Merge tag 'v0.24.0' into upgrade-0.24.1

Author: JasleenKaurSethi

.github/copilot-instructions.md

@@ -1 +1,22 @@
-Refer to [AGENTS.MD](../AGENTS.md) for all repo instructions.
+# Project instructions for Copilot
+
+## How to run (minimum)
+- Install:
+  - python -m venv .venv && source .venv/bin/activate
+  - pip install -r requirements.txt
+- Run:
+  - (fill) e.g. uvicorn app.main:app --reload
+- Verify:
+  - (fill) curl http://127.0.0.1:8000/health
+
+## Project layout (what matters)
+- app/: API entrypoints + routers
+- services/: business logic
+- configs/: config loading (.env)
+- docs/: documents
+- tests/: pytest
+
+## Conventions
+- Prefer small, incremental changes.
+- Add logging for new flows.
+- Add/adjust tests for behavior changes.

.github/workflows/tests.yml

@@ -44,13 +44,21 @@ cl100k_base.tiktoken
 chrome*
 huggingface.co/
 nltk_data/
+uv-x86_64*.tar.gz
 
 # Exclude hash-like temporary files like 9b5ad71b2ce5302211f9c61530b329a4922fc6a4
 *[0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f]*
 .lh/
 .venv
 docker/data
 
+# OceanBase data and conf
+docker/oceanbase/conf
+docker/oceanbase/data
+
+# SeekDB data and conf
+docker/seekdb
+
 
 #--------------------------------------------------#
 # The following was generated with gitignore.nvim: #
@@ -197,4 +205,9 @@ ragflow_cli.egg-info
 backup
 
 
-.hypothesis
+.hypothesis
+
+
+# Added by cargo
+
+/target

.gitignore

@@ -27,7 +27,7 @@ RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on d
 - **Document Processing**: `deepdoc/` - PDF parsing, OCR, layout analysis
 - **LLM Integration**: `rag/llm/` - Model abstractions for chat, embedding, reranking
 - **RAG Pipeline**: `rag/flow/` - Chunking, parsing, tokenization
-- **Graph RAG**: `graphrag/` - Knowledge graph construction and querying
+- **Graph RAG**: `rag/graphrag/` - Knowledge graph construction and querying
 
 ### Agent System (`/agent/`)
 - **Components**: Modular workflow components (LLM, retrieval, categorize, etc.)
@@ -113,4 +113,4 @@ RAGFlow supports switching between Elasticsearch (default) and Infinity:
 - Node.js >=18.20.4
 - Docker & Docker Compose
 - uv package manager
-- 16GB+ RAM, 50GB+ disk space
+- 16GB+ RAM, 50GB+ disk space

CLAUDE.md

@@ -19,17 +19,16 @@ RUN --mount=type=bind,from=infiniflow/ragflow_deps:latest,source=/huggingface.co
 # This is the only way to run python-tika without internet access. Without this set, the default is to check the tika version and pull latest every time from Apache.
 RUN --mount=type=bind,from=infiniflow/ragflow_deps:latest,source=/,target=/deps \
     cp -r /deps/nltk_data /root/ && \
-    cp /deps/tika-server-standard-3.0.0.jar /deps/tika-server-standard-3.0.0.jar.md5 /ragflow/ && \
+    cp /deps/tika-server-standard-3.2.3.jar /deps/tika-server-standard-3.2.3.jar.md5 /ragflow/ && \
     cp /deps/cl100k_base.tiktoken /ragflow/9b5ad71b2ce5302211f9c61530b329a4922fc6a4
 
-ENV TIKA_SERVER_JAR="file:///ragflow/tika-server-standard-3.0.0.jar"
+ENV TIKA_SERVER_JAR="file:///ragflow/tika-server-standard-3.2.3.jar"
 ENV DEBIAN_FRONTEND=noninteractive
 
 # Setup apt
 # Python package and implicit dependencies:
 # opencv-python: libglib2.0-0 libglx-mesa0 libgl1
-# aspose-slides: pkg-config libicu-dev libgdiplus         libssl1.1_1.1.1f-1ubuntu2_amd64.deb
-# python-pptx:   default-jdk                              tika-server-standard-3.0.0.jar
+# python-pptx:   default-jdk                              tika-server-standard-3.2.3.jar
 # selenium:      libatk-bridge2.0-0                       chrome-linux64-121-0-6167-85
 # Building C extensions: libpython3-dev libgtk-4-1 libnss3 xdg-utils libgbm-dev
 RUN --mount=type=cache,id=ragflow_apt,target=/var/cache/apt,sharing=locked \
@@ -49,11 +48,21 @@ RUN --mount=type=cache,id=ragflow_apt,target=/var/cache/apt,sharing=locked \
     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 nginx unzip curl wget git vim less && \
+    apt install -y gnupg unzip curl wget git vim less && \
     apt install -y ghostscript && \
     apt install -y pandoc && \
     apt install -y texlive && \
-    apt install -y fonts-freefont-ttf fonts-noto-cjk
+    apt install -y fonts-freefont-ttf fonts-noto-cjk && \
+    apt install -y postgresql-client
+
+ARG NGINX_VERSION=1.29.5-1~noble
+RUN --mount=type=cache,id=ragflow_apt,target=/var/cache/apt,sharing=locked \
+    mkdir -p /etc/apt/keyrings && \
+    curl -fsSL https://nginx.org/keys/nginx_signing.key | gpg --dearmor -o /etc/apt/keyrings/nginx-archive-keyring.gpg && \
+    echo "deb [signed-by=/etc/apt/keyrings/nginx-archive-keyring.gpg] https://nginx.org/packages/mainline/ubuntu/ noble nginx" > /etc/apt/sources.list.d/nginx.list && \
+    apt update && \
+    apt install -y nginx=${NGINX_VERSION} && \
+    apt-mark hold nginx
 
 # Install uv
 RUN --mount=type=bind,from=infiniflow/ragflow_deps:latest,source=/,target=/deps \
@@ -64,10 +73,12 @@ RUN --mount=type=bind,from=infiniflow/ragflow_deps:latest,source=/,target=/deps
         echo 'url = "https://pypi.tuna.tsinghua.edu.cn/simple"' >> /etc/uv/uv.toml && \
         echo 'default = true' >> /etc/uv/uv.toml; \
     fi; \
-    tar xzf /deps/uv-x86_64-unknown-linux-gnu.tar.gz \
-    && cp uv-x86_64-unknown-linux-gnu/* /usr/local/bin/ \
-    && rm -rf uv-x86_64-unknown-linux-gnu \
-    && uv python install 3.11
+    arch="$(uname -m)"; \
+    if [ "$arch" = "x86_64" ]; then uv_arch="x86_64"; else uv_arch="aarch64"; fi; \
+    tar xzf "/deps/uv-${uv_arch}-unknown-linux-gnu.tar.gz" \
+    && cp "uv-${uv_arch}-unknown-linux-gnu/"* /usr/local/bin/ \
+    && rm -rf "uv-${uv_arch}-unknown-linux-gnu" \
+    && uv python install 3.12
 
 ENV PYTHONDONTWRITEBYTECODE=1 DOTNET_SYSTEM_GLOBALIZATION_INVARIANT=1
 ENV PATH=/root/.local/bin:$PATH
@@ -125,8 +136,6 @@ RUN --mount=type=bind,from=infiniflow/ragflow_deps:latest,source=/chromedriver-l
     mv chromedriver /usr/local/bin/ && \
     rm -f /usr/bin/google-chrome
 
-# https://forum.aspose.com/t/aspose-slides-for-net-no-usable-version-of-libssl-found-with-linux-server/271344/13
-# aspose-slides on linux/arm64 is unavailable
 RUN --mount=type=bind,from=infiniflow/ragflow_deps:latest,source=/,target=/deps \
     if [ "$(uname -m)" = "x86_64" ]; then \
         dpkg -i /deps/libssl1.1_1.1.1f-1ubuntu2_amd64.deb; \
@@ -152,11 +161,14 @@ RUN --mount=type=cache,id=ragflow_uv,target=/root/.cache/uv,sharing=locked \
     else \
         sed -i 's|pypi.tuna.tsinghua.edu.cn|pypi.org|g' uv.lock; \
     fi; \
-    uv sync --python 3.12 --frozen
+    uv sync --python 3.12 --frozen && \
+    # Ensure pip is available in the venv for runtime package installation (fixes #12651)
+    .venv/bin/python3 -m ensurepip --upgrade
 
 COPY web web
 COPY docs docs
 RUN --mount=type=cache,id=ragflow_npm,target=/root/.npm,sharing=locked \
+    export NODE_OPTIONS="--max-old-space-size=4096" && \
     cd web && npm install && npm run build
 
 COPY .git /ragflow/.git
@@ -186,11 +198,8 @@ COPY conf conf
 COPY deepdoc deepdoc
 COPY rag rag
 COPY agent agent
-COPY graphrag graphrag
-COPY agentic_reasoning agentic_reasoning
 COPY pyproject.toml uv.lock ./
 COPY mcp mcp
-COPY plugin plugin
 COPY common common
 COPY memory memory
 

Dockerfile

@@ -3,7 +3,7 @@
 FROM scratch
 
 # Copy resources downloaded via download_deps.py
-COPY chromedriver-linux64-121-0-6167-85 chrome-linux64-121-0-6167-85 cl100k_base.tiktoken libssl1.1_1.1.1f-1ubuntu2_amd64.deb libssl1.1_1.1.1f-1ubuntu2_arm64.deb tika-server-standard-3.0.0.jar tika-server-standard-3.0.0.jar.md5 libssl*.deb uv-x86_64-unknown-linux-gnu.tar.gz /
+COPY chromedriver-linux64-121-0-6167-85 chrome-linux64-121-0-6167-85 cl100k_base.tiktoken libssl1.1_1.1.1f-1ubuntu2_amd64.deb libssl1.1_1.1.1f-1ubuntu2_arm64.deb tika-server-standard-3.2.3.jar tika-server-standard-3.2.3.jar.md5 libssl*.deb uv-x86_64-unknown-linux-gnu.tar.gz uv-aarch64-unknown-linux-gnu.tar.gz /
 
 COPY nltk_data /nltk_data
 

Dockerfile.deps

@@ -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/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.23.1">
+        <img src="https://img.shields.io/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.24.0">
     </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">
@@ -72,7 +72,7 @@
 
 ## 💡 What is RAGFlow?
 
-[RAGFlow](https://ragflow.io/) is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs. It offers a streamlined RAG workflow adaptable to enterprises of any scale. Powered by a converged context engine and pre-built agent templates, RAGFlow enables developers to transform complex data into high-fidelity, production-ready AI systems with exceptional efficiency and precision.
+[RAGFlow](https://ragflow.io/) is a leading open-source Retrieval-Augmented Generation ([RAG](https://ragflow.io/basics/what-is-rag)) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs. It offers a streamlined RAG workflow adaptable to enterprises of any scale. Powered by a converged [context engine](https://ragflow.io/basics/what-is-agent-context-engine) and pre-built agent templates, RAGFlow enables developers to transform complex data into high-fidelity, production-ready AI systems with exceptional efficiency and precision.
 
 ## 🎮 Demo
 
@@ -188,15 +188,15 @@ releases! 🌟
 > 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 `v0.23.1` edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.23.1`, update the `RAGFLOW_IMAGE` variable accordingly in **docker/.env** before using `docker compose` to start the server.
+> The command below downloads the `v0.24.0` edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.24.0`, update the `RAGFLOW_IMAGE` variable accordingly in **docker/.env** before using `docker compose` to start the server.
 
 ```bash
    $ cd ragflow/docker
-  
-   # git checkout v0.23.1
+
+   # git checkout v0.24.0
    # Optional: use a stable tag (see releases: https://github.com/infiniflow/ragflow/releases)
    # This step ensures the **entrypoint.sh** file in the code matches the Docker image version.
-   
+
    # Use CPU for DeepDoc tasks:
    $ docker compose -f docker-compose.yml up -d
 

README.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/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.23.1">
+        <img src="https://img.shields.io/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.24.0">
     </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">
@@ -72,7 +72,7 @@
 
 ## 💡 Apa Itu RAGFlow?
 
-[RAGFlow](https://ragflow.io/) adalah mesin RAG (Retrieval-Augmented Generation) open-source terkemuka yang mengintegrasikan teknologi RAG mutakhir dengan kemampuan Agent untuk menciptakan lapisan kontekstual superior bagi LLM. Menyediakan alur kerja RAG yang efisien dan dapat diadaptasi untuk perusahaan segala skala. Didukung oleh mesin konteks terkonvergensi dan template Agent yang telah dipra-bangun, RAGFlow memungkinkan pengembang mengubah data kompleks menjadi sistem AI kesetiaan-tinggi dan siap-produksi dengan efisiensi dan presisi yang luar biasa.
+[RAGFlow](https://ragflow.io/) adalah mesin [RAG](https://ragflow.io/basics/what-is-rag) (Retrieval-Augmented Generation) open-source terkemuka yang mengintegrasikan teknologi RAG mutakhir dengan kemampuan Agent untuk menciptakan lapisan kontekstual superior bagi LLM. Menyediakan alur kerja RAG yang efisien dan dapat diadaptasi untuk perusahaan segala skala. Didukung oleh mesin konteks terkonvergensi dan template Agent yang telah dipra-bangun, RAGFlow memungkinkan pengembang mengubah data kompleks menjadi sistem AI kesetiaan-tinggi dan siap-produksi dengan efisiensi dan presisi yang luar biasa.
 
 ## 🎮 Demo
 
@@ -188,12 +188,12 @@ Coba demo kami di [https://demo.ragflow.io](https://demo.ragflow.io).
 > Semua gambar Docker dibangun untuk platform x86. Saat ini, kami tidak menawarkan gambar Docker untuk ARM64.
 > Jika Anda menggunakan platform ARM64, [silakan gunakan panduan ini untuk membangun gambar Docker yang kompatibel dengan sistem Anda](https://ragflow.io/docs/dev/build_docker_image).
 
-> Perintah di bawah ini mengunduh edisi v0.23.1 dari gambar Docker RAGFlow. Silakan merujuk ke tabel berikut untuk deskripsi berbagai edisi RAGFlow. Untuk mengunduh edisi RAGFlow yang berbeda dari v0.23.1, perbarui variabel RAGFLOW_IMAGE di docker/.env sebelum menggunakan docker compose untuk memulai server.
+> Perintah di bawah ini mengunduh edisi v0.24.0 dari gambar Docker RAGFlow. Silakan merujuk ke tabel berikut untuk deskripsi berbagai edisi RAGFlow. Untuk mengunduh edisi RAGFlow yang berbeda dari v0.24.0, perbarui variabel RAGFLOW_IMAGE di docker/.env sebelum menggunakan docker compose untuk memulai server.
 
 ```bash
    $ cd ragflow/docker
-   
-   # git checkout v0.23.1
+
+   # git checkout v0.24.0
    # Opsional: gunakan tag stabil (lihat releases: https://github.com/infiniflow/ragflow/releases)
    # This steps ensures the **entrypoint.sh** file in the code matches the Docker image version.
 

README_id.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/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.23.1">
+        <img src="https://img.shields.io/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.24.0">
     </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">
@@ -53,7 +53,7 @@
 
 ## 💡 RAGFlow とは？
 
-[RAGFlow](https://ragflow.io/) は、先進的なRAG（Retrieval-Augmented Generation）技術と Agent 機能を融合し、大規模言語モデル（LLM）に優れたコンテキスト層を構築する最先端のオープンソース RAG エンジンです。あらゆる規模の企業に対応可能な合理化された RAG ワークフローを提供し、統合型コンテキストエンジンと事前構築されたAgentテンプレートにより、開発者が複雑なデータを驚異的な効率性と精度で高精細なプロダクションレディAIシステムへ変換することを可能にします。
+[RAGFlow](https://ragflow.io/) は、先進的な[RAG](https://ragflow.io/basics/what-is-rag)（Retrieval-Augmented Generation）技術と Agent 機能を融合し、大規模言語モデル（LLM）に優れたコンテキスト層を構築する最先端のオープンソース RAG エンジンです。あらゆる規模の企業に対応可能な合理化された RAG ワークフローを提供し、統合型[コンテキストエンジン](https://ragflow.io/basics/what-is-agent-context-engine)と事前構築されたAgentテンプレートにより、開発者が複雑なデータを驚異的な効率性と精度で高精細なプロダクションレディAIシステムへ変換することを可能にします。
 
 ## 🎮 Demo
 
@@ -168,12 +168,12 @@
 > 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 v0.23.1 エディションをダウンロードします。異なる RAGFlow エディションの説明については、以下の表を参照してください。v0.23.1 とは異なるエディションをダウンロードするには、docker/.env ファイルの RAGFLOW_IMAGE 変数を適宜更新し、docker compose を使用してサーバーを起動してください。
+> 以下のコマンドは、RAGFlow Docker イメージの v0.24.0 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。v0.24.0 とは異なるエディションをダウンロードするには、docker/.env ファイルの RAGFLOW_IMAGE 変数を適宜更新し、docker compose を使用してサーバーを起動してください。
 
 ```bash
    $ cd ragflow/docker
 
-   # git checkout v0.23.1
+   # git checkout v0.24.0
    # 任意: 安定版タグを利用 (一覧: https://github.com/infiniflow/ragflow/releases)
    # この手順は、コード内の entrypoint.sh ファイルが Docker イメージのバージョンと一致していることを確認します。
 
@@ -194,8 +194,8 @@
 
 > `v0.22.0` 以降、当プロジェクトでは slim エディションのみを提供し、イメージタグに **-slim** サフィックスを付けなくなりました。
 
-   1. サーバーを立ち上げた後、サーバーの状態を確認する:   
-   
+   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 docker-ragflow-cpu-1
    ```