Add docs category file (#12359 )

### What problem does this PR solve? As title. ### Type of change - [x] Documentation Update Signed-off-by: Jin Hai <haijin.chn@gmail.com>
Fix: Batch parsing problem (#12358 )
2026-01-04 03:25:30 +08:00 · 2025-12-31 13:56:11 +08:00 · 2025-12-31 13:52:50 +08:00 · 2025-12-31 13:52:33 +08:00 · 2025-12-31 13:49:38 +08:00 · 2025-12-31 12:49:42 +08:00
81 changed files with 1904 additions and 640 deletions
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@ -10,6 +10,12 @@ on:
    tags:
      - "v*.*.*"                  # normal release
 permissions:
  contents: write
  actions: read
  checks: read
  statuses: read
 # https://docs.github.com/en/actions/using-jobs/using-concurrency
 concurrency:
  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
@ -76,6 +82,14 @@ jobs:
          # The body field does not support environment variable substitution directly.
          body_path: release_body.md
      - name: Build and push image
        run: |
          sudo docker login --username infiniflow --password-stdin <<< ${{ secrets.DOCKERHUB_TOKEN }}
          sudo docker build --build-arg NEED_MIRROR=1 --build-arg HTTPS_PROXY=${HTTPS_PROXY} --build-arg HTTP_PROXY=${HTTP_PROXY} -t infiniflow/ragflow:${RELEASE_TAG} -f Dockerfile .
          sudo docker tag infiniflow/ragflow:${RELEASE_TAG} infiniflow/ragflow:latest
          sudo docker push infiniflow/ragflow:${RELEASE_TAG}
          sudo docker push infiniflow/ragflow:latest
      - name: Build and push ragflow-sdk
        if: startsWith(github.ref, 'refs/tags/v')
        run: |
@ -85,11 +99,3 @@ jobs:
        if: startsWith(github.ref, 'refs/tags/v')
        run: |
          cd admin/client && uv build && uv publish --token ${{ secrets.PYPI_API_TOKEN }}
      - name: Build and push image
        run: |
          sudo docker login --username infiniflow --password-stdin <<< ${{ secrets.DOCKERHUB_TOKEN }}
          sudo docker build --build-arg NEED_MIRROR=1 --build-arg HTTPS_PROXY=${HTTPS_PROXY} --build-arg HTTP_PROXY=${HTTP_PROXY} -t infiniflow/ragflow:${RELEASE_TAG} -f Dockerfile .
          sudo docker tag infiniflow/ragflow:${RELEASE_TAG} infiniflow/ragflow:latest
          sudo docker push infiniflow/ragflow:${RELEASE_TAG}
          sudo docker push infiniflow/ragflow:latest
--- a/README.md
+++ b/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/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.23.0">
+        <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">
    </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">
@ -37,7 +37,7 @@
 <h4 align="center">
  <a href="https://ragflow.io/docs/dev/">Document</a> |
-  <a href="https://github.com/infiniflow/ragflow/issues/4214">Roadmap</a> |
+  <a href="https://github.com/infiniflow/ragflow/issues/12241">Roadmap</a> |
  <a href="https://twitter.com/infiniflowai">Twitter</a> |
  <a href="https://discord.gg/NjYzJD3GM3">Discord</a> |
  <a href="https://demo.ragflow.io">Demo</a>
@ -188,12 +188,12 @@ 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 `v0.23.0` edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.23.0`, update the `RAGFLOW_IMAGE` variable accordingly in **docker/.env** before using `docker compose` to start the server.
+> 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.
 ```bash
   $ cd ragflow/docker
-   # git checkout v0.23.0
+   # git checkout v0.23.1
   # 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.
@ -396,7 +396,7 @@ docker build --platform linux/amd64 \
 ## 📜 Roadmap
-See the [RAGFlow Roadmap 2025](https://github.com/infiniflow/ragflow/issues/4214)
+See the [RAGFlow Roadmap 2026](https://github.com/infiniflow/ragflow/issues/12241)
 ## 🏄 Community
--- a/README_id.md
+++ b/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/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.23.0">
+        <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">
    </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">
@ -37,7 +37,7 @@
 <h4 align="center">
  <a href="https://ragflow.io/docs/dev/">Dokumentasi</a> |
-  <a href="https://github.com/infiniflow/ragflow/issues/4214">Peta Jalan</a> |
+  <a href="https://github.com/infiniflow/ragflow/issues/12241">Peta Jalan</a> |
  <a href="https://twitter.com/infiniflowai">Twitter</a> |
  <a href="https://discord.gg/NjYzJD3GM3">Discord</a> |
  <a href="https://demo.ragflow.io">Demo</a>
@ -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 v0.23.0 dari gambar Docker RAGFlow. Silakan merujuk ke tabel berikut untuk deskripsi berbagai edisi RAGFlow. Untuk mengunduh edisi RAGFlow yang berbeda dari v0.23.0, perbarui variabel RAGFLOW_IMAGE di docker/.env sebelum menggunakan docker compose untuk memulai server.
+> 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.
 ```bash
   $ cd ragflow/docker
-   # git checkout v0.23.0
+   # git checkout v0.23.1
   # 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.
@ -368,7 +368,7 @@ docker build --platform linux/amd64 \
 ## 📜 Roadmap
-Lihat [Roadmap RAGFlow 2025](https://github.com/infiniflow/ragflow/issues/4214)
+Lihat [Roadmap RAGFlow 2026](https://github.com/infiniflow/ragflow/issues/12241)
 ## 🏄 Komunitas
--- a/README_ja.md
+++ b/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/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.23.0">
+        <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">
    </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">
@ -37,7 +37,7 @@
 <h4 align="center">
  <a href="https://ragflow.io/docs/dev/">Document</a> |
-  <a href="https://github.com/infiniflow/ragflow/issues/4214">Roadmap</a> |
+  <a href="https://github.com/infiniflow/ragflow/issues/12241">Roadmap</a> |
  <a href="https://twitter.com/infiniflowai">Twitter</a> |
  <a href="https://discord.gg/NjYzJD3GM3">Discord</a> |
  <a href="https://demo.ragflow.io">Demo</a>
@ -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 v0.23.0 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。v0.23.0 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。
+> 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。
 ```bash
   $ cd ragflow/docker
-   # git checkout v0.23.0
+   # git checkout v0.23.1
   # 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。
@ -368,7 +368,7 @@ docker build --platform linux/amd64 \
 ## 📜 ロードマップ
-[RAGFlow ロードマップ 2025](https://github.com/infiniflow/ragflow/issues/4214) を参照
+[RAGFlow ロードマップ 2026](https://github.com/infiniflow/ragflow/issues/12241) を参照
 ## 🏄 コミュニティ
--- a/README_ko.md
+++ b/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/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.23.0">
+        <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">
    </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">
@ -37,7 +37,7 @@
 <h4 align="center">
  <a href="https://ragflow.io/docs/dev/">Document</a> |
-  <a href="https://github.com/infiniflow/ragflow/issues/4214">Roadmap</a> |
+  <a href="https://github.com/infiniflow/ragflow/issues/12241">Roadmap</a> |
  <a href="https://twitter.com/infiniflowai">Twitter</a> |
  <a href="https://discord.gg/NjYzJD3GM3">Discord</a> |
  <a href="https://demo.ragflow.io">Demo</a>
@ -170,12 +170,12 @@
 > 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 v0.23.0 버전을 다운로드합니다. 다양한 RAGFlow 버전에 대한 설명은 다음 표를 참조하십시오. v0.23.0과 다른 RAGFlow 버전을 다운로드하려면, docker/.env 파일에서 RAGFLOW_IMAGE 변수를 적절히 업데이트한 후 docker compose를 사용하여 서버를 시작하십시오.
+   > 아래 명령어는 RAGFlow Docker 이미지의 v0.23.1 버전을 다운로드합니다. 다양한 RAGFlow 버전에 대한 설명은 다음 표를 참조하십시오. v0.23.1과 다른 RAGFlow 버전을 다운로드하려면, docker/.env 파일에서 RAGFLOW_IMAGE 변수를 적절히 업데이트한 후 docker compose를 사용하여 서버를 시작하십시오.
   ```bash
   $ cd ragflow/docker
-   # git checkout v0.23.0
+   # git checkout v0.23.1
   # Optional: use a stable tag (see releases: https://github.com/infiniflow/ragflow/releases)
   # 이 단계는 코드의 entrypoint.sh 파일이 Docker 이미지 버전과 일치하도록 보장합니다.
@ -372,7 +372,7 @@ docker build --platform linux/amd64 \
 ## 📜 로드맵
-[RAGFlow 로드맵 2025](https://github.com/infiniflow/ragflow/issues/4214)을 확인하세요.
+[RAGFlow 로드맵 2026](https://github.com/infiniflow/ragflow/issues/12241)을 확인하세요.
 ## 🏄 커뮤니티
--- a/README_pt_br.md
+++ b/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/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.23.0">
+        <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">
    </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">
@ -37,7 +37,7 @@
 <h4 align="center">
  <a href="https://ragflow.io/docs/dev/">Documentação</a> |
-  <a href="https://github.com/infiniflow/ragflow/issues/4214">Roadmap</a> |
+  <a href="https://github.com/infiniflow/ragflow/issues/12241">Roadmap</a> |
  <a href="https://twitter.com/infiniflowai">Twitter</a> |
  <a href="https://discord.gg/NjYzJD3GM3">Discord</a> |
  <a href="https://demo.ragflow.io">Demo</a>
@ -188,12 +188,12 @@ Experimente nossa demo em [https://demo.ragflow.io](https://demo.ragflow.io).
 > 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`v0.23.0` 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 `v0.23.0`, atualize a variável `RAGFLOW_IMAGE` conforme necessário no **docker/.env** antes de usar `docker compose` para iniciar o servidor.
+    > O comando abaixo baixa a edição`v0.23.1` 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 `v0.23.1`, atualize a variável `RAGFLOW_IMAGE` conforme necessário no **docker/.env** antes de usar `docker compose` para iniciar o servidor.
 ```bash
   $ cd ragflow/docker
-   # git checkout v0.23.0
+   # git checkout v0.23.1
   # Opcional: use uma tag estável (veja releases: https://github.com/infiniflow/ragflow/releases)
   # Esta etapa garante que o arquivo entrypoint.sh no código corresponda à versão da imagem do Docker.
@ -385,7 +385,7 @@ docker build --platform linux/amd64 \
 ## 📜 Roadmap
-Veja o [RAGFlow Roadmap 2025](https://github.com/infiniflow/ragflow/issues/4214)
+Veja o [RAGFlow Roadmap 2026](https://github.com/infiniflow/ragflow/issues/12241)
 ## 🏄 Comunidade
--- a/README_tzh.md
+++ b/README_tzh.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:v0.23.0">
+        <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">
    </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">
@ -37,7 +37,7 @@
 <h4 align="center">
  <a href="https://ragflow.io/docs/dev/">Document</a> |
-  <a href="https://github.com/infiniflow/ragflow/issues/4214">Roadmap</a> |
+  <a href="https://github.com/infiniflow/ragflow/issues/12241">Roadmap</a> |
  <a href="https://twitter.com/infiniflowai">Twitter</a> |
  <a href="https://discord.gg/NjYzJD3GM3">Discord</a> |
  <a href="https://demo.ragflow.io">Demo</a>
@ -187,12 +187,12 @@
 > 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 Docker 映像 `v0.23.0`。請參考下表查看不同 Docker 發行版的說明。如需下載不同於 `v0.23.0` 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。
+> 執行以下指令會自動下載 RAGFlow Docker 映像 `v0.23.1`。請參考下表查看不同 Docker 發行版的說明。如需下載不同於 `v0.23.1` 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。
 ```bash
   $ cd ragflow/docker
-   # git checkout v0.23.0
+   # git checkout v0.23.1
   # 可選：使用穩定版標籤（查看發佈：https://github.com/infiniflow/ragflow/releases）
   # 此步驟確保程式碼中的 entrypoint.sh 檔案與 Docker 映像版本一致。
@ -399,7 +399,7 @@ docker build --platform linux/amd64 \
 ## 📜 路線圖
-詳見 [RAGFlow Roadmap 2025](https://github.com/infiniflow/ragflow/issues/4214) 。
+詳見 [RAGFlow Roadmap 2026](https://github.com/infiniflow/ragflow/issues/12241) 。
 ## 🏄 開源社群
--- a/README_zh.md
+++ b/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/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.23.0">
+        <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">
    </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">
@ -37,7 +37,7 @@
 <h4 align="center">
  <a href="https://ragflow.io/docs/dev/">Document</a> |
-  <a href="https://github.com/infiniflow/ragflow/issues/4214">Roadmap</a> |
+  <a href="https://github.com/infiniflow/ragflow/issues/12241">Roadmap</a> |
  <a href="https://twitter.com/infiniflowai">Twitter</a> |
  <a href="https://discord.gg/NjYzJD3GM3">Discord</a> |
  <a href="https://demo.ragflow.io">Demo</a>
@ -188,12 +188,12 @@
 > 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 Docker 镜像 `v0.23.0`。请参考下表查看不同 Docker 发行版的描述。如需下载不同于 `v0.23.0` 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。
+   > 运行以下命令会自动下载 RAGFlow Docker 镜像 `v0.23.1`。请参考下表查看不同 Docker 发行版的描述。如需下载不同于 `v0.23.1` 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。
   ```bash
   $ cd ragflow/docker
-   # git checkout v0.23.0
+   # git checkout v0.23.1
   # 可选：使用稳定版本标签（查看发布：https://github.com/infiniflow/ragflow/releases）
   # 这一步确保代码中的 entrypoint.sh 文件与 Docker 镜像的版本保持一致。
@ -402,7 +402,7 @@ docker build --platform linux/amd64 \
 ## 📜 路线图
-详见 [RAGFlow Roadmap 2025](https://github.com/infiniflow/ragflow/issues/4214) 。
+详见 [RAGFlow Roadmap 2026](https://github.com/infiniflow/ragflow/issues/12241) 。
 ## 🏄 开源社区
--- a/admin/client/README.md
+++ b/admin/client/README.md
@ -48,7 +48,7 @@ It consists of a server-side Service and a command-line client (CLI), both imple
 1.  Ensure the Admin Service is running.
 2.  Install ragflow-cli.
    ```bash
-    pip install ragflow-cli==0.23.0
+    pip install ragflow-cli==0.23.1
    ```
 3.  Launch the CLI client:
    ```bash
--- a/admin/client/admin_client.py
+++ b/admin/client/admin_client.py
@ -370,7 +370,7 @@ class AdminCLI(Cmd):
                    res_json = response.json()
                    error_code = res_json.get("code", -1)
                    if error_code == 0:
-                        self.session.headers.update({"Content-Type": "application/json", "Authorization": response.headers["Authorization"], "User-Agent": "RAGFlow-CLI/0.23.0"})
+                        self.session.headers.update({"Content-Type": "application/json", "Authorization": response.headers["Authorization"], "User-Agent": "RAGFlow-CLI/0.23.1"})
                        print("Authentication successful.")
                        return True
                    else:
--- a/admin/client/pyproject.toml
+++ b/admin/client/pyproject.toml
@ -1,6 +1,6 @@
 [project]
 name = "ragflow-cli"
-version = "0.23.0"
+version = "0.23.1"
 description = "Admin Service's client of [RAGFlow](https://github.com/infiniflow/ragflow). The Admin Service provides user management and system monitoring. "
 authors = [{ name = "Lynn", email = "lynn_inf@hotmail.com" }]
 license = { text = "Apache License, Version 2.0" }
--- a/admin/client/uv.lock
+++ b/admin/client/uv.lock
@ -196,7 +196,7 @@ wheels = [
 [[package]]
 name = "ragflow-cli"
-version = "0.23.0"
+version = "0.23.1"
 source = { virtual = "." }
 dependencies = [
    { name = "beartype" },
--- a/admin/server/services.py
+++ b/admin/server/services.py
@ -13,6 +13,8 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
 import os
 import logging
 import re
 from werkzeug.security import check_password_hash
@ -179,10 +181,14 @@ class ServiceMgr:
    @staticmethod
    def get_all_services():
        doc_engine = os.getenv('DOC_ENGINE', 'elasticsearch')
        result = []
        configs = SERVICE_CONFIGS.configs
        for service_id, config in enumerate(configs):
            config_dict = config.to_dict()
            if config_dict['service_type'] == 'retrieval':
                if config_dict['extra']['retrieval_type'] != doc_engine:
                    continue
            try:
                service_detail = ServiceMgr.get_service_details(service_id)
                if "status" in service_detail:
--- a/api/apps/sdk/session.py
+++ b/api/apps/sdk/session.py
@ -60,7 +60,7 @@ async def create(tenant_id, chat_id):
        "name": req.get("name", "New session"),
        "message": [{"role": "assistant", "content": dia[0].prompt_config.get("prologue")}],
        "user_id": req.get("user_id", ""),
-        "reference": [{}],
+        "reference": [],
    }
    if not conv.get("name"):
        return get_error_data_result(message="`name` can not be empty.")
--- a/api/db/services/user_service.py
+++ b/api/db/services/user_service.py
@ -164,7 +164,7 @@ class UserService(CommonService):
    @classmethod
    @DB.connection_context()
    def get_all_users(cls):
-        users = cls.model.select()
+        users = cls.model.select().order_by(cls.model.email)
        return list(users)
--- a/common/constants.py
+++ b/common/constants.py
@ -132,6 +132,7 @@ class FileSource(StrEnum):
    ASANA = "asana"
    GITHUB = "github"
    GITLAB = "gitlab"
    IMAP = "imap"
 class PipelineTaskType(StrEnum):
    PARSE = "Parse"
--- a/common/data_source/init.py
+++ b/common/data_source/init.py
@ -38,6 +38,7 @@ from .webdav_connector import WebDAVConnector
 from .moodle_connector import MoodleConnector
 from .airtable_connector import AirtableConnector
 from .asana_connector import AsanaConnector
 from .imap_connector import ImapConnector
 from .config import BlobType, DocumentSource
 from .models import Document, TextSection, ImageSection, BasicExpertInfo
 from .exceptions import (
@ -75,4 +76,5 @@ __all__ = [
    "UnexpectedValidationError",
    "AirtableConnector",
    "AsanaConnector",
    "ImapConnector"
 ]
--- a/common/data_source/airtable_connector.py
+++ b/common/data_source/airtable_connector.py
@ -1,6 +1,6 @@
 from datetime import datetime, timezone
 import logging
-from typing import Any
+from typing import Any, Generator
 import requests
@ -8,8 +8,8 @@ from pyairtable import Api as AirtableApi
 from common.data_source.config import AIRTABLE_CONNECTOR_SIZE_THRESHOLD, INDEX_BATCH_SIZE, DocumentSource
 from common.data_source.exceptions import ConnectorMissingCredentialError
-from common.data_source.interfaces import LoadConnector
+from common.data_source.interfaces import LoadConnector, PollConnector
-from common.data_source.models import Document, GenerateDocumentsOutput
+from common.data_source.models import Document, GenerateDocumentsOutput, SecondsSinceUnixEpoch
 from common.data_source.utils import extract_size_bytes, get_file_ext
 class AirtableClientNotSetUpError(PermissionError):
@ -19,7 +19,7 @@ class AirtableClientNotSetUpError(PermissionError):
        )
-class AirtableConnector(LoadConnector):
+class AirtableConnector(LoadConnector, PollConnector):
    """
    Lightweight Airtable connector.
@ -132,6 +132,26 @@ class AirtableConnector(LoadConnector):
        if batch:
            yield batch
    def poll_source(self, start: SecondsSinceUnixEpoch, end: SecondsSinceUnixEpoch) -> Generator[list[Document], None, None]:
        """Poll source to get documents"""
        start_dt = datetime.fromtimestamp(start, tz=timezone.utc)
        end_dt = datetime.fromtimestamp(end, tz=timezone.utc)
        for batch in self.load_from_state():
            filtered: list[Document] = []
            for doc in batch:
                if not doc.doc_updated_at:
                    continue
                doc_dt = doc.doc_updated_at.astimezone(timezone.utc)
                if start_dt <= doc_dt < end_dt:
                    filtered.append(doc)
            if filtered:
                yield filtered
 if __name__ == "__main__":
    import os
--- a/common/data_source/config.py
+++ b/common/data_source/config.py
@ -57,6 +57,7 @@ class DocumentSource(str, Enum):
    ASANA = "asana"
    GITHUB = "github"
    GITLAB = "gitlab"
    IMAP = "imap"
 class FileOrigin(str, Enum):
@ -266,6 +267,10 @@ ASANA_CONNECTOR_SIZE_THRESHOLD = int(
    os.environ.get("ASANA_CONNECTOR_SIZE_THRESHOLD", 10 * 1024 * 1024)
 )
 IMAP_CONNECTOR_SIZE_THRESHOLD = int(
    os.environ.get("IMAP_CONNECTOR_SIZE_THRESHOLD", 10 * 1024 * 1024)
 )
 _USER_NOT_FOUND = "Unknown Confluence User"
 _COMMENT_EXPANSION_FIELDS = ["body.storage.value"]
--- a/common/data_source/imap_connector.py
+++ b/common/data_source/imap_connector.py
@ -0,0 +1,724 @@
 import copy
 import email
 from email.header import decode_header
 import imaplib
 import logging
 import os
 import re
 from datetime import datetime, timedelta
 from datetime import timezone
 from email.message import Message
 from email.utils import collapse_rfc2231_value, parseaddr
 from enum import Enum
 from typing import Any
 from typing import cast
 import bs4
 from pydantic import BaseModel
 from common.data_source.config import IMAP_CONNECTOR_SIZE_THRESHOLD, DocumentSource
 from common.data_source.interfaces import CheckpointOutput, CheckpointedConnectorWithPermSync, CredentialsConnector, CredentialsProviderInterface
 from common.data_source.models import BasicExpertInfo, ConnectorCheckpoint, Document, ExternalAccess, SecondsSinceUnixEpoch
 _DEFAULT_IMAP_PORT_NUMBER = int(os.environ.get("IMAP_PORT", 993))
 _IMAP_OKAY_STATUS = "OK"
 _PAGE_SIZE = 100
 _USERNAME_KEY = "imap_username"
 _PASSWORD_KEY = "imap_password"
 class Header(str, Enum):
    SUBJECT_HEADER = "subject"
    FROM_HEADER = "from"
    TO_HEADER = "to"
    CC_HEADER = "cc"
    DELIVERED_TO_HEADER = (
        "Delivered-To"  # Used in mailing lists instead of the "to" header.
    )
    DATE_HEADER = "date"
    MESSAGE_ID_HEADER = "Message-ID"
 class EmailHeaders(BaseModel):
    """
    Model for email headers extracted from IMAP messages.
    """
    id: str
    subject: str
    sender: str
    recipients: str | None
    cc: str | None
    date: datetime
    @classmethod
    def from_email_msg(cls, email_msg: Message) -> "EmailHeaders":
        def _decode(header: str, default: str | None = None) -> str | None:
            value = email_msg.get(header, default)
            if not value:
                return None
            decoded_fragments = decode_header(value)
            decoded_strings: list[str] = []
            for decoded_value, encoding in decoded_fragments:
                if isinstance(decoded_value, bytes):
                    try:
                        decoded_strings.append(
                            decoded_value.decode(encoding or "utf-8", errors="replace")
                        )
                    except LookupError:
                        decoded_strings.append(
                            decoded_value.decode("utf-8", errors="replace")
                        )
                elif isinstance(decoded_value, str):
                    decoded_strings.append(decoded_value)
                else:
                    decoded_strings.append(str(decoded_value))
            return "".join(decoded_strings)
        def _parse_date(date_str: str | None) -> datetime | None:
            if not date_str:
                return None
            try:
                return email.utils.parsedate_to_datetime(date_str)
            except (TypeError, ValueError):
                return None
        message_id = _decode(header=Header.MESSAGE_ID_HEADER)
        if not message_id:
            message_id = f"<generated-{uuid.uuid4()}@imap.local>"
        # It's possible for the subject line to not exist or be an empty string.
        subject = _decode(header=Header.SUBJECT_HEADER) or "Unknown Subject"
        from_ = _decode(header=Header.FROM_HEADER)
        to = _decode(header=Header.TO_HEADER)
        if not to:
            to = _decode(header=Header.DELIVERED_TO_HEADER)
        cc = _decode(header=Header.CC_HEADER)
        date_str = _decode(header=Header.DATE_HEADER)
        date = _parse_date(date_str=date_str)
        if not date:
            date = datetime.now(tz=timezone.utc)
        # If any of the above are `None`, model validation will fail.
        # Therefore, no guards (i.e.: `if <header> is None: raise RuntimeError(..)`) were written.
        return cls.model_validate(
            {
                "id": message_id,
                "subject": subject,
                "sender": from_,
                "recipients": to,
                "cc": cc,
                "date": date,
            }
        )
 class CurrentMailbox(BaseModel):
    mailbox: str
    todo_email_ids: list[str]
 # An email has a list of mailboxes.
 # Each mailbox has a list of email-ids inside of it.
 #
 # Usage:
 # To use this checkpointer, first fetch all the mailboxes.
 # Then, pop a mailbox and fetch all of its email-ids.
 # Then, pop each email-id and fetch its content (and parse it, etc..).
 # When you have popped all email-ids for this mailbox, pop the next mailbox and repeat the above process until you're done.
 #
 # For initial checkpointing, set both fields to `None`.
 class ImapCheckpoint(ConnectorCheckpoint):
    todo_mailboxes: list[str] | None = None
    current_mailbox: CurrentMailbox | None = None
 class LoginState(str, Enum):
    LoggedIn = "logged_in"
    LoggedOut = "logged_out"
 class ImapConnector(
    CredentialsConnector,
    CheckpointedConnectorWithPermSync,
 ):
    def __init__(
        self,
        host: str,
        port: int = _DEFAULT_IMAP_PORT_NUMBER,
        mailboxes: list[str] | None = None,
    ) -> None:
        self._host = host
        self._port = port
        self._mailboxes = mailboxes
        self._credentials: dict[str, Any] | None = None
    @property
    def credentials(self) -> dict[str, Any]:
        if not self._credentials:
            raise RuntimeError(
                "Credentials have not been initialized; call `set_credentials_provider` first"
            )
        return self._credentials
    def _get_mail_client(self) -> imaplib.IMAP4_SSL:
        """
        Returns a new `imaplib.IMAP4_SSL` instance.
        The `imaplib.IMAP4_SSL` object is supposed to be an "ephemeral" object; it's not something that you can login,
        logout, then log back into again. I.e., the following will fail:
        ```py
        mail_client.login(..)
        mail_client.logout();
        mail_client.login(..)
        ```
        Therefore, you need a fresh, new instance in order to operate with IMAP. This function gives one to you.
        # Notes
        This function will throw an error if the credentials have not yet been set.
        """
        def get_or_raise(name: str) -> str:
            value = self.credentials.get(name)
            if not value:
                raise RuntimeError(f"Credential item {name=} was not found")
            if not isinstance(value, str):
                raise RuntimeError(
                    f"Credential item {name=} must be of type str, instead received {type(name)=}"
                )
            return value
        username = get_or_raise(_USERNAME_KEY)
        password = get_or_raise(_PASSWORD_KEY)
        mail_client = imaplib.IMAP4_SSL(host=self._host, port=self._port)
        status, _data = mail_client.login(user=username, password=password)
        if status != _IMAP_OKAY_STATUS:
            raise RuntimeError(f"Failed to log into imap server; {status=}")
        return mail_client
    def _load_from_checkpoint(
        self,
        start: SecondsSinceUnixEpoch,
        end: SecondsSinceUnixEpoch,
        checkpoint: ImapCheckpoint,
        include_perm_sync: bool,
    ) -> CheckpointOutput[ImapCheckpoint]:
        checkpoint = cast(ImapCheckpoint, copy.deepcopy(checkpoint))
        checkpoint.has_more = True
        mail_client = self._get_mail_client()
        if checkpoint.todo_mailboxes is None:
            # This is the dummy checkpoint.
            # Fill it with mailboxes first.
            if self._mailboxes:
                checkpoint.todo_mailboxes = _sanitize_mailbox_names(self._mailboxes)
            else:
                fetched_mailboxes = _fetch_all_mailboxes_for_email_account(
                    mail_client=mail_client
                )
                if not fetched_mailboxes:
                    raise RuntimeError(
                        "Failed to find any mailboxes for this email account"
                    )
                checkpoint.todo_mailboxes = _sanitize_mailbox_names(fetched_mailboxes)
            return checkpoint
        if (
            not checkpoint.current_mailbox
            or not checkpoint.current_mailbox.todo_email_ids
        ):
            if not checkpoint.todo_mailboxes:
                checkpoint.has_more = False
                return checkpoint
            mailbox = checkpoint.todo_mailboxes.pop()
            email_ids = _fetch_email_ids_in_mailbox(
                mail_client=mail_client,
                mailbox=mailbox,
                start=start,
                end=end,
            )
            checkpoint.current_mailbox = CurrentMailbox(
                mailbox=mailbox,
                todo_email_ids=email_ids,
            )
        _select_mailbox(
            mail_client=mail_client, mailbox=checkpoint.current_mailbox.mailbox
        )
        current_todos = cast(
            list, copy.deepcopy(checkpoint.current_mailbox.todo_email_ids[:_PAGE_SIZE])
        )
        checkpoint.current_mailbox.todo_email_ids = (
            checkpoint.current_mailbox.todo_email_ids[_PAGE_SIZE:]
        )
        for email_id in current_todos:
            email_msg = _fetch_email(mail_client=mail_client, email_id=email_id)
            if not email_msg:
                logging.warning(f"Failed to fetch message {email_id=}; skipping")
                continue
            email_headers = EmailHeaders.from_email_msg(email_msg=email_msg)
            msg_dt = email_headers.date
            if msg_dt.tzinfo is None:
                msg_dt = msg_dt.replace(tzinfo=timezone.utc)
            else:
                msg_dt = msg_dt.astimezone(timezone.utc)
            start_dt = datetime.fromtimestamp(start, tz=timezone.utc)
            end_dt = datetime.fromtimestamp(end, tz=timezone.utc)
            if not (start_dt < msg_dt <= end_dt):
                continue
            email_doc = _convert_email_headers_and_body_into_document(
                email_msg=email_msg,
                email_headers=email_headers,
                include_perm_sync=include_perm_sync,
            )
            yield email_doc
            attachments = extract_attachments(email_msg)
            for att in attachments:
                yield attachment_to_document(email_doc, att, email_headers)
        return checkpoint
    # impls for BaseConnector
    def load_credentials(self, credentials: dict[str, Any]) -> dict[str, Any] | None:
        self._credentials = credentials
        return None
    def validate_connector_settings(self) -> None:
        self._get_mail_client()
    # impls for CredentialsConnector
    def set_credentials_provider(
        self, credentials_provider: CredentialsProviderInterface
    ) -> None:
        self._credentials = credentials_provider.get_credentials()
    # impls for CheckpointedConnector
    def load_from_checkpoint(
        self,
        start: SecondsSinceUnixEpoch,
        end: SecondsSinceUnixEpoch,
        checkpoint: ImapCheckpoint,
    ) -> CheckpointOutput[ImapCheckpoint]:
        return self._load_from_checkpoint(
            start=start, end=end, checkpoint=checkpoint, include_perm_sync=False
        )
    def build_dummy_checkpoint(self) -> ImapCheckpoint:
        return ImapCheckpoint(has_more=True)
    def validate_checkpoint_json(self, checkpoint_json: str) -> ImapCheckpoint:
        return ImapCheckpoint.model_validate_json(json_data=checkpoint_json)
    # impls for CheckpointedConnectorWithPermSync
    def load_from_checkpoint_with_perm_sync(
        self,
        start: SecondsSinceUnixEpoch,
        end: SecondsSinceUnixEpoch,
        checkpoint: ImapCheckpoint,
    ) -> CheckpointOutput[ImapCheckpoint]:
        return self._load_from_checkpoint(
            start=start, end=end, checkpoint=checkpoint, include_perm_sync=True
        )
 def _fetch_all_mailboxes_for_email_account(mail_client: imaplib.IMAP4_SSL) -> list[str]:
    status, mailboxes_data = mail_client.list('""', "*")
    if status != _IMAP_OKAY_STATUS:
        raise RuntimeError(f"Failed to fetch mailboxes; {status=}")
    mailboxes = []
    for mailboxes_raw in mailboxes_data:
        if isinstance(mailboxes_raw, bytes):
            mailboxes_str = mailboxes_raw.decode()
        elif isinstance(mailboxes_raw, str):
            mailboxes_str = mailboxes_raw
        else:
            logging.warning(
                f"Expected the mailbox data to be of type str, instead got {type(mailboxes_raw)=} {mailboxes_raw}; skipping"
            )
            continue
        # The mailbox LIST response output can be found here:
        # https://www.rfc-editor.org/rfc/rfc3501.html#section-7.2.2
        #
        # The general format is:
        # `(<name-attributes>) <hierarchy-delimiter> <mailbox-name>`
        #
        # The below regex matches on that pattern; from there, we select the 3rd match (index 2), which is the mailbox-name.
        match = re.match(r'\([^)]*\)\s+"([^"]+)"\s+"?(.+?)"?$', mailboxes_str)
        if not match:
            logging.warning(
                f"Invalid mailbox-data formatting structure: {mailboxes_str=}; skipping"
            )
            continue
        mailbox = match.group(2)
        mailboxes.append(mailbox)
    if not mailboxes:
        logging.warning(
            "No mailboxes parsed from LIST response; falling back to INBOX"
        )
        return ["INBOX"]
    return mailboxes
 def _select_mailbox(mail_client: imaplib.IMAP4_SSL, mailbox: str) -> bool:
    try:
        status, _ = mail_client.select(mailbox=mailbox, readonly=True)
        if status != _IMAP_OKAY_STATUS:
            return False
        return True
    except Exception:
        return False
 def _fetch_email_ids_in_mailbox(
    mail_client: imaplib.IMAP4_SSL,
    mailbox: str,
    start: SecondsSinceUnixEpoch,
    end: SecondsSinceUnixEpoch,
 ) -> list[str]:
    if not _select_mailbox(mail_client, mailbox):
        logging.warning(f"Skip mailbox: {mailbox}")
        return []
    start_dt = datetime.fromtimestamp(start, tz=timezone.utc)
    end_dt = datetime.fromtimestamp(end, tz=timezone.utc) + timedelta(days=1)
    start_str = start_dt.strftime("%d-%b-%Y")
    end_str = end_dt.strftime("%d-%b-%Y")
    search_criteria = f'(SINCE "{start_str}" BEFORE "{end_str}")'
    status, email_ids_byte_array = mail_client.search(None, search_criteria)
    if status != _IMAP_OKAY_STATUS or not email_ids_byte_array:
        raise RuntimeError(f"Failed to fetch email ids; {status=}")
    email_ids: bytes = email_ids_byte_array[0]
    return [email_id.decode() for email_id in email_ids.split()]
 def _fetch_email(mail_client: imaplib.IMAP4_SSL, email_id: str) -> Message | None:
    status, msg_data = mail_client.fetch(message_set=email_id, message_parts="(RFC822)")
    if status != _IMAP_OKAY_STATUS or not msg_data:
        return None
    data = msg_data[0]
    if not isinstance(data, tuple):
        raise RuntimeError(
            f"Message data should be a tuple; instead got a {type(data)=} {data=}"
        )
    _, raw_email = data
    return email.message_from_bytes(raw_email)
 def _convert_email_headers_and_body_into_document(
    email_msg: Message,
    email_headers: EmailHeaders,
    include_perm_sync: bool,
 ) -> Document:
    sender_name, sender_addr = _parse_singular_addr(raw_header=email_headers.sender)
    to_addrs = (
        _parse_addrs(email_headers.recipients)
        if email_headers.recipients
        else []
    )
    cc_addrs = (
        _parse_addrs(email_headers.cc)
        if email_headers.cc
        else []
    )
    all_participants = to_addrs + cc_addrs
    expert_info_map = {
        recipient_addr: BasicExpertInfo(
            display_name=recipient_name, email=recipient_addr
        )
        for recipient_name, recipient_addr in all_participants
    }
    if sender_addr not in expert_info_map:
        expert_info_map[sender_addr] = BasicExpertInfo(
            display_name=sender_name, email=sender_addr
        )
    email_body = _parse_email_body(email_msg=email_msg, email_headers=email_headers)
    primary_owners = list(expert_info_map.values())
    external_access = (
        ExternalAccess(
            external_user_emails=set(expert_info_map.keys()),
            external_user_group_ids=set(),
            is_public=False,
        )
        if include_perm_sync
        else None
    )
    return Document(
        id=email_headers.id,
        title=email_headers.subject,
        blob=email_body,
        size_bytes=len(email_body),
        semantic_identifier=email_headers.subject,
        metadata={},
        extension='.txt',
        doc_updated_at=email_headers.date,
        source=DocumentSource.IMAP,
        primary_owners=primary_owners,
        external_access=external_access,
    )
 def extract_attachments(email_msg: Message, max_bytes: int = IMAP_CONNECTOR_SIZE_THRESHOLD):
    attachments = []
    if not email_msg.is_multipart():
        return attachments
    for part in email_msg.walk():
        if part.get_content_maintype() == "multipart":
            continue
        disposition = (part.get("Content-Disposition") or "").lower()
        filename = part.get_filename()
        if not (
            disposition.startswith("attachment")
            or (disposition.startswith("inline") and filename)
        ):
            continue
        payload = part.get_payload(decode=True)
        if not payload:
            continue
        if len(payload) > max_bytes:
            continue
        attachments.append({
            "filename": filename or "attachment.bin",
            "content_type": part.get_content_type(),
            "content_bytes": payload,
            "size_bytes": len(payload),
        })
    return attachments
 def decode_mime_filename(raw: str | None) -> str | None:
    if not raw:
        return None
    try:
        raw = collapse_rfc2231_value(raw)
    except Exception:
        pass
    parts = decode_header(raw)
    decoded = []
    for value, encoding in parts:
        if isinstance(value, bytes):
            decoded.append(value.decode(encoding or "utf-8", errors="replace"))
        else:
            decoded.append(value)
    return "".join(decoded)
 def attachment_to_document(
    parent_doc: Document,
    att: dict,
    email_headers: EmailHeaders,
 ):
    raw_filename = att["filename"]
    filename = decode_mime_filename(raw_filename) or "attachment.bin"
    ext = "." + filename.split(".")[-1] if "." in filename else ""
    return Document(
        id=f"{parent_doc.id}#att:{filename}",
        source=DocumentSource.IMAP,
        semantic_identifier=filename,
        extension=ext,
        blob=att["content_bytes"],
        size_bytes=att["size_bytes"],
        doc_updated_at=email_headers.date,
        primary_owners=parent_doc.primary_owners,
        metadata={
            "parent_email_id": parent_doc.id,
            "parent_subject": email_headers.subject,
            "attachment_filename": filename,
            "attachment_content_type": att["content_type"],
        },
    )
 def _parse_email_body(
    email_msg: Message,
    email_headers: EmailHeaders,
 ) -> str:
    body = None
    for part in email_msg.walk():
        if part.is_multipart():
            # Multipart parts are *containers* for other parts, not the actual content itself.
            # Therefore, we skip until we find the individual parts instead.
            continue
        charset = part.get_content_charset() or "utf-8"
        try:
            raw_payload = part.get_payload(decode=True)
            if not isinstance(raw_payload, bytes):
                logging.warning(
                    "Payload section from email was expected to be an array of bytes, instead got "
                    f"{type(raw_payload)=}, {raw_payload=}"
                )
                continue
            body = raw_payload.decode(charset)
            break
        except (UnicodeDecodeError, LookupError) as e:
            logging.warning(f"Could not decode part with charset {charset}. Error: {e}")
            continue
    if not body:
        logging.warning(
            f"Email with {email_headers.id=} has an empty body; returning an empty string"
        )
        return ""
    soup = bs4.BeautifulSoup(markup=body, features="html.parser")
    return " ".join(str_section for str_section in soup.stripped_strings)
 def _sanitize_mailbox_names(mailboxes: list[str]) -> list[str]:
    """
    Mailboxes with special characters in them must be enclosed by double-quotes, as per the IMAP protocol.
    Just to be safe, we wrap *all* mailboxes with double-quotes.
    """
    return [f'"{mailbox}"' for mailbox in mailboxes if mailbox]
 def _parse_addrs(raw_header: str) -> list[tuple[str, str]]:
    addrs = raw_header.split(",")
    name_addr_pairs = [parseaddr(addr=addr) for addr in addrs if addr]
    return [(name, addr) for name, addr in name_addr_pairs if addr]
 def _parse_singular_addr(raw_header: str) -> tuple[str, str]:
    addrs = _parse_addrs(raw_header=raw_header)
    if not addrs:
        return ("Unknown", "unknown@example.com")
    elif len(addrs) >= 2:
        raise RuntimeError(
            f"Expected a singular address, but instead got multiple; {raw_header=} {addrs=}"
        )
    return addrs[0]
 if __name__ == "__main__":
    import time
    import uuid
    from types import TracebackType
    from common.data_source.utils import load_all_docs_from_checkpoint_connector
    class OnyxStaticCredentialsProvider(
        CredentialsProviderInterface["OnyxStaticCredentialsProvider"]
    ):
        """Implementation (a very simple one!) to handle static credentials."""
        def __init__(
            self,
            tenant_id: str | None,
            connector_name: str,
            credential_json: dict[str, Any],
        ):
            self._tenant_id = tenant_id
            self._connector_name = connector_name
            self._credential_json = credential_json
            self._provider_key = str(uuid.uuid4())
        def __enter__(self) -> "OnyxStaticCredentialsProvider":
            return self
        def __exit__(
            self,
            exc_type: type[BaseException] | None,
            exc_value: BaseException | None,
            traceback: TracebackType | None,
        ) -> None:
            pass
        def get_tenant_id(self) -> str | None:
            return self._tenant_id
        def get_provider_key(self) -> str:
            return self._provider_key
        def get_credentials(self) -> dict[str, Any]:
            return self._credential_json
        def set_credentials(self, credential_json: dict[str, Any]) -> None:
            self._credential_json = credential_json
        def is_dynamic(self) -> bool:
            return False
    # from tests.daily.connectors.utils import load_all_docs_from_checkpoint_connector
    # from onyx.connectors.credentials_provider import OnyxStaticCredentialsProvider
    host = os.environ.get("IMAP_HOST")
    mailboxes_str = os.environ.get("IMAP_MAILBOXES","INBOX")
    username = os.environ.get("IMAP_USERNAME")
    password = os.environ.get("IMAP_PASSWORD")
    mailboxes = (
        [mailbox.strip() for mailbox in mailboxes_str.split(",")]
        if mailboxes_str
        else []
    )
    if not host:
        raise RuntimeError("`IMAP_HOST` must be set")
    imap_connector = ImapConnector(
        host=host,
        mailboxes=mailboxes,
    )
    imap_connector.set_credentials_provider(
        OnyxStaticCredentialsProvider(
            tenant_id=None,
            connector_name=DocumentSource.IMAP,
            credential_json={
                _USERNAME_KEY: username,
                _PASSWORD_KEY: password,
            },
        )
    )
    END = time.time()
    START = END - 1 * 24 * 60 * 60
    for doc in load_all_docs_from_checkpoint_connector(
        connector=imap_connector,
        start=START,
        end=END,
    ):
        print(doc.id,doc.extension)
--- a/deepdoc/parser/pdf_parser.py
+++ b/deepdoc/parser/pdf_parser.py
@ -476,11 +476,13 @@ class RAGFlowPdfParser:
        self.boxes = bxs
    def _naive_vertical_merge(self, zoomin=3):
-        bxs = self._assign_column(self.boxes, zoomin)
+        #bxs = self._assign_column(self.boxes, zoomin)
        bxs = self.boxes
        grouped = defaultdict(list)
        for b in bxs:
-            grouped[(b["page_number"], b.get("col_id", 0))].append(b)
+            # grouped[(b["page_number"], b.get("col_id", 0))].append(b)
            grouped[(b["page_number"], "x")].append(b)
        merged_boxes = []
        for (pg, col), bxs in grouped.items():
@ -551,7 +553,7 @@ class RAGFlowPdfParser:
            merged_boxes.extend(bxs)
-        self.boxes = sorted(merged_boxes, key=lambda x: (x["page_number"], x.get("col_id", 0), x["top"]))
+        #self.boxes = sorted(merged_boxes, key=lambda x: (x["page_number"], x.get("col_id", 0), x["top"]))
    def _final_reading_order_merge(self, zoomin=3):
        if not self.boxes:
@ -1206,7 +1208,7 @@ class RAGFlowPdfParser:
        start = timer()
        self._text_merge()
        self._concat_downward()
-        #self._naive_vertical_merge(zoomin)
+        self._naive_vertical_merge(zoomin)
        if callback:
            callback(0.92, "Text merged ({:.2f}s)".format(timer() - start))
--- a/docker/.env
+++ b/docker/.env
@ -137,11 +137,11 @@ ADMIN_SVR_HTTP_PORT=9381
 SVR_MCP_PORT=9382
 # The RAGFlow Docker image to download. v0.22+ doesn't include embedding models.
-RAGFLOW_IMAGE=infiniflow/ragflow:v0.23.0
+RAGFLOW_IMAGE=infiniflow/ragflow:v0.23.1
 # If you cannot download the RAGFlow Docker image:
-# RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:v0.23.0
+# RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:v0.23.1
-# RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:v0.23.0
+# RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:v0.23.1
 #
 # - For the `nightly` edition, uncomment either of the following:
 # RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:nightly
--- a/docker/README.md
+++ b/docker/README.md
@ -77,7 +77,7 @@ The [.env](./.env) file contains important environment variables for Docker.
 - `SVR_HTTP_PORT`  
  The port used to expose RAGFlow's HTTP API service to the host machine, allowing **external** access to the service running inside the Docker container. Defaults to `9380`.
 - `RAGFLOW-IMAGE`  
-  The Docker image edition. Defaults to `infiniflow/ragflow:v0.23.0`. The RAGFlow Docker image does not include embedding models.
+  The Docker image edition. Defaults to `infiniflow/ragflow:v0.23.1`. The RAGFlow Docker image does not include embedding models.
 > [!TIP]  
--- a/docs/basics/_category_.json
+++ b/docs/basics/_category_.json
@ -0,0 +1,8 @@
 {
  "label": "Basics",
  "position": 2,
  "link": {
    "type": "generated-index",
    "description": "Basic concepts."
  }
 }
--- a/docs/basics/agent_context_engine.md
+++ b/docs/basics/agent_context_engine.md
@ -0,0 +1,61 @@
 ---
 sidebar_position: 2
 slug: /what_is_agent_context_engine
 ---
 # What is Agent context engine?
 From 2025, a silent revolution began beneath the dazzling surface of AI Agents. While the world marveled at agents that could write code, analyze data, and automate workflows, a fundamental bottleneck emerged: why do even the most advanced agents still stumble on simple questions, forget previous conversations, or misuse available tools?
 The answer lies not in the intelligence of the Large Language Model (LLM) itself, but in the quality of the Context it receives. An LLM, no matter how powerful, is only as good as the information we feed it. Today’s cutting-edge agents are often crippled by a cumbersome, manual, and error-prone process of context assembly—a process known as Context Engineering.
 This is where the Agent Context Engine comes in. It is not merely an incremental improvement but a foundational shift, representing the evolution of RAG from a singular technique into the core data and intelligence substrate for the entire Agent ecosystem.
 ## Beyond the hype: The reality of today's "intelligent" Agents
 Today, the “intelligence” behind most AI Agents hides a mountain of human labor. Developers must:
 - Hand-craft elaborate prompt templates
 - Hard-code document-retrieval logic for every task
 - Juggle tool descriptions, conversation history, and knowledge snippets inside a tiny context window
 - Repeat the whole process for each new scenario
 This pattern is called Context Engineering. It is deeply tied to expert know-how, almost impossible to scale, and prohibitively expensive to maintain. When an enterprise needs to keep dozens of distinct agents alive, the artisanal workshop model collapses under its own weight.
 The mission of an Agent Context Engine is to turn Context Engineering from an “art” into an industrial-grade science.
 Deconstructing the Agent Context Engine
 So, what exactly is an Agent Context Engine? It is a unified, intelligent, and automated platform responsible for the end-to-end process of assembling the optimal context for an LLM or Agent at the moment of inference. It moves from artisanal crafting to industrialized production.
 At its core, an Agent Context Engine is built on a triumvirate of next-generation retrieval capabilities, seamlessly integrated into a single service layer:
 1. The Knowledge Core (Advanced RAG): This is the evolution of traditional RAG. It moves beyond simple chunk-and-embed to intelligently process static, private enterprise knowledge. Techniques like TreeRAG (building LLM-generated document outlines for "locate-then-expand" retrieval) and GraphRAG (extracting entity networks to find semantically distant connections) work to close the "semantic gap." The engine’s Ingestion Pipeline acts as the ETL for unstructured data, parsing multi-format documents and using LLMs to enrich content with summaries, metadata, and structure before indexing.
 2. The Memory Layer: An Agent’s intelligence is defined by its ability to learn from interaction. The Memory Layer is a specialized retrieval system for dynamic, episodic data: conversation history, user preferences, and the agent’s own internal state (e.g., "waiting for human input"). It manages the lifecycle of this data—storing raw dialogue, triggering summarization into semantic memory, and retrieving relevant past interactions to provide continuity and personalization. Technologically, it is a close sibling to RAG, but focused on a temporal stream of data.
 3. The Tool Orchestrator: As MCP (Model Context Protocol) enables the connection of hundreds of internal services as tools, a new problem arises: tool selection. The Context Engine solves this with Tool Retrieval. Instead of dumping all tool descriptions into the prompt, it maintains an index of tools and—critically—an index of Playbooks or Guidelines (best practices on when and how to use tools). For a given task, it retrieves only the most relevant tools and instructions, transforming the LLM’s job from "searching a haystack" to "following a recipe."
 ## Why we need a dedicated engine? The case for a unified substrate
 The necessity of an Agent Context Engine becomes clear when we examine the alternative: siloed, manually wired components.
 - The Data Silo Problem: Knowledge, memory, and tools reside in separate systems, requiring complex integration for each new agent.
 - The Assembly Line Bottleneck: Developers spend more time on context plumbing than on agent logic, slowing innovation to a crawl.
 - The "Context Ownership" Dilemma: In manually engineered systems, context logic is buried in code, owned by developers, and opaque to business users. An Engine makes context a configurable, observable, and customer-owned asset.
 The shift from Context Engineering to a Context Platform/Engine marks the maturation of enterprise AI, as summarized in the table below:
 | Dimension           | Context engineering (present)                                              | Context engineering/Platform (future)                                                               |
 | ------------------- | -------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------- |
 | Context creation    | Manual, artisanal work by developers and prompt engineers.                 | Automated, driven by intelligent ingestion pipelines and configurable rules.                        |
 | Context delivery    | Hard-coded prompts and static retrieval logic embedded in agent workflows. | Dynamic, real-time retrieval and assembly based on the agent's live state and intent.               |
 | Context maintenance | A development and operational burden, logic locked in code.                | A manageable platform function, with visibility and control returned to the business.               |
 ## RAGFlow: A resolute march toward the context engine of Agents
 This is the future RAGFlow is forging.
 We left behind the label of “yet another RAG system” long ago. From DeepDoc—our deeply-optimized, multimodal document parser—to the bleeding-edge architectures that bridge semantic chasms in complex RAG scenarios, all the way to a full-blown, enterprise-grade ingestion pipeline, every evolutionary step RAGFlow takes is a deliberate stride toward the ultimate form: an Agentic Context Engine.
 We believe tomorrow’s enterprise AI advantage will hinge not on who owns the largest model, but on who can feed that model the highest-quality, most real-time, and most relevant context. An Agentic Context Engine is the critical infrastructure that turns this vision into reality.
 In the paradigm shift from “hand-crafted prompts” to “intelligent context,” RAGFlow is determined to be the most steadfast propeller and enabler. We invite every developer, enterprise, and researcher who cares about the future of AI agents to follow RAGFlow’s journey—so together we can witness and build the cornerstone of the next-generation AI stack.
--- a/docs/basics/rag.md
+++ b/docs/basics/rag.md
@ -0,0 +1,107 @@
 ---
 sidebar_position: 1
 slug: /what_is_rag
 ---
 # What is Retreival-Augmented-Generation (RAG)？
 Since large language models (LLMs) became the focus of technology, their ability to handle general knowledge has been astonishing. However, when questions shift to internal corporate documents, proprietary knowledge bases, or real-time data, the limitations of LLMs become glaringly apparent: they cannot access private information outside their training data. Retrieval-Augmented Generation (RAG) was born precisely to address this core need. Before an LLM generates an answer, it first retrieves the most relevant context from an external knowledge base and inputs it as "reference material" to the LLM, thereby guiding it to produce accurate answers. In short, RAG elevates LLMs from "relying on memory" to "having evidence to rely on," significantly improving their accuracy and trustworthiness in specialized fields and real-time information queries.
 ## Why RAG is important?
 Although LLMs excel in language understanding and generation, they have inherent limitations:
 - Static Knowledge: The model's knowledge is based on a data snapshot from its training time and cannot be automatically updated, making it difficult to perceive the latest information.
 - Blind Spot to External Data: They cannot directly access corporate private documents, real-time information streams, or domain-specific content.
 - Hallucination Risk: When lacking accurate evidence, they may still fabricate plausible-sounding but false answers to maintain conversational fluency.
 The introduction of RAG provides LLMs with real-time, credible "factual grounding." Its core mechanism is divided into two stages:
 - Retrieval Stage: Based on the user's question, quickly retrieve the most relevant documents or data fragments from an external knowledge base.
 - Generation Stage: The LLM organizes and generates the final answer by incorporating the retrieved information as context, combined with its own linguistic capabilities.
 This upgrades LLMs from "speaking from memory" to "speaking with documentation," significantly enhancing reliability in professional and enterprise-level applications.
 ## How RAG works?
 Retrieval-Augmented Generation enables LLMs to generate higher-quality responses by leveraging real-time, external, or private data sources through the introduction of an information retrieval mechanism. Its workflow can be divided into following key steps:
 ### Data processing and vectorization
 The knowledge required by RAG comes from unstructured data in various formats, such as documents, database records, or API return content. This data typically needs to be chunked, then transformed into vectors via an embedding model, and stored in a vector database.
 Why is Chunking Needed? Indexing entire documents directly faces the following problems:
 - Decreased Retrieval Precision: Vectorizing long documents leads to semantic "averaging," losing details.
 - Context Length Limitation: LLMs have a finite context window, requiring filtering of the most relevant parts for input.
 - Cost and Efficiency: Embedding computation and retrieval costs are higher for long texts.
 Therefore, an intelligent chunking strategy is key to balancing information integrity, retrieval granularity, and computational efficiency.
 ### Retrieve relevant information
 The user's query is also converted into a vector to perform semantic relevance searches (e.g., calculating cosine similarity) in the vector database, matching and recalling the most relevant text fragments.
 ### Context construction and answer generation
 The retrieved relevant content is added to the LLM's context as factual grounding, and the LLM finally generates the answer. Therefore, RAG can be seen as Context Engineering 1.0 for automated context construction.
 ## Deep dive into existing RAG architecture: beyond vector retrieval
 An industrial-grade RAG system is far from being as simple as "vector search + LLM"; its complexity and challenges are primarily embedded in the retrieval process.
 ### Data complexity: multimodal document processing
 Core Challenge: Corporate knowledge mostly exists in the form of multimodal documents containing text, charts, tables, and formulas. Simple OCR extraction loses a large amount of semantic information.
 Advanced Practice: Leading solutions, such as RAGFlow, tend to use Visual Language Models (VLM) or specialized parsing models like DeepDoc to "translate" multimodal documents into unimodal text rich in structural and semantic information. Converting multimodal information into high-quality unimodal text has become standard practice for advanced RAG.
 ### The complexity of chunking: the trade-off between precision and context
 A simple "chunk-embed-retrieve" pipeline has an inherent contradiction:
 - Semantic Matching requires small text chunks to ensure clear semantic focus.
 - Context Understanding requires large text chunks to ensure complete and coherent information.
 This forces system design into a difficult trade-off between "precise but fragmented" and "complete but vague."
 Advanced Practice: Leading solutions, such as RAGFlow, employ semantic enhancement techniques like constructing semantic tables of contents and knowledge graphs. These not only address semantic fragmentation caused by physical chunking but also enable the discovery of relevant content across documents based on entity-relationship networks.
 ### Why is a vector database insufficient for serving RAG?
 Vector databases excel at semantic similarity search, but RAG requires precise and reliable answers, demanding more capabilities from the retrieval system:
 - Hybrid Search: Relying solely on vector retrieval may miss exact keyword matches (e.g., product codes, regulation numbers). Hybrid search, combining vector retrieval with keyword retrieval (BM25), ensures both semantic breadth and keyword precision.
 - Tensor or Multi-Vector Representation: To support cross-modal data, employing tensor or multi-vector representation has become an important trend.
 - Metadata Filtering: Filtering based on attributes like date, department, and type is a rigid requirement in business scenarios.
 Therefore, the retrieval layer of RAG is a composite system based on vector search but must integrate capabilities like full-text search, re-ranking, and metadata filtering.
 ## RAG and memory: Retrieval from the same source but different streams
 Within the agent framework, the essence of the memory mechanism is the same as RAG: both retrieve relevant information from storage based on current needs. The key difference lies in the data source:
 - RAG: Targets pre-existing static or dynamic private data provided by the user in advance (e.g., documents, databases).
 - Memory: Targets dynamic data generated or perceived by the agent in real-time during interaction (e.g., conversation history, environmental state, tool execution results).
 They are highly consistent at the technical base (e.g., vector retrieval, keyword matching) and can be seen as the same retrieval capability applied in different scenarios ("existing knowledge" vs. "interaction memory"). A complete agent system often includes both an RAG module for inherent knowledge and a Memory module for interaction history.
 ## RAG applications
 RAG has demonstrated clear value in several typical scenarios:
 1. Enterprise Knowledge Q&A and Internal Search  
   By vectorizing corporate private data and combining it with an LLM, RAG can directly return natural language answers based on authoritative sources, rather than document lists. While meeting intelligent Q&A needs, it inherently aligns with corporate requirements for data security, access control, and compliance.
 2. Complex Document Understanding and Professional Q&A  
   For structurally complex documents like contracts and regulations, the value of RAG lies in its ability to generate accurate, verifiable answers while maintaining context integrity. Its system accuracy largely depends on text chunking and semantic understanding strategies.
 3. Dynamic Knowledge Fusion and Decision Support  
   In business scenarios requiring the synthesis of information from multiple sources, RAG evolves into a knowledge orchestration and reasoning support system for business decisions. Through a multi-path recall mechanism, it fuses knowledge from different systems and formats, maintaining factual consistency and logical controllability during the generation phase.
 ## The future of RAG
 The evolution of RAG is unfolding along several clear paths:
 1. RAG as the data foundation for Agents  
   RAG and agents have an architecture vs. scenario relationship. For agents to achieve autonomous and reliable decision-making and execution, they must rely on accurate and timely knowledge. RAG provides them with a standardized capability to access private domain knowledge and is an inevitable choice for building knowledge-aware agents.
 2. Advanced RAG: Using LLMs to optimize retrieval itself  
   The core feature of next-generation RAG is fully utilizing the reasoning capabilities of LLMs to optimize the retrieval process, such as rewriting queries, summarizing or fusing results, or implementing intelligent routing. Empowering every aspect of retrieval with LLMs is key to breaking through current performance bottlenecks.
 3. Towards context engineering 2.0  
   Current RAG can be viewed as Context Engineering 1.0, whose core is assembling static knowledge context for single Q&A tasks. The forthcoming Context Engineering 2.0 will extend with RAG technology at its core, becoming a system that automatically and dynamically assembles comprehensive context for agents. The context fused by this system will come not only from documents but also include interaction memory, available tools/skills, and real-time environmental information. This marks the transition of agent development from a "handicraft workshop" model to the industrial starting point of automated context engineering.
 The essence of RAG is to build a dedicated, efficient, and trustworthy external data interface for large language models; its core is Retrieval, not Generation. Starting from the practical need to solve private data access, its technical depth is reflected in the optimization of retrieval for complex unstructured data. With its deep integration into agent architectures and its development towards automated context engineering, RAG is evolving from a technology that improves Q&A quality into the core infrastructure for building the next generation of trustworthy, controllable, and scalable intelligent applications.
--- a/docs/configurations.md
+++ b/docs/configurations.md
@ -99,7 +99,7 @@ RAGFlow utilizes MinIO as its object storage solution, leveraging its scalabilit
 - `SVR_HTTP_PORT`
  The port used to expose RAGFlow's HTTP API service to the host machine, allowing **external** access to the service running inside the Docker container. Defaults to `9380`.
 - `RAGFLOW-IMAGE`
-  The Docker image edition. Defaults to `infiniflow/ragflow:v0.23.0` (the RAGFlow Docker image without embedding models).
+  The Docker image edition. Defaults to `infiniflow/ragflow:v0.23.1` (the RAGFlow Docker image without embedding models).
 :::tip NOTE
 If you cannot download the RAGFlow Docker image, try the following mirrors.
--- a/docs/develop/build_docker_image.mdx
+++ b/docs/develop/build_docker_image.mdx
@ -47,7 +47,7 @@ After building the infiniflow/ragflow:nightly image, you are ready to launch a f
 1. Edit Docker Compose Configuration
-Open the `docker/.env` file. Find the `RAGFLOW_IMAGE` setting and change the image reference from `infiniflow/ragflow:v0.23.0` to `infiniflow/ragflow:nightly` to use the pre-built image.
+Open the `docker/.env` file. Find the `RAGFLOW_IMAGE` setting and change the image reference from `infiniflow/ragflow:v0.23.1` to `infiniflow/ragflow:nightly` to use the pre-built image.
 2. Launch the Service
--- a/docs/guides/dataset/configure_knowledge_base.md
+++ b/docs/guides/dataset/configure_knowledge_base.md
@ -133,7 +133,7 @@ See [Run retrieval test](./run_retrieval_test.md) for details.
 ## Search for dataset
-As of RAGFlow v0.23.0, the search feature is still in a rudimentary form, supporting only dataset search by name.
+As of RAGFlow v0.23.1, the search feature is still in a rudimentary form, supporting only dataset search by name.
 ![search dataset](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/search_datasets.jpg)
--- a/docs/guides/manage_files.md
+++ b/docs/guides/manage_files.md
@ -87,4 +87,4 @@ RAGFlow's file management allows you to download an uploaded file:
 ![download_file](https://github.com/infiniflow/ragflow/assets/93570324/cf3b297f-7d9b-4522-bf5f-4f45743e4ed5)
-> As of RAGFlow v0.23.0, bulk download is not supported, nor can you download an entire folder. 
+> As of RAGFlow v0.23.1, bulk download is not supported, nor can you download an entire folder. 
--- a/docs/guides/manage_users_and_services.md
+++ b/docs/guides/manage_users_and_services.md
@ -46,7 +46,7 @@ The Admin CLI and Admin Service form a client-server architectural suite for RAG
 2. Install ragflow-cli.
   ```bash
-   pip install ragflow-cli==0.23.0
+   pip install ragflow-cli==0.23.1
   ```
 3. Launch the CLI client:
--- a/docs/guides/migration/_category_.json
+++ b/docs/guides/migration/_category_.json
@ -0,0 +1,8 @@
 {
  "label": "Migration",
  "position": 5,
  "link": {
    "type": "generated-index",
    "description": "RAGFlow migration guide"
  }
 }
--- a/docs/guides/run_health_check.md
+++ b/docs/guides/run_health_check.md
@ -1,109 +0,0 @@
 ---
 sidebar_position: 8
 slug: /run_health_check
 ---
 # Monitoring
 Double-check the health status of RAGFlow's dependencies.
 ---
 The operation of RAGFlow depends on four services:
 - **Elasticsearch** (default) or [Infinity](https://github.com/infiniflow/infinity) as the document engine
 - **MySQL**
 - **Redis**
 - **MinIO** for object storage
 If an exception or error occurs related to any of the above services, such as `Exception: Can't connect to ES cluster`, refer to this document to check their health status.
 You can also click you avatar in the top right corner of the page **>** System to view the visualized health status of RAGFlow's core services. The following screenshot shows that all services are 'green' (running healthily). The task executor displays the *cumulative* number of completed and failed document parsing tasks from the past 30 minutes:
 ![system_status_page](https://github.com/user-attachments/assets/b0c1a11e-93e3-4947-b17a-1bfb4cdab6e4)
 Services with a yellow or red light are not running properly. The following is a screenshot of the system page after running `docker stop ragflow-es-10`:
 ![es_failed](https://github.com/user-attachments/assets/06056540-49f5-48bf-9cc9-a7086bc75790)
 You can click on a specific 30-second time interval to view the details of completed and failed tasks:
 ![done_tasks](https://github.com/user-attachments/assets/49b25ec4-03af-48cf-b2e5-c892f6eaa261)
 ![done_vs_failed](https://github.com/user-attachments/assets/eaa928d0-a31c-4072-adea-046091e04599)
 ## API Health Check
 In addition to checking the system dependencies from the **avatar > System** page in the UI, you can directly query the backend health check endpoint:
 ```bash
 http://IP_OF_YOUR_MACHINE/v1/system/healthz
 ```
 Here `<port>` refers to the actual port of your backend service (e.g., `7897`, `9222`, etc.).
 Key points:
 - **No login required** (no `@login_required` decorator)
 - Returns results in JSON format
 - If all dependencies are healthy → HTTP **200 OK**
 - If any dependency fails → HTTP **500 Internal Server Error**
 ### Example 1: All services healthy (HTTP 200)
 ```bash
 http://127.0.0.1/v1/system/healthz
 ```
 Response:
 ```http
 HTTP/1.1 200 OK
 Content-Type: application/json
 Content-Length: 120
 ```
 Explanation:
 - Database (MySQL/Postgres), Redis, document engine (Elasticsearch/Infinity), and object storage (MinIO) are all healthy.
 - The `status` field returns `"ok"`.
 ### Example 2: One service unhealthy (HTTP 500)
 For example, if Redis is down:
 Response:
 ```http
 HTTP/1.1 500 INTERNAL SERVER ERROR
 Content-Type: application/json
 Content-Length: 300
 ```
 Explanation:
 - `redis` is marked as `"nok"`, with detailed error info under `_meta.redis.error`.
 - The overall `status` is `"nok"`, so the endpoint returns 500.
 ---
 This endpoint allows you to monitor RAGFlow’s core dependencies programmatically in scripts or external monitoring systems, without relying on the frontend UI.
  "redis": "nok",
  "doc_engine": "ok",
  "storage": "ok",
  "status": "nok",
  "_meta": {
    "redis": {
      "elapsed": "5.2",
      "error": "Lost connection!"
    }
  }
 }
 ```
 Explanation:
 - `redis` is marked as `"nok"`, with detailed error info under `_meta.redis.error`.
 - The overall `status` is `"nok"`, so the endpoint returns 500.
 ---
 This endpoint allows you to monitor RAGFlow’s core dependencies programmatically in scripts or external monitoring systems, without relying on the frontend UI.
--- a/docs/guides/upgrade_ragflow.mdx
+++ b/docs/guides/upgrade_ragflow.mdx
@ -60,16 +60,16 @@ To upgrade RAGFlow, you must upgrade **both** your code **and** your Docker imag
   git pull
   ```
-3. Switch to the latest, officially published release, e.g., `v0.23.0`:
+3. Switch to the latest, officially published release, e.g., `v0.23.1`:
   ```bash
-   git checkout -f v0.23.0
+   git checkout -f v0.23.1
   ```
 4. Update **ragflow/docker/.env**:
   ```bash
-   RAGFLOW_IMAGE=infiniflow/ragflow:v0.23.0
+   RAGFLOW_IMAGE=infiniflow/ragflow:v0.23.1
   ```
 5. Update the RAGFlow image and restart RAGFlow:
@ -90,10 +90,10 @@ No, you do not need to. Upgrading RAGFlow in itself will *not* remove your uploa
 1. From an environment with Internet access, pull the required Docker image.
 2. Save the Docker image to a **.tar** file.
   ```bash
-   docker save -o ragflow.v0.23.0.tar infiniflow/ragflow:v0.23.0
+   docker save -o ragflow.v0.23.1.tar infiniflow/ragflow:v0.23.1
   ```
 3. Copy the **.tar** file to the target server.
 4. Load the **.tar** file into Docker:
   ```bash
-   docker load -i ragflow.v0.23.0.tar
+   docker load -i ragflow.v0.23.1.tar
   ```
--- a/docs/quickstart.mdx
+++ b/docs/quickstart.mdx
@ -46,7 +46,7 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
   `vm.max_map_count`. This value sets the maximum number of memory map areas a process may have. Its default value is 65530. While most applications require fewer than a thousand maps, reducing this value can result in abnormal behaviors, and the system will throw out-of-memory errors when a process reaches the limitation.
-   RAGFlow v0.23.0 uses Elasticsearch or [Infinity](https://github.com/infiniflow/infinity) for multiple recall. Setting the value of `vm.max_map_count` correctly is crucial to the proper functioning of the Elasticsearch component.
+   RAGFlow v0.23.1 uses Elasticsearch or [Infinity](https://github.com/infiniflow/infinity) for multiple recall. Setting the value of `vm.max_map_count` correctly is crucial to the proper functioning of the Elasticsearch component.
 <Tabs
  defaultValue="linux"
@ -186,7 +186,7 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
   ```bash
   $ git clone https://github.com/infiniflow/ragflow.git
   $ cd ragflow/docker
-   $ git checkout -f v0.23.0
+   $ git checkout -f v0.23.1
   ```
 3. Use the pre-built Docker images and start up the server:
@ -202,7 +202,7 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
   | RAGFlow image tag   | Image size (GB) | Stable?                  |
   | ------------------- | --------------- | ------------------------ |
-   | v0.23.0             | &approx;2       | Stable release           |
+   | v0.23.1             | &approx;2       | Stable release           |
   | nightly             | &approx;2       | _Unstable_ nightly build |
   ```mdx-code-block
--- a/docs/references/supported_models.mdx
+++ b/docs/references/supported_models.mdx
@ -13,61 +13,58 @@ A complete list of models supported by RAGFlow, which will continue to expand.
 <APITable>
 ```
-| Provider              | Chat               | Embedding          | Rerank             | Img2txt            | Speech2txt         | TTS                |
+| Provider              | LLM                | Image2Text         | Speech2text        | TTS                | Embedding          | Rerank             | OCR                |
-| --------------------- | ------------------ | ------------------ | ------------------ | ------------------ | ------------------ | ------------------ |
+| --------------------- | ------------------ | ------------------ | ------------------ | ------------------ | ------------------ | ------------------ | ------------------ |
-| Anthropic             | :heavy_check_mark: |                    |                    |                    |                    |                    |
+| Anthropic             | :heavy_check_mark: |                    |                    |                    |                    |                    |                    |
-| Azure-OpenAI          | :heavy_check_mark: | :heavy_check_mark: |                    | :heavy_check_mark: | :heavy_check_mark: |                    |
+| Azure-OpenAI          | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |                    | :heavy_check_mark: |                    |                    |
-| BAAI                  |                    | :heavy_check_mark: | :heavy_check_mark: |                    |                    |                    |
+| BaiChuan              | :heavy_check_mark: |                    |                    |                    | :heavy_check_mark: |                    |                    |
-| BaiChuan              | :heavy_check_mark: | :heavy_check_mark: |                    |                    |                    |                    |
+| BaiduYiyan            | :heavy_check_mark: | :heavy_check_mark: |                    |                    | :heavy_check_mark: | :heavy_check_mark: |                    |
-| BaiduYiyan            | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |                    |                    |
+| Bedrock               | :heavy_check_mark: |                    |                    |                    | :heavy_check_mark: |                    |                    |
-| Bedrock               | :heavy_check_mark: | :heavy_check_mark: |                    |                    |                    |                    |
+| Cohere                | :heavy_check_mark: | :heavy_check_mark: |                    |                    | :heavy_check_mark: | :heavy_check_mark: |                    |
-| Cohere                | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |                    |                    |
+| DeepSeek              | :heavy_check_mark: |                    |                    |                    |                    |                    |                    |
-| DeepSeek              | :heavy_check_mark: |                    |                    |                    |                    |                    |
+| Fish Audio            |                    |                    |                    | :heavy_check_mark: |                    |                    |                    |
-| FastEmbed             |                    | :heavy_check_mark: |                    |                    |                    |                    |
+| Gemini                | :heavy_check_mark: | :heavy_check_mark: |                    |                    | :heavy_check_mark: |                    |                    |
-| Fish Audio            |                    |                    |                    |                    |                    | :heavy_check_mark: |
+| Google Cloud          | :heavy_check_mark: |                    |                    |                    |                    |                    |                    |
-| Gemini                | :heavy_check_mark: | :heavy_check_mark: |                    | :heavy_check_mark: |                    |                    |
+| GPUStack              | :heavy_check_mark: |                    | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |                    |
-| Google Cloud          | :heavy_check_mark: |                    |                    |                    |                    |                    |
+| Groq                  | :heavy_check_mark: |                    |                    |                    |                    |                    |                    |
-| GPUStack              | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |                    | :heavy_check_mark: | :heavy_check_mark: |
+| HuggingFace           | :heavy_check_mark: |                    |                    |                    | :heavy_check_mark: |                    |                    |
-| Groq                  | :heavy_check_mark: |                    |                    |                    |                    |                    |
+| Jina                  |                    |                    |                    |                    | :heavy_check_mark: | :heavy_check_mark: |                    |
-| HuggingFace           | :heavy_check_mark: | :heavy_check_mark: |                    |                    |                    |                    |
+| LocalAI               | :heavy_check_mark: | :heavy_check_mark: |                    |                    | :heavy_check_mark: |                    |                    |
-| Jina                  |                    | :heavy_check_mark: | :heavy_check_mark: |                    |                    |                    |
+| LongCat               | :heavy_check_mark: |                    |                    |                    |                    |                    |                    |
-| LeptonAI              | :heavy_check_mark: |                    |                    |                    |                    |                    |
+| LM-Studio             | :heavy_check_mark: | :heavy_check_mark: |                    |                    | :heavy_check_mark: |                    |                    |
-| LocalAI               | :heavy_check_mark: | :heavy_check_mark: |                    | :heavy_check_mark: |                    |                    |
+| MiniMax               | :heavy_check_mark: |                    |                    |                    |                    |                    |                    |
-| LM-Studio             | :heavy_check_mark: | :heavy_check_mark: |                    | :heavy_check_mark: |                    |                    |
+| MinerU                |                    |                    |                    |                    |                    |                    | :heavy_check_mark: |
-| MiniMax               | :heavy_check_mark: |                    |                    |                    |                    |                    |
+| Mistral               | :heavy_check_mark: |                    |                    |                    | :heavy_check_mark: |                    |                    |
-| Mistral               | :heavy_check_mark: | :heavy_check_mark: |                    |                    |                    |                    |
+| ModelScope            | :heavy_check_mark: |                    |                    |                    |                    |                    |                    |
-| ModelScope            | :heavy_check_mark: |                    |                    |                    |                    |                    |
+| Moonshot              | :heavy_check_mark: | :heavy_check_mark: |                    |                    |                    |                    |                    |
-| Moonshot              | :heavy_check_mark: |                    |                    | :heavy_check_mark: |                    |                    |
+| NovitaAI              | :heavy_check_mark: |                    |                    |                    | :heavy_check_mark: |                    |                    |
-| Novita AI             | :heavy_check_mark: | :heavy_check_mark: |                    |                    |                    |                    |
+| NVIDIA                | :heavy_check_mark: | :heavy_check_mark: |                    |                    | :heavy_check_mark: | :heavy_check_mark: |                    |
-| NVIDIA                | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |                    |                    |
+| Ollama                | :heavy_check_mark: | :heavy_check_mark: |                    |                    | :heavy_check_mark: |                    |                    |
-| Ollama                | :heavy_check_mark: | :heavy_check_mark: |                    | :heavy_check_mark: |                    |                    |
+| OpenAI                | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |                    |                    |
-| OpenAI                | :heavy_check_mark: | :heavy_check_mark: |                    | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
+| OpenAI-API-Compatible | :heavy_check_mark: | :heavy_check_mark: |                    |                    | :heavy_check_mark: | :heavy_check_mark: |                    |
-| OpenAI-API-Compatible | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |                    |                    |
+| OpenRouter            | :heavy_check_mark: | :heavy_check_mark: |                    |                    |                    |                    |                    |
-| OpenRouter            | :heavy_check_mark: |                    |                    | :heavy_check_mark: |                    |                    |
+| Replicate             | :heavy_check_mark: |                    |                    |                    | :heavy_check_mark: |                    |                    |
-| PerfXCloud            | :heavy_check_mark: | :heavy_check_mark: |                    |                    |                    |                    |
+| PPIO                  | :heavy_check_mark: |                    |                    |                    |                    |                    |                    |
-| Replicate             | :heavy_check_mark: | :heavy_check_mark: |                    |                    |                    |                    |
+| SILICONFLOW           | :heavy_check_mark: | :heavy_check_mark: |                    |                    | :heavy_check_mark: | :heavy_check_mark: |                    |
-| PPIO                  | :heavy_check_mark: |                    |                    |                    |                    |                    |
+| StepFun               | :heavy_check_mark: |                    |                    |                    |                    |                    |                    |
-| SILICONFLOW           | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |                    |                    |
+| Tencent Hunyuan       | :heavy_check_mark: |                    |                    |                    |                    |                    |                    |
-| StepFun               | :heavy_check_mark: |                    |                    |                    |                    |                    |
+| Tencent Cloud         |                    |                    | :heavy_check_mark: |                    |                    |                    |                    |
-| Tencent Hunyuan       | :heavy_check_mark: |                    |                    |                    |                    |                    |
+| TogetherAI            | :heavy_check_mark: | :heavy_check_mark: |                    |                    | :heavy_check_mark: | :heavy_check_mark: |                    |
-| Tencent Cloud         |                    |                    |                    |                    | :heavy_check_mark: |                    |
+| TokenPony             | :heavy_check_mark: |                    |                    |                    |                    |                    |                    |
-| TogetherAI            | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |                    |                    |
+| Tongyi-Qianwen        | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |                    |
-| Tongyi-Qianwen        | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
+| Upstage               | :heavy_check_mark: |                    |                    |                    | :heavy_check_mark: |                    |                    |
-| Upstage               | :heavy_check_mark: | :heavy_check_mark: |                    |                    |                    |                    |
+| VLLM                  | :heavy_check_mark: | :heavy_check_mark: |                    |                    | :heavy_check_mark: | :heavy_check_mark: |                    |
-| VLLM                  | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |                    |                    |
+| VolcEngine            | :heavy_check_mark: |                    |                    |                    |                    |                    |                    |
-| VolcEngine            | :heavy_check_mark: |                    |                    |                    |                    |                    |
+| Voyage AI             |                    | :heavy_check_mark: |                    |                    | :heavy_check_mark: | :heavy_check_mark: |                    |
-| Voyage AI             |                    | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |                    |                    |
+| Xinference            | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |                    |
-| Xinference            | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
+| XunFei Spark          | :heavy_check_mark: |                    |                    | :heavy_check_mark: |                    |                    |                    |
-| XunFei Spark          | :heavy_check_mark: |                    |                    |                    |                    | :heavy_check_mark: |
+| xAI                   | :heavy_check_mark: | :heavy_check_mark: |                    |                    |                    |                    |                    |
-| xAI                   | :heavy_check_mark: |                    |                    | :heavy_check_mark: |                    |                    |
+| ZHIPU-AI              | :heavy_check_mark: | :heavy_check_mark: |                    |                    | :heavy_check_mark: |                    |                    |
-| Youdao                |                    | :heavy_check_mark: | :heavy_check_mark: |                    |                    |                    |
+| DeepInfra             | :heavy_check_mark: |                    | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |                    |                    |
-| ZHIPU-AI              | :heavy_check_mark: | :heavy_check_mark: |                    | :heavy_check_mark: |                    |                    |
+| 302.AI                | :heavy_check_mark: | :heavy_check_mark: |                    |                    | :heavy_check_mark: | :heavy_check_mark: |                    |
-| 01.AI                 | :heavy_check_mark: |                    |                    |                    |                    |                    |
+| CometAPI              | :heavy_check_mark: |                    |                    |                    | :heavy_check_mark: |                    |                    |
-| DeepInfra             | :heavy_check_mark: | :heavy_check_mark: |                    |                    | :heavy_check_mark: | :heavy_check_mark: |
+| DeerAPI               | :heavy_check_mark: | :heavy_check_mark: |                    | :heavy_check_mark: | :heavy_check_mark: |                    |                    |
-| 302.AI                | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |                    |                    |
+| Jiekou.AI             | :heavy_check_mark: |                    |                    |                    | :heavy_check_mark: | :heavy_check_mark: |                    |
 | CometAPI              | :heavy_check_mark: | :heavy_check_mark: |                    |                    |                    |                    |
 | DeerAPI               | :heavy_check_mark: | :heavy_check_mark: |                    | :heavy_check_mark: |                    | :heavy_check_mark: |
 | Jiekou.AI             | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |                    |                    |                    |
 ```mdx-code-block
 </APITable>
--- a/docs/release_notes.md
+++ b/docs/release_notes.md
@ -7,6 +7,24 @@ slug: /release_notes
 Key features, improvements and bug fixes in the latest releases.
 ## v0.23.1
 Released on December 31, 2025.
 ### Fixed issues
 - Resolved an issue where the RAGFlow Server would fail to start if an empty memory object existed, and corrected the inability to delete a newly created empty Memory.
 - Improved the stability of memory extraction across all memory types after selection.
 - Fixed MDX file parsing support.
 ### Data sources
 - GitHub
 - Gitlab
 - Asana
 - IMAP
 ## v0.23.0
 Released on December 27, 2025.
--- a/helm/values.yaml
+++ b/helm/values.yaml
@ -77,7 +77,7 @@ env:
 ragflow:
  image:
    repository: infiniflow/ragflow
-    tag: v0.23.0
+    tag: v0.23.1
    pullPolicy: IfNotPresent
    pullSecrets: []
  # Optional service configuration overrides
--- a/pyproject.toml
+++ b/pyproject.toml
@ -1,6 +1,6 @@
 [project]
 name = "ragflow"
-version = "0.23.0"
+version = "0.23.1"
 description = "[RAGFlow](https://ragflow.io/) is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It offers a streamlined RAG workflow for businesses of any scale, combining LLM (Large Language Models) to provide truthful question-answering capabilities, backed by well-founded citations from various complex formatted data."
 authors = [{ name = "Zhichang Yu", email = "yuzhichang@gmail.com" }]
 license-files = ["LICENSE"]
--- a/rag/app/naive.py
+++ b/rag/app/naive.py
@ -40,7 +40,7 @@ from deepdoc.parser.docling_parser import DoclingParser
 from deepdoc.parser.tcadp_parser import TCADPParser
 from common.parser_config_utils import normalize_layout_recognizer
 from rag.nlp import concat_img, find_codec, naive_merge, naive_merge_with_images, naive_merge_docx, rag_tokenizer, \
-    tokenize_chunks, tokenize_chunks_with_images, tokenize_table, attach_media_context
+    tokenize_chunks, tokenize_chunks_with_images, tokenize_table, attach_media_context, append_context2table_image4pdf
 def by_deepdoc(filename, binary=None, from_page=0, to_page=100000, lang="Chinese", callback=None, pdf_cls=None,
@ -487,7 +487,7 @@ class Pdf(PdfParser):
            tbls = self._extract_table_figure(True, zoomin, True, True)
            self._naive_vertical_merge()
            self._concat_downward()
-            self._final_reading_order_merge()
+            # self._final_reading_order_merge()
            # self._filter_forpages()
            logging.info("layouts cost: {}s".format(timer() - first_start))
            return [(b["text"], self._line_tag(b, zoomin)) for b in self.boxes], tbls
@ -776,6 +776,9 @@ def chunk(filename, binary=None, from_page=0, to_page=100000, lang="Chinese", ca
        if not sections and not tables:
            return []
        if table_context_size or image_context_size:
            tables = append_context2table_image4pdf(sections, tables, image_context_size)
        if name in ["tcadp", "docling", "mineru"]:
            parser_config["chunk_token_num"] = 0
@ -1006,8 +1009,8 @@ def chunk(filename, binary=None, from_page=0, to_page=100000, lang="Chinese", ca
        res.extend(embed_res)
    if url_res:
        res.extend(url_res)
-    if table_context_size or image_context_size:
+    #if table_context_size or image_context_size:
-        attach_media_context(res, table_context_size, image_context_size)
+    #    attach_media_context(res, table_context_size, image_context_size)
    return res
--- a/rag/nlp/init.py
+++ b/rag/nlp/init.py
@ -16,7 +16,7 @@
 import logging
 import random
-from collections import Counter
+from collections import Counter, defaultdict
 from common.token_utils import num_tokens_from_string
 import re
@ -667,6 +667,94 @@ def attach_media_context(chunks, table_context_size=0, image_context_size=0):
    return chunks
 def append_context2table_image4pdf(sections: list, tabls: list, table_context_size=0):
    from deepdoc.parser import PdfParser
    if table_context_size <=0:
        return tabls
    page_bucket = defaultdict(list)
    for i, (txt, poss) in enumerate(sections):
        poss = PdfParser.extract_positions(poss)
        for page, left, right, top, bottom in poss:
            page = page[0]
            page_bucket[page].append(((left, top, right, bottom), txt))
    def upper_context(page, i):
        txt = ""
        if page not in page_bucket:
            i = -1
        while num_tokens_from_string(txt) < table_context_size:
            if i < 0:
                page -= 1
                if page < 0 or page not in page_bucket:
                    break
                i = len(page_bucket[page]) -1
            blks = page_bucket[page]
            (_, _, _, _), cnt = blks[i]
            txts = re.split(r"([。!?？；！\n]|\. )", cnt, flags=re.DOTALL)[::-1]
            for j in range(0, len(txts), 2):
                txt = (txts[j+1] if j+1<len(txts) else "") + txts[j] + txt
                if num_tokens_from_string(txt) > table_context_size:
                    break
            i -= 1
        return txt
    def lower_context(page, i):
        txt = ""
        if page not in page_bucket:
            return txt
        while num_tokens_from_string(txt) < table_context_size:
            if i >= len(page_bucket[page]):
                page += 1
                if page not in page_bucket:
                    break
                i = 0
            blks = page_bucket[page]
            (_, _, _, _), cnt = blks[i]
            txts = re.split(r"([。!?？；！\n]|\. )", cnt, flags=re.DOTALL)
            for j in range(0, len(txts), 2):
                txt += txts[j] + (txts[j+1] if j+1<len(txts) else "")
                if num_tokens_from_string(txt) > table_context_size:
                    break
            i += 1
        return txt
    res = []
    for (img, tb), poss in tabls:
        page, left, top, right, bott = poss[0]
        _page, _left, _top, _right, _bott = poss[-1]
        if isinstance(tb, list):
            tb = "\n".join(tb)
        i = 0
        blks = page_bucket.get(page, [])
        _tb = tb
        while i < len(blks):
            if i + 1 >= len(blks):
                if _page > page:
                    page += 1
                    i = 0
                    blks = page_bucket.get(page, [])
                    continue
                tb = upper_context(page, i) + tb + lower_context(page+1, 0)
                break
            (_, t, r, b), txt = blks[i]
            if b > top:
                break
            (_, _t, _r, _b), _txt = blks[i+1]
            if _t < _bott:
                i += 1
                continue
            tb = upper_context(page, i) + tb + lower_context(page, i)
            break
        if _tb == tb:
            tb = upper_context(page, -1) + tb + lower_context(page+1, 0)
        res.append(((img, tb), poss))
    return res
 def add_positions(d, poss):
    if not poss:
        return
--- a/rag/prompts/generator.py
+++ b/rag/prompts/generator.py
@ -729,6 +729,8 @@ TOC_FROM_TEXT_USER = load_prompt("toc_from_text_user")
 # Generate TOC from text chunks with text llms
 async def gen_toc_from_text(txt_info: dict, chat_mdl, callback=None):
    if callback:
        callback(msg="")
    try:
        ans = await gen_json(
            PROMPT_JINJA_ENV.from_string(TOC_FROM_TEXT_SYSTEM).render(),
@ -738,8 +740,6 @@ async def gen_toc_from_text(txt_info: dict, chat_mdl, callback=None):
            gen_conf={"temperature": 0.0, "top_p": 0.9}
        )
        txt_info["toc"] = ans if ans and not isinstance(ans, str) else []
        if callback:
            callback(msg="")
    except Exception as e:
        logging.exception(e)
--- a/rag/prompts/meta_data.md
+++ b/rag/prompts/meta_data.md
@ -1,14 +1,11 @@
-## Role: Metadata extraction expert
+## Role: Metadata extraction expert.
-## Constraints:
+## Rules:
- - Core Directive: Extract important structured information from the given content. Output ONLY a valid JSON string. No Markdown (e.g., ```json), no explanations, and no notes.
+ - Strict Evidence Only: Extract a value ONLY if it is explicitly mentioned in the Content. 
- - Schema Parsing: In the `properties` object provided in Schema, the attribute name (e.g., 'author') is the target Key. Extract values based on the `description`; if no `description` is provided, refer to the key's literal meaning.
+ - Enum Filter: For any field with an 'enum' list, the list acts as a strict filter. If no element from the list (or its direct synonym) is found in the Content, you MUST NOT extract that field.
- - Extraction Rules: Extract only when there is an explicit semantic correlation. If multiple values or data points match a field's definition, extract and include all of them. Strictly follow the Schema below and only output matched key-value pairs. If the content is irrelevant or no matching information is identified, you **MUST** output {}.
+ - No Meta-Inference: Do not infer values based on the document's nature, format, or category. If the text does not literally state the information, treat it as missing.
- - Data Source: Extraction must be based solely on content below. Semantic mapping (synonyms) is allowed, but strictly prohibit hallucinations or fabricated facts.
+ - Zero-Hallucination: Never invent information or pick a "likely" value from the enum to fill a field.
-
+ - Empty Result: If no matches are found for any field, or if the content is irrelevant, output ONLY {}. 
-## Enum Rules (Triggered ONLY if an enum list is present): 
+ - Output: ONLY a valid JSON string. No Markdown, no notes.
 - Value Lock: All extracted values MUST strictly match the provided enum list.
 - Normalization: Map synonyms or variants in the text back to the standard enum value (e.g., "Dec" to "December").
 - Fallback: Output {} if no explicit match or synonym is identified.
 ## Schema for extraction:
 {{ schema }}
--- a/rag/svr/sync_data_source.py
+++ b/rag/svr/sync_data_source.py
@ -49,6 +49,7 @@ from common.data_source import (
    WebDAVConnector,
    AirtableConnector,
    AsanaConnector,
    ImapConnector
 )
 from common.constants import FileSource, TaskStatus
 from common.data_source.config import INDEX_BATCH_SIZE
@ -915,6 +916,70 @@ class Github(SyncBase):
        return async_wrapper()
 class IMAP(SyncBase):
    SOURCE_NAME: str = FileSource.IMAP
    async def _generate(self, task):
        from common.data_source.config import DocumentSource
        from common.data_source.interfaces import StaticCredentialsProvider
        self.connector = ImapConnector(
            host=self.conf.get("imap_host"),
            port=self.conf.get("imap_port"),
            mailboxes=self.conf.get("imap_mailbox"),
        )
        credentials_provider = StaticCredentialsProvider(tenant_id=task["tenant_id"], connector_name=DocumentSource.IMAP, credential_json=self.conf["credentials"])
        self.connector.set_credentials_provider(credentials_provider)
        end_time = datetime.now(timezone.utc).timestamp()
        if task["reindex"] == "1" or not task["poll_range_start"]:
            start_time = end_time - self.conf.get("poll_range",30) * 24 * 60 * 60
            begin_info = "totally"
        else:
            start_time = task["poll_range_start"].timestamp()
            begin_info = f"from {task['poll_range_start']}"
        raw_batch_size = self.conf.get("sync_batch_size") or self.conf.get("batch_size") or INDEX_BATCH_SIZE
        try:
            batch_size = int(raw_batch_size)
        except (TypeError, ValueError):
            batch_size = INDEX_BATCH_SIZE
        if batch_size <= 0:
            batch_size = INDEX_BATCH_SIZE
        def document_batches():
            checkpoint = self.connector.build_dummy_checkpoint()
            pending_docs = []
            iterations = 0
            iteration_limit = 100_000
            while checkpoint.has_more:
                wrapper = CheckpointOutputWrapper()
                doc_generator = wrapper(self.connector.load_from_checkpoint(start_time, end_time, checkpoint))
                for document, failure, next_checkpoint in doc_generator:
                    if failure is not None:
                        logging.warning("IMAP connector failure: %s", getattr(failure, "failure_message", failure))
                        continue
                    if document is not None:
                        pending_docs.append(document)
                        if len(pending_docs) >= batch_size:
                            yield pending_docs
                            pending_docs = []
                    if next_checkpoint is not None:
                        checkpoint = next_checkpoint
                iterations += 1
                if iterations > iteration_limit:
                    raise RuntimeError("Too many iterations while loading IMAP documents.")
            if pending_docs:
                yield pending_docs
        logging.info(
            "Connect to IMAP: host(%s) port(%s) user(%s) folder(%s) %s",
            self.conf["imap_host"],
            self.conf["imap_port"],
            self.conf["credentials"]["imap_username"],
            self.conf["imap_mailbox"],
            begin_info
        )
        return document_batches()
 class Gitlab(SyncBase):
@ -977,6 +1042,7 @@ func_factory = {
    FileSource.BOX: BOX,
    FileSource.AIRTABLE: Airtable,
    FileSource.ASANA: Asana,
    FileSource.IMAP: IMAP,
    FileSource.GITHUB: Github,
    FileSource.GITLAB: Gitlab,
 }
--- a/rag/svr/task_executor.py
+++ b/rag/svr/task_executor.py
@ -332,6 +332,9 @@ async def build_chunks(task, progress_callback):
        async def doc_keyword_extraction(chat_mdl, d, topn):
            cached = get_llm_cache(chat_mdl.llm_name, d["content_with_weight"], "keywords", {"topn": topn})
            if not cached:
                if has_canceled(task["id"]):
                    progress_callback(-1, msg="Task has been canceled.")
                    return
                async with chat_limiter:
                    cached = await keyword_extraction(chat_mdl, d["content_with_weight"], topn)
                set_llm_cache(chat_mdl.llm_name, d["content_with_weight"], cached, "keywords", {"topn": topn})
@ -362,6 +365,9 @@ async def build_chunks(task, progress_callback):
        async def doc_question_proposal(chat_mdl, d, topn):
            cached = get_llm_cache(chat_mdl.llm_name, d["content_with_weight"], "question", {"topn": topn})
            if not cached:
                if has_canceled(task["id"]):
                    progress_callback(-1, msg="Task has been canceled.")
                    return
                async with chat_limiter:
                    cached = await question_proposal(chat_mdl, d["content_with_weight"], topn)
                set_llm_cache(chat_mdl.llm_name, d["content_with_weight"], cached, "question", {"topn": topn})
@ -392,6 +398,9 @@ async def build_chunks(task, progress_callback):
            cached = get_llm_cache(chat_mdl.llm_name, d["content_with_weight"], "metadata",
                                   task["parser_config"]["metadata"])
            if not cached:
                if has_canceled(task["id"]):
                    progress_callback(-1, msg="Task has been canceled.")
                    return
                async with chat_limiter:
                    cached = await gen_metadata(chat_mdl,
                                                metadata_schema(task["parser_config"]["metadata"]),
@ -457,6 +466,9 @@ async def build_chunks(task, progress_callback):
        async def doc_content_tagging(chat_mdl, d, topn_tags):
            cached = get_llm_cache(chat_mdl.llm_name, d["content_with_weight"], all_tags, {"topn": topn_tags})
            if not cached:
                if has_canceled(task["id"]):
                    progress_callback(-1, msg="Task has been canceled.")
                    return
                picked_examples = random.choices(examples, k=2) if len(examples) > 2 else examples
                if not picked_examples:
                    picked_examples.append({"content": "This is an example", TAG_FLD: {'example': 1}})
@ -890,6 +902,7 @@ async def do_handle_task(task):
    task_embedding_id = task["embd_id"]
    task_language = task["language"]
    task_llm_id = task["parser_config"].get("llm_id") or task["llm_id"]
    task["llm_id"] = task_llm_id
    task_dataset_id = task["kb_id"]
    task_doc_id = task["doc_id"]
    task_document_name = task["name"]
--- a/sdk/python/pyproject.toml
+++ b/sdk/python/pyproject.toml
@ -1,6 +1,6 @@
 [project]
 name = "ragflow-sdk"
-version = "0.23.0"
+version = "0.23.1"
 description = "Python client sdk of [RAGFlow](https://github.com/infiniflow/ragflow). RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding."
 authors = [{ name = "Zhichang Yu", email = "yuzhichang@gmail.com" }]
 license = { text = "Apache License, Version 2.0" }
--- a/sdk/python/uv.lock
+++ b/sdk/python/uv.lock
@ -353,7 +353,7 @@ wheels = [
 [[package]]
 name = "ragflow-sdk"
-version = "0.23.0"
+version = "0.23.1"
 source = { virtual = "." }
 dependencies = [
    { name = "beartype" },
--- a/uv.lock
+++ b/uv.lock
@ -6163,7 +6163,7 @@ wheels = [
 [[package]]
 name = "ragflow"
-version = "0.23.0"
+version = "0.23.1"
 source = { virtual = "." }
 dependencies = [
    { name = "aiosmtplib" },
--- a/web/src/assets/svg/data-source/imap.svg
+++ b/web/src/assets/svg/data-source/imap.svg
@ -0,0 +1,7 @@
 <svg stroke="currentColor" fill="none" stroke-width="2" viewBox="0 0 24 24"
  stroke-linecap="round" stroke-linejoin="round"
  class="text-text-04" height="32" width="32"
  xmlns="http://www.w3.org/2000/svg">
  <path d="M4 4h16c1.1 0 2 .9 2 2v12c0 1.1-.9 2-2 2H4c-1.1 0-2-.9-2-2V6c0-1.1.9-2 2-2z"></path>
  <polyline points="22,6 12,13 2,6"></polyline>
 </svg>
--- a/web/src/components/api-service/chat-overview-modal/api-content.tsx
+++ b/web/src/components/api-service/chat-overview-modal/api-content.tsx
@ -1,3 +1,4 @@
 import { useIsDarkTheme } from '@/components/theme-provider';
 import { useSetModalState, useTranslate } from '@/hooks/common-hooks';
 import { LangfuseCard } from '@/pages/user-setting/setting-model/langfuse';
 import apiDoc from '@parent/docs/references/http_api_reference.md';
@ -28,6 +29,8 @@ const ApiContent = ({
  const { handlePreview } = usePreviewChat(idKey);
  const isDarkTheme = useIsDarkTheme();
  return (
    <div className="pb-2">
      <Flex vertical gap={'middle'}>
@ -47,7 +50,10 @@ const ApiContent = ({
        <div style={{ position: 'relative' }}>
          <MarkdownToc content={apiDoc} />
        </div>
-        <MarkdownPreview source={apiDoc}></MarkdownPreview>
+        <MarkdownPreview
          source={apiDoc}
          wrapperElement={{ 'data-color-mode': isDarkTheme ? 'dark' : 'light' }}
        ></MarkdownPreview>
      </Flex>
      {apiKeyVisible && (
        <ChatApiKeyModal
--- a/web/src/components/knowledge-base-item.tsx
+++ b/web/src/components/knowledge-base-item.tsx
@ -1,79 +1,72 @@
 import { DocumentParserType } from '@/constants/knowledge';
 import { useTranslate } from '@/hooks/common-hooks';
 import { useFetchKnowledgeList } from '@/hooks/use-knowledge-request';
 import { IKnowledge } from '@/interfaces/database/knowledge';
 import { useBuildQueryVariableOptions } from '@/pages/agent/hooks/use-get-begin-query';
 import { UserOutlined } from '@ant-design/icons';
 import { Avatar as AntAvatar, Form, Select, Space } from 'antd';
 import { toLower } from 'lodash';
-import { useMemo } from 'react';
+import { useEffect, useMemo, useState } from 'react';
 import { useFormContext } from 'react-hook-form';
 import { useTranslation } from 'react-i18next';
 import { RAGFlowAvatar } from './ragflow-avatar';
 import { FormControl, FormField, FormItem, FormLabel } from './ui/form';
-import { MultiSelect } from './ui/multi-select';
+import { MultiSelect, MultiSelectOptionType } from './ui/multi-select';
 interface KnowledgeBaseItemProps {
  label?: string;
  tooltipText?: string;
  name?: string;
  required?: boolean;
  onChange?(): void;
 }
 const KnowledgeBaseItem = ({
  label,
  tooltipText,
  name,
  required = true,
  onChange,
 }: KnowledgeBaseItemProps) => {
  const { t } = useTranslate('chat');
  const { list: knowledgeList } = useFetchKnowledgeList(true);
  const filteredKnowledgeList = knowledgeList.filter(
    (x) => x.parser_id !== DocumentParserType.Tag,
  );
  const knowledgeOptions = filteredKnowledgeList.map((x) => ({
    label: (
      <Space>
        <AntAvatar size={20} icon={<UserOutlined />} src={x.avatar} />
        {x.name}
      </Space>
    ),
    value: x.id,
  }));
  return (
    <Form.Item
      label={label || t('knowledgeBases')}
      name={name || 'kb_ids'}
      tooltip={tooltipText || t('knowledgeBasesTip')}
      rules={[
        {
          required,
          message: t('knowledgeBasesMessage'),
          type: 'array',
        },
      ]}
    >
      <Select
        mode="multiple"
        options={knowledgeOptions}
        placeholder={t('knowledgeBasesMessage')}
        onChange={onChange}
      ></Select>
    </Form.Item>
  );
 };
 export default KnowledgeBaseItem;
 function buildQueryVariableOptionsByShowVariable(showVariable?: boolean) {
  return showVariable ? useBuildQueryVariableOptions : () => [];
 }
 export function useDisableDifferenceEmbeddingDataset() {
  const [datasetOptions, setDatasetOptions] = useState<MultiSelectOptionType[]>(
    [],
  );
  const [datasetSelectEmbedId, setDatasetSelectEmbedId] = useState('');
  const { list: datasetListOrigin } = useFetchKnowledgeList(true);
  useEffect(() => {
    const datasetListMap = datasetListOrigin
      .filter((x) => x.parser_id !== DocumentParserType.Tag)
      .map((item: IKnowledge) => {
        return {
          label: item.name,
          icon: () => (
            <RAGFlowAvatar
              className="size-4"
              avatar={item.avatar}
              name={item.name}
            />
          ),
          suffix: (
            <div className="text-xs px-4 p-1 bg-bg-card text-text-secondary rounded-lg border border-bg-card">
              {item.embd_id}
            </div>
          ),
          value: item.id,
          disabled:
            item.embd_id !== datasetSelectEmbedId &&
            datasetSelectEmbedId !== '',
        };
      });
    setDatasetOptions(datasetListMap);
  }, [datasetListOrigin, datasetSelectEmbedId]);
  const handleDatasetSelectChange = (
    value: string[],
    onChange: (value: string[]) => void,
  ) => {
    if (value.length) {
      const data = datasetListOrigin?.find((item) => item.id === value[0]);
      setDatasetSelectEmbedId(data?.embd_id ?? '');
    } else {
      setDatasetSelectEmbedId('');
    }
    onChange?.(value);
  };
  return {
    datasetOptions,
    handleDatasetSelectChange,
  };
 }
 export function KnowledgeBaseFormField({
  showVariable = false,
 }: {
@ -82,22 +75,12 @@ export function KnowledgeBaseFormField({
  const form = useFormContext();
  const { t } = useTranslation();
-  const { list: knowledgeList } = useFetchKnowledgeList(true);
+  const { datasetOptions, handleDatasetSelectChange } =
-
+    useDisableDifferenceEmbeddingDataset();
  const filteredKnowledgeList = knowledgeList.filter(
    (x) => x.parser_id !== DocumentParserType.Tag,
  );
  const nextOptions = buildQueryVariableOptionsByShowVariable(showVariable)();
-  const knowledgeOptions = filteredKnowledgeList.map((x) => ({
+  const knowledgeOptions = datasetOptions;
    label: x.name,
    value: x.id,
    icon: () => (
      <RAGFlowAvatar className="size-4 mr-2" avatar={x.avatar} name={x.name} />
    ),
  }));
  const options = useMemo(() => {
    if (showVariable) {
      return [
@ -140,11 +123,14 @@ export function KnowledgeBaseFormField({
          <FormControl>
            <MultiSelect
              options={options}
-              onValueChange={field.onChange}
+              onValueChange={(value) => {
                handleDatasetSelectChange(value, field.onChange);
              }}
              placeholder={t('chat.knowledgeBasesMessage')}
              variant="inverted"
              maxCount={100}
              defaultValue={field.value}
              showSelectAll={false}
              {...field}
            />
          </FormControl>
--- a/web/src/components/originui/select-with-search.tsx
+++ b/web/src/components/originui/select-with-search.tsx
@ -109,6 +109,19 @@ export const SelectWithSearch = forwardRef<
      }
    }, [options, value]);
    const showSearch = useMemo(() => {
      if (Array.isArray(options) && options.length > 5) {
        return true;
      }
      if (Array.isArray(options)) {
        const optionsNum = options.reduce((acc, option) => {
          return acc + (option?.options?.length || 0);
        }, 0);
        return optionsNum > 5;
      }
      return false;
    }, [options]);
    const handleSelect = useCallback(
      (val: string) => {
        setValue(val);
@ -179,7 +192,7 @@ export const SelectWithSearch = forwardRef<
          align="start"
        >
          <Command className="p-5">
-            {options && options.length > 5 && (
+            {showSearch && (
              <CommandInput
                placeholder={t('common.search') + '...'}
                className=" placeholder:text-text-disabled"
--- a/web/src/components/ui/message.ts
+++ b/web/src/components/ui/message.ts
@ -1,35 +1,19 @@
-import { toast } from 'sonner';
+import { ExternalToast, toast } from 'sonner';
-const duration = { duration: 2500 };
+const configuration: ExternalToast = { duration: 2500, position: 'top-center' };
 const message = {
  success: (msg: string) => {
-    toast.success(msg, {
+    toast.success(msg, configuration);
      position: 'top-center',
      closeButton: false,
      ...duration,
    });
  },
  error: (msg: string) => {
-    toast.error(msg, {
+    toast.error(msg, configuration);
      position: 'top-center',
      closeButton: false,
      ...duration,
    });
  },
  warning: (msg: string) => {
-    toast.warning(msg, {
+    toast.warning(msg, configuration);
      position: 'top-center',
      closeButton: false,
      ...duration,
    });
  },
  info: (msg: string) => {
-    toast.info(msg, {
+    toast.info(msg, configuration);
      position: 'top-center',
      closeButton: false,
      ...duration,
    });
  },
 };
 export default message;
--- a/web/src/constants/agent.tsx
+++ b/web/src/constants/agent.tsx
@ -211,3 +211,14 @@ export const WebhookJWTAlgorithmList = [
  'ps512',
  'none',
 ] as const;
 export enum AgentDialogueMode {
  Conversational = 'conversational',
  Task = 'task',
  Webhook = 'Webhook',
 }
 export const initialBeginValues = {
  mode: AgentDialogueMode.Conversational,
  prologue: `Hi! I'm your assistant. What can I do for you?`,
 };
--- a/web/src/hooks/use-agent-request.ts
+++ b/web/src/hooks/use-agent-request.ts
@ -1,7 +1,7 @@
 import { FileUploadProps } from '@/components/file-upload';
 import { useHandleFilterSubmit } from '@/components/list-filter-bar/use-handle-filter-submit';
 import message from '@/components/ui/message';
-import { AgentGlobals } from '@/constants/agent';
+import { AgentGlobals, initialBeginValues } from '@/constants/agent';
 import {
  IAgentLogsRequest,
  IAgentLogsResponse,
@ -76,6 +76,7 @@ export const EmptyDsl = {
        data: {
          label: 'Begin',
          name: 'begin',
          form: initialBeginValues,
        },
        sourcePosition: 'left',
        targetPosition: 'right',
--- a/web/src/interfaces/database/knowledge.ts
+++ b/web/src/interfaces/database/knowledge.ts
@ -116,7 +116,7 @@ export interface ITenantInfo {
  tts_id: string;
 }
-export type ChunkDocType = 'image' | 'table';
+export type ChunkDocType = 'image' | 'table' | 'text';
 export interface IChunk {
  available_int: number; // Whether to enable, 0: not enabled, 1: enabled
--- a/web/src/locales/en.ts
+++ b/web/src/locales/en.ts
@ -147,6 +147,8 @@ Procedural Memory: Learned skills, habits, and automated procedures.`,
        action: 'Action',
      },
      config: {
        memorySizeTooltip: `Accounts for each message's content + its embedding vector (≈ Content + Dimensions × 8 Bytes). 
 Example: A 1 KB message with 1024-dim embedding uses ~9 KB. The 5 MB default limit holds ~500 such messages.`,
        avatar: 'Avatar',
        description: 'Description',
        memorySize: 'Memory size',
@ -181,6 +183,8 @@ Procedural Memory: Learned skills, habits, and automated procedures.`,
    },
    knowledgeDetails: {
      metadata: {
        toMetadataSetting: 'Generation settings',
        toMetadataSettingTip: 'Set auto-metadata in Configuration.',
        descriptionTip:
          'Provide descriptions or examples to guide LLM extract values for this field. If left empty, it will rely on the field name.',
        restrictTDefinedValuesTip:
@ -939,6 +943,8 @@ Example: Virtual Hosted Style`,
        'Connect GitLab to sync repositories, issues, merge requests, and related documentation.',
      asanaDescription:
        'Connect to Asana and synchronize files from a specified workspace.',
      imapDescription:
        'Connect to your IMAP mailbox to sync emails for knowledge retrieval.',
      dropboxAccessTokenTip:
        'Generate a long-lived access token in the Dropbox App Console with files.metadata.read, files.content.read, and sharing.read scopes.',
      moodleDescription:
--- a/web/src/locales/ru.ts
+++ b/web/src/locales/ru.ts
@ -755,6 +755,8 @@ export default {
        'Подключите GitLab для синхронизации репозиториев, задач, merge requests и связанной документации.',
      asanaDescription:
        'Подключите Asana и синхронизируйте файлы из рабочего пространства.',
      imapDescription:
        'Подключите почтовый ящик IMAP для синхронизации писем из указанных почтовых ящиков (mailboxes) с целью поиска и анализа знаний.',
      google_driveDescription:
        'Подключите ваш Google Drive через OAuth и синхронизируйте определенные папки или диски.',
      gmailDescription:
--- a/web/src/locales/zh.ts
+++ b/web/src/locales/zh.ts
@ -124,12 +124,11 @@ export default {
        forgetMessageTip: '确定遗忘吗？',
        messageDescription: '记忆提取使用高级设置中的提示词和温度值进行配置。',
        copied: '已复制！',
        contentEmbed: '内容嵌入',
        content: '内容',
        delMessageWarn: `遗忘后，代理将无法检索此消息。`,
        forgetMessage: '遗忘消息',
        sessionId: '会话ID',
-        agent: '代理',
+        agent: '智能体',
        type: '类型',
        validDate: '有效日期',
        forgetAt: '遗忘于',
@ -138,6 +137,8 @@ export default {
        action: '操作',
      },
      config: {
        memorySizeTooltip: `记录每条消息的内容 + 其嵌入向量（≈ 内容 + 维度 × 8 字节）。
 例如：一条带有 1024 维嵌入的 1 KB 消息大约使用 9 KB。5 MB 的默认限制大约可容纳 500 条此类消息。`,
        avatar: '头像',
        description: '描述',
        memorySize: '记忆大小',
@ -172,6 +173,8 @@ export default {
    },
    knowledgeDetails: {
      metadata: {
        toMetadataSettingTip: '在配置中设置自动元数据',
        toMetadataSetting: '生成设置',
        descriptionTip:
          '提供描述或示例来指导大语言模型为此字段提取值。如果留空，将依赖字段名称。',
        restrictTDefinedValuesTip:
@ -867,6 +870,8 @@ General：实体和关系提取提示来自 GitHub - microsoft/graphrag：基于
      gitlabDescription:
        '连接 GitLab，同步仓库、Issue、合并请求（MR）及相关文档内容。',
      asanaDescription: '连接 Asana，同步工作区中的文件。',
      imapDescription:
        '连接你的 IMAP 邮箱，同步指定mailboxes中的邮件，用于知识检索与分析',
      r2Description: '连接你的 Cloudflare R2 存储桶以导入和同步文件。',
      dropboxAccessTokenTip:
        '请在 Dropbox App Console 生成 Access Token，并勾选 files.metadata.read、files.content.read、sharing.read 等必要权限。',
--- a/web/src/pages/admin/users.tsx
+++ b/web/src/pages/admin/users.tsx
@ -1,4 +1,4 @@
-import { useMemo, useState } from 'react';
+import { useLayoutEffect, useMemo, useState } from 'react';
 import { useTranslation } from 'react-i18next';
 import { useNavigate } from 'umi';
@ -12,7 +12,12 @@ import {
  useReactTable,
 } from '@tanstack/react-table';
-import { useMutation, useQuery, useQueryClient } from '@tanstack/react-query';
+import {
  keepPreviousData,
  useMutation,
  useQuery,
  useQueryClient,
 } from '@tanstack/react-query';
 import {
  LucideClipboardList,
@ -125,12 +130,14 @@ function AdminUserManagement() {
    queryFn: async () => (await listRoles()).data.data.roles,
    enabled: IS_ENTERPRISE,
    retry: false,
    placeholderData: keepPreviousData,
  });
  const { data: usersList } = useQuery({
    queryKey: ['admin/listUsers'],
    queryFn: async () => (await listUsers()).data.data,
    retry: false,
    placeholderData: keepPreviousData,
  });
  // Delete user mutation
@ -354,8 +361,16 @@ function AdminUserManagement() {
    getFilteredRowModel: getFilteredRowModel(),
    getSortedRowModel: getSortedRowModel(),
    getPaginationRowModel: getPaginationRowModel(),
    autoResetPageIndex: false,
  });
  useLayoutEffect(() => {
    if (table.getState().pagination.pageIndex > table.getPageCount()) {
      table.setPageIndex(Math.max(0, table.getPageCount() - 1));
    }
  }, [usersList, table]);
  return (
    <>
      <Card className="!shadow-none relative h-full bg-transparent overflow-hidden">
@ -538,7 +553,7 @@ function AdminUserManagement() {
          <CardFooter className="flex items-center justify-end">
            <RAGFlowPagination
-              total={usersList?.length ?? 0}
+              total={table.getFilteredRowModel().rows.length}
              current={table.getState().pagination.pageIndex + 1}
              pageSize={table.getState().pagination.pageSize}
              onChange={(page, pageSize) => {
--- a/web/src/pages/agent/canvas/node/begin-node.tsx
+++ b/web/src/pages/agent/canvas/node/begin-node.tsx
@ -1,16 +1,19 @@
-import { IBeginNode } from '@/interfaces/database/flow';
+import { BaseNode } from '@/interfaces/database/flow';
 import { cn } from '@/lib/utils';
 import { NodeProps, Position } from '@xyflow/react';
 import get from 'lodash/get';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
 import {
  AgentDialogueMode,
  BeginQueryType,
  BeginQueryTypeIconMap,
  NodeHandleId,
  Operator,
 } from '../../constant';
-import { BeginQuery } from '../../interface';
+import { BeginFormSchemaType } from '../../form/begin-form/schema';
 import { useBuildWebhookUrl } from '../../hooks/use-build-webhook-url';
 import { useIsPipeline } from '../../hooks/use-is-pipeline';
 import OperatorIcon from '../../operator-icon';
 import { LabelCard } from './card';
 import { CommonHandle } from './handle';
@ -18,10 +21,21 @@ import { RightHandleStyle } from './handle-icon';
 import styles from './index.less';
 import { NodeWrapper } from './node-wrapper';
-// TODO: do not allow other nodes to connect to this node
+function InnerBeginNode({
-function InnerBeginNode({ data, id, selected }: NodeProps<IBeginNode>) {
+  data,
  id,
  selected,
 }: NodeProps<BaseNode<BeginFormSchemaType>>) {
  const { t } = useTranslation();
-  const inputs: Record<string, BeginQuery> = get(data, 'form.inputs', {});
+  const inputs = get(data, 'form.inputs', {});
  const mode = data.form?.mode;
  const isWebhookMode = mode === AgentDialogueMode.Webhook;
  const url = useBuildWebhookUrl();
  const isPipeline = useIsPipeline();
  return (
    <NodeWrapper selected={selected} id={id}>
@ -34,29 +48,46 @@ function InnerBeginNode({ data, id, selected }: NodeProps<IBeginNode>) {
        id={NodeHandleId.Start}
      ></CommonHandle>
-      <section className="flex items-center  gap-2">
+      <section className="flex items-center gap-2">
        <OperatorIcon name={data.label as Operator}></OperatorIcon>
        <div className="truncate text-center font-semibold text-sm">
          {t(`flow.begin`)}
        </div>
      </section>
-      <section className={cn(styles.generateParameters, 'flex gap-2 flex-col')}>
+      {isPipeline || (
-        {Object.entries(inputs).map(([key, val], idx) => {
+        <div className="text-accent-primary mt-2 p-1 bg-bg-accent w-fit rounded-sm text-xs">
-          const Icon = BeginQueryTypeIconMap[val.type as BeginQueryType];
+          {t(`flow.${isWebhookMode ? 'webhook.name' : mode}`)}
-          return (
+        </div>
-            <LabelCard key={idx} className={cn('flex gap-1.5 items-center')}>
+      )}
-              <Icon className="size-3.5" />
+      {isWebhookMode ? (
-              <label htmlFor="" className="text-accent-primary text-sm italic">
+        <LabelCard className="mt-2 flex gap-1 items-center">
-                {key}
+          <span className="font-bold">URL</span>
-              </label>
+          <span className="flex-1 truncate">{url}</span>
-              <LabelCard className="py-0.5 truncate flex-1">
+        </LabelCard>
-                {val.name}
+      ) : (
        <section
          className={cn(styles.generateParameters, 'flex gap-2 flex-col')}
        >
          {Object.entries(inputs).map(([key, val], idx) => {
            const Icon = BeginQueryTypeIconMap[val.type as BeginQueryType];
            return (
              <LabelCard key={idx} className={cn('flex gap-1.5 items-center')}>
                <Icon className="size-3.5" />
                <label
                  htmlFor=""
                  className="text-accent-primary text-sm italic"
                >
                  {key}
                </label>
                <LabelCard className="py-0.5 truncate flex-1">
                  {val.name}
                </LabelCard>
                <span className="flex-1">{val.optional ? 'Yes' : 'No'}</span>
              </LabelCard>
-              <span className="flex-1">{val.optional ? 'Yes' : 'No'}</span>
+            );
-            </LabelCard>
+          })}
-          );
+        </section>
-        })}
+      )}
      </section>
    </NodeWrapper>
  );
 }
--- a/web/src/pages/agent/constant/index.tsx
+++ b/web/src/pages/agent/constant/index.tsx
@ -15,19 +15,15 @@ import {
  initialLlmBaseValues,
 } from '@/constants/agent';
 export {
  AgentDialogueMode,
  AgentStructuredOutputField,
  JsonSchemaDataType,
  Operator,
  initialBeginValues,
 } from '@/constants/agent';
 export * from './pipeline';
 export enum AgentDialogueMode {
  Conversational = 'conversational',
  Task = 'task',
  Webhook = 'Webhook',
 }
 import { ModelVariableType } from '@/constants/knowledge';
 import { t } from 'i18next';
@ -109,11 +105,6 @@ export const initialRetrievalValues = {
  },
 };
 export const initialBeginValues = {
  mode: AgentDialogueMode.Conversational,
  prologue: `Hi! I'm your assistant. What can I do for you?`,
 };
 export const initialRewriteQuestionValues = {
  ...initialLlmBaseValues,
  language: '',
@ -750,6 +741,8 @@ export const NodeMap = {
  [Operator.Loop]: 'loopNode',
  [Operator.LoopStart]: 'loopStartNode',
  [Operator.ExitLoop]: 'exitLoopNode',
  [Operator.ExcelProcessor]: 'ragNode',
  [Operator.PDFGenerator]: 'ragNode',
 };
 export enum BeginQueryType {
--- a/web/src/pages/agent/form/begin-form/webhook/index.tsx
+++ b/web/src/pages/agent/form/begin-form/webhook/index.tsx
@ -3,13 +3,16 @@ import { CopyToClipboardWithText } from '@/components/copy-to-clipboard';
 import NumberInput from '@/components/originui/number-input';
 import { SelectWithSearch } from '@/components/originui/select-with-search';
 import { RAGFlowFormItem } from '@/components/ragflow-form';
 import { Label } from '@/components/ui/label';
 import { MultiSelect } from '@/components/ui/multi-select';
 import { Separator } from '@/components/ui/separator';
 import { Textarea } from '@/components/ui/textarea';
 import { useBuildWebhookUrl } from '@/pages/agent/hooks/use-build-webhook-url';
 import { buildOptions } from '@/utils/form';
 import { upperFirst } from 'lodash';
 import { useCallback } from 'react';
 import { useFormContext, useWatch } from 'react-hook-form';
 import { useTranslation } from 'react-i18next';
 import { useParams } from 'umi';
 import {
  RateLimitPerList,
  WebhookMaxBodySize,
@ -22,7 +25,10 @@ import { Auth } from './auth';
 import { WebhookRequestSchema } from './request-schema';
 import { WebhookResponse } from './response';
-const RateLimitPerOptions = buildOptions(RateLimitPerList);
+const RateLimitPerOptions = RateLimitPerList.map((x) => ({
  value: x,
  label: upperFirst(x),
 }));
 const RequestLimitMap = {
  [WebhookRateLimitPer.Second]: 100,
@ -33,7 +39,6 @@ const RequestLimitMap = {
 export function WebHook() {
  const { t } = useTranslation();
  const { id } = useParams();
  const form = useFormContext();
  const rateLimitPer = useWatch({
@ -45,7 +50,7 @@ export function WebHook() {
    return RequestLimitMap[rateLimitPer as keyof typeof RequestLimitMap] ?? 100;
  }, []);
-  const text = `${location.protocol}//${location.host}/api/v1/webhook/${id}`;
+  const text = useBuildWebhookUrl();
  return (
    <>
@ -74,33 +79,36 @@ export function WebHook() {
            ></SelectWithSearch>
          </RAGFlowFormItem>
          <Auth></Auth>
-          <RAGFlowFormItem
+          <section>
-            name="security.rate_limit.limit"
+            <Label>{t('flow.webhook.limit')}</Label>
-            label={t('flow.webhook.limit')}
+            <div className="flex items-center mt-1 gap-2">
-          >
+              <RAGFlowFormItem
-            <NumberInput
+                name="security.rate_limit.limit"
-              max={getLimitRateLimitPerMax(rateLimitPer)}
+                className="flex-1"
-              className="w-full"
+              >
-            ></NumberInput>
+                <NumberInput
-          </RAGFlowFormItem>
+                  max={getLimitRateLimitPerMax(rateLimitPer)}
-          <RAGFlowFormItem
+                  className="w-full"
-            name="security.rate_limit.per"
+                ></NumberInput>
-            label={t('flow.webhook.per')}
+              </RAGFlowFormItem>
-          >
+              <Separator className="w-2" />
-            {(field) => (
+              <RAGFlowFormItem name="security.rate_limit.per">
-              <SelectWithSearch
+                {(field) => (
-                options={RateLimitPerOptions}
+                  <SelectWithSearch
-                value={field.value}
+                    options={RateLimitPerOptions}
-                onChange={(val) => {
+                    value={field.value}
-                  field.onChange(val);
+                    onChange={(val) => {
-                  form.setValue(
+                      field.onChange(val);
-                    'security.rate_limit.limit',
+                      form.setValue(
-                    getLimitRateLimitPerMax(val),
+                        'security.rate_limit.limit',
-                  );
+                        getLimitRateLimitPerMax(val),
-                }}
+                      );
-              ></SelectWithSearch>
+                    }}
-            )}
+                  ></SelectWithSearch>
-          </RAGFlowFormItem>
+                )}
              </RAGFlowFormItem>
            </div>
          </section>
          <RAGFlowFormItem
            name="security.max_body_size"
            label={t('flow.webhook.maxBodySize')}
--- a/web/src/pages/agent/hooks/use-add-node.ts
+++ b/web/src/pages/agent/hooks/use-add-node.ts
@ -179,6 +179,8 @@ export const useInitializeOperatorParams = () => {
      [Operator.Loop]: initialLoopValues,
      [Operator.LoopStart]: {},
      [Operator.ExitLoop]: {},
      [Operator.PDFGenerator]: {},
      [Operator.ExcelProcessor]: {},
    };
  }, [llmId]);
--- a/web/src/pages/agent/hooks/use-build-webhook-url.ts
+++ b/web/src/pages/agent/hooks/use-build-webhook-url.ts
@ -0,0 +1,8 @@
 import { useParams } from 'umi';
 export function useBuildWebhookUrl() {
  const { id } = useParams();
  const text = `${location.protocol}//${location.host}/api/v1/webhook/${id}`;
  return text;
 }
--- a/web/src/pages/chunk/parsed-result/add-knowledge/components/knowledge-chunk/components/chunk-card/index.tsx
+++ b/web/src/pages/chunk/parsed-result/add-knowledge/components/knowledge-chunk/components/chunk-card/index.tsx
@ -8,7 +8,7 @@ import {
  TooltipContent,
  TooltipTrigger,
 } from '@/components/ui/tooltip';
-import { IChunk } from '@/interfaces/database/knowledge';
+import type { ChunkDocType, IChunk } from '@/interfaces/database/knowledge';
 import { cn } from '@/lib/utils';
 import { CheckedState } from '@radix-ui/react-checkbox';
 import classNames from 'classnames';
@ -67,6 +67,10 @@ const ChunkCard = ({
    setEnabled(available === 1);
  }, [available]);
  const chunkType =
    ((item.doc_type_kwd &&
      String(item.doc_type_kwd)?.toLowerCase()) as ChunkDocType) || 'text';
  return (
    <Card
      className={classNames('relative flex-none', styles.chunkCard, {
@ -81,9 +85,7 @@ const ChunkCard = ({
        bg-bg-card rounded-bl-2xl rounded-tr-lg
        border-l-0.5 border-b-0.5 border-border-button"
      >
-        {t(
+        {t(`chunk.docType.${chunkType}`)}
          `chunk.docType.${item.doc_type_kwd ? String(item.doc_type_kwd).toLowerCase() : 'text'}`,
        )}
      </span>
      <div className="flex items-start justify-between gap-2">
--- a/web/src/pages/chunk/parsed-result/add-knowledge/components/knowledge-chunk/components/chunk-creating-modal/index.tsx
+++ b/web/src/pages/chunk/parsed-result/add-knowledge/components/knowledge-chunk/components/chunk-creating-modal/index.tsx
@ -22,6 +22,7 @@ import { Switch } from '@/components/ui/switch';
 import { Textarea } from '@/components/ui/textarea';
 import { useFetchChunk } from '@/hooks/use-chunk-request';
 import { IModalProps } from '@/interfaces/common';
 import type { ChunkDocType } from '@/interfaces/database/knowledge';
 import React, { useCallback, useEffect, useState } from 'react';
 import { FieldValues, FormProvider, useForm } from 'react-hook-form';
 import { useTranslation } from 'react-i18next';
@ -151,20 +152,25 @@ const ChunkCreatingModal: React.FC<IModalProps<any> & kFProps> = ({
            <FormField
              control={form.control}
              name="doc_type_kwd"
-              render={({ field }) => (
+              render={({ field }) => {
-                <FormItem>
+                const chunkType =
-                  <FormLabel>{t(`chunk.type`)}</FormLabel>
+                  ((field.value &&
-                  <FormControl>
+                    String(field.value)?.toLowerCase()) as ChunkDocType) ||
-                    <Input
+                  'text';
-                      type="text"
+
-                      value={t(
+                return (
-                        `chunk.docType.${field.value ? String(field.value).toLowerCase() : 'text'}`,
+                  <FormItem>
-                      )}
+                    <FormLabel>{t(`chunk.type`)}</FormLabel>
-                      readOnly
+                    <FormControl>
-                    />
+                      <Input
-                  </FormControl>
+                        type="text"
-                </FormItem>
+                        value={t(`chunk.docType.${chunkType}`)}
-              )}
+                        readOnly
                      />
                    </FormControl>
                  </FormItem>
                );
              }}
            />
          )}
--- a/web/src/pages/dataset/components/metedata/manage-modal.tsx
+++ b/web/src/pages/dataset/components/metedata/manage-modal.tsx
@ -15,6 +15,7 @@ import {
  TableRow,
 } from '@/components/ui/table';
 import { useSetModalState } from '@/hooks/common-hooks';
 import { Routes } from '@/routes';
 import {
  ColumnDef,
  flexRender,
@ -27,6 +28,7 @@ import {
 import { Plus, Settings, Trash2 } from 'lucide-react';
 import { useCallback, useEffect, useMemo, useState } from 'react';
 import { useTranslation } from 'react-i18next';
 import { useHandleMenuClick } from '../../sidebar/hooks';
 import {
  MetadataDeleteMap,
  MetadataType,
@ -78,7 +80,7 @@ export const ManageMetadataModal = (props: IManageModalProps) => {
    addUpdateValue,
    addDeleteValue,
  } = useManageMetaDataModal(originalTableData, metadataType, otherData);
-
+  const { handleMenuClick } = useHandleMenuClick();
  const {
    visible: manageValuesVisible,
    showModal: showManageValuesModal,
@ -335,67 +337,87 @@ export const ManageMetadataModal = (props: IManageModalProps) => {
          success?.(res);
        }}
      >
-        <div className="flex flex-col gap-2">
+        <>
-          <div className="flex items-center justify-between">
+          <div className="flex flex-col gap-2">
-            <div>{t('knowledgeDetails.metadata.metadata')}</div>
+            <div className="flex items-center justify-between">
-            {isCanAdd && (
+              <div>{t('knowledgeDetails.metadata.metadata')}</div>
-              <Button
+              {metadataType === MetadataType.Manage && false && (
-                variant={'ghost'}
+                <Button
-                className="border border-border-button"
+                  variant={'ghost'}
-                onClick={handAddValueRow}
+                  className="border border-border-button"
-              >
+                  type="button"
-                <Plus />
+                  onClick={handleMenuClick(Routes.DataSetSetting, {
-              </Button>
+                    openMetadata: true,
-            )}
+                  })}
-          </div>
+                >
-          <Table rootClassName="max-h-[800px]">
+                  {t('knowledgeDetails.metadata.toMetadataSetting')}
-            <TableHeader>
+                </Button>
-              {table.getHeaderGroups().map((headerGroup) => (
+              )}
-                <TableRow key={headerGroup.id}>
+              {isCanAdd && (
-                  {headerGroup.headers.map((header) => (
+                <Button
-                    <TableHead key={header.id}>
+                  variant={'ghost'}
-                      {header.isPlaceholder
+                  className="border border-border-button"
-                        ? null
+                  type="button"
-                        : flexRender(
+                  onClick={handAddValueRow}
-                            header.column.columnDef.header,
+                >
-                            header.getContext(),
+                  <Plus />
-                          )}
+                </Button>
-                    </TableHead>
+              )}
-                  ))}
+            </div>
-                </TableRow>
+            <Table rootClassName="max-h-[800px]">
-              ))}
+              <TableHeader>
-            </TableHeader>
+                {table.getHeaderGroups().map((headerGroup) => (
-            <TableBody className="relative">
+                  <TableRow key={headerGroup.id}>
-              {table.getRowModel().rows?.length ? (
+                    {headerGroup.headers.map((header) => (
-                table.getRowModel().rows.map((row) => (
+                      <TableHead key={header.id}>
-                  <TableRow
+                        {header.isPlaceholder
-                    key={row.id}
+                          ? null
-                    data-state={row.getIsSelected() && 'selected'}
+                          : flexRender(
-                    className="group"
+                              header.column.columnDef.header,
-                  >
+                              header.getContext(),
-                    {row.getVisibleCells().map((cell) => (
+                            )}
-                      <TableCell key={cell.id}>
+                      </TableHead>
                        {flexRender(
                          cell.column.columnDef.cell,
                          cell.getContext(),
                        )}
                      </TableCell>
                    ))}
                  </TableRow>
-                ))
+                ))}
-              ) : (
+              </TableHeader>
-                <TableRow>
+              <TableBody className="relative">
-                  <TableCell
+                {table.getRowModel().rows?.length ? (
-                    colSpan={columns.length}
+                  table.getRowModel().rows.map((row) => (
-                    className="h-24 text-center"
+                    <TableRow
-                  >
+                      key={row.id}
-                    <Empty type={EmptyType.Data} />
+                      data-state={row.getIsSelected() && 'selected'}
-                  </TableCell>
+                      className="group"
-                </TableRow>
+                    >
-              )}
+                      {row.getVisibleCells().map((cell) => (
-            </TableBody>
+                        <TableCell key={cell.id}>
-          </Table>
+                          {flexRender(
-        </div>
+                            cell.column.columnDef.cell,
                            cell.getContext(),
                          )}
                        </TableCell>
                      ))}
                    </TableRow>
                  ))
                ) : (
                  <TableRow>
                    <TableCell
                      colSpan={columns.length}
                      className="h-24 text-center"
                    >
                      <Empty type={EmptyType.Data} />
                    </TableCell>
                  </TableRow>
                )}
              </TableBody>
            </Table>
          </div>
          {metadataType === MetadataType.Manage && (
            <div className=" absolute bottom-6 left-5 text-text-secondary text-sm">
              {t('knowledgeDetails.metadata.toMetadataSettingTip')}
            </div>
          )}
        </>
      </Modal>
      {manageValuesVisible && (
        <ManageValuesModal
--- a/web/src/pages/dataset/dataset-setting/configuration/common-item.tsx
+++ b/web/src/pages/dataset/dataset-setting/configuration/common-item.tsx
@ -25,12 +25,13 @@ import { useComposeLlmOptionsByModelTypes } from '@/hooks/use-llm-request';
 import { cn } from '@/lib/utils';
 import { t } from 'i18next';
 import { Settings } from 'lucide-react';
-import { useMemo, useState } from 'react';
+import { useCallback, useEffect, useMemo, useState } from 'react';
 import {
  ControllerRenderProps,
  FieldValues,
  useFormContext,
 } from 'react-hook-form';
 import { useLocation } from 'umi';
 import {
  MetadataType,
  useManageMetadata,
@ -368,6 +369,7 @@ export function AutoMetadata({
  otherData?: Record<string, any>;
 }) {
  // get metadata field
  const location = useLocation();
  const form = useFormContext();
  const {
    manageMetadataVisible,
@ -377,6 +379,29 @@ export function AutoMetadata({
    config: metadataConfig,
  } = useManageMetadata();
  const handleClickOpenMetadata = useCallback(() => {
    const metadata = form.getValues('parser_config.metadata');
    const tableMetaData = util.metaDataSettingJSONToMetaDataTableData(metadata);
    showManageMetadataModal({
      metadata: tableMetaData,
      isCanAdd: true,
      type: type,
      record: otherData,
    });
  }, [form, otherData, showManageMetadataModal, type]);
  useEffect(() => {
    const locationState = location.state as
      | { openMetadata?: boolean }
      | undefined;
    if (locationState?.openMetadata) {
      setTimeout(() => {
        handleClickOpenMetadata();
      }, 100);
      locationState.openMetadata = false;
    }
  }, [location, handleClickOpenMetadata]);
  const autoMetadataField: FormFieldConfig = {
    name: 'parser_config.enable_metadata',
    label: t('knowledgeConfiguration.autoMetadata'),
@ -386,21 +411,7 @@ export function AutoMetadata({
    tooltip: t('knowledgeConfiguration.autoMetadataTip'),
    render: (fieldProps: ControllerRenderProps) => (
      <div className="flex items-center justify-between">
-        <Button
+        <Button type="button" variant="ghost" onClick={handleClickOpenMetadata}>
          type="button"
          variant="ghost"
          onClick={() => {
            const metadata = form.getValues('parser_config.metadata');
            const tableMetaData =
              util.metaDataSettingJSONToMetaDataTableData(metadata);
            showManageMetadataModal({
              metadata: tableMetaData,
              isCanAdd: true,
              type: type,
              record: otherData,
            });
          }}
        >
          <div className="flex items-center gap-2">
            <Settings />
            {t('knowledgeConfiguration.settings')}
--- a/web/src/pages/dataset/dataset/index.tsx
+++ b/web/src/pages/dataset/dataset/index.tsx
@ -79,6 +79,7 @@ export default function Dataset() {
    useRowSelection();
  const {
    chunkNum,
    list,
    visible: reparseDialogVisible,
    hideModal: hideReparseDialogModal,
@ -216,9 +217,9 @@ export default function Dataset() {
        {reparseDialogVisible && (
          <ReparseDialog
            // hidden={isZeroChunk || isRunning}
-            hidden={true}
+            hidden={false}
            handleOperationIconClick={handleOperationIconClick}
-            chunk_num={0}
+            chunk_num={chunkNum}
            visible={reparseDialogVisible}
            hideModal={hideReparseDialogModal}
          ></ReparseDialog>
--- a/web/src/pages/dataset/dataset/parsing-status-cell.tsx
+++ b/web/src/pages/dataset/dataset/parsing-status-cell.tsx
@ -183,7 +183,7 @@ export function ParsingStatusCell({
      )}
      {reparseDialogVisible && (
        <ReparseDialog
-          hidden={isZeroChunk || isRunning}
+          hidden={isRunning}
          // hidden={false}
          handleOperationIconClick={handleOperationIconClick}
          chunk_num={chunk_num}
--- a/web/src/pages/dataset/dataset/reparse-dialog.tsx
+++ b/web/src/pages/dataset/dataset/reparse-dialog.tsx
@ -2,12 +2,14 @@ import { ConfirmDeleteDialog } from '@/components/confirm-delete-dialog';
 import {
  DynamicForm,
  DynamicFormRef,
  FormFieldConfig,
  FormFieldType,
 } from '@/components/dynamic-form';
 import { Checkbox } from '@/components/ui/checkbox';
 import { DialogProps } from '@radix-ui/react-dialog';
-import { t } from 'i18next';
+import { memo, useCallback, useEffect, useRef, useState } from 'react';
-import { memo, useCallback, useEffect, useRef } from 'react';
+import { ControllerRenderProps } from 'react-hook-form';
 import { useTranslation } from 'react-i18next';
 export const ReparseDialog = memo(
  ({
@ -26,18 +28,77 @@ export const ReparseDialog = memo(
    hideModal: () => void;
    hidden?: boolean;
  }) => {
-    // const [formInstance, setFormInstance] = useState<DynamicFormRef | null>(
+    const [defaultValues, setDefaultValues] = useState<any>(null);
-    //   null,
+    const [fields, setFields] = useState<FormFieldConfig[]>([]);
-    // );
+    const { t } = useTranslation();
    const handleOperationIconClickRef = useRef(handleOperationIconClick);
    const hiddenRef = useRef(hidden);
-    // const formCallbackRef = useCallback((node: DynamicFormRef | null) => {
+    useEffect(() => {
-    //   if (node) {
+      handleOperationIconClickRef.current = handleOperationIconClick;
-    //     setFormInstance(node);
+      hiddenRef.current = hidden;
-    //     console.log('Form instance assigned:', node);
+    });
-    //   } else {
+
-    //     console.log('Form instance removed');
+    useEffect(() => {
-    //   }
+      if (hiddenRef.current) {
-    // }, []);
+        handleOperationIconClickRef.current();
      }
    }, []);
    useEffect(() => {
      setDefaultValues({
        delete: chunk_num > 0,
        apply_kb: false,
      });
      const deleteField = {
        name: 'delete',
        label: '',
        type: FormFieldType.Checkbox,
        render: (fieldProps: ControllerRenderProps) => (
          <div className="flex items-center text-text-secondary p-5 border border-border-button rounded-lg">
            <Checkbox
              {...fieldProps}
              checked={fieldProps.value}
              onCheckedChange={(checked: boolean) => {
                fieldProps.onChange(checked);
              }}
            />
            <span className="ml-2">
              {chunk_num > 0
                ? t(`knowledgeDetails.redo`, {
                    chunkNum: chunk_num,
                  })
                : t('knowledgeDetails.redoAll')}
            </span>
          </div>
        ),
      };
      const applyKBField = {
        name: 'apply_kb',
        label: '',
        type: FormFieldType.Checkbox,
        defaultValue: false,
        render: (fieldProps: ControllerRenderProps) => (
          <div className="flex items-center text-text-secondary p-5 border border-border-button rounded-lg">
            <Checkbox
              {...fieldProps}
              checked={fieldProps.value}
              onCheckedChange={(checked: boolean) => {
                fieldProps.onChange(checked);
              }}
            />
            <span className="ml-2">
              {t('knowledgeDetails.applyAutoMetadataSettings')}
            </span>
          </div>
        ),
      };
      if (chunk_num > 0) {
        setFields([deleteField, applyKBField]);
      }
      if (chunk_num <= 0) {
        setFields([applyKBField]);
      }
    }, [chunk_num, t]);
    const formCallbackRef = useRef<DynamicFormRef>(null);
@ -68,12 +129,6 @@ export const ReparseDialog = memo(
      }
    }, [formCallbackRef, handleOperationIconClick]);
    useEffect(() => {
      if (hidden) {
        handleOperationIconClick();
      }
    }, []);
    return (
      <ConfirmDeleteDialog
        title={t(`knowledgeDetails.parseFile`)}
@ -91,48 +146,8 @@ export const ReparseDialog = memo(
                  console.log('submit', data);
                }}
                ref={formCallbackRef}
-                fields={[
+                fields={fields}
-                  {
+                defaultValues={defaultValues}
                    name: 'delete',
                    label: '',
                    type: FormFieldType.Checkbox,
                    render: (fieldProps) => (
                      <div className="flex items-center text-text-secondary p-5 border border-border-button rounded-lg">
                        <Checkbox
                          {...fieldProps}
                          onCheckedChange={(checked: boolean) => {
                            fieldProps.onChange(checked);
                          }}
                        />
                        <span className="ml-2">
                          {chunk_num > 0
                            ? t(`knowledgeDetails.redo`, {
                                chunkNum: chunk_num,
                              })
                            : t('knowledgeDetails.redoAll')}
                        </span>
                      </div>
                    ),
                  },
                  {
                    name: 'apply_kb',
                    label: '',
                    type: FormFieldType.Checkbox,
                    render: (fieldProps) => (
                      <div className="flex items-center text-text-secondary p-5 border border-border-button rounded-lg">
                        <Checkbox
                          {...fieldProps}
                          onCheckedChange={(checked: boolean) => {
                            fieldProps.onChange(checked);
                          }}
                        />
                        <span className="ml-2">
                          {t('knowledgeDetails.applyAutoMetadataSettings')}
                        </span>
                      </div>
                    ),
                  },
                ]}
              >
                {/* <DynamicForm.CancelButton
                handleCancel={() => handleOperationIconClick(false)}
--- a/web/src/pages/dataset/dataset/use-bulk-operate-dataset.tsx
+++ b/web/src/pages/dataset/dataset/use-bulk-operate-dataset.tsx
@ -10,7 +10,7 @@ import {
 } from '@/hooks/use-document-request';
 import { IDocumentInfo } from '@/interfaces/database/document';
 import { Ban, CircleCheck, CircleX, Play, Trash2 } from 'lucide-react';
-import { useCallback } from 'react';
+import { useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { toast } from 'sonner';
 import { DocumentType, RunningStatus } from './constant';
@ -32,6 +32,16 @@ export function useBulkOperateDataset({
  const { setDocumentStatus } = useSetDocumentStatus();
  const { removeDocument } = useRemoveDocument();
  const { visible, showModal, hideModal } = useSetModalState();
  const chunkNum = useMemo(() => {
    if (!documents.length) {
      return 0;
    }
    return documents.reduce((acc, cur) => {
      return acc + cur.chunk_num;
    }, 0);
  }, [documents]);
  const runDocument = useCallback(
    async (run: number, option?: { delete: boolean; apply_kb: boolean }) => {
      const nonVirtualKeys = selectedRowKeys.filter(
@ -132,5 +142,5 @@ export function useBulkOperateDataset({
    },
  ];
-  return { list, visible, hideModal, showModal, handleRunClick };
+  return { chunkNum, list, visible, hideModal, showModal, handleRunClick };
 }
--- a/web/src/pages/dataset/process-log-modal.tsx
+++ b/web/src/pages/dataset/process-log-modal.tsx
@ -38,21 +38,27 @@ interface ProcessLogModalProps {
 }
 const InfoItem: React.FC<{
  overflowTip?: boolean;
  label: string;
  value: string | React.ReactNode;
  className?: string;
-}> = ({ label, value, className = '' }) => {
+}> = ({ label, value, className = '', overflowTip = false }) => {
  return (
    <div className={`flex flex-col mb-4 ${className}`}>
      <span className="text-text-secondary text-sm">{label}</span>
-      <Tooltip>
+      {overflowTip && (
-        <TooltipTrigger asChild>
+        <Tooltip>
-          <span className="text-text-primary mt-1 truncate w-full">
+          <TooltipTrigger asChild>
-            {value}
+            <span className="text-text-primary mt-1 truncate w-full">
-          </span>
+              {value}
-        </TooltipTrigger>
+            </span>
-        <TooltipContent>{value}</TooltipContent>
+          </TooltipTrigger>
-      </Tooltip>
+          <TooltipContent>{value}</TooltipContent>
        </Tooltip>
      )}
      {!overflowTip && (
        <span className="text-text-primary mt-1 truncate w-full">{value}</span>
      )}
    </div>
  );
 };
@ -139,6 +145,7 @@ const ProcessLogModal: React.FC<ProcessLogModalProps> = ({
            return (
              <div className="w-1/2" key={key}>
                <InfoItem
                  overflowTip={true}
                  label={t(key)}
                  value={logInfo[key as keyof typeof logInfo]}
                />
--- a/web/src/pages/dataset/sidebar/hooks.tsx
+++ b/web/src/pages/dataset/sidebar/hooks.tsx
@ -7,8 +7,8 @@ export const useHandleMenuClick = () => {
  const { id } = useParams();
  const handleMenuClick = useCallback(
-    (key: Routes) => () => {
+    (key: Routes, data?: any) => () => {
-      navigate(`${Routes.DatasetBase}${key}/${id}`);
+      navigate(`${Routes.DatasetBase}${key}/${id}`, { state: data });
    },
    [id, navigate],
  );
--- a/web/src/pages/memory/memory-message/message-table.tsx
+++ b/web/src/pages/memory/memory-message/message-table.tsx
@ -18,6 +18,7 @@ import {
 } from '@/components/ui/table';
 import { Pagination } from '@/interfaces/common';
 import { replaceText } from '@/pages/dataset/process-log-modal';
 import { MemoryOptions } from '@/pages/memories/constants';
 import {
  ColumnDef,
  ColumnFiltersState,
@ -99,7 +100,12 @@ export function MemoryTable({
        header: () => <span>{t('memory.messages.type')}</span>,
        cell: ({ row }) => (
          <div className="text-sm font-medium  capitalize">
-            {row.getValue('message_type')}
+            {row.getValue('message_type')
              ? MemoryOptions(t).find(
                  (item) =>
                    item.value === (row.getValue('message_type') as string),
                )?.label
              : row.getValue('message_type')}
          </div>
        ),
      },
@ -117,13 +123,13 @@ export function MemoryTable({
          <div className="text-sm ">{row.getValue('forget_at')}</div>
        ),
      },
-      {
+      // {
-        accessorKey: 'source_id',
+      //   accessorKey: 'source_id',
-        header: () => <span>{t('memory.messages.source')}</span>,
+      //   header: () => <span>{t('memory.messages.source')}</span>,
-        cell: ({ row }) => (
+      //   cell: ({ row }) => (
-          <div className="text-sm ">{row.getValue('source_id')}</div>
+      //     <div className="text-sm ">{row.getValue('source_id')}</div>
-        ),
+      //   ),
-      },
+      // },
      {
        accessorKey: 'status',
        header: () => <span>{t('memory.messages.enable')}</span>,
--- a/web/src/pages/memory/memory-setting/memory-model-form.tsx
+++ b/web/src/pages/memory/memory-setting/memory-model-form.tsx
@ -92,6 +92,7 @@ export const MemoryModelForm = () => {
          label: t('memory.config.memorySize') + ' (Bytes)',
          type: FormFieldType.Number,
          horizontal: true,
          tooltip: t('memory.config.memorySizeTooltip'),
          // placeholder: t('memory.config.memorySizePlaceholder'),
          required: false,
        }}
--- a/web/src/pages/user-setting/data-source/constant/index.tsx
+++ b/web/src/pages/user-setting/data-source/constant/index.tsx
@ -27,6 +27,7 @@ export enum DataSourceKey {
  AIRTABLE = 'airtable',
  GITLAB = 'gitlab',
  ASANA = 'asana',
  IMAP = 'imap',
  GITHUB = 'github',
  //   SHAREPOINT = 'sharepoint',
  //   SLACK = 'slack',
@ -127,6 +128,11 @@ export const generateDataSourceInfo = (t: TFunction) => {
      description: t(`setting.${DataSourceKey.GITHUB}Description`),
      icon: <SvgIcon name={'data-source/github'} width={38} />,
    },
    [DataSourceKey.IMAP]: {
      name: 'IMAP',
      description: t(`setting.${DataSourceKey.IMAP}Description`),
      icon: <SvgIcon name={'data-source/imap'} width={38} />,
    },
  };
 };
@ -654,7 +660,7 @@ export const DataSourceFormFields = {
    {
      label: 'Access Token',
      name: 'config.credentials.airtable_access_token',
-      type: FormFieldType.Text,
+      type: FormFieldType.Password,
      required: true,
    },
    {
@ -722,7 +728,7 @@ export const DataSourceFormFields = {
    {
      label: 'API Token',
      name: 'config.credentials.asana_api_token_secret',
-      type: FormFieldType.Text,
+      type: FormFieldType.Password,
      required: true,
    },
    {
@ -778,6 +784,44 @@ export const DataSourceFormFields = {
      defaultValue: false,
    },
  ],
  [DataSourceKey.IMAP]: [
    {
      label: 'Username',
      name: 'config.credentials.imap_username',
      type: FormFieldType.Text,
      required: true,
    },
    {
      label: 'Password',
      name: 'config.credentials.imap_password',
      type: FormFieldType.Password,
      required: true,
    },
    {
      label: 'Host',
      name: 'config.imap_host',
      type: FormFieldType.Text,
      required: true,
    },
    {
      label: 'Port',
      name: 'config.imap_port',
      type: FormFieldType.Number,
      required: true,
    },
    {
      label: 'Mailboxes',
      name: 'config.imap_mailbox',
      type: FormFieldType.Tag,
      required: false,
    },
    {
      label: 'Poll Range',
      name: 'config.poll_range',
      type: FormFieldType.Number,
      required: false,
    },
  ],
 };
 export const DataSourceFormDefaultValues = {
@ -1017,4 +1061,19 @@ export const DataSourceFormDefaultValues = {
      },
    },
  },
  [DataSourceKey.IMAP]: {
    name: '',
    source: DataSourceKey.IMAP,
    config: {
      name: '',
      imap_host: '',
      imap_port: 993,
      imap_mailbox: [],
      poll_range: 30,
      credentials: {
        imap_username: '',
        imap_password: '',
      },
    },
  },
 };
--- a/web/src/pages/user-setting/data-source/data-source-detail-page/index.tsx
+++ b/web/src/pages/user-setting/data-source/data-source-detail-page/index.tsx
@ -127,9 +127,21 @@ const SourceDetailPage = () => {
  }, []);
  useEffect(() => {
    const baseFields = DataSourceFormBaseFields.map((field) => {
      if (field.name === 'name') {
        return {
          ...field,
          disabled: true,
        };
      } else {
        return {
          ...field,
        };
      }
    });
    if (detail) {
      const fields = [
-        ...DataSourceFormBaseFields,
+        ...baseFields,
        ...DataSourceFormFields[
          detail.source as keyof typeof DataSourceFormFields
        ],
--- a/web/src/pages/user-setting/setting-model/components/modal-card.tsx
+++ b/web/src/pages/user-setting/setting-model/components/modal-card.tsx
@ -14,6 +14,7 @@ import { ChevronsDown, ChevronsUp, Trash2 } from 'lucide-react';
 import { FC } from 'react';
 import { isLocalLlmFactory } from '../../utils';
 import { useHandleDeleteFactory, useHandleEnableLlm } from '../hooks';
 import { mapModelKey } from './un-add-model';
 interface IModelCardProps {
  item: LlmItem;
@ -145,7 +146,8 @@ export const ModelProviderCard: FC<IModelCardProps> = ({
                key={index}
                className="px-2 py-1 text-xs bg-bg-card text-text-secondary rounded-md"
              >
-                {tag}
+                {mapModelKey[tag.trim() as keyof typeof mapModelKey] ||
                  tag.trim()}
              </span>
            ))}
          </div>
--- a/web/src/pages/user-setting/setting-model/components/un-add-model.tsx
+++ b/web/src/pages/user-setting/setting-model/components/un-add-model.tsx
@ -7,7 +7,21 @@ import { useTranslate } from '@/hooks/common-hooks';
 import { useSelectLlmList } from '@/hooks/use-llm-request';
 import { ArrowUpRight, Plus } from 'lucide-react';
 import { FC, useMemo, useState } from 'react';
-
+export const mapModelKey = {
  IMAGE2TEXT: 'VLM',
  'TEXT EMBEDDING': 'Embedding',
  SPEECH2TEXT: 'ASR',
  'TEXT RE-RANK': 'Rerank',
 };
 const orderMap: Record<TagType, number> = {
  LLM: 1,
  'TEXT EMBEDDING': 2,
  'TEXT RE-RANK': 3,
  TTS: 4,
  SPEECH2TEXT: 5,
  IMAGE2TEXT: 6,
  MODERATION: 7,
 };
 type TagType =
  | 'LLM'
  | 'TEXT EMBEDDING'
@ -18,16 +32,6 @@ type TagType =
  | 'MODERATION';
 const sortTags = (tags: string) => {
  const orderMap: Record<TagType, number> = {
    LLM: 1,
    'TEXT EMBEDDING': 2,
    'TEXT RE-RANK': 3,
    TTS: 4,
    SPEECH2TEXT: 5,
    IMAGE2TEXT: 6,
    MODERATION: 7,
  };
  return tags
    .split(',')
    .map((tag) => tag.trim())
@ -64,7 +68,10 @@ export const AvailableModels: FC<{
    factoryList.forEach((model) => {
      model.tags.split(',').forEach((tag) => tagsSet.add(tag.trim()));
    });
-    return Array.from(tagsSet).sort();
+    return Array.from(tagsSet).sort(
      (a, b) =>
        (orderMap[a as TagType] || 999) - (orderMap[b as TagType] || 999),
    );
  }, [factoryList]);
  const handleTagClick = (tag: string) => {
@ -114,7 +121,7 @@ export const AvailableModels: FC<{
                : 'text-text-secondary  border-none bg-bg-card'
            }`}
          >
-            {tag}
+            {mapModelKey[tag.trim() as keyof typeof mapModelKey] || tag.trim()}
          </Button>
        ))}
      </div>
@ -162,7 +169,9 @@ export const AvailableModels: FC<{
                  key={index}
                  className="px-1 flex items-center h-5 text-xs bg-bg-card text-text-secondary rounded-md"
                >
-                  {tag}
+                  {/* {tag} */}
                  {mapModelKey[tag.trim() as keyof typeof mapModelKey] ||
                    tag.trim()}
                </span>
              ))}
            </div>
Author	SHA1	Message	Date
Jin Hai	675d18d359	Add docs category file (#12359 ) ### What problem does this PR solve? As title. ### Type of change - [x] Documentation Update Signed-off-by: Jin Hai <haijin.chn@gmail.com>	2025-12-31 13:56:11 +08:00
chanx	750335978c	Fix: Batch parsing problem (#12358 ) ### What problem does this PR solve? Fix: Batch parsing problem ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)	2025-12-31 13:52:50 +08:00
Liu An	ae7c623a35	fix(rag/prompts): Restructure metadata extraction rules for precision (#12360 ) ### What problem does this PR solve? - Simplified and consolidated extraction rules - Emphasized strict evidence-based extraction only - Strengthened enum handling and hallucination prevention - Clarified output requirements for empty results ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)	2025-12-31 13:52:33 +08:00
Jin Hai	f24bdc0f83	Remove doc of health check (#12363 ) ### What problem does this PR solve? System web page is disabled since v0.22.0, and the health check API is also described in API reference. This document is obsolete. ### Type of change - [x] Documentation Update Signed-off-by: Jin Hai <haijin.chn@gmail.com>	2025-12-31 13:49:38 +08:00
Liu An	07ef35b7e6	Docs: Update version references to v0.23.1 in READMEs and docs (#12349 ) ### What problem does this PR solve? - Update version tags in README files (including translations) from v0.23.0 to v0.23.1 - Modify Docker image references and documentation to reflect new version - Update version badges and image descriptions - Maintain consistency across all language variants of README files ### Type of change - [x] Documentation Update	2025-12-31 12:49:42 +08:00
Jin Hai	7c9823a1ff	Update release notes (#12356 ) ### What problem does this PR solve? As title ### Type of change - [x] Documentation Update Signed-off-by: Jin Hai <haijin.chn@gmail.com>	2025-12-31 12:49:09 +08:00
Jin Hai	a0c3bcf798	[Bug] Don't display not used component status in admin status dashboard (#12355 ) ### What problem does this PR solve? Currently, all components in configs are displayed even they are not used. This PR is to fix it. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) Signed-off-by: Jin Hai <haijin.chn@gmail.com>	2025-12-31 12:40:52 +08:00
Kevin Hu	1a4a7d1705	Fix: apply kb configured llm issue. (#12354 ) ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)	2025-12-31 12:40:28 +08:00
Jin Hai	f141947085	[Feature] Admin: sort user list by email (#12350 ) ### What problem does this PR solve? Sort the user list by user email address. ### Type of change - [x] New Feature (non-breaking change which adds functionality) Signed-off-by: Jin Hai <haijin.chn@gmail.com>	2025-12-31 11:55:18 +08:00
balibabu	a07e947644	Feat: Fixed the issue where the newly created agent begin node displayed "undefined". #10427 (#12348 ) ### What problem does this PR solve? Feat: Fixed the issue where the newly created agent begin node displayed "undefined". #10427 ### Type of change - [x] New Feature (non-breaking change which adds functionality)	2025-12-31 11:19:33 +08:00
chanx	ae4692a845	Fix: Bug fixed (#12345 ) ### What problem does this PR solve? Fix: Bug fixed - Memory type multilingual display - Name modification is prohibited in Data source - Jump directly to Metadata settings ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)	2025-12-31 10:43:57 +08:00
Daniel Derefaka	7dac269429	fix: correct session reference initialization to prevent dialogue misalignment (#12343 ) ## Summary Fixes #12311 Changes the `reference` field initialization from `[{}]` to `[]` in session creation. ### Problem When creating a session via the SDK API, the `reference` field was incorrectly initialized as `[{}]`. This caused: - First dialogue round: Empty reference - Second dialogue round: Reference pointing to first round's data - Overall misalignment between dialogue rounds and their references ### Solution Changed the initialization to `[]` (empty list), which: - Matches the `Conversation` model's expected default - Ensures references grow correctly one-to-one with assistant responses - Aligns with the service layer's expectations ### Testing After applying this fix: 1. Create a session via `POST /api/v1/chats/{conversation_id}/sessions` 2. Send multiple questions via `POST /api/v1/chats/{conversation_id}/completions` 3. View the conversation on web - references should now align correctly with each dialogue round	2025-12-31 10:25:49 +08:00
Jimmy Ben Klieve	ec5575dce2	fix(admin-ui): pagination auto reset to first page when after refetching data (#12339 ) ### What problem does this PR solve? Admin user enabling/disabling a user will causing user list reset to first page ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)	2025-12-31 09:39:13 +08:00
writinwaters	6fee60e110	Docs: What is RAG & What is Agent context engine (#12341 ) ### Type of change - [x] Documentation Update	2025-12-30 21:29:21 +08:00
Kevin Hu	52f91c2388	Refine: image/table context. (#12336 ) ### What problem does this PR solve? #12303 ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)	2025-12-30 20:24:27 +08:00
balibabu	348265afc1	Feat: Display mode at the begin node #10427 (#12326 ) ### What problem does this PR solve? Feat: Display mode at the begin node #10427 ### Type of change - [x] New Feature (non-breaking change which adds functionality)	2025-12-30 19:53:24 +08:00
chanx	a7e466142d	Fix: Dataset parse logic (#12330 ) ### What problem does this PR solve? Fix: Dataset logic of parser ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)	2025-12-30 19:53:00 +08:00
balibabu	2fccf3924d	Feat: Adapt the theme of the documentation page. #10427 (#12337 ) ### What problem does this PR solve? Feat: Adapt the theme of the documentation page. #10427 ### Type of change - [x] New Feature (non-breaking change which adds functionality)	2025-12-30 19:35:44 +08:00
Jimmy Ben Klieve	4705d07e11	fix: malformed dynamic translation key `chunk.docType.${chunkType}` (#12329 ) ### What problem does this PR solve? Back-end may returns empty array on `"doc_type_kwd"` property which causes translation key malformed. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)	2025-12-30 19:33:20 +08:00
Jin Hai	68be3b9a3d	Update release workflow (#12335 ) ### What problem does this PR solve? As title ### Type of change - [x] Refactoring Signed-off-by: Jin Hai <haijin.chn@gmail.com>	2025-12-30 19:06:27 +08:00
Yingfeng	e2d17d808b	Potential fix for code scanning alert no. 62: Workflow does not contain permissions (#12334 ) Potential fix for [https://github.com/infiniflow/ragflow/security/code-scanning/62](https://github.com/infiniflow/ragflow/security/code-scanning/62) In general, the fix is to explicitly declare a `permissions:` block so the GITHUB_TOKEN used by this workflow only has the scopes required: read access to repository contents and write access to contents/releases. Since this workflow creates or moves tags and creates/overwrites releases via `softprops/action-gh-release`, it needs `contents: write`. There is no evidence that it needs other elevated scopes (issues, pull-requests, actions, etc.), so these should remain at their default of `none` by omission. The best minimal fix without changing existing functionality is to add a workflow-level `permissions:` block near the top of `.github/workflows/release.yml`, after `name:` and before `on:` (or anywhere at the root level, but this is conventional). This will apply to all jobs (there is only `jobs.release`) and ensure that the GITHUB_TOKEN has only `contents: write`. No additional imports or methods are needed because this is a YAML configuration change only. Concretely: - Edit `.github/workflows/release.yml`. - Insert: ```yaml permissions: contents: write ``` between line 2 (empty line after `name: release`) and line 3 (`on:`). No other lines need to be changed. _Suggested fixes powered by Copilot Autofix. Review carefully before merging._ Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>	2025-12-30 18:59:51 +08:00
Jin Hai	95edbd43ba	Update model providers (#12333 ) ### What problem does this PR solve? As title ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) Signed-off-by: Jin Hai <haijin.chn@gmail.com>	2025-12-30 18:51:35 +08:00
Jin Hai	b96d553cd8	Update release workflow (#12327 ) ### What problem does this PR solve? As title. ### Type of change - [x] Refactoring Signed-off-by: Jin Hai <haijin.chn@gmail.com>	2025-12-30 17:25:27 +08:00
buua436	bffdb5fb11	Feat: add IMAP data source integration with configuration and sync capabilities (#12316 ) ### What problem does this PR solve? issue: #12217 [#12313](https://github.com/infiniflow/ragflow/issues/12313) change: add IMAP data source integration with configuration and sync capabilities ### Type of change - [x] New Feature (non-breaking change which adds functionality)	2025-12-30 17:09:13 +08:00
balibabu	109e782493	Feat: On the agent page and chat page, you can only select knowledge bases that use the same embedding model. #12320 (#12321 ) ### What problem does this PR solve? Feat: On the agent page and chat page, you can only select knowledge bases that use the same embedding model. #12320 ### Type of change - [x] New Feature (non-breaking change which adds functionality)	2025-12-30 17:08:30 +08:00
`@ -87,4 +87,4 @@ RAGFlow's file management allows you to download an uploaded file:`

	`![download_file](https://github.com/infiniflow/ragflow/assets/93570324/cf3b297f-7d9b-4522-bf5f-4f45743e4ed5)`	`![download_file](https://github.com/infiniflow/ragflow/assets/93570324/cf3b297f-7d9b-4522-bf5f-4f45743e4ed5)`

	`> As of RAGFlow v0.23.0, bulk download is not supported, nor can you download an entire folder.`	`> As of RAGFlow v0.23.1, bulk download is not supported, nor can you download an entire folder.`