Don't release full image (#10654)

### What problem does this PR solve? Introduced gpu profile in .env Added Dockerfile_tei fix datrie Removed LIGHTEN flag ### Type of change - [x] Documentation Update - [x] Refactoring
2025-12-08 12:32:30 +08:00 · 2025-10-23 23:02:27 +08:00
parent 92739ea804
commit 73144e278b
67 changed files with 2792 additions and 3608 deletions
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@ -83,30 +83,17 @@ jobs:
          password: ${{ secrets.DOCKERHUB_TOKEN }}

      # https://github.com/marketplace/actions/build-and-push-docker-images
-      - name: Build and push full image
+      - name: Build and push image
        uses: docker/build-push-action@v6
        with:
          context: .
          push: true
          tags: |
            infiniflow/ragflow:${{ env.RELEASE_TAG }}
-            infiniflow/ragflow:latest-full
+            infiniflow/ragflow:latest
          file: Dockerfile
          platforms: linux/amd64

-      # https://github.com/marketplace/actions/build-and-push-docker-images
-      - name: Build and push slim image
-        uses: docker/build-push-action@v6
-        with:
-          context: .
-          push: true
-          tags: |
-            infiniflow/ragflow:${{ env.RELEASE_TAG }}-slim
-            infiniflow/ragflow:latest-slim
-          file: Dockerfile
-          build-args: LIGHTEN=1
-          platforms: linux/amd64
-
      - name: Build ragflow-sdk
        if: startsWith(github.ref, 'refs/tags/v')
        run: |
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@ -49,20 +49,20 @@ jobs:
      - name: Check workflow duplication
        if: ${{ !cancelled() && !failure() && (github.event_name != 'pull_request' || contains(github.event.pull_request.labels.*.name, 'ci')) }}
        run: |
-          if [[ ${{ github.event_name }} != 'pull_request' ]]; then
+          if [[ "$GITHUB_EVENT_NAME" != "pull_request" && "$GITHUB_EVENT_NAME" != "schedule" ]]; then
            HEAD=$(git rev-parse HEAD)
            # Find a PR that introduced a given commit
            gh auth login --with-token <<< "${{ secrets.GITHUB_TOKEN }}"
            PR_NUMBER=$(gh pr list --search ${HEAD} --state merged --json number --jq .[0].number)
            echo "HEAD=${HEAD}"
            echo "PR_NUMBER=${PR_NUMBER}"
-            if [[ -n ${PR_NUMBER} ]]; then
+            if [[ -n "${PR_NUMBER}" ]]; then
              PR_SHA_FP=${RUNNER_WORKSPACE_PREFIX}/artifacts/${GITHUB_REPOSITORY}/PR_${PR_NUMBER}
-              if [[ -f ${PR_SHA_FP} ]]; then
+              if [[ -f "${PR_SHA_FP}" ]]; then
                read -r PR_SHA PR_RUN_ID < "${PR_SHA_FP}"
                # Calculate the hash of the current workspace content
                HEAD_SHA=$(git rev-parse HEAD^{tree})
-                if [[ ${HEAD_SHA} == ${PR_SHA} ]]; then
+                if [[ "${HEAD_SHA}" == "${PR_SHA}" ]]; then
                  echo "Cancel myself since the workspace content hash is the same with PR #${PR_NUMBER} merged. See ${GITHUB_SERVER_URL}/${GITHUB_REPOSITORY}/actions/runs/${PR_RUN_ID} for details."
                  gh run cancel ${GITHUB_RUN_ID}
                  while true; do
@ -91,122 +91,98 @@ jobs:
          version: ">=0.11.x"
          args: "check"

-      - name: Build ragflow:nightly-slim
+      - name: Build ragflow:nightly
        run: |
          RUNNER_WORKSPACE_PREFIX=${RUNNER_WORKSPACE_PREFIX:-$HOME}
          sudo docker pull ubuntu:22.04
-          sudo DOCKER_BUILDKIT=1 docker build --build-arg LIGHTEN=1 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
-
-      - name: Build ragflow:nightly
-        run: |
          sudo DOCKER_BUILDKIT=1 docker build --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly .
-
-      - name: Start ragflow:nightly-slim
-        run: |
-          sudo docker compose -f docker/docker-compose.yml down --volumes --remove-orphans
-          echo -e "\nRAGFLOW_IMAGE=infiniflow/ragflow:nightly-slim" >> docker/.env
-          sudo docker compose -f docker/docker-compose.yml up -d
-
-      - name: Stop ragflow:nightly-slim
-        if: always()  # always run this step even if previous steps failed
-        run: |
-          sudo docker compose -f docker/docker-compose.yml down -v
+          if [[ "$GITHUB_EVENT_NAME" == "schedule" ]]; then
+            export HTTP_API_TEST_LEVEL=p3
+          else
+            export HTTP_API_TEST_LEVEL=p2
+          fi
+          echo "HTTP_API_TEST_LEVEL=${HTTP_API_TEST_LEVEL}" >> $GITHUB_ENV
+          echo "RAGFLOW_CONTAINER=${GITHUB_RUN_ID}-ragflow-cpu-1" >> $GITHUB_ENV

      - name: Start ragflow:nightly
        run: |
+          echo -e "\nCOMPOSE_PROFILES=\${COMPOSE_PROFILES},tei-cpu" >> docker/.env
+          echo -e "\nTEI_MODEL=BAAI/bge-small-en-v1.5" >> docker/.env
          echo -e "\nRAGFLOW_IMAGE=infiniflow/ragflow:nightly" >> docker/.env
-          sudo docker compose -f docker/docker-compose.yml up -d
+          sudo docker compose -f docker/docker-compose.yml -p ${GITHUB_RUN_ID} up -d
+          uv sync --python 3.10 --only-group test --no-default-groups --frozen && uv pip install sdk/python

      - name: Run sdk tests against Elasticsearch
        run: |
          export http_proxy=""; export https_proxy=""; export no_proxy=""; export HTTP_PROXY=""; export HTTPS_PROXY=""; export NO_PROXY=""
          export HOST_ADDRESS=http://host.docker.internal:9380
-          until sudo docker exec ragflow-server curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
+          until sudo docker exec ${RAGFLOW_CONTAINER} curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
            echo "Waiting for service to be available..."
            sleep 5
          done
-          if [[ $GITHUB_EVENT_NAME == 'schedule' ]]; then
-            export HTTP_API_TEST_LEVEL=p3
-          else
-            export HTTP_API_TEST_LEVEL=p2
-          fi
-          UV_LINK_MODE=copy uv sync --python 3.10 --only-group test --no-default-groups --frozen && uv pip install sdk/python && uv run --only-group test --no-default-groups pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_sdk_api
+          source .venv/bin/activate && pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_sdk_api

      - name: Run frontend api tests against Elasticsearch
        run: |
          export http_proxy=""; export https_proxy=""; export no_proxy=""; export HTTP_PROXY=""; export HTTPS_PROXY=""; export NO_PROXY=""
          export HOST_ADDRESS=http://host.docker.internal:9380
-          until sudo docker exec ragflow-server curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
+          until sudo docker exec ${RAGFLOW_CONTAINER} curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
            echo "Waiting for service to be available..."
            sleep 5
          done
-          cd sdk/python && UV_LINK_MODE=copy uv sync --python 3.10 --group test --frozen && source .venv/bin/activate && cd test/test_frontend_api && pytest -s --tb=short get_email.py test_dataset.py
+          source .venv/bin/activate && pytest -s --tb=short sdk/python/test/test_frontend_api/get_email.py sdk/python/test/test_frontend_api/test_dataset.py
          
      - name: Run http api tests against Elasticsearch
        run: |
          export http_proxy=""; export https_proxy=""; export no_proxy=""; export HTTP_PROXY=""; export HTTPS_PROXY=""; export NO_PROXY=""
          export HOST_ADDRESS=http://host.docker.internal:9380
-          until sudo docker exec ragflow-server curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
+          until sudo docker exec ${RAGFLOW_CONTAINER} curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
            echo "Waiting for service to be available..."
            sleep 5
          done
-          if [[ $GITHUB_EVENT_NAME == 'schedule' ]]; then
-            export HTTP_API_TEST_LEVEL=p3
-          else
-            export HTTP_API_TEST_LEVEL=p2
-          fi
-          UV_LINK_MODE=copy uv sync --python 3.10 --only-group test --no-default-groups --frozen && uv run --only-group test --no-default-groups pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_http_api
+          source .venv/bin/activate && pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_http_api

      - name: Stop ragflow:nightly
        if: always()  # always run this step even if previous steps failed
        run: |
-          sudo docker compose -f docker/docker-compose.yml down -v
+          sudo docker compose -f docker/docker-compose.yml -p ${GITHUB_RUN_ID} down -v

      - name: Start ragflow:nightly
        run: |
-          sudo DOC_ENGINE=infinity docker compose -f docker/docker-compose.yml up -d
+          sed -i '1i DOC_ENGINE=infinity' docker/.env
+          sudo docker compose -f docker/docker-compose.yml -p ${GITHUB_RUN_ID} up -d

      - name: Run sdk tests against Infinity
        run: |
          export http_proxy=""; export https_proxy=""; export no_proxy=""; export HTTP_PROXY=""; export HTTPS_PROXY=""; export NO_PROXY=""
          export HOST_ADDRESS=http://host.docker.internal:9380
-          until sudo docker exec ragflow-server curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
+          until sudo docker exec ${RAGFLOW_CONTAINER} curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
            echo "Waiting for service to be available..."
            sleep 5
          done
-          if [[ $GITHUB_EVENT_NAME == 'schedule' ]]; then
-            export HTTP_API_TEST_LEVEL=p3
-          else
-            export HTTP_API_TEST_LEVEL=p2
-          fi
-          UV_LINK_MODE=copy uv sync --python 3.10 --only-group test --no-default-groups --frozen && uv pip install sdk/python && DOC_ENGINE=infinity uv run --only-group test --no-default-groups pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_sdk_api
+          source .venv/bin/activate && DOC_ENGINE=infinity pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_sdk_api

      - name: Run frontend api tests against Infinity
        run: |
          export http_proxy=""; export https_proxy=""; export no_proxy=""; export HTTP_PROXY=""; export HTTPS_PROXY=""; export NO_PROXY=""
          export HOST_ADDRESS=http://host.docker.internal:9380
-          until sudo docker exec ragflow-server curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
+          until sudo docker exec ${RAGFLOW_CONTAINER} curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
            echo "Waiting for service to be available..."
            sleep 5
          done
-          cd sdk/python && UV_LINK_MODE=copy uv sync --python 3.10 --group test --frozen && source .venv/bin/activate && cd test/test_frontend_api && pytest -s --tb=short get_email.py test_dataset.py
+          source .venv/bin/activate && DOC_ENGINE=infinity pytest -s --tb=short sdk/python/test/test_frontend_api/get_email.py sdk/python/test/test_frontend_api/test_dataset.py

      - name: Run http api tests against Infinity
        run: |
          export http_proxy=""; export https_proxy=""; export no_proxy=""; export HTTP_PROXY=""; export HTTPS_PROXY=""; export NO_PROXY=""
          export HOST_ADDRESS=http://host.docker.internal:9380
-          until sudo docker exec ragflow-server curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
+          until sudo docker exec ${RAGFLOW_CONTAINER} curl -s --connect-timeout 5 ${HOST_ADDRESS} > /dev/null; do
            echo "Waiting for service to be available..."
            sleep 5
          done
-          if [[ $GITHUB_EVENT_NAME == 'schedule' ]]; then
-            export HTTP_API_TEST_LEVEL=p3
-          else
-            export HTTP_API_TEST_LEVEL=p2
-          fi
-          UV_LINK_MODE=copy uv sync --python 3.10 --only-group test --no-default-groups --frozen && DOC_ENGINE=infinity uv run --only-group test --no-default-groups pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_http_api
+          source .venv/bin/activate && DOC_ENGINE=infinity pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_http_api

      - name: Stop ragflow:nightly
        if: always()  # always run this step even if previous steps failed
        run: |
-          sudo DOC_ENGINE=infinity docker compose -f docker/docker-compose.yml down -v
+          sudo docker compose -f docker/docker-compose.yml -p ${GITHUB_RUN_ID} down -v
--- a/31
+++ b/31
@ -4,8 +4,6 @@ USER root
 SHELL ["/bin/bash", "-c"]

 ARG NEED_MIRROR=0
-ARG LIGHTEN=0
-ENV LIGHTEN=${LIGHTEN}

 WORKDIR /ragflow

@ -17,13 +15,6 @@ RUN --mount=type=bind,from=infiniflow/ragflow_deps:latest,source=/huggingface.co
        /huggingface.co/InfiniFlow/text_concat_xgb_v1.0 \
        /huggingface.co/InfiniFlow/deepdoc \
        | tar -xf - --strip-components=3 -C /ragflow/rag/res/deepdoc 
-RUN --mount=type=bind,from=infiniflow/ragflow_deps:latest,source=/huggingface.co,target=/huggingface.co \
-    if [ "$LIGHTEN" != "1" ]; then \
-        (tar -cf - \
-            /huggingface.co/BAAI/bge-large-zh-v1.5 \
-            /huggingface.co/maidalun1020/bce-embedding-base_v1 \
-            | tar -xf - --strip-components=2 -C /root/.ragflow) \
-    fi

 # https://github.com/chrismattmann/tika-python
 # This is the only way to run python-tika without internet access. Without this set, the default is to check the tika version and pull latest every time from Apache.
@ -63,11 +54,11 @@ RUN --mount=type=cache,id=ragflow_apt,target=/var/cache/apt,sharing=locked \
    apt install -y ghostscript

 RUN if [ "$NEED_MIRROR" == "1" ]; then \
-        pip3 config set global.index-url https://mirrors.aliyun.com/pypi/simple && \
-        pip3 config set global.trusted-host mirrors.aliyun.com; \
+        pip3 config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple && \
+        pip3 config set global.trusted-host pypi.tuna.tsinghua.edu.cn; \
        mkdir -p /etc/uv && \
        echo "[[index]]" > /etc/uv/uv.toml && \
-        echo 'url = "https://mirrors.aliyun.com/pypi/simple"' >> /etc/uv/uv.toml && \
+        echo 'url = "https://pypi.tuna.tsinghua.edu.cn/simple"' >> /etc/uv/uv.toml && \
        echo "default = true" >> /etc/uv/uv.toml; \
    fi; \
    pipx install uv
@ -151,15 +142,11 @@ COPY pyproject.toml uv.lock ./
 # uv records index url into uv.lock but doesn't failover among multiple indexes
 RUN --mount=type=cache,id=ragflow_uv,target=/root/.cache/uv,sharing=locked \
    if [ "$NEED_MIRROR" == "1" ]; then \
-        sed -i 's|pypi.org|mirrors.aliyun.com/pypi|g' uv.lock; \
+        sed -i 's|pypi.org|pypi.tuna.tsinghua.edu.cn|g' uv.lock; \
    else \
-        sed -i 's|mirrors.aliyun.com/pypi|pypi.org|g' uv.lock; \
+        sed -i 's|pypi.tuna.tsinghua.edu.cn|pypi.org|g' uv.lock; \
    fi; \
-    if [ "$LIGHTEN" == "1" ]; then \
-        uv sync --python 3.10 --frozen; \
-    else \
-        uv sync --python 3.10 --frozen --all-extras; \
-    fi
+    uv sync --python 3.10 --frozen

 COPY web web
 COPY docs docs
@ -169,11 +156,7 @@ RUN --mount=type=cache,id=ragflow_npm,target=/root/.npm,sharing=locked \
 COPY .git /ragflow/.git

 RUN version_info=$(git describe --tags --match=v* --first-parent --always); \
-    if [ "$LIGHTEN" == "1" ]; then \
-        version_info="$version_info slim"; \
-    else \
-        version_info="$version_info full"; \
-    fi; \
+    version_info="$version_info"; \
    echo "RAGFlow version: $version_info"; \
    echo $version_info > /ragflow/VERSION

--- a/14
+++ b/14
@ -0,0 +1,14 @@
+FROM ghcr.io/huggingface/text-embeddings-inference:cpu-1.8
+
+# uv tool install huggingface_hub
+# hf download --local-dir tei_data/BAAI/bge-small-en-v1.5 BAAI/bge-small-en-v1.5
+# hf download --local-dir tei_data/BAAI/bge-m3 BAAI/bge-m3
+# hf download --local-dir tei_data/Qwen/Qwen3-Embedding-0.6B Qwen/Qwen3-Embedding-0.6B
+COPY tei_data /data
+
+# curl -X POST http://localhost:6380/embed -H "Content-Type: application/json" -d '{"inputs": "Hello, world! This is a test sentence."}'
+# curl -X POST http://tei:80/embed -H "Content-Type: application/json" -d '{"inputs": "Hello, world! This is a test sentence."}'
+# [[-0.058816575,0.019564206,0.026697718,...]]
+
+# curl -X POST http://localhost:6380/v1/embeddings -H "Content-Type: application/json" -d '{"input": "Hello, world! This is a test sentence."}'
+# {"object":"list","data":[{"object":"embedding","embedding":[-0.058816575,0.019564206,...],"index":0}],"model":"BAAI/bge-small-en-v1.5","usage":{"prompt_tokens":12,"total_tokens":12}}
--- a/README.md
+++ b/README.md
@ -187,7 +187,7 @@ 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 `v0.21.1-slim` edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.21.1-slim`, update the `RAGFLOW_IMAGE` variable accordingly in **docker/.env** before using `docker compose` to start the server. For example: set `RAGFLOW_IMAGE=infiniflow/ragflow:v0.21.1` for the full edition `v0.21.1`.
+   > The command below downloads the `v0.21.1-slim` edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.21.1-slim`, update the `RAGFLOW_IMAGE` variable accordingly in **docker/.env** before using `docker compose` to start the server.

   ```bash
   $ cd ragflow/docker
@ -195,20 +195,23 @@ releases! 🌟
   $ docker compose -f docker-compose.yml up -d

   # To use GPU to accelerate embedding and DeepDoc tasks:
-   # docker compose -f docker-compose-gpu.yml up -d
+   # sed -i '1i DEVICE=gpu' .env
+   # docker compose -f docker-compose.yml up -d
   ```

   | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
   |-------------------|-----------------|-----------------------|--------------------------|
-   | v0.21.1           | &approx;9       | :heavy_check_mark:    | Stable release           |
-   | v0.21.1-slim      | &approx;2       | ❌                   | Stable release            |
-   | nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
-   | nightly-slim      | &approx;2       | ❌                   | _Unstable_ nightly build  |
+   | v0.21.1           | &approx;9       | ✔️                    | Stable release           |
+   | v0.21.1-slim      | &approx;2       | ❌                    | Stable release           |
+   | nightly           | &approx;2       | ❌                    | _Unstable_ nightly build |
+
+
+   > Note: Starting with `v0.22.0`, we ship only the slim edition and no longer append the **-slim** suffix to the image tag.

 4. Check the server status after having the server up and running:

   ```bash
-   $ docker logs -f ragflow-server
+   $ docker logs -f docker-ragflow-cpu-1
   ```

   _The following output confirms a successful launch of the system:_
@ -286,16 +289,6 @@ RAGFlow uses Elasticsearch by default for storing full text and vectors. To swit

 This image is approximately 2 GB in size and relies on external LLM and embedding services.

-```bash
-git clone https://github.com/infiniflow/ragflow.git
-cd ragflow/
-docker build --platform linux/amd64 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
-```
-
-## 🔧 Build a Docker image including embedding models
-
-This image is approximately 9 GB in size. As it includes embedding models, it relies on external LLM services only.
-
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
@ -315,7 +308,7 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
   ```bash
   git clone https://github.com/infiniflow/ragflow.git
   cd ragflow/
-   uv sync --python 3.10 --all-extras # install RAGFlow dependent python modules
+   uv sync --python 3.10 # install RAGFlow dependent python modules
   uv run download_deps.py
   pre-commit install
   ```
--- a/README_id.md
+++ b/README_id.md
@ -181,28 +181,28 @@ 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 v0.21.1-slim dari gambar Docker RAGFlow. Silakan merujuk ke tabel berikut untuk deskripsi berbagai edisi RAGFlow. Untuk mengunduh edisi RAGFlow yang berbeda dari v0.21.1-slim, perbarui variabel RAGFLOW_IMAGE di docker/.env sebelum menggunakan docker compose untuk memulai server. Misalnya, atur RAGFLOW_IMAGE=infiniflow/ragflow:v0.21.1 untuk edisi lengkap v0.21.1.
+> Perintah di bawah ini mengunduh edisi v0.21.1 dari gambar Docker RAGFlow. Silakan merujuk ke tabel berikut untuk deskripsi berbagai edisi RAGFlow. Untuk mengunduh edisi RAGFlow yang berbeda dari v0.21.1, perbarui variabel RAGFLOW_IMAGE di docker/.env sebelum menggunakan docker compose untuk memulai server.

-```bash
-$ cd ragflow/docker
-# Use CPU for embedding and DeepDoc tasks:
-$ docker compose -f docker-compose.yml up -d
+   ```bash
+   $ cd ragflow/docker
+   # Use CPU for embedding and DeepDoc tasks:
+   $ docker compose -f docker-compose.yml up -d

-# To use GPU to accelerate embedding and DeepDoc tasks:
-# docker compose -f docker-compose-gpu.yml up -d
-```
+   # To use GPU to accelerate embedding and DeepDoc tasks:
+   # sed -i '1i DEVICE=gpu' .env
+   # docker compose -f docker-compose.yml up -d
+   ```

 | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
 | ----------------- | --------------- | --------------------- | ------------------------ |
-| v0.21.1           | &approx;9       | :heavy_check_mark:    | Stable release           |
+| v0.21.1           | &approx;9       | ✔️                    | Stable release           |
 | v0.21.1-slim      | &approx;2       | ❌                    | Stable release           |
-| nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
-| nightly-slim      | &approx;2       | ❌                    | _Unstable_ nightly build |
+| nightly           | &approx;2       | ❌                    | _Unstable_ nightly build |

 1. Periksa status server setelah server aktif dan berjalan:

   ```bash
-   $ docker logs -f ragflow-server
+   $ docker logs -f docker-ragflow-cpu-1
   ```

   _Output berikut menandakan bahwa sistem berhasil diluncurkan:_
@ -253,16 +253,6 @@ Pembaruan konfigurasi ini memerlukan reboot semua kontainer agar efektif:

 Image ini berukuran sekitar 2 GB dan bergantung pada aplikasi LLM eksternal dan embedding.

-```bash
-git clone https://github.com/infiniflow/ragflow.git
-cd ragflow/
-docker build --platform linux/amd64 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
-```
-
-## 🔧 Membangun Docker Image Termasuk Model Embedding
-
-Image ini berukuran sekitar 9 GB. Karena sudah termasuk model embedding, ia hanya bergantung pada aplikasi LLM eksternal.
-
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
@ -282,7 +272,7 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
   ```bash
   git clone https://github.com/infiniflow/ragflow.git
   cd ragflow/
-   uv sync --python 3.10 --all-extras # install RAGFlow dependent python modules
+   uv sync --python 3.10 # install RAGFlow dependent python modules
   uv run download_deps.py
   pre-commit install
   ```
--- a/README_ja.md
+++ b/README_ja.md
@ -160,7 +160,7 @@
 > 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 v0.21.1-slim エディションをダウンロードします。異なる RAGFlow エディションの説明については、以下の表を参照してください。v0.21.1-slim とは異なるエディションをダウンロードするには、docker/.env ファイルの RAGFLOW_IMAGE 変数を適宜更新し、docker compose を使用してサーバーを起動してください。例えば、完全版 v0.21.1 をダウンロードするには、RAGFLOW_IMAGE=infiniflow/ragflow:v0.21.1 と設定します。
+   > 以下のコマンドは、RAGFlow Docker イメージの v0.21.1 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。v0.21.1 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
@ -168,20 +168,20 @@
   $ docker compose -f docker-compose.yml up -d

   # To use GPU to accelerate embedding and DeepDoc tasks:
-   # docker compose -f docker-compose-gpu.yml up -d
+   # sed -i '1i DEVICE=gpu' .env
+   # docker compose -f docker-compose.yml up -d
   ```

   | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
   | ----------------- | --------------- | --------------------- | ------------------------ |
-   | v0.21.1           | &approx;9       | :heavy_check_mark:    | Stable release           |
+   | v0.21.1           | &approx;9       | ✔️                    | Stable release           |
   | v0.21.1-slim      | &approx;2       | ❌                    | Stable release           |
-   | nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
-   | nightly-slim      | &approx;2       | ❌                     | _Unstable_ nightly build |
+   | nightly           | &approx;2       | ❌                    | _Unstable_ nightly build |

 1. サーバーを立ち上げた後、サーバーの状態を確認する:

   ```bash
-   $ docker logs -f ragflow-server
+   $ docker logs -f docker-ragflow-cpu-1
   ```

   _以下の出力は、システムが正常に起動したことを確認するものです:_
@ -248,16 +248,6 @@ RAGFlow はデフォルトで Elasticsearch を使用して全文とベクトル

 この Docker イメージのサイズは約 1GB で、外部の大モデルと埋め込みサービスに依存しています。

-```bash
-git clone https://github.com/infiniflow/ragflow.git
-cd ragflow/
-docker build --platform linux/amd64 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
-```
-
-## 🔧 ソースコードをコンパイルした Docker イメージ（埋め込みモデルを含む）
-
-この Docker のサイズは約 9GB で、埋め込みモデルを含むため、外部の大モデルサービスのみが必要です。
-
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
@ -277,7 +267,7 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
   ```bash
   git clone https://github.com/infiniflow/ragflow.git
   cd ragflow/
-   uv sync --python 3.10 --all-extras # install RAGFlow dependent python modules
+   uv sync --python 3.10 # install RAGFlow dependent python modules
   uv run download_deps.py
   pre-commit install
   ```
--- a/README_ko.md
+++ b/README_ko.md
@ -160,7 +160,7 @@
 > 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 v0.21.1-slim 버전을 다운로드합니다. 다양한 RAGFlow 버전에 대한 설명은 다음 표를 참조하십시오. v0.21.1-slim과 다른 RAGFlow 버전을 다운로드하려면, docker/.env 파일에서 RAGFLOW_IMAGE 변수를 적절히 업데이트한 후 docker compose를 사용하여 서버를 시작하십시오. 예를 들어, 전체 버전인 v0.21.1을 다운로드하려면 RAGFLOW_IMAGE=infiniflow/ragflow:v0.21.1로 설정합니다.
+   > 아래 명령어는 RAGFlow Docker 이미지의 v0.21.1 버전을 다운로드합니다. 다양한 RAGFlow 버전에 대한 설명은 다음 표를 참조하십시오. v0.21.1과 다른 RAGFlow 버전을 다운로드하려면, docker/.env 파일에서 RAGFLOW_IMAGE 변수를 적절히 업데이트한 후 docker compose를 사용하여 서버를 시작하십시오.

   ```bash
   $ cd ragflow/docker
@ -168,20 +168,20 @@
   $ docker compose -f docker-compose.yml up -d

   # To use GPU to accelerate embedding and DeepDoc tasks:
-   # docker compose -f docker-compose-gpu.yml up -d
+   # sed -i '1i DEVICE=gpu' .env
+   # docker compose -f docker-compose.yml up -d
   ```

   | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
   | ----------------- | --------------- | --------------------- | ------------------------ |
-   | v0.21.1           | &approx;9       | :heavy_check_mark:    | Stable release           |
+   | v0.21.1           | &approx;9       | ✔️                    | Stable release           |
   | v0.21.1-slim      | &approx;2       | ❌                    | Stable release           |
-   | nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
-   | nightly-slim      | &approx;2       | ❌                     | _Unstable_ nightly build |
+   | nightly           | &approx;2       | ❌                    | _Unstable_ nightly build |

 1. 서버가 시작된 후 서버 상태를 확인하세요:

   ```bash
-   $ docker logs -f ragflow-server
+   $ docker logs -f docker-ragflow-cpu-1
   ```

   _다음 출력 결과로 시스템이 성공적으로 시작되었음을 확인합니다:_
@ -247,16 +247,6 @@ RAGFlow 는 기본적으로 Elasticsearch 를 사용하여 전체 텍스트 및

 이 Docker 이미지의 크기는 약 1GB이며, 외부 대형 모델과 임베딩 서비스에 의존합니다.

-```bash
-git clone https://github.com/infiniflow/ragflow.git
-cd ragflow/
-docker build --platform linux/amd64 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
-```
-
-## 🔧 소스 코드로 Docker 이미지를 컴파일합니다(임베딩 모델 포함)
-
-이 Docker의 크기는 약 9GB이며, 이미 임베딩 모델을 포함하고 있으므로 외부 대형 모델 서비스에만 의존하면 됩니다.
-
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
@ -276,7 +266,7 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
   ```bash
   git clone https://github.com/infiniflow/ragflow.git
   cd ragflow/
-   uv sync --python 3.10 --all-extras # install RAGFlow dependent python modules
+   uv sync --python 3.10 # install RAGFlow dependent python modules
   uv run download_deps.py
   pre-commit install
   ```
--- a/README_pt_br.md
+++ b/README_pt_br.md
@ -180,28 +180,28 @@ 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 `v0.21.1-slim` da imagem Docker do RAGFlow. Consulte a tabela a seguir para descrições de diferentes edições do RAGFlow. Para baixar uma edição do RAGFlow diferente da `v0.21.1-slim`, atualize a variável `RAGFLOW_IMAGE` conforme necessário no **docker/.env** antes de usar `docker compose` para iniciar o servidor. Por exemplo: defina `RAGFLOW_IMAGE=infiniflow/ragflow:v0.21.1` para a edição completa `v0.21.1`.
+    > O comando abaixo baixa a edição `v0.21.1` 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 `v0.21.1`, atualize a variável `RAGFLOW_IMAGE` conforme necessário no **docker/.env** antes de usar `docker compose` para iniciar o servidor.

-    ```bash
-    $ cd ragflow/docker
-    # Use CPU for embedding and DeepDoc tasks:
-    $ docker compose -f docker-compose.yml up -d
+   ```bash
+   $ cd ragflow/docker
+   # Use CPU for embedding and DeepDoc tasks:
+   $ docker compose -f docker-compose.yml up -d

-    # To use GPU to accelerate embedding and DeepDoc tasks:
-    # docker compose -f docker-compose-gpu.yml up -d
-    ```
+   # To use GPU to accelerate embedding and DeepDoc tasks:
+   # sed -i '1i DEVICE=gpu' .env
+   # docker compose -f docker-compose.yml up -d
+   ```

-    | Tag da imagem RAGFlow | Tamanho da imagem (GB) | Possui modelos de incorporação? | Estável?                 |
-    | --------------------- | ---------------------- | ------------------------------- | ------------------------ |
-    | v0.21.1               | ~9                     | :heavy_check_mark:              | Lançamento estável       |
-    | v0.21.1-slim          | ~2                     | ❌                              | Lançamento estável       |
-    | nightly               | ~9                     | :heavy_check_mark:              | _Instável_ build noturno |
-    | nightly-slim          | ~2                     | ❌                               | _Instável_ build noturno |
+    | Tag da imagem RAGFlow | Tamanho da imagem (GB) | Possui modelos de incorporação? | Estável?                    |
+    | --------------------- | ---------------------- | ------------------------------- | --------------------------- |
+    | v0.21.1               | &approx;9              | ✔️                              | Lançamento estável          |
+    | v0.21.1-slim          | &approx;2              | ❌                              | Lançamento estável          |
+    | nightly               | &approx;2              | ❌                              | Construção noturna instável |

 4.  Verifique o status do servidor após tê-lo iniciado:

    ```bash
-    $ docker logs -f ragflow-server
+    $ docker logs -f docker-ragflow-cpu-1
    ```

    _O seguinte resultado confirma o lançamento bem-sucedido do sistema:_
@ -271,16 +271,6 @@ O RAGFlow usa o Elasticsearch por padrão para armazenar texto completo e vetore

 Esta imagem tem cerca de 2 GB de tamanho e depende de serviços externos de LLM e incorporação.

-```bash
-git clone https://github.com/infiniflow/ragflow.git
-cd ragflow/
-docker build --platform linux/amd64 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
-```
-
-## 🔧 Criar uma imagem Docker incluindo modelos de incorporação
-
-Esta imagem tem cerca de 9 GB de tamanho. Como inclui modelos de incorporação, depende apenas de serviços externos de LLM.
-
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
@ -300,7 +290,7 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
   ```bash
   git clone https://github.com/infiniflow/ragflow.git
   cd ragflow/
-   uv sync --python 3.10 --all-extras # instala os módulos Python dependentes do RAGFlow
+   uv sync --python 3.10 # instala os módulos Python dependentes do RAGFlow
   uv run download_deps.py
   pre-commit install
   ```
--- a/README_tzh.md
+++ b/README_tzh.md
@ -183,7 +183,7 @@
 > 所有 Docker 映像檔都是為 x86 平台建置的。目前，我們不提供 ARM64 平台的 Docker 映像檔。
 > 如果您使用的是 ARM64 平台，請使用 [這份指南](https://ragflow.io/docs/dev/build_docker_image) 來建置適合您系統的 Docker 映像檔。

-   > 執行以下指令會自動下載 RAGFlow slim Docker 映像 `v0.21.1-slim`。請參考下表查看不同 Docker 發行版的說明。如需下載不同於 `v0.21.1-slim` 的 Docker 映像，請在執行 `docker compose` 啟動服務之前先更新 **docker/.env** 檔案內的 `RAGFLOW_IMAGE` 變數。例如，你可以透過設定 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.21.1` 來下載 RAGFlow 鏡像的 `v0.21.1` 完整發行版。
+   > 執行以下指令會自動下載 RAGFlow slim Docker 映像 `v0.21.1`。請參考下表查看不同 Docker 發行版的說明。如需下載不同於 `v0.21.1` 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
@ -191,15 +191,15 @@
   $ docker compose -f docker-compose.yml up -d

   # To use GPU to accelerate embedding and DeepDoc tasks:
-   # docker compose -f docker-compose-gpu.yml up -d
+   # sed -i '1i DEVICE=gpu' .env
+   # docker compose -f docker-compose.yml up -d
   ```

   | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
   | ----------------- | --------------- | --------------------- | ------------------------ |
-   | v0.21.1           | &approx;9       | :heavy_check_mark:    | Stable release           |
+   | v0.21.1           | &approx;9       | ✔️                    | Stable release           |
   | v0.21.1-slim      | &approx;2       | ❌                    | Stable release           |
-   | nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
-   | nightly-slim      | &approx;2       | ❌                     | _Unstable_ nightly build |
+   | nightly           | &approx;2       | ❌                    | _Unstable_ nightly build |

   > [!TIP]
   > 如果你遇到 Docker 映像檔拉不下來的問題，可以在 **docker/.env** 檔案內根據變數 `RAGFLOW_IMAGE` 的註解提示選擇華為雲或阿里雲的對應映像。
@ -210,7 +210,7 @@
 4. 伺服器啟動成功後再次確認伺服器狀態：

   ```bash
-   $ docker logs -f ragflow-server
+   $ docker logs -f docker-ragflow-cpu-1
   ```

   _出現以下介面提示說明伺服器啟動成功：_
@ -286,17 +286,7 @@ RAGFlow 預設使用 Elasticsearch 儲存文字和向量資料. 如果要切換
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build --platform linux/amd64 --build-arg LIGHTEN=1 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
-```
-
-## 🔧 原始碼編譯 Docker 映像（包含 embedding 模型）
-
-本 Docker 大小約 9 GB 左右。由於已包含 embedding 模型，所以只需依賴外部的大模型服務即可。
-
-```bash
-git clone https://github.com/infiniflow/ragflow.git
-cd ragflow/
-docker build --platform linux/amd64 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly .
+docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .
 ```

 ## 🔨 以原始碼啟動服務
@ -313,7 +303,7 @@ docker build --platform linux/amd64 --build-arg NEED_MIRROR=1 -f Dockerfile -t i
   ```bash
   git clone https://github.com/infiniflow/ragflow.git
   cd ragflow/
-   uv sync --python 3.10 --all-extras # install RAGFlow dependent python modules
+   uv sync --python 3.10 # install RAGFlow dependent python modules
   uv run download_deps.py
   pre-commit install
   ```
--- a/README_zh.md
+++ b/README_zh.md
@ -183,7 +183,7 @@
 > 请注意，目前官方提供的所有 Docker 镜像均基于 x86 架构构建，并不提供基于 ARM64 的 Docker 镜像。
 > 如果你的操作系统是 ARM64 架构，请参考[这篇文档](https://ragflow.io/docs/dev/build_docker_image)自行构建 Docker 镜像。

-   > 运行以下命令会自动下载 RAGFlow slim Docker 镜像 `v0.21.1-slim`。请参考下表查看不同 Docker 发行版的描述。如需下载不同于 `v0.21.1-slim` 的 Docker 镜像，请在运行 `docker compose` 启动服务之前先更新 **docker/.env** 文件内的 `RAGFLOW_IMAGE` 变量。比如，你可以通过设置 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.21.1` 来下载 RAGFlow 镜像的 `v0.21.1` 完整发行版。
+   > 运行以下命令会自动下载 RAGFlow slim Docker 镜像 `v0.21.1`。请参考下表查看不同 Docker 发行版的描述。如需下载不同于 `v0.21.1` 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
@ -191,15 +191,15 @@
   $ docker compose -f docker-compose.yml up -d

   # To use GPU to accelerate embedding and DeepDoc tasks:
-   # docker compose -f docker-compose-gpu.yml up -d
+   # sed -i '1i DEVICE=gpu' .env
+   # docker compose -f docker-compose.yml up -d
   ```

   | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
   | ----------------- | --------------- | --------------------- | ------------------------ |
-   | v0.21.1           | &approx;9       | :heavy_check_mark:    | Stable release           |
+   | v0.21.1           | &approx;9       | ✔️                    | Stable release           |
   | v0.21.1-slim      | &approx;2       | ❌                    | Stable release           |
-   | nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
-   | nightly-slim      | &approx;2       | ❌                     | _Unstable_ nightly build |
+   | nightly           | &approx;2       | ❌                    | _Unstable_ nightly build |

   > [!TIP]
   > 如果你遇到 Docker 镜像拉不下来的问题，可以在 **docker/.env** 文件内根据变量 `RAGFLOW_IMAGE` 的注释提示选择华为云或者阿里云的相应镜像。
@ -210,7 +210,7 @@
 4. 服务器启动成功后再次确认服务器状态：

   ```bash
-   $ docker logs -f ragflow-server
+   $ docker logs -f docker-ragflow-cpu-1
   ```

   _出现以下界面提示说明服务器启动成功：_
@ -286,17 +286,7 @@ RAGFlow 默认使用 Elasticsearch 存储文本和向量数据. 如果要切换
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build --platform linux/amd64 --build-arg LIGHTEN=1 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
-```
-
-## 🔧 源码编译 Docker 镜像（包含 embedding 模型）
-
-本 Docker 大小约 9 GB 左右。由于已包含 embedding 模型，所以只需依赖外部的大模型服务即可。
-
-```bash
-git clone https://github.com/infiniflow/ragflow.git
-cd ragflow/
-docker build --platform linux/amd64 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly .
+docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .
 ```

 ## 🔨 以源代码启动服务
@ -313,7 +303,7 @@ docker build --platform linux/amd64 --build-arg NEED_MIRROR=1 -f Dockerfile -t i
   ```bash
   git clone https://github.com/infiniflow/ragflow.git
   cd ragflow/
-   uv sync --python 3.10 --all-extras # install RAGFlow dependent python modules
+   uv sync --python 3.10 # install RAGFlow dependent python modules
   uv run download_deps.py
   pre-commit install
   ```
--- a/api/apps/llm_app.py
+++ b/api/apps/llm_app.py
@ -15,11 +15,11 @@
 #
 import logging
 import json
+import os
 from flask import request
 from flask_login import login_required, current_user
 from api.db.services.tenant_llm_service import LLMFactoriesService, TenantLLMService
 from api.db.services.llm_service import LLMService
-from api import settings
 from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
 from api.db import StatusEnum, LLMType
 from api.db.db_models import TenantLLM
@ -369,7 +369,7 @@ def my_llms():
@login_required
 def list_app():
    self_deployed = ["Youdao", "FastEmbed", "BAAI", "Ollama", "Xinference", "LocalAI", "LM-Studio", "GPUStack"]
-    weighted = ["Youdao", "FastEmbed", "BAAI"] if settings.LIGHTEN != 0 else []
+    weighted = []
    model_type = request.args.get("model_type")
    try:
        objs = TenantLLMService.query(tenant_id=current_user.id)
@ -379,6 +379,8 @@ def list_app():
                for m in llms if m.status == StatusEnum.VALID.value and m.fid not in weighted]
        for m in llms:
            m["available"] = m["fid"] in facts or m["llm_name"].lower() == "flag-embedding" or m["fid"] in self_deployed
+            if "tei-" in os.getenv("COMPOSE_PROFILES", "") and m["model_type"]==LLMType.EMBEDDING and m["fid"]=="Builtin" and m["llm_name"]==os.getenv('TEI_MODEL', ''):
+                m["available"] = True

        llm_set = set([m["llm_name"] + "@" + m["fid"] for m in llms])
        for o in objs:
--- a/api/db/services/llm_service.py
+++ b/api/db/services/llm_service.py
@ -59,21 +59,6 @@ def get_init_tenant_llm(user_id):
                }
            )

-    if settings.LIGHTEN != 1:
-        for buildin_embedding_model in settings.BUILTIN_EMBEDDING_MODELS:
-            mdlnm, fid = TenantLLMService.split_model_name_and_factory(buildin_embedding_model)
-            tenant_llm.append(
-                {
-                    "tenant_id": user_id,
-                    "llm_factory": fid,
-                    "llm_name": mdlnm,
-                    "model_type": "embedding",
-                    "api_key": "",
-                    "api_base": "",
-                    "max_tokens": 1024 if buildin_embedding_model == "BAAI/bge-large-zh-v1.5@BAAI" else 512,
-                }
-            )
-
    unique = {}
    for item in tenant_llm:
        key = (item["tenant_id"], item["llm_factory"], item["llm_name"])
--- a/api/db/services/tenant_llm_service.py
+++ b/api/db/services/tenant_llm_service.py
@ -13,6 +13,7 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import os
 import logging
 from langfuse import Langfuse
 from api import settings
@ -112,24 +113,17 @@ class TenantLLMService(CommonService):
            model_config = cls.get_api_key(tenant_id, mdlnm)
        if model_config:
            model_config = model_config.to_dict()
-            llm = LLMService.query(llm_name=mdlnm) if not fid else LLMService.query(llm_name=mdlnm, fid=fid)
-            if not llm and fid:  # for some cases seems fid mismatch
-                llm = LLMService.query(llm_name=mdlnm)
-            if llm:
-                model_config["is_tools"] = llm[0].is_tools
-        if not model_config:
-            if llm_type in [LLMType.EMBEDDING, LLMType.RERANK]:
-                llm = LLMService.query(llm_name=mdlnm) if not fid else LLMService.query(llm_name=mdlnm, fid=fid)
-                if llm and llm[0].fid in ["Youdao", "FastEmbed", "BAAI"]:
-                    model_config = {"llm_factory": llm[0].fid, "api_key": "", "llm_name": mdlnm, "api_base": ""}
-            if not model_config:
-                if mdlnm == "flag-embedding":
-                    model_config = {"llm_factory": "Tongyi-Qianwen", "api_key": "", "llm_name": llm_name,
-                                    "api_base": ""}
-                else:
-                    if not mdlnm:
-                        raise LookupError(f"Type of {llm_type} model is not set.")
-                    raise LookupError("Model({}) not authorized".format(mdlnm))
+        elif llm_type == LLMType.EMBEDDING and fid == 'Builtin' and "tei-" in os.getenv("COMPOSE_PROFILES", "") and mdlnm == os.getenv('TEI_MODEL', ''):
+            embedding_cfg = settings.EMBEDDING_CFG
+            model_config = {"llm_factory": 'Builtin', "api_key": embedding_cfg["api_key"], "llm_name": mdlnm, "api_base": embedding_cfg["base_url"]}
+        else:
+            raise LookupError(f"Model({mdlnm}@{fid}) not authorized")
+
+        llm = LLMService.query(llm_name=mdlnm) if not fid else LLMService.query(llm_name=mdlnm, fid=fid)
+        if not llm and fid:  # for some cases seems fid mismatch
+            llm = LLMService.query(llm_name=mdlnm)
+        if llm:
+            model_config["is_tools"] = llm[0].is_tools
        return model_config

    @classmethod
--- a/api/settings.py
+++ b/api/settings.py
@ -28,8 +28,6 @@ from api.utils.configs import decrypt_database_config, get_base_config
 from api.utils.file_utils import get_project_base_directory
 from rag.nlp import search

-LIGHTEN = int(os.environ.get("LIGHTEN", "0"))
-
 LLM = None
 LLM_FACTORY = None
 LLM_BASE_URL = None
@ -77,8 +75,6 @@ SANDBOX_ENABLED = 0
 SANDBOX_HOST = None
 STRONG_TEST_COUNT = int(os.environ.get("STRONG_TEST_COUNT", "8"))

-BUILTIN_EMBEDDING_MODELS = ["BAAI/bge-large-zh-v1.5@BAAI", "maidalun1020/bce-embedding-base_v1@Youdao"]
-
 SMTP_CONF = None
 MAIL_SERVER = ""
 MAIL_PORT = 000
@ -109,8 +105,7 @@ def get_or_create_secret_key():


 def init_settings():
-    global LLM, LLM_FACTORY, LLM_BASE_URL, LIGHTEN, DATABASE_TYPE, DATABASE, FACTORY_LLM_INFOS, REGISTER_ENABLED
-    LIGHTEN = int(os.environ.get("LIGHTEN", "0"))
+    global LLM, LLM_FACTORY, LLM_BASE_URL, DATABASE_TYPE, DATABASE, FACTORY_LLM_INFOS, REGISTER_ENABLED
    DATABASE_TYPE = os.getenv("DB_TYPE", "mysql")
    DATABASE = decrypt_database_config(name=DATABASE_TYPE)
    LLM = get_base_config("user_default_llm", {}) or {}
@ -130,8 +125,6 @@ def init_settings():

    global CHAT_MDL, EMBEDDING_MDL, RERANK_MDL, ASR_MDL, IMAGE2TEXT_MDL
    global CHAT_CFG, EMBEDDING_CFG, RERANK_CFG, ASR_CFG, IMAGE2TEXT_CFG
-    if not LIGHTEN:
-        EMBEDDING_MDL = BUILTIN_EMBEDDING_MODELS[0]

    global API_KEY, PARSERS, HOST_IP, HOST_PORT, SECRET_KEY
    API_KEY = LLM.get("api_key")
@ -152,7 +145,7 @@ def init_settings():
    IMAGE2TEXT_CFG = _resolve_per_model_config(image2text_entry, LLM_FACTORY, API_KEY, LLM_BASE_URL)

    CHAT_MDL = CHAT_CFG.get("model", "") or ""
-    EMBEDDING_MDL = EMBEDDING_CFG.get("model", "") or ""
+    EMBEDDING_MDL = os.getenv("TEI_MODEL", "BAAI/bge-small-en-v1.5") if "tei-" in os.getenv("COMPOSE_PROFILES", "") else ""
    RERANK_MDL = RERANK_CFG.get("model", "") or ""
    ASR_MDL = ASR_CFG.get("model", "") or ""
    IMAGE2TEXT_MDL = IMAGE2TEXT_CFG.get("model", "") or ""
--- a/api/utils/api_utils.py
+++ b/api/utils/api_utils.py
@ -588,7 +588,7 @@ def verify_embedding_availability(embd_id: str, tenant_id: str) -> tuple[bool, R
            llm["llm_name"] == llm_name and llm["llm_factory"] == llm_factory and llm["model_type"] == "embedding" for
            llm in tenant_llms)

-        is_builtin_model = embd_id in settings.BUILTIN_EMBEDDING_MODELS
+        is_builtin_model = llm_factory=='Builtin'
        if not (is_builtin_model or is_tenant_model or in_llm_service):
            return False, get_error_argument_result(f"Unsupported model: <{embd_id}>")

--- a/api/utils/common.py
+++ b/api/utils/common.py
@ -14,6 +14,12 @@
 #  limitations under the License.
 #

+import threading
+import subprocess
+import sys
+import os
+import logging
+
 def string_to_bytes(string):
    return string if isinstance(
        string, bytes) else string.encode(encoding="utf-8")
@ -44,3 +50,48 @@ def convert_bytes(size_in_bytes: int) -> str:
        return f"{size:.1f} {units[i]}"
    else:
        return f"{size:.2f} {units[i]}"
+
+
+def once(func):
+    """
+    A thread-safe decorator that ensures the decorated function runs exactly once,
+    caching and returning its result for all subsequent calls. This prevents
+    race conditions in multi-threaded environments by using a lock to protect
+    the execution state.
+
+    Args:
+        func (callable): The function to be executed only once.
+
+    Returns:
+        callable: A wrapper function that executes `func` on the first call
+                  and returns the cached result thereafter.
+
+    Example:
+        @once
+        def compute_expensive_value():
+            print("Computing...")
+            return 42
+
+        # First call: executes and prints
+        # Subsequent calls: return 42 without executing
+    """
+    executed = False
+    result = None
+    lock = threading.Lock()
+    def wrapper(*args, **kwargs):
+        nonlocal executed, result
+        with lock:
+            if not executed:
+                executed = True
+                result = func(*args, **kwargs)
+        return result
+    return wrapper
+
+@once
+def pip_install_torch():
+    device = os.getenv("DEVICE", "cpu")
+    if device=="cpu":
+        return
+    logging.info("Installing pytorch")
+    pkg_names = ["torch>=2.5.0,<3.0.0"]
+    subprocess.check_call([sys.executable, "-m", "pip", "install", *pkg_names])
--- a/api/versions.py
+++ b/api/versions.py
@ -34,8 +34,6 @@ def get_ragflow_version() -> str:
            RAGFLOW_VERSION_INFO = f.read().strip()
    else:
        RAGFLOW_VERSION_INFO = get_closest_tag_and_count()
-        LIGHTEN = int(os.environ.get("LIGHTEN", "0"))
-        RAGFLOW_VERSION_INFO += " slim" if LIGHTEN == 1 else " full"
    return RAGFLOW_VERSION_INFO


--- a/conf/llm_factories.json
+++ b/conf/llm_factories.json
@ -1112,6 +1112,32 @@
                }
            ]
        },
+        {
+            "name": "Builtin",
+            "logo": "",
+            "tags": "TEXT EMBEDDING",
+            "status": "1",
+            "llm": [
+                {
+                    "llm_name": "BAAI/bge-small-en-v1.5",
+                    "tags": "TEXT EMBEDDING,512",
+                    "max_tokens": 512,
+                    "model_type": "embedding"
+                },
+                {
+                    "llm_name": "BAAI/bge-m3",
+                    "tags": "TEXT EMBEDDING,8k",
+                    "max_tokens": 8192,
+                    "model_type": "embedding"
+                },
+                {
+                    "llm_name": "Qwen/Qwen3-Embedding-0.6B",
+                    "tags": "TEXT EMBEDDING,32k",
+                    "max_tokens": 32768,
+                    "model_type": "embedding"
+                }
+            ]
+        },
        {
            "name": "MiniMax",
            "logo": "",
--- a/conf/service_conf.yaml
+++ b/conf/service_conf.yaml
@ -32,6 +32,11 @@ redis:
  db: 1
  password: 'infini_rag_flow'
  host: 'localhost:6379'
+user_default_llm:
+  default_models:
+    embedding_model:
+      api_key: 'xxx'
+      base_url: 'http://localhost:6380'
 # postgres:
 #   name: 'rag_flow'
 #   user: 'rag_flow'
@ -77,7 +82,8 @@ redis:
 #       api_key: 'xxxx'
 #       base_url: 'https://api.xx.com'
 #     embedding_model:
-#       name: 'bge-m3'
+#       api_key: 'xxx'
+#       base_url: 'http://localhost:6380'
 #     rerank_model: 'bge-reranker-v2'
 #     asr_model:
 #       model: 'whisper-large-v3' # alias of name
--- a/deepdoc/parser/pdf_parser.py
+++ b/deepdoc/parser/pdf_parser.py
@ -34,8 +34,8 @@ from huggingface_hub import snapshot_download
 from PIL import Image
 from pypdf import PdfReader as pdf2_read

-from api import settings
 from api.utils.file_utils import get_project_base_directory
+from api.utils.common import pip_install_torch
 from deepdoc.vision import OCR, AscendLayoutRecognizer, LayoutRecognizer, Recognizer, TableStructureRecognizer
 from rag.app.picture import vision_llm_chunk as picture_vision_llm_chunk
 from rag.nlp import rag_tokenizer
@ -84,14 +84,13 @@ class RAGFlowPdfParser:
        self.tbl_det = TableStructureRecognizer()

        self.updown_cnt_mdl = xgb.Booster()
-        if not settings.LIGHTEN:
-            try:
-                import torch.cuda
-
-                if torch.cuda.is_available():
-                    self.updown_cnt_mdl.set_param({"device": "cuda"})
-            except Exception:
-                logging.exception("RAGFlowPdfParser __init__")
+        try:
+            pip_install_torch()
+            import torch.cuda
+            if torch.cuda.is_available():
+                self.updown_cnt_mdl.set_param({"device": "cuda"})
+        except Exception:
+            logging.exception("RAGFlowPdfParser __init__")
        try:
            model_dir = os.path.join(get_project_base_directory(), "rag/res/deepdoc")
            self.updown_cnt_mdl.load_model(os.path.join(model_dir, "updown_concat_xgb.model"))
--- a/deepdoc/vision/ocr.py
+++ b/deepdoc/vision/ocr.py
@ -22,6 +22,7 @@ import os
 from huggingface_hub import snapshot_download

 from api.utils.file_utils import get_project_base_directory
+from api.utils.common import pip_install_torch
 from rag.settings import PARALLEL_DEVICES
 from .operators import *  # noqa: F403
 from . import operators
@ -83,6 +84,7 @@ def load_model(model_dir, nm, device_id: int | None = None):

    def cuda_is_available():
        try:
+            pip_install_torch()
            import torch
            target_id = 0 if device_id is None else device_id
            if torch.cuda.is_available() and torch.cuda.device_count() > target_id:
--- a/docker/.env
+++ b/docker/.env
@ -1,3 +1,8 @@
+# ------------------------------
+# docker env var for specifying vector db type at startup
+# (based on the vector db type, the corresponding docker
+# compose profile will be used)
+# ------------------------------
 # The type of doc engine to use.
 # Available options:
 # - `elasticsearch` (default)
@ -5,12 +10,13 @@
 # - `opensearch` (https://github.com/opensearch-project/OpenSearch)
 DOC_ENGINE=${DOC_ENGINE:-elasticsearch}

-# ------------------------------
-# docker env var for specifying vector db type at startup
-# (based on the vector db type, the corresponding docker
-# compose profile will be used)
-# ------------------------------
-COMPOSE_PROFILES=${DOC_ENGINE}
+# Device on which deepdoc inference run.
+# Available levels:
+# - `cpu` (default)
+# - `gpu`
+DEVICE=${DEVICE:-cpu}
+
+COMPOSE_PROFILES=${DOC_ENGINE},${DEVICE}

 # The version of Elasticsearch.
 STACK_VERSION=8.11.3
@ -38,7 +44,7 @@ OPENSEARCH_PASSWORD=infini_rag_flow_OS_01
 # The port used to expose the Kibana service to the host machine,
 # allowing EXTERNAL access to the service running inside the Docker container.
 # To enable kibana, you need to:
-# 1. Ensure that COMPOSE_PROFILES includes kibana, for example: COMPOSE_PROFILES=${DOC_ENGINE},kibana
+# 1. Ensure that COMPOSE_PROFILES includes kibana, for example: COMPOSE_PROFILES=${COMPOSE_PROFILES},kibana
 # 2. Comment out or delete the following configurations of the es service in docker-compose-base.yml: xpack.security.enabled、xpack.security.http.ssl.enabled、xpack.security.transport.ssl.enabled (for details: https://www.elastic.co/docs/deploy-manage/security/self-auto-setup#stack-existing-settings-detected)
 # 3. Adjust the es.hosts in conf/service_config.yaml or docker/service_conf.yaml.template to 'https://localhost:1200'
 # 4. After the startup is successful, in the es container, execute the command to generate the kibana token: `bin/elasticsearch-create-enrollment-token -s kibana`, then you can use kibana normally
@ -96,30 +102,47 @@ REDIS_PASSWORD=infini_rag_flow
 SVR_HTTP_PORT=9380
 ADMIN_SVR_HTTP_PORT=9381

-# The RAGFlow Docker image to download.
+# The RAGFlow Docker image to download. v0.22+ doesn't include embedding models.
 # Defaults to the v0.21.1-slim edition, which is the RAGFlow Docker image without embedding models.
 RAGFLOW_IMAGE=infiniflow/ragflow:v0.21.1-slim
-#
-# To download the RAGFlow Docker image with embedding models, uncomment the following line instead:
 # RAGFLOW_IMAGE=infiniflow/ragflow:v0.21.1
-#
 # The Docker image of the v0.21.1 edition includes built-in embedding models:
 #   - BAAI/bge-large-zh-v1.5
 #   - maidalun1020/bce-embedding-base_v1
 #

 # If you cannot download the RAGFlow Docker image:
-#
-# - For the `nightly-slim` edition, uncomment either of the following:
-# RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:nightly-slim
-# RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:nightly-slim
+# RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:v0.21.1
+# RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:v0.21.1
 #
 # - For the `nightly` edition, uncomment either of the following:
 # RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:nightly
 # RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:nightly

+# The embedding service image, model and port.
+# Important: To enable the embedding service, you need to uncomment one of the following two lines:
+# COMPOSE_PROFILES=${COMPOSE_PROFILES},tei-cpu
+# COMPOSE_PROFILES=${COMPOSE_PROFILES},tei-gpu
+
+# The embedding service image:
+TEI_IMAGE_CPU=infiniflow/text-embeddings-inference:cpu-1.8
+TEI_IMAGE_GPU=infiniflow/text-embeddings-inference:1.8
+
+# The embedding service model:
+# Available options:
+# - `Qwen/Qwen3-Embedding-0.6B` (default, requires 25GB RAM/vRAM to load)
+# - `BAAI/bge-m3` (requires 21GB RAM/vRAM to load)
+# - `BAAI/bge-small-en-v1.5` (requires 1.2GB RAM/vRAM to load)
+TEI_MODEL=${TEI_MODEL:-Qwen/Qwen3-Embedding-0.6B}
+
+# The embedding service port:
+TEI_HOST=tei
+# The port used to expose the TEI service to the host machine,
+# allowing EXTERNAL access to the service running inside the Docker container.
+TEI_PORT=6380
+
 # The local time zone.
-TIMEZONE=Asia/Shanghai
+TZ=Asia/Shanghai

 # Uncomment the following line if you have limited access to huggingface.co:
 # HF_ENDPOINT=https://hf-mirror.com
@ -165,8 +188,11 @@ EMBEDDING_BATCH_SIZE=${EMBEDDING_BATCH_SIZE:-16}
 # - Disable registration: 0
 REGISTER_ENABLED=1

+# Important: To enable sandbox, you need to uncomment following two lines:
+# SANDBOX_ENABLED=1
+# COMPOSE_PROFILES=${COMPOSE_PROFILES},sandbox
+
 # Sandbox settings
-# Important: To enable sandbox, you must re-declare the compose profiles. See hints at the end of file.
 # Double check if you add `sandbox-executor-manager` to your `/etc/hosts`
 # Pull the required base images before running:
 #   docker pull infiniflow/sandbox-base-nodejs:latest
@ -175,7 +201,6 @@ REGISTER_ENABLED=1
 #   - Node.js base image: includes axios
 #   - Python base image: includes requests, numpy, and pandas
 # Specify custom executor images below if you're using non-default environments.
-# SANDBOX_ENABLED=1
 # SANDBOX_HOST=sandbox-executor-manager
 # SANDBOX_EXECUTOR_MANAGER_IMAGE=infiniflow/sandbox-executor-manager:latest
 # SANDBOX_EXECUTOR_MANAGER_POOL_SIZE=3
--- a/docker/README.md
+++ b/docker/README.md
@ -18,7 +18,7 @@
  Sets up environment for RAGFlow's dependencies: Elasticsearch/[Infinity](https://github.com/infiniflow/infinity), MySQL, MinIO, and Redis.

 > [!CAUTION]
-> We do not actively maintain **docker-compose-CN-oc9.yml**, **docker-compose-gpu-CN-oc9.yml**, or **docker-compose-gpu.yml**, so use them at your own risk. However, you are welcome to file a pull request to improve any of them.
+> We do not actively maintain **docker-compose-CN-oc9.yml**, **docker-compose-macos.yml**, so use them at your own risk. However, you are welcome to file a pull request to improve any of them.

 ## 🐬 Docker environment variables

@ -89,16 +89,13 @@ The [.env](./.env) file contains important environment variables for Docker.
 > [!TIP]  
 > If you cannot download the RAGFlow Docker image, try the following mirrors.  
 > 
-> - For the `nightly-slim` edition:  
->   - `RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:nightly-slim` or,
->   - `RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:nightly-slim`.
 > - For the `nightly` edition:  
 >   - `RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:nightly` or,
 >   - `RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:nightly`.

 ### Timezone

- `TIMEZONE`  
+- `TZ`  
  The local time zone. Defaults to `'Asia/Shanghai'`.

 ### Hugging Face mirror site
--- a/docker/docker-compose-CN-oc9.yml
+++ b/docker/docker-compose-CN-oc9.yml
@ -4,12 +4,13 @@ include:
  - ./docker-compose-base.yml

 services:
-  ragflow:
+  ragflow-cpu:
    depends_on:
      mysql:
        condition: service_healthy
+    profiles:
+      - cpu
    image: edwardelric233/ragflow:oc9
-    container_name: ragflow-server
    ports:
      - ${SVR_HTTP_PORT}:9380
      - 80:80
@ -20,10 +21,6 @@ services:
      - ./nginx/proxy.conf:/etc/nginx/proxy.conf
      - ./nginx/nginx.conf:/etc/nginx/nginx.conf
    env_file: .env
-    environment:
-      - TZ=${TIMEZONE}
-      - HF_ENDPOINT=${HF_ENDPOINT}
-      - MACOS=${MACOS}
    networks:
      - ragflow
    restart: on-failure
@ -31,3 +28,35 @@ services:
    # If you're using Docker Desktop, the --add-host flag is optional. This flag makes sure that the host's internal IP gets exposed to the Prometheus container.
    extra_hosts:
      - "host.docker.internal:host-gateway"
+
+  ragflow-gpu:
+    depends_on:
+      mysql:
+        condition: service_healthy
+    profiles:
+      - gpu
+    image: edwardelric233/ragflow:oc9
+    ports:
+      - ${SVR_HTTP_PORT}:9380
+      - 80:80
+      - 443:443
+    volumes:
+      - ./ragflow-logs:/ragflow/logs
+      - ./nginx/ragflow.conf:/etc/nginx/conf.d/ragflow.conf
+      - ./nginx/proxy.conf:/etc/nginx/proxy.conf
+      - ./nginx/nginx.conf:/etc/nginx/nginx.conf
+    env_file: .env
+    networks:
+      - ragflow
+    restart: on-failure
+    # https://docs.docker.com/engine/daemon/prometheus/#create-a-prometheus-configuration
+    # If you're using Docker Desktop, the --add-host flag is optional. This flag makes sure that the host's internal IP gets exposed to the Prometheus container.
+    extra_hosts:
+      - "host.docker.internal:host-gateway"
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all
+              capabilities: [gpu]
--- a/docker/docker-compose-base.yml
+++ b/docker/docker-compose-base.yml
@ -1,6 +1,5 @@
 services:
  es01:
-    container_name: ragflow-es-01
    profiles:
      - elasticsearch
    image: elasticsearch:${STACK_VERSION}
@ -20,7 +19,6 @@ services:
      - cluster.routing.allocation.disk.watermark.low=5gb
      - cluster.routing.allocation.disk.watermark.high=3gb
      - cluster.routing.allocation.disk.watermark.flood_stage=2gb
-      - TZ=${TIMEZONE}
    mem_limit: ${MEM_LIMIT}
    ulimits:
      memlock:
@ -36,7 +34,6 @@ services:
    restart: on-failure

  opensearch01:
-    container_name: ragflow-opensearch-01
    profiles:
      - opensearch
    image: hub.icert.top/opensearchproject/opensearch:2.19.1
@ -57,7 +54,6 @@ services:
      - cluster.routing.allocation.disk.watermark.low=5gb
      - cluster.routing.allocation.disk.watermark.high=3gb
      - cluster.routing.allocation.disk.watermark.flood_stage=2gb
-      - TZ=${TIMEZONE}
      - http.port=9201
    mem_limit: ${MEM_LIMIT}
    ulimits:
@ -74,7 +70,6 @@ services:
    restart: on-failure

  infinity:
-    container_name: ragflow-infinity
    profiles:
      - infinity
    image: infiniflow/infinity:v0.6.1
@ -87,8 +82,6 @@ services:
      - ${INFINITY_HTTP_PORT}:23820
      - ${INFINITY_PSQL_PORT}:5432
    env_file: .env
-    environment:
-      - TZ=${TIMEZONE}
    mem_limit: ${MEM_LIMIT}
    ulimits:
      nofile:
@ -104,7 +97,6 @@ services:
    restart: on-failure

  sandbox-executor-manager:
-    container_name: ragflow-sandbox-executor-manager
    profiles:
      - sandbox
    image: ${SANDBOX_EXECUTOR_MANAGER_IMAGE-infiniflow/sandbox-executor-manager:latest}
@ -119,7 +111,6 @@ services:
    security_opt:
      - no-new-privileges:true
    environment:
-      - TZ=${TIMEZONE}
      - SANDBOX_EXECUTOR_MANAGER_POOL_SIZE=${SANDBOX_EXECUTOR_MANAGER_POOL_SIZE:-3}
      - SANDBOX_BASE_PYTHON_IMAGE=${SANDBOX_BASE_PYTHON_IMAGE:-infiniflow/sandbox-base-python:latest}
      - SANDBOX_BASE_NODEJS_IMAGE=${SANDBOX_BASE_NODEJS_IMAGE:-infiniflow/sandbox-base-nodejs:latest}
@ -136,11 +127,9 @@ services:
  mysql:
    # mysql:5.7 linux/arm64 image is unavailable.
    image: mysql:8.0.39
-    container_name: ragflow-mysql
    env_file: .env
    environment:
      - MYSQL_ROOT_PASSWORD=${MYSQL_PASSWORD}
-      - TZ=${TIMEZONE}
    command:
      --max_connections=1000
      --character-set-server=utf8mb4
@ -165,7 +154,6 @@ services:

  minio:
    image: quay.io/minio/minio:RELEASE.2025-06-13T11-33-47Z
-    container_name: ragflow-minio
    command: server --console-address ":9001" /data
    ports:
      - ${MINIO_PORT}:9000
@ -174,7 +162,6 @@ services:
    environment:
      - MINIO_ROOT_USER=${MINIO_USER}
      - MINIO_ROOT_PASSWORD=${MINIO_PASSWORD}
-      - TZ=${TIMEZONE}
    volumes:
      - minio_data:/data
    networks:
@ -189,7 +176,6 @@ services:
  redis:
    # swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/valkey/valkey:8
    image: valkey/valkey:8
-    container_name: ragflow-redis
    command: redis-server --requirepass ${REDIS_PASSWORD} --maxmemory 128mb --maxmemory-policy allkeys-lru
    env_file: .env
    ports:
@ -207,16 +193,48 @@ services:
      start_period: 10s    


+  tei-cpu:
+    profiles:
+      - tei-cpu
+    image: ${TEI_IMAGE_CPU}
+    hostname: tei
+    ports:
+      - ${TEI_PORT-6380}:80
+    env_file: .env
+    networks:
+      - ragflow
+    command: ["--model-id", "/data/${TEI_MODEL}"]
+    restart: on-failure
+
+
+  tei-gpu:
+    profiles:
+      - tei-gpu
+    image: ${TEI_IMAGE_GPU}
+    hostname: tei
+    ports:
+      - ${TEI_PORT-6380}:80
+    env_file: .env
+    networks:
+      - ragflow
+    command: ["--model-id", "/data/${TEI_MODEL}"]
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all
+              capabilities: [gpu]
+    restart: on-failure
+
+
  kibana:
-    container_name: ragflow-kibana
    profiles:
      - kibana
    image: kibana:${STACK_VERSION}
    ports:
      - ${KIBANA_PORT-5601}:5601
    env_file: .env
-    environment:
-      - TZ=${TIMEZONE}
    volumes:
      - kibana_data:/usr/share/kibana/data
    depends_on:
@ -245,6 +263,8 @@ volumes:
    driver: local
  redis_data:
    driver: local
+  tei_data:
+    driver: local
  kibana_data:
    driver: local

--- a/docker/docker-compose-gpu-CN-oc9.yml
+++ b/docker/docker-compose-gpu-CN-oc9.yml
@ -1,40 +0,0 @@
-# The RAGFlow team do not actively maintain docker-compose-gpu-CN-oc9.yml, so use them at your own risk. 
-# However, you are welcome to file a pull request to improve it.
-include:
-  - ./docker-compose-base.yml
-
-services:
-  ragflow:
-    depends_on:
-      mysql:
-        condition: service_healthy
-    image: edwardelric233/ragflow:oc9
-    container_name: ragflow-server
-    ports:
-      - ${SVR_HTTP_PORT}:9380
-      - 80:80
-      - 443:443
-    volumes:
-      - ./ragflow-logs:/ragflow/logs
-      - ./nginx/ragflow.conf:/etc/nginx/conf.d/ragflow.conf
-      - ./nginx/proxy.conf:/etc/nginx/proxy.conf
-      - ./nginx/nginx.conf:/etc/nginx/nginx.conf
-    env_file: .env
-    environment:
-      - TZ=${TIMEZONE}
-      - HF_ENDPOINT=${HF_ENDPOINT}
-      - MACOS=${MACOS}
-    networks:
-      - ragflow
-    restart: on-failure
-    # https://docs.docker.com/engine/daemon/prometheus/#create-a-prometheus-configuration
-    # If you're using Docker Desktop, the --add-host flag is optional. This flag makes sure that the host's internal IP gets exposed to the Prometheus container.
-    extra_hosts:
-      - "host.docker.internal:host-gateway"
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: all
-              capabilities: [gpu]
--- a/docker/docker-compose-gpu.yml
+++ b/docker/docker-compose-gpu.yml
@ -1,40 +0,0 @@
-# The RAGFlow team do not actively maintain docker-compose-gpu.yml, so use them at your own risk. 
-# Pull requests to improve it are welcome.
-include:
-  - ./docker-compose-base.yml
-
-services:
-  ragflow:
-    depends_on:
-      mysql:
-        condition: service_healthy
-    image: ${RAGFLOW_IMAGE}
-    container_name: ragflow-server
-    ports:
-      - ${SVR_HTTP_PORT}:9380
-      - 80:80
-      - 443:443
-    volumes:
-      - ./ragflow-logs:/ragflow/logs
-      - ./nginx/ragflow.conf:/etc/nginx/conf.d/ragflow.conf
-      - ./nginx/proxy.conf:/etc/nginx/proxy.conf
-      - ./nginx/nginx.conf:/etc/nginx/nginx.conf
-    env_file: .env
-    environment:
-      - TZ=${TIMEZONE}
-      - HF_ENDPOINT=${HF_ENDPOINT}
-      - MACOS=${MACOS}
-    networks:
-      - ragflow
-    restart: on-failure
-    # https://docs.docker.com/engine/daemon/prometheus/#create-a-prometheus-configuration
-    # If you're using Docker Desktop, the --add-host flag is optional. This flag makes sure that the host's internal IP gets exposed to the Prometheus container.
-    extra_hosts:
-      - "host.docker.internal:host-gateway"
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: all
-              capabilities: [gpu]
--- a/docker/docker-compose-macos.yml
+++ b/docker/docker-compose-macos.yml
@ -10,7 +10,6 @@ services:
    build:
      context: ../
      dockerfile: Dockerfile
-    container_name: ragflow-server
    ports:
      - ${SVR_HTTP_PORT}:9380
      - 80:80
@ -21,11 +20,6 @@ services:
      - ./nginx/proxy.conf:/etc/nginx/proxy.conf
      - ./nginx/nginx.conf:/etc/nginx/nginx.conf
    env_file: .env
-    environment:
-      - TZ=${TIMEZONE}
-      - HF_ENDPOINT=${HF_ENDPOINT}
-      - MACOS=${MACOS:-1}
-      - LIGHTEN=${LIGHTEN:-1}
    networks:
      - ragflow
    restart: on-failure
@ -38,15 +32,10 @@ services:
  #     mysql:
  #       condition: service_healthy
  #   image: ${RAGFLOW_IMAGE}
-  #   container_name: ragflow-executor
  #   volumes:
  #     - ./ragflow-logs:/ragflow/logs
  #     - ./nginx/ragflow.conf:/etc/nginx/conf.d/ragflow.conf
  #   env_file: .env
-  #   environment:
-  #     - TZ=${TIMEZONE}
-  #     - HF_ENDPOINT=${HF_ENDPOINT}
-  #     - MACOS=${MACOS}
  #   entrypoint: "/ragflow/entrypoint_task_executor.sh 1 3"
  #   networks:
  #     - ragflow
--- a/docker/docker-compose.yml
+++ b/docker/docker-compose.yml
@ -2,10 +2,12 @@ include:
  - ./docker-compose-base.yml
 # To ensure that the container processes the locally modified `service_conf.yaml.template` instead of the one included in its image, you need to mount the local `service_conf.yaml.template` to the container.
 services:
-  ragflow:
+  ragflow-cpu:
    depends_on:
      mysql:
        condition: service_healthy
+    profiles:
+      - cpu
    image: ${RAGFLOW_IMAGE}
    # Example configuration to set up an MCP server:
    # command:
@ -26,7 +28,6 @@ services:
    # Example configration to start Admin server:
    # command:
    #   - --enable-adminserver
-    container_name: ragflow-server
    ports:
      - ${SVR_HTTP_PORT}:9380
      - ${ADMIN_SVR_HTTP_PORT}:9381
@ -44,10 +45,6 @@ services:
      - ./service_conf.yaml.template:/ragflow/conf/service_conf.yaml.template
      - ./entrypoint.sh:/ragflow/entrypoint.sh
    env_file: .env
-    environment:
-      - TZ=${TIMEZONE}
-      - HF_ENDPOINT=${HF_ENDPOINT-}
-      - MACOS=${MACOS-}
    networks:
      - ragflow
    restart: on-failure
@ -55,20 +52,75 @@ services:
    # If you use Docker Desktop, the --add-host flag is optional. This flag ensures that the host's internal IP is exposed to the Prometheus container.
    extra_hosts:
      - "host.docker.internal:host-gateway"
+
+  ragflow-gpu:
+    depends_on:
+      mysql:
+        condition: service_healthy
+    profiles:
+      - gpu
+    image: ${RAGFLOW_IMAGE}
+    # Example configuration to set up an MCP server:
+    # command:
+    #   - --enable-mcpserver
+    #   - --mcp-host=0.0.0.0
+    #   - --mcp-port=9382
+    #   - --mcp-base-url=http://127.0.0.1:9380
+    #   - --mcp-script-path=/ragflow/mcp/server/server.py
+    #   - --mcp-mode=self-host
+    #   - --mcp-host-api-key=ragflow-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
+    # Optional transport flags for MCP (customize if needed).
+    # Host mode need to combined with --no-transport-streamable-http-enabled flag, namely, host+streamable-http is not supported yet.
+    # The following are enabled by default unless explicitly disabled with --no-<flag>.
+    #   - --no-transport-sse-enabled # Disable legacy SSE endpoints (/sse and /messages/)
+    #   - --no-transport-streamable-http-enabled #  Disable Streamable HTTP transport (/mcp endpoint)
+    #   - --no-json-response # Disable JSON response mode in Streamable HTTP transport (instead of SSE over HTTP)
+
+    # Example configration to start Admin server:
+    # command:
+    #   - --enable-adminserver
+    ports:
+      - ${SVR_HTTP_PORT}:9380
+      - ${ADMIN_SVR_HTTP_PORT}:9381
+      - 80:80
+      - 443:443
+      - 5678:5678
+      - 5679:5679
+      - 9382:9382 # entry for MCP (host_port:docker_port). The docker_port must match the value you set for `mcp-port` above.
+    volumes:
+      - ./ragflow-logs:/ragflow/logs
+      - ./nginx/ragflow.conf:/etc/nginx/conf.d/ragflow.conf
+      - ./nginx/proxy.conf:/etc/nginx/proxy.conf
+      - ./nginx/nginx.conf:/etc/nginx/nginx.conf
+      - ../history_data_agent:/ragflow/history_data_agent
+      - ./service_conf.yaml.template:/ragflow/conf/service_conf.yaml.template
+      - ./entrypoint.sh:/ragflow/entrypoint.sh
+    env_file: .env
+    networks:
+      - ragflow
+    restart: on-failure
+    # https://docs.docker.com/engine/daemon/prometheus/#create-a-prometheus-configuration
+    # If you use Docker Desktop, the --add-host flag is optional. This flag ensures that the host's internal IP is exposed to the Prometheus container.
+    extra_hosts:
+      - "host.docker.internal:host-gateway"
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all
+              capabilities: [gpu]
+
+
  # executor:
  #   depends_on:
  #     mysql:
  #       condition: service_healthy
  #   image: ${RAGFLOW_IMAGE}
-  #   container_name: ragflow-executor
  #   volumes:
  #     - ./ragflow-logs:/ragflow/logs
  #     - ./nginx/ragflow.conf:/etc/nginx/conf.d/ragflow.conf
  #   env_file: .env
-  #   environment:
-  #     - TZ=${TIMEZONE}
-  #     - HF_ENDPOINT=${HF_ENDPOINT}
-  #     - MACOS=${MACOS}
  #   entrypoint: "/ragflow/entrypoint_task_executor.sh 1 3"
  #   networks:
  #     - ragflow
@ -77,3 +129,10 @@ services:
  #   # If you're using Docker Desktop, the --add-host flag is optional. This flag makes sure that the host's internal IP gets exposed to the Prometheus container.
  #   extra_hosts:
  #     - "host.docker.internal:host-gateway"
+  #   deploy:
+  #     resources:
+  #       reservations:
+  #         devices:
+  #           - driver: nvidia
+  #             count: all
+  #             capabilities: [gpu]
--- a/docker/nginx/ragflow.conf
+++ b/docker/nginx/ragflow.conf
@ -11,7 +11,7 @@ server {
    gzip_disable "MSIE [1-6]\.";

    location ~ ^/(v1|api) {
-        proxy_pass http://ragflow:9380;
+        proxy_pass http://localhost:9380;
        include proxy.conf;
    }

--- a/docker/service_conf.yaml.template
+++ b/docker/service_conf.yaml.template
@ -32,6 +32,11 @@ redis:
  db: 1
  password: '${REDIS_PASSWORD:-infini_rag_flow}'
  host: '${REDIS_HOST:-redis}:6379'
+user_default_llm:
+  default_models:
+    embedding_model:
+      api_key: 'xxx'
+      base_url: 'http://${TEI_HOST}:80'
 # postgres:
 #   name: '${POSTGRES_DBNAME:-rag_flow}'
 #   user: '${POSTGRES_USER:-rag_flow}'
--- a/docs/configurations.md
+++ b/docs/configurations.md
@ -35,7 +35,7 @@ docker compose -f docker/docker-compose.yml up -d
  Sets up environment for RAGFlow's dependencies: Elasticsearch/[Infinity](https://github.com/infiniflow/infinity), MySQL, MinIO, and Redis.

 :::danger IMPORTANT
-We do not actively maintain **docker-compose-CN-oc9.yml**, **docker-compose-gpu-CN-oc9.yml**, or **docker-compose-gpu.yml**, so use them at your own risk. However, you are welcome to file a pull request to improve any of them.
+We do not actively maintain **docker-compose-CN-oc9.yml**, **docker-compose-macos.yml**, so use them at your own risk. However, you are welcome to file a pull request to improve them.
 :::

 ## Docker environment variables
@ -109,18 +109,23 @@ RAGFlow utilizes MinIO as its object storage solution, leveraging its scalabilit
 :::tip NOTE  
 If you cannot download the RAGFlow Docker image, try the following mirrors.  

- For the `nightly-slim` edition:  
-  - `RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:nightly-slim` or,
-  - `RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:nightly-slim`.
 - For the `nightly` edition:  
  - `RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:nightly` or,
  - `RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:nightly`.
 :::

+### Embedding service
+
+- `TEI_MODEL`  
+  The embedding model which text-embeddings-inference serves. Allowed values are one of `Qwen/Qwen3-Embedding-0.6B`(default), `BAAI/bge-m3`, and `BAAI/bge-small-en-v1.5`.
+
+- `TEI_PORT`  
+  The port used to expose the text-embeddings-inference service to the host machine, allowing **external** access to the text-embeddings-inference service running inside the Docker container. Defaults to `6380`.
+
 ### Timezone

- `TIMEZONE`  
-  The local time zone. Defaults to `'Asia/Shanghai'`.
+- `TZ`  
+  The local time zone. Defaults to `Asia/Shanghai`.

 ### Hugging Face mirror site

--- a/docs/develop/build_docker_image.mdx
+++ b/docs/develop/build_docker_image.mdx
@ -39,25 +39,6 @@ This image is approximately 2 GB in size and relies on external LLM and embeddin
 - For ARM64 platforms, please upgrade the `xgboost` version in **pyproject.toml** to `1.6.0` and ensure **unixODBC** is properly installed.
 :::

-```bash
-git clone https://github.com/infiniflow/ragflow.git
-cd ragflow/
-uv run download_deps.py
-docker build -f Dockerfile.deps -t infiniflow/ragflow_deps .
-docker build --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
-```
-
-
-  </TabItem>
-  <TabItem value="including">
-
-This image is approximately 9 GB in size. As it includes embedding models, it relies on external LLM services only.  
-
-:::danger IMPORTANT
- While we also test RAGFlow on ARM64 platforms, we do not maintain RAGFlow Docker images for ARM. However, you can build an image yourself on a `linux/arm64` or `darwin/arm64` host machine as well.
- For ARM64 platforms, please upgrade the `xgboost` version in **pyproject.toml** to `1.6.0` and ensure **unixODBC** is properly installed.
-:::
-
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
@ -66,18 +47,19 @@ docker build -f Dockerfile.deps -t infiniflow/ragflow_deps .
 docker build -f Dockerfile -t infiniflow/ragflow:nightly .
 ```

+
  </TabItem>
 </Tabs>

 ## Launch a RAGFlow Service from Docker for MacOS

-After building the infiniflow/ragflow:nightly-slim image, you are ready to launch a fully-functional RAGFlow service with all the required components, such as Elasticsearch, MySQL, MinIO, Redis, and more.
+After building the infiniflow/ragflow:nightly image, you are ready to launch a fully-functional RAGFlow service with all the required components, such as Elasticsearch, MySQL, MinIO, Redis, and more.

 ## Example: Apple M2 Pro (Sequoia)

 1. Edit Docker Compose Configuration

-Open the `docker/.env` file. Find the `RAGFLOW_IMAGE` setting and change the image reference from `infiniflow/ragflow:v0.21.1-slim` to `infiniflow/ragflow:nightly-slim` to use the pre-built image.
+Open the `docker/.env` file. Find the `RAGFLOW_IMAGE` setting and change the image reference from `infiniflow/ragflow:v0.21.1` to `infiniflow/ragflow:nightly` to use the pre-built image.


 2. Launch the Service
--- a/docs/develop/launch_ragflow_from_source.md
+++ b/docs/develop/launch_ragflow_from_source.md
@ -48,7 +48,7 @@ cd ragflow/
   ```
   - full:
   ```bash
-   uv sync --python 3.10 --all-extras # install RAGFlow dependent python modules
+   uv sync --python 3.10 # install RAGFlow dependent python modules
   ```
   *A virtual environment named `.venv` is created, and all Python dependencies are installed into the new environment.*

--- a/docs/develop/mcp/launch_mcp_server.md
+++ b/docs/develop/mcp/launch_mcp_server.md
@ -116,51 +116,51 @@ Run `docker compose -f docker-compose.yml up` to launch the RAGFlow server toget
 *The following ASCII art confirms a successful launch:*

 ```bash
-  ragflow-server  | Starting MCP Server on 0.0.0.0:9382 with base URL http://127.0.0.1:9380...
-  ragflow-server  | Starting 1 task executor(s) on host 'dd0b5e07e76f'...
-  ragflow-server  | 2025-04-18 15:41:18,816 INFO     27 ragflow_server log path: /ragflow/logs/ragflow_server.log, log levels: {'peewee': 'WARNING', 'pdfminer': 'WARNING', 'root': 'INFO'}
-  ragflow-server  | 
-  ragflow-server  | __  __  ____ ____       ____  _____ ______     _______ ____
-  ragflow-server  | |  \/  |/ ___|  _ \     / ___|| ____|  _ \ \   / / ____|  _ \
-  ragflow-server  | | |\/| | |   | |_) |    \___ \|  _| | |_) \ \ / /|  _| | |_) |
-  ragflow-server  | | |  | | |___|  __/      ___) | |___|  _ < \ V / | |___|  _ <
-  ragflow-server  | |_|  |_|\____|_|        |____/|_____|_| \_\ \_/  |_____|_| \_\
-  ragflow-server  |     
-  ragflow-server  | MCP launch mode: self-host
-  ragflow-server  | MCP host: 0.0.0.0
-  ragflow-server  | MCP port: 9382
-  ragflow-server  | MCP base_url: http://127.0.0.1:9380
-  ragflow-server  | INFO:     Started server process [26]
-  ragflow-server  | INFO:     Waiting for application startup.
-  ragflow-server  | INFO:     Application startup complete.
-  ragflow-server  | INFO:     Uvicorn running on http://0.0.0.0:9382 (Press CTRL+C to quit)
-  ragflow-server  | 2025-04-18 15:41:20,469 INFO     27 found 0 gpus
-  ragflow-server  | 2025-04-18 15:41:23,263 INFO     27 init database on cluster mode successfully
-  ragflow-server  | 2025-04-18 15:41:25,318 INFO     27 load_model /ragflow/rag/res/deepdoc/det.onnx uses CPU
-  ragflow-server  | 2025-04-18 15:41:25,367 INFO     27 load_model /ragflow/rag/res/deepdoc/rec.onnx uses CPU
-  ragflow-server  |         ____   ___    ______ ______ __               
-  ragflow-server  |        / __ \ /   |  / ____// ____// /____  _      __
-  ragflow-server  |       / /_/ // /| | / / __ / /_   / // __ \| | /| / /
-  ragflow-server  |      / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ / 
-  ragflow-server  |     /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/                             
-  ragflow-server  | 
-  ragflow-server  |     
-  ragflow-server  | 2025-04-18 15:41:29,088 INFO     27 RAGFlow version: v0.18.0-285-gb2c299fa full
-  ragflow-server  | 2025-04-18 15:41:29,088 INFO     27 project base: /ragflow
-  ragflow-server  | 2025-04-18 15:41:29,088 INFO     27 Current configs, from /ragflow/conf/service_conf.yaml:
-  ragflow-server  |  ragflow: {'host': '0.0.0.0', 'http_port': 9380}
+  docker-ragflow-cpu-1  | Starting MCP Server on 0.0.0.0:9382 with base URL http://127.0.0.1:9380...
+  docker-ragflow-cpu-1  | Starting 1 task executor(s) on host 'dd0b5e07e76f'...
+  docker-ragflow-cpu-1  | 2025-04-18 15:41:18,816 INFO     27 ragflow_server log path: /ragflow/logs/ragflow_server.log, log levels: {'peewee': 'WARNING', 'pdfminer': 'WARNING', 'root': 'INFO'}
+  docker-ragflow-cpu-1  | 
+  docker-ragflow-cpu-1  | __  __  ____ ____       ____  _____ ______     _______ ____
+  docker-ragflow-cpu-1  | |  \/  |/ ___|  _ \     / ___|| ____|  _ \ \   / / ____|  _ \
+  docker-ragflow-cpu-1  | | |\/| | |   | |_) |    \___ \|  _| | |_) \ \ / /|  _| | |_) |
+  docker-ragflow-cpu-1  | | |  | | |___|  __/      ___) | |___|  _ < \ V / | |___|  _ <
+  docker-ragflow-cpu-1  | |_|  |_|\____|_|        |____/|_____|_| \_\ \_/  |_____|_| \_\
+  docker-ragflow-cpu-1  |     
+  docker-ragflow-cpu-1  | MCP launch mode: self-host
+  docker-ragflow-cpu-1  | MCP host: 0.0.0.0
+  docker-ragflow-cpu-1  | MCP port: 9382
+  docker-ragflow-cpu-1  | MCP base_url: http://127.0.0.1:9380
+  docker-ragflow-cpu-1  | INFO:     Started server process [26]
+  docker-ragflow-cpu-1  | INFO:     Waiting for application startup.
+  docker-ragflow-cpu-1  | INFO:     Application startup complete.
+  docker-ragflow-cpu-1  | INFO:     Uvicorn running on http://0.0.0.0:9382 (Press CTRL+C to quit)
+  docker-ragflow-cpu-1  | 2025-04-18 15:41:20,469 INFO     27 found 0 gpus
+  docker-ragflow-cpu-1  | 2025-04-18 15:41:23,263 INFO     27 init database on cluster mode successfully
+  docker-ragflow-cpu-1  | 2025-04-18 15:41:25,318 INFO     27 load_model /ragflow/rag/res/deepdoc/det.onnx uses CPU
+  docker-ragflow-cpu-1  | 2025-04-18 15:41:25,367 INFO     27 load_model /ragflow/rag/res/deepdoc/rec.onnx uses CPU
+  docker-ragflow-cpu-1  |         ____   ___    ______ ______ __               
+  docker-ragflow-cpu-1  |        / __ \ /   |  / ____// ____// /____  _      __
+  docker-ragflow-cpu-1  |       / /_/ // /| | / / __ / /_   / // __ \| | /| / /
+  docker-ragflow-cpu-1  |      / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ / 
+  docker-ragflow-cpu-1  |     /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/                             
+  docker-ragflow-cpu-1  | 
+  docker-ragflow-cpu-1  |     
+  docker-ragflow-cpu-1  | 2025-04-18 15:41:29,088 INFO     27 RAGFlow version: v0.18.0-285-gb2c299fa full
+  docker-ragflow-cpu-1  | 2025-04-18 15:41:29,088 INFO     27 project base: /ragflow
+  docker-ragflow-cpu-1  | 2025-04-18 15:41:29,088 INFO     27 Current configs, from /ragflow/conf/service_conf.yaml:
+  docker-ragflow-cpu-1  |  ragflow: {'host': '0.0.0.0', 'http_port': 9380}
  ...
-  ragflow-server  |  * Running on all addresses (0.0.0.0)
-  ragflow-server  |  * Running on http://127.0.0.1:9380
-  ragflow-server  |  * Running on http://172.19.0.6:9380
-  ragflow-server  |   ______           __      ______                     __            
-  ragflow-server  |  /_  __/___ ______/ /__   / ____/  _____  _______  __/ /_____  _____
-  ragflow-server  |   / / / __ `/ ___/ //_/  / __/ | |/_/ _ \/ ___/ / / / __/ __ \/ ___/
-  ragflow-server  |  / / / /_/ (__  ) ,<    / /____>  </  __/ /__/ /_/ / /_/ /_/ / /    
-  ragflow-server  | /_/  \__,_/____/_/|_|  /_____/_/|_|\___/\___/\__,_/\__/\____/_/                               
-  ragflow-server  |     
-  ragflow-server  | 2025-04-18 15:41:34,501 INFO     32 TaskExecutor: RAGFlow version: v0.18.0-285-gb2c299fa full
-  ragflow-server  | 2025-04-18 15:41:34,501 INFO     32 Use Elasticsearch http://es01:9200 as the doc engine.
+  docker-ragflow-cpu-1  |  * Running on all addresses (0.0.0.0)
+  docker-ragflow-cpu-1  |  * Running on http://127.0.0.1:9380
+  docker-ragflow-cpu-1  |  * Running on http://172.19.0.6:9380
+  docker-ragflow-cpu-1  |   ______           __      ______                     __            
+  docker-ragflow-cpu-1  |  /_  __/___ ______/ /__   / ____/  _____  _______  __/ /_____  _____
+  docker-ragflow-cpu-1  |   / / / __ `/ ___/ //_/  / __/ | |/_/ _ \/ ___/ / / / __/ __ \/ ___/
+  docker-ragflow-cpu-1  |  / / / /_/ (__  ) ,<    / /____>  </  __/ /__/ /_/ / /_/ /_/ / /    
+  docker-ragflow-cpu-1  | /_/  \__,_/____/_/|_|  /_____/_/|_|\___/\___/\__,_/\__/\____/_/                               
+  docker-ragflow-cpu-1  |     
+  docker-ragflow-cpu-1  | 2025-04-18 15:41:34,501 INFO     32 TaskExecutor: RAGFlow version: v0.18.0-285-gb2c299fa full
+  docker-ragflow-cpu-1  | 2025-04-18 15:41:34,501 INFO     32 Use Elasticsearch http://es01:9200 as the doc engine.
  ...
 ```

@ -176,7 +176,7 @@ This section is contributed by our community contributor [yiminghub2024](https:/
   iii. Copy [docker/entrypoint.sh](https://github.com/infiniflow/ragflow/blob/main/docker/entrypoint.sh) locally.  
   iv. Install the required dependencies using `uv`:  
       - Run `uv add mcp` or
-       - Copy [pyproject.toml](https://github.com/infiniflow/ragflow/blob/main/pyproject.toml) locally and run `uv sync --python 3.10 --all-extras`.
+       - Copy [pyproject.toml](https://github.com/infiniflow/ragflow/blob/main/pyproject.toml) locally and run `uv sync --python 3.10`.
 2. Edit **docker-compose.yml** to enable MCP (disabled by default).
 3. Launch the MCP server:

@ -189,7 +189,7 @@ docker compose -f docker-compose.yml up -d
 Run the following to check the logs the RAGFlow server and the MCP server:

 ```bash
-docker logs ragflow-server
+docker logs docker-ragflow-cpu-1
 ```

 ## Security considerations
--- a/docs/develop/mcp/mcp_client_example.md
+++ b/docs/develop/mcp/mcp_client_example.md
@ -99,7 +99,7 @@ _The server replies with an `initialize` response, including the supported proto

 ```bash
 event: message
-data: {"jsonrpc":"2.0","id":1,"result":{"protocolVersion":"2025-03-26","capabilities":{"experimental":{"headers":{"host":"127.0.0.1:9382","user-agent":"curl/8.7.1","accept":"*/*","api_key":"ragflow-xxxxxxxxxxxx","accept-encoding":"gzip"}},"tools":{"listChanged":false}},"serverInfo":{"name":"ragflow-server","version":"1.9.4"}}}
+data: {"jsonrpc":"2.0","id":1,"result":{"protocolVersion":"2025-03-26","capabilities":{"experimental":{"headers":{"host":"127.0.0.1:9382","user-agent":"curl/8.7.1","accept":"*/*","api_key":"ragflow-xxxxxxxxxxxx","accept-encoding":"gzip"}},"tools":{"listChanged":false}},"serverInfo":{"name":"docker-ragflow-cpu-1","version":"1.9.4"}}}
 ```

 ### 3. Acknowledge readiness
--- a/docs/faq.mdx
+++ b/docs/faq.mdx
@ -33,6 +33,8 @@ Each RAGFlow release is available in two editions:
 - **Slim edition**: excludes built-in embedding models and is identified by a **-slim** suffix added to the version name. Example: `infiniflow/ragflow:v0.21.1-slim`
 - **Full edition**: includes built-in embedding models and has no suffix added to the version name. Example: `infiniflow/ragflow:v0.21.1`

+Note: Starting with `v0.22.0`, we ship only the slim edition and no longer append the **-slim** suffix to the image tag.
+
 ---

 ### Which embedding models can be deployed locally?
@ -44,6 +46,8 @@ RAGFlow offers two Docker image editions, `v0.21.1-slim` and `v0.21.1`:
  - `BAAI/bge-large-zh-v1.5`
  - `maidalun1020/bce-embedding-base_v1`

+Note: Starting with `v0.22.0`, we ship only the slim edition and no longer append the **-slim** suffix to the image tag.
+
 ---

 ### Where to find the version of RAGFlow? How to interpret it?
@ -220,7 +224,7 @@ Ignore this warning and continue. All system warnings can be ignored.

 ![anomaly](https://github.com/infiniflow/ragflow/assets/93570324/beb7ad10-92e4-4a58-8886-bfb7cbd09e5d)

-You will not log in to RAGFlow unless the server is fully initialized. Run `docker logs -f ragflow-server`.
+You will not log in to RAGFlow unless the server is fully initialized. Run `docker logs -f docker-ragflow-cpu-1`.

 *The server is successfully initialized, if your system displays the following:*

@ -256,7 +260,7 @@ Click the red cross beside the 'parsing status' bar, then restart the parsing pr
 1. Check the log of your RAGFlow server to see if it is running properly:

   ```bash
-   docker logs -f ragflow-server
+   docker logs -f docker-ragflow-cpu-1
   ```

 2. Check if the **task_executor.py** process exists.
@ -310,7 +314,7 @@ tail -f ragflow/docker/ragflow-logs/*.log
   *The following is an example result:*

   ```bash
-   5bc45806b680   infiniflow/ragflow:latest     "./entrypoint.sh"        11 hours ago   Up 11 hours               0.0.0.0:80->80/tcp, :::80->80/tcp, 0.0.0.0:443->443/tcp, :::443->443/tcp, 0.0.0.0:9380->9380/tcp, :::9380->9380/tcp   ragflow-server
+   5bc45806b680   infiniflow/ragflow:latest     "./entrypoint.sh"        11 hours ago   Up 11 hours               0.0.0.0:80->80/tcp, :::80->80/tcp, 0.0.0.0:443->443/tcp, :::443->443/tcp, 0.0.0.0:9380->9380/tcp, :::9380->9380/tcp   docker-ragflow-cpu-1
   91220e3285dd   docker.elastic.co/elasticsearch/elasticsearch:8.11.3   "/bin/tini -- /usr/l…"   11 hours ago   Up 11 hours (healthy)     9300/tcp, 0.0.0.0:9200->9200/tcp, :::9200->9200/tcp           ragflow-es-01
   d8c86f06c56b   mysql:5.7.18        "docker-entrypoint.s…"   7 days ago     Up 16 seconds (healthy)   0.0.0.0:3306->3306/tcp, :::3306->3306/tcp     ragflow-mysql
   cd29bcb254bc   quay.io/minio/minio:RELEASE.2023-12-20T01-00-02Z       "/usr/bin/docker-ent…"   2 weeks ago    Up 11 hours      0.0.0.0:9001->9001/tcp, :::9001->9001/tcp, 0.0.0.0:9000->9000/tcp, :::9000->9000/tcp     ragflow-minio
--- a/docs/guides/agent/agent_component_reference/retrieval.mdx
+++ b/docs/guides/agent/agent_component_reference/retrieval.mdx
@ -47,7 +47,7 @@ By default, a combination of weighted keyword similarity and weighted vector cos
 As a starter, you can skip this step to stay with the default retrieval method.

 :::caution WARNING
-Using a rerank model will *significantly* increase the system's response time. If you must use a rerank model, ensure you use a SaaS reranker; if you prefer a locally deployed rerank model, ensure you start RAGFlow with **docker-compose-gpu.yml**.
+Using a rerank model will *significantly* increase the system's response time.
 :::

 ### 5. Enable cross-language search
@ -140,6 +140,5 @@ The global variable name for the output of the **Retrieval** component, which ca

 Go through the checklist below for best performance:

- Leave the **Rerank model** field empty.
- If you must use a rerank model, ensure you use a SaaS reranker; if you prefer a locally deployed rerank model, ensure you start RAGFlow with **docker-compose-gpu.yml**.
+- Leave the **Rerank model** field empty to disable rerank.
 - Disable **Use knowledge graph**.
--- a/docs/guides/models/deploy_local_llm.mdx
+++ b/docs/guides/models/deploy_local_llm.mdx
@ -62,7 +62,7 @@ $ sudo docker exec ollama ollama pull bge-m3

 - If RAGFlow runs in Docker, the localhost is mapped within the RAGFlow Docker container as `host.docker.internal`. If Ollama runs on the same host machine, the right URL to use for Ollama would be `http://host.docker.internal:11434/' and you should check that Ollama is accessible from inside the RAGFlow container with:
 ```bash
-$ sudo docker exec -it ragflow-server bash
+$ sudo docker exec -it docker-ragflow-cpu-1 bash
 $ curl http://host.docker.internal:11434/
 > Ollama is running
 ```
--- a/docs/guides/upgrade_ragflow.mdx
+++ b/docs/guides/upgrade_ragflow.mdx
@ -7,15 +7,15 @@ slug: /upgrade_ragflow
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';

-Upgrade RAGFlow to `nightly-slim`/`nightly` or the latest, published release.
+Upgrade RAGFlow to `nightly` or the latest, published release.

 :::info NOTE
 Upgrading RAGFlow in itself will *not* remove your uploaded/historical data. However, be aware that `docker compose -f docker/docker-compose.yml down -v` will remove Docker container volumes, resulting in data loss.
 :::

-## Upgrade RAGFlow to `nightly-slim`/`nightly`, the most recent, tested Docker image
+## Upgrade RAGFlow to `nightly`, the most recent, tested Docker image

-`nightly-slim` refers to the RAGFlow Docker image *without* embedding models, while `nightly` refers to the RAGFlow Docker image with embedding models. For details on their differences, see [ragflow/docker/.env](https://github.com/infiniflow/ragflow/blob/main/docker/.env).
+`nightly` refers to the RAGFlow Docker image without embedding models.

 To upgrade RAGFlow, you must upgrade **both** your code **and** your Docker image:

@ -27,27 +27,9 @@ To upgrade RAGFlow, you must upgrade **both** your code **and** your Docker imag

 2. Update **ragflow/docker/.env**:

-<Tabs
-  defaultValue="nightly-slim"
-  values={[
-    {label: 'nightly-slim', value: 'nightly-slim'},
-    {label: 'nightly', value: 'nightly'},
-  ]}>
-  <TabItem value="nightly-slim">
-
-```bash
-RAGFLOW_IMAGE=infiniflow/ragflow:nightly-slim
-```
-
-  </TabItem>
-  <TabItem value="nightly">
-
-```bash
-RAGFLOW_IMAGE=infiniflow/ragflow:nightly
-```
-
-  </TabItem>
-</Tabs>
+   ```bash
+   RAGFLOW_IMAGE=infiniflow/ragflow:nightly
+   ```

 3. Update RAGFlow image and restart RAGFlow:

@ -74,27 +56,9 @@ To upgrade RAGFlow, you must upgrade **both** your code **and** your Docker imag

 3. Update **ragflow/docker/.env**:

-<Tabs
-  defaultValue="slim"
-  values={[
-    {label: 'slim', value: 'slim'},
-    {label: 'full', value: 'full'},
-  ]}>
-  <TabItem value="slim">
-
-```bash
-RAGFLOW_IMAGE=infiniflow/ragflow:v0.21.1-slim
-```
-
-  </TabItem>
-  <TabItem value="full">
-
-```bash
-RAGFLOW_IMAGE=infiniflow/ragflow:v0.21.1
-```
-
-  </TabItem>
-</Tabs>
+   ```bash
+   RAGFLOW_IMAGE=infiniflow/ragflow:v0.21.1
+   ```

 4. Update the RAGFlow image and restart RAGFlow:

--- a/docs/quickstart.mdx
+++ b/docs/quickstart.mdx
@ -196,9 +196,6 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
   ```bash
   # Use CPU for embedding and DeepDoc tasks:
   $ docker compose -f docker-compose.yml up -d
-
-   # To use GPU to accelerate embedding and DeepDoc tasks:
-   # docker compose -f docker-compose-gpu.yml up -d
   ```

 ```mdx-code-block
@ -207,10 +204,9 @@ This section provides instructions on setting up the RAGFlow server on Linux. If

 | RAGFlow image tag   | Image size (GB) | Has embedding models and Python packages? | Stable?                  |
 | ------------------- | --------------- | ----------------------------------------- | ------------------------ |
-| `v0.21.1`           | &approx;9       | :heavy_check_mark:                        | Stable release           |
-| `v0.21.1-slim`      | &approx;2       | ❌                                        | Stable release           |
-| `nightly`           | &approx;9       | :heavy_check_mark:                        | *Unstable* nightly build |
-| `nightly-slim`      | &approx;2       | ❌                                        | *Unstable* nightly build |
+| v0.21.1             | &approx;9       | ✔️                                        | Stable release           |
+| v0.21.1-slim        | &approx;2       | ❌                                        | Stable release           |
+| nightly             | &approx;2       | ❌                                        | _Unstable_ nightly build |

 ```mdx-code-block
 </APITable>
@ -232,7 +228,7 @@ The image size shown refers to the size of the *downloaded* Docker image, which
 4. Check the server status after having the server up and running:

   ```bash
-   $ docker logs -f ragflow-server
+   $ docker logs -f docker-ragflow-cpu-1
   ```

   _The following output confirms a successful launch of the system:_
--- a/download_deps.py
+++ b/download_deps.py
@ -42,8 +42,6 @@ repos = [
    "InfiniFlow/text_concat_xgb_v1.0",
    "InfiniFlow/deepdoc",
    "InfiniFlow/huqie",
-    "BAAI/bge-large-zh-v1.5",
-    "maidalun1020/bce-embedding-base_v1",
 ]

 def download_model(repo_id):
--- a/helm/templates/elasticsearch-config.yaml
+++ b/helm/templates/elasticsearch-config.yaml
@ -13,5 +13,5 @@ data:
  cluster.routing.allocation.disk.watermark.low: 5gb
  cluster.routing.allocation.disk.watermark.high: 3gb
  cluster.routing.allocation.disk.watermark.flood_stage: 2gb
-  TZ: {{ .Values.env.TIMEZONE }}
+  TZ: {{ .Values.env.TZ }}
 {{- end -}}
--- a/helm/templates/opensearch-config.yaml
+++ b/helm/templates/opensearch-config.yaml
@ -13,6 +13,6 @@ data:
  cluster.routing.allocation.disk.watermark.low: 5gb
  cluster.routing.allocation.disk.watermark.high: 3gb
  cluster.routing.allocation.disk.watermark.flood_stage: 2gb
-  TZ: {{ .Values.env.TIMEZONE }}
+  TZ: {{ .Values.env.TZ }}
  http.port: "9201"
 {{- end -}}
--- a/helm/values.yaml
+++ b/helm/values.yaml
@ -37,7 +37,7 @@ env:
  REDIS_PASSWORD: infini_rag_flow_helm

  # The local time zone.
-  TIMEZONE: "Asia/Shanghai"
+  TZ: "Asia/Shanghai"

  # Uncomment the following line if you have limited access to huggingface.co:
  # HF_ENDPOINT: https://hf-mirror.com
--- a/intergrations/firecrawl/INSTALLATION.md
+++ b/intergrations/firecrawl/INSTALLATION.md
@ -131,7 +131,7 @@ python example_usage.py

 ```bash
 # Check RAGFlow server logs
-docker logs ragflow-server
+docker logs docker-ragflow-cpu-1

 # Look for messages like:
 # "Firecrawl plugin loaded successfully"
--- a/pyproject.toml
+++ b/pyproject.toml
@ -7,14 +7,14 @@ license-files = ["LICENSE"]
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "datrie==0.8.2",
+    "datrie>=0.8.3,<0.9.0",
    "akshare>=1.15.78,<2.0.0",
    "azure-storage-blob==12.22.0",
    "azure-identity==1.17.1",
    "azure-storage-file-datalake==12.16.0",
    "anthropic==0.34.1",
    "arxiv==2.1.3",
-    "aspose-slides>=24.9.0,<25.0.0; platform_machine == 'x86_64' or (sys_platform == 'darwin' and platform_machine == 'arm64')",
+    "aspose-slides>=25.10.0,<26.0.0; platform_machine == 'x86_64' or (sys_platform == 'darwin' and platform_machine == 'arm64')",
    "beartype>=0.18.5,<0.19.0",
    "bio==1.7.1",
    "blinker==1.7.0",
@ -137,16 +137,7 @@ dependencies = [
    "mammoth>=1.11.0",
    "markdownify>=1.2.0",
    "captcha>=0.7.1",
-]
-
-[project.optional-dependencies]
-full = [
-    "bcembedding==0.1.5",
-    "fastembed>=0.3.6,<0.4.0; sys_platform == 'darwin' or platform_machine != 'x86_64'",
-    "fastembed-gpu>=0.3.6,<0.4.0; sys_platform != 'darwin' and platform_machine == 'x86_64'",
-    "flagembedding==1.2.10",
-    "torch>=2.5.0,<3.0.0",
-    "transformers>=4.35.0,<5.0.0",
+    "pip>=25.2",
 ]

 [dependency-groups]
--- a/rag/llm/embedding_model.py
+++ b/rag/llm/embedding_model.py
@ -14,9 +14,7 @@
 #  limitations under the License.
 #
 import json
-import logging
 import os
-import re
 import threading
 from abc import ABC
 from urllib.parse import urljoin
@ -25,15 +23,14 @@ import dashscope
 import google.generativeai as genai
 import numpy as np
 import requests
-from huggingface_hub import snapshot_download
 from ollama import Client
 from openai import OpenAI
 from zhipuai import ZhipuAI

-from api import settings
-from api.utils.file_utils import get_home_cache_dir
 from api.utils.log_utils import log_exception
 from rag.utils import num_tokens_from_string, truncate
+from api import settings
+import logging


 class Base(ABC):
@ -63,71 +60,42 @@ class Base(ABC):
        return 0


-class DefaultEmbedding(Base):
-    _FACTORY_NAME = "BAAI"
+class BuiltinEmbed(Base):
+    _FACTORY_NAME = "Builtin"
+    MAX_TOKENS = {"Qwen/Qwen3-Embedding-0.6B": 30000, "BAAI/bge-m3": 8000, "BAAI/bge-small-en-v1.5": 500}
    _model = None
    _model_name = ""
+    _max_tokens = 500
    _model_lock = threading.Lock()

    def __init__(self, key, model_name, **kwargs):
-        """
-        If you have trouble downloading HuggingFace models, -_^ this might help!!
-
-        For Linux:
-        export HF_ENDPOINT=https://hf-mirror.com
-
-        For Windows:
-        Good luck
-        ^_-
-
-        """
-        if not settings.LIGHTEN:
-            input_cuda_visible_devices = None
-            with DefaultEmbedding._model_lock:
-                import torch
-                from FlagEmbedding import FlagModel
-
-                if "CUDA_VISIBLE_DEVICES" in os.environ:
-                    input_cuda_visible_devices = os.environ["CUDA_VISIBLE_DEVICES"]
-                    os.environ["CUDA_VISIBLE_DEVICES"] = "0"  # handle some issues with multiple GPUs when initializing the model
-
-                if not DefaultEmbedding._model or model_name != DefaultEmbedding._model_name:
-                    try:
-                        DefaultEmbedding._model = FlagModel(
-                            os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z0-9]+/", "", model_name)),
-                            query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
-                            use_fp16=torch.cuda.is_available(),
-                        )
-                        DefaultEmbedding._model_name = model_name
-                    except Exception:
-                        model_dir = snapshot_download(
-                            repo_id="BAAI/bge-large-zh-v1.5", local_dir=os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z0-9]+/", "", model_name)), local_dir_use_symlinks=False
-                        )
-                        DefaultEmbedding._model = FlagModel(model_dir, query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", use_fp16=torch.cuda.is_available())
-                    finally:
-                        if input_cuda_visible_devices:
-                            # restore CUDA_VISIBLE_DEVICES
-                            os.environ["CUDA_VISIBLE_DEVICES"] = input_cuda_visible_devices
-        self._model = DefaultEmbedding._model
-        self._model_name = DefaultEmbedding._model_name
+        logging.info(f"Initialize BuiltinEmbed according to settings.EMBEDDING_CFG: {settings.EMBEDDING_CFG}")
+        embedding_cfg = settings.EMBEDDING_CFG
+        if not BuiltinEmbed._model and "tei-" in os.getenv("COMPOSE_PROFILES", ""):
+            with BuiltinEmbed._model_lock:
+                BuiltinEmbed._model_name = settings.EMBEDDING_MDL
+                BuiltinEmbed._max_tokens = BuiltinEmbed.MAX_TOKENS.get(settings.EMBEDDING_MDL, 500)
+                BuiltinEmbed._model = HuggingFaceEmbed(embedding_cfg["api_key"], settings.EMBEDDING_MDL, base_url=embedding_cfg["base_url"])
+        self._model = BuiltinEmbed._model
+        self._model_name = BuiltinEmbed._model_name
+        self._max_tokens = BuiltinEmbed._max_tokens

    def encode(self, texts: list):
        batch_size = 16
-        texts = [truncate(t, 2048) for t in texts]
+        texts = [truncate(t, self._max_tokens) for t in texts]
        token_count = 0
-        for t in texts:
-            token_count += num_tokens_from_string(t)
        ress = None
        for i in range(0, len(texts), batch_size):
+            embeddings, token_count_delta = self._model.encode(texts[i : i + batch_size])
+            token_count += token_count_delta
            if ress is None:
-                ress = self._model.encode(texts[i : i + batch_size], convert_to_numpy=True)
+                ress = embeddings
            else:
-                ress = np.concatenate((ress, self._model.encode(texts[i : i + batch_size], convert_to_numpy=True)), axis=0)
+                ress = np.concatenate((ress, embeddings), axis=0)
        return ress, token_count

    def encode_queries(self, text: str):
-        token_count = num_tokens_from_string(text)
-        return self._model.encode_queries([text], convert_to_numpy=False)[0][0].cpu().numpy(), token_count
+        return self._model.encode_queries(text)


 class OpenAIEmbed(Base):
@ -326,51 +294,6 @@ class OllamaEmbed(Base):
            log_exception(_e, res)


-class FastEmbed(DefaultEmbedding):
-    _FACTORY_NAME = "FastEmbed"
-
-    def __init__(
-        self,
-        key: str | None = None,
-        model_name: str = "BAAI/bge-small-en-v1.5",
-        cache_dir: str | None = None,
-        threads: int | None = None,
-        **kwargs,
-    ):
-        if not settings.LIGHTEN:
-            with FastEmbed._model_lock:
-                from fastembed import TextEmbedding
-
-                if not DefaultEmbedding._model or model_name != DefaultEmbedding._model_name:
-                    try:
-                        DefaultEmbedding._model = TextEmbedding(model_name, cache_dir, threads, **kwargs)
-                        DefaultEmbedding._model_name = model_name
-                    except Exception:
-                        cache_dir = snapshot_download(
-                            repo_id="BAAI/bge-small-en-v1.5", local_dir=os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z0-9]+/", "", model_name)), local_dir_use_symlinks=False
-                        )
-                        DefaultEmbedding._model = TextEmbedding(model_name, cache_dir, threads, **kwargs)
-        self._model = DefaultEmbedding._model
-        self._model_name = model_name
-
-    def encode(self, texts: list):
-        # Using the internal tokenizer to encode the texts and get the total
-        # number of tokens
-        encodings = self._model.model.tokenizer.encode_batch(texts)
-        total_tokens = sum(len(e) for e in encodings)
-
-        embeddings = [e.tolist() for e in self._model.embed(texts, batch_size=16)]
-
-        return np.array(embeddings), total_tokens
-
-    def encode_queries(self, text: str):
-        # Using the internal tokenizer to encode the texts and get the total
-        # number of tokens
-        encoding = self._model.model.tokenizer.encode(text)
-        embedding = next(self._model.query_embed(text))
-        return np.array(embedding), len(encoding.ids)
-
-
 class XinferenceEmbed(Base):
    _FACTORY_NAME = "Xinference"

@ -407,14 +330,7 @@ class YoudaoEmbed(Base):
    _client = None

    def __init__(self, key=None, model_name="maidalun1020/bce-embedding-base_v1", **kwargs):
-        if not settings.LIGHTEN and not YoudaoEmbed._client:
-            from BCEmbedding import EmbeddingModel as qanthing
-
-            try:
-                logging.info("LOADING BCE...")
-                YoudaoEmbed._client = qanthing(model_name_or_path=os.path.join(get_home_cache_dir(), "bce-embedding-base_v1"))
-            except Exception:
-                YoudaoEmbed._client = qanthing(model_name_or_path=model_name.replace("maidalun1020", "InfiniFlow"))
+        pass

    def encode(self, texts: list):
        batch_size = 10
@ -885,21 +801,18 @@ class HuggingFaceEmbed(Base):
        self.base_url = base_url or "http://127.0.0.1:8080"

    def encode(self, texts: list):
-        embeddings = []
-        for text in texts:
-            response = requests.post(f"{self.base_url}/embed", json={"inputs": text}, headers={"Content-Type": "application/json"})
-            if response.status_code == 200:
-                embedding = response.json()
-                embeddings.append(embedding[0])
-            else:
-                raise Exception(f"Error: {response.status_code} - {response.text}")
+        response = requests.post(f"{self.base_url}/embed", json={"inputs": texts}, headers={"Content-Type": "application/json"})
+        if response.status_code == 200:
+            embeddings = response.json()
+        else:
+            raise Exception(f"Error: {response.status_code} - {response.text}")
        return np.array(embeddings), sum([num_tokens_from_string(text) for text in texts])

-    def encode_queries(self, text):
+    def encode_queries(self, text: str):
        response = requests.post(f"{self.base_url}/embed", json={"inputs": text}, headers={"Content-Type": "application/json"})
        if response.status_code == 200:
-            embedding = response.json()
-            return np.array(embedding[0]), num_tokens_from_string(text)
+            embedding = response.json()[0]
+            return np.array(embedding), num_tokens_from_string(text)
        else:
            raise Exception(f"Error: {response.status_code} - {response.text}")

--- a/rag/llm/rerank_model.py
+++ b/rag/llm/rerank_model.py
@ -14,21 +14,14 @@
 #  limitations under the License.
 #
 import json
-import os
-import re
-import threading
 from abc import ABC
-from collections.abc import Iterable
 from urllib.parse import urljoin

 import httpx
 import numpy as np
 import requests
-from huggingface_hub import snapshot_download
 from yarl import URL

-from api import settings
-from api.utils.file_utils import get_home_cache_dir
 from api.utils.log_utils import log_exception
 from rag.utils import num_tokens_from_string, truncate, total_token_count_from_response

@ -47,100 +40,6 @@ class Base(ABC):
        return total_token_count_from_response(resp)


-class DefaultRerank(Base):
-    _FACTORY_NAME = "BAAI"
-    _model = None
-    _model_lock = threading.Lock()
-
-    def __init__(self, key, model_name, **kwargs):
-        """
-        If you have trouble downloading HuggingFace models, -_^ this might help!!
-
-        For Linux:
-        export HF_ENDPOINT=https://hf-mirror.com
-
-        For Windows:
-        Good luck
-        ^_-
-
-        """
-        if not settings.LIGHTEN and not DefaultRerank._model:
-            import torch
-            from FlagEmbedding import FlagReranker
-
-            with DefaultRerank._model_lock:
-                if not DefaultRerank._model:
-                    try:
-                        DefaultRerank._model = FlagReranker(os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z0-9]+/", "", model_name)), use_fp16=torch.cuda.is_available())
-                    except Exception:
-                        model_dir = snapshot_download(repo_id=model_name, local_dir=os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z0-9]+/", "", model_name)), local_dir_use_symlinks=False)
-                        DefaultRerank._model = FlagReranker(model_dir, use_fp16=torch.cuda.is_available())
-        self._model = DefaultRerank._model
-        self._dynamic_batch_size = 8
-        self._min_batch_size = 1
-
-    def torch_empty_cache(self):
-        try:
-            import torch
-
-            torch.cuda.empty_cache()
-        except Exception as e:
-            log_exception(e)
-
-    def _process_batch(self, pairs, max_batch_size=None):
-        """template method for subclass call"""
-        old_dynamic_batch_size = self._dynamic_batch_size
-        if max_batch_size is not None:
-            self._dynamic_batch_size = max_batch_size
-        res = np.array(len(pairs), dtype=float)
-        i = 0
-        while i < len(pairs):
-            cur_i = i
-            current_batch = self._dynamic_batch_size
-            max_retries = 5
-            retry_count = 0
-            while retry_count < max_retries:
-                try:
-                    # call subclass implemented batch processing calculation
-                    batch_scores = self._compute_batch_scores(pairs[i : i + current_batch])
-                    res[i : i + current_batch] = batch_scores
-                    i += current_batch
-                    self._dynamic_batch_size = min(self._dynamic_batch_size * 2, 8)
-                    break
-                except RuntimeError as e:
-                    if "CUDA out of memory" in str(e) and current_batch > self._min_batch_size:
-                        current_batch = max(current_batch // 2, self._min_batch_size)
-                        self.torch_empty_cache()
-                        i = cur_i # reset i to the start of the current batch
-                        retry_count += 1
-                    else:
-                        raise
-            if retry_count >= max_retries:
-                raise RuntimeError("max retry times, still cannot process batch, please check your GPU memory")
-            
-        self.torch_empty_cache()
-        self._dynamic_batch_size = old_dynamic_batch_size
-        return np.array(res)
-
-    def _compute_batch_scores(self, batch_pairs, max_length=None):
-        if max_length is None:
-            scores = self._model.compute_score(batch_pairs, normalize=True)
-        else:
-            scores = self._model.compute_score(batch_pairs, max_length=max_length, normalize=True)
-        if not isinstance(scores, Iterable):
-            scores = [scores]
-        return scores
-
-    def similarity(self, query: str, texts: list):
-        pairs = [(query, truncate(t, 2048)) for t in texts]
-        token_count = 0
-        for _, t in pairs:
-            token_count += num_tokens_from_string(t)
-        batch_size = 4096
-        res = self._process_batch(pairs, max_batch_size=batch_size)
-        return np.array(res), token_count
-
-
 class JinaRerank(Base):
    _FACTORY_NAME = "Jina"

@ -162,36 +61,6 @@ class JinaRerank(Base):
        return rank, self.total_token_count(res)


-class YoudaoRerank(DefaultRerank):
-    _FACTORY_NAME = "Youdao"
-    _model = None
-    _model_lock = threading.Lock()
-
-    def __init__(self, key=None, model_name="maidalun1020/bce-reranker-base_v1", **kwargs):
-        if not settings.LIGHTEN and not YoudaoRerank._model:
-            from BCEmbedding import RerankerModel
-
-            with YoudaoRerank._model_lock:
-                if not YoudaoRerank._model:
-                    try:
-                        YoudaoRerank._model = RerankerModel(model_name_or_path=os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z0-9]+/", "", model_name)))
-                    except Exception:
-                        YoudaoRerank._model = RerankerModel(model_name_or_path=model_name.replace("maidalun1020", "InfiniFlow"))
-
-        self._model = YoudaoRerank._model
-        self._dynamic_batch_size = 8
-        self._min_batch_size = 1
-
-    def similarity(self, query: str, texts: list):
-        pairs = [(query, truncate(t, self._model.max_length)) for t in texts]
-        token_count = 0
-        for _, t in pairs:
-            token_count += num_tokens_from_string(t)
-        batch_size = 8
-        res = self._process_batch(pairs, max_batch_size=batch_size)
-        return np.array(res), token_count
-
-
 class XInferenceRerank(Base):
    _FACTORY_NAME = "Xinference"

@ -514,7 +383,7 @@ class QWenRerank(Base):
            raise ValueError(f"Error calling QWenRerank model {self.model_name}: {resp.status_code} - {resp.text}")


-class HuggingfaceRerank(DefaultRerank):
+class HuggingfaceRerank(Base):
    _FACTORY_NAME = "HuggingFace"

    @staticmethod
--- a/rag/settings.py
+++ b/rag/settings.py
@ -17,6 +17,7 @@ import os
 import logging
 from api.utils.configs import get_base_config, decrypt_database_config
 from api.utils.file_utils import get_project_base_directory
+from api.utils.common import pip_install_torch

 # Server
 RAG_CONF_PATH = os.path.join(get_project_base_directory(), "conf")
@ -65,6 +66,7 @@ TAG_FLD = "tag_feas"

 PARALLEL_DEVICES = 0
 try:
+    pip_install_torch()
    import torch.cuda
    PARALLEL_DEVICES = torch.cuda.device_count()
    logging.info(f"found {PARALLEL_DEVICES} gpus")
--- a/rag/svr/jina_server.py
+++ b/rag/svr/jina_server.py
@ -1,109 +0,0 @@
-#
-#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-
-from jina import Deployment
-from docarray import BaseDoc
-from jina import Executor, requests
-from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
-import argparse
-import torch
-
-
-class Prompt(BaseDoc):
-    message: list[dict]
-    gen_conf: dict
-
-
-class Generation(BaseDoc):
-    text: str
-
-
-tokenizer = None
-model_name = ""
-
-
-class TokenStreamingExecutor(Executor):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self.model = AutoModelForCausalLM.from_pretrained(
-            model_name, device_map="auto", torch_dtype="auto"
-        )
-
-    @requests(on="/chat")
-    async def generate(self, doc: Prompt, **kwargs) -> Generation:
-        text = tokenizer.apply_chat_template(
-            doc.message,
-            tokenize=False,
-        )
-        inputs = tokenizer([text], return_tensors="pt")
-        generation_config = GenerationConfig(
-            **doc.gen_conf,
-            eos_token_id=tokenizer.eos_token_id,
-            pad_token_id=tokenizer.eos_token_id
-        )
-        generated_ids = self.model.generate(
-            inputs.input_ids, generation_config=generation_config
-        )
-        generated_ids = [
-            output_ids[len(input_ids) :]
-            for input_ids, output_ids in zip(inputs.input_ids, generated_ids)
-        ]
-
-        response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-        yield Generation(text=response)
-
-    @requests(on="/stream")
-    async def task(self, doc: Prompt, **kwargs) -> Generation:
-        text = tokenizer.apply_chat_template(
-            doc.message,
-            tokenize=False,
-        )
-        input = tokenizer([text], return_tensors="pt")
-        input_len = input["input_ids"].shape[1]
-        max_new_tokens = 512
-        if "max_new_tokens" in doc.gen_conf:
-            max_new_tokens = doc.gen_conf.pop("max_new_tokens")
-        generation_config = GenerationConfig(
-            **doc.gen_conf,
-            eos_token_id=tokenizer.eos_token_id,
-            pad_token_id=tokenizer.eos_token_id
-        )
-        for _ in range(max_new_tokens):
-            output = self.model.generate(
-                **input, max_new_tokens=1, generation_config=generation_config
-            )
-            if output[0][-1] == tokenizer.eos_token_id:
-                break
-            yield Generation(
-                text=tokenizer.decode(output[0][input_len:], skip_special_tokens=True)
-            )
-            input = {
-                "input_ids": output,
-                "attention_mask": torch.ones(1, len(output[0])),
-            }
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model_name", type=str, help="Model name or path")
-    parser.add_argument("--port", default=12345, type=int, help="Jina serving port")
-    args = parser.parse_args()
-    model_name = args.model_name
-    tokenizer = AutoTokenizer.from_pretrained(args.model_name)
-    with Deployment(
-        uses=TokenStreamingExecutor, port=args.port, protocol="grpc"
-    ) as dep:
-        dep.block()
--- a/rag/svr/task_executor.py
+++ b/rag/svr/task_executor.py
@ -29,6 +29,7 @@ from api.db.services.pipeline_operation_log_service import PipelineOperationLogS
 from api.utils.api_utils import timeout
 from api.utils.base64_image import image2id
 from api.utils.log_utils import init_root_logger, get_project_base_directory
+from api.utils.configs import show_configs
 from graphrag.general.index import run_graphrag_for_kb
 from graphrag.utils import get_llm_cache, set_llm_cache, get_tags_from_cache, set_tags_to_cache
 from rag.flow.pipeline import Pipeline
@ -475,7 +476,7 @@ async def embedding(docs, mdl, parser_config=None, callback=None):
    tk_count = 0
    if len(tts) == len(cnts):
        vts, c = await trio.to_thread.run_sync(lambda: mdl.encode(tts[0: 1]))
-        tts = np.concatenate([vts for _ in range(len(tts))], axis=0)
+        tts = np.concatenate([vts[0] for _ in range(len(tts))], axis=0)
        tk_count += c

    @timeout(60)
@ -1061,7 +1062,10 @@ async def main():
          /____/
    """)
    logging.info(f'RAGFlow version: {get_ragflow_version()}')
+    show_configs()
    settings.init_settings()
+    from api.settings import EMBEDDING_CFG
+    logging.info(f'api.settings.EMBEDDING_CFG: {EMBEDDING_CFG}')
    print_rag_settings()
    if sys.platform != "win32":
        signal.signal(signal.SIGUSR1, start_tracemalloc_and_snapshot)
--- a/sandbox/docker-compose.yml
+++ b/sandbox/docker-compose.yml
@ -1,6 +1,5 @@
 services:
  sandbox-executor-manager:
-    container_name: sandbox-executor-manager
    build:
      context: ./executor_manager
      dockerfile: Dockerfile
--- a/sdk/python/test/conftest.py
+++ b/sdk/python/test/conftest.py
@ -141,7 +141,7 @@ def set_tenant_info(get_auth):
    tenant_info = {
        "tenant_id": tenant_id,
        "llm_id": "glm-4-flash@ZHIPU-AI",
-        "embd_id": "BAAI/bge-large-zh-v1.5@BAAI",
+        "embd_id": "BAAI/bge-small-en-v1.5@Builtin",
        "img2txt_id": "glm-4v@ZHIPU-AI",
        "asr_id": "",
        "tts_id": None,
--- a/sdk/python/test/test_http_api/test_dataset_mangement/test_create_dataset.py
+++ b/sdk/python/test/test_http_api/test_dataset_mangement/test_create_dataset.py
@ -221,11 +221,10 @@ class TestDatasetCreate:
    @pytest.mark.parametrize(
        "name, embedding_model",
        [
-            ("BAAI/bge-large-zh-v1.5@BAAI", "BAAI/bge-large-zh-v1.5@BAAI"),
-            ("maidalun1020/bce-embedding-base_v1@Youdao", "maidalun1020/bce-embedding-base_v1@Youdao"),
+            ("BAAI/bge-small-en-v1.5@Builtin", "BAAI/bge-small-en-v1.5@Builtin"),
            ("embedding-3@ZHIPU-AI", "embedding-3@ZHIPU-AI"),
        ],
-        ids=["builtin_baai", "builtin_youdao", "tenant_zhipu"],
+        ids=["builtin_baai", "tenant_zhipu"],
    )
    def test_embedding_model(self, get_http_api_auth, name, embedding_model):
        payload = {"name": name, "embedding_model": embedding_model}
@ -257,11 +256,11 @@ class TestDatasetCreate:
    @pytest.mark.parametrize(
        "name, embedding_model",
        [
-            ("missing_at", "BAAI/bge-large-zh-v1.5BAAI"),
-            ("missing_model_name", "@BAAI"),
-            ("missing_provider", "BAAI/bge-large-zh-v1.5@"),
-            ("whitespace_only_model_name", " @BAAI"),
-            ("whitespace_only_provider", "BAAI/bge-large-zh-v1.5@ "),
+            ("missing_at", "BAAI/bge-small-en-v1.5Builtin"),
+            ("missing_model_name", "@Builtin"),
+            ("missing_provider", "BAAI/bge-small-en-v1.5@"),
+            ("whitespace_only_model_name", " @Builtin"),
+            ("whitespace_only_provider", "BAAI/bge-small-en-v1.5@ "),
        ],
        ids=["missing_at", "empty_model_name", "empty_provider", "whitespace_only_model_name", "whitespace_only_provider"],
    )
@ -279,7 +278,7 @@ class TestDatasetCreate:
        payload = {"name": "embedding_model_unset"}
        res = create_dataset(get_http_api_auth, payload)
        assert res["code"] == 0, res
-        assert res["data"]["embedding_model"] == "BAAI/bge-large-zh-v1.5@BAAI", res
+        assert res["data"]["embedding_model"] == "BAAI/bge-small-en-v1.5@Builtin", res

    @pytest.mark.p2
    def test_embedding_model_none(self, get_http_api_auth):
--- a/sdk/python/test/test_http_api/test_dataset_mangement/test_update_dataset.py
+++ b/sdk/python/test/test_http_api/test_dataset_mangement/test_update_dataset.py
@ -255,11 +255,10 @@ class TestDatasetUpdate:
    @pytest.mark.parametrize(
        "embedding_model",
        [
-            "BAAI/bge-large-zh-v1.5@BAAI",
-            "maidalun1020/bce-embedding-base_v1@Youdao",
+            "BAAI/bge-small-en-v1.5@Builtin",
            "embedding-3@ZHIPU-AI",
        ],
-        ids=["builtin_baai", "builtin_youdao", "tenant_zhipu"],
+        ids=["builtin_baai", "tenant_zhipu"],
    )
    def test_embedding_model(self, get_http_api_auth, add_dataset_func, embedding_model):
        dataset_id = add_dataset_func
@ -296,11 +295,11 @@ class TestDatasetUpdate:
    @pytest.mark.parametrize(
        "name, embedding_model",
        [
-            ("missing_at", "BAAI/bge-large-zh-v1.5BAAI"),
-            ("missing_model_name", "@BAAI"),
-            ("missing_provider", "BAAI/bge-large-zh-v1.5@"),
-            ("whitespace_only_model_name", " @BAAI"),
-            ("whitespace_only_provider", "BAAI/bge-large-zh-v1.5@ "),
+            ("missing_at", "BAAI/bge-small-en-v1.5Builtin"),
+            ("missing_model_name", "@Builtin"),
+            ("missing_provider", "BAAI/bge-small-en-v1.5@"),
+            ("whitespace_only_model_name", " @Builtin"),
+            ("whitespace_only_provider", "BAAI/bge-small-en-v1.5@ "),
        ],
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-    { url = "https://mirrors.aliyun.com/pypi/packages/6b/11/cc635220681e93a0183390e26485430ca2c7b5f9d33b15c74c2861cb8091/urllib3-2.4.0-py3-none-any.whl", hash = "sha256:4e16665048960a0900c702d4a66415956a584919c03361cac9f1df5c5dd7e813" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/a7/c2/fe1e52489ae3122415c51f387e221dd0773709bad6c6cdaa599e8a2c5185/urllib3-2.5.0-py3-none-any.whl", hash = "sha256:e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc", size = 129795, upload-time = "2025-06-18T14:07:40.39Z" },
 ]

 [[package]]
 name = "xlsxwriter"
-version = "3.2.3"
-source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
-sdist = { url = "https://mirrors.aliyun.com/pypi/packages/a7/d1/e026d33dd5d552e5bf3a873dee54dad66b550230df8290d79394f09b2315/xlsxwriter-3.2.3.tar.gz", hash = "sha256:ad6fd41bdcf1b885876b1f6b7087560aecc9ae5a9cc2ba97dcac7ab2e210d3d5" }
+version = "3.2.9"
+source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" }
+sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/46/2c/c06ef49dc36e7954e55b802a8b231770d286a9758b3d936bd1e04ce5ba88/xlsxwriter-3.2.9.tar.gz", hash = "sha256:254b1c37a368c444eac6e2f867405cc9e461b0ed97a3233b2ac1e574efb4140c", size = 215940, upload-time = "2025-09-16T00:16:21.63Z" }
 wheels = [
-    { url = "https://mirrors.aliyun.com/pypi/packages/37/b1/a252d499f2760b314fcf264d2b36fcc4343a1ecdb25492b210cb0db70a68/XlsxWriter-3.2.3-py3-none-any.whl", hash = "sha256:593f8296e8a91790c6d0378ab08b064f34a642b3feb787cf6738236bd0a4860d" },
+    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/3a/0c/3662f4a66880196a590b202f0db82d919dd2f89e99a27fadef91c4a33d41/xlsxwriter-3.2.9-py3-none-any.whl", hash = "sha256:9a5db42bc5dff014806c58a20b9eae7322a134abb6fce3c92c181bfb275ec5b3", size = 175315, upload-time = "2025-09-16T00:16:20.108Z" },
 ]
--- a/test/testcases/conftest.py
+++ b/test/testcases/conftest.py
@ -141,7 +141,7 @@ def set_tenant_info(auth):
    tenant_info = {
        "tenant_id": tenant_id,
        "llm_id": "glm-4-flash@ZHIPU-AI",
-        "embd_id": "BAAI/bge-large-zh-v1.5@BAAI",
+        "embd_id": "BAAI/bge-small-en-v1.5@Builtin",
        "img2txt_id": "",
        "asr_id": "",
        "tts_id": None,
--- a/test/testcases/test_http_api/test_dataset_mangement/test_create_dataset.py
+++ b/test/testcases/test_http_api/test_dataset_mangement/test_create_dataset.py
@ -224,11 +224,10 @@ class TestDatasetCreate:
    @pytest.mark.parametrize(
        "name, embedding_model",
        [
-            ("BAAI/bge-large-zh-v1.5@BAAI", "BAAI/bge-large-zh-v1.5@BAAI"),
-            ("maidalun1020/bce-embedding-base_v1@Youdao", "maidalun1020/bce-embedding-base_v1@Youdao"),
+            ("BAAI/bge-small-en-v1.5@Builtin", "BAAI/bge-small-en-v1.5@Builtin"),
            ("embedding-3@ZHIPU-AI", "embedding-3@ZHIPU-AI"),
        ],
-        ids=["builtin_baai", "builtin_youdao", "tenant_zhipu"],
+        ids=["builtin_baai", "tenant_zhipu"],
    )
    def test_embedding_model(self, HttpApiAuth, name, embedding_model):
        payload = {"name": name, "embedding_model": embedding_model}
@ -262,11 +261,11 @@ class TestDatasetCreate:
        [
            ("empty", ""),
            ("space", " "),
-            ("missing_at", "BAAI/bge-large-zh-v1.5BAAI"),
-            ("missing_model_name", "@BAAI"),
-            ("missing_provider", "BAAI/bge-large-zh-v1.5@"),
-            ("whitespace_only_model_name", " @BAAI"),
-            ("whitespace_only_provider", "BAAI/bge-large-zh-v1.5@ "),
+            ("missing_at", "BAAI/bge-small-en-v1.5Builtin"),
+            ("missing_model_name", "@Builtin"),
+            ("missing_provider", "BAAI/bge-small-en-v1.5@"),
+            ("whitespace_only_model_name", " @Builtin"),
+            ("whitespace_only_provider", "BAAI/bge-small-env1.5@ "),
        ],
        ids=["empty", "space", "missing_at", "empty_model_name", "empty_provider", "whitespace_only_model_name", "whitespace_only_provider"],
    )
@ -284,14 +283,14 @@ class TestDatasetCreate:
        payload = {"name": "embedding_model_unset"}
        res = create_dataset(HttpApiAuth, payload)
        assert res["code"] == 0, res
-        assert res["data"]["embedding_model"] == "BAAI/bge-large-zh-v1.5@BAAI", res
+        assert res["data"]["embedding_model"] == "BAAI/bge-small-en-v1.5@Builtin", res

    @pytest.mark.p2
    def test_embedding_model_none(self, HttpApiAuth):
        payload = {"name": "embedding_model_none", "embedding_model": None}
        res = create_dataset(HttpApiAuth, payload)
        assert res["code"] == 0, res
-        assert res["data"]["embedding_model"] == "BAAI/bge-large-zh-v1.5@BAAI", res
+        assert res["data"]["embedding_model"] == "BAAI/bge-small-en-v1.5@Builtin", res

    @pytest.mark.p1
    @pytest.mark.parametrize(
--- a/test/testcases/test_http_api/test_dataset_mangement/test_update_dataset.py
+++ b/test/testcases/test_http_api/test_dataset_mangement/test_update_dataset.py
@ -259,11 +259,10 @@ class TestDatasetUpdate:
    @pytest.mark.parametrize(
        "embedding_model",
        [
-            "BAAI/bge-large-zh-v1.5@BAAI",
-            "maidalun1020/bce-embedding-base_v1@Youdao",
+            "BAAI/bge-small-en-v1.5@Builtin",
            "embedding-3@ZHIPU-AI",
        ],
-        ids=["builtin_baai", "builtin_youdao", "tenant_zhipu"],
+        ids=["builtin_baai", "tenant_zhipu"],
    )
    def test_embedding_model(self, HttpApiAuth, add_dataset_func, embedding_model):
        dataset_id = add_dataset_func
@ -302,11 +301,11 @@ class TestDatasetUpdate:
        [
            ("empty", ""),
            ("space", " "),
-            ("missing_at", "BAAI/bge-large-zh-v1.5BAAI"),
-            ("missing_model_name", "@BAAI"),
-            ("missing_provider", "BAAI/bge-large-zh-v1.5@"),
-            ("whitespace_only_model_name", " @BAAI"),
-            ("whitespace_only_provider", "BAAI/bge-large-zh-v1.5@ "),
+            ("missing_at", "BAAI/bge-small-en-v1.5Builtin"),
+            ("missing_model_name", "@Builtin"),
+            ("missing_provider", "BAAI/bge-small-en-v1.5@"),
+            ("whitespace_only_model_name", " @Builtin"),
+            ("whitespace_only_provider", "BAAI/bge-small-en-v1.5@ "),
        ],
        ids=["empty", "space", "missing_at", "empty_model_name", "empty_provider", "whitespace_only_model_name", "whitespace_only_provider"],
    )
@ -329,7 +328,7 @@ class TestDatasetUpdate:

        res = list_datasets(HttpApiAuth)
        assert res["code"] == 0, res
-        assert res["data"][0]["embedding_model"] == "BAAI/bge-large-zh-v1.5@BAAI", res
+        assert res["data"][0]["embedding_model"] == "BAAI/bge-small-en-v1.5@Builtin", res

    @pytest.mark.p1
    @pytest.mark.parametrize(
--- a/test/testcases/test_sdk_api/test_dataset_mangement/test_create_dataset.py
+++ b/test/testcases/test_sdk_api/test_dataset_mangement/test_create_dataset.py
@ -182,11 +182,10 @@ class TestDatasetCreate:
    @pytest.mark.parametrize(
        "name, embedding_model",
        [
-            ("BAAI/bge-large-zh-v1.5@BAAI", "BAAI/bge-large-zh-v1.5@BAAI"),
-            ("maidalun1020/bce-embedding-base_v1@Youdao", "maidalun1020/bce-embedding-base_v1@Youdao"),
+            ("BAAI/bge-small-en-v1.5@Builtin", "BAAI/bge-small-en-v1.5@Builtin"),
            ("embedding-3@ZHIPU-AI", "embedding-3@ZHIPU-AI"),
        ],
-        ids=["builtin_baai", "builtin_youdao", "tenant_zhipu"],
+        ids=["builtin_baai", "tenant_zhipu"],
    )
    def test_embedding_model(self, client, name, embedding_model):
        payload = {"name": name, "embedding_model": embedding_model}
@ -219,11 +218,11 @@ class TestDatasetCreate:
        [
            ("empty", ""),
            ("space", " "),
-            ("missing_at", "BAAI/bge-large-zh-v1.5BAAI"),
-            ("missing_model_name", "@BAAI"),
-            ("missing_provider", "BAAI/bge-large-zh-v1.5@"),
-            ("whitespace_only_model_name", " @BAAI"),
-            ("whitespace_only_provider", "BAAI/bge-large-zh-v1.5@ "),
+            ("missing_at", "BAAI/bge-small-en-v1.5Builtin"),
+            ("missing_model_name", "@Builtin"),
+            ("missing_provider", "BAAI/bge-small-en-v1.5@"),
+            ("whitespace_only_model_name", " @Builtin"),
+            ("whitespace_only_provider", "BAAI/bge-small-en-v1.5@ "),
        ],
        ids=["empty", "space", "missing_at", "empty_model_name", "empty_provider", "whitespace_only_model_name", "whitespace_only_provider"],
    )
@ -240,13 +239,13 @@ class TestDatasetCreate:
    def test_embedding_model_unset(self, client):
        payload = {"name": "embedding_model_unset"}
        dataset = client.create_dataset(**payload)
-        assert dataset.embedding_model == "BAAI/bge-large-zh-v1.5@BAAI", str(dataset)
+        assert dataset.embedding_model == "BAAI/bge-small-en-v1.5@Builtin", str(dataset)

    @pytest.mark.p2
    def test_embedding_model_none(self, client):
        payload = {"name": "embedding_model_none", "embedding_model": None}
        dataset = client.create_dataset(**payload)
-        assert dataset.embedding_model == "BAAI/bge-large-zh-v1.5@BAAI", str(dataset)
+        assert dataset.embedding_model == "BAAI/bge-small-en-v1.5@Builtin", str(dataset)

    @pytest.mark.p1
    @pytest.mark.parametrize(
--- a/test/testcases/test_sdk_api/test_dataset_mangement/test_update_dataset.py
+++ b/test/testcases/test_sdk_api/test_dataset_mangement/test_update_dataset.py
@ -168,11 +168,10 @@ class TestDatasetUpdate:
    @pytest.mark.parametrize(
        "embedding_model",
        [
-            "BAAI/bge-large-zh-v1.5@BAAI",
-            "maidalun1020/bce-embedding-base_v1@Youdao",
+            "BAAI/bge-small-en-v1.5@Builtin",
            "embedding-3@ZHIPU-AI",
        ],
-        ids=["builtin_baai", "builtin_youdao", "tenant_zhipu"],
+        ids=["builtin_baai", "tenant_zhipu"],
    )
    def test_embedding_model(self, client, add_dataset_func, embedding_model):
        dataset = add_dataset_func
@ -209,11 +208,11 @@ class TestDatasetUpdate:
        [
            ("empty", ""),
            ("space", " "),
-            ("missing_at", "BAAI/bge-large-zh-v1.5BAAI"),
-            ("missing_model_name", "@BAAI"),
-            ("missing_provider", "BAAI/bge-large-zh-v1.5@"),
-            ("whitespace_only_model_name", " @BAAI"),
-            ("whitespace_only_provider", "BAAI/bge-large-zh-v1.5@ "),
+            ("missing_at", "BAAI/bge-small-en-v1.5Builtin"),
+            ("missing_model_name", "@Builtin"),
+            ("missing_provider", "BAAI/bge-small-en-v1.5@"),
+            ("whitespace_only_model_name", " @Builtin"),
+            ("whitespace_only_provider", "BAAI/bge-small-en-v1.5@ "),
        ],
        ids=["empty", "space", "missing_at", "empty_model_name", "empty_provider", "whitespace_only_model_name", "whitespace_only_provider"],
    )
@ -231,10 +230,10 @@ class TestDatasetUpdate:
    def test_embedding_model_none(self, client, add_dataset_func):
        dataset = add_dataset_func
        dataset.update({"embedding_model": None})
-        assert dataset.embedding_model == "BAAI/bge-large-zh-v1.5@BAAI", str(dataset)
+        assert dataset.embedding_model == "BAAI/bge-small-en-v1.5@Builtin", str(dataset)

        retrieved_dataset = client.get_dataset(name=dataset.name)
-        assert retrieved_dataset.embedding_model == "BAAI/bge-large-zh-v1.5@BAAI", str(retrieved_dataset)
+        assert retrieved_dataset.embedding_model == "BAAI/bge-small-en-v1.5@Builtin", str(retrieved_dataset)

    @pytest.mark.p1
    @pytest.mark.parametrize(
--- a/test/testcases/test_web_api/test_chunk_app/test_create_chunk.py
+++ b/test/testcases/test_web_api/test_chunk_app/test_create_chunk.py
@ -57,7 +57,7 @@ class TestAddChunk:
        "payload, expected_code, expected_message",
        [
            ({"content_with_weight": None}, 100, """TypeError("unsupported operand type(s) for +: 'NoneType' and 'str'")"""),
-            ({"content_with_weight": ""}, 0, ""),
+            ({"content_with_weight": ""}, 100, """Exception('Error: 413 - {"error":"Input validation error: `inputs` cannot be empty","error_type":"Validation"}')"""),
            pytest.param(
                {"content_with_weight": 1},
                100,
@ -124,7 +124,7 @@ class TestAddChunk:
        "payload, expected_code, expected_message",
        [
            ({"content_with_weight": "chunk test", "question_kwd": ["a", "b", "c"]}, 0, ""),
-            ({"content_with_weight": "chunk test", "question_kwd": [""]}, 0, ""),
+            ({"content_with_weight": "chunk test", "question_kwd": [""]}, 100, """Exception('Error: 413 - {"error":"Input validation error: `inputs` cannot be empty","error_type":"Validation"}')"""),
            ({"content_with_weight": "chunk test", "question_kwd": [1]}, 100, "TypeError('sequence item 0: expected str instance, int found')"),
            ({"content_with_weight": "chunk test", "question_kwd": ["a", "a"]}, 0, ""),
            ({"content_with_weight": "chunk test", "question_kwd": "abc"}, 102, "`question_kwd` is required to be a list"),
--- a/test/testcases/test_web_api/test_chunk_app/test_update_chunk.py
+++ b/test/testcases/test_web_api/test_chunk_app/test_update_chunk.py
@ -45,7 +45,7 @@ class TestUpdateChunk:
        "payload, expected_code, expected_message",
        [
            ({"content_with_weight": None}, 100, "TypeError('expected string or bytes-like object')"),
-            ({"content_with_weight": ""}, 0, ""),
+            ({"content_with_weight": ""}, 100, """Exception('Error: 413 - {"error":"Input validation error: `inputs` cannot be empty","error_type":"Validation"}')"""),
            ({"content_with_weight": 1}, 100, "TypeError('expected string or bytes-like object')"),
            ({"content_with_weight": "update chunk"}, 0, ""),
            ({"content_with_weight": " "}, 0, ""),
@ -103,7 +103,7 @@ class TestUpdateChunk:
        "payload, expected_code, expected_message",
        [
            ({"question_kwd": ["a", "b", "c"]}, 0, ""),
-            ({"question_kwd": [""]}, 0, ""),
+            ({"question_kwd": [""]}, 100, """Exception('Error: 413 - {"error":"Input validation error: `inputs` cannot be empty","error_type":"Validation"}')"""),
            ({"question_kwd": [1]}, 100, "TypeError('sequence item 0: expected str instance, int found')"),
            ({"question_kwd": ["a", "a"]}, 0, ""),
            ({"question_kwd": "abc"}, 102, "`question_kwd` should be a list"),
--- a/test/testcases/test_web_api/test_kb_app/test_update_kb.py
+++ b/test/testcases/test_web_api/test_kb_app/test_update_kb.py
@ -149,11 +149,10 @@ class TestDatasetUpdate:
    @pytest.mark.parametrize(
        "embedding_model",
        [
-            "BAAI/bge-large-zh-v1.5@BAAI",
-            "maidalun1020/bce-embedding-base_v1@Youdao",
+            "BAAI/bge-small-en-v1.5@Builtin",
            "embedding-3@ZHIPU-AI",
        ],
-        ids=["builtin_baai", "builtin_youdao", "tenant_zhipu"],
+        ids=["builtin_baai", "tenant_zhipu"],
    )
    def test_embedding_model(self, WebApiAuth, add_dataset_func, embedding_model):
        kb_id = add_dataset_func
--- a/uv.lock
+++ b/uv.lock