diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 2613255c1f7..e902bb73be2 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -55,28 +55,29 @@ jobs:
         with:
           version: ">=0.8.2"
 
-      - name: Build ragflow:dev-slim
+      - name: Build ragflow:nightly-slim
         run: |
           RUNNER_WORKSPACE_PREFIX=${RUNNER_WORKSPACE_PREFIX:-$HOME}
           sudo docker pull ubuntu:22.04
-          sudo docker build --progress=plain --build-arg LIGHTEN=1 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:dev-slim .
+          sudo docker build --progress=plain --build-arg LIGHTEN=1 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
 
-      - name: Build ragflow:dev
+      - name: Build ragflow:nightly
         run: |
-          sudo docker build --progress=plain --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:dev .
+          sudo docker build --progress=plain --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly .
 
-      - name: Start ragflow:dev-slim
+      - name: Start ragflow:nightly-slim
         run: |
+          echo "RAGFLOW_IMAGE=infiniflow/ragflow:nightly-slim" >> docker/.env
           sudo docker compose -f docker/docker-compose.yml up -d
 
-      - name: Stop ragflow:dev-slim
+      - 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
 
-      - name: Start ragflow:dev
+      - name: Start ragflow:nightly
         run: |
-          echo "RAGFLOW_IMAGE=infiniflow/ragflow:dev" >> docker/.env
+          echo "RAGFLOW_IMAGE=infiniflow/ragflow:nightly" >> docker/.env
           sudo docker compose -f docker/docker-compose.yml up -d
 
       - name: Run sdk tests against Elasticsearch
@@ -100,12 +101,12 @@ jobs:
           cd sdk/python && poetry install && source .venv/bin/activate && cd test/test_frontend_api && pytest -s --tb=short get_email.py test_dataset.py 
           
 
-      - name: Stop ragflow:dev
+      - 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
 
-      - name: Start ragflow:dev
+      - name: Start ragflow:nightly
         run: |
           sudo DOC_ENGINE=infinity docker compose -f docker/docker-compose.yml up -d
 
@@ -129,7 +130,7 @@ jobs:
           done
           cd sdk/python && poetry install && source .venv/bin/activate && cd test/test_frontend_api && pytest -s --tb=short get_email.py test_dataset.py
 
-      - name: Stop ragflow:dev
+      - 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
diff --git a/Dockerfile b/Dockerfile
index f00ef060c8d..3e316e87a1b 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -43,6 +43,7 @@ RUN --mount=type=cache,id=ragflow_apt,target=/var/cache/apt,sharing=locked \
     fi; \
     rm -f /etc/apt/apt.conf.d/docker-clean && \
     echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache && \
+    chmod 1777 /tmp && \
     apt update && \
     apt --no-install-recommends install -y ca-certificates && \
     apt update && \
diff --git a/Dockerfile.deps b/Dockerfile.deps
index 438ab5ce9fe..c16ad446201 100644
--- a/Dockerfile.deps
+++ b/Dockerfile.deps
@@ -1,6 +1,6 @@
 # This builds an image that contains the resources needed by Dockerfile
 #
-FROM ubuntu:22.04
+FROM scratch
 
 # Copy resources downloaded via download_deps.py
 COPY chromedriver-linux64-121-0-6167-85 chrome-linux64-121-0-6167-85 cl100k_base.tiktoken libssl1.1_1.1.1f-1ubuntu2_amd64.deb libssl1.1_1.1.1f-1ubuntu2_arm64.deb tika-server-standard-3.0.0.jar tika-server-standard-3.0.0.jar.md5 libssl*.deb /
diff --git a/README.md b/README.md
index 8be1526b35e..ed3681da905 100644
--- a/README.md
+++ b/README.md
@@ -165,29 +165,25 @@ releases! 🌟
    $ git clone https://github.com/infiniflow/ragflow.git
    ```
 
-3. Build the pre-built Docker images and start up the server:
+3. Start up the server using the pre-built Docker images:
 
-   > The command below downloads the dev version Docker image for RAGFlow slim (`dev-slim`). Note that RAGFlow slim
-   Docker images do not include embedding models or Python libraries and hence are approximately 1GB in size.
+   > The command below downloads the v0.14.1 version Docker image for RAGFlow slim (`v0.14.1-slim`). Note that RAGFlow slim
+   Docker images do not include embedding models or Python libraries and hence are approximately 2 GB in size.
 
    ```bash
    $ cd ragflow/docker
    $ docker compose -f docker-compose.yml up -d
    ```
 
-   > - To download a RAGFlow slim Docker image of a specific version, update the `RAGFLOW_IMAGE` variable in *
-       *docker/.env** to your desired version. For example, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1-slim`. After
-       making this change, rerun the command above to initiate the download.
-   > - To download the dev version of RAGFlow Docker image *including* embedding models and Python libraries, update the
-       `RAGFLOW_IMAGE` variable in **docker/.env** to `RAGFLOW_IMAGE=infiniflow/ragflow:dev`. After making this change,
-       rerun the command above to initiate the download.
-   > - To download a specific version of RAGFlow Docker image *including* embedding models and Python libraries, update
-       the `RAGFLOW_IMAGE` variable in **docker/.env** to your desired version. For example,
-       `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1`. After making this change, rerun the command above to initiate the
-       download.
+   | RAGFLOW_IMAGE tag in docker/.env | size  | Including embedding models and related Python packages? | comments               |
+   | -------------------------------- | ----- | ------------------------------------------------------- | ---------------------- |
+   | v0.14.1                          | ~9 GB | YES                                                     | stable release         |
+   | v0.14.1-slim                     | ~2 GB | NO                                                      | stable release         |
+   | v0.15.0-dev1                     | ~9 GB | YES                                                     | unstable beta release  |
+   | v0.15.0-dev1-slim                | ~2 GB | NO                                                      | unstable beta release  |
+   | nightly                          | ~9 GB | YES                                                     | unstable nightly build |
+   | nightly-slim                     | ~2 GB | NO                                                      | unstable nightly build |
 
-   > **NOTE:** A RAGFlow Docker image that includes embedding models and Python libraries is approximately 9GB in size
-   and may take significantly longer time to load.
 
 4. Check the server status after having the server up and running:
 
@@ -267,12 +263,12 @@ RAGFlow uses Elasticsearch by default for storing full text and vectors. To swit
 
 ## 🔧 Build a Docker image without embedding models
 
-This image is approximately 1 GB in size and relies on external LLM and embedding services.
+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 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:dev-slim .
+docker build --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
 ```
 
 ## 🔧 Build a Docker image including embedding models
@@ -282,7 +278,7 @@ This image is approximately 9 GB in size. As it includes embedding models, it re
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build -f Dockerfile -t infiniflow/ragflow:dev .
+docker build -f Dockerfile -t infiniflow/ragflow:nightly .
 ```
 
 ## 🔨 Launch service from source for development
diff --git a/README_id.md b/README_id.md
index fd06d63b8cb..a48a5005e4c 100644
--- a/README_id.md
+++ b/README_id.md
@@ -160,7 +160,7 @@ Coba demo kami di [https://demo.ragflow.io](https://demo.ragflow.io).
 
 3. Bangun image Docker pre-built dan jalankan server:
 
-   > Perintah di bawah ini akan mengunduh versi dev dari Docker image RAGFlow slim (`dev-slim`). Image RAGFlow slim
+   > Perintah di bawah ini akan mengunduh versi v0.14.1 dari Docker image RAGFlow slim (`v0.14.1-slim`). Image RAGFlow slim
    tidak termasuk model embedding atau library Python dan berukuran sekitar 1GB.
 
    ```bash
@@ -168,18 +168,16 @@ Coba demo kami di [https://demo.ragflow.io](https://demo.ragflow.io).
    $ docker compose -f docker-compose.yml up -d
    ```
 
-   > - Untuk mengunduh versi tertentu dari image Docker RAGFlow slim, perbarui variabel `RAGFlow_IMAGE` di *
-       *docker/.env** sesuai dengan versi yang diinginkan. Misalnya, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1-slim`.
-       Setelah mengubah ini, jalankan ulang perintah di atas untuk memulai unduhan.
-   > - Untuk mengunduh versi dev dari image Docker RAGFlow *termasuk* model embedding dan library Python, perbarui
-       variabel `RAGFlow_IMAGE` di **docker/.env** menjadi `RAGFLOW_IMAGE=infiniflow/ragflow:dev`. Setelah mengubah ini,
-       jalankan ulang perintah di atas untuk memulai unduhan.
-   > - Untuk mengunduh versi tertentu dari image Docker RAGFlow *termasuk* model embedding dan library Python, perbarui
-       variabel `RAGFlow_IMAGE` di **docker/.env** sesuai dengan versi yang diinginkan. Misalnya,
-       `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1`. Setelah mengubah ini, jalankan ulang perintah di atas untuk memulai unduhan.
+   | RAGFLOW_IMAGE tag in docker/.env | size  | Including embedding models and related Python packages? | comments               |
+   | -------------------------------- | ----- | ------------------------------------------------------- | ---------------------- |
+   | v0.14.1                          | ~9 GB | YES                                                     | stable release         |
+   | v0.14.1-slim                     | ~2 GB | NO                                                      | stable release         |
+   | v0.15.0-dev1                     | ~9 GB | YES                                                     | unstable beta release  |
+   | v0.15.0-dev1-slim                | ~2 GB | NO                                                      | unstable beta release  |
+   | nightly                          | ~9 GB | YES                                                     | unstable nightly build |
+   | nightly-slim                     | ~2 GB | NO                                                      | unstable nightly build |
+
 
-   > **CATATAN:** Image Docker RAGFlow yang mencakup model embedding dan library Python berukuran sekitar 9GB
-   dan mungkin memerlukan waktu lebih lama untuk dimuat.
 
 4. Periksa status server setelah server aktif dan berjalan:
 
@@ -242,12 +240,12 @@ Pembaruan konfigurasi ini memerlukan reboot semua kontainer agar efektif:
 
 ## 🔧 Membangun Docker Image tanpa Model Embedding
 
-Image ini berukuran sekitar 1 GB dan bergantung pada aplikasi LLM eksternal dan embedding.
+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 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:dev-slim .
+docker build --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
 ```
 
 ## 🔧 Membangun Docker Image Termasuk Model Embedding
@@ -257,7 +255,7 @@ Image ini berukuran sekitar 9 GB. Karena sudah termasuk model embedding, ia hany
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build -f Dockerfile -t infiniflow/ragflow:dev .
+docker build -f Dockerfile -t infiniflow/ragflow:nightly .
 ```
 
 ## 🔨 Menjalankan Aplikasi dari untuk Pengembangan
diff --git a/README_ja.md b/README_ja.md
index 22135c88e39..94369c95ce5 100644
--- a/README_ja.md
+++ b/README_ja.md
@@ -141,18 +141,22 @@
 
 3. ビルド済みの Docker イメージをビルドし、サーバーを起動する:
 
-   > 以下のコマンドは、RAGFlow slim（`dev-slim`）の開発版Dockerイメージをダウンロードします。RAGFlow slimのDockerイメージには、埋め込みモデルやPythonライブラリが含まれていないため、サイズは約1GBです。
+   > 以下のコマンドは、RAGFlow slim（`v0.14.1-slim`）の開発版Dockerイメージをダウンロードします。RAGFlow slimのDockerイメージには、埋め込みモデルやPythonライブラリが含まれていないため、サイズは約1GBです。
 
    ```bash
    $ cd ragflow/docker
    $ docker compose -f docker-compose.yml up -d
    ```
 
-   > - 特定のバージョンのRAGFlow slim Dockerイメージをダウンロードするには、**docker/.env**内の`RAGFlow_IMAGE`変数を希望のバージョンに更新します。例えば、`RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1`とします。この変更を行った後、上記のコマンドを再実行してダウンロードを開始してください。
-   > - RAGFlowの埋め込みモデルとPythonライブラリを含む開発版Dockerイメージをダウンロードするには、**docker/.env**内の`RAGFlow_IMAGE`変数を`RAGFLOW_IMAGE=infiniflow/ragflow:dev`に更新します。この変更を行った後、上記のコマンドを再実行してダウンロードを開始してください。
-   > - 特定のバージョンのRAGFlow Dockerイメージ（埋め込みモデルとPythonライブラリを含む）をダウンロードするには、**docker/.env**内の`RAGFlow_IMAGE`変数を希望のバージョンに更新します。例えば、`RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1`とします。この変更を行った後、上記のコマンドを再実行してダウンロードを開始してください。  
-   
-   > **NOTE:** 埋め込みモデルとPythonライブラリを含むRAGFlow Dockerイメージのサイズは約9GBであり、読み込みにかなりの時間がかかる場合があります。
+   | RAGFLOW_IMAGE tag in docker/.env | size  | Including embedding models and related Python packages? | comments               |
+   | -------------------------------- | ----- | ------------------------------------------------------- | ---------------------- |
+   | v0.14.1                          | ~9 GB | YES                                                     | stable release         |
+   | v0.14.1-slim                     | ~2 GB | NO                                                      | stable release         |
+   | v0.15.0-dev1                     | ~9 GB | YES                                                     | unstable beta release  |
+   | v0.15.0-dev1-slim                | ~2 GB | NO                                                      | unstable beta release  |
+   | nightly                          | ~9 GB | YES                                                     | unstable nightly build |
+   | nightly-slim                     | ~2 GB | NO                                                      | unstable nightly build |
+
 
 4. サーバーを立ち上げた後、サーバーの状態を確認する:
 
@@ -228,7 +232,7 @@ RAGFlow はデフォルトで Elasticsearch を使用して全文とベクトル
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:dev-slim .
+docker build --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）
@@ -238,7 +242,7 @@ docker build --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:dev-slim
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build -f Dockerfile -t infiniflow/ragflow:dev .
+docker build -f Dockerfile -t infiniflow/ragflow:nightly .
 ```
 
 ## 🔨 ソースコードからサービスを起動する方法
diff --git a/README_ko.md b/README_ko.md
index 87cde3d4ec0..d3769a9a19a 100644
--- a/README_ko.md
+++ b/README_ko.md
@@ -145,18 +145,24 @@
 
 3. 미리 빌드된 Docker 이미지를 생성하고 서버를 시작하세요:
 
-   > 아래의 명령은 RAGFlow slim(dev-slim)의 개발 버전 Docker 이미지를 다운로드합니다. RAGFlow slim Docker 이미지에는 임베딩 모델이나 Python 라이브러리가 포함되어 있지 않으므로 크기는 약 1GB입니다.
+   > 아래의 명령은 RAGFlow slim(v0.14.1-slim)의 개발 버전 Docker 이미지를 다운로드합니다. RAGFlow slim Docker 이미지에는 임베딩 모델이나 Python 라이브러리가 포함되어 있지 않으므로 크기는 약 1GB입니다.
 
    ```bash
    $ cd ragflow/docker
    $ docker compose -f docker-compose.yml up -d
    ```
    
-   > - 특정 버전의 RAGFlow slim Docker 이미지를 다운로드하려면, **docker/.env**에서 `RAGFlow_IMAGE` 변수를 원하는 버전으로 업데이트하세요. 예를 들어, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1-slim`으로 설정합니다. 이 변경을 완료한 후, 위의 명령을 다시 실행하여 다운로드를 시작하세요.
-   > - RAGFlow의 임베딩 모델과 Python 라이브러리를 포함한 개발 버전 Docker 이미지를 다운로드하려면, **docker/.env**에서 `RAGFlow_IMAGE` 변수를 `RAGFLOW_IMAGE=infiniflow/ragflow:dev`로 업데이트하세요. 이 변경을 완료한 후, 위의 명령을 다시 실행하여 다운로드를 시작하세요.
-   > - 특정 버전의 RAGFlow Docker 이미지를 임베딩 모델과 Python 라이브러리를 포함하여 다운로드하려면, **docker/.env**에서 `RAGFlow_IMAGE` 변수를 원하는 버전으로 업데이트하세요. 예를 들어, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1` 로 설정합니다. 이 변경을 완료한 후, 위의 명령을 다시 실행하여 다운로드를 시작하세요.  
-   
-   > **NOTE:** 임베딩 모델과 Python 라이브러리를 포함한 RAGFlow Docker 이미지의 크기는 약 9GB이며, 로드하는 데 상당히 오랜 시간이 걸릴 수 있습니다.
+
+   | RAGFLOW_IMAGE tag in docker/.env | size  | Including embedding models and related Python packages? | comments               |
+   | -------------------------------- | ----- | ------------------------------------------------------- | ---------------------- |
+   | v0.14.1                          | ~9 GB | YES                                                     | stable release         |
+   | v0.14.1-slim                     | ~2 GB | NO                                                      | stable release         |
+   | v0.15.0-dev1                     | ~9 GB | YES                                                     | unstable beta release  |
+   | v0.15.0-dev1-slim                | ~2 GB | NO                                                      | unstable beta release  |
+   | nightly                          | ~9 GB | YES                                                     | unstable nightly build |
+   | nightly-slim                     | ~2 GB | NO                                                      | unstable nightly build |
+
+
 
 
 4. 서버가 시작된 후 서버 상태를 확인하세요:
@@ -230,7 +236,7 @@ RAGFlow 는 기본적으로 Elasticsearch 를 사용하여 전체 텍스트 및
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:dev-slim .
+docker build --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
 ```
 
 ## 🔧 소스 코드로 Docker 이미지를 컴파일합니다(임베딩 모델 포함)
@@ -240,7 +246,7 @@ docker build --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:dev-slim
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build -f Dockerfile -t infiniflow/ragflow:dev .
+docker build -f Dockerfile -t infiniflow/ragflow:nightly .
 ```
 
 ## 🔨 소스 코드로 서비스를 시작합니다.
diff --git a/README_zh.md b/README_zh.md
index f7a5b1b25a4..0d9e7ddc9a5 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -142,18 +142,23 @@
 
 3. 进入 **docker** 文件夹，利用提前编译好的 Docker 镜像启动服务器：
 
-   > 运行以下命令会自动下载 dev 版的 RAGFlow slim Docker 镜像（`dev-slim`），该镜像并不包含 embedding 模型以及一些 Python 库，因此镜像大小约 1GB。
+   > 运行以下命令会自动下载 v0.14.1 版的 RAGFlow slim Docker 镜像（`v0.14.1-slim`），该镜像并不包含 embedding 模型以及一些 Python 库，因此镜像大小约 1GB。
 
    ```bash
    $ cd ragflow/docker
    $ docker compose -f docker-compose.yml up -d
    ```
 
-   > - 如果你想下载并运行特定版本的 RAGFlow slim Docker 镜像，请在 **docker/.env** 文件中找到 `RAGFLOW_IMAGE` 变量，将其改为对应版本。例如 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1-slim`，然后再运行上述命令。
-   > - 如果您想安装内置 embedding 模型和 Python 库的 dev 版本的 Docker 镜像，需要将 **docker/.env** 文件中的 `RAGFLOW_IMAGE` 变量修改为： `RAGFLOW_IMAGE=infiniflow/ragflow:dev`。
-   > - 如果您想安装内置 embedding 模型和 Python 库的指定版本的 RAGFlow Docker 镜像，需要将 **docker/.env** 文件中的 `RAGFLOW_IMAGE` 变量修改为： `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1`。修改后，再运行上面的命令。
-   > **注意：** 安装内置 embedding 模型和 Python 库的指定版本的 RAGFlow Docker 镜像大小约 9 GB，可能需要更长时间下载，请耐心等待。
-   
+   | RAGFLOW_IMAGE tag in docker/.env | 尺寸  | 内置 embedding 模型和相关 Python 库?                    | 描述                   |
+   | -------------------------------- | ----- | ------------------------------------------------------- | ---------------------- |
+   | v0.14.1                          | ~9 GB | YES                                                     | stable release         |
+   | v0.14.1-slim                     | ~2 GB | NO                                                      | stable release         |
+   | v0.15.0-dev1                     | ~9 GB | YES                                                     | unstable beta release  |
+   | v0.15.0-dev1-slim                | ~2 GB | NO                                                      | unstable beta release  |
+   | nightly                          | ~9 GB | YES                                                     | unstable nightly build |
+   | nightly-slim                     | ~2 GB | NO                                                      | unstable nightly build |
+
+
 4. 服务器启动成功后再次确认服务器状态：
 
    ```bash
@@ -230,12 +235,12 @@ RAGFlow 默认使用 Elasticsearch 存储文本和向量数据. 如果要切换
 
 ## 🔧 源码编译 Docker 镜像（不含 embedding 模型）
 
-本 Docker 镜像大小约 1 GB 左右并且依赖外部的大模型和 embedding 服务。
+本 Docker 镜像大小约 2 GB 左右并且依赖外部的大模型和 embedding 服务。
 
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build --build-arg LIGHTEN=1 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:dev-slim .
+docker build --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）
@@ -245,7 +250,7 @@ docker build --build-arg LIGHTEN=1 --build-arg NEED_MIRROR=1 -f Dockerfile -t in
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:dev .
+docker build --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly .
 ```
 
 ## 🔨 以源代码启动服务
diff --git a/agent/canvas.py b/agent/canvas.py
index bfffabd59ce..d8b04a9835b 100644
--- a/agent/canvas.py
+++ b/agent/canvas.py
@@ -21,6 +21,7 @@
 from agent.component import component_class
 from agent.component.base import ComponentBase
 
+
 class Canvas(ABC):
     """
     dsl = {
@@ -320,3 +321,13 @@ def _find_loop(self, max_loops=6):
 
     def get_prologue(self):
         return self.components["begin"]["obj"]._param.prologue
+
+    def set_global_param(self, **kwargs):
+        for k, v in kwargs.items():
+            for q in self.components["begin"]["obj"]._param.query:
+                if k != q["key"]:
+                    continue
+                q["value"] = v
+
+    def get_preset_param(self):
+        return self.components["begin"]["obj"]._param.query
\ No newline at end of file
diff --git a/agent/component/base.py b/agent/component/base.py
index 2660be7d373..2dc0cd49beb 100644
--- a/agent/component/base.py
+++ b/agent/component/base.py
@@ -383,9 +383,6 @@ def __str__(self):
             "params": {}
         }
         """
-        out = json.loads(str(self._param)).get("output", {})
-        if isinstance(out, dict) and "vector" in out:
-            del out["vector"]
         return """{{
             "component_name": "{}",
             "params": {},
@@ -393,7 +390,7 @@ def __str__(self):
             "inputs": {}
         }}""".format(self.component_name,
                      self._param,
-                     json.dumps(out, ensure_ascii=False),
+                     json.dumps(json.loads(str(self._param)).get("output", {}), ensure_ascii=False),
                      json.dumps(json.loads(str(self._param)).get("inputs", []), ensure_ascii=False)
         )
 
@@ -462,7 +459,7 @@ def get_input(self):
             self._param.inputs = []
             outs = []
             for q in self._param.query:
-                if q["component_id"]:
+                if q.get("component_id"):
                     if q["component_id"].split("@")[0].lower().find("begin") >= 0:
                         cpn_id, key = q["component_id"].split("@")
                         for p in self._canvas.get_component(cpn_id)["obj"]._param.query:
diff --git a/agent/component/begin.py b/agent/component/begin.py
index 037a8a057c2..766c0c66719 100644
--- a/agent/component/begin.py
+++ b/agent/component/begin.py
@@ -26,6 +26,7 @@ class BeginParam(ComponentParamBase):
     def __init__(self):
         super().__init__()
         self.prologue = "Hi! I'm your smart assistant. What can I do for you?"
+        self.query = []
 
     def check(self):
         return True
diff --git a/agent/templates/customer_service.json b/agent/templates/customer_service.json
index edc9931c188..e8aa89b6303 100644
--- a/agent/templates/customer_service.json
+++ b/agent/templates/customer_service.json
@@ -336,7 +336,7 @@
                 "parameters": [],
                 "presencePenaltyEnabled": true,
                 "presence_penalty": 0.4,
-                "prompt": "Role: You are a customer support.  \n\nTask: Please answer the question based on content of knowledge base. \n\nRequirements & restrictions:\n  -  DO NOT make things up when all knowledge base content is irrelevant to the question. \n  - Answers need to consider chat history.\n  - Request about customer's contact information like, Wechat number, LINE number, twitter, discord, etc,. , when knowledge base content can't answer his question. So,  product expert could contact him soon to solve his problem.\n\n      Knowledge base content is as following:\n      {input}\n      The above is the content of knowledge base.",
+                "prompt": "Role: You are a customer support.  \n\nTask: Please answer the question based on content of knowledge base. \n\nReuirements & restrictions:\n  -  DO NOT make things up when all knowledge base content is irrelevant to the question. \n  - Answers need to consider chat history.\n  - Request about customer's contact information like, Wechat number, LINE number, twitter, discord, etc,. , when knowlegebase content can't answer his question. So,  product expert could contact him soon to solve his problem.\n\n      Knowledge base content is as following:\n      {input}\n      The above is the content of knowledge base.",
                 "temperature": 0.1,
                 "temperatureEnabled": true,
                 "topPEnabled": true,
@@ -603,7 +603,7 @@
           {
             "data": {
               "form": {
-                "text": "Static messages.\nDefine response after receive user's contact information."
+                "text": "Static messages.\nDefine replys after recieve user's contact information."
               },
               "label": "Note",
               "name": "N: What else?"
@@ -691,7 +691,7 @@
           {
             "data": {
               "form": {
-                "text": "Complete questions by conversation history.\nUser: What's RAGFlow?\nAssistant: RAGFlow is xxx.\nUser: How to deploy it?\n\nRefine it: How to deploy RAGFlow?"
+                "text": "Complete questions by conversation history.\nUser: What's RAGFlow?\nAssistant: RAGFlow is xxx.\nUser: How to deloy it?\n\nRefine it: How to deploy RAGFlow?"
               },
               "label": "Note",
               "name": "N: Refine Question"
diff --git a/api/apps/conversation_app.py b/api/apps/conversation_app.py
index cce87c337e8..d787e5f1f65 100644
--- a/api/apps/conversation_app.py
+++ b/api/apps/conversation_app.py
@@ -17,12 +17,14 @@
 import re
 import traceback
 from copy import deepcopy
+
+from api.db.services.conversation_service import ConversationService
 from api.db.services.user_service import UserTenantService
 from flask import request, Response
 from flask_login import login_required, current_user
 
 from api.db import LLMType
-from api.db.services.dialog_service import DialogService, ConversationService, chat, ask
+from api.db.services.dialog_service import DialogService, chat, ask
 from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.db.services.llm_service import LLMBundle, TenantService, TenantLLMService
 from api import settings
diff --git a/api/apps/sdk/session.py b/api/apps/sdk/session.py
index 54851f984d9..ff458000229 100644
--- a/api/apps/sdk/session.py
+++ b/api/apps/sdk/session.py
@@ -16,17 +16,19 @@
 import logging
 import re
 import json
-from copy import deepcopy
-from uuid import uuid4
 from api.db import LLMType
 from flask import request, Response
+
+from api.db.services.conversation_service import ConversationService, iframe_completion
+from api.db.services.conversation_service import completion as rag_completion
+from api.db.services.canvas_service import completion as agent_completion
 from api.db.services.dialog_service import ask
 from agent.canvas import Canvas
 from api.db import StatusEnum
-from api.db.db_models import API4Conversation
+from api.db.db_models import APIToken
 from api.db.services.api_service import API4ConversationService
 from api.db.services.canvas_service import UserCanvasService
-from api.db.services.dialog_service import DialogService, ConversationService, chat
+from api.db.services.dialog_service import DialogService
 from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.utils import get_uuid
 from api.utils.api_utils import get_error_data_result
@@ -67,8 +69,6 @@ def create_agent_session(tenant_id, agent_id):
     e, cvs = UserCanvasService.get_by_id(agent_id)
     if not e:
         return get_error_data_result("Agent not found.")
-    if cvs.user_id != tenant_id:
-        return get_error_data_result(message="You do not own the agent.")
 
     if not isinstance(cvs.dsl, str):
         cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False)
@@ -111,98 +111,10 @@ def update(tenant_id, chat_id, session_id):
 
 @manager.route('/chats/<chat_id>/completions', methods=['POST'])  # noqa: F821
 @token_required
-def completion(tenant_id, chat_id):
-    dia = DialogService.query(id=chat_id, tenant_id=tenant_id, status=StatusEnum.VALID.value)
-    if not dia:
-        return get_error_data_result(message="You do not own the chat")
+def chat_completion(tenant_id, chat_id):
     req = request.json
-    if not req.get("session_id"):
-        conv = {
-            "id": get_uuid(),
-            "dialog_id": chat_id,
-            "name": req.get("name", "New session"),
-            "message": [{"role": "assistant", "content": dia[0].prompt_config.get("prologue")}]
-        }
-        if not conv.get("name"):
-            return get_error_data_result(message="`name` can not be empty.")
-        ConversationService.save(**conv)
-        e, conv = ConversationService.get_by_id(conv["id"])
-        session_id = conv.id
-    else:
-        session_id = req.get("session_id")
-    if not req.get("question"):
-        return get_error_data_result(message="Please input your question.")
-    conv = ConversationService.query(id=session_id, dialog_id=chat_id)
-    if not conv:
-        return get_error_data_result(message="Session does not exist")
-    conv = conv[0]
-    msg = []
-    question = {
-        "content": req.get("question"),
-        "role": "user",
-        "id": str(uuid4())
-    }
-    conv.message.append(question)
-    for m in conv.message:
-        if m["role"] == "system":
-            continue
-        if m["role"] == "assistant" and not msg:
-            continue
-        msg.append(m)
-    message_id = msg[-1].get("id")
-    e, dia = DialogService.get_by_id(conv.dialog_id)
-
-    if not conv.reference:
-        conv.reference = []
-    conv.message.append({"role": "assistant", "content": "", "id": message_id})
-    conv.reference.append({"chunks": [], "doc_aggs": []})
-
-    def fillin_conv(ans):
-        reference = ans["reference"]
-        temp_reference = deepcopy(ans["reference"])
-        nonlocal conv, message_id
-        if not conv.reference:
-            conv.reference.append(temp_reference)
-        else:
-            conv.reference[-1] = temp_reference
-        conv.message[-1] = {"role": "assistant", "content": ans["answer"],
-                            "id": message_id, "prompt": ans.get("prompt", "")}
-        if "chunks" in reference:
-            chunks = reference.get("chunks")
-            chunk_list = []
-            for chunk in chunks:
-                new_chunk = {
-                    "id": chunk["chunk_id"],
-                    "content": chunk["content_with_weight"],
-                    "document_id": chunk["doc_id"],
-                    "document_name": chunk["docnm_kwd"],
-                    "dataset_id": chunk["kb_id"],
-                    "image_id": chunk.get("image_id", ""),
-                    "similarity": chunk["similarity"],
-                    "vector_similarity": chunk["vector_similarity"],
-                    "term_similarity": chunk["term_similarity"],
-                    "positions": chunk.get("positions", []),
-                }
-                chunk_list.append(new_chunk)
-            reference["chunks"] = chunk_list
-        ans["id"] = message_id
-        ans["session_id"] = session_id
-
-    def stream():
-        nonlocal dia, msg, req, conv
-        try:
-            for ans in chat(dia, msg, **req):
-                fillin_conv(ans)
-                yield "data:" + json.dumps({"code": 0, "data": ans}, ensure_ascii=False) + "\n\n"
-            ConversationService.update_by_id(conv.id, conv.to_dict())
-        except Exception as e:
-            yield "data:" + json.dumps({"code": 500, "message": str(e),
-                                        "data": {"answer": "**ERROR**: " + str(e), "reference": []}},
-                                       ensure_ascii=False) + "\n\n"
-        yield "data:" + json.dumps({"code": 0, "data": True}, ensure_ascii=False) + "\n\n"
-
     if req.get("stream", True):
-        resp = Response(stream(), mimetype="text/event-stream")
+        resp = Response(rag_completion(tenant_id, chat_id, **req), mimetype="text/event-stream")
         resp.headers.add_header("Cache-control", "no-cache")
         resp.headers.add_header("Connection", "keep-alive")
         resp.headers.add_header("X-Accel-Buffering", "no")
@@ -212,172 +124,26 @@ def stream():
 
     else:
         answer = None
-        for ans in chat(dia, msg, **req):
+        for ans in rag_completion(tenant_id, chat_id, **req):
             answer = ans
-            fillin_conv(ans)
-            ConversationService.update_by_id(conv.id, conv.to_dict())
             break
         return get_result(data=answer)
 
 
 @manager.route('/agents/<agent_id>/completions', methods=['POST'])  # noqa: F821
 @token_required
-def agent_completion(tenant_id, agent_id):
+def agent_completions(tenant_id, agent_id):
     req = request.json
-
-    e, cvs = UserCanvasService.get_by_id(agent_id)
-    if not e:
-        return get_error_data_result("Agent not found.")
-    if cvs.user_id != tenant_id:
-        return get_error_data_result(message="You do not own the agent.")
-    if not isinstance(cvs.dsl, str):
-        cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False)
-    canvas = Canvas(cvs.dsl, tenant_id)
-
-    if not req.get("session_id"):
-        session_id = get_uuid()
-        conv = {
-            "id": session_id,
-            "dialog_id": cvs.id,
-            "user_id": req.get("user_id", ""),
-            "message": [{"role": "assistant", "content": canvas.get_prologue()}],
-            "source": "agent",
-            "dsl": json.loads(cvs.dsl)
-        }
-        API4ConversationService.save(**conv)
-        conv = API4Conversation(**conv)
-    else:
-        session_id = req.get("session_id")
-        e, conv = API4ConversationService.get_by_id(req["session_id"])
-        if not e:
-            return get_error_data_result(message="Session not found!")
-        canvas = Canvas(json.dumps(conv.dsl), tenant_id)
-
-    messages = conv.message
-    question = req.get("question")
-    if not question:
-        return get_error_data_result("`question` is required.")
-    question = {
-        "role": "user",
-        "content": question,
-        "id": str(uuid4())
-    }
-    messages.append(question)
-    msg = []
-    for m in messages:
-        if m["role"] == "system":
-            continue
-        if m["role"] == "assistant" and not msg:
-            continue
-        msg.append(m)
-    if not msg[-1].get("id"):
-        msg[-1]["id"] = get_uuid()
-    message_id = msg[-1]["id"]
-
-    stream = req.get("stream", True)
-
-    def fillin_conv(ans):
-        reference = ans["reference"]
-        temp_reference = deepcopy(ans["reference"])
-        nonlocal conv, message_id
-        if not conv.reference:
-            conv.reference.append(temp_reference)
-        else:
-            conv.reference[-1] = temp_reference
-        conv.message[-1] = {"role": "assistant", "content": ans["answer"], "id": message_id}
-        if "chunks" in reference:
-            chunks = reference.get("chunks")
-            chunk_list = []
-            for chunk in chunks:
-                new_chunk = {
-                    "id": chunk["chunk_id"],
-                    "content": chunk["content"],
-                    "document_id": chunk["doc_id"],
-                    "document_name": chunk["docnm_kwd"],
-                    "dataset_id": chunk["kb_id"],
-                    "image_id": chunk["image_id"],
-                    "similarity": chunk["similarity"],
-                    "vector_similarity": chunk["vector_similarity"],
-                    "term_similarity": chunk["term_similarity"],
-                    "positions": chunk["positions"],
-                }
-                chunk_list.append(new_chunk)
-            reference["chunks"] = chunk_list
-        ans["id"] = message_id
-        ans["session_id"] = session_id
-
-    def rename_field(ans):
-        reference = ans['reference']
-        if not isinstance(reference, dict):
-            return
-        for chunk_i in reference.get('chunks', []):
-            if 'docnm_kwd' in chunk_i:
-                chunk_i['doc_name'] = chunk_i['docnm_kwd']
-                chunk_i.pop('docnm_kwd')
-
-    if not conv.reference:
-        conv.reference = []
-    conv.message.append({"role": "assistant", "content": "", "id": message_id})
-    conv.reference.append({"chunks": [], "doc_aggs": []})
-
-    final_ans = {"reference": [], "content": ""}
-
-    canvas.add_user_input(msg[-1]["content"])
-
-    if stream:
-        def sse():
-            nonlocal answer, cvs
-            try:
-                for ans in canvas.run(stream=stream):
-                    if ans.get("running_status"):
-                        yield "data:" + json.dumps({"code": 0, "message": "",
-                                                    "data": {"answer": ans["content"],
-                                                             "running_status": True}},
-                                                   ensure_ascii=False) + "\n\n"
-                        continue
-                    for k in ans.keys():
-                        final_ans[k] = ans[k]
-                    ans = {"answer": ans["content"], "reference": ans.get("reference", [])}
-                    fillin_conv(ans)
-                    rename_field(ans)
-                    yield "data:" + json.dumps({"code": 0, "message": "", "data": ans},
-                                               ensure_ascii=False) + "\n\n"
-
-                canvas.messages.append({"role": "assistant", "content": final_ans["content"], "id": message_id})
-                canvas.history.append(("assistant", final_ans["content"]))
-                if final_ans.get("reference"):
-                    canvas.reference.append(final_ans["reference"])
-                conv.dsl = json.loads(str(canvas))
-                API4ConversationService.append_message(conv.id, conv.to_dict())
-            except Exception as e:
-                conv.dsl = json.loads(str(canvas))
-                API4ConversationService.append_message(conv.id, conv.to_dict())
-                yield "data:" + json.dumps({"code": 500, "message": str(e),
-                                            "data": {"answer": "**ERROR**: " + str(e), "reference": []}},
-                                           ensure_ascii=False) + "\n\n"
-            yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n"
-
-        resp = Response(sse(), mimetype="text/event-stream")
+    if req.get("stream", True):
+        resp = Response(agent_completion(tenant_id, agent_id, **req), mimetype="text/event-stream")
         resp.headers.add_header("Cache-control", "no-cache")
         resp.headers.add_header("Connection", "keep-alive")
         resp.headers.add_header("X-Accel-Buffering", "no")
         resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8")
         return resp
 
-    for answer in canvas.run(stream=False):
-        if answer.get("running_status"):
-            continue
-        final_ans["content"] = "\n".join(answer["content"]) if "content" in answer else ""
-        canvas.messages.append({"role": "assistant", "content": final_ans["content"], "id": message_id})
-        if final_ans.get("reference"):
-            canvas.reference.append(final_ans["reference"])
-        conv.dsl = json.loads(str(canvas))
-
-        result = {"answer": final_ans["content"], "reference": final_ans.get("reference", [])}
-        fillin_conv(result)
-        API4ConversationService.append_message(conv.id, conv.to_dict())
-        rename_field(result)
-        return get_result(data=result)
+    for answer in agent_completion(tenant_id, agent_id, **req):
+        return get_result(data=answer)
 
 
 @manager.route('/chats/<chat_id>/sessions', methods=['GET'])  # noqa: F821
@@ -591,3 +357,57 @@ def related_questions(tenant_id):
 Related search terms:
     """}], {"temperature": 0.9})
     return get_result(data=[re.sub(r"^[0-9]\. ", "", a) for a in ans.split("\n") if re.match(r"^[0-9]\. ", a)])
+
+
+@manager.route('/chatbots/<dialog_id>/completions', methods=['POST'])  # noqa: F821
+def chatbot_completions(dialog_id):
+    req = request.json
+
+    token = request.headers.get('Authorization').split()
+    if len(token) != 2:
+        return get_error_data_result(message='Authorization is not valid!"')
+    token = token[1]
+    objs = APIToken.query(beta=token)
+    if not objs:
+        return get_error_data_result(message='Token is not valid!"')
+
+    if "quote" not in req:
+        req["quote"] = False
+
+    if req.get("stream", True):
+        resp = Response(iframe_completion(objs[0].tenant_id, dialog_id, **req), mimetype="text/event-stream")
+        resp.headers.add_header("Cache-control", "no-cache")
+        resp.headers.add_header("Connection", "keep-alive")
+        resp.headers.add_header("X-Accel-Buffering", "no")
+        resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8")
+        return resp
+
+    for answer in agent_completion(objs[0].tenant_id, dialog_id, **req):
+        return get_result(data=answer)
+
+
+@manager.route('/agentbots/<agent_id>/completions', methods=['POST'])  # noqa: F821
+def agent_bot_completions(agent_id):
+    req = request.json
+
+    token = request.headers.get('Authorization').split()
+    if len(token) != 2:
+        return get_error_data_result(message='Authorization is not valid!"')
+    token = token[1]
+    objs = APIToken.query(beta=token)
+    if not objs:
+        return get_error_data_result(message='Token is not valid!"')
+
+    if "quote" not in req:
+        req["quote"] = False
+
+    if req.get("stream", True):
+        resp = Response(agent_completion(objs[0].tenant_id, agent_id, **req), mimetype="text/event-stream")
+        resp.headers.add_header("Cache-control", "no-cache")
+        resp.headers.add_header("Connection", "keep-alive")
+        resp.headers.add_header("X-Accel-Buffering", "no")
+        resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8")
+        return resp
+
+    for answer in agent_completion(objs[0].tenant_id, agent_id, **req):
+        return get_result(data=answer)
diff --git a/api/apps/system_app.py b/api/apps/system_app.py
index 7b715f14b1c..28b74d65820 100644
--- a/api/apps/system_app.py
+++ b/api/apps/system_app.py
@@ -205,6 +205,7 @@ def new_token():
         obj = {
             "tenant_id": tenant_id,
             "token": generate_confirmation_token(tenant_id),
+            "beta": generate_confirmation_token(generate_confirmation_token(tenant_id)).replace("ragflow-", "")[:32],
             "create_time": current_timestamp(),
             "create_date": datetime_format(datetime.now()),
             "update_time": None,
diff --git a/api/db/db_models.py b/api/db/db_models.py
index 0c052ca18a4..0c4d12c034c 100644
--- a/api/db/db_models.py
+++ b/api/db/db_models.py
@@ -934,6 +934,7 @@ class APIToken(DataBaseModel):
     token = CharField(max_length=255, null=False, index=True)
     dialog_id = CharField(max_length=32, null=False, index=True)
     source = CharField(max_length=16, null=True, help_text="none|agent|dialog", index=True)
+    beta = CharField(max_length=255, null=True, index=True)
 
     class Meta:
         db_table = "api_token"
@@ -1083,4 +1084,10 @@ def migrate_db():
             )
         except Exception:
             pass
+        try:
+            migrate(
+                migrator.add_column("api_token", "beta", CharField(max_length=255, null=True, index=True))
+            )
+        except Exception:
+            pass
 
diff --git a/api/db/services/canvas_service.py b/api/db/services/canvas_service.py
index 0fac2f24850..e7aada1fdef 100644
--- a/api/db/services/canvas_service.py
+++ b/api/db/services/canvas_service.py
@@ -13,8 +13,14 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+import json
+from uuid import uuid4
+from agent.canvas import Canvas
 from api.db.db_models import DB, CanvasTemplate, UserCanvas
+from api.db.services.api_service import API4ConversationService
 from api.db.services.common_service import CommonService
+from api.db.services.conversation_service import structure_answer
+from api.utils import get_uuid
 
 
 class CanvasTemplateService(CommonService):
@@ -42,3 +48,115 @@ def get_list(cls, tenant_id,
         agents = agents.paginate(page_number, items_per_page)
 
         return list(agents.dicts())
+
+
+def completion(tenant_id, agent_id, question, session_id=None, stream=True, **kwargs):
+    e, cvs = UserCanvasService.get_by_id(agent_id)
+    assert e, "Agent not found."
+    assert cvs.user_id == tenant_id, "You do not own the agent."
+
+    if not isinstance(cvs.dsl, str):
+        cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False)
+    canvas = Canvas(cvs.dsl, tenant_id)
+
+    if not session_id:
+        session_id = get_uuid()
+        conv = {
+            "id": session_id,
+            "dialog_id": cvs.id,
+            "user_id": kwargs.get("user_id", ""),
+            "message": [{"role": "assistant", "content": canvas.get_prologue()}],
+            "source": "agent",
+            "dsl": json.loads(cvs.dsl)
+        }
+        API4ConversationService.save(**conv)
+        yield "data:" + json.dumps({"code": 0,
+                                    "message": "",
+                                    "data": {
+                                        "session_id": session_id,
+                                        "answer": canvas.get_prologue(),
+                                        "reference": [],
+                                        "param": canvas.get_preset_param()
+                                      }
+                                    },
+                                   ensure_ascii=False) + "\n\n"
+        yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n"
+        return
+    else:
+        session_id = session_id
+        e, conv = API4ConversationService.get_by_id(session_id)
+        assert e, "Session not found!"
+        canvas = Canvas(json.dumps(conv.dsl), tenant_id)
+
+    messages = conv.message
+    question = {
+        "role": "user",
+        "content": question,
+        "id": str(uuid4())
+    }
+    messages.append(question)
+    msg = []
+    for m in messages:
+        if m["role"] == "system":
+            continue
+        if m["role"] == "assistant" and not msg:
+            continue
+        msg.append(m)
+    if not msg[-1].get("id"):
+        msg[-1]["id"] = get_uuid()
+    message_id = msg[-1]["id"]
+
+    if not conv.reference:
+        conv.reference = []
+    conv.message.append({"role": "assistant", "content": "", "id": message_id})
+    conv.reference.append({"chunks": [], "doc_aggs": []})
+
+    final_ans = {"reference": [], "content": ""}
+
+    canvas.add_user_input(msg[-1]["content"])
+
+    if stream:
+        try:
+            for ans in canvas.run(stream=stream):
+                if ans.get("running_status"):
+                    yield "data:" + json.dumps({"code": 0, "message": "",
+                                                "data": {"answer": ans["content"],
+                                                         "running_status": True}},
+                                               ensure_ascii=False) + "\n\n"
+                    continue
+                for k in ans.keys():
+                    final_ans[k] = ans[k]
+                ans = {"answer": ans["content"], "reference": ans.get("reference", [])}
+                ans = structure_answer(conv, ans, message_id, session_id)
+                yield "data:" + json.dumps({"code": 0, "message": "", "data": ans},
+                                           ensure_ascii=False) + "\n\n"
+
+            canvas.messages.append({"role": "assistant", "content": final_ans["content"], "id": message_id})
+            canvas.history.append(("assistant", final_ans["content"]))
+            if final_ans.get("reference"):
+                canvas.reference.append(final_ans["reference"])
+            conv.dsl = json.loads(str(canvas))
+            API4ConversationService.append_message(conv.id, conv.to_dict())
+        except Exception as e:
+            conv.dsl = json.loads(str(canvas))
+            API4ConversationService.append_message(conv.id, conv.to_dict())
+            yield "data:" + json.dumps({"code": 500, "message": str(e),
+                                        "data": {"answer": "**ERROR**: " + str(e), "reference": []}},
+                                       ensure_ascii=False) + "\n\n"
+        yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n"
+
+    else:
+        for answer in canvas.run(stream=False):
+            if answer.get("running_status"):
+                continue
+            final_ans["content"] = "\n".join(answer["content"]) if "content" in answer else ""
+            canvas.messages.append({"role": "assistant", "content": final_ans["content"], "id": message_id})
+            if final_ans.get("reference"):
+                canvas.reference.append(final_ans["reference"])
+            conv.dsl = json.loads(str(canvas))
+
+            result = {"answer": final_ans["content"], "reference": final_ans.get("reference", [])}
+            result = structure_answer(conv, result, message_id, session_id)
+            API4ConversationService.append_message(conv.id, conv.to_dict())
+            yield result
+            break
\ No newline at end of file
diff --git a/api/db/services/conversation_service.py b/api/db/services/conversation_service.py
new file mode 100644
index 00000000000..7844bcc6a87
--- /dev/null
+++ b/api/db/services/conversation_service.py
@@ -0,0 +1,221 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from uuid import uuid4
+from api.db import StatusEnum
+from api.db.db_models import Conversation, DB
+from api.db.services.api_service import API4ConversationService
+from api.db.services.common_service import CommonService
+from api.db.services.dialog_service import DialogService, chat
+from api.utils import get_uuid
+import json
+from copy import deepcopy
+
+
+class ConversationService(CommonService):
+    model = Conversation
+
+    @classmethod
+    @DB.connection_context()
+    def get_list(cls,dialog_id,page_number, items_per_page, orderby, desc, id , name):
+        sessions = cls.model.select().where(cls.model.dialog_id ==dialog_id)
+        if id:
+            sessions = sessions.where(cls.model.id == id)
+        if name:
+            sessions = sessions.where(cls.model.name == name)
+        if desc:
+            sessions = sessions.order_by(cls.model.getter_by(orderby).desc())
+        else:
+            sessions = sessions.order_by(cls.model.getter_by(orderby).asc())
+
+        sessions = sessions.paginate(page_number, items_per_page)
+
+        return list(sessions.dicts())
+
+
+def structure_answer(conv, ans, message_id, session_id):
+    reference = ans["reference"]
+    temp_reference = deepcopy(ans["reference"])
+    if not conv.reference:
+        conv.reference.append(temp_reference)
+    else:
+        conv.reference[-1] = temp_reference
+    conv.message[-1] = {"role": "assistant", "content": ans["answer"], "id": message_id}
+
+    chunk_list = [{
+            "id": chunk["chunk_id"],
+            "content": chunk["content"],
+            "document_id": chunk["doc_id"],
+            "document_name": chunk["docnm_kwd"],
+            "dataset_id": chunk["kb_id"],
+            "image_id": chunk["image_id"],
+            "similarity": chunk["similarity"],
+            "vector_similarity": chunk["vector_similarity"],
+            "term_similarity": chunk["term_similarity"],
+            "positions": chunk["positions"],
+        } for chunk in reference.get("chunks", [])]
+
+    reference["chunks"] = chunk_list
+    ans["id"] = message_id
+    ans["session_id"] = session_id
+
+    return ans
+
+
+def completion(tenant_id, chat_id, question, name="New session", session_id=None, stream=True, **kwargs):
+    assert name, "`name` can not be empty."
+    dia = DialogService.query(id=chat_id, tenant_id=tenant_id, status=StatusEnum.VALID.value)
+    assert dia, "You do not own the chat."
+
+    if not session_id:
+        conv = {
+            "id": get_uuid(),
+            "dialog_id": chat_id,
+            "name": name,
+            "message": [{"role": "assistant", "content": dia[0].prompt_config.get("prologue")}]
+        }
+        ConversationService.save(**conv)
+        yield "data:" + json.dumps({"code": 0, "message": "",
+                                    "data": {
+                                        "answer": conv["message"][0]["content"],
+                                        "reference": {},
+                                        "audio_binary": None,
+                                        "id": None,
+                                        "session_id": session_id
+                                    }},
+                                   ensure_ascii=False) + "\n\n"
+        yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n"
+        return
+
+    conv = ConversationService.query(id=session_id, dialog_id=chat_id)
+    if not conv:
+        raise LookupError("Session does not exist")
+
+    conv = conv[0]
+    msg = []
+    question = {
+        "content": question,
+        "role": "user",
+        "id": str(uuid4())
+    }
+    conv.message.append(question)
+    for m in conv.message:
+        if m["role"] == "system":
+            continue
+        if m["role"] == "assistant" and not msg:
+            continue
+        msg.append(m)
+    message_id = msg[-1].get("id")
+    e, dia = DialogService.get_by_id(conv.dialog_id)
+
+    if not conv.reference:
+        conv.reference = []
+    conv.message.append({"role": "assistant", "content": "", "id": message_id})
+    conv.reference.append({"chunks": [], "doc_aggs": []})
+
+    if stream:
+        try:
+            for ans in chat(dia, msg, True, **kwargs):
+                ans = structure_answer(conv, ans, message_id, session_id)
+                yield "data:" + json.dumps({"code": 0, "data": ans}, ensure_ascii=False) + "\n\n"
+            ConversationService.update_by_id(conv.id, conv.to_dict())
+        except Exception as e:
+            yield "data:" + json.dumps({"code": 500, "message": str(e),
+                                        "data": {"answer": "**ERROR**: " + str(e), "reference": []}},
+                                       ensure_ascii=False) + "\n\n"
+        yield "data:" + json.dumps({"code": 0, "data": True}, ensure_ascii=False) + "\n\n"
+
+    else:
+        answer = None
+        for ans in chat(dia, msg, False, **kwargs):
+            answer = structure_answer(conv, ans, message_id, session_id)
+            ConversationService.update_by_id(conv.id, conv.to_dict())
+            break
+        yield answer
+
+
+def iframe_completion(dialog_id, question, session_id=None, stream=True, **kwargs):
+    e, dia = DialogService.get_by_id(dialog_id)
+    assert e, "Dialog not found"
+    if not session_id:
+        session_id = get_uuid()
+        conv = {
+            "id": session_id,
+            "dialog_id": dialog_id,
+            "user_id": kwargs.get("user_id", ""),
+            "message": [{"role": "assistant", "content": dia.prompt_config["prologue"]}]
+        }
+        API4ConversationService.save(**conv)
+        yield "data:" + json.dumps({"code": 0, "message": "",
+                                    "data": {
+                                        "answer": conv["message"][0]["content"],
+                                        "reference": {},
+                                        "audio_binary": None,
+                                        "id": None,
+                                        "session_id": session_id
+                                    }},
+                                   ensure_ascii=False) + "\n\n"
+        yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n"
+        return
+    else:
+        session_id = session_id
+        e, conv = API4ConversationService.get_by_id(session_id)
+        assert e, "Session not found!"
+
+    messages = conv.message
+    question = {
+        "role": "user",
+        "content": question,
+        "id": str(uuid4())
+    }
+    messages.append(question)
+
+    msg = []
+    for m in messages:
+        if m["role"] == "system":
+            continue
+        if m["role"] == "assistant" and not msg:
+            continue
+        msg.append(m)
+    if not msg[-1].get("id"):
+        msg[-1]["id"] = get_uuid()
+    message_id = msg[-1]["id"]
+
+    if not conv.reference:
+        conv.reference = []
+    conv.message.append({"role": "assistant", "content": "", "id": message_id})
+    conv.reference.append({"chunks": [], "doc_aggs": []})
+
+    if stream:
+        try:
+            for ans in chat(dia, msg, True, **kwargs):
+                ans = structure_answer(conv, ans, message_id, session_id)
+                yield "data:" + json.dumps({"code": 0, "message": "", "data": ans},
+                                           ensure_ascii=False) + "\n\n"
+            API4ConversationService.append_message(conv.id, conv.to_dict())
+        except Exception as e:
+            yield "data:" + json.dumps({"code": 500, "message": str(e),
+                                        "data": {"answer": "**ERROR**: " + str(e), "reference": []}},
+                                       ensure_ascii=False) + "\n\n"
+        yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n"
+
+    else:
+        answer = None
+        for ans in chat(dia, msg, False, **kwargs):
+            answer = structure_answer(conv, ans, message_id, session_id)
+            API4ConversationService.append_message(conv.id, conv.to_dict())
+            break
+        yield answer
+
diff --git a/api/db/services/dialog_service.py b/api/db/services/dialog_service.py
index a092a50cad7..cd22fdc26c4 100644
--- a/api/db/services/dialog_service.py
+++ b/api/db/services/dialog_service.py
@@ -23,7 +23,7 @@
 import datetime
 from datetime import timedelta
 from api.db import LLMType, ParserType,StatusEnum
-from api.db.db_models import Dialog, Conversation,DB
+from api.db.db_models import Dialog, DB
 from api.db.services.common_service import CommonService
 from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.db.services.llm_service import LLMService, TenantLLMService, LLMBundle
@@ -61,27 +61,6 @@ def get_list(cls, tenant_id,
         return list(chats.dicts())
 
 
-class ConversationService(CommonService):
-    model = Conversation
-
-    @classmethod
-    @DB.connection_context()
-    def get_list(cls,dialog_id,page_number, items_per_page, orderby, desc, id , name):
-        sessions = cls.model.select().where(cls.model.dialog_id ==dialog_id)
-        if id:
-            sessions = sessions.where(cls.model.id == id)
-        if name:
-            sessions = sessions.where(cls.model.name == name)
-        if desc:
-            sessions = sessions.order_by(cls.model.getter_by(orderby).desc())
-        else:
-            sessions = sessions.order_by(cls.model.getter_by(orderby).asc())
-
-        sessions = sessions.paginate(page_number, items_per_page)
-
-        return list(sessions.dicts())
-
-
 def message_fit_in(msg, max_length=4000):
     def count():
         nonlocal msg
diff --git a/api/db/services/document_service.py b/api/db/services/document_service.py
index aea4931ebf3..f4b6f6874b8 100644
--- a/api/db/services/document_service.py
+++ b/api/db/services/document_service.py
@@ -425,11 +425,12 @@ def new_task():
 
 def doc_upload_and_parse(conversation_id, file_objs, user_id):
     from rag.app import presentation, picture, naive, audio, email
-    from api.db.services.dialog_service import ConversationService, DialogService
+    from api.db.services.dialog_service import DialogService
     from api.db.services.file_service import FileService
     from api.db.services.llm_service import LLMBundle
     from api.db.services.user_service import TenantService
     from api.db.services.api_service import API4ConversationService
+    from api.db.services.conversation_service import ConversationService
 
     e, conv = ConversationService.get_by_id(conversation_id)
     if not e:
diff --git a/docker/.env b/docker/.env
index 7a8eb62d339..f8ad038a181 100644
--- a/docker/.env
+++ b/docker/.env
@@ -80,13 +80,13 @@ REDIS_PASSWORD=infini_rag_flow
 SVR_HTTP_PORT=9380
 
 # The RAGFlow Docker image to download.
-# Defaults to the dev-slim edition, which is the RAGFlow Docker image without embedding models.
-RAGFLOW_IMAGE=infiniflow/ragflow:dev
+# Defaults to the v0.14.1-slim edition, which is the RAGFlow Docker image without embedding models.
+RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1-slim
 #
 # To download the RAGFlow Docker image with embedding models, uncomment the following line instead:
-# RAGFLOW_IMAGE=infiniflow/ragflow:dev
+# RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1
 # 
-# The Docker image of the dev edition includes:
+# The Docker image of the v0.14.1 edition includes:
 # - Built-in embedding models:
 #   - BAAI/bge-large-zh-v1.5
 #   - BAAI/bge-reranker-v2-m3
@@ -107,13 +107,13 @@ RAGFLOW_IMAGE=infiniflow/ragflow:dev
 
 # If you cannot download the RAGFlow Docker image:
 #
-# - For the `dev-slim` edition, uncomment either of the following:
-# RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:dev-slim
-# RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:dev-slim
+# - 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
 #
-# - For the `dev` edition, uncomment either of the following:
-# RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:dev
-# RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:dev
+# - 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 local time zone.
 TIMEZONE='Asia/Shanghai'
diff --git a/docker/README.md b/docker/README.md
index 1813d2f5e81..5b95803fb0d 100644
--- a/docker/README.md
+++ b/docker/README.md
@@ -75,8 +75,8 @@ The [.env](./.env) file contains important environment variables for Docker.
 - `RAGFLOW-IMAGE`  
   The Docker image edition. Available editions:  
   
-  - `infiniflow/ragflow:dev-slim` (default): The RAGFlow Docker image without embedding models.  
-  - `infiniflow/ragflow:dev`: The RAGFlow Docker image with embedding models including:
+  - `infiniflow/ragflow:v0.14.1-slim` (default): The RAGFlow Docker image without embedding models.  
+  - `infiniflow/ragflow:v0.14.1`: The RAGFlow Docker image with embedding models including:
     - Built-in embedding models:
       - `BAAI/bge-large-zh-v1.5` 
       - `BAAI/bge-reranker-v2-m3`
@@ -95,12 +95,12 @@ 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 `dev-slim` edition:  
->   - `RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:dev-slim` or,
->   - `RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:dev-slim`.
-> - For the `dev` edition:  
->   - `RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:dev` or,
->   - `RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:dev`.
+> - 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
 
diff --git a/docs/configurations.md b/docs/configurations.md
index 1ccb0d74bed..84c8388db89 100644
--- a/docs/configurations.md
+++ b/docs/configurations.md
@@ -93,8 +93,8 @@ The [.env](https://github.com/infiniflow/ragflow/blob/main/docker/.env) file con
 - `RAGFLOW-IMAGE`  
   The Docker image edition. Available editions:  
   
-  - `infiniflow/ragflow:dev-slim` (default): The RAGFlow Docker image without embedding models.  
-  - `infiniflow/ragflow:dev`: The RAGFlow Docker image with embedding models including:
+  - `infiniflow/ragflow:v0.14.1-slim` (default): The RAGFlow Docker image without embedding models.  
+  - `infiniflow/ragflow:v0.14.1`: The RAGFlow Docker image with embedding models including:
     - Built-in embedding models:
       - `BAAI/bge-large-zh-v1.5` 
       - `BAAI/bge-reranker-v2-m3`
@@ -113,12 +113,12 @@ The [.env](https://github.com/infiniflow/ragflow/blob/main/docker/.env) file con
 :::tip NOTE  
 If you cannot download the RAGFlow Docker image, try the following mirrors.  
 
-- For the `dev-slim` edition:  
-  - `RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:dev-slim` or,
-  - `RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:dev-slim`.
-- For the `dev` edition:  
-  - `RAGFLOW_IMAGE=swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow:dev` or,
-  - `RAGFLOW_IMAGE=registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow:dev`.
+- 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
diff --git a/docs/guides/develop/build_docker_image.mdx b/docs/guides/develop/build_docker_image.mdx
index 3cfc1666fee..a653ca91fa5 100644
--- a/docs/guides/develop/build_docker_image.mdx
+++ b/docs/guides/develop/build_docker_image.mdx
@@ -31,7 +31,7 @@ A guide explaining how to build a RAGFlow Docker image from its source code. By
   ]}>
   <TabItem value="without">
 
-This image is approximately 1 GB in size and relies on external LLM and embedding services.
+This image is approximately 2 GB in size and relies on external LLM and embedding services.
 
 :::tip NOTE
 While we also test RAGFlow on ARM64 platforms, we do not plan to maintain RAGFlow Docker images for ARM. However, you can build an image yourself on a `linux/arm64` or `darwin/arm64` host machine as well.
@@ -40,7 +40,7 @@ While we also test RAGFlow on ARM64 platforms, we do not plan to maintain RAGFlo
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:dev-slim .
+docker build --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
 ```
 
 
@@ -56,7 +56,7 @@ While we also test RAGFlow on ARM64 platforms, we do not plan to maintain RAGFlo
 ```bash
 git clone https://github.com/infiniflow/ragflow.git
 cd ragflow/
-docker build -f Dockerfile -t infiniflow/ragflow:dev .
+docker build -f Dockerfile -t infiniflow/ragflow:nightly .
 ```
 
   </TabItem>
diff --git a/docs/guides/upgrade_ragflow.mdx b/docs/guides/upgrade_ragflow.mdx
index e329e579a3b..306cd7d0199 100644
--- a/docs/guides/upgrade_ragflow.mdx
+++ b/docs/guides/upgrade_ragflow.mdx
@@ -7,11 +7,11 @@ slug: /upgrade_ragflow
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 
-Upgrade RAGFlow to `dev-slim`/`dev` or the latest, published release.
+Upgrade RAGFlow to `nightly-slim`/`nightly` or the latest, published release.
 
-## Upgrade RAGFlow to `dev-slim`/`dev`, the most recent, tested Docker image
+## Upgrade RAGFlow to `nightly-slim`/`nightly`, the most recent, tested Docker image
 
-`dev-slim` refers to the RAGFlow Docker image *without* embedding models, while `dev` 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-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).
 
 To upgrade RAGFlow, you must upgrade **both** your code **and** your Docker image:
 
@@ -24,22 +24,22 @@ To upgrade RAGFlow, you must upgrade **both** your code **and** your Docker imag
 2. Update **ragflow/docker/.env**:
 
 <Tabs
-  defaultValue="dev-slim"
+  defaultValue="nightly-slim"
   values={[
-    {label: 'dev-slim', value: 'dev-slim'},
-    {label: 'dev', value: 'dev'},
+    {label: 'nightly-slim', value: 'nightly-slim'},
+    {label: 'nightly', value: 'nightly'},
   ]}>
-  <TabItem value="dev-slim">
+  <TabItem value="nightly-slim">
 
 ```bash
-RAGFLOW_IMAGE=infiniflow/ragflow:dev-slim
+RAGFLOW_IMAGE=infiniflow/ragflow:nightly-slim
 ```
 
   </TabItem>
-  <TabItem value="dev">
+  <TabItem value="nightly">
 
 ```bash
-RAGFLOW_IMAGE=infiniflow/ragflow:dev
+RAGFLOW_IMAGE=infiniflow/ragflow:nightly
 ```
 
   </TabItem>
@@ -65,7 +65,7 @@ To upgrade RAGFlow, you must upgrade **both** your code **and** your Docker imag
 2. Switch to the latest, officially published release, e.g., `v0.14.1`:
 
    ```bash
-   git checkout v0.14.1
+   git checkout -f v0.14.1
    ```
 
 3. Update **ragflow/docker/.env** as follows:
diff --git a/docs/quickstart.mdx b/docs/quickstart.mdx
index efa7c07cf40..6ee8f5d77db 100644
--- a/docs/quickstart.mdx
+++ b/docs/quickstart.mdx
@@ -177,20 +177,27 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
 
    ```bash
    $ git clone https://github.com/infiniflow/ragflow.git
+   $ cd ragflow
+   $ git checkout -f v0.14.1
    ```
 
-3. Build the pre-built Docker images and start up the server:
+3. Use the pre-built Docker images and start up the server:
 
-   > The command below downloads the dev version Docker image for RAGFlow slim (`dev-slim`). Note that RAGFlow slim Docker images do not include embedding models or Python libraries and hence are approximately 1GB in size.
+   > The command below downloads the v0.14.1 version Docker image for RAGFlow slim (`v0.14.1-slim`). Note that RAGFlow slim Docker images do not include embedding models or Python libraries and hence are approximately 2 GB in size.
 
    ```bash
-   $ cd ragflow/docker
-   $ docker compose -f docker-compose.yml up -d
+   $ docker compose -f docker/docker-compose.yml up -d
    ```
 
-   > - To download a RAGFlow slim Docker image of a specific version, update the `RAGFlOW_IMAGE` variable in **docker/.env** to your desired version. For example, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1-slim`. After making this change, rerun the command above to initiate the download.
-   > - To download the dev version of RAGFlow Docker image *including* embedding models and Python libraries, update the `RAGFlOW_IMAGE` variable in **docker/.env** to `RAGFLOW_IMAGE=infiniflow/ragflow:dev`. After making this change, rerun the command above to initiate the download.
-   > - To download a specific version of RAGFlow Docker image *including* embedding models and Python libraries, update the `RAGFlOW_IMAGE` variable in **docker/.env** to your desired version. For example, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1`. After making this change, rerun the command above to initiate the download.
+   | RAGFLOW_IMAGE tag in docker/.env | size  | Including embedding models and related Python packages? | comments               |
+   | -------------------------------- | ----- | ------------------------------------------------------- | ---------------------- |
+   | v0.14.1                          | ~9 GB | YES                                                     | stable release         |
+   | v0.14.1-slim                     | ~2 GB | NO                                                      | stable release         |
+   | v0.15.0-dev1                     | ~9 GB | YES                                                     | unstable beta release  |
+   | v0.15.0-dev1-slim                | ~2 GB | NO                                                      | unstable beta release  |
+   | nightly                          | ~9 GB | YES                                                     | unstable nightly build |
+   | nightly-slim                     | ~2 GB | NO                                                      | unstable nightly build |
+
 
 :::tip NOTE
 A RAGFlow Docker image that includes embedding models and Python libraries is approximately 9GB in size and may take significantly longer time to load.
diff --git a/docs/references/faq.md b/docs/references/faq.md
index 60d38280d3f..842a6185ab7 100644
--- a/docs/references/faq.md
+++ b/docs/references/faq.md
@@ -42,10 +42,10 @@ We officially support x86 CPU and nvidia GPU. While we also test RAGFlow on ARM6
 
 ### Which embedding models can be deployed locally?
 
-RAGFlow offers two Docker image editions, `dev-slim` and `dev`:  
+RAGFlow offers two Docker image editions, `v0.14.1-slim` and `v0.14.1`:  
   
-- `infiniflow/ragflow:dev-slim` (default): The RAGFlow Docker image without embedding models.  
-- `infiniflow/ragflow:dev`: The RAGFlow Docker image with embedding models including:
+- `infiniflow/ragflow:v0.14.1-slim` (default): The RAGFlow Docker image without embedding models.  
+- `infiniflow/ragflow:v0.14.1`: The RAGFlow Docker image with embedding models including:
   - Built-in embedding models:
     - `BAAI/bge-large-zh-v1.5`
     - `BAAI/bge-reranker-v2-m3`
diff --git a/docs/release_notes.md b/docs/release_notes.md
index a5463015e13..91f3a9e7195 100644
--- a/docs/release_notes.md
+++ b/docs/release_notes.md
@@ -102,11 +102,11 @@ Released on September 30, 2024.
 
 As of this release, RAGFlow offers slim editions of its Docker images to improve the experience for users with limited Internet access. A slim edition of RAGFlow's Docker image does not include built-in BGE/BCE embedding models and has a size of about 1GB; a full edition of RAGFlow is approximately 9GB and includes both built-in embedding models and embedding models that will be downloaded once you select them in the RAGFlow UI.
 
-The default Docker image edition is `dev-slim`. The following list clarifies the differences between various editions:
+The default Docker image edition is `nightly-slim`. The following list clarifies the differences between various editions:
 
-- `dev-slim`: The slim edition of the most recent tested Docker image.
+- `nightly-slim`: The slim edition of the most recent tested Docker image.
 - `v0.12.0-slim`: The slim edition of the most recent **officially released** Docker image.
-- `dev`: The full edition of the most recent tested Docker image.
+- `nightly`: The full edition of the most recent tested Docker image.
 - `v0.12.0`: The full edition of the most recent **officially released** Docker image.
 
 See [Upgrade RAGFlow](https://ragflow.io/docs/dev/upgrade_ragflow) for instructions on upgrading.
diff --git a/helm/values.yaml b/helm/values.yaml
index 66f94c004b6..3eaafc48e79 100644
--- a/helm/values.yaml
+++ b/helm/values.yaml
@@ -27,13 +27,13 @@ env:
   REDIS_PASSWORD: infini_rag_flow_helm
 
   # The RAGFlow Docker image to download.
-  # Defaults to the dev-slim edition, which is the RAGFlow Docker image without embedding models.
-  RAGFLOW_IMAGE: infiniflow/ragflow:dev-slim
+  # Defaults to the v0.14.1-slim edition, which is the RAGFlow Docker image without embedding models.
+  RAGFLOW_IMAGE: infiniflow/ragflow:v0.14.1-slim
   #
   # To download the RAGFlow Docker image with embedding models, uncomment the following line instead:
-  # RAGFLOW_IMAGE=infiniflow/ragflow:dev
+  # RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1
   #
-  # The Docker image of the dev edition includes:
+  # The Docker image of the v0.14.1 edition includes:
   # - Built-in embedding models:
   #   - BAAI/bge-large-zh-v1.5
   #   - BAAI/bge-reranker-v2-m3
diff --git a/rag/llm/cv_model.py b/rag/llm/cv_model.py
index 33d8a79704e..11df9660a78 100644
--- a/rag/llm/cv_model.py
+++ b/rag/llm/cv_model.py
@@ -25,7 +25,6 @@
 from io import BytesIO
 import json
 import requests
-from transformers import GenerationConfig
 
 from rag.nlp import is_english
 from api.utils import get_uuid
@@ -513,6 +512,7 @@ def describe(self, image, max_tokens=2048):
         return res.text,res.usage_metadata.total_token_count
 
     def chat(self, system, history, gen_conf, image=""):
+        from transformers import GenerationConfig
         if system:
             history[-1]["content"] = system + history[-1]["content"] + "user query: " + history[-1]["content"]
         try:
@@ -536,6 +536,7 @@ def chat(self, system, history, gen_conf, image=""):
             return "**ERROR**: " + str(e), 0
 
     def chat_streamly(self, system, history, gen_conf, image=""):
+        from transformers import GenerationConfig
         if system:
             history[-1]["content"] = system + history[-1]["content"] + "user query: " + history[-1]["content"]
 
diff --git a/rag/svr/task_executor.py b/rag/svr/task_executor.py
index aed8b7fa4a3..a8061a817f1 100644
--- a/rag/svr/task_executor.py
+++ b/rag/svr/task_executor.py
@@ -273,7 +273,7 @@ def embedding(docs, mdl, parser_config=None, callback=None):
     batch_size = 16
     tts, cnts = [], []
     for d in docs:
-        tts.append(rmSpace(d["title_tks"]))
+        tts.append(rmSpace(d.get("docnm_kwd", "Title")))
         c = "\n".join(d.get("question_kwd", []))
         if not c:
             c = d["content_with_weight"]