()[0]
+        except Exception as e:
+            print(e)
+            return False, None
+     
+    @classmethod
+    @DB.connection_context()
+    def get_by_tenant_ids(cls, joined_tenant_ids, user_id,
+                          page_number, items_per_page,
+                          orderby, desc, keywords,
+                          ):
+        fields = [
+            cls.model.id,
+            cls.model.avatar,
+            cls.model.title,
+            cls.model.dsl,
+            cls.model.description,
+            cls.model.permission,
+            User.nickname,
+            User.avatar.alias('tenant_avatar'),
+            cls.model.update_time
+        ]
+        if keywords:
+            angents = cls.model.select(*fields).join(User, on=(cls.model.user_id == User.id)).where(
+                ((cls.model.user_id.in_(joined_tenant_ids) & (cls.model.permission == 
+                                                                TenantPermission.TEAM.value)) | (
+                    cls.model.user_id == user_id)),
+                (fn.LOWER(cls.model.title).contains(keywords.lower()))
+            )
+        else:
+            angents = cls.model.select(*fields).join(User, on=(cls.model.user_id == User.id)).where(
+                ((cls.model.user_id.in_(joined_tenant_ids) & (cls.model.permission == 
+                                                                TenantPermission.TEAM.value)) | (
+                    cls.model.user_id == user_id))
+            )
+        if desc:
+            angents = angents.order_by(cls.model.getter_by(orderby).desc())
+        else:
+            angents = angents.order_by(cls.model.getter_by(orderby).asc())
+        count = angents.count()
+        angents = angents.paginate(page_number, items_per_page)
+        return list(angents.dicts()), count
+   
 
 def completion(tenant_id, agent_id, question, session_id=None, stream=True, **kwargs):
     e, cvs = UserCanvasService.get_by_id(agent_id)
@@ -86,21 +155,7 @@ def completion(tenant_id, agent_id, question, session_id=None, stream=True, **kw
             "dsl": cvs.dsl
         }
         API4ConversationService.save(**conv)
-        if query:
-            yield "data:" + json.dumps({"code": 0,
-                                        "message": "",
-                                        "data": {
-                                            "session_id": session_id,
-                                            "answer": canvas.get_prologue(),
-                                            "reference": [],
-                                            "param": canvas.get_preset_param()
-                                        }
-                                        },
-                                       ensure_ascii=False) + "\n\n"
-            yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n"
-            return
-        else:
-            conv = API4Conversation(**conv)
+        conv = API4Conversation(**conv)
     else:
         e, conv = API4ConversationService.get_by_id(session_id)
         assert e, "Session not found!"
@@ -130,7 +185,7 @@ def completion(tenant_id, agent_id, question, session_id=None, stream=True, **kw
                     continue
                 for k in ans.keys():
                     final_ans[k] = ans[k]
-                ans = {"answer": ans["content"], "reference": ans.get("reference", [])}
+                ans = {"answer": ans["content"], "reference": ans.get("reference", []), "param": canvas.get_preset_param()}
                 ans = structure_answer(conv, ans, message_id, session_id)
                 yield "data:" + json.dumps({"code": 0, "message": "", "data": ans},
                                            ensure_ascii=False) + "\n\n"
@@ -160,8 +215,211 @@ def completion(tenant_id, agent_id, question, session_id=None, stream=True, **kw
                 canvas.reference.append(final_ans["reference"])
             conv.dsl = json.loads(str(canvas))
 
-            result = {"answer": final_ans["content"], "reference": final_ans.get("reference", [])}
+            result = {"answer": final_ans["content"], "reference": final_ans.get("reference", []) , "param": canvas.get_preset_param()}
             result = structure_answer(conv, result, message_id, session_id)
             API4ConversationService.append_message(conv.id, conv.to_dict())
             yield result
             break
+def completionOpenAI(tenant_id, agent_id, question, session_id=None, stream=True, **kwargs):
+    """Main function for OpenAI-compatible completions, structured similarly to the completion function."""
+    tiktokenenc = tiktoken.get_encoding("cl100k_base")
+    e, cvs = UserCanvasService.get_by_id(agent_id)
+    
+    if not e:
+        yield get_data_openai(
+            id=session_id,
+            model=agent_id,
+            content="**ERROR**: Agent not found."
+        )
+        return
+    
+    if cvs.user_id != tenant_id:
+        yield get_data_openai(
+            id=session_id,
+            model=agent_id,
+            content="**ERROR**: You do not own the agent"
+        )
+        return
+    
+    if not isinstance(cvs.dsl, str):
+        cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False)
+    
+    canvas = Canvas(cvs.dsl, tenant_id)
+    canvas.reset()
+    message_id = str(uuid4())
+    
+    # Handle new session creation
+    if not session_id:
+        query = canvas.get_preset_param()
+        if query:
+            for ele in query:
+                if not ele["optional"]:
+                    if not kwargs.get(ele["key"]):
+                        yield get_data_openai(
+                            id=None,
+                            model=agent_id,
+                            content=f"`{ele['key']}` is required",
+                            completion_tokens=len(tiktokenenc.encode(f"`{ele['key']}` is required")),
+                            prompt_tokens=len(tiktokenenc.encode(question if question else ""))
+                        )
+                        return
+                    ele["value"] = kwargs[ele["key"]]
+                if ele["optional"]:
+                    if kwargs.get(ele["key"]):
+                        ele["value"] = kwargs[ele['key']]
+                    else:
+                        if "value" in ele:
+                            ele.pop("value")
+        
+        cvs.dsl = json.loads(str(canvas))
+        session_id = get_uuid()
+        conv = {
+            "id": session_id,
+            "dialog_id": cvs.id,
+            "user_id": kwargs.get("user_id", "") if isinstance(kwargs, dict) else "",
+            "message": [{"role": "assistant", "content": canvas.get_prologue(), "created_at": time.time()}],
+            "source": "agent",
+            "dsl": cvs.dsl
+        }
+        API4ConversationService.save(**conv)
+        conv = API4Conversation(**conv)
+            
+    # Handle existing session
+    else:
+        e, conv = API4ConversationService.get_by_id(session_id)
+        if not e:
+            yield get_data_openai(
+                id=session_id,
+                model=agent_id,
+                content="**ERROR**: Session not found!"
+            )
+            return
+        
+        canvas = Canvas(json.dumps(conv.dsl), tenant_id)
+        canvas.messages.append({"role": "user", "content": question, "id": message_id})
+        canvas.add_user_input(question)
+        
+        if not conv.message:
+            conv.message = []
+        conv.message.append({
+            "role": "user",
+            "content": question,
+            "id": message_id
+        })
+        
+        if not conv.reference:
+            conv.reference = []
+        conv.reference.append({"chunks": [], "doc_aggs": []})
+    
+    # Process request based on stream mode
+    final_ans = {"reference": [], "content": ""}
+    prompt_tokens = len(tiktokenenc.encode(str(question)))
+    
+    if stream:
+        try:
+            completion_tokens = 0
+            for ans in canvas.run(stream=True):
+                if ans.get("running_status"):
+                    completion_tokens += len(tiktokenenc.encode(ans.get("content", "")))
+                    yield "data: " + json.dumps(
+                        get_data_openai(
+                            id=session_id,
+                            model=agent_id,
+                            content=ans["content"],
+                            object="chat.completion.chunk",
+                            completion_tokens=completion_tokens,
+                            prompt_tokens=prompt_tokens
+                        ),
+                        ensure_ascii=False
+                    ) + "\n\n"
+                    continue
+                
+                for k in ans.keys():
+                    final_ans[k] = ans[k]
+                
+                completion_tokens += len(tiktokenenc.encode(final_ans.get("content", "")))
+                yield "data: " + json.dumps(
+                    get_data_openai(
+                        id=session_id,
+                        model=agent_id,
+                        content=final_ans["content"],
+                        object="chat.completion.chunk",
+                        finish_reason="stop",
+                        completion_tokens=completion_tokens,
+                        prompt_tokens=prompt_tokens
+                    ),
+                    ensure_ascii=False
+                ) + "\n\n"
+            
+            # Update conversation
+            canvas.messages.append({"role": "assistant", "content": final_ans["content"], "created_at": time.time(), "id": message_id})
+            canvas.history.append(("assistant", final_ans["content"]))
+            if final_ans.get("reference"):
+                canvas.reference.append(final_ans["reference"])
+            conv.dsl = json.loads(str(canvas))
+            API4ConversationService.append_message(conv.id, conv.to_dict())
+            
+            yield "data: [DONE]\n\n"
+            
+        except Exception as e:
+            traceback.print_exc()
+            conv.dsl = json.loads(str(canvas))
+            API4ConversationService.append_message(conv.id, conv.to_dict())
+            yield "data: " + json.dumps(
+                get_data_openai(
+                    id=session_id,
+                    model=agent_id,
+                    content="**ERROR**: " + str(e),
+                    finish_reason="stop",
+                    completion_tokens=len(tiktokenenc.encode("**ERROR**: " + str(e))),
+                    prompt_tokens=prompt_tokens
+                ),
+                ensure_ascii=False
+            ) + "\n\n"
+            yield "data: [DONE]\n\n"
+    
+    else:  # Non-streaming mode
+        try:
+            all_answer_content = ""
+            for answer in canvas.run(stream=False):
+                if answer.get("running_status"):
+                    continue
+                
+                final_ans["content"] = "\n".join(answer["content"]) if "content" in answer else ""
+                final_ans["reference"] = answer.get("reference", [])
+                all_answer_content += final_ans["content"]
+            
+            final_ans["content"] = all_answer_content
+            
+            # Update conversation
+            canvas.messages.append({"role": "assistant", "content": final_ans["content"], "created_at": time.time(), "id": message_id})
+            canvas.history.append(("assistant", final_ans["content"]))
+            if final_ans.get("reference"):
+                canvas.reference.append(final_ans["reference"])
+            conv.dsl = json.loads(str(canvas))
+            API4ConversationService.append_message(conv.id, conv.to_dict())
+            
+            # Return the response in OpenAI format
+            yield get_data_openai(
+                id=session_id,
+                model=agent_id,
+                content=final_ans["content"],
+                finish_reason="stop",
+                completion_tokens=len(tiktokenenc.encode(final_ans["content"])),
+                prompt_tokens=prompt_tokens,
+                param=canvas.get_preset_param()  # Added param info like in completion
+            )
+            
+        except Exception as e:
+            traceback.print_exc()
+            conv.dsl = json.loads(str(canvas))
+            API4ConversationService.append_message(conv.id, conv.to_dict())
+            yield get_data_openai(
+                id=session_id,
+                model=agent_id,
+                content="**ERROR**: " + str(e),
+                finish_reason="stop",
+                completion_tokens=len(tiktokenenc.encode("**ERROR**: " + str(e))),
+                prompt_tokens=prompt_tokens
+            )
+

api/db/services/common_service.py

@@ -22,17 +22,56 @@ from api.utils import datetime_format, current_timestamp, get_uuid
 
 
 class CommonService:
+    """Base service class that provides common database operations.
+    
+    This class serves as a foundation for all service classes in the application,
+    implementing standard CRUD operations and common database query patterns.
+    It uses the Peewee ORM for database interactions and provides a consistent
+    interface for database operations across all derived service classes.
+    
+    Attributes:
+        model: The Peewee model class that this service operates on. Must be set by subclasses.
+    """
     model = None
 
     @classmethod
     @DB.connection_context()
     def query(cls, cols=None, reverse=None, order_by=None, **kwargs):
+        """Execute a database query with optional column selection and ordering.
+    
+        This method provides a flexible way to query the database with various filters
+        and sorting options. It supports column selection, sort order control, and
+        additional filter conditions.
+    
+        Args:
+            cols (list, optional): List of column names to select. If None, selects all columns.
+            reverse (bool, optional): If True, sorts in descending order. If False, sorts in ascending order.
+            order_by (str, optional): Column name to sort results by.
+            **kwargs: Additional filter conditions passed as keyword arguments.
+    
+        Returns:
+            peewee.ModelSelect: A query result containing matching records.
+        """
         return cls.model.query(cols=cols, reverse=reverse,
                                order_by=order_by, **kwargs)
 
     @classmethod
     @DB.connection_context()
     def get_all(cls, cols=None, reverse=None, order_by=None):
+        """Retrieve all records from the database with optional column selection and ordering.
+    
+        This method fetches all records from the model's table with support for
+        column selection and result ordering. If no order_by is specified and reverse
+        is True, it defaults to ordering by create_time.
+    
+        Args:
+            cols (list, optional): List of column names to select. If None, selects all columns.
+            reverse (bool, optional): If True, sorts in descending order. If False, sorts in ascending order.
+            order_by (str, optional): Column name to sort results by. Defaults to 'create_time' if reverse is specified.
+    
+        Returns:
+            peewee.ModelSelect: A query containing all matching records.
+        """
         if cols:
             query_records = cls.model.select(*cols)
         else:
@@ -51,11 +90,36 @@ class CommonService:
     @classmethod
     @DB.connection_context()
     def get(cls, **kwargs):
+        """Get a single record matching the given criteria.
+    
+        This method retrieves a single record from the database that matches
+        the specified filter conditions.
+    
+        Args:
+            **kwargs: Filter conditions as keyword arguments.
+    
+        Returns:
+            Model instance: Single matching record.
+    
+        Raises:
+            peewee.DoesNotExist: If no matching record is found.
+        """
         return cls.model.get(**kwargs)
 
     @classmethod
     @DB.connection_context()
     def get_or_none(cls, **kwargs):
+        """Get a single record or None if not found.
+    
+        This method attempts to retrieve a single record matching the given criteria,
+        returning None if no match is found instead of raising an exception.
+    
+        Args:
+            **kwargs: Filter conditions as keyword arguments.
+    
+        Returns:
+            Model instance or None: Matching record if found, None otherwise.
+        """
         try:
             return cls.model.get(**kwargs)
         except peewee.DoesNotExist:
@@ -64,14 +128,34 @@ class CommonService:
     @classmethod
     @DB.connection_context()
     def save(cls, **kwargs):
-        # if "id" not in kwargs:
-        #    kwargs["id"] = get_uuid()
+        """Save a new record to database.
+    
+        This method creates a new record in the database with the provided field values,
+        forcing an insert operation rather than an update.
+    
+        Args:
+            **kwargs: Record field values as keyword arguments.
+    
+        Returns:
+            Model instance: The created record object.
+        """
         sample_obj = cls.model(**kwargs).save(force_insert=True)
         return sample_obj
 
     @classmethod
     @DB.connection_context()
     def insert(cls, **kwargs):
+        """Insert a new record with automatic ID and timestamps.
+    
+        This method creates a new record with automatically generated ID and timestamp fields.
+        It handles the creation of create_time, create_date, update_time, and update_date fields.
+    
+        Args:
+            **kwargs: Record field values as keyword arguments.
+    
+        Returns:
+            Model instance: The newly created record object.
+        """
         if "id" not in kwargs:
             kwargs["id"] = get_uuid()
         kwargs["create_time"] = current_timestamp()
@@ -84,6 +168,15 @@ class CommonService:
     @classmethod
     @DB.connection_context()
     def insert_many(cls, data_list, batch_size=100):
+        """Insert multiple records in batches.
+    
+        This method efficiently inserts multiple records into the database using batch processing.
+        It automatically sets creation timestamps for all records.
+    
+        Args:
+            data_list (list): List of dictionaries containing record data to insert.
+            batch_size (int, optional): Number of records to insert in each batch. Defaults to 100.
+        """
         with DB.atomic():
             for d in data_list:
                 d["create_time"] = current_timestamp()
@@ -94,6 +187,15 @@ class CommonService:
     @classmethod
     @DB.connection_context()
     def update_many_by_id(cls, data_list):
+        """Update multiple records by their IDs.
+    
+        This method updates multiple records in the database, identified by their IDs.
+        It automatically updates the update_time and update_date fields for each record.
+    
+        Args:
+            data_list (list): List of dictionaries containing record data to update.
+                             Each dictionary must include an 'id' field.
+        """
         with DB.atomic():
             for data in data_list:
                 data["update_time"] = current_timestamp()
@@ -104,6 +206,12 @@ class CommonService:
     @classmethod
     @DB.connection_context()
     def update_by_id(cls, pid, data):
+        # Update a single record by ID
+        # Args:
+        #     pid: Record ID
+        #     data: Updated field values
+        # Returns:
+        #     Number of records updated
         data["update_time"] = current_timestamp()
         data["update_date"] = datetime_format(datetime.now())
         num = cls.model.update(data).where(cls.model.id == pid).execute()
@@ -112,15 +220,28 @@ class CommonService:
     @classmethod
     @DB.connection_context()
     def get_by_id(cls, pid):
+        # Get a record by ID
+        # Args:
+        #     pid: Record ID
+        # Returns:
+        #     Tuple of (success, record)
         try:
-            obj = cls.model.query(id=pid)[0]
-            return True, obj
+            obj = cls.model.get_or_none(cls.model.id == pid)
+            if obj:
+                return True, obj
         except Exception:
-            return False, None
+            pass
+        return False, None
 
     @classmethod
     @DB.connection_context()
     def get_by_ids(cls, pids, cols=None):
+        # Get multiple records by their IDs
+        # Args:
+        #     pids: List of record IDs
+        #     cols: List of columns to select
+        # Returns:
+        #     Query of matching records
         if cols:
             objs = cls.model.select(*cols)
         else:
@@ -130,11 +251,21 @@ class CommonService:
     @classmethod
     @DB.connection_context()
     def delete_by_id(cls, pid):
+        # Delete a record by ID
+        # Args:
+        #     pid: Record ID
+        # Returns:
+        #     Number of records deleted
         return cls.model.delete().where(cls.model.id == pid).execute()
 
     @classmethod
     @DB.connection_context()
     def filter_delete(cls, filters):
+        # Delete records matching given filters
+        # Args:
+        #     filters: List of filter conditions
+        # Returns:
+        #     Number of records deleted
         with DB.atomic():
             num = cls.model.delete().where(*filters).execute()
             return num
@@ -142,11 +273,23 @@ class CommonService:
     @classmethod
     @DB.connection_context()
     def filter_update(cls, filters, update_data):
+        # Update records matching given filters
+        # Args:
+        #     filters: List of filter conditions
+        #     update_data: Updated field values
+        # Returns:
+        #     Number of records updated
         with DB.atomic():
             return cls.model.update(update_data).where(*filters).execute()
 
     @staticmethod
     def cut_list(tar_list, n):
+        # Split a list into chunks of size n
+        # Args:
+        #     tar_list: List to split
+        #     n: Chunk size
+        # Returns:
+        #     List of tuples containing chunks
         length = len(tar_list)
         arr = range(length)
         result = [tuple(tar_list[x:(x + n)]) for x in arr[::n]]
@@ -156,6 +299,14 @@ class CommonService:
     @DB.connection_context()
     def filter_scope_list(cls, in_key, in_filters_list,
                           filters=None, cols=None):
+        # Get records matching IN clause filters with optional column selection
+        # Args:
+        #     in_key: Field name for IN clause
+        #     in_filters_list: List of values for IN clause
+        #     filters: Additional filter conditions
+        #     cols: List of columns to select
+        # Returns:
+        #     List of matching records
         in_filters_tuple_list = cls.cut_list(in_filters_list, 20)
         if not filters:
             filters = []

api/db/services/dialog_service.py

@@ -13,44 +13,79 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-import logging
 import binascii
-import time
-from functools import partial
+from datetime import datetime
+import logging
 import re
+import time
 from copy import deepcopy
+from functools import partial
 from timeit import default_timer as timer
+
+from langfuse import Langfuse
+
 from agentic_reasoning import DeepResearcher
+from api import settings
 from api.db import LLMType, ParserType, StatusEnum
-from api.db.db_models import Dialog, DB
+from api.db.db_models import DB, Dialog
 from api.db.services.common_service import CommonService
 from api.db.services.knowledgebase_service import KnowledgebaseService
-from api.db.services.llm_service import TenantLLMService, LLMBundle
-from api import settings
+from api.db.services.langfuse_service import TenantLangfuseService
+from api.db.services.llm_service import LLMBundle, TenantLLMService
+from api.utils import current_timestamp, datetime_format
 from rag.app.resume import forbidden_select_fields4resume
 from rag.app.tag import label_question
 from rag.nlp.search import index_name
-from rag.prompts import kb_prompt, message_fit_in, llm_id2llm_type, keyword_extraction, full_question, chunks_format
-from rag.utils import rmSpace, num_tokens_from_string
+from rag.prompts import chunks_format, citation_prompt, full_question, kb_prompt, keyword_extraction, llm_id2llm_type, message_fit_in
+from rag.utils import num_tokens_from_string, rmSpace
 from rag.utils.tavily_conn import Tavily
 
 
 class DialogService(CommonService):
     model = Dialog
 
+    @classmethod
+    def save(cls, **kwargs):
+        """Save a new record to database.
+
+        This method creates a new record in the database with the provided field values,
+        forcing an insert operation rather than an update.
+
+        Args:
+            **kwargs: Record field values as keyword arguments.
+
+        Returns:
+            Model instance: The created record object.
+        """
+        sample_obj = cls.model(**kwargs).save(force_insert=True)
+        return sample_obj
+
+    @classmethod
+    def update_many_by_id(cls, data_list):
+        """Update multiple records by their IDs.
+
+        This method updates multiple records in the database, identified by their IDs.
+        It automatically updates the update_time and update_date fields for each record.
+
+        Args:
+            data_list (list): List of dictionaries containing record data to update.
+                             Each dictionary must include an 'id' field.
+        """
+        with DB.atomic():
+            for data in data_list:
+                data["update_time"] = current_timestamp()
+                data["update_date"] = datetime_format(datetime.now())
+                cls.model.update(data).where(cls.model.id == data["id"]).execute()
+
     @classmethod
     @DB.connection_context()
-    def get_list(cls, tenant_id,
-                 page_number, items_per_page, orderby, desc, id, name):
+    def get_list(cls, tenant_id, page_number, items_per_page, orderby, desc, id, name):
         chats = cls.model.select()
         if id:
             chats = chats.where(cls.model.id == id)
         if name:
             chats = chats.where(cls.model.name == name)
-        chats = chats.where(
-            (cls.model.tenant_id == tenant_id)
-            & (cls.model.status == StatusEnum.VALID.value)
-        )
+        chats = chats.where((cls.model.tenant_id == tenant_id) & (cls.model.status == StatusEnum.VALID.value))
         if desc:
             chats = chats.order_by(cls.model.getter_by(orderby).desc())
         else:
@@ -71,17 +106,18 @@ def chat_solo(dialog, messages, stream=True):
     tts_mdl = None
     if prompt_config.get("tts"):
         tts_mdl = LLMBundle(dialog.tenant_id, LLMType.TTS)
-    msg = [{"role": m["role"], "content": re.sub(r"##\d+\$\$", "", m["content"])}
-                for m in messages if m["role"] != "system"]
+    msg = [{"role": m["role"], "content": re.sub(r"##\d+\$\$", "", m["content"])} for m in messages if m["role"] != "system"]
     if stream:
         last_ans = ""
         for ans in chat_mdl.chat_streamly(prompt_config.get("system", ""), msg, dialog.llm_setting):
             answer = ans
-            delta_ans = ans[len(last_ans):]
+            delta_ans = ans[len(last_ans) :]
             if num_tokens_from_string(delta_ans) < 16:
                 continue
             last_ans = answer
-            yield {"answer": answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans), "prompt":"", "created_at": time.time()}
+            yield {"answer": answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans), "prompt": "", "created_at": time.time()}
+        if delta_ans:
+            yield {"answer": answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans), "prompt": "", "created_at": time.time()}
     else:
         answer = chat_mdl.chat(prompt_config.get("system", ""), msg, dialog.llm_setting)
         user_content = msg[-1].get("content", "[content not available]")
@@ -107,6 +143,16 @@ def chat(dialog, messages, stream=True, **kwargs):
 
     check_llm_ts = timer()
 
+    langfuse_tracer = None
+    langfuse_keys = TenantLangfuseService.filter_by_tenant(tenant_id=dialog.tenant_id)
+    if langfuse_keys:
+        langfuse = Langfuse(public_key=langfuse_keys.public_key, secret_key=langfuse_keys.secret_key, host=langfuse_keys.host)
+        if langfuse.auth_check():
+            langfuse_tracer = langfuse
+            langfuse.trace = langfuse_tracer.trace(name=f"{dialog.name}-{llm_model_config['llm_name']}")
+
+    check_langfuse_tracer_ts = timer()
+
     kbs = KnowledgebaseService.get_by_ids(dialog.kb_ids)
     embedding_list = list(set([kb.embd_id for kb in kbs]))
     if len(embedding_list) != 1:
@@ -134,6 +180,9 @@ def chat(dialog, messages, stream=True, **kwargs):
         chat_mdl = LLMBundle(dialog.tenant_id, LLMType.IMAGE2TEXT, dialog.llm_id)
     else:
         chat_mdl = LLMBundle(dialog.tenant_id, LLMType.CHAT, dialog.llm_id)
+        toolcall_session, tools = kwargs.get("toolcall_session"), kwargs.get("tools")
+        if toolcall_session and tools:
+            chat_mdl.bind_tools(toolcall_session, tools)
 
     bind_llm_ts = timer()
 
@@ -156,8 +205,7 @@ def chat(dialog, messages, stream=True, **kwargs):
         if p["key"] not in kwargs and not p["optional"]:
             raise KeyError("Miss parameter: " + p["key"])
         if p["key"] not in kwargs:
-            prompt_config["system"] = prompt_config["system"].replace(
-                "{%s}" % p["key"], " ")
+            prompt_config["system"] = prompt_config["system"].replace("{%s}" % p["key"], " ")
 
     if len(questions) > 1 and prompt_config.get("refine_multiturn"):
         questions = [full_question(dialog.tenant_id, dialog.llm_id, messages)]
@@ -186,9 +234,11 @@ def chat(dialog, messages, stream=True, **kwargs):
 
         knowledges = []
         if prompt_config.get("reasoning", False):
-            reasoner = DeepResearcher(chat_mdl,
-                                      prompt_config,
-                                      partial(retriever.retrieval, embd_mdl=embd_mdl, tenant_ids=tenant_ids, kb_ids=dialog.kb_ids, page=1, page_size=dialog.top_n, similarity_threshold=0.2, vector_similarity_weight=0.3))
+            reasoner = DeepResearcher(
+                chat_mdl,
+                prompt_config,
+                partial(retriever.retrieval, embd_mdl=embd_mdl, tenant_ids=tenant_ids, kb_ids=dialog.kb_ids, page=1, page_size=dialog.top_n, similarity_threshold=0.2, vector_similarity_weight=0.3),
+            )
 
             for think in reasoner.thinking(kbinfos, " ".join(questions)):
                 if isinstance(think, str):
@@ -197,56 +247,58 @@ def chat(dialog, messages, stream=True, **kwargs):
                 elif stream:
                     yield think
         else:
-            kbinfos = retriever.retrieval(" ".join(questions), embd_mdl, tenant_ids, dialog.kb_ids, 1, dialog.top_n,
-                                          dialog.similarity_threshold,
-                                          dialog.vector_similarity_weight,
-                                          doc_ids=attachments,
-                                          top=dialog.top_k, aggs=False, rerank_mdl=rerank_mdl,
-                                          rank_feature=label_question(" ".join(questions), kbs)
-                                          )
+            kbinfos = retriever.retrieval(
+                " ".join(questions),
+                embd_mdl,
+                tenant_ids,
+                dialog.kb_ids,
+                1,
+                dialog.top_n,
+                dialog.similarity_threshold,
+                dialog.vector_similarity_weight,
+                doc_ids=attachments,
+                top=dialog.top_k,
+                aggs=False,
+                rerank_mdl=rerank_mdl,
+                rank_feature=label_question(" ".join(questions), kbs),
+            )
             if prompt_config.get("tavily_api_key"):
                 tav = Tavily(prompt_config["tavily_api_key"])
                 tav_res = tav.retrieve_chunks(" ".join(questions))
                 kbinfos["chunks"].extend(tav_res["chunks"])
                 kbinfos["doc_aggs"].extend(tav_res["doc_aggs"])
             if prompt_config.get("use_kg"):
-                ck = settings.kg_retrievaler.retrieval(" ".join(questions),
-                                                       tenant_ids,
-                                                       dialog.kb_ids,
-                                                       embd_mdl,
-                                                       LLMBundle(dialog.tenant_id, LLMType.CHAT))
+                ck = settings.kg_retrievaler.retrieval(" ".join(questions), tenant_ids, dialog.kb_ids, embd_mdl, LLMBundle(dialog.tenant_id, LLMType.CHAT))
                 if ck["content_with_weight"]:
                     kbinfos["chunks"].insert(0, ck)
 
             knowledges = kb_prompt(kbinfos, max_tokens)
 
-    logging.debug(
-        "{}->{}".format(" ".join(questions), "\n->".join(knowledges)))
+    logging.debug("{}->{}".format(" ".join(questions), "\n->".join(knowledges)))
 
     retrieval_ts = timer()
     if not knowledges and prompt_config.get("empty_response"):
         empty_res = prompt_config["empty_response"]
-        yield {"answer": empty_res, "reference": kbinfos, "audio_binary": tts(tts_mdl, empty_res)}
+        yield {"answer": empty_res, "reference": kbinfos, "prompt": "\n\n### Query:\n%s" % " ".join(questions), "audio_binary": tts(tts_mdl, empty_res)}
         return {"answer": prompt_config["empty_response"], "reference": kbinfos}
 
     kwargs["knowledge"] = "\n------\n" + "\n\n------\n\n".join(knowledges)
     gen_conf = dialog.llm_setting
 
     msg = [{"role": "system", "content": prompt_config["system"].format(**kwargs)}]
-    msg.extend([{"role": m["role"], "content": re.sub(r"##\d+\$\$", "", m["content"])}
-                for m in messages if m["role"] != "system"])
-    used_token_count, msg = message_fit_in(msg, int(max_tokens * 0.97))
+    prompt4citation = ""
+    if knowledges and (prompt_config.get("quote", True) and kwargs.get("quote", True)):
+        prompt4citation = citation_prompt()
+    msg.extend([{"role": m["role"], "content": re.sub(r"##\d+\$\$", "", m["content"])} for m in messages if m["role"] != "system"])
+    used_token_count, msg = message_fit_in(msg, int(max_tokens * 0.95))
     assert len(msg) >= 2, f"message_fit_in has bug: {msg}"
     prompt = msg[0]["content"]
-    prompt += "\n\n### Query:\n%s" % " ".join(questions)
 
     if "max_tokens" in gen_conf:
-        gen_conf["max_tokens"] = min(
-            gen_conf["max_tokens"],
-            max_tokens - used_token_count)
+        gen_conf["max_tokens"] = min(gen_conf["max_tokens"], max_tokens - used_token_count)
 
     def decorate_answer(answer):
-        nonlocal prompt_config, knowledges, kwargs, kbinfos, prompt, retrieval_ts
+        nonlocal prompt_config, knowledges, kwargs, kbinfos, prompt, retrieval_ts, questions, langfuse_tracer
 
         refs = []
         ans = answer.split("</think>")
@@ -254,18 +306,37 @@ def chat(dialog, messages, stream=True, **kwargs):
         if len(ans) == 2:
             think = ans[0] + "</think>"
             answer = ans[1]
+
         if knowledges and (prompt_config.get("quote", True) and kwargs.get("quote", True)):
-            answer, idx = retriever.insert_citations(answer,
-                                                     [ck["content_ltks"]
-                                                      for ck in kbinfos["chunks"]],
-                                                     [ck["vector"]
-                                                      for ck in kbinfos["chunks"]],
-                                                     embd_mdl,
-                                                     tkweight=1 - dialog.vector_similarity_weight,
-                                                     vtweight=dialog.vector_similarity_weight)
+            answer = re.sub(r"##[ij]\$\$", "", answer, flags=re.DOTALL)
+            idx = set([])
+            if not re.search(r"##[0-9]+\$\$", answer):
+                answer, idx = retriever.insert_citations(
+                    answer,
+                    [ck["content_ltks"] for ck in kbinfos["chunks"]],
+                    [ck["vector"] for ck in kbinfos["chunks"]],
+                    embd_mdl,
+                    tkweight=1 - dialog.vector_similarity_weight,
+                    vtweight=dialog.vector_similarity_weight,
+                )
+            else:
+                for match in re.finditer(r"##([0-9]+)\$\$", answer):
+                    i = int(match.group(1))
+                    if i < len(kbinfos["chunks"]):
+                        idx.add(i)
+
+            # handle (ID: 1), ID: 2 etc.
+            for match in re.finditer(r"\(\s*ID:\s*(\d+)\s*\)|ID[: ]+\s*(\d+)", answer):
+                full_match = match.group(0)
+                id = match.group(1) or match.group(2)
+                if id:
+                    i = int(id)
+                    if i < len(kbinfos["chunks"]):
+                        idx.add(i)
+                    answer = answer.replace(full_match, f"##{i}$$")
+
             idx = set([kbinfos["chunks"][int(i)]["doc_id"] for i in idx])
-            recall_docs = [
-                d for d in kbinfos["doc_aggs"] if d["doc_id"] in idx]
+            recall_docs = [d for d in kbinfos["doc_aggs"] if d["doc_id"] in idx]
             if not recall_docs:
                 recall_docs = kbinfos["doc_aggs"]
             kbinfos["doc_aggs"] = recall_docs
@@ -281,7 +352,8 @@ def chat(dialog, messages, stream=True, **kwargs):
 
         total_time_cost = (finish_chat_ts - chat_start_ts) * 1000
         check_llm_time_cost = (check_llm_ts - chat_start_ts) * 1000
-        create_retriever_time_cost = (create_retriever_ts - check_llm_ts) * 1000
+        check_langfuse_tracer_cost = (check_langfuse_tracer_ts - check_llm_ts) * 1000
+        create_retriever_time_cost = (create_retriever_ts - check_langfuse_tracer_ts) * 1000
         bind_embedding_time_cost = (bind_embedding_ts - create_retriever_ts) * 1000
         bind_llm_time_cost = (bind_llm_ts - bind_embedding_ts) * 1000
         refine_question_time_cost = (refine_question_ts - bind_llm_ts) * 1000
@@ -290,27 +362,57 @@ def chat(dialog, messages, stream=True, **kwargs):
         retrieval_time_cost = (retrieval_ts - generate_keyword_ts) * 1000
         generate_result_time_cost = (finish_chat_ts - retrieval_ts) * 1000
 
-        prompt = f"{prompt}\n\n - Total: {total_time_cost:.1f}ms\n  - Check LLM: {check_llm_time_cost:.1f}ms\n  - Create retriever: {create_retriever_time_cost:.1f}ms\n  - Bind embedding: {bind_embedding_time_cost:.1f}ms\n  - Bind LLM: {bind_llm_time_cost:.1f}ms\n  - Tune question: {refine_question_time_cost:.1f}ms\n  - Bind reranker: {bind_reranker_time_cost:.1f}ms\n  - Generate keyword: {generate_keyword_time_cost:.1f}ms\n  - Retrieval: {retrieval_time_cost:.1f}ms\n  - Generate answer: {generate_result_time_cost:.1f}ms"
-        return {"answer": think+answer, "reference": refs, "prompt": re.sub(r"\n", "  \n", prompt), "created_at": time.time()}
+        tk_num = num_tokens_from_string(think + answer)
+        prompt += "\n\n### Query:\n%s" % " ".join(questions)
+        prompt = (
+            f"{prompt}\n\n"
+            "## Time elapsed:\n"
+            f"  - Total: {total_time_cost:.1f}ms\n"
+            f"  - Check LLM: {check_llm_time_cost:.1f}ms\n"
+            f"  - Check Langfuse tracer: {check_langfuse_tracer_cost:.1f}ms\n"
+            f"  - Create retriever: {create_retriever_time_cost:.1f}ms\n"
+            f"  - Bind embedding: {bind_embedding_time_cost:.1f}ms\n"
+            f"  - Bind LLM: {bind_llm_time_cost:.1f}ms\n"
+            f"  - Multi-turn optimization: {refine_question_time_cost:.1f}ms\n"
+            f"  - Bind reranker: {bind_reranker_time_cost:.1f}ms\n"
+            f"  - Generate keyword: {generate_keyword_time_cost:.1f}ms\n"
+            f"  - Retrieval: {retrieval_time_cost:.1f}ms\n"
+            f"  - Generate answer: {generate_result_time_cost:.1f}ms\n\n"
+            "## Token usage:\n"
+            f"  - Generated tokens(approximately): {tk_num}\n"
+            f"  - Token speed: {int(tk_num / (generate_result_time_cost / 1000.0))}/s"
+        )
+
+        langfuse_output = "\n" + re.sub(r"^.*?(### Query:.*)", r"\1", prompt, flags=re.DOTALL)
+        langfuse_output = {"time_elapsed:": re.sub(r"\n", "  \n", langfuse_output), "created_at": time.time()}
+
+        # Add a condition check to call the end method only if langfuse_tracer exists
+        if langfuse_tracer and "langfuse_generation" in locals():
+            langfuse_generation.end(output=langfuse_output)
+
+        return {"answer": think + answer, "reference": refs, "prompt": re.sub(r"\n", "  \n", prompt), "created_at": time.time()}
+
+    if langfuse_tracer:
+        langfuse_generation = langfuse_tracer.trace.generation(name="chat", model=llm_model_config["llm_name"], input={"prompt": prompt, "prompt4citation": prompt4citation, "messages": msg})
 
     if stream:
         last_ans = ""
         answer = ""
-        for ans in chat_mdl.chat_streamly(prompt, msg[1:], gen_conf):
+        for ans in chat_mdl.chat_streamly(prompt + prompt4citation, msg[1:], gen_conf):
             if thought:
                 ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
             answer = ans
-            delta_ans = ans[len(last_ans):]
+            delta_ans = ans[len(last_ans) :]
             if num_tokens_from_string(delta_ans) < 16:
                 continue
             last_ans = answer
-            yield {"answer": thought+answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans)}
-        delta_ans = answer[len(last_ans):]
+            yield {"answer": thought + answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans)}
+        delta_ans = answer[len(last_ans) :]
         if delta_ans:
-            yield {"answer": thought+answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans)}
-        yield decorate_answer(thought+answer)
+            yield {"answer": thought + answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans)}
+        yield decorate_answer(thought + answer)
     else:
-        answer = chat_mdl.chat(prompt, msg[1:], gen_conf)
+        answer = chat_mdl.chat(prompt + prompt4citation, msg[1:], gen_conf)
         user_content = msg[-1].get("content", "[content not available]")
         logging.debug("User: {}|Assistant: {}".format(user_content, answer))
         res = decorate_answer(answer)
@@ -328,26 +430,22 @@ Table of database fields are as follows:
 Question are as follows:
 {}
 Please write the SQL, only SQL, without any other explanations or text.
-""".format(
-        index_name(tenant_id),
-        "\n".join([f"{k}: {v}" for k, v in field_map.items()]),
-        question
-    )
+""".format(index_name(tenant_id), "\n".join([f"{k}: {v}" for k, v in field_map.items()]), question)
     tried_times = 0
 
     def get_table():
         nonlocal sys_prompt, user_prompt, question, tried_times
-        sql = chat_mdl.chat(sys_prompt, [{"role": "user", "content": user_prompt}], {
-            "temperature": 0.06})
+        sql = chat_mdl.chat(sys_prompt, [{"role": "user", "content": user_prompt}], {"temperature": 0.06})
+        sql = re.sub(r"<think>.*</think>", "", sql, flags=re.DOTALL)
         logging.debug(f"{question} ==> {user_prompt} get SQL: {sql}")
         sql = re.sub(r"[\r\n]+", " ", sql.lower())
         sql = re.sub(r".*select ", "select ", sql.lower())
         sql = re.sub(r" +", " ", sql)
         sql = re.sub(r"([;；]|```).*", "", sql)
-        if sql[:len("select ")] != "select ":
+        if sql[: len("select ")] != "select ":
             return None, None
         if not re.search(r"((sum|avg|max|min)\(|group by )", sql.lower()):
-            if sql[:len("select *")] != "select *":
+            if sql[: len("select *")] != "select *":
                 sql = "select doc_id,docnm_kwd," + sql[6:]
             else:
                 flds = []
@@ -384,11 +482,7 @@ Please write the SQL, only SQL, without any other explanations or text.
         {}
 
         Please correct the error and write SQL again, only SQL, without any other explanations or text.
-        """.format(
-            index_name(tenant_id),
-            "\n".join([f"{k}: {v}" for k, v in field_map.items()]),
-            question, sql, tbl["error"]
-        )
+        """.format(index_name(tenant_id), "\n".join([f"{k}: {v}" for k, v in field_map.items()]), question, sql, tbl["error"])
         tbl, sql = get_table()
         logging.debug("TRY it again: {}".format(sql))
 
@@ -396,24 +490,18 @@ Please write the SQL, only SQL, without any other explanations or text.
     if tbl.get("error") or len(tbl["rows"]) == 0:
         return None
 
-    docid_idx = set([ii for ii, c in enumerate(
-        tbl["columns"]) if c["name"] == "doc_id"])
-    doc_name_idx = set([ii for ii, c in enumerate(
-        tbl["columns"]) if c["name"] == "docnm_kwd"])
-    column_idx = [ii for ii in range(
-        len(tbl["columns"])) if ii not in (docid_idx | doc_name_idx)]
+    docid_idx = set([ii for ii, c in enumerate(tbl["columns"]) if c["name"] == "doc_id"])
+    doc_name_idx = set([ii for ii, c in enumerate(tbl["columns"]) if c["name"] == "docnm_kwd"])
+    column_idx = [ii for ii in range(len(tbl["columns"])) if ii not in (docid_idx | doc_name_idx)]
 
     # compose Markdown table
-    columns = "|" + "|".join([re.sub(r"(/.*|（[^（）]+）)", "", field_map.get(tbl["columns"][i]["name"],
-                                                                          tbl["columns"][i]["name"])) for i in
-                              column_idx]) + ("|Source|" if docid_idx and docid_idx else "|")
+    columns = (
+            "|" + "|".join([re.sub(r"(/.*|（[^（）]+）)", "", field_map.get(tbl["columns"][i]["name"], tbl["columns"][i]["name"])) for i in column_idx]) + ("|Source|" if docid_idx and docid_idx else "|")
+    )
 
-    line = "|" + "|".join(["------" for _ in range(len(column_idx))]) + \
-           ("|------|" if docid_idx and docid_idx else "")
+    line = "|" + "|".join(["------" for _ in range(len(column_idx))]) + ("|------|" if docid_idx and docid_idx else "")
 
-    rows = ["|" +
-            "|".join([rmSpace(str(r[i])) for i in column_idx]).replace("None", " ") +
-            "|" for r in tbl["rows"]]
+    rows = ["|" + "|".join([rmSpace(str(r[i])) for i in column_idx]).replace("None", " ") + "|" for r in tbl["rows"]]
     rows = [r for r in rows if re.sub(r"[ |]+", "", r)]
     if quota:
         rows = "\n".join([r + f" ##{ii}$$ |" for ii, r in enumerate(rows)])
@@ -423,11 +511,7 @@ Please write the SQL, only SQL, without any other explanations or text.
 
     if not docid_idx or not doc_name_idx:
         logging.warning("SQL missing field: " + sql)
-        return {
-            "answer": "\n".join([columns, line, rows]),
-            "reference": {"chunks": [], "doc_aggs": []},
-            "prompt": sys_prompt
-        }
+        return {"answer": "\n".join([columns, line, rows]), "reference": {"chunks": [], "doc_aggs": []}, "prompt": sys_prompt}
 
     docid_idx = list(docid_idx)[0]
     doc_name_idx = list(doc_name_idx)[0]
@@ -438,10 +522,11 @@ Please write the SQL, only SQL, without any other explanations or text.
         doc_aggs[r[docid_idx]]["count"] += 1
     return {
         "answer": "\n".join([columns, line, rows]),
-        "reference": {"chunks": [{"doc_id": r[docid_idx], "docnm_kwd": r[doc_name_idx]} for r in tbl["rows"]],
-                      "doc_aggs": [{"doc_id": did, "doc_name": d["doc_name"], "count": d["count"]} for did, d in
-                                   doc_aggs.items()]},
-        "prompt": sys_prompt
+        "reference": {
+            "chunks": [{"doc_id": r[docid_idx], "docnm_kwd": r[doc_name_idx]} for r in tbl["rows"]],
+            "doc_aggs": [{"doc_id": did, "doc_name": d["doc_name"], "count": d["count"]} for did, d in doc_aggs.items()],
+        },
+        "prompt": sys_prompt,
     }
 
 
@@ -465,10 +550,7 @@ def ask(question, kb_ids, tenant_id):
     chat_mdl = LLMBundle(tenant_id, LLMType.CHAT)
     max_tokens = chat_mdl.max_length
     tenant_ids = list(set([kb.tenant_id for kb in kbs]))
-    kbinfos = retriever.retrieval(question, embd_mdl, tenant_ids, kb_ids,
-                                  1, 12, 0.1, 0.3, aggs=False,
-                                  rank_feature=label_question(question, kbs)
-                                  )
+    kbinfos = retriever.retrieval(question, embd_mdl, tenant_ids, kb_ids, 1, 12, 0.1, 0.3, aggs=False, rank_feature=label_question(question, kbs))
     knowledges = kb_prompt(kbinfos, max_tokens)
     prompt = """
     Role: You're a smart assistant. Your name is Miss R.
@@ -490,17 +572,9 @@ def ask(question, kb_ids, tenant_id):
 
     def decorate_answer(answer):
         nonlocal knowledges, kbinfos, prompt
-        answer, idx = retriever.insert_citations(answer,
-                                                 [ck["content_ltks"]
-                                                  for ck in kbinfos["chunks"]],
-                                                 [ck["vector"]
-                                                  for ck in kbinfos["chunks"]],
-                                                 embd_mdl,
-                                                 tkweight=0.7,
-                                                 vtweight=0.3)
+        answer, idx = retriever.insert_citations(answer, [ck["content_ltks"] for ck in kbinfos["chunks"]], [ck["vector"] for ck in kbinfos["chunks"]], embd_mdl, tkweight=0.7, vtweight=0.3)
         idx = set([kbinfos["chunks"][int(i)]["doc_id"] for i in idx])
-        recall_docs = [
-            d for d in kbinfos["doc_aggs"] if d["doc_id"] in idx]
+        recall_docs = [d for d in kbinfos["doc_aggs"] if d["doc_id"] in idx]
         if not recall_docs:
             recall_docs = kbinfos["doc_aggs"]
         kbinfos["doc_aggs"] = recall_docs
@@ -511,12 +585,11 @@ def ask(question, kb_ids, tenant_id):
 
         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'"
-        return {"answer": answer, "reference": chunks_format(refs)}
+        refs["chunks"] = chunks_format(refs)
+        return {"answer": answer, "reference": refs}
 
     answer = ""
     for ans in chat_mdl.chat_streamly(prompt, msg, {"temperature": 0.1}):
         answer = ans
         yield {"answer": answer, "reference": {}}
     yield decorate_answer(answer)
-
-

api/db/services/document_service.py

@@ -13,9 +13,8 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-import logging
-import xxhash
 import json
+import logging
 import random
 import re
 from concurrent.futures import ThreadPoolExecutor
@@ -23,23 +22,21 @@ from copy import deepcopy
 from datetime import datetime
 from io import BytesIO
 
+import trio
+import xxhash
 from peewee import fn
 
-from api.db.db_utils import bulk_insert_into_db
 from api import settings
-from api.utils import current_timestamp, get_format_time, get_uuid
-from graphrag.general.mind_map_extractor import MindMapExtractor
-from rag.settings import SVR_QUEUE_NAME
-from rag.utils.storage_factory import STORAGE_IMPL
-from rag.nlp import search, rag_tokenizer
-
-from api.db import FileType, TaskStatus, ParserType, LLMType
-from api.db.db_models import DB, Knowledgebase, Tenant, Task, UserTenant
-from api.db.db_models import Document
+from api.db import FileType, LLMType, ParserType, StatusEnum, TaskStatus, UserTenantRole
+from api.db.db_models import DB, Document, Knowledgebase, Task, Tenant, UserTenant
+from api.db.db_utils import bulk_insert_into_db
 from api.db.services.common_service import CommonService
 from api.db.services.knowledgebase_service import KnowledgebaseService
-from api.db import StatusEnum
+from api.utils import current_timestamp, get_format_time, get_uuid
+from rag.nlp import rag_tokenizer, search
+from rag.settings import get_svr_queue_name
 from rag.utils.redis_conn import REDIS_CONN
+from rag.utils.storage_factory import STORAGE_IMPL
 
 
 class DocumentService(CommonService):
@@ -96,9 +93,7 @@ class DocumentService(CommonService):
     def insert(cls, doc):
         if not cls.save(**doc):
             raise RuntimeError("Database error (Document)!")
-        e, kb = KnowledgebaseService.get_by_id(doc["kb_id"])
-        if not KnowledgebaseService.update_by_id(
-                kb.id, {"doc_num": kb.doc_num + 1}):
+        if not KnowledgebaseService.atomic_increase_doc_num_by_id(doc["kb_id"]):
             raise RuntimeError("Database error (Knowledgebase)!")
         return Document(**doc)
 
@@ -108,13 +103,13 @@ class DocumentService(CommonService):
         cls.clear_chunk_num(doc.id)
         try:
             settings.docStoreConn.delete({"doc_id": doc.id}, search.index_name(tenant_id), doc.kb_id)
-            settings.docStoreConn.update({"kb_id": doc.kb_id, "knowledge_graph_kwd": ["entity", "relation", "graph", "community_report"], "source_id": doc.id},
+            settings.docStoreConn.update({"kb_id": doc.kb_id, "knowledge_graph_kwd": ["entity", "relation", "graph", "subgraph", "community_report"], "source_id": doc.id},
                                          {"remove": {"source_id": doc.id}},
                                          search.index_name(tenant_id), doc.kb_id)
             settings.docStoreConn.update({"kb_id": doc.kb_id, "knowledge_graph_kwd": ["graph"]},
                                          {"removed_kwd": "Y"},
                                          search.index_name(tenant_id), doc.kb_id)
-            settings.docStoreConn.delete({"kb_id": doc.kb_id, "knowledge_graph_kwd": ["entity", "relation", "graph", "community_report"], "must_not": {"exists": "source_id"}},
+            settings.docStoreConn.delete({"kb_id": doc.kb_id, "knowledge_graph_kwd": ["entity", "relation", "graph", "subgraph", "community_report"], "must_not": {"exists": "source_id"}},
                                          search.index_name(tenant_id), doc.kb_id)
         except Exception:
             pass
@@ -174,9 +169,9 @@ class DocumentService(CommonService):
                 "Document not found which is supposed to be there")
         num = Knowledgebase.update(
             token_num=Knowledgebase.token_num +
-                      token_num,
+            token_num,
             chunk_num=Knowledgebase.chunk_num +
-                      chunk_num).where(
+            chunk_num).where(
             Knowledgebase.id == kb_id).execute()
         return num
 
@@ -192,9 +187,9 @@ class DocumentService(CommonService):
                 "Document not found which is supposed to be there")
         num = Knowledgebase.update(
             token_num=Knowledgebase.token_num -
-                      token_num,
+            token_num,
             chunk_num=Knowledgebase.chunk_num -
-                      chunk_num
+            chunk_num
         ).where(
             Knowledgebase.id == kb_id).execute()
         return num
@@ -207,9 +202,9 @@ class DocumentService(CommonService):
 
         num = Knowledgebase.update(
             token_num=Knowledgebase.token_num -
-                      doc.token_num,
+            doc.token_num,
             chunk_num=Knowledgebase.chunk_num -
-                      doc.chunk_num,
+            doc.chunk_num,
             doc_num=Knowledgebase.doc_num - 1
         ).where(
             Knowledgebase.id == doc.kb_id).execute()
@@ -221,7 +216,7 @@ class DocumentService(CommonService):
         docs = cls.model.select(
             Knowledgebase.tenant_id).join(
             Knowledgebase, on=(
-                    Knowledgebase.id == cls.model.kb_id)).where(
+                Knowledgebase.id == cls.model.kb_id)).where(
             cls.model.id == doc_id, Knowledgebase.status == StatusEnum.VALID.value)
         docs = docs.dicts()
         if not docs:
@@ -243,7 +238,7 @@ class DocumentService(CommonService):
         docs = cls.model.select(
             Knowledgebase.tenant_id).join(
             Knowledgebase, on=(
-                    Knowledgebase.id == cls.model.kb_id)).where(
+                Knowledgebase.id == cls.model.kb_id)).where(
             cls.model.name == name, Knowledgebase.status == StatusEnum.VALID.value)
         docs = docs.dicts()
         if not docs:
@@ -256,7 +251,7 @@ class DocumentService(CommonService):
         docs = cls.model.select(
             cls.model.id).join(
             Knowledgebase, on=(
-                    Knowledgebase.id == cls.model.kb_id)
+                Knowledgebase.id == cls.model.kb_id)
         ).join(UserTenant, on=(UserTenant.tenant_id == Knowledgebase.tenant_id)
                ).where(cls.model.id == doc_id, UserTenant.user_id == user_id).paginate(0, 1)
         docs = docs.dicts()
@@ -267,11 +262,18 @@ class DocumentService(CommonService):
     @classmethod
     @DB.connection_context()
     def accessible4deletion(cls, doc_id, user_id):
-        docs = cls.model.select(
-            cls.model.id).join(
+        docs = cls.model.select(cls.model.id
+        ).join(
             Knowledgebase, on=(
-                    Knowledgebase.id == cls.model.kb_id)
-        ).where(cls.model.id == doc_id, Knowledgebase.created_by == user_id).paginate(0, 1)
+                Knowledgebase.id == cls.model.kb_id)
+        ).join(
+            UserTenant, on=(
+                (UserTenant.tenant_id == Knowledgebase.created_by) & (UserTenant.user_id == user_id))
+        ).where(
+            cls.model.id == doc_id,
+            UserTenant.status == StatusEnum.VALID.value,
+            ((UserTenant.role == UserTenantRole.NORMAL) | (UserTenant.role == UserTenantRole.OWNER))
+        ).paginate(0, 1)
         docs = docs.dicts()
         if not docs:
             return False
@@ -283,7 +285,7 @@ class DocumentService(CommonService):
         docs = cls.model.select(
             Knowledgebase.embd_id).join(
             Knowledgebase, on=(
-                    Knowledgebase.id == cls.model.kb_id)).where(
+                Knowledgebase.id == cls.model.kb_id)).where(
             cls.model.id == doc_id, Knowledgebase.status == StatusEnum.VALID.value)
         docs = docs.dicts()
         if not docs:
@@ -306,9 +308,9 @@ class DocumentService(CommonService):
                 Tenant.asr_id,
                 Tenant.llm_id,
             )
-                .join(Knowledgebase, on=(cls.model.kb_id == Knowledgebase.id))
-                .join(Tenant, on=(Knowledgebase.tenant_id == Tenant.id))
-                .where(cls.model.id == doc_id)
+            .join(Knowledgebase, on=(cls.model.kb_id == Knowledgebase.id))
+            .join(Tenant, on=(Knowledgebase.tenant_id == Tenant.id))
+            .where(cls.model.id == doc_id)
         )
         configs = configs.dicts()
         if not configs:
@@ -336,6 +338,8 @@ class DocumentService(CommonService):
     @classmethod
     @DB.connection_context()
     def update_parser_config(cls, id, config):
+        if not config:
+            return
         e, d = cls.get_by_id(id)
         if not e:
             raise LookupError(f"Document({id}) not found.")
@@ -372,6 +376,7 @@ class DocumentService(CommonService):
                     "progress_msg": "Task is queued...",
                     "process_begin_at": get_format_time()
                     })
+
     @classmethod
     @DB.connection_context()
     def update_meta_fields(cls, doc_id, meta_fields):
@@ -380,12 +385,6 @@ class DocumentService(CommonService):
     @classmethod
     @DB.connection_context()
     def update_progress(cls):
-        MSG = {
-            "raptor": "Start RAPTOR (Recursive Abstractive Processing for Tree-Organized Retrieval).",
-            "graphrag": "Entities extraction progress",
-            "graph_resolution": "Start Graph Resolution",
-            "graph_community": "Start Graph Community Reports Generation"
-        }
         docs = cls.get_unfinished_docs()
         for d in docs:
             try:
@@ -396,37 +395,33 @@ class DocumentService(CommonService):
                 prg = 0
                 finished = True
                 bad = 0
+                has_raptor = False
+                has_graphrag = False
                 e, doc = DocumentService.get_by_id(d["id"])
                 status = doc.run  # TaskStatus.RUNNING.value
+                priority = 0
                 for t in tsks:
                     if 0 <= t.progress < 1:
                         finished = False
-                    prg += t.progress if t.progress >= 0 else 0
-                    if t.progress_msg not in msg:
-                        msg.append(t.progress_msg)
                     if t.progress == -1:
                         bad += 1
+                    prg += t.progress if t.progress >= 0 else 0
+                    msg.append(t.progress_msg)
+                    if t.task_type == "raptor":
+                        has_raptor = True
+                    elif t.task_type == "graphrag":
+                        has_graphrag = True
+                    priority = max(priority, t.priority)
                 prg /= len(tsks)
                 if finished and bad:
                     prg = -1
                     status = TaskStatus.FAIL.value
                 elif finished:
-                    m = "\n".join(sorted(msg))
-                    if d["parser_config"].get("raptor", {}).get("use_raptor") and m.find(MSG["raptor"]) < 0:
-                        queue_raptor_o_graphrag_tasks(d, "raptor", MSG["raptor"])
+                    if d["parser_config"].get("raptor", {}).get("use_raptor") and not has_raptor:
+                        queue_raptor_o_graphrag_tasks(d, "raptor", priority)
                         prg = 0.98 * len(tsks) / (len(tsks) + 1)
-                    elif d["parser_config"].get("graphrag", {}).get("use_graphrag") and m.find(MSG["graphrag"]) < 0:
-                        queue_raptor_o_graphrag_tasks(d, "graphrag", MSG["graphrag"])
-                        prg = 0.98 * len(tsks) / (len(tsks) + 1)
-                    elif d["parser_config"].get("graphrag", {}).get("use_graphrag") \
-                        and d["parser_config"].get("graphrag", {}).get("resolution") \
-                        and m.find(MSG["graph_resolution"]) < 0:
-                        queue_raptor_o_graphrag_tasks(d, "graph_resolution", MSG["graph_resolution"])
-                        prg = 0.98 * len(tsks) / (len(tsks) + 1)
-                    elif d["parser_config"].get("graphrag", {}).get("use_graphrag") \
-                        and d["parser_config"].get("graphrag", {}).get("community") \
-                        and m.find(MSG["graph_community"]) < 0:
-                        queue_raptor_o_graphrag_tasks(d, "graph_community", MSG["graph_community"])
+                    elif d["parser_config"].get("graphrag", {}).get("use_graphrag") and not has_graphrag:
+                        queue_raptor_o_graphrag_tasks(d, "graphrag", priority)
                         prg = 0.98 * len(tsks) / (len(tsks) + 1)
                     else:
                         status = TaskStatus.DONE.value
@@ -435,7 +430,7 @@ class DocumentService(CommonService):
                 info = {
                     "process_duation": datetime.timestamp(
                         datetime.now()) -
-                                       d["process_begin_at"].timestamp(),
+                    d["process_begin_at"].timestamp(),
                     "run": status}
                 if prg != 0:
                     info["progress"] = prg
@@ -463,7 +458,7 @@ class DocumentService(CommonService):
         return False
 
 
-def queue_raptor_o_graphrag_tasks(doc, ty, msg):
+def queue_raptor_o_graphrag_tasks(doc, ty, priority):
     chunking_config = DocumentService.get_chunking_config(doc["id"])
     hasher = xxhash.xxh64()
     for field in sorted(chunking_config.keys()):
@@ -476,7 +471,8 @@ def queue_raptor_o_graphrag_tasks(doc, ty, msg):
             "doc_id": doc["id"],
             "from_page": 100000000,
             "to_page": 100000000,
-            "progress_msg":  datetime.now().strftime("%H:%M:%S") + " " + msg
+            "task_type": ty,
+            "progress_msg":  datetime.now().strftime("%H:%M:%S") + " created task " + ty
         }
 
     task = new_task()
@@ -485,18 +481,17 @@ def queue_raptor_o_graphrag_tasks(doc, ty, msg):
     hasher.update(ty.encode("utf-8"))
     task["digest"] = hasher.hexdigest()
     bulk_insert_into_db(Task, [task], True)
-    task["task_type"] = ty
-    assert REDIS_CONN.queue_product(SVR_QUEUE_NAME, message=task), "Can't access Redis. Please check the Redis' status."
+    assert REDIS_CONN.queue_product(get_svr_queue_name(priority), message=task), "Can't access Redis. Please check the Redis' status."
 
 
 def doc_upload_and_parse(conversation_id, file_objs, user_id):
-    from rag.app import presentation, picture, naive, audio, email
+    from api.db.services.api_service import API4ConversationService
+    from api.db.services.conversation_service import ConversationService
     from api.db.services.dialog_service import DialogService
     from api.db.services.file_service import FileService
     from api.db.services.llm_service import LLMBundle
     from api.db.services.user_service import TenantService
-    from api.db.services.api_service import API4ConversationService
-    from api.db.services.conversation_service import ConversationService
+    from rag.app import audio, email, naive, picture, presentation
 
     e, conv = ConversationService.get_by_id(conversation_id)
     if not e:
@@ -595,10 +590,11 @@ def doc_upload_and_parse(conversation_id, file_objs, user_id):
         cks = [c for c in docs if c["doc_id"] == doc_id]
 
         if parser_ids[doc_id] != ParserType.PICTURE.value:
+            from graphrag.general.mind_map_extractor import MindMapExtractor
             mindmap = MindMapExtractor(llm_bdl)
             try:
-                mind_map = json.dumps(mindmap([c["content_with_weight"] for c in docs if c["doc_id"] == doc_id]).output,
-                                      ensure_ascii=False, indent=2)
+                mind_map = trio.run(mindmap, [c["content_with_weight"] for c in docs if c["doc_id"] == doc_id])
+                mind_map = json.dumps(mind_map.output, ensure_ascii=False, indent=2)
                 if len(mind_map) < 32:
                     raise Exception("Few content: " + mind_map)
                 cks.append({

api/db/services/file_service.py

@@ -34,12 +34,24 @@ from rag.utils.storage_factory import STORAGE_IMPL
 
 
 class FileService(CommonService):
+    # Service class for managing file operations and storage
     model = File
 
     @classmethod
     @DB.connection_context()
     def get_by_pf_id(cls, tenant_id, pf_id, page_number, items_per_page,
                      orderby, desc, keywords):
+        # Get files by parent folder ID with pagination and filtering
+        # Args:
+        #     tenant_id: ID of the tenant
+        #     pf_id: Parent folder ID
+        #     page_number: Page number for pagination
+        #     items_per_page: Number of items per page
+        #     orderby: Field to order by
+        #     desc: Boolean indicating descending order
+        #     keywords: Search keywords
+        # Returns:
+        #     Tuple of (file_list, total_count)
         if keywords:
             files = cls.model.select().where(
                 (cls.model.tenant_id == tenant_id),
@@ -80,6 +92,11 @@ class FileService(CommonService):
     @classmethod
     @DB.connection_context()
     def get_kb_id_by_file_id(cls, file_id):
+        # Get knowledge base IDs associated with a file
+        # Args:
+        #     file_id: File ID
+        # Returns:
+        #     List of dictionaries containing knowledge base IDs and names
         kbs = (cls.model.select(*[Knowledgebase.id, Knowledgebase.name])
                .join(File2Document, on=(File2Document.file_id == file_id))
                .join(Document, on=(File2Document.document_id == Document.id))
@@ -95,6 +112,12 @@ class FileService(CommonService):
     @classmethod
     @DB.connection_context()
     def get_by_pf_id_name(cls, id, name):
+        # Get file by parent folder ID and name
+        # Args:
+        #     id: Parent folder ID
+        #     name: File name
+        # Returns:
+        #     File object or None if not found
         file = cls.model.select().where((cls.model.parent_id == id) & (cls.model.name == name))
         if file.count():
             e, file = cls.get_by_id(file[0].id)
@@ -106,6 +129,14 @@ class FileService(CommonService):
     @classmethod
     @DB.connection_context()
     def get_id_list_by_id(cls, id, name, count, res):
+        # Recursively get list of file IDs by traversing folder structure
+        # Args:
+        #     id: Starting folder ID
+        #     name: List of folder names to traverse
+        #     count: Current depth in traversal
+        #     res: List to store results
+        # Returns:
+        #     List of file IDs
         if count < len(name):
             file = cls.get_by_pf_id_name(id, name[count])
             if file:
@@ -119,6 +150,12 @@ class FileService(CommonService):
     @classmethod
     @DB.connection_context()
     def get_all_innermost_file_ids(cls, folder_id, result_ids):
+        # Get IDs of all files in the deepest level of folders
+        # Args:
+        #     folder_id: Starting folder ID
+        #     result_ids: List to store results
+        # Returns:
+        #     List of file IDs
         subfolders = cls.model.select().where(cls.model.parent_id == folder_id)
         if subfolders.exists():
             for subfolder in subfolders:
@@ -130,6 +167,14 @@ class FileService(CommonService):
     @classmethod
     @DB.connection_context()
     def create_folder(cls, file, parent_id, name, count):
+        # Recursively create folder structure
+        # Args:
+        #     file: Current file object
+        #     parent_id: Parent folder ID
+        #     name: List of folder names to create
+        #     count: Current depth in creation
+        # Returns:
+        #     Created file object
         if count > len(name) - 2:
             return file
         else:
@@ -148,6 +193,11 @@ class FileService(CommonService):
     @classmethod
     @DB.connection_context()
     def is_parent_folder_exist(cls, parent_id):
+        # Check if parent folder exists
+        # Args:
+        #     parent_id: Parent folder ID
+        # Returns:
+        #     Boolean indicating if folder exists
         parent_files = cls.model.select().where(cls.model.id == parent_id)
         if parent_files.count():
             return True
@@ -157,6 +207,11 @@ class FileService(CommonService):
     @classmethod
     @DB.connection_context()
     def get_root_folder(cls, tenant_id):
+        # Get or create root folder for tenant
+        # Args:
+        #     tenant_id: Tenant ID
+        # Returns:
+        #     Root folder dictionary
         for file in cls.model.select().where((cls.model.tenant_id == tenant_id),
                                         (cls.model.parent_id == cls.model.id)
                                         ):
@@ -179,6 +234,11 @@ class FileService(CommonService):
     @classmethod
     @DB.connection_context()
     def get_kb_folder(cls, tenant_id):
+        # Get knowledge base folder for tenant
+        # Args:
+        #     tenant_id: Tenant ID
+        # Returns:
+        #     Knowledge base folder dictionary
         for root in cls.model.select().where(
                 (cls.model.tenant_id == tenant_id), (cls.model.parent_id == cls.model.id)):
             for folder in cls.model.select().where(
@@ -190,6 +250,16 @@ class FileService(CommonService):
     @classmethod
     @DB.connection_context()
     def new_a_file_from_kb(cls, tenant_id, name, parent_id, ty=FileType.FOLDER.value, size=0, location=""):
+        # Create a new file from knowledge base
+        # Args:
+        #     tenant_id: Tenant ID
+        #     name: File name
+        #     parent_id: Parent folder ID
+        #     ty: File type
+        #     size: File size
+        #     location: File location
+        # Returns:
+        #     Created file dictionary
         for file in cls.query(tenant_id=tenant_id, parent_id=parent_id, name=name):
             return file.to_dict()
         file = {
@@ -209,6 +279,10 @@ class FileService(CommonService):
     @classmethod
     @DB.connection_context()
     def init_knowledgebase_docs(cls, root_id, tenant_id):
+        # Initialize knowledge base documents
+        # Args:
+        #     root_id: Root folder ID
+        #     tenant_id: Tenant ID
         for _ in cls.model.select().where((cls.model.name == KNOWLEDGEBASE_FOLDER_NAME)\
                                           & (cls.model.parent_id == root_id)):
             return
@@ -222,6 +296,11 @@ class FileService(CommonService):
     @classmethod
     @DB.connection_context()
     def get_parent_folder(cls, file_id):
+        # Get parent folder of a file
+        # Args:
+        #     file_id: File ID
+        # Returns:
+        #     Parent folder object
         file = cls.model.select().where(cls.model.id == file_id)
         if file.count():
             e, file = cls.get_by_id(file[0].parent_id)
@@ -234,6 +313,11 @@ class FileService(CommonService):
     @classmethod
     @DB.connection_context()
     def get_all_parent_folders(cls, start_id):
+        # Get all parent folders in path
+        # Args:
+        #     start_id: Starting file ID
+        # Returns:
+        #     List of parent folder objects
         parent_folders = []
         current_id = start_id
         while current_id:
@@ -249,6 +333,11 @@ class FileService(CommonService):
     @classmethod
     @DB.connection_context()
     def insert(cls, file):
+        # Insert a new file record
+        # Args:
+        #     file: File data dictionary
+        # Returns:
+        #     Created file object
         if not cls.save(**file):
             raise RuntimeError("Database error (File)!")
         return File(**file)
@@ -256,6 +345,7 @@ class FileService(CommonService):
     @classmethod
     @DB.connection_context()
     def delete(cls, file):
+        #
         return cls.delete_by_id(file.id)
 
     @classmethod

api/db/services/knowledgebase_service.py

@@ -13,22 +13,115 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-from api.db import StatusEnum, TenantPermission
-from api.db.db_models import Knowledgebase, DB, Tenant, User, UserTenant,Document
-from api.db.services.common_service import CommonService
+from datetime import datetime
+
 from peewee import fn
 
+from api.db import StatusEnum, TenantPermission
+from api.db.db_models import DB, Document, Knowledgebase, Tenant, User, UserTenant
+from api.db.services.common_service import CommonService
+from api.utils import current_timestamp, datetime_format
+
 
 class KnowledgebaseService(CommonService):
+    """Service class for managing knowledge base operations.
+
+    This class extends CommonService to provide specialized functionality for knowledge base
+    management, including document parsing status tracking, access control, and configuration
+    management. It handles operations such as listing, creating, updating, and deleting
+    knowledge bases, as well as managing their associated documents and permissions.
+
+    The class implements a comprehensive set of methods for:
+    - Document parsing status verification
+    - Knowledge base access control
+    - Parser configuration management
+    - Tenant-based knowledge base organization
+
+    Attributes:
+        model: The Knowledgebase model class for database operations.
+    """
     model = Knowledgebase
 
     @classmethod
     @DB.connection_context()
-    def list_documents_by_ids(cls,kb_ids):
-        doc_ids=cls.model.select(Document.id.alias("document_id")).join(Document,on=(cls.model.id == Document.kb_id)).where(
+    def accessible4deletion(cls, kb_id, user_id):
+        """Check if a knowledge base can be deleted by a specific user.
+
+        This method verifies whether a user has permission to delete a knowledge base
+        by checking if they are the creator of that knowledge base.
+
+        Args:
+            kb_id (str): The unique identifier of the knowledge base to check.
+            user_id (str): The unique identifier of the user attempting the deletion.
+
+        Returns:
+            bool: True if the user has permission to delete the knowledge base,
+                  False if the user doesn't have permission or the knowledge base doesn't exist.
+
+        Example:
+            >>> KnowledgebaseService.accessible4deletion("kb123", "user456")
+            True
+
+        Note:
+            - This method only checks creator permissions
+            - A return value of False can mean either:
+                1. The knowledge base doesn't exist
+                2. The user is not the creator of the knowledge base
+        """
+        # Check if a knowledge base can be deleted by a user
+        docs = cls.model.select(
+            cls.model.id).where(cls.model.id == kb_id, cls.model.created_by == user_id).paginate(0, 1)
+        docs = docs.dicts()
+        if not docs:
+            return False
+        return True
+
+    @classmethod
+    @DB.connection_context()
+    def is_parsed_done(cls, kb_id):
+        # Check if all documents in the knowledge base have completed parsing
+        #
+        # Args:
+        #     kb_id: Knowledge base ID
+        #
+        # Returns:
+        #     If all documents are parsed successfully, returns (True, None)
+        #     If any document is not fully parsed, returns (False, error_message)
+        from api.db import TaskStatus
+        from api.db.services.document_service import DocumentService
+
+        # Get knowledge base information
+        kbs = cls.query(id=kb_id)
+        if not kbs:
+            return False, "Knowledge base not found"
+        kb = kbs[0]
+
+        # Get all documents in the knowledge base
+        docs, _ = DocumentService.get_by_kb_id(kb_id, 1, 1000, "create_time", True, "")
+
+        # Check parsing status of each document
+        for doc in docs:
+            # If document is being parsed, don't allow chat creation
+            if doc['run'] == TaskStatus.RUNNING.value or doc['run'] == TaskStatus.CANCEL.value or doc['run'] == TaskStatus.FAIL.value:
+                return False, f"Document '{doc['name']}' in dataset '{kb.name}' is still being parsed. Please wait until all documents are parsed before starting a chat."
+            # If document is not yet parsed and has no chunks, don't allow chat creation
+            if doc['run'] == TaskStatus.UNSTART.value and doc['chunk_num'] == 0:
+                return False, f"Document '{doc['name']}' in dataset '{kb.name}' has not been parsed yet. Please parse all documents before starting a chat."
+
+        return True, None
+
+    @classmethod
+    @DB.connection_context()
+    def list_documents_by_ids(cls, kb_ids):
+        # Get document IDs associated with given knowledge base IDs
+        # Args:
+        #     kb_ids: List of knowledge base IDs
+        # Returns:
+        #     List of document IDs
+        doc_ids = cls.model.select(Document.id.alias("document_id")).join(Document, on=(cls.model.id == Document.kb_id)).where(
             cls.model.id.in_(kb_ids)
         )
-        doc_ids =list(doc_ids.dicts())
+        doc_ids = list(doc_ids.dicts())
         doc_ids = [doc["document_id"] for doc in doc_ids]
         return doc_ids
 
@@ -39,12 +132,25 @@ class KnowledgebaseService(CommonService):
                           orderby, desc, keywords,
                           parser_id=None
                           ):
+        # Get knowledge bases by tenant IDs with pagination and filtering
+        # Args:
+        #     joined_tenant_ids: List of tenant IDs
+        #     user_id: Current user ID
+        #     page_number: Page number for pagination
+        #     items_per_page: Number of items per page
+        #     orderby: Field to order by
+        #     desc: Boolean indicating descending order
+        #     keywords: Search keywords
+        #     parser_id: Optional parser ID filter
+        # Returns:
+        #     Tuple of (knowledge_base_list, total_count)
         fields = [
             cls.model.id,
             cls.model.avatar,
             cls.model.name,
             cls.model.language,
             cls.model.description,
+            cls.model.tenant_id,
             cls.model.permission,
             cls.model.doc_num,
             cls.model.token_num,
@@ -79,13 +185,19 @@ class KnowledgebaseService(CommonService):
 
         count = kbs.count()
 
-        kbs = kbs.paginate(page_number, items_per_page)
+        if page_number and items_per_page:
+            kbs = kbs.paginate(page_number, items_per_page)
 
         return list(kbs.dicts()), count
 
     @classmethod
     @DB.connection_context()
     def get_kb_ids(cls, tenant_id):
+        # Get all knowledge base IDs for a tenant
+        # Args:
+        #     tenant_id: Tenant ID
+        # Returns:
+        #     List of knowledge base IDs
         fields = [
             cls.model.id,
         ]
@@ -96,9 +208,13 @@ class KnowledgebaseService(CommonService):
     @classmethod
     @DB.connection_context()
     def get_detail(cls, kb_id):
+        # Get detailed information about a knowledge base
+        # Args:
+        #     kb_id: Knowledge base ID
+        # Returns:
+        #     Dictionary containing knowledge base details
         fields = [
             cls.model.id,
-            # Tenant.embd_id,
             cls.model.embd_id,
             cls.model.avatar,
             cls.model.name,
@@ -112,24 +228,28 @@ class KnowledgebaseService(CommonService):
             cls.model.parser_config,
             cls.model.pagerank]
         kbs = cls.model.select(*fields).join(Tenant, on=(
-                (Tenant.id == cls.model.tenant_id) & (Tenant.status == StatusEnum.VALID.value))).where(
+            (Tenant.id == cls.model.tenant_id) & (Tenant.status == StatusEnum.VALID.value))).where(
             (cls.model.id == kb_id),
             (cls.model.status == StatusEnum.VALID.value)
         )
         if not kbs:
             return
         d = kbs[0].to_dict()
-        # d["embd_id"] = kbs[0].tenant.embd_id
         return d
 
     @classmethod
     @DB.connection_context()
     def update_parser_config(cls, id, config):
+        # Update parser configuration for a knowledge base
+        # Args:
+        #     id: Knowledge base ID
+        #     config: New parser configuration
         e, m = cls.get_by_id(id)
         if not e:
             raise LookupError(f"knowledgebase({id}) not found.")
 
         def dfs_update(old, new):
+            # Deep update of nested configuration
             for k, v in new.items():
                 if k not in old:
                     old[k] = v
@@ -149,6 +269,11 @@ class KnowledgebaseService(CommonService):
     @classmethod
     @DB.connection_context()
     def get_field_map(cls, ids):
+        # Get field mappings for knowledge bases
+        # Args:
+        #     ids: List of knowledge base IDs
+        # Returns:
+        #     Dictionary of field mappings
         conf = {}
         for k in cls.get_by_ids(ids):
             if k.parser_config and "field_map" in k.parser_config:
@@ -158,6 +283,12 @@ class KnowledgebaseService(CommonService):
     @classmethod
     @DB.connection_context()
     def get_by_name(cls, kb_name, tenant_id):
+        # Get knowledge base by name and tenant ID
+        # Args:
+        #     kb_name: Knowledge base name
+        #     tenant_id: Tenant ID
+        # Returns:
+        #     Tuple of (exists, knowledge_base)
         kb = cls.model.select().where(
             (cls.model.name == kb_name)
             & (cls.model.tenant_id == tenant_id)
@@ -170,12 +301,27 @@ class KnowledgebaseService(CommonService):
     @classmethod
     @DB.connection_context()
     def get_all_ids(cls):
+        # Get all knowledge base IDs
+        # Returns:
+        #     List of all knowledge base IDs
         return [m["id"] for m in cls.model.select(cls.model.id).dicts()]
 
     @classmethod
     @DB.connection_context()
     def get_list(cls, joined_tenant_ids, user_id,
                  page_number, items_per_page, orderby, desc, id, name):
+        # Get list of knowledge bases with filtering and pagination
+        # Args:
+        #     joined_tenant_ids: List of tenant IDs
+        #     user_id: Current user ID
+        #     page_number: Page number for pagination
+        #     items_per_page: Number of items per page
+        #     orderby: Field to order by
+        #     desc: Boolean indicating descending order
+        #     id: Optional ID filter
+        #     name: Optional name filter
+        # Returns:
+        #     List of knowledge bases
         kbs = cls.model.select()
         if id:
             kbs = kbs.where(cls.model.id == id)
@@ -184,7 +330,7 @@ class KnowledgebaseService(CommonService):
         kbs = kbs.where(
             ((cls.model.tenant_id.in_(joined_tenant_ids) & (cls.model.permission ==
                                                             TenantPermission.TEAM.value)) | (
-                     cls.model.tenant_id == user_id))
+                cls.model.tenant_id == user_id))
             & (cls.model.status == StatusEnum.VALID.value)
         )
         if desc:
@@ -199,9 +345,15 @@ class KnowledgebaseService(CommonService):
     @classmethod
     @DB.connection_context()
     def accessible(cls, kb_id, user_id):
+        # Check if a knowledge base is accessible by a user
+        # Args:
+        #     kb_id: Knowledge base ID
+        #     user_id: User ID
+        # Returns:
+        #     Boolean indicating accessibility
         docs = cls.model.select(
             cls.model.id).join(UserTenant, on=(UserTenant.tenant_id == Knowledgebase.tenant_id)
-            ).where(cls.model.id == kb_id, UserTenant.user_id == user_id).paginate(0, 1)
+                               ).where(cls.model.id == kb_id, UserTenant.user_id == user_id).paginate(0, 1)
         docs = docs.dicts()
         if not docs:
             return False
@@ -210,26 +362,64 @@ class KnowledgebaseService(CommonService):
     @classmethod
     @DB.connection_context()
     def get_kb_by_id(cls, kb_id, user_id):
+        # Get knowledge base by ID and user ID
+        # Args:
+        #     kb_id: Knowledge base ID
+        #     user_id: User ID
+        # Returns:
+        #     List containing knowledge base information
         kbs = cls.model.select().join(UserTenant, on=(UserTenant.tenant_id == Knowledgebase.tenant_id)
-            ).where(cls.model.id == kb_id, UserTenant.user_id == user_id).paginate(0, 1)
+                                      ).where(cls.model.id == kb_id, UserTenant.user_id == user_id).paginate(0, 1)
         kbs = kbs.dicts()
         return list(kbs)
 
     @classmethod
     @DB.connection_context()
     def get_kb_by_name(cls, kb_name, user_id):
+        # Get knowledge base by name and user ID
+        # Args:
+        #     kb_name: Knowledge base name
+        #     user_id: User ID
+        # Returns:
+        #     List containing knowledge base information
         kbs = cls.model.select().join(UserTenant, on=(UserTenant.tenant_id == Knowledgebase.tenant_id)
-            ).where(cls.model.name == kb_name, UserTenant.user_id == user_id).paginate(0, 1)
+                                      ).where(cls.model.name == kb_name, UserTenant.user_id == user_id).paginate(0, 1)
         kbs = kbs.dicts()
         return list(kbs)
 
     @classmethod
     @DB.connection_context()
-    def accessible4deletion(cls, kb_id, user_id):
-        docs = cls.model.select(
-            cls.model.id).where(cls.model.id == kb_id, cls.model.created_by == user_id).paginate(0, 1)
-        docs = docs.dicts()
-        if not docs:
-            return False
-        return True
+    def atomic_increase_doc_num_by_id(cls, kb_id):
+        data = {}
+        data["update_time"] = current_timestamp()
+        data["update_date"] = datetime_format(datetime.now())
+        data["doc_num"] = cls.model.doc_num + 1
+        num = cls.model.update(data).where(cls.model.id == kb_id).execute()
+        return num
+
+    @classmethod
+    @DB.connection_context()
+    def update_document_number_in_init(cls, kb_id, doc_num):
+        """
+        Only use this function when init system
+        """
+        ok, kb = cls.get_by_id(kb_id)
+        if not ok:
+            return
+        kb.doc_num = doc_num
+
+        dirty_fields = kb.dirty_fields
+        if cls.model._meta.combined.get("update_time") in dirty_fields:
+            dirty_fields.remove(cls.model._meta.combined["update_time"])
+
+        if cls.model._meta.combined.get("update_date") in dirty_fields:
+            dirty_fields.remove(cls.model._meta.combined["update_date"])
+
+        try:
+            kb.save(only=dirty_fields)
+        except ValueError as e:
+            if str(e) == "no data to save!":
+                pass # that's OK
+            else:
+                raise e
 

api/db/services/langfuse_service.py

@@ -0,0 +1,71 @@
+#
+#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+
+from datetime import datetime
+
+import peewee
+
+from api.db.db_models import DB, TenantLangfuse
+from api.db.services.common_service import CommonService
+from api.utils import current_timestamp, datetime_format
+
+
+class TenantLangfuseService(CommonService):
+    """
+    All methods that modify the status should be enclosed within a DB.atomic() context to ensure atomicity
+    and maintain data integrity in case of errors during execution.
+    """
+
+    model = TenantLangfuse
+
+    @classmethod
+    @DB.connection_context()
+    def filter_by_tenant(cls, tenant_id):
+        fields = [cls.model.tenant_id, cls.model.host, cls.model.secret_key, cls.model.public_key]
+        try:
+            keys = cls.model.select(*fields).where(cls.model.tenant_id == tenant_id).first()
+            return keys
+        except peewee.DoesNotExist:
+            return None
+
+    @classmethod
+    @DB.connection_context()
+    def filter_by_tenant_with_info(cls, tenant_id):
+        fields = [cls.model.tenant_id, cls.model.host, cls.model.secret_key, cls.model.public_key]
+        try:
+            keys = cls.model.select(*fields).where(cls.model.tenant_id == tenant_id).dicts().first()
+            return keys
+        except peewee.DoesNotExist:
+            return None
+
+    @classmethod
+    def update_by_tenant(cls, tenant_id, langfuse_keys):
+        langfuse_keys["update_time"] = current_timestamp()
+        langfuse_keys["update_date"] = datetime_format(datetime.now())
+        return cls.model.update(**langfuse_keys).where(cls.model.tenant_id == tenant_id).execute()
+
+    @classmethod
+    def save(cls, **kwargs):
+        kwargs["create_time"] = current_timestamp()
+        kwargs["create_date"] = datetime_format(datetime.now())
+        kwargs["update_time"] = current_timestamp()
+        kwargs["update_date"] = datetime_format(datetime.now())
+        obj = cls.model.create(**kwargs)
+        return obj
+
+    @classmethod
+    def delete_model(cls, langfuse_model):
+        langfuse_model.delete_instance()

api/db/services/llm_service.py

@@ -13,17 +13,17 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-import json
 import logging
-import os
 
-from api.db.services.user_service import TenantService
-from api.utils.file_utils import get_project_base_directory
-from rag.llm import EmbeddingModel, CvModel, ChatModel, RerankModel, Seq2txtModel, TTSModel
+from langfuse import Langfuse
+
+from api import settings
 from api.db import LLMType
-from api.db.db_models import DB
-from api.db.db_models import LLMFactories, LLM, TenantLLM
+from api.db.db_models import DB, LLM, LLMFactories, TenantLLM
 from api.db.services.common_service import CommonService
+from api.db.services.langfuse_service import TenantLangfuseService
+from api.db.services.user_service import TenantService
+from rag.llm import ChatModel, CvModel, EmbeddingModel, RerankModel, Seq2txtModel, TTSModel
 
 
 class LLMFactoriesService(CommonService):
@@ -52,16 +52,8 @@ class TenantLLMService(CommonService):
     @classmethod
     @DB.connection_context()
     def get_my_llms(cls, tenant_id):
-        fields = [
-            cls.model.llm_factory,
-            LLMFactories.logo,
-            LLMFactories.tags,
-            cls.model.model_type,
-            cls.model.llm_name,
-            cls.model.used_tokens
-        ]
-        objs = cls.model.select(*fields).join(LLMFactories, on=(cls.model.llm_factory == LLMFactories.name)).where(
-            cls.model.tenant_id == tenant_id, ~cls.model.api_key.is_null()).dicts()
+        fields = [cls.model.llm_factory, LLMFactories.logo, LLMFactories.tags, cls.model.model_type, cls.model.llm_name, cls.model.used_tokens]
+        objs = cls.model.select(*fields).join(LLMFactories, on=(cls.model.llm_factory == LLMFactories.name)).where(cls.model.tenant_id == tenant_id, ~cls.model.api_key.is_null()).dicts()
 
         return list(objs)
 
@@ -75,7 +67,7 @@ class TenantLLMService(CommonService):
 
         # model name must be xxx@yyy
         try:
-            model_factories = json.load(open(os.path.join(get_project_base_directory(), "conf/llm_factories.json"), "r"))["factory_llm_infos"]
+            model_factories = settings.FACTORY_LLM_INFOS
             model_providers = set([f["name"] for f in model_factories])
             if arr[-1] not in model_providers:
                 return model_name, None
@@ -110,6 +102,9 @@ class TenantLLMService(CommonService):
         mdlnm, fid = TenantLLMService.split_model_name_and_factory(mdlnm)
         if model_config:
             model_config = model_config.to_dict()
+            llm = LLMService.query(llm_name=mdlnm) if not fid else LLMService.query(llm_name=mdlnm, fid=fid)
+            if llm:
+                model_config["is_tools"] = llm[0].is_tools
         if not model_config:
             if llm_type in [LLMType.EMBEDDING, LLMType.RERANK]:
                 llm = LLMService.query(llm_name=mdlnm) if not fid else LLMService.query(llm_name=mdlnm, fid=fid)
@@ -117,8 +112,7 @@ class TenantLLMService(CommonService):
                     model_config = {"llm_factory": llm[0].fid, "api_key": "", "llm_name": mdlnm, "api_base": ""}
             if not model_config:
                 if mdlnm == "flag-embedding":
-                    model_config = {"llm_factory": "Tongyi-Qianwen", "api_key": "",
-                                    "llm_name": llm_name, "api_base": ""}
+                    model_config = {"llm_factory": "Tongyi-Qianwen", "api_key": "", "llm_name": llm_name, "api_base": ""}
                 else:
                     if not mdlnm:
                         raise LookupError(f"Type of {llm_type} model is not set.")
@@ -127,43 +121,32 @@ class TenantLLMService(CommonService):
 
     @classmethod
     @DB.connection_context()
-    def model_instance(cls, tenant_id, llm_type,
-                       llm_name=None, lang="Chinese"):
+    def model_instance(cls, tenant_id, llm_type, llm_name=None, lang="Chinese"):
         model_config = TenantLLMService.get_model_config(tenant_id, llm_type, llm_name)
         if llm_type == LLMType.EMBEDDING.value:
             if model_config["llm_factory"] not in EmbeddingModel:
                 return
-            return EmbeddingModel[model_config["llm_factory"]](
-                model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])
+            return EmbeddingModel[model_config["llm_factory"]](model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])
 
         if llm_type == LLMType.RERANK:
             if model_config["llm_factory"] not in RerankModel:
                 return
-            return RerankModel[model_config["llm_factory"]](
-                model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])
+            return RerankModel[model_config["llm_factory"]](model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])
 
         if llm_type == LLMType.IMAGE2TEXT.value:
             if model_config["llm_factory"] not in CvModel:
                 return
-            return CvModel[model_config["llm_factory"]](
-                model_config["api_key"], model_config["llm_name"], lang,
-                base_url=model_config["api_base"]
-            )
+            return CvModel[model_config["llm_factory"]](model_config["api_key"], model_config["llm_name"], lang, base_url=model_config["api_base"])
 
         if llm_type == LLMType.CHAT.value:
             if model_config["llm_factory"] not in ChatModel:
                 return
-            return ChatModel[model_config["llm_factory"]](
-                model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])
+            return ChatModel[model_config["llm_factory"]](model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])
 
         if llm_type == LLMType.SPEECH2TEXT:
             if model_config["llm_factory"] not in Seq2txtModel:
                 return
-            return Seq2txtModel[model_config["llm_factory"]](
-                key=model_config["api_key"], model_name=model_config["llm_name"],
-                lang=lang,
-                base_url=model_config["api_base"]
-            )
+            return Seq2txtModel[model_config["llm_factory"]](key=model_config["api_key"], model_name=model_config["llm_name"], lang=lang, base_url=model_config["api_base"])
         if llm_type == LLMType.TTS:
             if model_config["llm_factory"] not in TTSModel:
                 return
@@ -176,6 +159,12 @@ class TenantLLMService(CommonService):
     @classmethod
     @DB.connection_context()
     def increase_usage(cls, tenant_id, llm_type, used_tokens, llm_name=None):
+        try:
+            if not DB.is_connection_usable():
+                DB.connect()
+        except Exception:
+            DB.close()
+            DB.connect()
         e, tenant = TenantService.get_by_id(tenant_id)
         if not e:
             logging.error(f"Tenant not found: {tenant_id}")
@@ -187,7 +176,7 @@ class TenantLLMService(CommonService):
             LLMType.IMAGE2TEXT.value: tenant.img2txt_id,
             LLMType.CHAT.value: tenant.llm_id if not llm_name else llm_name,
             LLMType.RERANK.value: tenant.rerank_id if not llm_name else llm_name,
-            LLMType.TTS.value: tenant.tts_id if not llm_name else llm_name
+            LLMType.TTS.value: tenant.tts_id if not llm_name else llm_name,
         }
 
         mdlnm = llm_map.get(llm_type)
@@ -198,17 +187,13 @@ class TenantLLMService(CommonService):
         llm_name, llm_factory = TenantLLMService.split_model_name_and_factory(mdlnm)
 
         try:
-            num = cls.model.update(
-                used_tokens=cls.model.used_tokens + used_tokens
-            ).where(
-                cls.model.tenant_id == tenant_id,
-                cls.model.llm_name == llm_name,
-                cls.model.llm_factory == llm_factory if llm_factory else True
-            ).execute()
+            num = (
+                cls.model.update(used_tokens=cls.model.used_tokens + used_tokens)
+                .where(cls.model.tenant_id == tenant_id, cls.model.llm_name == llm_name, cls.model.llm_factory == llm_factory if llm_factory else True)
+                .execute()
+            )
         except Exception:
-            logging.exception(
-                "TenantLLMService.increase_usage got exception,Failed to update used_tokens for tenant_id=%s, llm_name=%s",
-                tenant_id, llm_name)
+            logging.exception("TenantLLMService.increase_usage got exception,Failed to update used_tokens for tenant_id=%s, llm_name=%s", tenant_id, llm_name)
             return 0
 
         return num
@@ -216,92 +201,183 @@ class TenantLLMService(CommonService):
     @classmethod
     @DB.connection_context()
     def get_openai_models(cls):
-        objs = cls.model.select().where(
-            (cls.model.llm_factory == "OpenAI"),
-            ~(cls.model.llm_name == "text-embedding-3-small"),
-            ~(cls.model.llm_name == "text-embedding-3-large")
-        ).dicts()
+        objs = cls.model.select().where((cls.model.llm_factory == "OpenAI"), ~(cls.model.llm_name == "text-embedding-3-small"), ~(cls.model.llm_name == "text-embedding-3-large")).dicts()
         return list(objs)
 
 
-class LLMBundle(object):
+class LLMBundle:
     def __init__(self, tenant_id, llm_type, llm_name=None, lang="Chinese"):
         self.tenant_id = tenant_id
         self.llm_type = llm_type
         self.llm_name = llm_name
-        self.mdl = TenantLLMService.model_instance(
-            tenant_id, llm_type, llm_name, lang=lang)
-        assert self.mdl, "Can't find model for {}/{}/{}".format(
-            tenant_id, llm_type, llm_name)
+        self.mdl = TenantLLMService.model_instance(tenant_id, llm_type, llm_name, lang=lang)
+        assert self.mdl, "Can't find model for {}/{}/{}".format(tenant_id, llm_type, llm_name)
         model_config = TenantLLMService.get_model_config(tenant_id, llm_type, llm_name)
         self.max_length = model_config.get("max_tokens", 8192)
 
+        self.is_tools = model_config.get("is_tools", False)
+
+        langfuse_keys = TenantLangfuseService.filter_by_tenant(tenant_id=tenant_id)
+        if langfuse_keys:
+            langfuse = Langfuse(public_key=langfuse_keys.public_key, secret_key=langfuse_keys.secret_key, host=langfuse_keys.host)
+            if langfuse.auth_check():
+                self.langfuse = langfuse
+                self.trace = self.langfuse.trace(name=f"{self.llm_type}-{self.llm_name}")
+        else:
+            self.langfuse = None
+
+    def bind_tools(self, toolcall_session, tools):
+        if not self.is_tools:
+            return
+        self.mdl.bind_tools(toolcall_session, tools)
+
     def encode(self, texts: list):
+        if self.langfuse:
+            generation = self.trace.generation(name="encode", model=self.llm_name, input={"texts": texts})
+
         embeddings, used_tokens = self.mdl.encode(texts)
-        if not TenantLLMService.increase_usage(
-                self.tenant_id, self.llm_type, used_tokens):
-            logging.error(
-                "LLMBundle.encode can't update token usage for {}/EMBEDDING used_tokens: {}".format(self.tenant_id, used_tokens))
+        if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens):
+            logging.error("LLMBundle.encode can't update token usage for {}/EMBEDDING used_tokens: {}".format(self.tenant_id, used_tokens))
+
+        if self.langfuse:
+            generation.end(usage_details={"total_tokens": used_tokens})
+
         return embeddings, used_tokens
 
     def encode_queries(self, query: str):
+        if self.langfuse:
+            generation = self.trace.generation(name="encode_queries", model=self.llm_name, input={"query": query})
+
         emd, used_tokens = self.mdl.encode_queries(query)
-        if not TenantLLMService.increase_usage(
-                self.tenant_id, self.llm_type, used_tokens):
-            logging.error(
-                "LLMBundle.encode_queries can't update token usage for {}/EMBEDDING used_tokens: {}".format(self.tenant_id, used_tokens))
+        if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens):
+            logging.error("LLMBundle.encode_queries can't update token usage for {}/EMBEDDING used_tokens: {}".format(self.tenant_id, used_tokens))
+
+        if self.langfuse:
+            generation.end(usage_details={"total_tokens": used_tokens})
+
         return emd, used_tokens
 
     def similarity(self, query: str, texts: list):
+        if self.langfuse:
+            generation = self.trace.generation(name="similarity", model=self.llm_name, input={"query": query, "texts": texts})
+
         sim, used_tokens = self.mdl.similarity(query, texts)
-        if not TenantLLMService.increase_usage(
-                self.tenant_id, self.llm_type, used_tokens):
-            logging.error(
-                "LLMBundle.similarity can't update token usage for {}/RERANK used_tokens: {}".format(self.tenant_id, used_tokens))
+        if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens):
+            logging.error("LLMBundle.similarity can't update token usage for {}/RERANK used_tokens: {}".format(self.tenant_id, used_tokens))
+
+        if self.langfuse:
+            generation.end(usage_details={"total_tokens": used_tokens})
+
         return sim, used_tokens
 
     def describe(self, image, max_tokens=300):
-        txt, used_tokens = self.mdl.describe(image, max_tokens)
-        if not TenantLLMService.increase_usage(
-                self.tenant_id, self.llm_type, used_tokens):
-            logging.error(
-                "LLMBundle.describe can't update token usage for {}/IMAGE2TEXT used_tokens: {}".format(self.tenant_id, used_tokens))
+        if self.langfuse:
+            generation = self.trace.generation(name="describe", metadata={"model": self.llm_name})
+
+        txt, used_tokens = self.mdl.describe(image)
+        if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens):
+            logging.error("LLMBundle.describe can't update token usage for {}/IMAGE2TEXT used_tokens: {}".format(self.tenant_id, used_tokens))
+
+        if self.langfuse:
+            generation.end(output={"output": txt}, usage_details={"total_tokens": used_tokens})
+
+        return txt
+
+    def describe_with_prompt(self, image, prompt):
+        if self.langfuse:
+            generation = self.trace.generation(name="describe_with_prompt", metadata={"model": self.llm_name, "prompt": prompt})
+
+        txt, used_tokens = self.mdl.describe_with_prompt(image, prompt)
+        if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens):
+            logging.error("LLMBundle.describe can't update token usage for {}/IMAGE2TEXT used_tokens: {}".format(self.tenant_id, used_tokens))
+
+        if self.langfuse:
+            generation.end(output={"output": txt}, usage_details={"total_tokens": used_tokens})
+
         return txt
 
     def transcription(self, audio):
+        if self.langfuse:
+            generation = self.trace.generation(name="transcription", metadata={"model": self.llm_name})
+
         txt, used_tokens = self.mdl.transcription(audio)
-        if not TenantLLMService.increase_usage(
-                self.tenant_id, self.llm_type, used_tokens):
-            logging.error(
-                "LLMBundle.transcription can't update token usage for {}/SEQUENCE2TXT used_tokens: {}".format(self.tenant_id, used_tokens))
+        if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens):
+            logging.error("LLMBundle.transcription can't update token usage for {}/SEQUENCE2TXT used_tokens: {}".format(self.tenant_id, used_tokens))
+
+        if self.langfuse:
+            generation.end(output={"output": txt}, usage_details={"total_tokens": used_tokens})
+
         return txt
 
     def tts(self, text):
+        if self.langfuse:
+            span = self.trace.span(name="tts", input={"text": text})
+
         for chunk in self.mdl.tts(text):
             if isinstance(chunk, int):
-                if not TenantLLMService.increase_usage(
-                        self.tenant_id, self.llm_type, chunk, self.llm_name):
-                    logging.error(
-                        "LLMBundle.tts can't update token usage for {}/TTS".format(self.tenant_id))
+                if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, chunk, self.llm_name):
+                    logging.error("LLMBundle.tts can't update token usage for {}/TTS".format(self.tenant_id))
                 return
             yield chunk
 
+        if self.langfuse:
+            span.end()
+
+    def _remove_reasoning_content(self, txt: str) -> str:
+        first_think_start = txt.find("<think>")
+        if first_think_start == -1:
+            return txt
+
+        last_think_end = txt.rfind("</think>")
+        if last_think_end == -1:
+            return txt
+
+        if last_think_end < first_think_start:
+            return txt
+
+        return txt[last_think_end + len("</think>") :]
+
     def chat(self, system, history, gen_conf):
-        txt, used_tokens = self.mdl.chat(system, history, gen_conf)
-        if isinstance(txt, int) and not TenantLLMService.increase_usage(
-                self.tenant_id, self.llm_type, used_tokens, self.llm_name):
-            logging.error(
-                "LLMBundle.chat can't update token usage for {}/CHAT llm_name: {}, used_tokens: {}".format(self.tenant_id, self.llm_name,
-                                                                                                           used_tokens))
+        if self.langfuse:
+            generation = self.trace.generation(name="chat", model=self.llm_name, input={"system": system, "history": history})
+
+        chat = self.mdl.chat
+        if self.is_tools and self.mdl.is_tools:
+            chat = self.mdl.chat_with_tools
+
+        txt, used_tokens = chat(system, history, gen_conf)
+        txt = self._remove_reasoning_content(txt)
+
+        if isinstance(txt, int) and not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens, self.llm_name):
+            logging.error("LLMBundle.chat can't update token usage for {}/CHAT llm_name: {}, used_tokens: {}".format(self.tenant_id, self.llm_name, used_tokens))
+
+        if self.langfuse:
+            generation.end(output={"output": txt}, usage_details={"total_tokens": used_tokens})
+
         return txt
 
     def chat_streamly(self, system, history, gen_conf):
-        for txt in self.mdl.chat_streamly(system, history, gen_conf):
+        if self.langfuse:
+            generation = self.trace.generation(name="chat_streamly", model=self.llm_name, input={"system": system, "history": history})
+
+        ans = ""
+        chat_streamly = self.mdl.chat_streamly
+        total_tokens = 0 
+        if self.is_tools and self.mdl.is_tools:
+            chat_streamly = self.mdl.chat_streamly_with_tools
+
+        for txt in chat_streamly(system, history, gen_conf):
             if isinstance(txt, int):
-                if not TenantLLMService.increase_usage(
-                        self.tenant_id, self.llm_type, txt, self.llm_name):
-                    logging.error(
-                        "LLMBundle.chat_streamly can't update token usage for {}/CHAT llm_name: {}, content: {}".format(self.tenant_id, self.llm_name,
-                                                                                                                        txt))
-                return
-            yield txt
+                total_tokens = txt
+                if self.langfuse:
+                    generation.end(output={"output": ans})
+                break
+
+            if txt.endswith("</think>"):
+                ans = ans.rstrip("</think>")
+
+            ans += txt
+            yield ans
+        if total_tokens > 0:
+            if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, txt, self.llm_name):
+                logging.error("LLMBundle.chat_streamly can't update token usage for {}/CHAT llm_name: {}, content: {}".format(self.tenant_id, self.llm_name, txt))

api/db/services/task_service.py

@@ -28,7 +28,7 @@ from api.db.services.common_service import CommonService
 from api.db.services.document_service import DocumentService
 from api.utils import current_timestamp, get_uuid
 from deepdoc.parser.excel_parser import RAGFlowExcelParser
-from rag.settings import SVR_QUEUE_NAME
+from rag.settings import get_svr_queue_name
 from rag.utils.storage_factory import STORAGE_IMPL
 from rag.utils.redis_conn import REDIS_CONN
 from api import settings
@@ -36,6 +36,12 @@ from rag.nlp import search
 
 
 def trim_header_by_lines(text: str, max_length) -> str:
+    # Trim header text to maximum length while preserving line breaks
+    # Args:
+    #     text: Input text to trim
+    #     max_length: Maximum allowed length
+    # Returns:
+    #     Trimmed text
     len_text = len(text)
     if len_text <= max_length:
         return text
@@ -46,11 +52,37 @@ def trim_header_by_lines(text: str, max_length) -> str:
 
 
 class TaskService(CommonService):
+    """Service class for managing document processing tasks.
+    
+    This class extends CommonService to provide specialized functionality for document
+    processing task management, including task creation, progress tracking, and chunk
+    management. It handles various document types (PDF, Excel, etc.) and manages their
+    processing lifecycle.
+    
+    The class implements a robust task queue system with retry mechanisms and progress
+    tracking, supporting both synchronous and asynchronous task execution.
+    
+    Attributes:
+        model: The Task model class for database operations.
+    """
     model = Task
 
     @classmethod
     @DB.connection_context()
     def get_task(cls, task_id):
+        """Retrieve detailed task information by task ID.
+    
+        This method fetches comprehensive task details including associated document,
+        knowledge base, and tenant information. It also handles task retry logic and
+        progress updates.
+    
+        Args:
+            task_id (str): The unique identifier of the task to retrieve.
+    
+        Returns:
+            dict: Task details dictionary containing all task information and related metadata.
+                 Returns None if task is not found or has exceeded retry limit.
+        """
         fields = [
             cls.model.id,
             cls.model.doc_id,
@@ -105,6 +137,18 @@ class TaskService(CommonService):
     @classmethod
     @DB.connection_context()
     def get_tasks(cls, doc_id: str):
+        """Retrieve all tasks associated with a document.
+    
+        This method fetches all processing tasks for a given document, ordered by page
+        number and creation time. It includes task progress and chunk information.
+    
+        Args:
+            doc_id (str): The unique identifier of the document.
+    
+        Returns:
+            list[dict]: List of task dictionaries containing task details.
+                       Returns None if no tasks are found.
+        """
         fields = [
             cls.model.id,
             cls.model.from_page,
@@ -124,11 +168,31 @@ class TaskService(CommonService):
     @classmethod
     @DB.connection_context()
     def update_chunk_ids(cls, id: str, chunk_ids: str):
+        """Update the chunk IDs associated with a task.
+    
+        This method updates the chunk_ids field of a task, which stores the IDs of
+        processed document chunks in a space-separated string format.
+    
+        Args:
+            id (str): The unique identifier of the task.
+            chunk_ids (str): Space-separated string of chunk identifiers.
+        """
         cls.model.update(chunk_ids=chunk_ids).where(cls.model.id == id).execute()
 
     @classmethod
     @DB.connection_context()
     def get_ongoing_doc_name(cls):
+        """Get names of documents that are currently being processed.
+    
+        This method retrieves information about documents that are in the processing state,
+        including their locations and associated IDs. It uses database locking to ensure
+        thread safety when accessing the task information.
+    
+        Returns:
+            list[tuple]: A list of tuples, each containing (parent_id/kb_id, location)
+                        for documents currently being processed. Returns empty list if
+                        no documents are being processed.
+        """
         with DB.lock("get_task", -1):
             docs = (
                 cls.model.select(
@@ -172,6 +236,18 @@ class TaskService(CommonService):
     @classmethod
     @DB.connection_context()
     def do_cancel(cls, id):
+        """Check if a task should be cancelled based on its document status.
+    
+        This method determines whether a task should be cancelled by checking the
+        associated document's run status and progress. A task should be cancelled
+        if its document is marked for cancellation or has negative progress.
+    
+        Args:
+            id (str): The unique identifier of the task to check.
+    
+        Returns:
+            bool: True if the task should be cancelled, False otherwise.
+        """
         task = cls.model.get_by_id(id)
         _, doc = DocumentService.get_by_id(task.doc_id)
         return doc.run == TaskStatus.CANCEL.value or doc.progress < 0
@@ -179,6 +255,18 @@ class TaskService(CommonService):
     @classmethod
     @DB.connection_context()
     def update_progress(cls, id, info):
+        """Update the progress information for a task.
+    
+        This method updates both the progress message and completion percentage of a task.
+        It handles platform-specific behavior (macOS vs others) and uses database locking
+        when necessary to ensure thread safety.
+    
+        Args:
+            id (str): The unique identifier of the task to update.
+            info (dict): Dictionary containing progress information with keys:
+                        - progress_msg (str, optional): Progress message to append
+                        - progress (float, optional): Progress percentage (0.0 to 1.0)
+        """
         if os.environ.get("MACOS"):
             if info["progress_msg"]:
                 task = cls.model.get_by_id(id)
@@ -201,7 +289,26 @@ class TaskService(CommonService):
                 ).execute()
 
 
-def queue_tasks(doc: dict, bucket: str, name: str):
+def queue_tasks(doc: dict, bucket: str, name: str, priority: int):
+    """Create and queue document processing tasks.
+    
+    This function creates processing tasks for a document based on its type and configuration.
+    It handles different document types (PDF, Excel, etc.) differently and manages task
+    chunking and configuration. It also implements task reuse optimization by checking
+    for previously completed tasks.
+    
+    Args:
+        doc (dict): Document dictionary containing metadata and configuration.
+        bucket (str): Storage bucket name where the document is stored.
+        name (str): File name of the document.
+        priority (int, optional): Priority level for task queueing (default is 0).
+    
+    Note:
+        - For PDF documents, tasks are created per page range based on configuration
+        - For Excel documents, tasks are created per row range
+        - Task digests are calculated for optimization and reuse
+        - Previous task chunks may be reused if available
+    """
     def new_task():
         return {"id": get_uuid(), "doc_id": doc["id"], "progress": 0.0, "from_page": 0, "to_page": 100000000}
 
@@ -252,6 +359,7 @@ def queue_tasks(doc: dict, bucket: str, name: str):
         task_digest = hasher.hexdigest()
         task["digest"] = task_digest
         task["progress"] = 0.0
+        task["priority"] = priority
 
     prev_tasks = TaskService.get_tasks(doc["id"])
     ck_num = 0
@@ -274,11 +382,31 @@ def queue_tasks(doc: dict, bucket: str, name: str):
     unfinished_task_array = [task for task in parse_task_array if task["progress"] < 1.0]
     for unfinished_task in unfinished_task_array:
         assert REDIS_CONN.queue_product(
-            SVR_QUEUE_NAME, message=unfinished_task
+            get_svr_queue_name(priority), message=unfinished_task
         ), "Can't access Redis. Please check the Redis' status."
 
 
 def reuse_prev_task_chunks(task: dict, prev_tasks: list[dict], chunking_config: dict):
+    """Attempt to reuse chunks from previous tasks for optimization.
+    
+    This function checks if chunks from previously completed tasks can be reused for
+    the current task, which can significantly improve processing efficiency. It matches
+    tasks based on page ranges and configuration digests.
+    
+    Args:
+        task (dict): Current task dictionary to potentially reuse chunks for.
+        prev_tasks (list[dict]): List of previous task dictionaries to check for reuse.
+        chunking_config (dict): Configuration dictionary for chunk processing.
+    
+    Returns:
+        int: Number of chunks successfully reused. Returns 0 if no chunks could be reused.
+    
+    Note:
+        Chunks can only be reused if:
+        - A previous task exists with matching page range and configuration digest
+        - The previous task was completed successfully (progress = 1.0)
+        - The previous task has valid chunk IDs
+    """
     idx = 0
     while idx < len(prev_tasks):
         prev_task = prev_tasks[idx]

api/db/services/user_canvas_version.py

@@ -0,0 +1,43 @@
+from api.db.db_models import UserCanvasVersion, DB
+from api.db.services.common_service import CommonService
+from peewee import DoesNotExist
+
+class UserCanvasVersionService(CommonService):
+    model = UserCanvasVersion
+    
+    
+    @classmethod
+    @DB.connection_context()
+    def list_by_canvas_id(cls, user_canvas_id):
+        try:
+            user_canvas_version = cls.model.select(
+                *[cls.model.id, 
+                cls.model.create_time,
+                cls.model.title,
+                cls.model.create_date, 
+                cls.model.update_date,
+                cls.model.user_canvas_id, 
+                cls.model.update_time]
+            ).where(cls.model.user_canvas_id == user_canvas_id)
+            return user_canvas_version
+        except DoesNotExist:
+            return None
+        except Exception:
+            return None
+    
+    @classmethod
+    @DB.connection_context()
+    def delete_all_versions(cls, user_canvas_id):
+        try:
+            user_canvas_version = cls.model.select().where(cls.model.user_canvas_id == user_canvas_id).order_by(cls.model.create_time.desc())
+            if user_canvas_version.count() > 20:
+                for i in range(20, user_canvas_version.count()):
+                    cls.delete(user_canvas_version[i].id)
+            return True
+        except DoesNotExist:
+            return None
+        except Exception:
+            return None
+
+
+

api/db/services/user_service.py

@@ -29,11 +29,27 @@ from rag.settings import MINIO
 
 
 class UserService(CommonService):
+    """Service class for managing user-related database operations.
+
+    This class extends CommonService to provide specialized functionality for user management,
+    including authentication, user creation, updates, and deletions.
+
+    Attributes:
+        model: The User model class for database operations.
+    """
     model = User
 
     @classmethod
     @DB.connection_context()
     def filter_by_id(cls, user_id):
+        """Retrieve a user by their ID.
+
+        Args:
+            user_id: The unique identifier of the user.
+
+        Returns:
+            User object if found, None otherwise.
+        """
         try:
             user = cls.model.select().where(cls.model.id == user_id).get()
             return user
@@ -43,6 +59,15 @@ class UserService(CommonService):
     @classmethod
     @DB.connection_context()
     def query_user(cls, email, password):
+        """Authenticate a user with email and password.
+
+        Args:
+            email: User's email address.
+            password: User's password in plain text.
+
+        Returns:
+            User object if authentication successful, None otherwise.
+        """
         user = cls.model.select().where((cls.model.email == email),
                                         (cls.model.status == StatusEnum.VALID.value)).first()
         if user and check_password_hash(str(user.password), password):
@@ -85,6 +110,14 @@ class UserService(CommonService):
 
 
 class TenantService(CommonService):
+    """Service class for managing tenant-related database operations.
+
+    This class extends CommonService to provide functionality for tenant management,
+    including tenant information retrieval and credit management.
+
+    Attributes:
+        model: The Tenant model class for database operations.
+    """
     model = Tenant
 
     @classmethod
@@ -136,8 +169,25 @@ class TenantService(CommonService):
 
 
 class UserTenantService(CommonService):
+    """Service class for managing user-tenant relationship operations.
+
+    This class extends CommonService to handle the many-to-many relationship
+    between users and tenants, managing user roles and tenant memberships.
+
+    Attributes:
+        model: The UserTenant model class for database operations.
+    """
     model = UserTenant
 
+    @classmethod
+    @DB.connection_context()
+    def filter_by_id(cls, user_tenant_id):
+        try:
+            user_tenant = cls.model.select().where((cls.model.id == user_tenant_id) & (cls.model.status == StatusEnum.VALID.value)).get()
+            return user_tenant
+        except peewee.DoesNotExist:
+            return None
+
     @classmethod
     @DB.connection_context()
     def save(cls, **kwargs):
@@ -150,6 +200,7 @@ class UserTenantService(CommonService):
     @DB.connection_context()
     def get_by_tenant_id(cls, tenant_id):
         fields = [
+            cls.model.id,
             cls.model.user_id,
             cls.model.status,
             cls.model.role,
@@ -181,3 +232,21 @@ class UserTenantService(CommonService):
         return list(cls.model.select(*fields)
                     .join(User, on=((cls.model.tenant_id == User.id) & (UserTenant.user_id == user_id) & (UserTenant.status == StatusEnum.VALID.value)))
                     .where(cls.model.status == StatusEnum.VALID.value).dicts())
+
+    @classmethod
+    @DB.connection_context()
+    def get_num_members(cls, user_id: str):
+        cnt_members = cls.model.select(peewee.fn.COUNT(cls.model.id)).where(cls.model.tenant_id == user_id).scalar()
+        return cnt_members
+
+    @classmethod
+    @DB.connection_context()
+    def filter_by_tenant_and_user_id(cls, tenant_id, user_id):
+        try:
+            user_tenant = cls.model.select().where(
+                (cls.model.tenant_id == tenant_id) & (cls.model.status == StatusEnum.VALID.value) &
+                (cls.model.user_id == user_id)
+            ).first()
+            return user_tenant
+        except peewee.DoesNotExist:
+            return None

api/ragflow_server.py

@@ -29,6 +29,7 @@ import time
 import traceback
 from concurrent.futures import ThreadPoolExecutor
 import threading
+import uuid
 
 from werkzeug.serving import run_simple
 from api import settings
@@ -42,16 +43,26 @@ from api.db.init_data import init_web_data
 from api.versions import get_ragflow_version
 from api.utils import show_configs
 from rag.settings import print_rag_settings
+from rag.utils.redis_conn import RedisDistributedLock
 
 stop_event = threading.Event()
 
+RAGFLOW_DEBUGPY_LISTEN = int(os.environ.get('RAGFLOW_DEBUGPY_LISTEN', "0"))
+
 def update_progress():
+    lock_value = str(uuid.uuid4())
+    redis_lock = RedisDistributedLock("update_progress", lock_value=lock_value, timeout=60)
+    logging.info(f"update_progress lock_value: {lock_value}")
     while not stop_event.is_set():
         try:
-            DocumentService.update_progress()
+            if redis_lock.acquire():
+                DocumentService.update_progress()
+                redis_lock.release()
             stop_event.wait(6)
         except Exception:
             logging.exception("update_progress exception")
+        finally:
+            redis_lock.release()
 
 def signal_handler(sig, frame):
     logging.info("Received interrupt signal, shutting down...")
@@ -78,6 +89,11 @@ if __name__ == '__main__':
     settings.init_settings()
     print_rag_settings()
 
+    if RAGFLOW_DEBUGPY_LISTEN > 0:
+        logging.info(f"debugpy listen on {RAGFLOW_DEBUGPY_LISTEN}")
+        import debugpy
+        debugpy.listen(("0.0.0.0", RAGFLOW_DEBUGPY_LISTEN))
+
     # init db
     init_web_db()
     init_web_data()

api/settings.py

@@ -16,6 +16,7 @@
 import os
 from datetime import date
 from enum import IntEnum, Enum
+import json
 import rag.utils.es_conn
 import rag.utils.infinity_conn
 
@@ -24,6 +25,7 @@ from rag.nlp import search
 from graphrag import search as kg_search
 from api.utils import get_base_config, decrypt_database_config
 from api.constants import RAG_FLOW_SERVICE_NAME
+from api.utils.file_utils import get_project_base_directory
 
 LIGHTEN = int(os.environ.get('LIGHTEN', "0"))
 
@@ -40,6 +42,7 @@ PARSERS = None
 HOST_IP = None
 HOST_PORT = None
 SECRET_KEY = None
+FACTORY_LLM_INFOS = None
 
 DATABASE_TYPE = os.getenv("DB_TYPE", 'mysql')
 DATABASE = decrypt_database_config(name=DATABASE_TYPE)
@@ -59,9 +62,12 @@ docStoreConn = None
 retrievaler = None
 kg_retrievaler = None
 
+# user registration switch
+REGISTER_ENABLED = 1
+
 
 def init_settings():
-    global LLM, LLM_FACTORY, LLM_BASE_URL, LIGHTEN, DATABASE_TYPE, DATABASE
+    global LLM, LLM_FACTORY, LLM_BASE_URL, LIGHTEN, DATABASE_TYPE, DATABASE, FACTORY_LLM_INFOS, REGISTER_ENABLED
     LIGHTEN = int(os.environ.get('LIGHTEN', "0"))
     DATABASE_TYPE = os.getenv("DB_TYPE", 'mysql')
     DATABASE = decrypt_database_config(name=DATABASE_TYPE)
@@ -69,6 +75,16 @@ def init_settings():
     LLM_DEFAULT_MODELS = LLM.get("default_models", {})
     LLM_FACTORY = LLM.get("factory", "Tongyi-Qianwen")
     LLM_BASE_URL = LLM.get("base_url")
+    try:
+        REGISTER_ENABLED = int(os.environ.get("REGISTER_ENABLED", "1"))
+    except Exception:
+        pass  
+    
+    try:
+        with open(os.path.join(get_project_base_directory(), "conf", "llm_factories.json"), "r") as f:
+            FACTORY_LLM_INFOS = json.load(f)["factory_llm_infos"]
+    except Exception:
+        FACTORY_LLM_INFOS = []
 
     global CHAT_MDL, EMBEDDING_MDL, RERANK_MDL, ASR_MDL, IMAGE2TEXT_MDL
     if not LIGHTEN:
@@ -93,7 +109,7 @@ def init_settings():
     API_KEY = LLM.get("api_key", "")
     PARSERS = LLM.get(
         "parsers",
-        "naive:General,qa:Q&A,resume:Resume,manual:Manual,table:Table,paper:Paper,book:Book,laws:Laws,presentation:Presentation,picture:Picture,one:One,audio:Audio,knowledge_graph:Knowledge Graph,email:Email,tag:Tag")
+        "naive:General,qa:Q&A,resume:Resume,manual:Manual,table:Table,paper:Paper,book:Book,laws:Laws,presentation:Presentation,picture:Picture,one:One,audio:Audio,email:Email,tag:Tag")
 
     HOST_IP = get_base_config(RAG_FLOW_SERVICE_NAME, {}).get("host", "127.0.0.1")
     HOST_PORT = get_base_config(RAG_FLOW_SERVICE_NAME, {}).get("http_port")

api/utils/api_utils.py

@@ -13,9 +13,9 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-import logging
 import functools
 import json
+import logging
 import random
 import time
 from base64 import b64encode
@@ -27,59 +27,60 @@ from uuid import uuid1
 
 import requests
 from flask import (
-    Response, jsonify, send_file, make_response,
+    Response,
+    jsonify,
+    make_response,
+    send_file,
+)
+from flask import (
     request as flask_request,
 )
 from itsdangerous import URLSafeTimedSerializer
 from werkzeug.http import HTTP_STATUS_CODES
 
-from api.db.db_models import APIToken
 from api import settings
+from api.constants import REQUEST_MAX_WAIT_SEC, REQUEST_WAIT_SEC
+from api.db.db_models import APIToken
+from api.utils import CustomJSONEncoder, get_uuid, json_dumps
 
-from api.utils import CustomJSONEncoder, get_uuid
-from api.utils import json_dumps
-from api.constants import REQUEST_WAIT_SEC, REQUEST_MAX_WAIT_SEC
-
-requests.models.complexjson.dumps = functools.partial(
-    json.dumps, cls=CustomJSONEncoder)
+requests.models.complexjson.dumps = functools.partial(json.dumps, cls=CustomJSONEncoder)
 
 
 def request(**kwargs):
     sess = requests.Session()
-    stream = kwargs.pop('stream', sess.stream)
-    timeout = kwargs.pop('timeout', None)
-    kwargs['headers'] = {
-        k.replace(
-            '_',
-            '-').upper(): v for k,
-                                v in kwargs.get(
-            'headers',
-            {}).items()}
+    stream = kwargs.pop("stream", sess.stream)
+    timeout = kwargs.pop("timeout", None)
+    kwargs["headers"] = {k.replace("_", "-").upper(): v for k, v in kwargs.get("headers", {}).items()}
     prepped = requests.Request(**kwargs).prepare()
 
     if settings.CLIENT_AUTHENTICATION and settings.HTTP_APP_KEY and settings.SECRET_KEY:
         timestamp = str(round(time() * 1000))
         nonce = str(uuid1())
-        signature = b64encode(HMAC(settings.SECRET_KEY.encode('ascii'), b'\n'.join([
-            timestamp.encode('ascii'),
-            nonce.encode('ascii'),
-            settings.HTTP_APP_KEY.encode('ascii'),
-            prepped.path_url.encode('ascii'),
-            prepped.body if kwargs.get('json') else b'',
-            urlencode(
-                sorted(
-                    kwargs['data'].items()),
-                quote_via=quote,
-                safe='-._~').encode('ascii')
-            if kwargs.get('data') and isinstance(kwargs['data'], dict) else b'',
-        ]), 'sha1').digest()).decode('ascii')
+        signature = b64encode(
+            HMAC(
+                settings.SECRET_KEY.encode("ascii"),
+                b"\n".join(
+                    [
+                        timestamp.encode("ascii"),
+                        nonce.encode("ascii"),
+                        settings.HTTP_APP_KEY.encode("ascii"),
+                        prepped.path_url.encode("ascii"),
+                        prepped.body if kwargs.get("json") else b"",
+                        urlencode(sorted(kwargs["data"].items()), quote_via=quote, safe="-._~").encode("ascii") if kwargs.get("data") and isinstance(kwargs["data"], dict) else b"",
+                    ]
+                ),
+                "sha1",
+            ).digest()
+        ).decode("ascii")
 
-        prepped.headers.update({
-            'TIMESTAMP': timestamp,
-            'NONCE': nonce,
-            'APP-KEY': settings.HTTP_APP_KEY,
-            'SIGNATURE': signature,
-        })
+        prepped.headers.update(
+            {
+                "TIMESTAMP": timestamp,
+                "NONCE": nonce,
+                "APP-KEY": settings.HTTP_APP_KEY,
+                "SIGNATURE": signature,
+            }
+        )
 
     return sess.send(prepped, stream=stream, timeout=timeout)
 
@@ -87,7 +88,7 @@ def request(**kwargs):
 def get_exponential_backoff_interval(retries, full_jitter=False):
     """Calculate the exponential backoff wait time."""
     # Will be zero if factor equals 0
-    countdown = min(REQUEST_MAX_WAIT_SEC, REQUEST_WAIT_SEC * (2 ** retries))
+    countdown = min(REQUEST_MAX_WAIT_SEC, REQUEST_WAIT_SEC * (2**retries))
     # Full jitter according to
     # https://aws.amazon.com/blogs/architecture/exponential-backoff-and-jitter/
     if full_jitter:
@@ -96,12 +97,9 @@ def get_exponential_backoff_interval(retries, full_jitter=False):
     return max(0, countdown)
 
 
-def get_data_error_result(code=settings.RetCode.DATA_ERROR,
-                          message='Sorry! Data missing!'):
+def get_data_error_result(code=settings.RetCode.DATA_ERROR, message="Sorry! Data missing!"):
     logging.exception(Exception(message))
-    result_dict = {
-        "code": code,
-        "message": message}
+    result_dict = {"code": code, "message": message}
     response = {}
     for key, value in result_dict.items():
         if value is None and key != "code":
@@ -119,23 +117,27 @@ def server_error_response(e):
     except BaseException:
         pass
     if len(e.args) > 1:
-        return get_json_result(
-            code=settings.RetCode.EXCEPTION_ERROR, message=repr(e.args[0]), data=e.args[1])
+        return get_json_result(code=settings.RetCode.EXCEPTION_ERROR, message=repr(e.args[0]), data=e.args[1])
     if repr(e).find("index_not_found_exception") >= 0:
-        return get_json_result(code=settings.RetCode.EXCEPTION_ERROR,
-                               message="No chunk found, please upload file and parse it.")
+        return get_json_result(code=settings.RetCode.EXCEPTION_ERROR, message="No chunk found, please upload file and parse it.")
 
     return get_json_result(code=settings.RetCode.EXCEPTION_ERROR, message=repr(e))
 
 
 def error_response(response_code, message=None):
     if message is None:
-        message = HTTP_STATUS_CODES.get(response_code, 'Unknown Error')
+        message = HTTP_STATUS_CODES.get(response_code, "Unknown Error")
 
-    return Response(json.dumps({
-        'message': message,
-        'code': response_code,
-    }), status=response_code, mimetype='application/json')
+    return Response(
+        json.dumps(
+            {
+                "message": message,
+                "code": response_code,
+            }
+        ),
+        status=response_code,
+        mimetype="application/json",
+    )
 
 
 def validate_request(*args, **kwargs):
@@ -160,13 +162,10 @@ def validate_request(*args, **kwargs):
             if no_arguments or error_arguments:
                 error_string = ""
                 if no_arguments:
-                    error_string += "required argument are missing: {}; ".format(
-                        ",".join(no_arguments))
+                    error_string += "required argument are missing: {}; ".format(",".join(no_arguments))
                 if error_arguments:
-                    error_string += "required argument values: {}".format(
-                        ",".join(["{}={}".format(a[0], a[1]) for a in error_arguments]))
-                return get_json_result(
-                    code=settings.RetCode.ARGUMENT_ERROR, message=error_string)
+                    error_string += "required argument values: {}".format(",".join(["{}={}".format(a[0], a[1]) for a in error_arguments]))
+                return get_json_result(code=settings.RetCode.ARGUMENT_ERROR, message=error_string)
             return func(*_args, **_kwargs)
 
         return decorated_function
@@ -180,8 +179,7 @@ def not_allowed_parameters(*params):
             input_arguments = flask_request.json or flask_request.form.to_dict()
             for param in params:
                 if param in input_arguments:
-                    return get_json_result(
-                        code=settings.RetCode.ARGUMENT_ERROR, message=f"Parameter {param} isn't allowed")
+                    return get_json_result(code=settings.RetCode.ARGUMENT_ERROR, message=f"Parameter {param} isn't allowed")
             return f(*args, **kwargs)
 
         return wrapper
@@ -190,14 +188,14 @@ def not_allowed_parameters(*params):
 
 
 def is_localhost(ip):
-    return ip in {'127.0.0.1', '::1', '[::1]', 'localhost'}
+    return ip in {"127.0.0.1", "::1", "[::1]", "localhost"}
 
 
 def send_file_in_mem(data, filename):
     if not isinstance(data, (str, bytes)):
         data = json_dumps(data)
     if isinstance(data, str):
-        data = data.encode('utf-8')
+        data = data.encode("utf-8")
 
     f = BytesIO()
     f.write(data)
@@ -206,7 +204,7 @@ def send_file_in_mem(data, filename):
     return send_file(f, as_attachment=True, attachment_filename=filename)
 
 
-def get_json_result(code=settings.RetCode.SUCCESS, message='success', data=None):
+def get_json_result(code=settings.RetCode.SUCCESS, message="success", data=None):
     response = {"code": code, "message": message, "data": data}
     return jsonify(response)
 
@@ -214,27 +212,24 @@ def get_json_result(code=settings.RetCode.SUCCESS, message='success', data=None)
 def apikey_required(func):
     @wraps(func)
     def decorated_function(*args, **kwargs):
-        token = flask_request.headers.get('Authorization').split()[1]
+        token = flask_request.headers.get("Authorization").split()[1]
         objs = APIToken.query(token=token)
         if not objs:
-            return build_error_result(
-                message='API-KEY is invalid!', code=settings.RetCode.FORBIDDEN
-            )
-        kwargs['tenant_id'] = objs[0].tenant_id
+            return build_error_result(message="API-KEY is invalid!", code=settings.RetCode.FORBIDDEN)
+        kwargs["tenant_id"] = objs[0].tenant_id
         return func(*args, **kwargs)
 
     return decorated_function
 
 
-def build_error_result(code=settings.RetCode.FORBIDDEN, message='success'):
+def build_error_result(code=settings.RetCode.FORBIDDEN, message="success"):
     response = {"code": code, "message": message}
     response = jsonify(response)
     response.status_code = code
     return response
 
 
-def construct_response(code=settings.RetCode.SUCCESS,
-                       message='success', data=None, auth=None):
+def construct_response(code=settings.RetCode.SUCCESS, message="success", data=None, auth=None):
     result_dict = {"code": code, "message": message, "data": data}
     response_dict = {}
     for key, value in result_dict.items():
@@ -253,7 +248,7 @@ def construct_response(code=settings.RetCode.SUCCESS,
     return response
 
 
-def construct_result(code=settings.RetCode.DATA_ERROR, message='data is missing'):
+def construct_result(code=settings.RetCode.DATA_ERROR, message="data is missing"):
     result_dict = {"code": code, "message": message}
     response = {}
     for key, value in result_dict.items():
@@ -264,7 +259,7 @@ def construct_result(code=settings.RetCode.DATA_ERROR, message='data is missing'
     return jsonify(response)
 
 
-def construct_json_result(code=settings.RetCode.SUCCESS, message='success', data=None):
+def construct_json_result(code=settings.RetCode.SUCCESS, message="success", data=None):
     if data is None:
         return jsonify({"code": code, "message": message})
     else:
@@ -286,7 +281,7 @@ def construct_error_response(e):
 def token_required(func):
     @wraps(func)
     def decorated_function(*args, **kwargs):
-        authorization_str = flask_request.headers.get('Authorization')
+        authorization_str = flask_request.headers.get("Authorization")
         if not authorization_str:
             return get_json_result(data=False, message="`Authorization` can't be empty")
         authorization_list = authorization_str.split()
@@ -295,11 +290,8 @@ def token_required(func):
         token = authorization_list[1]
         objs = APIToken.query(token=token)
         if not objs:
-            return get_json_result(
-                data=False, message='Authentication error: API key is invalid!',
-                code=settings.RetCode.AUTHENTICATION_ERROR
-            )
-        kwargs['tenant_id'] = objs[0].tenant_id
+            return get_json_result(data=False, message="Authentication error: API key is invalid!", code=settings.RetCode.AUTHENTICATION_ERROR)
+        kwargs["tenant_id"] = objs[0].tenant_id
         return func(*args, **kwargs)
 
     return decorated_function
@@ -316,11 +308,11 @@ def get_result(code=settings.RetCode.SUCCESS, message="", data=None):
     return jsonify(response)
 
 
-def get_error_data_result(message='Sorry! Data missing!', code=settings.RetCode.DATA_ERROR,
-                          ):
-    result_dict = {
-        "code": code,
-        "message": message}
+def get_error_data_result(
+    message="Sorry! Data missing!",
+    code=settings.RetCode.DATA_ERROR,
+):
+    result_dict = {"code": code, "message": message}
     response = {}
     for key, value in result_dict.items():
         if value is None and key != "code":
@@ -335,11 +327,9 @@ def generate_confirmation_token(tenent_id):
     return "ragflow-" + serializer.dumps(get_uuid(), salt=tenent_id)[2:34]
 
 
-def valid(permission, valid_permission, language, valid_language, chunk_method, valid_chunk_method):
+def valid(permission, valid_permission, chunk_method, valid_chunk_method):
     if valid_parameter(permission, valid_permission):
         return valid_parameter(permission, valid_permission)
-    if valid_parameter(language, valid_language):
-        return valid_parameter(language, valid_language)
     if valid_parameter(chunk_method, valid_chunk_method):
         return valid_parameter(chunk_method, valid_chunk_method)
 
@@ -349,14 +339,17 @@ def valid_parameter(parameter, valid_values):
         return get_error_data_result(f"'{parameter}' is not in {valid_values}")
 
 
+def dataset_readonly_fields(field_name):
+    return field_name in ["chunk_count", "create_date", "create_time", "update_date", "update_time", "created_by", "document_count", "token_num", "status", "tenant_id", "id"]
+
+
 def get_parser_config(chunk_method, parser_config):
     if parser_config:
         return parser_config
     if not chunk_method:
         chunk_method = "naive"
     key_mapping = {
-        "naive": {"chunk_token_num": 128, "delimiter": "\\n!?;。；！？", "html4excel": False, "layout_recognize": "DeepDOC",
-                  "raptor": {"use_raptor": False}},
+        "naive": {"chunk_token_num": 128, "delimiter": "\\n!?;。；！？", "html4excel": False, "layout_recognize": "DeepDOC", "raptor": {"use_raptor": False}},
         "qa": {"raptor": {"use_raptor": False}},
         "tag": None,
         "resume": None,
@@ -367,9 +360,115 @@ def get_parser_config(chunk_method, parser_config):
         "laws": {"raptor": {"use_raptor": False}},
         "presentation": {"raptor": {"use_raptor": False}},
         "one": None,
-        "knowledge_graph": {"chunk_token_num": 8192, "delimiter": "\\n!?;。；！？",
-                            "entity_types": ["organization", "person", "location", "event", "time"]},
+        "knowledge_graph": {"chunk_token_num": 8192, "delimiter": "\\n!?;。；！？", "entity_types": ["organization", "person", "location", "event", "time"]},
         "email": None,
-        "picture": None}
+        "picture": None,
+    }
     parser_config = key_mapping[chunk_method]
     return parser_config
+
+
+def get_data_openai(id=None, 
+                    created=None, 
+                    model=None, 
+                    prompt_tokens= 0, 
+                    completion_tokens=0, 
+                    content = None, 
+                    finish_reason= None,
+                    object="chat.completion",
+                    param=None,
+):
+   
+    total_tokens= prompt_tokens + completion_tokens
+    return {
+        "id":f"{id}",
+        "object": object,
+        "created": int(time.time()) if created else None,
+        "model": model,
+        "param":param,
+        "usage": {
+            "prompt_tokens": prompt_tokens,
+            "completion_tokens": completion_tokens,
+            "total_tokens": total_tokens,
+            "completion_tokens_details": {
+                "reasoning_tokens": 0,
+                "accepted_prediction_tokens": 0,
+                "rejected_prediction_tokens": 0
+            }
+        },
+        "choices": [
+            {
+                "message": {
+                    "role": "assistant",
+                    "content": content
+                },
+                "logprobs": None,
+                "finish_reason": finish_reason,
+                "index": 0
+            }
+        ]
+    } 
+def valid_parser_config(parser_config):
+    if not parser_config:
+        return
+    scopes = set(
+        [
+            "chunk_token_num",
+            "delimiter",
+            "raptor",
+            "graphrag",
+            "layout_recognize",
+            "task_page_size",
+            "pages",
+            "html4excel",
+            "auto_keywords",
+            "auto_questions",
+            "tag_kb_ids",
+            "topn_tags",
+            "filename_embd_weight",
+        ]
+    )
+    for k in parser_config.keys():
+        assert k in scopes, f"Abnormal 'parser_config'. Invalid key: {k}"
+
+    assert isinstance(parser_config.get("chunk_token_num", 1), int), "chunk_token_num should be int"
+    assert 1 <= parser_config.get("chunk_token_num", 1) < 100000000, "chunk_token_num should be in range from 1 to 100000000"
+    assert isinstance(parser_config.get("task_page_size", 1), int), "task_page_size should be int"
+    assert 1 <= parser_config.get("task_page_size", 1) < 100000000, "task_page_size should be in range from 1 to 100000000"
+    assert isinstance(parser_config.get("auto_keywords", 1), int), "auto_keywords should be int"
+    assert 0 <= parser_config.get("auto_keywords", 0) < 32, "auto_keywords should be in range from 0 to 32"
+    assert isinstance(parser_config.get("auto_questions", 1), int), "auto_questions should be int"
+    assert 0 <= parser_config.get("auto_questions", 0) < 10, "auto_questions should be in range from 0 to 10"
+    assert isinstance(parser_config.get("topn_tags", 1), int), "topn_tags should be int"
+    assert 0 <= parser_config.get("topn_tags", 0) < 10, "topn_tags should be in range from 0 to 10"
+    assert isinstance(parser_config.get("html4excel", False), bool), "html4excel should be True or False"
+    assert isinstance(parser_config.get("delimiter", ""), str), "delimiter should be str"
+
+
+def check_duplicate_ids(ids, id_type="item"):
+    """
+    Check for duplicate IDs in a list and return unique IDs and error messages.
+
+    Args:
+        ids (list): List of IDs to check for duplicates
+        id_type (str): Type of ID for error messages (e.g., 'document', 'dataset', 'chunk')
+
+    Returns:
+        tuple: (unique_ids, error_messages)
+            - unique_ids (list): List of unique IDs
+            - error_messages (list): List of error messages for duplicate IDs
+    """
+    id_count = {}
+    duplicate_messages = []
+
+    # Count occurrences of each ID
+    for id_value in ids:
+        id_count[id_value] = id_count.get(id_value, 0) + 1
+
+    # Check for duplicates
+    for id_value, count in id_count.items():
+        if count > 1:
+            duplicate_messages.append(f"Duplicate {id_type} ids: {id_value}")
+
+    # Return unique IDs and error messages
+    return list(set(ids)), duplicate_messages

api/utils/file_utils.py

@@ -17,6 +17,8 @@ import base64
 import json
 import os
 import re
+import sys
+import threading
 from io import BytesIO
 
 import pdfplumber
@@ -30,6 +32,10 @@ from api.constants import IMG_BASE64_PREFIX
 PROJECT_BASE = os.getenv("RAG_PROJECT_BASE") or os.getenv("RAG_DEPLOY_BASE")
 RAG_BASE = os.getenv("RAG_BASE")
 
+LOCK_KEY_pdfplumber = "global_shared_lock_pdfplumber"
+if LOCK_KEY_pdfplumber not in sys.modules:
+    sys.modules[LOCK_KEY_pdfplumber] = threading.Lock()
+
 
 def get_project_base_directory(*args):
     global PROJECT_BASE
@@ -175,19 +181,21 @@ def thumbnail_img(filename, blob):
     """
     filename = filename.lower()
     if re.match(r".*\.pdf$", filename):
-        pdf = pdfplumber.open(BytesIO(blob))
-        buffered = BytesIO()
-        resolution = 32
-        img = None
-        for _ in range(10):
-            # https://github.com/jsvine/pdfplumber?tab=readme-ov-file#creating-a-pageimage-with-to_image
-            pdf.pages[0].to_image(resolution=resolution).annotated.save(buffered, format="png")
-            img = buffered.getvalue()
-            if len(img) >= 64000 and resolution >= 2:
-                resolution = resolution / 2
-                buffered = BytesIO()
-            else:
-                break
+        with sys.modules[LOCK_KEY_pdfplumber]:
+            pdf = pdfplumber.open(BytesIO(blob))
+            buffered = BytesIO()
+            resolution = 32
+            img = None
+            for _ in range(10):
+                # https://github.com/jsvine/pdfplumber?tab=readme-ov-file#creating-a-pageimage-with-to_image
+                pdf.pages[0].to_image(resolution=resolution).annotated.save(buffered, format="png")
+                img = buffered.getvalue()
+                if len(img) >= 64000 and resolution >= 2:
+                    resolution = resolution / 2
+                    buffered = BytesIO()
+                else:
+                    break
+        pdf.close()
         return img
 
     elif re.match(r".*\.(jpg|jpeg|png|tif|gif|icon|ico|webp)$", filename):

api/utils/log_utils.py

@@ -18,6 +18,8 @@ import os.path
 import logging
 from logging.handlers import RotatingFileHandler
 
+initialized_root_logger = False
+
 def get_project_base_directory():
     PROJECT_BASE = os.path.abspath(
         os.path.join(
@@ -29,10 +31,13 @@ def get_project_base_directory():
     return PROJECT_BASE
 
 def initRootLogger(logfile_basename: str, log_format: str = "%(asctime)-15s %(levelname)-8s %(process)d %(message)s"):
-    logger = logging.getLogger()
-    if logger.hasHandlers():
+    global initialized_root_logger
+    if initialized_root_logger:
         return
+    initialized_root_logger = True
 
+    logger = logging.getLogger()
+    logger.handlers.clear()
     log_path = os.path.abspath(os.path.join(get_project_base_directory(), "logs", f"{logfile_basename}.log"))
 
     os.makedirs(os.path.dirname(log_path), exist_ok=True)

conf/infinity_mapping.json

@@ -5,14 +5,14 @@
 	"create_time": {"type": "varchar", "default": ""},
 	"create_timestamp_flt": {"type": "float", "default": 0.0},
 	"img_id": {"type": "varchar", "default": ""},
-	"docnm_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace"},
+	"docnm_kwd": {"type": "varchar", "default": ""},
 	"title_tks": {"type": "varchar", "default": "", "analyzer": "whitespace"},
 	"title_sm_tks": {"type": "varchar", "default": "", "analyzer": "whitespace"},
-	"name_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace"},
-	"important_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace"},
-	"tag_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace"},
+	"name_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"},
+	"important_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"},
+	"tag_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"},
 	"important_tks": {"type": "varchar", "default": "", "analyzer": "whitespace"},
-	"question_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace"},
+	"question_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"},
 	"question_tks": {"type": "varchar", "default": "", "analyzer": "whitespace"},
 	"content_with_weight": {"type": "varchar", "default": ""},
 	"content_ltks": {"type": "varchar", "default": "", "analyzer": "whitespace"},
@@ -27,16 +27,16 @@
 	"rank_int": {"type": "integer", "default": 0},
 	"rank_flt": {"type": "float", "default": 0},
 	"available_int": {"type": "integer", "default": 1},
-	"knowledge_graph_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace"},
-	"entities_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace"},
+	"knowledge_graph_kwd": {"type": "varchar", "default": ""},
+	"entities_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"},
 	"pagerank_fea": {"type": "integer", "default":  0},
 	"tag_feas": {"type": "varchar", "default":  ""},
 
-	"from_entity_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace"},
-	"to_entity_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace"},
-	"entity_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace"},
-	"entity_type_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace"},
-	"source_id": {"type": "varchar", "default": ""},
+	"from_entity_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"},
+	"to_entity_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"},
+	"entity_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"},
+	"entity_type_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"},
+	"source_id": {"type": "varchar", "default": "", "analyzer": "whitespace-#"},
 	"n_hop_with_weight": {"type": "varchar", "default": ""},
-	"removed_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace"}
+	"removed_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"}
 }

deepdoc/README_zh.md

@@ -113,4 +113,4 @@ PDF、DOCX、EXCEL和PPT四种文档格式都有相应的解析器。最复杂
   
 ### 简历
 
-简历是一种非常复杂的文件。一份由各种布局的非结构化文本组成的简历可以分解为由近百个字段组成的结构化数据。我们还没有打开解析器，因为我们在解析过程之后打开了处理方法。
+简历是一种非常复杂的文档。由各种格式的非结构化文本构成的简历可以被解析为包含近百个字段的结构化数据。我们还没有启用解析器，因为在解析过程之后才会启动处理方法。

deepdoc/parser/excel_parser.py

@@ -11,52 +11,68 @@
 #  limitations under the License.
 #
 
-from openpyxl import load_workbook, Workbook
+import logging
 import sys
 from io import BytesIO
 
-from rag.nlp import find_codec
-
 import pandas as pd
+from openpyxl import Workbook, load_workbook
+
+from rag.nlp import find_codec
 
 
 class RAGFlowExcelParser:
-    def html(self, fnm, chunk_rows=256):
 
-        # if isinstance(fnm, str):
-        #     wb = load_workbook(fnm)
-        # else:
-        #     wb = load_workbook(BytesIO(fnm))++
+    @staticmethod
+    def _load_excel_to_workbook(file_like_object):
+        if isinstance(file_like_object, bytes):
+            file_like_object = BytesIO(file_like_object)
 
-        s_fnm = fnm
-        if not isinstance(fnm, str):
-            s_fnm = BytesIO(fnm)
-        else:
-            pass
+        # Read first 4 bytes to determine file type
+        file_like_object.seek(0)
+        file_head = file_like_object.read(4)
+        file_like_object.seek(0)
+
+        if not (file_head.startswith(b'PK\x03\x04') or file_head.startswith(b'\xD0\xCF\x11\xE0')):
+            logging.info("****wxy: Not an Excel file, converting CSV to Excel Workbook")
+
+            try:
+                file_like_object.seek(0)
+                df = pd.read_csv(file_like_object)
+                return RAGFlowExcelParser._dataframe_to_workbook(df)
+
+            except Exception as e_csv:
+                raise Exception(f"****wxy: Failed to parse CSV and convert to Excel Workbook: {e_csv}")
 
         try:
-            wb = load_workbook(s_fnm)
+            return load_workbook(file_like_object,data_only= True)
         except Exception as e:
-            print(f'****wxy: file parser error: {e}, s_fnm={s_fnm}, trying convert files')
-            df = pd.read_excel(s_fnm)
-            wb = Workbook()
-            # if len(wb.worksheets) > 0:
-            #   del wb.worksheets[0]
-            # else: pass
-            ws = wb.active
-            ws.title = "Data"
-            for col_num, column_name in enumerate(df.columns, 1):
-                ws.cell(row=1, column=col_num, value=column_name)
-            else:
-                pass
-            for row_num, row in enumerate(df.values, 2):
-                for col_num, value in enumerate(row, 1):
-                    ws.cell(row=row_num, column=col_num, value=value)
-                else:
-                    pass
-            else:
-                pass
+            logging.info(f"****wxy: openpyxl load error: {e}, try pandas instead")
+            try:
+                file_like_object.seek(0)
+                df = pd.read_excel(file_like_object)
+                return RAGFlowExcelParser._dataframe_to_workbook(df)
+            except Exception as e_pandas:
+                raise Exception(f"****wxy: pandas.read_excel error: {e_pandas}, original openpyxl error: {e}")
 
+    @staticmethod
+    def _dataframe_to_workbook(df):
+        wb = Workbook()
+        ws = wb.active
+        ws.title = "Data"
+
+        for col_num, column_name in enumerate(df.columns, 1):
+            ws.cell(row=1, column=col_num, value=column_name)
+
+        for row_num, row in enumerate(df.values, 2):
+            for col_num, value in enumerate(row, 1):
+                ws.cell(row=row_num, column=col_num, value=value)
+
+        return wb
+
+    def html(self, fnm, chunk_rows=256):
+        file_like_object = BytesIO(fnm) if not isinstance(fnm, str) else fnm
+        wb = RAGFlowExcelParser._load_excel_to_workbook(file_like_object)
         tb_chunks = []
         for sheetname in wb.sheetnames:
             ws = wb[sheetname]
@@ -74,7 +90,7 @@ class RAGFlowExcelParser:
                 tb += f"<table><caption>{sheetname}</caption>"
                 tb += tb_rows_0
                 for r in list(
-                  rows[1 + chunk_i * chunk_rows: 1 + (chunk_i + 1) * chunk_rows]
+                    rows[1 + chunk_i * chunk_rows: 1 + (chunk_i + 1) * chunk_rows]
                 ):
                     tb += "<tr>"
                     for i, c in enumerate(r):
@@ -89,40 +105,8 @@ class RAGFlowExcelParser:
         return tb_chunks
 
     def __call__(self, fnm):
-        # if isinstance(fnm, str):
-        #   wb = load_workbook(fnm)
-        # else:
-        #   wb = load_workbook(BytesIO(fnm))
-
-        s_fnm = fnm
-        if not isinstance(fnm, str):
-            s_fnm = BytesIO(fnm)
-        else:
-            pass
-
-        try:
-            wb = load_workbook(s_fnm)
-        except Exception as e:
-            print(f'****wxy: file parser error: {e}, s_fnm={s_fnm}, trying convert files')
-            df = pd.read_excel(s_fnm)
-            wb = Workbook()
-            if len(wb.worksheets) > 0:
-                del wb.worksheets[0]
-            else:
-                pass
-            ws = wb.active
-            ws.title = "Data"
-            for col_num, column_name in enumerate(df.columns, 1):
-                ws.cell(row=1, column=col_num, value=column_name)
-            else:
-                pass
-            for row_num, row in enumerate(df.values, 2):
-                for col_num, value in enumerate(row, 1):
-                    ws.cell(row=row_num, column=col_num, value=value)
-                else:
-                    pass
-            else:
-                pass
+        file_like_object = BytesIO(fnm) if not isinstance(fnm, str) else fnm
+        wb = RAGFlowExcelParser._load_excel_to_workbook(file_like_object)
 
         res = []
         for sheetname in wb.sheetnames:
@@ -148,7 +132,7 @@ class RAGFlowExcelParser:
     @staticmethod
     def row_number(fnm, binary):
         if fnm.split(".")[-1].lower().find("xls") >= 0:
-            wb = load_workbook(BytesIO(binary))
+            wb = RAGFlowExcelParser._load_excel_to_workbook(BytesIO(binary))
             total = 0
             for sheetname in wb.sheetnames:
                 ws = wb[sheetname]
@@ -164,4 +148,3 @@ class RAGFlowExcelParser:
 if __name__ == "__main__":
     psr = RAGFlowExcelParser()
     psr(sys.argv[1])
-

deepdoc/parser/figure_parser.py

@@ -0,0 +1,91 @@
+#
+#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+
+
+from PIL import Image
+
+from rag.app.picture import vision_llm_chunk as picture_vision_llm_chunk
+from rag.prompts import vision_llm_figure_describe_prompt
+
+
+def vision_figure_parser_figure_data_wraper(figures_data_without_positions):
+    return [(
+        (figure_data[1], [figure_data[0]]),
+        [(0, 0, 0, 0, 0)]
+    ) for figure_data in figures_data_without_positions if isinstance(figure_data[1], Image.Image)]
+
+
+class VisionFigureParser:
+    def __init__(self, vision_model, figures_data, *args, **kwargs):
+        self.vision_model = vision_model
+        self._extract_figures_info(figures_data)
+        assert len(self.figures) == len(self.descriptions)
+        assert not self.positions or (len(self.figures) == len(self.positions))
+
+    def _extract_figures_info(self, figures_data):
+        self.figures = []
+        self.descriptions = []
+        self.positions = []
+
+        for item in figures_data:
+            # position
+            if len(item) == 2 and isinstance(item[1], list) and len(item[1]) == 1 and isinstance(item[1][0], tuple) and len(item[1][0]) == 5:
+                img_desc = item[0]
+                assert len(img_desc) == 2 and isinstance(img_desc[0], Image.Image) and isinstance(img_desc[1], list), "Should be (figure, [description])"
+                self.figures.append(img_desc[0])
+                self.descriptions.append(img_desc[1])
+                self.positions.append(item[1])
+            else:
+                assert len(item) == 2 and isinstance(item, tuple) and isinstance(item[1], list), f"get {len(item)=}, {item=}"
+                self.figures.append(item[0])
+                self.descriptions.append(item[1])
+
+    def _assemble(self):
+        self.assembled = []
+        self.has_positions = len(self.positions) != 0
+        for i in range(len(self.figures)):
+            figure = self.figures[i]
+            desc = self.descriptions[i]
+            pos = self.positions[i] if self.has_positions else None
+
+            figure_desc = (figure, desc)
+
+            if pos is not None:
+                self.assembled.append((figure_desc, pos))
+            else:
+                self.assembled.append((figure_desc,))
+
+        return self.assembled
+
+    def __call__(self, **kwargs):
+        callback = kwargs.get("callback", lambda prog, msg: None)
+
+        for idx, img_binary in enumerate(self.figures or []):
+            figure_num = idx  # 0-based
+
+            txt = picture_vision_llm_chunk(
+                binary=img_binary,
+                vision_model=self.vision_model,
+                prompt=vision_llm_figure_describe_prompt(),
+                callback=callback,
+            )
+
+            if txt:
+                self.descriptions[figure_num] = txt + "\n".join(self.descriptions[figure_num])
+
+        self._assemble()
+
+        return self.assembled

deepdoc/parser/markdown_parser.py

@@ -22,27 +22,56 @@ class RAGFlowMarkdownParser:
         self.chunk_token_num = int(chunk_token_num)
 
     def extract_tables_and_remainder(self, markdown_text):
-        # Standard Markdown table
-        table_pattern = re.compile(
-            r'''
-            (?:\n|^)                     
-            (?:\|.*?\|.*?\|.*?\n)        
-            (?:\|(?:\s*[:-]+[-| :]*\s*)\|.*?\n) 
-            (?:\|.*?\|.*?\|.*?\n)+
+        tables = []
+        remainder = markdown_text
+        if "|" in markdown_text: # for optimize performance
+            # Standard Markdown table
+            border_table_pattern = re.compile(
+                r'''
+                (?:\n|^)                     
+                (?:\|.*?\|.*?\|.*?\n)        
+                (?:\|(?:\s*[:-]+[-| :]*\s*)\|.*?\n) 
+                (?:\|.*?\|.*?\|.*?\n)+
             ''', re.VERBOSE)
-        tables = table_pattern.findall(markdown_text)
-        remainder = table_pattern.sub('', markdown_text)
+            border_tables = border_table_pattern.findall(markdown_text)
+            tables.extend(border_tables)
+            remainder = border_table_pattern.sub('', remainder)
 
-        # Borderless Markdown table
-        no_border_table_pattern = re.compile(
+            # Borderless Markdown table
+            no_border_table_pattern = re.compile(
+                r'''
+                (?:\n|^)                 
+                (?:\S.*?\|.*?\n)
+                (?:(?:\s*[:-]+[-| :]*\s*).*?\n)
+                (?:\S.*?\|.*?\n)+
+                ''', re.VERBOSE)
+            no_border_tables = no_border_table_pattern.findall(remainder)
+            tables.extend(no_border_tables)
+            remainder = no_border_table_pattern.sub('', remainder)
+
+        if "<table>" in remainder.lower(): # for optimize performance
+            #HTML table extraction - handle possible html/body wrapper tags
+            html_table_pattern = re.compile(
             r'''
             (?:\n|^)
-            (?:\S.*?\|.*?\n)
-            (?:(?:\s*[:-]+[-| :]*\s*).*?\n)
-            (?:\S.*?\|.*?\n)+
-            ''', re.VERBOSE)
-        no_border_tables = no_border_table_pattern.findall(remainder)
-        tables.extend(no_border_tables)
-        remainder = no_border_table_pattern.sub('', remainder)
+            \s*
+            (?:
+                # case1: <html><body><table>...</table></body></html>
+                (?:<html[^>]*>\s*<body[^>]*>\s*<table[^>]*>.*?</table>\s*</body>\s*</html>)
+                |
+                # case2: <body><table>...</table></body>
+                (?:<body[^>]*>\s*<table[^>]*>.*?</table>\s*</body>)
+                |
+                # case3: only<table>...</table>
+                (?:<table[^>]*>.*?</table>)
+            )
+            \s*
+            (?=\n|$)
+            ''',
+            re.VERBOSE | re.DOTALL | re.IGNORECASE
+            )
+            html_tables = html_table_pattern.findall(remainder)
+            tables.extend(html_tables)
+            remainder = html_table_pattern.sub('', remainder)
 
         return remainder, tables

deepdoc/parser/pdf_parser.py

@@ -17,26 +17,53 @@
 import logging
 import os
 import random
+import re
+import sys
+import threading
+from copy import deepcopy
+from io import BytesIO
 from timeit import default_timer as timer
 
-import xgboost as xgb
-from io import BytesIO
-import re
-import pdfplumber
-from PIL import Image
 import numpy as np
+import pdfplumber
+import trio
+import xgboost as xgb
+from huggingface_hub import snapshot_download
+from PIL import Image
 from pypdf import PdfReader as pdf2_read
 
 from api import settings
 from api.utils.file_utils import get_project_base_directory
-from deepdoc.vision import OCR, Recognizer, LayoutRecognizer, TableStructureRecognizer
+from deepdoc.vision import OCR, LayoutRecognizer, Recognizer, TableStructureRecognizer
+from rag.app.picture import vision_llm_chunk as picture_vision_llm_chunk
 from rag.nlp import rag_tokenizer
-from copy import deepcopy
-from huggingface_hub import snapshot_download
+from rag.prompts import vision_llm_describe_prompt
+from rag.settings import PARALLEL_DEVICES
+
+LOCK_KEY_pdfplumber = "global_shared_lock_pdfplumber"
+if LOCK_KEY_pdfplumber not in sys.modules:
+    sys.modules[LOCK_KEY_pdfplumber] = threading.Lock()
+
 
 class RAGFlowPdfParser:
-    def __init__(self):
+    def __init__(self, **kwargs):
+        """
+        If you have trouble downloading HuggingFace models, -_^ this might help!!
+
+        For Linux:
+        export HF_ENDPOINT=https://hf-mirror.com
+
+        For Windows:
+        Good luck
+        ^_-
+
+        """
+
         self.ocr = OCR()
+        self.parallel_limiter = None
+        if PARALLEL_DEVICES is not None and PARALLEL_DEVICES > 1:
+            self.parallel_limiter = [trio.CapacityLimiter(1) for _ in range(PARALLEL_DEVICES)]
+
         if hasattr(self, "model_speciess"):
             self.layouter = LayoutRecognizer("layout." + self.model_speciess)
         else:
@@ -46,7 +73,7 @@ class RAGFlowPdfParser:
         self.updown_cnt_mdl = xgb.Booster()
         if not settings.LIGHTEN:
             try:
-                import torch
+                import torch.cuda
                 if torch.cuda.is_available():
                     self.updown_cnt_mdl.set_param({"device": "cuda"})
             except Exception:
@@ -66,17 +93,6 @@ class RAGFlowPdfParser:
                 model_dir, "updown_concat_xgb.model"))
 
         self.page_from = 0
-        """
-        If you have trouble downloading HuggingFace models, -_^ this might help!!
-
-        For Linux:
-        export HF_ENDPOINT=https://hf-mirror.com
-
-        For Windows:
-        Good luck
-        ^_-
-
-        """
 
     def __char_width(self, c):
         return (c["x1"] - c["x0"]) // max(len(c["text"]), 1)
@@ -91,7 +107,7 @@ class RAGFlowPdfParser:
     def _y_dis(
             self, a, b):
         return (
-                       b["top"] + b["bottom"] - a["top"] - a["bottom"]) / 2
+            b["top"] + b["bottom"] - a["top"] - a["bottom"]) / 2
 
     def _match_proj(self, b):
         proj_patt = [
@@ -114,9 +130,9 @@ class RAGFlowPdfParser:
         tks_down = rag_tokenizer.tokenize(down["text"][:LEN]).split()
         tks_up = rag_tokenizer.tokenize(up["text"][-LEN:]).split()
         tks_all = up["text"][-LEN:].strip() \
-                  + (" " if re.match(r"[a-zA-Z0-9]+",
-                                     up["text"][-1] + down["text"][0]) else "") \
-                  + down["text"][:LEN].strip()
+            + (" " if re.match(r"[a-zA-Z0-9]+",
+                               up["text"][-1] + down["text"][0]) else "") \
+            + down["text"][:LEN].strip()
         tks_all = rag_tokenizer.tokenize(tks_all).split()
         fea = [
             up.get("R", -1) == down.get("R", -1),
@@ -138,7 +154,7 @@ class RAGFlowPdfParser:
             True if re.search(r"[，,][^。.]+$", up["text"]) else False,
             True if re.search(r"[，,][^。.]+$", up["text"]) else False,
             True if re.search(r"[\(（][^\)）]+$", up["text"])
-                    and re.search(r"[\)）]", down["text"]) else False,
+            and re.search(r"[\)）]", down["text"]) else False,
             self._match_proj(down),
             True if re.match(r"[A-Z]", down["text"]) else False,
             True if re.match(r"[A-Z]", up["text"][-1]) else False,
@@ -200,7 +216,7 @@ class RAGFlowPdfParser:
                 continue
             for tb in tbls:  # for table
                 left, top, right, bott = tb["x0"] - MARGIN, tb["top"] - MARGIN, \
-                                         tb["x1"] + MARGIN, tb["bottom"] + MARGIN
+                    tb["x1"] + MARGIN, tb["bottom"] + MARGIN
                 left *= ZM
                 top *= ZM
                 right *= ZM
@@ -277,9 +293,9 @@ class RAGFlowPdfParser:
                 b["H_right"] = spans[ii]["x1"]
                 b["SP"] = ii
 
-    def __ocr(self, pagenum, img, chars, ZM=3):
+    def __ocr(self, pagenum, img, chars, ZM=3, device_id: int | None = None):
         start = timer()
-        bxs = self.ocr.detect(np.array(img))
+        bxs = self.ocr.detect(np.array(img), device_id)
         logging.info(f"__ocr detecting boxes of a image cost ({timer() - start}s)")
 
         start = timer()
@@ -324,7 +340,7 @@ class RAGFlowPdfParser:
                 b["box_image"] = self.ocr.get_rotate_crop_image(img_np, np.array([[left, top], [right, top], [right, bott], [left, bott]], dtype=np.float32))
                 boxes_to_reg.append(b)
             del b["txt"]
-        texts = self.ocr.recognize_batch([b["box_image"] for b in boxes_to_reg])
+        texts = self.ocr.recognize_batch([b["box_image"] for b in boxes_to_reg], device_id)
         for i in range(len(boxes_to_reg)):
             boxes_to_reg[i]["text"] = texts[i]
             del boxes_to_reg[i]["box_image"]
@@ -442,7 +458,7 @@ class RAGFlowPdfParser:
                     b_["text"],
                     any(feats),
                     any(concatting_feats),
-                    ))
+                ))
                 i += 1
                 continue
             # merge up and down
@@ -637,8 +653,7 @@ class RAGFlowPdfParser:
             b_["top"] = b["top"]
             self.boxes.pop(i)
 
-    def _extract_table_figure(self, need_image, ZM,
-                              return_html, need_position):
+    def _extract_table_figure(self, need_image, ZM, return_html, need_position, separate_tables_figures=False):
         tables = {}
         figures = {}
         # extract figure and table boxes
@@ -650,7 +665,7 @@ class RAGFlowPdfParser:
                 i += 1
                 continue
             lout_no = str(self.boxes[i]["page_number"]) + \
-                      "-" + str(self.boxes[i]["layoutno"])
+                "-" + str(self.boxes[i]["layoutno"])
             if TableStructureRecognizer.is_caption(self.boxes[i]) or self.boxes[i]["layout_type"] in ["table caption",
                                                                                                       "title",
                                                                                                       "figure caption",
@@ -752,9 +767,6 @@ class RAGFlowPdfParser:
                     tk)
             self.boxes.pop(i)
 
-        res = []
-        positions = []
-
         def cropout(bxs, ltype, poss):
             nonlocal ZM
             pn = set([b["page_number"] - 1 for b in bxs])
@@ -802,6 +814,10 @@ class RAGFlowPdfParser:
                 height += img.size[1]
             return pic
 
+        res = []
+        positions = []
+        figure_results = []
+        figure_positions = []
         # crop figure out and add caption
         for k, bxs in figures.items():
             txt = "\n".join([b["text"] for b in bxs])
@@ -809,28 +825,46 @@ class RAGFlowPdfParser:
                 continue
 
             poss = []
-            res.append(
-                (cropout(
-                    bxs,
-                    "figure", poss),
-                 [txt]))
-            positions.append(poss)
+
+            if separate_tables_figures:
+                figure_results.append(
+                    (cropout(
+                        bxs,
+                        "figure", poss),
+                     [txt]))
+                figure_positions.append(poss)
+            else:
+                res.append(
+                    (cropout(
+                        bxs,
+                        "figure", poss),
+                     [txt]))
+                positions.append(poss)
 
         for k, bxs in tables.items():
             if not bxs:
                 continue
             bxs = Recognizer.sort_Y_firstly(bxs, np.mean(
                 [(b["bottom"] - b["top"]) / 2 for b in bxs]))
+
             poss = []
+
             res.append((cropout(bxs, "table", poss),
                         self.tbl_det.construct_table(bxs, html=return_html, is_english=self.is_english)))
             positions.append(poss)
 
-        assert len(positions) == len(res)
-
-        if need_position:
-            return list(zip(res, positions))
-        return res
+        if separate_tables_figures:
+            assert len(positions) + len(figure_positions) == len(res) + len(figure_results)
+            if need_position:
+                return list(zip(res, positions)), list(zip(figure_results, figure_positions))
+            else:
+                return res, figure_results
+        else:
+            assert len(positions) == len(res)
+            if need_position:
+                return list(zip(res, positions))
+            else:
+                return res
 
     def proj_match(self, line):
         if len(line) <= 2:
@@ -948,9 +982,12 @@ class RAGFlowPdfParser:
     @staticmethod
     def total_page_number(fnm, binary=None):
         try:
-            pdf = pdfplumber.open(
-                fnm) if not binary else pdfplumber.open(BytesIO(binary))
-            return len(pdf.pages)
+            with sys.modules[LOCK_KEY_pdfplumber]:
+                pdf = pdfplumber.open(
+                    fnm) if not binary else pdfplumber.open(BytesIO(binary))
+            total_page = len(pdf.pages)
+            pdf.close()
+            return total_page
         except Exception:
             logging.exception("total_page_number")
 
@@ -966,59 +1003,57 @@ class RAGFlowPdfParser:
         self.page_from = page_from
         start = timer()
         try:
-            self.pdf = pdfplumber.open(fnm) if isinstance(
-                fnm, str) else pdfplumber.open(BytesIO(fnm))
-            self.page_images = [p.to_image(resolution=72 * zoomin).annotated for i, p in
-                                enumerate(self.pdf.pages[page_from:page_to])]
-            try:
-                self.page_chars = [[c for c in page.dedupe_chars().chars if self._has_color(c)] for page in self.pdf.pages[page_from:page_to]]
-            except Exception as e:
-                logging.warning(f"Failed to extract characters for pages {page_from}-{page_to}: {str(e)}")
-                self.page_chars = [[] for _ in range(page_to - page_from)]  # If failed to extract, using empty list instead.
+            with sys.modules[LOCK_KEY_pdfplumber]:
+                with (pdfplumber.open(fnm) if isinstance(fnm, str) else pdfplumber.open(BytesIO(fnm))) as pdf:
+                    self.pdf = pdf
+                    self.page_images = [p.to_image(resolution=72 * zoomin).annotated for i, p in
+                                        enumerate(self.pdf.pages[page_from:page_to])]
+
+                    try:
+                        self.page_chars = [[c for c in page.dedupe_chars().chars if self._has_color(c)] for page in self.pdf.pages[page_from:page_to]]
+                    except Exception as e:
+                        logging.warning(f"Failed to extract characters for pages {page_from}-{page_to}: {str(e)}")
+                        self.page_chars = [[] for _ in range(page_to - page_from)]  # If failed to extract, using empty list instead.
+
+                    self.total_page = len(self.pdf.pages)
 
-            self.total_page = len(self.pdf.pages)
         except Exception:
             logging.exception("RAGFlowPdfParser __images__")
         logging.info(f"__images__ dedupe_chars cost {timer() - start}s")
 
         self.outlines = []
         try:
-            self.pdf = pdf2_read(fnm if isinstance(fnm, str) else BytesIO(fnm))
-            outlines = self.pdf.outline
+            with (pdf2_read(fnm if isinstance(fnm, str)
+                            else BytesIO(fnm))) as pdf:
+                self.pdf = pdf
 
-            def dfs(arr, depth):
-                for a in arr:
-                    if isinstance(a, dict):
-                        self.outlines.append((a["/Title"], depth))
-                        continue
-                    dfs(a, depth + 1)
+                outlines = self.pdf.outline
+                def dfs(arr, depth):
+                    for a in arr:
+                        if isinstance(a, dict):
+                            self.outlines.append((a["/Title"], depth))
+                            continue
+                        dfs(a, depth + 1)
+
+                dfs(outlines, 0)
 
-            dfs(outlines, 0)
         except Exception as e:
             logging.warning(f"Outlines exception: {e}")
+
         if not self.outlines:
             logging.warning("Miss outlines")
 
         logging.debug("Images converted.")
         self.is_english = [re.search(r"[a-zA-Z0-9,/¸;:'\[\]\(\)!@#$%^&*\"?<>._-]{30,}", "".join(
             random.choices([c["text"] for c in self.page_chars[i]], k=min(100, len(self.page_chars[i]))))) for i in
-                           range(len(self.page_chars))]
+            range(len(self.page_chars))]
         if sum([1 if e else 0 for e in self.is_english]) > len(
                 self.page_images) / 2:
             self.is_english = True
         else:
             self.is_english = False
 
-        start = timer()
-        for i, img in enumerate(self.page_images):
-            chars = self.page_chars[i] if not self.is_english else []
-            self.mean_height.append(
-                np.median(sorted([c["height"] for c in chars])) if chars else 0
-            )
-            self.mean_width.append(
-                np.median(sorted([c["width"] for c in chars])) if chars else 8
-            )
-            self.page_cum_height.append(img.size[1] / zoomin)
+        async def __img_ocr(i, id, img, chars, limiter):
             j = 0
             while j + 1 < len(chars):
                 if chars[j]["text"] and chars[j + 1]["text"] \
@@ -1028,9 +1063,44 @@ class RAGFlowPdfParser:
                     chars[j]["text"] += " "
                 j += 1
 
-            self.__ocr(i + 1, img, chars, zoomin)
+            if limiter:
+                async with limiter:
+                    await trio.to_thread.run_sync(lambda: self.__ocr(i + 1, img, chars, zoomin, id))
+            else:
+                self.__ocr(i + 1, img, chars, zoomin, id)
+
             if callback and i % 6 == 5:
                 callback(prog=(i + 1) * 0.6 / len(self.page_images), msg="")
+
+        async def __img_ocr_launcher():
+            def __ocr_preprocess():
+                chars = self.page_chars[i] if not self.is_english else []
+                self.mean_height.append(
+                    np.median(sorted([c["height"] for c in chars])) if chars else 0
+                )
+                self.mean_width.append(
+                    np.median(sorted([c["width"] for c in chars])) if chars else 8
+                )
+                self.page_cum_height.append(img.size[1] / zoomin)
+                return chars
+
+            if self.parallel_limiter:
+                async with trio.open_nursery() as nursery:
+                    for i, img in enumerate(self.page_images):
+                        chars = __ocr_preprocess()
+
+                        nursery.start_soon(__img_ocr, i, i % PARALLEL_DEVICES, img, chars,
+                                           self.parallel_limiter[i % PARALLEL_DEVICES])
+                        await trio.sleep(0.1)
+            else:
+                for i, img in enumerate(self.page_images):
+                    chars = __ocr_preprocess()
+                    await __img_ocr(i, 0, img, chars, None)
+
+        start = timer()
+
+        trio.run(__img_ocr_launcher)
+
         logging.info(f"__images__ {len(self.page_images)} pages cost {timer() - start}s")
 
         if not self.is_english and not any(
@@ -1095,7 +1165,7 @@ class RAGFlowPdfParser:
                 self.page_images[pns[0]].crop((left * ZM, top * ZM,
                                                right *
                                                ZM, min(
-                    bottom, self.page_images[pns[0]].size[1])
+                                                   bottom, self.page_images[pns[0]].size[1])
                                                ))
             )
             if 0 < ii < len(poss) - 1:
@@ -1157,7 +1227,7 @@ class RAGFlowPdfParser:
         return poss
 
 
-class PlainParser(object):
+class PlainParser:
     def __call__(self, filename, from_page=0, to_page=100000, **kwargs):
         self.outlines = []
         lines = []
@@ -1193,5 +1263,52 @@ class PlainParser(object):
         raise NotImplementedError
 
 
+class VisionParser(RAGFlowPdfParser):
+    def __init__(self, vision_model, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.vision_model = vision_model
+
+    def __images__(self, fnm, zoomin=3, page_from=0, page_to=299, callback=None):
+        try:
+            with sys.modules[LOCK_KEY_pdfplumber]:
+                self.pdf = pdfplumber.open(fnm) if isinstance(
+                    fnm, str) else pdfplumber.open(BytesIO(fnm))
+                self.page_images = [p.to_image(resolution=72 * zoomin).annotated for i, p in
+                                    enumerate(self.pdf.pages[page_from:page_to])]
+                self.total_page = len(self.pdf.pages)
+        except Exception:
+            self.page_images = None
+            self.total_page = 0
+            logging.exception("VisionParser __images__")
+
+    def __call__(self, filename, from_page=0, to_page=100000, **kwargs):
+        callback = kwargs.get("callback", lambda prog, msg: None)
+
+        self.__images__(fnm=filename, zoomin=3, page_from=from_page, page_to=to_page, **kwargs)
+
+        total_pdf_pages = self.total_page
+
+        start_page = max(0, from_page)
+        end_page = min(to_page, total_pdf_pages)
+
+        all_docs = []
+
+        for idx, img_binary in enumerate(self.page_images or []):
+            pdf_page_num = idx  # 0-based
+            if pdf_page_num < start_page or pdf_page_num >= end_page:
+                continue
+
+            docs = picture_vision_llm_chunk(
+                binary=img_binary,
+                vision_model=self.vision_model,
+                prompt=vision_llm_describe_prompt(page=pdf_page_num+1),
+                callback=callback,
+            )
+
+            if docs:
+                all_docs.append(docs)
+        return [(doc, "") for doc in all_docs], []
+
+
 if __name__ == "__main__":
     pass

deepdoc/parser/ppt_parser.py

@@ -19,29 +19,60 @@ from io import BytesIO
 from pptx import Presentation
 
 
-class RAGFlowPptParser(object):
+class RAGFlowPptParser:
     def __init__(self):
         super().__init__()
 
+    def __get_bulleted_text(self, paragraph):
+        is_bulleted = bool(paragraph._p.xpath("./a:pPr/a:buChar")) or bool(paragraph._p.xpath("./a:pPr/a:buAutoNum")) or bool(paragraph._p.xpath("./a:pPr/a:buBlip"))
+        if is_bulleted:
+            return f"{'  '* paragraph.level}.{paragraph.text}"
+        else:
+            return paragraph.text
+
     def __extract(self, shape):
-        if shape.shape_type == 19:
-            tb = shape.table
-            rows = []
-            for i in range(1, len(tb.rows)):
-                rows.append("; ".join([tb.cell(
-                    0, j).text + ": " + tb.cell(i, j).text for j in range(len(tb.columns)) if tb.cell(i, j)]))
-            return "\n".join(rows)
+        try:
+            # First try to get text content
+            if hasattr(shape, 'has_text_frame') and shape.has_text_frame:
+                text_frame = shape.text_frame
+                texts = []
+                for paragraph in text_frame.paragraphs:
+                    if paragraph.text.strip():
+                        texts.append(self.__get_bulleted_text(paragraph))
+                return "\n".join(texts)
 
-        if shape.has_text_frame:
-            return shape.text_frame.text
+            # Safely get shape_type
+            try:
+                shape_type = shape.shape_type
+            except NotImplementedError:
+                # If shape_type is not available, try to get text content
+                if hasattr(shape, 'text'):
+                    return shape.text.strip()
+                return ""
 
-        if shape.shape_type == 6:
-            texts = []
-            for p in sorted(shape.shapes, key=lambda x: (x.top // 10, x.left)):
-                t = self.__extract(p)
-                if t:
-                    texts.append(t)
-            return "\n".join(texts)
+            # Handle table
+            if shape_type == 19:
+                tb = shape.table
+                rows = []
+                for i in range(1, len(tb.rows)):
+                    rows.append("; ".join([tb.cell(
+                        0, j).text + ": " + tb.cell(i, j).text for j in range(len(tb.columns)) if tb.cell(i, j)]))
+                return "\n".join(rows)
+
+            # Handle group shape
+            if shape_type == 6:
+                texts = []
+                for p in sorted(shape.shapes, key=lambda x: (x.top // 10, x.left)):
+                    t = self.__extract_texts(p)
+                    if t:
+                        texts.append(t)
+                return "\n".join(texts)
+
+            return ""
+
+        except Exception as e:
+            logging.error(f"Error processing shape: {str(e)}")
+            return ""
 
     def __call__(self, fnm, from_page, to_page, callback=None):
         ppt = Presentation(fnm) if isinstance(

deepdoc/parser/resume/entities/corporations.py

@@ -30,10 +30,10 @@ GOODS = pd.read_csv(
 GOODS["cid"] = GOODS["cid"].astype(str)
 GOODS = GOODS.set_index(["cid"])
 CORP_TKS = json.load(
-    open(os.path.join(current_file_path, "res/corp.tks.freq.json"), "r")
+    open(os.path.join(current_file_path, "res/corp.tks.freq.json"), "r",encoding="utf-8")
 )
-GOOD_CORP = json.load(open(os.path.join(current_file_path, "res/good_corp.json"), "r"))
-CORP_TAG = json.load(open(os.path.join(current_file_path, "res/corp_tag.json"), "r"))
+GOOD_CORP = json.load(open(os.path.join(current_file_path, "res/good_corp.json"), "r",encoding="utf-8"))
+CORP_TAG = json.load(open(os.path.join(current_file_path, "res/corp_tag.json"), "r",encoding="utf-8"))
 
 
 def baike(cid, default_v=0):

deepdoc/parser/resume/entities/schools.py

@@ -25,7 +25,7 @@ TBL = pd.read_csv(
     os.path.join(current_file_path, "res/schools.csv"), sep="\t", header=0
 ).fillna("")
 TBL["name_en"] = TBL["name_en"].map(lambda x: x.lower().strip())
-GOOD_SCH = json.load(open(os.path.join(current_file_path, "res/good_sch.json"), "r"))
+GOOD_SCH = json.load(open(os.path.join(current_file_path, "res/good_sch.json"), "r",encoding="utf-8"))
 GOOD_SCH = set([re.sub(r"[,. &（）()]+", "", c) for c in GOOD_SCH])
 
 

deepdoc/parser/txt_parser.py

@@ -31,6 +31,7 @@ class RAGFlowTxtParser:
             raise TypeError("txt type should be str!")
         cks = [""]
         tk_nums = [0]
+        delimiter = delimiter.encode('utf-8').decode('unicode_escape').encode('latin1').decode('utf-8')
 
         def add_chunk(t):
             nonlocal cks, tk_nums, delimiter

deepdoc/vision/__init__.py

@@ -14,7 +14,8 @@
 #  limitations under the License.
 #
 import io
-
+import sys
+import threading
 import pdfplumber
 
 from .ocr import OCR
@@ -23,6 +24,11 @@ from .layout_recognizer import LayoutRecognizer4YOLOv10 as LayoutRecognizer
 from .table_structure_recognizer import TableStructureRecognizer
 
 
+LOCK_KEY_pdfplumber = "global_shared_lock_pdfplumber"
+if LOCK_KEY_pdfplumber not in sys.modules:
+    sys.modules[LOCK_KEY_pdfplumber] = threading.Lock()
+
+
 def init_in_out(args):
     from PIL import Image
     import os
@@ -36,12 +42,14 @@ def init_in_out(args):
 
     def pdf_pages(fnm, zoomin=3):
         nonlocal outputs, images
-        pdf = pdfplumber.open(fnm)
-        images = [p.to_image(resolution=72 * zoomin).annotated for i, p in
-                            enumerate(pdf.pages)]
+        with sys.modules[LOCK_KEY_pdfplumber]:
+            pdf = pdfplumber.open(fnm)
+            images = [p.to_image(resolution=72 * zoomin).annotated for i, p in
+                                enumerate(pdf.pages)]
 
         for i, page in enumerate(images):
             outputs.append(os.path.split(fnm)[-1] + f"_{i}.jpg")
+        pdf.close()
 
     def images_and_outputs(fnm):
         nonlocal outputs, images

deepdoc/vision/layout_recognizer.py

@@ -46,8 +46,8 @@ class LayoutRecognizer(Recognizer):
     def __init__(self, domain):
         try:
             model_dir = os.path.join(
-                    get_project_base_directory(),
-                    "rag/res/deepdoc")
+                get_project_base_directory(),
+                "rag/res/deepdoc")
             super().__init__(self.labels, domain, model_dir)
         except Exception:
             model_dir = snapshot_download(repo_id="InfiniFlow/deepdoc",
@@ -56,18 +56,23 @@ class LayoutRecognizer(Recognizer):
             super().__init__(self.labels, domain, model_dir)
 
         self.garbage_layouts = ["footer", "header", "reference"]
+        self.client = None
+        if os.environ.get("TENSORRT_DLA_SVR"):
+            from deepdoc.vision.dla_cli import DLAClient
+            self.client = DLAClient(os.environ["TENSORRT_DLA_SVR"])
 
-    def __call__(self, image_list, ocr_res, scale_factor=3,
-                 thr=0.2, batch_size=16, drop=True):
+    def __call__(self, image_list, ocr_res, scale_factor=3, thr=0.2, batch_size=16, drop=True):
         def __is_garbage(b):
-            patt = [r"^•+$", r"(版权归©|免责条款|地址[:：])", r"\.{3,}", "^[0-9]{1,2} / ?[0-9]{1,2}$",
+            patt = [r"^•+$", "^[0-9]{1,2} / ?[0-9]{1,2}$",
                     r"^[0-9]{1,2} of [0-9]{1,2}$", "^http://[^ ]{12,}",
-                    "(资料|数据)来源[:：]", "[0-9a-z._-]+@[a-z0-9-]+\\.[a-z]{2,3}",
                     "\\(cid *: *[0-9]+ *\\)"
                     ]
             return any([re.search(p, b["text"]) for p in patt])
 
-        layouts = super().__call__(image_list, thr, batch_size)
+        if self.client:
+            layouts = self.client.predict(image_list)
+        else:
+            layouts = super().__call__(image_list, thr, batch_size)
         # save_results(image_list, layouts, self.labels, output_dir='output/', threshold=0.7)
         assert len(image_list) == len(ocr_res)
         # Tag layout type
@@ -160,6 +165,7 @@ class LayoutRecognizer(Recognizer):
     def forward(self, image_list, thr=0.7, batch_size=16):
         return super().__call__(image_list, thr, batch_size)
 
+
 class LayoutRecognizer4YOLOv10(LayoutRecognizer):
     labels = [
         "title",
@@ -185,9 +191,9 @@ class LayoutRecognizer4YOLOv10(LayoutRecognizer):
 
     def preprocess(self, image_list):
         inputs = []
-        new_shape = self.input_shape # height, width
+        new_shape = self.input_shape  # height, width
         for img in image_list:
-            shape = img.shape[:2]# current shape [height, width]
+            shape = img.shape[:2]  # current shape [height, width]
             # Scale ratio (new / old)
             r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
             # Compute padding
@@ -242,4 +248,3 @@ class LayoutRecognizer4YOLOv10(LayoutRecognizer):
             "bbox": [float(t) for t in boxes[i].tolist()],
             "score": float(scores[i])
         } for i in indices]
-

deepdoc/vision/ocr.py

@@ -22,6 +22,7 @@ import os
 from huggingface_hub import snapshot_download
 
 from api.utils.file_utils import get_project_base_directory
+from rag.settings import PARALLEL_DEVICES
 from .operators import *  # noqa: F403
 from . import operators
 import math
@@ -66,10 +67,12 @@ def create_operators(op_param_list, global_config=None):
     return ops
 
 
-def load_model(model_dir, nm):
+def load_model(model_dir, nm, device_id: int | None = None):
     model_file_path = os.path.join(model_dir, nm + ".onnx")
+    model_cached_tag = model_file_path + str(device_id) if device_id is not None else model_file_path
+
     global loaded_models
-    loaded_model = loaded_models.get(model_file_path)
+    loaded_model = loaded_models.get(model_cached_tag)
     if loaded_model:
         logging.info(f"load_model {model_file_path} reuses cached model")
         return loaded_model
@@ -81,7 +84,7 @@ def load_model(model_dir, nm):
     def cuda_is_available():
         try:
             import torch
-            if torch.cuda.is_available():
+            if torch.cuda.is_available() and torch.cuda.device_count() > device_id:
                 return True
         except Exception:
             return False
@@ -98,7 +101,7 @@ def load_model(model_dir, nm):
     run_options = ort.RunOptions()
     if cuda_is_available():
         cuda_provider_options = {
-            "device_id": 0, # Use specific GPU
+            "device_id": device_id, # Use specific GPU
             "gpu_mem_limit": 512 * 1024 * 1024, # Limit gpu memory
             "arena_extend_strategy": "kNextPowerOfTwo",  # gpu memory allocation strategy
         }
@@ -108,7 +111,7 @@ def load_model(model_dir, nm):
             providers=['CUDAExecutionProvider'],
             provider_options=[cuda_provider_options]
             )
-        run_options.add_run_config_entry("memory.enable_memory_arena_shrinkage", "gpu:0")
+        run_options.add_run_config_entry("memory.enable_memory_arena_shrinkage", "gpu:" + str(device_id))
         logging.info(f"load_model {model_file_path} uses GPU")
     else:
         sess = ort.InferenceSession(
@@ -118,12 +121,12 @@ def load_model(model_dir, nm):
         run_options.add_run_config_entry("memory.enable_memory_arena_shrinkage", "cpu")
         logging.info(f"load_model {model_file_path} uses CPU")
     loaded_model = (sess, run_options)
-    loaded_models[model_file_path] = loaded_model
+    loaded_models[model_cached_tag] = loaded_model
     return loaded_model
 
 
-class TextRecognizer(object):
-    def __init__(self, model_dir):
+class TextRecognizer:
+    def __init__(self, model_dir, device_id: int | None = None):
         self.rec_image_shape = [int(v) for v in "3, 48, 320".split(",")]
         self.rec_batch_num = 16
         postprocess_params = {
@@ -132,7 +135,7 @@ class TextRecognizer(object):
             "use_space_char": True
         }
         self.postprocess_op = build_post_process(postprocess_params)
-        self.predictor, self.run_options = load_model(model_dir, 'rec')
+        self.predictor, self.run_options = load_model(model_dir, 'rec', device_id)
         self.input_tensor = self.predictor.get_inputs()[0]
 
     def resize_norm_img(self, img, max_wh_ratio):
@@ -393,8 +396,8 @@ class TextRecognizer(object):
         return rec_res, time.time() - st
 
 
-class TextDetector(object):
-    def __init__(self, model_dir):
+class TextDetector:
+    def __init__(self, model_dir, device_id: int | None = None):
         pre_process_list = [{
             'DetResizeForTest': {
                 'limit_side_len': 960,
@@ -418,7 +421,7 @@ class TextDetector(object):
                               "unclip_ratio": 1.5, "use_dilation": False, "score_mode": "fast", "box_type": "quad"}
 
         self.postprocess_op = build_post_process(postprocess_params)
-        self.predictor, self.run_options = load_model(model_dir, 'det')
+        self.predictor, self.run_options = load_model(model_dir, 'det', device_id)
         self.input_tensor = self.predictor.get_inputs()[0]
 
         img_h, img_w = self.input_tensor.shape[2:]
@@ -506,7 +509,7 @@ class TextDetector(object):
         return dt_boxes, time.time() - st
 
 
-class OCR(object):
+class OCR:
     def __init__(self, model_dir=None):
         """
         If you have trouble downloading HuggingFace models, -_^ this might help!!
@@ -524,14 +527,33 @@ class OCR(object):
                 model_dir = os.path.join(
                         get_project_base_directory(),
                         "rag/res/deepdoc")
-                self.text_detector = TextDetector(model_dir)
-                self.text_recognizer = TextRecognizer(model_dir)
+                
+                # Append muti-gpus task to the list
+                if PARALLEL_DEVICES is not None and PARALLEL_DEVICES > 0:
+                    self.text_detector = []
+                    self.text_recognizer = []
+                    for device_id in range(PARALLEL_DEVICES):
+                        self.text_detector.append(TextDetector(model_dir, device_id))
+                        self.text_recognizer.append(TextRecognizer(model_dir, device_id))
+                else:
+                    self.text_detector = [TextDetector(model_dir, 0)]
+                    self.text_recognizer = [TextRecognizer(model_dir, 0)]
+
             except Exception:
                 model_dir = snapshot_download(repo_id="InfiniFlow/deepdoc",
                                               local_dir=os.path.join(get_project_base_directory(), "rag/res/deepdoc"),
                                               local_dir_use_symlinks=False)
-                self.text_detector = TextDetector(model_dir)
-                self.text_recognizer = TextRecognizer(model_dir)
+                
+                if PARALLEL_DEVICES is not None:
+                    assert PARALLEL_DEVICES > 0, "Number of devices must be >= 1"
+                    self.text_detector = []
+                    self.text_recognizer = []
+                    for device_id in range(PARALLEL_DEVICES):
+                        self.text_detector.append(TextDetector(model_dir, device_id))
+                        self.text_recognizer.append(TextRecognizer(model_dir, device_id))
+                else:
+                    self.text_detector = [TextDetector(model_dir, 0)]
+                    self.text_recognizer = [TextRecognizer(model_dir, 0)]
 
         self.drop_score = 0.5
         self.crop_image_res_index = 0
@@ -593,14 +615,17 @@ class OCR(object):
                     break
         return _boxes
 
-    def detect(self, img):
+    def detect(self, img, device_id: int | None = None):
+        if device_id is None:
+            device_id = 0
+
         time_dict = {'det': 0, 'rec': 0, 'cls': 0, 'all': 0}
 
         if img is None:
             return None, None, time_dict
 
         start = time.time()
-        dt_boxes, elapse = self.text_detector(img)
+        dt_boxes, elapse = self.text_detector[device_id](img)
         time_dict['det'] = elapse
 
         if dt_boxes is None:
@@ -611,17 +636,22 @@ class OCR(object):
         return zip(self.sorted_boxes(dt_boxes), [
                    ("", 0) for _ in range(len(dt_boxes))])
 
-    def recognize(self, ori_im, box):
+    def recognize(self, ori_im, box, device_id: int | None = None):
+        if device_id is None:
+            device_id = 0
+
         img_crop = self.get_rotate_crop_image(ori_im, box)
 
-        rec_res, elapse = self.text_recognizer([img_crop])
+        rec_res, elapse = self.text_recognizer[device_id]([img_crop])
         text, score = rec_res[0]
         if score < self.drop_score:
             return ""
         return text
 
-    def recognize_batch(self, img_list):
-        rec_res, elapse = self.text_recognizer(img_list)
+    def recognize_batch(self, img_list, device_id: int | None = None):
+        if device_id is None:
+            device_id = 0
+        rec_res, elapse = self.text_recognizer[device_id](img_list)
         texts = []
         for i in range(len(rec_res)):
             text, score = rec_res[i]
@@ -630,15 +660,17 @@ class OCR(object):
             texts.append(text)
         return texts
 
-    def __call__(self, img, cls=True):
+    def __call__(self, img, device_id = 0, cls=True):
         time_dict = {'det': 0, 'rec': 0, 'cls': 0, 'all': 0}
+        if device_id is None:
+            device_id = 0
 
         if img is None:
             return None, None, time_dict
 
         start = time.time()
         ori_im = img.copy()
-        dt_boxes, elapse = self.text_detector(img)
+        dt_boxes, elapse = self.text_detector[device_id](img)
         time_dict['det'] = elapse
 
         if dt_boxes is None:
@@ -655,7 +687,7 @@ class OCR(object):
             img_crop = self.get_rotate_crop_image(ori_im, tmp_box)
             img_crop_list.append(img_crop)
 
-        rec_res, elapse = self.text_recognizer(img_crop_list)
+        rec_res, elapse = self.text_recognizer[device_id](img_crop_list)
 
         time_dict['rec'] = elapse
 

deepdoc/vision/operators.py

@@ -23,7 +23,7 @@ import math
 from PIL import Image
 
 
-class DecodeImage(object):
+class DecodeImage:
     """ decode image """
 
     def __init__(self,
@@ -65,7 +65,7 @@ class DecodeImage(object):
         return data
 
 
-class StandardizeImage(object):
+class StandardizeImag:
     """normalize image
     Args:
         mean (list): im - mean
@@ -102,7 +102,7 @@ class StandardizeImage(object):
         return im, im_info
 
 
-class NormalizeImage(object):
+class NormalizeImage:
     """ normalize image such as subtract mean, divide std
     """
 
@@ -129,7 +129,7 @@ class NormalizeImage(object):
         return data
 
 
-class ToCHWImage(object):
+class ToCHWImage:
     """ convert hwc image to chw image
     """
 
@@ -145,7 +145,7 @@ class ToCHWImage(object):
         return data
 
 
-class KeepKeys(object):
+class KeepKeys:
     def __init__(self, keep_keys, **kwargs):
         self.keep_keys = keep_keys
 
@@ -156,7 +156,7 @@ class KeepKeys(object):
         return data_list
 
 
-class Pad(object):
+class Pad:
     def __init__(self, size=None, size_div=32, **kwargs):
         if size is not None and not isinstance(size, (int, list, tuple)):
             raise TypeError("Type of target_size is invalid. Now is {}".format(
@@ -194,7 +194,7 @@ class Pad(object):
         return data
 
 
-class LinearResize(object):
+class LinearResize:
     """resize image by target_size and max_size
     Args:
         target_size (int): the target size of image
@@ -261,7 +261,7 @@ class LinearResize(object):
         return im_scale_y, im_scale_x
 
 
-class Resize(object):
+class Resize:
     def __init__(self, size=(640, 640), **kwargs):
         self.size = size
 
@@ -291,7 +291,7 @@ class Resize(object):
         return data
 
 
-class DetResizeForTest(object):
+class DetResizeForTest:
     def __init__(self, **kwargs):
         super(DetResizeForTest, self).__init__()
         self.resize_type = 0
@@ -421,7 +421,7 @@ class DetResizeForTest(object):
         return img, [ratio_h, ratio_w]
 
 
-class E2EResizeForTest(object):
+class E2EResizeForTest:
     def __init__(self, **kwargs):
         super(E2EResizeForTest, self).__init__()
         self.max_side_len = kwargs['max_side_len']
@@ -489,7 +489,7 @@ class E2EResizeForTest(object):
         return im, (ratio_h, ratio_w)
 
 
-class KieResize(object):
+class KieResize:
     def __init__(self, **kwargs):
         super(KieResize, self).__init__()
         self.max_side, self.min_side = kwargs['img_scale'][0], kwargs[
@@ -539,7 +539,7 @@ class KieResize(object):
         return points
 
 
-class SRResize(object):
+class SRResize:
     def __init__(self,
                  imgH=32,
                  imgW=128,
@@ -576,7 +576,7 @@ class SRResize(object):
         return data
 
 
-class ResizeNormalize(object):
+class ResizeNormalize:
     def __init__(self, size, interpolation=Image.BICUBIC):
         self.size = size
         self.interpolation = interpolation
@@ -588,7 +588,7 @@ class ResizeNormalize(object):
         return img_numpy
 
 
-class GrayImageChannelFormat(object):
+class GrayImageChannelFormat:
     """
     format gray scale image's channel: (3,h,w) -> (1,h,w)
     Args:
@@ -612,7 +612,7 @@ class GrayImageChannelFormat(object):
         return data
 
 
-class Permute(object):
+class Permute:
     """permute image
     Args:
         to_bgr (bool): whether convert RGB to BGR
@@ -635,7 +635,7 @@ class Permute(object):
         return im, im_info
 
 
-class PadStride(object):
+class PadStride:
     """ padding image for model with FPN, instead PadBatch(pad_to_stride) in original config
     Args:
         stride (bool): model with FPN need image shape % stride == 0

deepdoc/vision/postprocess.py

@@ -38,7 +38,7 @@ def build_post_process(config, global_config=None):
     return module_class(**config)
 
 
-class DBPostProcess(object):
+class DBPostProcess:
     """
     The post process for Differentiable Binarization (DB).
     """
@@ -259,7 +259,7 @@ class DBPostProcess(object):
         return boxes_batch
 
 
-class BaseRecLabelDecode(object):
+class BaseRecLabelDecode:
     """ Convert between text-label and text-index """
 
     def __init__(self, character_dict_path=None, use_space_char=False):

deepdoc/vision/recognizer.py

@@ -28,7 +28,7 @@ from .operators import preprocess
 from . import operators
 from .ocr import load_model
 
-class Recognizer(object):
+class Recognizer:
     def __init__(self, label_list, task_name, model_dir=None):
         """
         If you have trouble downloading HuggingFace models, -_^ this might help!!
@@ -195,9 +195,8 @@ class Recognizer(object):
                 (im_info[0]['scale_factor'],)).astype('float32')
             return inputs
         
-        for e in im_info:
-            im_shape.append(np.array((e['im_shape'],)).astype('float32'))
-            scale_factor.append(np.array((e['scale_factor'],)).astype('float32'))
+        im_shape = np.array([info['im_shape'] for info in im_info], dtype='float32')
+        scale_factor = np.array([info['scale_factor'] for info in im_info], dtype='float32')
 
         inputs['im_shape'] = np.concatenate(im_shape, axis=0)
         inputs['scale_factor'] = np.concatenate(scale_factor, axis=0)

deepdoc/vision/t_ocr.py

@@ -28,14 +28,24 @@ from deepdoc.vision.seeit import draw_box
 from deepdoc.vision import OCR, init_in_out
 import argparse
 import numpy as np
+import trio
+
+# os.environ['CUDA_VISIBLE_DEVICES'] = '0,2' #2 gpus, uncontinuous
+os.environ['CUDA_VISIBLE_DEVICES'] = '0' #1 gpu
+# os.environ['CUDA_VISIBLE_DEVICES'] = '' #cpu
 
 
 def main(args):
+    import torch.cuda
+
+    cuda_devices = torch.cuda.device_count()
+    limiter = [trio.CapacityLimiter(1) for _ in range(cuda_devices)] if cuda_devices > 1 else None
     ocr = OCR()
     images, outputs = init_in_out(args)
 
-    for i, img in enumerate(images):
-        bxs = ocr(np.array(img))
+    def __ocr(i, id, img):
+        print("Task {} start".format(i))
+        bxs = ocr(np.array(img), id)
         bxs = [(line[0], line[1][0]) for line in bxs]
         bxs = [{
             "text": t,
@@ -47,6 +57,30 @@ def main(args):
         with open(outputs[i] + ".txt", "w+", encoding='utf-8') as f:
             f.write("\n".join([o["text"] for o in bxs]))
 
+        print("Task {} done".format(i))
+
+    async def __ocr_thread(i, id, img, limiter = None):
+        if limiter:
+            async with limiter:
+                print("Task {} use device {}".format(i, id))
+                await trio.to_thread.run_sync(lambda: __ocr(i, id, img))
+        else:
+            __ocr(i, id, img)
+
+    async def __ocr_launcher():
+        if cuda_devices > 1:
+            async with trio.open_nursery() as nursery:
+                for i, img in enumerate(images):
+                    nursery.start_soon(__ocr_thread, i, i % cuda_devices, img, limiter[i % cuda_devices])
+                    await trio.sleep(0.1)
+        else:
+            for i, img in enumerate(images):
+                await __ocr_thread(i, 0, img)
+
+    trio.run(__ocr_launcher)
+
+    print("OCR tasks are all done")
+
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()

deepdoc/vision/table_structure_recognizer.py

@@ -133,7 +133,7 @@ class TableStructureRecognizer(Recognizer):
         return "Ot"
 
     @staticmethod
-    def construct_table(boxes, is_english=False, html=False):
+    def construct_table(boxes, is_english=False, html=True, **kwargs):
         cap = ""
         i = 0
         while i < len(boxes):

docker/.env

@@ -80,28 +80,16 @@ REDIS_PASSWORD=infini_rag_flow
 SVR_HTTP_PORT=9380
 
 # The RAGFlow Docker image to download.
-# Defaults to the v0.17.0-slim edition, which is the RAGFlow Docker image without embedding models.
-RAGFLOW_IMAGE=infiniflow/ragflow:v0.17.0-slim
+# Defaults to the v0.18.0-slim edition, which is the RAGFlow Docker image without embedding models.
+RAGFLOW_IMAGE=infiniflow/ragflow:v0.18.0-slim
 #
 # To download the RAGFlow Docker image with embedding models, uncomment the following line instead:
-# RAGFLOW_IMAGE=infiniflow/ragflow:v0.17.0
+# RAGFLOW_IMAGE=infiniflow/ragflow:v0.18.0
 # 
-# The Docker image of the v0.17.0 edition includes:
-# - Built-in embedding models:
+# The Docker image of the v0.18.0 edition includes built-in embedding models:
 #   - BAAI/bge-large-zh-v1.5
-#   - BAAI/bge-reranker-v2-m3
 #   - maidalun1020/bce-embedding-base_v1
-#   - maidalun1020/bce-reranker-base_v1
-# - Embedding models that will be downloaded once you select them in the RAGFlow UI:
-#   - 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
-#
+
 # 
 
 
@@ -122,13 +110,15 @@ TIMEZONE='Asia/Shanghai'
 # HF_ENDPOINT=https://hf-mirror.com
 
 # Optimizations for MacOS
-# Uncomment the following line if your OS is MacOS:
+# Uncomment the following line if your operating system is MacOS:
 # MACOS=1
 
-# The maximum file size for each uploaded file, in bytes.
-# You can uncomment this line and update the value if you wish to change the 128M file size limit
-# MAX_CONTENT_LENGTH=134217728
-# After making the change, ensure you update `client_max_body_size` in nginx/nginx.conf correspondingly.
+# The maximum file size limit (in bytes) for each upload to your knowledge base or File Management.
+# To change the 1GB file size limit, uncomment the line below and update as needed.
+# MAX_CONTENT_LENGTH=1073741824
+# After updating, ensure `client_max_body_size` in nginx/nginx.conf is updated accordingly.
+# Note that neither `MAX_CONTENT_LENGTH` nor `client_max_body_size` sets the maximum size for files uploaded to an agent.
+# See https://ragflow.io/docs/dev/begin_component for details.
 
 # The log level for the RAGFlow's owned packages and imported packages.
 # Available level:
@@ -146,3 +136,6 @@ TIMEZONE='Asia/Shanghai'
 # ENDPOINT=http://oss-cn-hangzhou.aliyuncs.com
 # REGION=cn-hangzhou
 # BUCKET=ragflow65536
+
+# user registration switch
+REGISTER_ENABLED=1

docker/README.md

@@ -78,22 +78,12 @@ The [.env](./.env) file contains important environment variables for Docker.
 - `RAGFLOW-IMAGE`  
   The Docker image edition. Available editions:  
   
-  - `infiniflow/ragflow:v0.17.0-slim` (default): The RAGFlow Docker image without embedding models.  
-  - `infiniflow/ragflow:v0.17.0`: The RAGFlow Docker image with embedding models including:
+  - `infiniflow/ragflow:v0.18.0-slim` (default): The RAGFlow Docker image without embedding models.  
+  - `infiniflow/ragflow:v0.18.0`: The RAGFlow Docker image with embedding models including:
     - Built-in embedding models:
       - `BAAI/bge-large-zh-v1.5` 
-      - `BAAI/bge-reranker-v2-m3`
       - `maidalun1020/bce-embedding-base_v1`
-      - `maidalun1020/bce-reranker-base_v1`
-    - Embedding models that will be downloaded once you select them in the RAGFlow UI:
-      - `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`
+
   
 > [!TIP]  
 > If you cannot download the RAGFlow Docker image, try the following mirrors.  
@@ -146,6 +136,24 @@ The [.env](./.env) file contains important environment variables for Docker.
   - `password`: The password for MinIO.
   - `host`: The MinIO serving IP *and* port inside the Docker container. Defaults to `minio:9000`.
 
+- `oss`
+  - `access_key`: The access key ID used to authenticate requests to the OSS service.
+  - `secret_key`: The secret access key used to authenticate requests to the OSS service.
+  - `endpoint_url`: The URL of the OSS service endpoint.
+  - `region`: The OSS region where the bucket is located.
+  - `bucket`: The name of the OSS bucket where files will be stored. When you want to store all files in a specified bucket, you need this configuration item.
+  - `prefix_path`: Optional. A prefix path to prepend to file names in the OSS bucket, which can help organize files within the bucket.
+
+- `s3`:
+  - `access_key`: The access key ID used to authenticate requests to the S3 service.
+  - `secret_key`: The secret access key used to authenticate requests to the S3 service.
+  - `endpoint_url`: The URL of the S3-compatible service endpoint. This is necessary when using an S3-compatible protocol instead of the default AWS S3 endpoint.
+  - `bucket`: The name of the S3 bucket where files will be stored. When you want to store all files in a specified bucket, you need this configuration item.
+  - `region`: The AWS region where the S3 bucket is located. This is important for directing requests to the correct data center.
+  - `signature_version`: Optional. The version of the signature to use for authenticating requests. Common versions include `v4`.
+  - `addressing_style`: Optional. The style of addressing to use for the S3 endpoint. This can be `path` or `virtual`.
+  - `prefix_path`: Optional. A prefix path to prepend to file names in the S3 bucket, which can help organize files within the bucket.
+
 - `oauth`  
   The OAuth configuration for signing up or signing in to RAGFlow using a third-party account.  It is disabled by default. To enable this feature, uncomment the corresponding lines in **service_conf.yaml.template**.
   - `github`: The GitHub authentication settings for your application. Visit the [Github Developer Settings page](https://github.com/settings/developers) to obtain your client_id and secret_key.

docker/docker-compose-base.yml

@@ -81,6 +81,7 @@ services:
       --default-authentication-plugin=mysql_native_password
       --tls_version="TLSv1.2,TLSv1.3"
       --init-file /data/application/init.sql
+      --binlog_expire_logs_seconds=604800
     ports:
       - ${MYSQL_PORT}:3306
     volumes:

docker/docker-compose.yml

@@ -1,22 +1,38 @@
 include:
   - ./docker-compose-base.yml
 
+# To ensure that the container processes the locally modified `service_conf.yaml.template` instead of the one included in its image, you need to mount the local `service_conf.yaml.template` to the container.
 services:
   ragflow:
     depends_on:
       mysql:
         condition: service_healthy
     image: ${RAGFLOW_IMAGE}
+    # example to setup MCP server
+    # command:
+    #   - --enable-mcpserver
+    #   - --mcp-host=0.0.0.0
+    #   - --mcp-port=9382
+    #   - --mcp-base-url=http://127.0.0.1:9380
+    #   - --mcp-script-path=/ragflow/mcp/server/server.py
+    #   - --mcp-mode=self-host
+    #   - --mcp--host-api-key="ragflow-xxxxxxx"
     container_name: ragflow-server
     ports:
       - ${SVR_HTTP_PORT}:9380
       - 80:80
       - 443:443
+      - 5678:5678
+      - 5679:5679
+      - 9382:9382 # entry for MCP (host_port:docker_port). The docker_port should match with the value you set for `mcp-port` above
     volumes:
       - ./ragflow-logs:/ragflow/logs
       - ./nginx/ragflow.conf:/etc/nginx/conf.d/ragflow.conf
       - ./nginx/proxy.conf:/etc/nginx/proxy.conf
       - ./nginx/nginx.conf:/etc/nginx/nginx.conf
+      - ../history_data_agent:/ragflow/history_data_agent
+      - ./service_conf.yaml.template:/ragflow/conf/service_conf.yaml.template
+
     env_file: .env
     environment:
       - TZ=${TIMEZONE}

docker/entrypoint-parser.sh

@@ -1,28 +0,0 @@
-#!/bin/bash
-
-# replace env variables in the service_conf.yaml file
-rm -rf /ragflow/conf/service_conf.yaml
-while IFS= read -r line || [[ -n "$line" ]]; do
-    # Use eval to interpret the variable with default values
-    eval "echo \"$line\"" >> /ragflow/conf/service_conf.yaml
-done < /ragflow/conf/service_conf.yaml.template
-
-export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu/
-
-PY=python3
-
-CONSUMER_NO_BEG=$1
-CONSUMER_NO_END=$2
-
-function task_exe(){
-    while [ 1 -eq 1 ]; do
-      $PY rag/svr/task_executor.py $1;
-    done
-}
-
-for ((i=CONSUMER_NO_BEG; i<CONSUMER_NO_END; i++))
-do
-  task_exe $i &
-done
-
-wait;

docker/entrypoint.sh

@@ -1,34 +1,192 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
-# replace env variables in the service_conf.yaml file
-rm -rf /ragflow/conf/service_conf.yaml
-while IFS= read -r line || [[ -n "$line" ]]; do
-    # Use eval to interpret the variable with default values
-    eval "echo \"$line\"" >> /ragflow/conf/service_conf.yaml
-done < /ragflow/conf/service_conf.yaml.template
+set -e
 
-/usr/sbin/nginx
+# -----------------------------------------------------------------------------
+# Usage and command-line argument parsing
+# -----------------------------------------------------------------------------
+function usage() {
+    echo "Usage: $0 [--disable-webserver] [--disable-taskexecutor] [--consumer-no-beg=<num>] [--consumer-no-end=<num>] [--workers=<num>] [--host-id=<string>]"
+    echo
+    echo "  --disable-webserver             Disables the web server (nginx + ragflow_server)."
+    echo "  --disable-taskexecutor          Disables task executor workers."
+    echo "  --enable-mcpserver              Enables the MCP server."
+    echo "  --consumer-no-beg=<num>         Start range for consumers (if using range-based)."
+    echo "  --consumer-no-end=<num>         End range for consumers (if using range-based)."
+    echo "  --workers=<num>                 Number of task executors to run (if range is not used)."
+    echo "  --host-id=<string>              Unique ID for the host (defaults to \`hostname\`)."
+    echo
+    echo "Examples:"
+    echo "  $0 --disable-taskexecutor"
+    echo "  $0 --disable-webserver --consumer-no-beg=0 --consumer-no-end=5"
+    echo "  $0 --disable-webserver --workers=2 --host-id=myhost123"
+    echo "  $0 --enable-mcpserver"
+    exit 1
+}
 
-export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu/
+ENABLE_WEBSERVER=1 # Default to enable web server
+ENABLE_TASKEXECUTOR=1  # Default to enable task executor
+ENABLE_MCP_SERVER=0
+CONSUMER_NO_BEG=0
+CONSUMER_NO_END=0
+WORKERS=1
 
-PY=python3
-if [[ -z "$WS" || $WS -lt 1 ]]; then
-  WS=1
+MCP_HOST="127.0.0.1"
+MCP_PORT=9382
+MCP_BASE_URL="http://127.0.0.1:9380"
+MCP_SCRIPT_PATH="/ragflow/mcp/server/server.py"
+MCP_MODE="self-host"
+MCP_HOST_API_KEY=""
+
+# -----------------------------------------------------------------------------
+# Host ID logic:
+#   1. By default, use the system hostname if length <= 32
+#   2. Otherwise, use the full MD5 hash of the hostname (32 hex chars)
+# -----------------------------------------------------------------------------
+CURRENT_HOSTNAME="$(hostname)"
+if [ ${#CURRENT_HOSTNAME} -le 32 ]; then
+  DEFAULT_HOST_ID="$CURRENT_HOSTNAME"
+else
+  DEFAULT_HOST_ID="$(echo -n "$CURRENT_HOSTNAME" | md5sum | cut -d ' ' -f 1)"
 fi
 
-function task_exe(){
-    while [ 1 -eq 1 ];do
-      $PY rag/svr/task_executor.py $1;
+HOST_ID="$DEFAULT_HOST_ID"
+
+# Parse arguments
+for arg in "$@"; do
+  case $arg in
+    --disable-webserver)
+      ENABLE_WEBSERVER=0
+      shift
+      ;;
+    --disable-taskexecutor)
+      ENABLE_TASKEXECUTOR=0
+      shift
+      ;;
+    --enable-mcpserver)
+      ENABLE_MCP_SERVER=1
+      shift
+      ;;
+    --mcp-host=*)
+      MCP_HOST="${arg#*=}"
+      shift
+      ;;
+    --mcp-port=*)
+      MCP_PORT="${arg#*=}"
+      shift
+      ;;
+    --mcp-base-url=*)
+      MCP_BASE_URL="${arg#*=}"
+      shift
+      ;;
+    --mcp-mode=*)
+      MCP_MODE="${arg#*=}"
+      shift
+      ;;
+    --mcp-host-api-key=*)
+      MCP_HOST_API_KEY="${arg#*=}"
+      shift
+      ;;
+    --mcp-script-path=*)
+      MCP_SCRIPT_PATH="${arg#*=}"
+      shift
+      ;;
+    --consumer-no-beg=*)
+      CONSUMER_NO_BEG="${arg#*=}"
+      shift
+      ;;
+    --consumer-no-end=*)
+      CONSUMER_NO_END="${arg#*=}"
+      shift
+      ;;
+    --workers=*)
+      WORKERS="${arg#*=}"
+      shift
+      ;;
+    --host-id=*)
+      HOST_ID="${arg#*=}"
+      shift
+      ;;
+    *)
+      usage
+      ;;
+  esac
+done
+
+# -----------------------------------------------------------------------------
+# Replace env variables in the service_conf.yaml file
+# -----------------------------------------------------------------------------
+CONF_DIR="/ragflow/conf"
+TEMPLATE_FILE="${CONF_DIR}/service_conf.yaml.template"
+CONF_FILE="${CONF_DIR}/service_conf.yaml"
+
+rm -f "${CONF_FILE}"
+while IFS= read -r line || [[ -n "$line" ]]; do
+    eval "echo \"$line\"" >> "${CONF_FILE}"
+done < "${TEMPLATE_FILE}"
+
+export LD_LIBRARY_PATH="/usr/lib/x86_64-linux-gnu/"
+PY=python3
+
+# -----------------------------------------------------------------------------
+# Function(s)
+# -----------------------------------------------------------------------------
+
+function task_exe() {
+    local consumer_id="$1"
+    local host_id="$2"
+
+    JEMALLOC_PATH="$(pkg-config --variable=libdir jemalloc)/libjemalloc.so"
+    while true; do
+        LD_PRELOAD="$JEMALLOC_PATH" \
+        "$PY" rag/svr/task_executor.py "${host_id}_${consumer_id}"
     done
 }
 
-for ((i=0;i<WS;i++))
-do
-  task_exe  $i &
-done
+function start_mcp_server() {
+    echo "Starting MCP Server on ${MCP_HOST}:${MCP_PORT} with base URL ${MCP_BASE_URL}..."
+    "$PY" "${MCP_SCRIPT_PATH}" \
+        --host="${MCP_HOST}" \
+        --port="${MCP_PORT}" \
+        --base_url="${MCP_BASE_URL}" \
+        --mode="${MCP_MODE}" \
+        --api_key="${MCP_HOST_API_KEY}" \ &
+}
 
-while [ 1 -eq 1 ];do
-    $PY api/ragflow_server.py
-done
+# -----------------------------------------------------------------------------
+# Start components based on flags
+# -----------------------------------------------------------------------------
 
-wait;
+if [[ "${ENABLE_WEBSERVER}" -eq 1 ]]; then
+    echo "Starting nginx..."
+    /usr/sbin/nginx
+
+    echo "Starting ragflow_server..."
+    while true; do
+        "$PY" api/ragflow_server.py
+    done &
+fi
+
+
+if [[ "${ENABLE_MCP_SERVER}" -eq 1 ]]; then
+    start_mcp_server
+fi
+
+if [[ "${ENABLE_TASKEXECUTOR}" -eq 1 ]]; then
+    if [[ "${CONSUMER_NO_END}" -gt "${CONSUMER_NO_BEG}" ]]; then
+        echo "Starting task executors on host '${HOST_ID}' for IDs in [${CONSUMER_NO_BEG}, ${CONSUMER_NO_END})..."
+        for (( i=CONSUMER_NO_BEG; i<CONSUMER_NO_END; i++ ))
+        do
+          task_exe "${i}" "${HOST_ID}" &
+        done
+    else
+        # Otherwise, start a fixed number of workers
+        echo "Starting ${WORKERS} task executor(s) on host '${HOST_ID}'..."
+        for (( i=0; i<WORKERS; i++ ))
+        do
+          task_exe "${i}" "${HOST_ID}" &
+        done
+    fi
+fi
+
+wait

docker/launch_backend_service.sh

@@ -3,10 +3,33 @@
 # Exit immediately if a command exits with a non-zero status
 set -e
 
+# Function to load environment variables from .env file
+load_env_file() {
+    # Get the directory of the current script
+    local script_dir="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+    local env_file="$script_dir/.env"
+
+    # Check if .env file exists
+    if [ -f "$env_file" ]; then
+        echo "Loading environment variables from: $env_file"
+        # Source the .env file
+        set -a
+        source "$env_file" 
+        set +a
+    else
+        echo "Warning: .env file not found at: $env_file"
+    fi
+}
+
+# Load environment variables
+load_env_file
+
 # Unset HTTP proxies that might be set by Docker daemon
 export http_proxy=""; export https_proxy=""; export no_proxy=""; export HTTP_PROXY=""; export HTTPS_PROXY=""; export NO_PROXY=""
+export PYTHONPATH=$(pwd)
 
 export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu/
+JEMALLOC_PATH=$(pkg-config --variable=libdir jemalloc)/libjemalloc.so
 
 PY=python3
 
@@ -47,7 +70,7 @@ task_exe(){
     local retry_count=0
     while ! $STOP && [ $retry_count -lt $MAX_RETRIES ]; do
         echo "Starting task_executor.py for task $task_id (Attempt $((retry_count+1)))"
-        $PY rag/svr/task_executor.py "$task_id"
+        LD_PRELOAD=$JEMALLOC_PATH $PY rag/svr/task_executor.py "$task_id"
         EXIT_CODE=$?
         if [ $EXIT_CODE -eq 0 ]; then
             echo "task_executor.py for task $task_id exited successfully."

docker/nginx/nginx.conf

@@ -26,7 +26,7 @@ http {
     keepalive_timeout  65;
 
     #gzip  on;
-    client_max_body_size 128M;
+    client_max_body_size 1024M;
 
     include /etc/nginx/conf.d/ragflow.conf;
 }

docker/service_conf.yaml.template

@@ -37,6 +37,11 @@ redis:
 #   access_key: 'access_key'
 #   secret_key: 'secret_key'
 #   region: 'region'
+#   endpoint_url: 'endpoint_url'
+#   bucket: 'bucket'
+#   prefix_path: 'prefix_path'
+#   signature_version: 'v4'
+#   addressing_style: 'path'
 # oss:
 #   access_key: '${ACCESS_KEY}'
 #   secret_key: '${SECRET_KEY}'

docs/configurations.md

@@ -3,7 +3,7 @@ sidebar_position: 1
 slug: /configurations
 ---
 
-# Configurations
+# Configuration
 
 Configurations for deploying RAGFlow via Docker.
 
@@ -15,7 +15,7 @@ When it comes to system configurations, you will need to manage the following fi
 - [service_conf.yaml.template](https://github.com/infiniflow/ragflow/blob/main/docker/service_conf.yaml.template): Configures the back-end services. It specifies the system-level configuration for RAGFlow and is used by its API server and task executor. Upon container startup, the `service_conf.yaml` file will be generated based on this template file. This process replaces any environment variables within the template, allowing for dynamic configuration tailored to the container's environment.
 - [docker-compose.yml](https://github.com/infiniflow/ragflow/blob/main/docker/docker-compose.yml): The Docker Compose file for starting up the RAGFlow service.
 
-To update the default HTTP serving port (80), go to [docker-compose.yml](./docker/docker-compose.yml) and change `80:80`
+To update the default HTTP serving port (80), go to [docker-compose.yml](https://github.com/infiniflow/ragflow/blob/main/docker/docker-compose.yml) and change `80:80`
 to `<YOUR_SERVING_PORT>:80`.
 
 :::tip NOTE
@@ -74,6 +74,8 @@ The [.env](https://github.com/infiniflow/ragflow/blob/main/docker/.env) file con
 
 ### MinIO
 
+RAGFlow utilizes MinIO as its object storage solution, leveraging its scalability to store and manage all uploaded files.
+
 - `MINIO_CONSOLE_PORT`  
   The port used to expose the MinIO console interface to the host machine, allowing **external** access to the web-based console running inside the Docker container. Defaults to `9001`
 - `MINIO_PORT`  
@@ -97,22 +99,12 @@ The [.env](https://github.com/infiniflow/ragflow/blob/main/docker/.env) file con
 - `RAGFLOW-IMAGE`  
   The Docker image edition. Available editions:  
   
-  - `infiniflow/ragflow:v0.17.0-slim` (default): The RAGFlow Docker image without embedding models.  
-  - `infiniflow/ragflow:v0.17.0`: The RAGFlow Docker image with embedding models including:
+  - `infiniflow/ragflow:v0.18.0-slim` (default): The RAGFlow Docker image without embedding models.  
+  - `infiniflow/ragflow:v0.18.0`: The RAGFlow Docker image with embedding models including:
     - Built-in embedding models:
       - `BAAI/bge-large-zh-v1.5` 
-      - `BAAI/bge-reranker-v2-m3`
       - `maidalun1020/bce-embedding-base_v1`
-      - `maidalun1020/bce-reranker-base_v1`
-    - Embedding models that will be downloaded once you select them in the RAGFlow UI:
-      - `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`
+
 
 :::tip NOTE  
 If you cannot download the RAGFlow Docker image, try the following mirrors.  

docs/develop/_category_.json

@@ -0,0 +1,8 @@
+{
+  "label": "Developers",
+  "position": 4,
+  "link": {
+    "type": "generated-index",
+    "description": "Guides for hardcore developers"
+  }
+}

docs/develop/acquire_ragflow_api_key.md

@@ -3,7 +3,7 @@ sidebar_position: 3
 slug: /acquire_ragflow_api_key
 ---
 
-# Acquire a RAGFlow API key
+# Acquire RAGFlow API key
 
 A key is required for the RAGFlow server to authenticate your requests via HTTP or a Python API. This documents provides instructions on obtaining a RAGFlow API key.
 
@@ -14,5 +14,5 @@ A key is required for the RAGFlow server to authenticate your requests via HTTP
 ![ragflow_api_key](https://github.com/user-attachments/assets/f461ed61-04c6-4faf-b3d8-6b5fa56be4e7)
 
 :::tip NOTE
-See the [RAGFlow HTTP API reference](../../references/http_api_reference.md) or the [RAGFlow Python API reference](../../references/python_api_reference.md) for a complete reference of RAGFlow's HTTP or Python APIs.
+See the [RAGFlow HTTP API reference](../references/http_api_reference.md) or the [RAGFlow Python API reference](../references/python_api_reference.md) for a complete reference of RAGFlow's HTTP or Python APIs.
 :::

docs/develop/build_docker_image.mdx

@@ -3,7 +3,7 @@ sidebar_position: 1
 slug: /build_docker_image
 ---
 
-# Build a RAGFlow Docker Image
+# Build RAGFlow Docker image
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 
@@ -21,7 +21,6 @@ A guide explaining how to build a RAGFlow Docker image from its source code. By
 - RAM ≥ 16 GB
 - Disk ≥ 50 GB
 - Docker ≥ 24.0.0 & Docker Compose ≥ v2.26.1
-- For ARM64 platforms, please upgrade the `xgboost` version in **pyproject.toml** to `1.6.0` and ensure **unixODBC** is properly installed.
 
 ## Build a Docker image
 
@@ -35,8 +34,9 @@ A guide explaining how to build a RAGFlow Docker image from its source code. By
 
 This image is approximately 2 GB in size and relies on external LLM and embedding services.
 
-:::tip NOTE
-While we also test RAGFlow on ARM64 platforms, we do not maintain RAGFlow Docker images for ARM. However, you can build an image yourself on a `linux/arm64` or `darwin/arm64` host machine as well.
+:::danger IMPORTANT
+- While we also test RAGFlow on ARM64 platforms, we do not maintain RAGFlow Docker images for ARM. However, you can build an image yourself on a `linux/arm64` or `darwin/arm64` host machine as well.
+- For ARM64 platforms, please upgrade the `xgboost` version in **pyproject.toml** to `1.6.0` and ensure **unixODBC** is properly installed.
 :::
 
 ```bash
@@ -53,8 +53,9 @@ docker build --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-s
 
 This image is approximately 9 GB in size. As it includes embedding models, it relies on external LLM services only.  
 
-:::tip NOTE
-While we also test RAGFlow on ARM64 platforms, we do not maintain RAGFlow Docker images for ARM. However, you can build an image yourself on a `linux/arm64` or `darwin/arm64` host machine.
+:::danger IMPORTANT
+- While we also test RAGFlow on ARM64 platforms, we do not maintain RAGFlow Docker images for ARM. However, you can build an image yourself on a `linux/arm64` or `darwin/arm64` host machine as well.
+- For ARM64 platforms, please upgrade the `xgboost` version in **pyproject.toml** to `1.6.0` and ensure **unixODBC** is properly installed.
 :::
 
 ```bash
@@ -76,17 +77,8 @@ After building the infiniflow/ragflow:nightly-slim image, you are ready to launc
 
 1. Edit Docker Compose Configuration
 
-Open the `docker/docker-compose-base.yml` file. Find the `infinity.image` setting and change the image reference from `infiniflow/infinity:v0.6.0-dev3` to `infiniflow/ragflow:nightly-slim` to use the pre-built image.
+Open the `docker/.env` file. Find the `RAGFLOW_IMAGE` setting and change the image reference from `infiniflow/ragflow:v0.18.0-slim` to `infiniflow/ragflow:nightly-slim` to use the pre-built image.
 
-```yaml
-infinity:
-  container_name: ragflow-infinity
-  image: infiniflow/ragflow:nightly-slim # here
-  volumes:
-    - ...
-    - ...
-  ...
-```
 
 2. Launch the Service
 

docs/develop/launch_ragflow_from_source.md

@@ -3,11 +3,11 @@ sidebar_position: 2
 slug: /launch_ragflow_from_source
 ---
 
-# Launch a RAGFlow Service from Source
+# Launch service from source
 
 A guide explaining how to set up a RAGFlow service from its source code. By following this guide, you'll be able to debug using the source code.
 
-## Target Audience
+## Target audience
 
 Developers who have added new features or modified existing code and wish to debug using the source code, *provided that* their machine has the target deployment environment set up.
 
@@ -22,11 +22,11 @@ Developers who have added new features or modified existing code and wish to deb
 If you have not installed Docker on your local machine (Windows, Mac, or Linux), see the [Install Docker Engine](https://docs.docker.com/engine/install/) guide.
 :::
 
-## Launch the Service from Source
+## Launch a service from source
 
-To launch the RAGFlow service from source code:
+To launch a RAGFlow service from source code:
 
-### Clone the RAGFlow Repository
+### Clone the RAGFlow repository
 
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
@@ -52,7 +52,7 @@ cd ragflow/
    ```
    *A virtual environment named `.venv` is created, and all Python dependencies are installed into the new environment.*
 
-### Launch Third-party Services
+### Launch third-party services
 
 The following command launches the 'base' services (MinIO, Elasticsearch, Redis, and MySQL) using Docker Compose:
 
@@ -70,7 +70,7 @@ docker compose -f docker/docker-compose-base.yml up -d
 
 2. In **docker/service_conf.yaml.template**, update mysql port to `5455` and es port to `1200`, as specified in **docker/.env**.
 
-### Launch the RAGFlow Backend Service
+### Launch the RAGFlow backend service
 
 1. Comment out the `nginx` line in **docker/entrypoint.sh**.
 
@@ -91,10 +91,16 @@ docker compose -f docker/docker-compose-base.yml up -d
    export HF_ENDPOINT=https://hf-mirror.com
    ```
 
-4. Run the **entrypoint.sh** script to launch the backend service:
+4. Check the configuration in **conf/service_conf.yaml**, ensuring all hosts and ports are correctly set.
    
+5. Run the **entrypoint.sh** script to launch the backend service:
+
+   ```shell
+   JEMALLOC_PATH=$(pkg-config --variable=libdir jemalloc)/libjemalloc.so;
+   LD_PRELOAD=$JEMALLOC_PATH python rag/svr/task_executor.py 1;
    ```
-   bash docker/entrypoint.sh
+   ```shell
+   python api/ragflow_server.py;
    ```
 
 ### Launch the RAGFlow frontend service

docs/develop/mcp.md

@@ -0,0 +1,195 @@
+---
+sidebar_position: 4
+slug: /mcp_server
+---
+
+# RAGFlow MCP server overview
+
+The RAGFlow Model Context Protocol (MCP) server operates as an independent component that complements the RAGFlow server. However, it requires a RAGFlow server to work functionally well, meaning, the MCP client and server communicate with each other in MCP HTTP+SSE mode (once the connection is established, server pushes messages to client only), and responses are expected from RAGFlow server.
+
+The MCP server currently offers a specific tool to assist users in searching for relevant information powered by RAGFlow DeepDoc technology:
+
+- **retrieve**: Fetches relevant chunks from specified `dataset_ids` and optional `document_ids` using the RAGFlow retrieve interface, based on a given question. Details of all available datasets, namely, `id` and `description`, are provided within the tool description for each individual dataset.
+
+## Launching the MCP Server
+
+Similar to launching the RAGFlow server, the MCP server can be started either from source code or via Docker.
+
+### Launch Modes
+
+The MCP server supports two launch modes:
+
+1. **Self-Host Mode**:
+
+   - In this mode, the MCP server is launched to access a specific tenant's datasets.
+   - This is the default mode.
+   - The `--api_key` argument is **required** to authenticate the server with the RAGFlow server.
+   - Example:
+     ```bash
+     uv run mcp/server/server.py --host=127.0.0.1 --port=9382 --base_url=http://127.0.0.1:9380 --mode=self-host --api_key=ragflow-xxxxx
+     ```
+
+1. **Host Mode**:
+
+   - In this mode, the MCP server allows each user to access their own datasets.
+   - To ensure secure access, a valid API key must be included in the request headers to identify the user.
+   - The `--api_key` argument is **not required** during server launch but must be provided in the headers on each client request for user authentication.
+   - Example:
+     ```bash
+     uv run mcp/server/server.py --host=127.0.0.1 --port=9382 --base_url=http://127.0.0.1:9380 --mode=host
+     ```
+
+### Launching from Source Code
+
+All you need to do is stand on the right place and strike out command, assuming you are on the project working directory.
+
+```bash
+uv run mcp/server/server.py --host=127.0.0.1 --port=9382 --base_url=http://127.0.0.1:9380 --api_key=ragflow-xxxxx
+```
+
+For testing purposes, there is an [MCP client example](#example_mcp_client) provided, free to take!
+
+#### Required Arguments
+
+- **`host`**: Specifies the server's host address.
+- **`port`**: Defines the server's listening port.
+- **`base_url`**: The address of the RAGFlow server that is already running and ready to handle tasks.
+- **`mode`**: Launch mode, only accept `self-host` or `host`.
+- **`api_key`**: Required when `mode` is `self-host` to authenticate the MCP server with the RAGFlow server.
+
+Here are three augments required, the first two,`host` and `port`, are self-explained. The`base_url` is the address of the ready-to-serve RAGFlow server to actually perform the task.
+
+### Launching from Docker
+
+Building a standalone MCP server image is straightforward and easy, so we just proposed a way to launch it with RAGFlow server here.
+
+#### Alongside RAGFlow
+
+As MCP server is an extra and optional component of RAGFlow server, we consume that not everybody going to use it. Thus, it is disable by default.
+To enable it, simply find `docker/docker-compose.yml` to uncomment `services.ragflow.command` section.
+
+```yaml
+services:
+  ragflow:
+    ...
+    image: ${RAGFLOW_IMAGE}
+    # example to setup MCP server
+    command:
+      - --enable-mcpserver
+      - --mcp-host=0.0.0.0
+      - --mcp-port=9382
+      - --mcp-base-url=http://127.0.0.1:9380
+      - --mcp-script-path=/ragflow/mcp/server/server.py
+      - --mcp-mode=self-host # `self-host` or `host`
+      - --mcp--host-api-key="ragflow-xxxxxxx" # only need to privide when mode is `self-host`
+```
+
+Then launch it normally `docker compose -f docker-compose.yml`.
+
+```bash
+ragflow-server  | Starting MCP Server on 0.0.0.0:9382 with base URL http://127.0.0.1:9380...
+ragflow-server  | Starting 1 task executor(s) on host 'dd0b5e07e76f'...
+ragflow-server  | 2025-04-18 15:41:18,816 INFO     27 ragflow_server log path: /ragflow/logs/ragflow_server.log, log levels: {'peewee': 'WARNING', 'pdfminer': 'WARNING', 'root': 'INFO'}
+ragflow-server  | 
+ragflow-server  | __  __  ____ ____       ____  _____ ______     _______ ____
+ragflow-server  | |  \/  |/ ___|  _ \     / ___|| ____|  _ \ \   / / ____|  _ \
+ragflow-server  | | |\/| | |   | |_) |    \___ \|  _| | |_) \ \ / /|  _| | |_) |
+ragflow-server  | | |  | | |___|  __/      ___) | |___|  _ < \ V / | |___|  _ <
+ragflow-server  | |_|  |_|\____|_|        |____/|_____|_| \_\ \_/  |_____|_| \_\
+ragflow-server  |     
+ragflow-server  | MCP launch mode: self-host
+ragflow-server  | MCP host: 0.0.0.0
+ragflow-server  | MCP port: 9382
+ragflow-server  | MCP base_url: http://127.0.0.1:9380
+ragflow-server  | INFO:     Started server process [26]
+ragflow-server  | INFO:     Waiting for application startup.
+ragflow-server  | INFO:     Application startup complete.
+ragflow-server  | INFO:     Uvicorn running on http://0.0.0.0:9382 (Press CTRL+C to quit)
+ragflow-server  | 2025-04-18 15:41:20,469 INFO     27 found 0 gpus
+ragflow-server  | 2025-04-18 15:41:23,263 INFO     27 init database on cluster mode successfully
+ragflow-server  | 2025-04-18 15:41:25,318 INFO     27 load_model /ragflow/rag/res/deepdoc/det.onnx uses CPU
+ragflow-server  | 2025-04-18 15:41:25,367 INFO     27 load_model /ragflow/rag/res/deepdoc/rec.onnx uses CPU
+ragflow-server  |         ____   ___    ______ ______ __               
+ragflow-server  |        / __ \ /   |  / ____// ____// /____  _      __
+ragflow-server  |       / /_/ // /| | / / __ / /_   / // __ \| | /| / /
+ragflow-server  |      / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ / 
+ragflow-server  |     /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/                             
+ragflow-server  | 
+ragflow-server  |     
+ragflow-server  | 2025-04-18 15:41:29,088 INFO     27 RAGFlow version: v0.18.0-285-gb2c299fa full
+ragflow-server  | 2025-04-18 15:41:29,088 INFO     27 project base: /ragflow
+ragflow-server  | 2025-04-18 15:41:29,088 INFO     27 Current configs, from /ragflow/conf/service_conf.yaml:
+ragflow-server  |  ragflow: {'host': '0.0.0.0', 'http_port': 9380}
+...
+ragflow-server  |  * Running on all addresses (0.0.0.0)
+ragflow-server  |  * Running on http://127.0.0.1:9380
+ragflow-server  |  * Running on http://172.19.0.6:9380
+ragflow-server  |   ______           __      ______                     __            
+ragflow-server  |  /_  __/___ ______/ /__   / ____/  _____  _______  __/ /_____  _____
+ragflow-server  |   / / / __ `/ ___/ //_/  / __/ | |/_/ _ \/ ___/ / / / __/ __ \/ ___/
+ragflow-server  |  / / / /_/ (__  ) ,<    / /____>  </  __/ /__/ /_/ / /_/ /_/ / /    
+ragflow-server  | /_/  \__,_/____/_/|_|  /_____/_/|_|\___/\___/\__,_/\__/\____/_/                               
+ragflow-server  |     
+ragflow-server  | 2025-04-18 15:41:34,501 INFO     32 TaskExecutor: RAGFlow version: v0.18.0-285-gb2c299fa full
+ragflow-server  | 2025-04-18 15:41:34,501 INFO     32 Use Elasticsearch http://es01:9200 as the doc engine.
+...
+```
+
+You are ready to brew🍺!
+
+## Testing and Usage
+
+Typically, there are various ways to utilize an MCP server. You can integrate it with LLMs or use it as a standalone tool. You find the way.
+
+### Example MCP Client {#example_mcp_client}
+
+```python
+#
+#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+
+
+from mcp.client.session import ClientSession
+from mcp.client.sse import sse_client
+
+
+async def main():
+    try:
+        # To access RAGFlow server in `host` mode, you need to attach `api_key` for each request to indicate identification.
+        # async with sse_client("http://localhost:9382/sse", headers={"api_key": "ragflow-IyMGI1ZDhjMTA2ZTExZjBiYTMyMGQ4Zm"}) as streams:
+        async with sse_client("http://localhost:9382/sse") as streams:
+            async with ClientSession(
+                streams[0],
+                streams[1],
+            ) as session:
+                await session.initialize()
+                tools = await session.list_tools()
+                print(f"{tools.tools=}")
+                response = await session.call_tool(name="ragflow_retrieval", arguments={"dataset_ids": ["ce3bb17cf27a11efa69751e139332ced"], "document_ids": [], "question": "How to install neovim?"})
+                print(f"Tool response: {response.model_dump()}")
+
+    except Exception as e:
+        print(e)
+
+
+if __name__ == "__main__":
+    from anyio import run
+
+    run(main)
+```
+
+## Security and Concerns
+
+Since MCP technology is still in booming age and there are still no official Authentication and Authorization best practices to follow, RAGFlow uses `api_key` to validate the identification, and it is required to perform any operations mentioned in the preview section. Obviously, this is not a premium solution to do so, thus this RAGFlow MCP server is not expected to exposed to public use as it could be highly venerable to be attacked. For local SSE server, bind only to localhost (127.0.0.1) instead of all interfaces (0.0.0.0). For additional guidance, you can refer to [MCP official website](https://modelcontextprotocol.io/docs/concepts/transports#security-considerations).

docs/faq.mdx

@@ -3,12 +3,16 @@ sidebar_position: 10
 slug: /faq
 ---
 
-# Frequently asked questions
+# FAQs
 
-Queries regarding general features, troubleshooting, usage, and more.
+Answers to questions about general features, troubleshooting, usage, and more.
 
 ---
 
+import TOCInline from '@theme/TOCInline';
+
+<TOCInline toc={toc} />
+
 ## General features
 
 ---
@@ -37,12 +41,12 @@ If you build RAGFlow from source, the version number is also in the system log:
      / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ / 
     /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/                             
 
-2025-02-18 10:10:43,835 INFO     1445658 RAGFlow version: v0.17.0-50-g6daae7f2 full
+2025-02-18 10:10:43,835 INFO     1445658 RAGFlow version: v0.15.0-50-g6daae7f2 full
 ```
 
 Where:
 
-- `v0.17.0`: The officially published release.
+- `v0.15.0`: The officially published release.
 - `50`: The number of git commits since the official release.
 - `g6daae7f2`: `g` is the prefix, and `6daae7f2` is the first seven characters of the current commit ID.
 - `full`/`slim`: The RAGFlow edition.
@@ -65,16 +69,16 @@ RAGFlow has a number of built-in models for document structure parsing, which ac
 
 ### Which architectures or devices does RAGFlow support?
 
-We officially support x86 CPU and nvidia GPU. While we also test RAGFlow on ARM64 platforms, we do not maintain RAGFlow Docker images for ARM. If you are on an ARM platform, follow [this guide](https://ragflow.io/docs/dev/build_docker_image) to build a RAGFlow Docker image.
+We officially support x86 CPU and nvidia GPU. While we also test RAGFlow on ARM64 platforms, we do not maintain RAGFlow Docker images for ARM. If you are on an ARM platform, follow [this guide](./develop/build_docker_image.mdx) to build a RAGFlow Docker image.
 
 ---
 
 ### Which embedding models can be deployed locally?
 
-RAGFlow offers two Docker image editions, `v0.17.0-slim` and `v0.17.0`:  
+RAGFlow offers two Docker image editions, `v0.18.0-slim` and `v0.18.0`:  
   
-- `infiniflow/ragflow:v0.17.0-slim` (default): The RAGFlow Docker image without embedding models.  
-- `infiniflow/ragflow:v0.17.0`: The RAGFlow Docker image with embedding models including:
+- `infiniflow/ragflow:v0.18.0-slim` (default): The RAGFlow Docker image without embedding models.  
+- `infiniflow/ragflow:v0.18.0`: The RAGFlow Docker image with embedding models including:
   - Built-in embedding models:
     - `BAAI/bge-large-zh-v1.5`
     - `BAAI/bge-reranker-v2-m3`
@@ -94,7 +98,7 @@ RAGFlow offers two Docker image editions, `v0.17.0-slim` and `v0.17.0`:
 
 ### Do you offer an API for integration with third-party applications?
 
-The corresponding APIs are now available. See the [RAGFlow HTTP API Reference](./http_api_reference.md) or the [RAGFlow Python API Reference](./python_api_reference.md) for more information.
+The corresponding APIs are now available. See the [RAGFlow HTTP API Reference](./references/http_api_reference.md) or the [RAGFlow Python API Reference](./references/python_api_reference.md) for more information.
 
 ---
 
@@ -104,7 +108,7 @@ Yes, we do.
 
 ---
 
-### Is it possible to share dialogue through URL?
+### Do you support sharing dialogue through URL?
 
 No, this feature is not supported.
 
@@ -115,30 +119,29 @@ No, this feature is not supported.
 Yes, we support enhancing user queries based on existing context of an ongoing conversation:
 
 1. On the **Chat** page, hover over the desired assistant and select **Edit**.
-2. In the **Chat Configuration** popup, click the **Prompt Engine** tab.
+2. In the **Chat Configuration** popup, click the **Prompt engine** tab.
 3. Switch on **Multi-turn optimization** to enable this feature.
 
 ---
 
+### Key differences between AI search and chat?
+
+- **AI search**: This is a single-turn AI conversation using a predefined retrieval strategy (a hybrid search of weighted keyword similarity and weighted vector similarity) and the system's default chat model. It does not involve advanced RAG strategies like knowledge graph, auto-keyword, or auto-question. Retrieved chunks will be listed below the chat model's response.
+- **AI chat**: This is a multi-turn AI conversation where you can define your retrieval strategy (a weighted reranking score can be used to replace the weighted vector similarity in a hybrid search) and choose your chat model. In an AI chat, you can configure advanced RAG strategies, such as knowledge graphs, auto-keyword, and auto-question, for your specific case. Retrieved chunks are not displayed along with the answer.
+
+When debugging your chat assistant, you can use AI search as a reference to verify your model settings and retrieval strategy.
+
+---
+
 ## Troubleshooting
 
 ---
 
-### Issues with Docker images
+### How to build the RAGFlow image from scratch?
 
----
+See [Build a RAGFlow Docker image](./develop/build_docker_image.mdx).
 
-#### How to build the RAGFlow image from scratch?
-
-See [Build a RAGFlow Docker image](https://ragflow.io/docs/dev/build_docker_image).
-
----
-
-### Issues with huggingface models
-
----
-
-#### Cannot access https://huggingface.co
+### Cannot access https://huggingface.co
 
 A locally deployed RAGflow downloads OCR and embedding modules from [Huggingface website](https://huggingface.co) by default. If your machine is unable to access this site, the following error occurs and PDF parsing fails:
 
@@ -169,7 +172,7 @@ To fix this issue, use https://hf-mirror.com instead:
 
 ---
 
-#### `MaxRetryError: HTTPSConnectionPool(host='hf-mirror.com', port=443)`
+### `MaxRetryError: HTTPSConnectionPool(host='hf-mirror.com', port=443)`
 
 This error suggests that you do not have Internet access or are unable to connect to hf-mirror.com. Try the following:
 
@@ -182,17 +185,13 @@ This error suggests that you do not have Internet access or are unable to connec
 
 ---
 
-### Issues with RAGFlow servers
-
----
-
-#### `WARNING: can't find /raglof/rag/res/borker.tm`
+### `WARNING: can't find /raglof/rag/res/borker.tm`
 
 Ignore this warning and continue. All system warnings can be ignored.
 
 ---
 
-#### `network anomaly There is an abnormality in your network and you cannot connect to the server.`
+### `network anomaly There is an abnormality in your network and you cannot connect to the server.`
 
 ![anomaly](https://github.com/infiniflow/ragflow/assets/93570324/beb7ad10-92e4-4a58-8886-bfb7cbd09e5d)
 
@@ -215,11 +214,7 @@ You will not log in to RAGFlow unless the server is fully initialized. Run `dock
 
 ---
 
-### Issues with RAGFlow backend services
-
----
-
-#### `Realtime synonym is disabled, since no redis connection`
+### `Realtime synonym is disabled, since no redis connection`
 
 Ignore this warning and continue. All system warnings can be ignored.
 
@@ -227,7 +222,7 @@ Ignore this warning and continue. All system warnings can be ignored.
 
 ---
 
-#### Why does my document parsing stall at under one percent?
+### Why does my document parsing stall at under one percent?
 
 ![stall](https://github.com/infiniflow/ragflow/assets/93570324/3589cc25-c733-47d5-bbfc-fedb74a3da50)
 
@@ -244,7 +239,7 @@ Click the red cross beside the 'parsing status' bar, then restart the parsing pr
 
 ---
 
-#### Why does my pdf parsing stall near completion, while the log does not show any error?
+### Why does my pdf parsing stall near completion, while the log does not show any error?
 
 Click the red cross beside the 'parsing status' bar, then restart the parsing process to see if the issue remains. If the issue persists and your RAGFlow is deployed locally, the parsing process is likely killed due to insufficient RAM. Try increasing your memory allocation by increasing the `MEM_LIMIT` value in **docker/.env**.
 
@@ -265,13 +260,13 @@ docker compose up -d
 
 ---
 
-#### `Index failure`
+### `Index failure`
 
 An index failure usually indicates an unavailable Elasticsearch service.
 
 ---
 
-#### How to check the log of RAGFlow?
+### How to check the log of RAGFlow?
 
 ```bash
 tail -f ragflow/docker/ragflow-logs/*.log
@@ -279,7 +274,7 @@ tail -f ragflow/docker/ragflow-logs/*.log
 
 ---
 
-#### How to check the status of each component in RAGFlow?
+### How to check the status of each component in RAGFlow?
 
 1. Check the status of the Elasticsearch Docker container:
 
@@ -296,7 +291,7 @@ tail -f ragflow/docker/ragflow-logs/*.log
    cd29bcb254bc   quay.io/minio/minio:RELEASE.2023-12-20T01-00-02Z       "/usr/bin/docker-ent…"   2 weeks ago    Up 11 hours      0.0.0.0:9001->9001/tcp, :::9001->9001/tcp, 0.0.0.0:9000->9000/tcp, :::9000->9000/tcp     ragflow-minio
    ```
 
-2. Follow [this document](../guides/run_health_check.md) to check the health status of the Elasticsearch service.
+2. Follow [this document](./guides/run_health_check.md) to check the health status of the Elasticsearch service.
 
 :::danger IMPORTANT
 The status of a Docker container status does not necessarily reflect the status of the service. You may find that your services are unhealthy even when the corresponding Docker containers are up running. Possible reasons for this include network failures, incorrect port numbers, or DNS issues.
@@ -304,7 +299,7 @@ The status of a Docker container status does not necessarily reflect the status
 
 ---
 
-#### `Exception: Can't connect to ES cluster`
+### `Exception: Can't connect to ES cluster`
 
 1. Check the status of the Elasticsearch Docker container:
 
@@ -318,7 +313,7 @@ The status of a Docker container status does not necessarily reflect the status
    91220e3285dd   docker.elastic.co/elasticsearch/elasticsearch:8.11.3   "/bin/tini -- /usr/l…"   11 hours ago   Up 11 hours (healthy)     9300/tcp, 0.0.0.0:9200->9200/tcp, :::9200->9200/tcp           ragflow-es-01
    ```
 
-2. Follow [this document](../guides/run_health_check.md) to check the health status of the Elasticsearch service.
+2. Follow [this document](./guides/run_health_check.md) to check the health status of the Elasticsearch service.
 
 :::danger IMPORTANT
 The status of a Docker container status does not necessarily reflect the status of the service. You may find that your services are unhealthy even when the corresponding Docker containers are up running. Possible reasons for this include network failures, incorrect port numbers, or DNS issues.
@@ -328,60 +323,36 @@ The status of a Docker container status does not necessarily reflect the status
 
 ---
 
-#### Can't start ES container and get `Elasticsearch did not exit normally`
+### Can't start ES container and get `Elasticsearch did not exit normally`
 
 This is because you forgot to update the `vm.max_map_count` value in **/etc/sysctl.conf** and your change to this value was reset after a system reboot.
 
 ---
 
-#### `{"data":null,"code":100,"message":"<NotFound '404: Not Found'>"}`
+### `{"data":null,"code":100,"message":"<NotFound '404: Not Found'>"}`
 
 Your IP address or port number may be incorrect. If you are using the default configurations, enter `http://<IP_OF_YOUR_MACHINE>` (**NOT 9380, AND NO PORT NUMBER REQUIRED!**) in your browser. This should work.
 
 ---
 
-#### `Ollama - Mistral instance running at 127.0.0.1:11434 but cannot add Ollama as model in RagFlow`
+### `Ollama - Mistral instance running at 127.0.0.1:11434 but cannot add Ollama as model in RagFlow`
 
 A correct Ollama IP address and port is crucial to adding models to Ollama:
 
 - If you are on demo.ragflow.io, ensure that the server hosting Ollama has a publicly accessible IP address. Note that 127.0.0.1 is not a publicly accessible IP address.
 - If you deploy RAGFlow locally, ensure that Ollama and RAGFlow are in the same LAN and can communicate with each other.
 
-See [Deploy a local LLM](../guides/deploy_local_llm.mdx) for more information.
+See [Deploy a local LLM](./guides/models/deploy_local_llm.mdx) for more information.
 
 ---
 
-#### Do you offer examples of using DeepDoc to parse PDF or other files?
+### Do you offer examples of using DeepDoc to parse PDF or other files?
 
 Yes, we do. See the Python files under the **rag/app** folder.
 
 ---
 
-#### Why did I fail to upload a 128MB+ file to my locally deployed RAGFlow?
-
-Ensure that you update the **MAX_CONTENT_LENGTH** environment variable:
-
-1. In **ragflow/docker/.env**, uncomment environment variable `MAX_CONTENT_LENGTH`:
-
-   ```
-   MAX_CONTENT_LENGTH=176160768 # 168MB
-   ```
-
-2. Update **ragflow/docker/nginx/nginx.conf**:
-
-   ```
-   client_max_body_size 168M
-   ```
-
-3. Restart the RAGFlow server:
-
-   ```
-   docker compose up ragflow -d
-   ```
-
----
-
-#### `FileNotFoundError: [Errno 2] No such file or directory`
+### `FileNotFoundError: [Errno 2] No such file or directory`
 
 1. Check the status of the MinIO Docker container:
 
@@ -395,7 +366,7 @@ Ensure that you update the **MAX_CONTENT_LENGTH** environment variable:
    cd29bcb254bc   quay.io/minio/minio:RELEASE.2023-12-20T01-00-02Z       "/usr/bin/docker-ent…"   2 weeks ago    Up 11 hours      0.0.0.0:9001->9001/tcp, :::9001->9001/tcp, 0.0.0.0:9000->9000/tcp, :::9000->9000/tcp     ragflow-minio
    ```
 
-2. Follow [this document](../guides/run_health_check.md) to check the health status of the Elasticsearch service.
+2. Follow [this document](./guides/run_health_check.md) to check the health status of the Elasticsearch service.
 
 :::danger IMPORTANT
 The status of a Docker container status does not necessarily reflect the status of the service. You may find that your services are unhealthy even when the corresponding Docker containers are up running. Possible reasons for this include network failures, incorrect port numbers, or DNS issues.
@@ -407,21 +378,13 @@ The status of a Docker container status does not necessarily reflect the status
 
 ---
 
-### How to increase the length of RAGFlow responses?
-
-1. Right-click the desired dialog to display the **Chat Configuration** window.
-2. Switch to the **Model Setting** tab and adjust the **Max Tokens** slider to get the desired length.
-3. Click **OK** to confirm your change.
-
----
-
 ### How to run RAGFlow with a locally deployed LLM?
 
-You can use Ollama or Xinference to deploy local LLM. See [here](../guides/deploy_local_llm.mdx) for more information.
+You can use Ollama or Xinference to deploy local LLM. See [here](./guides/models/deploy_local_llm.mdx) for more information.
 
 ---
 
-### Is it possible to add an LLM that is not supported?
+### How to add an LLM that is not supported?
 
 If your model is not currently supported but has APIs compatible with those of OpenAI, click **OpenAI-API-Compatible** on the **Model providers** page to configure your model:
 
@@ -429,12 +392,25 @@ If your model is not currently supported but has APIs compatible with those of O
 
 ---
 
-### How to interconnect RAGFlow with Ollama?
+### How to integrate RAGFlow with Ollama?
 
 - If RAGFlow is locally deployed, ensure that your RAGFlow and Ollama are in the same LAN.
 - If you are using our online demo, ensure that the IP address of your Ollama server is public and accessible.
 
-See [here](../guides/deploy_local_llm.mdx) for more information.
+See [here](./guides/models/deploy_local_llm.mdx) for more information.
+
+---
+
+### How to change the file size limit?
+
+For a locally deployed RAGFlow: the total file size limit per upload is 1GB, with a batch upload limit of 32 files. There is no cap on the total number of files per account. To update this 1GB file size limit:
+
+- In **docker/.env**, upcomment `# MAX_CONTENT_LENGTH=1073741824`, adjust the value as needed, and note that `1073741824` represents 1GB in bytes.
+- If you update the value of `MAX_CONTENT_LENGTH` in **docker/.env**, ensure that you update `client_max_body_size` in **nginx/nginx.conf** accordingly.
+
+:::tip NOTE
+It is not recommended to manually change the 32-file batch upload limit. However, if you use RAGFlow's HTTP API or Python SDK to upload files, the 32-file batch upload limit is automatically removed.
+:::
 
 ---
 
@@ -443,7 +419,7 @@ See [here](../guides/deploy_local_llm.mdx) for more information.
 This error occurs because there are too many chunks matching your search criteria. Try reducing the **TopN** and increasing **Similarity threshold** to fix this issue:
 
 1. Click **Chat** in the middle top of the page.
-2. Right-click the desired conversation > **Edit** > **Prompt Engine**
+2. Right-click the desired conversation > **Edit** > **Prompt engine**
 3. Reduce the **TopN** and/or raise **Similarity threshold**.
 4. Click **OK** to confirm your changes.
 
@@ -453,12 +429,40 @@ This error occurs because there are too many chunks matching your search criteri
 
 ### How to get an API key for integration with third-party applications?
 
-See [Acquire a RAGFlow API key](../guides/develop/acquire_ragflow_api_key.md).
+See [Acquire a RAGFlow API key](./develop/acquire_ragflow_api_key.md).
 
 ---
 
 ### How to upgrade RAGFlow?
 
-See [Upgrade RAGFlow](../guides/upgrade_ragflow.mdx) for more information.
+See [Upgrade RAGFlow](./guides/upgrade_ragflow.mdx) for more information.
+
+---
+
+### How to switch the document engine to Infinity?
+
+To switch your document engine from Elasticsearch to [Infinity](https://github.com/infiniflow/infinity):
+
+1. Stop all running containers:  
+
+   ```bash
+   $ docker compose -f docker/docker-compose.yml down -v
+   ```
+:::caution WARNING
+`-v` will delete all Docker container volumes, and the existing data will be cleared.
+:::
+
+2. In **docker/.env**, set `DOC_ENGINE=${DOC_ENGINE:-infinity}`
+3. Restart your Docker image: 
+
+   ```bash
+   $ docker compose -f docker-compose.yml up -d
+   ```
+
+---
+
+### Where are my uploaded files stored in RAGFlow's image?
+
+All uploaded files are stored in Minio, RAGFlow's object storage solution. For instance, if you upload your file directly to a knowledge base, it is located at `<knowledgebase_id>/filename`.
 
 ---

docs/guides/agent/agent_component_reference/begin.mdx

@@ -46,6 +46,17 @@ You can set global variables within the **Begin** component, which can be either
   - **boolean**: Requires the user to toggle between on and off.
 - **Optional**: A toggle indicating whether the variable is optional. 
 
+:::tip NOTE
+To pass in parameters from a client, call:
+- HTTP method [Converse with agent](../../../references/http_api_reference.md#converse-with-agent), or
+- Python method [Converse with agent](../../../references/python_api_reference.md#converse-with-agent).
+:::
+
+:::danger IMPORTANT
+- If you set the key type as **file**, ensure the token count of the uploaded file does not exceed your model provider's maximum token limit; otherwise, the plain text in your file will be truncated and incomplete.
+- If your agent's **Begin** component takes a variable, you *cannot* embed it into a webpage.
+:::
+
 ## Examples
 
 As mentioned earlier, the **Begin** component is indispensable for an agent. Still, you can take a look at our three-step interpreter agent template, where the **Begin** component takes two global variables:
@@ -60,7 +71,7 @@ As mentioned earlier, the **Begin** component is indispensable for an agent. Sti
 
 ### Is the uploaded file in a knowledge base?
 
-No. Files uploaded to an agent as input are not stored in a knowledge base and will not be chunked using RAGFlow's built-in chunk methods. However, RAGFlow's built-in OSR, DLR, and TSR models will still be applied to process the document.
+No. Files uploaded to an agent as input are not stored in a knowledge base and hence will not be processed using RAGFlow's built-in OCR, DLR or TSR models, or chunked using RAGFlow's built-in chunk methods. 
 
 ### How to upload a webpage or file from a URL?
 
@@ -70,5 +81,8 @@ If you set the type of a variable as **file**, your users will be able to upload
 
 ### File size limit for an uploaded file
 
-The maximum file size for each uploaded file is determined by the variable `MAX_CONTENT_LENGTH` in `/docker/.env`. It defaults to 128 MB. If you change the default file size, ensure you also update the value of `client_max_body_size` in `/docker/nginx/nginx.conf` accordingly.
+There is no *specific* file size limit for a file uploaded to an agent. However, note that model providers typically have a default or explicit maximum token setting, which can range from 8196 to 128k: The plain text part of the uploaded file will be passed in as the key value, but if the file's token count exceeds this limit, the string will be truncated and incomplete.
 
+:::tip NOTE
+The variables `MAX_CONTENT_LENGTH` in `/docker/.env` and `client_max_body_size` in `/docker/nginx/nginx.conf` set the file size limit for each upload to a knowledge base or **File Management**. These settings DO NOT apply in this scenario.
+:::

docs/guides/agent/agent_component_reference/categorize.mdx

@@ -33,7 +33,7 @@ Click the dropdown menu of **Model** to show the model configuration window.
 - **Model**: The chat model to use.  
   - Ensure you set the chat model correctly on the **Model providers** page.
   - You can use different models for different components to increase flexibility or improve overall performance.
-- **Preset configurations**: A shortcut to **Temperature**, **Top P**, **Presence penalty**, and **Frequency penalty** settings, indicating the freedom level of the model. From **Improvise**, **Precise**, to **Balance**, each preset configuration corresponds to a unique combination of **Temperature**, **Top P**, **Presence penalty**, and **Frequency penalty**.  
+- **Freedom**: A shortcut to **Temperature**, **Top P**, **Presence penalty**, and **Frequency penalty** settings, indicating the freedom level of the model. From **Improvise**, **Precise**, to **Balance**, each preset configuration corresponds to a unique combination of **Temperature**, **Top P**, **Presence penalty**, and **Frequency penalty**.  
   This parameter has three options:  
   - **Improvise**: Produces more creative responses.
   - **Precise**: (Default) Produces more conservative responses.
@@ -52,9 +52,6 @@ Click the dropdown menu of **Model** to show the model configuration window.
 - **Frequency penalty**: Discourages the model from repeating the same words or phrases too frequently in the generated text.  
   - A higher **frequency penalty** value results in the model being more conservative in its use of repeated tokens.
   - Defaults to 0.7.
-- **Max tokens**: Sets the maximum length of the model's output, measured in the number of tokens.  
-  - Defaults to 512. 
-  - If disabled, you lift the maximum token limit, allowing the model to determine the number of tokens in its responses.
 
 :::tip NOTE
 - It is not necessary to stick with the same model for all components. If a specific model is not performing well for a particular task, consider using a different one.