Merge pull request #2754 from chatchat-space/dev

更新文档细节和readme细节
2026-02-01 11:53:24 +08:00 · 2024-01-22 13:44:36 +08:00 · 2024-01-22 13:44:36 +08:00 · 2c92f426d9
commit 2c92f426d9
parent d4ff6beee6 31631e56ca
35 changed files with 302 additions and 366 deletions
--- a/README.md
+++ b/README.md
@ -1,12 +1,13 @@
 ![](img/logo-long-chatchat-trans-v2.png)

-
 🌍 [READ THIS IN ENGLISH](README_en.md)

 📃 **LangChain-Chatchat** (原 Langchain-ChatGLM)

 基于 ChatGLM 等大语言模型与 Langchain 等应用框架实现，开源、可离线部署的检索增强生成(RAG)大模型知识库项目。

+⚠️`0.2.10`将会是`0.2.x`系列的最后一个版本，`0.2.x`系列版本将会停止更新和技术支持，全力研发具有更强应用性的 `Langchain-Chatchat 0.3.x`。
+
 ---

 ## 目录
@ -14,23 +15,31 @@
 * [介绍](README.md#介绍)
 * [解决的痛点](README.md#解决的痛点)
 * [快速上手](README.md#快速上手)
-  * [1. 环境配置](README.md#1-环境配置)
-  * [2. 模型下载](README.md#2-模型下载)
-  * [3. 初始化知识库和配置文件](README.md#3-初始化知识库和配置文件)
-  * [4. 一键启动](README.md#4-一键启动)
-  * [5. 启动界面示例](README.md#5-启动界面示例)
+    * [1. 环境配置](README.md#1-环境配置)
+    * [2. 模型下载](README.md#2-模型下载)
+    * [3. 初始化知识库和配置文件](README.md#3-初始化知识库和配置文件)
+    * [4. 一键启动](README.md#4-一键启动)
+    * [5. 启动界面示例](README.md#5-启动界面示例)
 * [联系我们](README.md#联系我们)

-
 ## 介绍

-🤖️ 一种利用 [langchain](https://github.com/hwchase17/langchain) 思想实现的基于本地知识库的问答应用，目标期望建立一套对中文场景与开源模型支持友好、可离线运行的知识库问答解决方案。
+🤖️ 一种利用 [langchain](https://github.com/langchain-ai/langchain)
+思想实现的基于本地知识库的问答应用，目标期望建立一套对中文场景与开源模型支持友好、可离线运行的知识库问答解决方案。

-💡 受 [GanymedeNil](https://github.com/GanymedeNil) 的项目 [document.ai](https://github.com/GanymedeNil/document.ai) 和 [AlexZhangji](https://github.com/AlexZhangji) 创建的 [ChatGLM-6B Pull Request](https://github.com/THUDM/ChatGLM-6B/pull/216) 启发，建立了全流程可使用开源模型实现的本地知识库问答应用。本项目的最新版本中通过使用 [FastChat](https://github.com/lm-sys/FastChat) 接入 Vicuna, Alpaca, LLaMA, Koala, RWKV 等模型，依托于 [langchain](https://github.com/langchain-ai/langchain) 框架支持通过基于 [FastAPI](https://github.com/tiangolo/fastapi) 提供的 API 调用服务，或使用基于 [Streamlit](https://github.com/streamlit/streamlit) 的 WebUI 进行操作。
+💡 受 [GanymedeNil](https://github.com/GanymedeNil) 的项目 [document.ai](https://github.com/GanymedeNil/document.ai)
+和 [AlexZhangji](https://github.com/AlexZhangji)
+创建的 [ChatGLM-6B Pull Request](https://github.com/THUDM/ChatGLM-6B/pull/216)
+启发，建立了全流程可使用开源模型实现的本地知识库问答应用。本项目的最新版本中通过使用 [FastChat](https://github.com/lm-sys/FastChat)
+接入 Vicuna, Alpaca, LLaMA, Koala, RWKV 等模型，依托于 [langchain](https://github.com/langchain-ai/langchain)
+框架支持通过基于 [FastAPI](https://github.com/tiangolo/fastapi) 提供的 API
+调用服务，或使用基于 [Streamlit](https://github.com/streamlit/streamlit) 的 WebUI 进行操作。

-✅ 依托于本项目支持的开源 LLM 与 Embedding 模型，本项目可实现全部使用**开源**模型**离线私有部署**。与此同时，本项目也支持 OpenAI GPT API 的调用，并将在后续持续扩充对各类模型及模型 API 的接入。
+✅ 依托于本项目支持的开源 LLM 与 Embedding 模型，本项目可实现全部使用**开源**模型**离线私有部署**。与此同时，本项目也支持
+OpenAI GPT API 的调用，并将在后续持续扩充对各类模型及模型 API 的接入。

-⛓️ 本项目实现原理如下图所示，过程包括加载文件 -> 读取文本 -> 文本分割 -> 文本向量化 -> 问句向量化 -> 在文本向量中匹配出与问句向量最相似的 `top k`个 -> 匹配出的文本作为上下文和问题一起添加到 `prompt`中 -> 提交给 `LLM`生成回答。
+⛓️ 本项目实现原理如下图所示，过程包括加载文件 -> 读取文本 -> 文本分割 -> 文本向量化 -> 问句向量化 ->
+在文本向量中匹配出与问句向量最相似的 `top k`个 -> 匹配出的文本作为上下文和问题一起添加到 `prompt`中 -> 提交给 `LLM`生成回答。

 📺 [原理介绍视频](https://www.bilibili.com/video/BV13M4y1e7cN/?share_source=copy_web&vd_source=e6c5aafe684f30fbe41925d61ca6d514)

@ -42,7 +51,8 @@

 🚩 本项目未涉及微调、训练过程，但可利用微调或训练对本项目效果进行优化。

-🌐 [AutoDL 镜像](https://www.codewithgpu.com/i/chatchat-space/Langchain-Chatchat/Langchain-Chatchat) 中 `v13` 版本所使用代码已更新至本项目 `v0.2.9` 版本。
+🌐 [AutoDL 镜像](https://www.codewithgpu.com/i/chatchat-space/Langchain-Chatchat/Langchain-Chatchat) 中 `v13`
+版本所使用代码已更新至本项目 `v0.2.9` 版本。

 🐳 [Docker 镜像](registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.6) 已经更新到 ```0.2.7``` 版本。

@ -52,7 +62,10 @@
 docker run -d --gpus all -p 80:8501 registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.7
 ```

-🧩 本项目有一个非常完整的[Wiki](https://github.com/chatchat-space/Langchain-Chatchat/wiki/) ， README只是一个简单的介绍，__仅仅是入门教程，能够基础运行__。 如果你想要更深入的了解本项目，或者想对本项目做出贡献。请移步 [Wiki](https://github.com/chatchat-space/Langchain-Chatchat/wiki/)  界面
+🧩 本项目有一个非常完整的[Wiki](https://github.com/chatchat-space/Langchain-Chatchat/wiki/) ， README只是一个简单的介绍，_
+_仅仅是入门教程，能够基础运行__。
+如果你想要更深入的了解本项目，或者想对本项目做出贡献。请移步 [Wiki](https://github.com/chatchat-space/Langchain-Chatchat/wiki/)
+界面

 ## 解决的痛点

@ -62,17 +75,19 @@ docker run -d --gpus all -p 80:8501 registry.cn-beijing.aliyuncs.com/chatchat/ch
 我们支持市面上主流的本地大语言模型和Embedding模型，支持开源的本地向量数据库。
 支持列表详见[Wiki](https://github.com/chatchat-space/Langchain-Chatchat/wiki/)

-
 ## 快速上手

 ### 1. 环境配置

-+ 首先，确保你的机器安装了 Python 3.8 - 3.11
+ 首先，确保你的机器安装了 Python 3.8 - 3.11 (我们强烈推荐使用 Python3.11)。
+
 ```
 $ python --version
 Python 3.11.7
 ```
+
 接着，创建一个虚拟环境，并在虚拟环境内安装项目的依赖
+
 ```shell

 # 拉取仓库
@ -88,33 +103,44 @@ $ pip install -r requirements_webui.txt

 # 默认依赖包括基本运行环境（FAISS向量库）。如果要使用 milvus/pg_vector 等向量库，请将 requirements.txt 中相应依赖取消注释再安装。
 ```
-请注意，LangChain-Chatchat `0.2.x` 系列是针对 Langchain `0.0.x` 系列版本的，如果你使用的是 Langchain `0.1.x` 系列版本，需要降级。
+
+请注意，LangChain-Chatchat `0.2.x` 系列是针对 Langchain `0.0.x` 系列版本的，如果你使用的是 Langchain `0.1.x`
+系列版本，需要降级您的`Langchain`版本。
+
 ### 2， 模型下载

-如需在本地或离线环境下运行本项目，需要首先将项目所需的模型下载至本地，通常开源 LLM 与 Embedding 模型可以从 [HuggingFace](https://huggingface.co/models) 下载。
+如需在本地或离线环境下运行本项目，需要首先将项目所需的模型下载至本地，通常开源 LLM 与 Embedding
+模型可以从 [HuggingFace](https://huggingface.co/models) 下载。

-以本项目中默认使用的 LLM 模型 [THUDM/ChatGLM3-6B](https://huggingface.co/THUDM/chatglm3-6b) 与 Embedding 模型 [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) 为例：
+以本项目中默认使用的 LLM 模型 [THUDM/ChatGLM3-6B](https://huggingface.co/THUDM/chatglm3-6b) 与 Embedding
+模型 [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) 为例：

-下载模型需要先[安装 Git LFS](https://docs.github.com/zh/repositories/working-with-files/managing-large-files/installing-git-large-file-storage)，然后运行
+下载模型需要先[安装 Git LFS](https://docs.github.com/zh/repositories/working-with-files/managing-large-files/installing-git-large-file-storage)
+，然后运行

 ```Shell
 $ git lfs install
 $ git clone https://huggingface.co/THUDM/chatglm3-6b
 $ git clone https://huggingface.co/BAAI/bge-large-zh
 ```
+
 ### 3. 初始化知识库和配置文件

 按照下列方式初始化自己的知识库和简单的复制配置文件
+
 ```shell
 $ python copy_config_example.py
 $ python init_database.py --recreate-vs
 ```
+
 ### 4. 一键启动

 按照以下命令启动项目
+
 ```shell
 $ python startup.py -a
 ```
+
 ### 5. 启动界面示例

 如果正常启动，你将能看到以下界面
@ -133,29 +159,37 @@ $ python startup.py -a

 ![](img/init_knowledge_base.jpg)

-
 ### 注意

-以上方式只是为了快速上手，如果需要更多的功能和自定义启动方式 ，请参考[Wiki](https://github.com/chatchat-space/Langchain-Chatchat/wiki/)
+以上方式只是为了快速上手，如果需要更多的功能和自定义启动方式
+，请参考[Wiki](https://github.com/chatchat-space/Langchain-Chatchat/wiki/)


 ---
+
 ## 项目里程碑

 + `2023年4月`: `Langchain-ChatGLM 0.1.0` 发布，支持基于 ChatGLM-6B 模型的本地知识库问答。
 + `2023年8月`: `Langchain-ChatGLM` 改名为 `Langchain-Chatchat`，`0.2.0` 发布，使用 `fastchat` 作为模型加载方案，支持更多的模型和数据库。
-+ `2023年10月`: `Langchain-Chatchat 0.2.5` 发布，推出 Agent 内容，开源项目在`Founder Park & Zhipu AI & Zilliz` 举办的黑客马拉松获得三等奖。 
+ `2023年10月`: `Langchain-Chatchat 0.2.5` 发布，推出 Agent 内容，开源项目在`Founder Park & Zhipu AI & Zilliz`
+  举办的黑客马拉松获得三等奖。
 + `2023年12月`: `Langchain-Chatchat` 开源项目获得超过 **20K** stars.
-+ `2024年1月`: `LangChain 0.1.x` 推出，`Langchain-Chatchat 0.2.x` 停止更新和技术支持，全力研发具有更强应用性的 `Langchain-Chatchat 0.3.x`。
+ `2024年1月`: `LangChain 0.1.x` 推出，`Langchain-Chatchat 0.2.x` 发布稳定版本`0.2.10`
+  后将停止更新和技术支持，全力研发具有更强应用性的 `Langchain-Chatchat 0.3.x`。

 + 🔥 让我们一起期待未来 Chatchat 的故事 ···
+
 ---
+
 ## 联系我们
+
 ### Telegram
+
 [![Telegram](https://img.shields.io/badge/Telegram-2CA5E0?style=for-the-badge&logo=telegram&logoColor=white "langchain-chatglm")](https://t.me/+RjliQ3jnJ1YyN2E9)

 ### 项目交流群
-<img src="img/qr_code_85.jpg" alt="二维码" width="300" />
+
+<img src="img/qr_code_87.jpg" alt="二维码" width="300" />

 🎉 Langchain-Chatchat 项目微信交流群，如果你也对本项目感兴趣，欢迎加入群聊参与讨论交流。

--- a/README_en.md
+++ b/README_en.md
@ -7,6 +7,10 @@
 A LLM application aims to implement knowledge and search engine based QA based on Langchain and open-source or remote
 LLM API.

+⚠️`0.2.10` will be the last version of the `0.2.x` series. The `0.2.x` series will stop updating and technical support,
+and strive to develop `Langchain-Chachat 0.3.x with stronger applicability. `.
+
+
 ---

 ## Table of Contents
@ -24,7 +28,8 @@ LLM API.
 ## Introduction

 🤖️ A Q&A application based on local knowledge base implemented using the idea
-of [langchain](https://github.com/hwchase17/langchain). The goal is to build a KBQA(Knowledge based Q&A) solution that
+of [langchain](https://github.com/langchain-ai/langchain). The goal is to build a KBQA(Knowledge based Q&A) solution
+that
 is friendly to Chinese scenarios and open source models and can run both offline and online.

 💡 Inspired by [document.ai](https://github.com/GanymedeNil/document.ai)
@ -55,10 +60,9 @@ The main process analysis from the aspect of document process:
 🚩 The training or fine-tuning are not involved in the project, but still, one always can improve performance by do
 these.

-🌐 [AutoDL image](registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.5) is supported, and in v13 the codes are update
-to v0.2.9.
+🌐 [AutoDL image](https://www.codewithgpu.com/i/chatchat-space/Langchain-Chatchat/Langchain-Chatchat) is supported, and in v13 the codes are update to v0.2.9.

-🐳 [Docker image](registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.7)
+🐳 [Docker image](registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.7) is supported to 0.2.7

 ## Pain Points Addressed

@ -98,7 +102,9 @@ $ pip install -r requirements_webui.txt

 # 默认依赖包括基本运行环境（FAISS向量库）。如果要使用 milvus/pg_vector 等向量库，请将 requirements.txt 中相应依赖取消注释再安装。
 ```
-Please note that the LangChain-Chachat `0.2.x` series is for the Langchain `0.0.x` series version. If you are using the Langchain `0.1.x` series version, you need to downgrade.
+
+Please note that the LangChain-Chachat `0.2.x` series is for the Langchain `0.0.x` series version. If you are using the
+Langchain `0.1.x` series version, you need to downgrade.

 ### Model Download

@ -157,15 +163,23 @@ The above instructions are provided for a quick start. If you need more features
 please refer to the [Wiki](https://github.com/chatchat-space/Langchain-Chatchat/wiki/).

 ---
+
 ## Project Milestones

-+ `April 2023`: `Langchain-ChatGLM 0.1.0` released, supporting local knowledge base question and answer based on the ChatGLM-6B model.
-+ `August 2023`: `Langchain-ChatGLM` was renamed to `Langchain-Chatchat`, `0.2.0` was released, using `fastchat` as the model loading solution, supporting more models and databases.
-+ `October 2023`: `Langchain-Chachat 0.2.5` was released, Agent content was launched, and the open source project won the third prize in the hackathon held by `Founder Park & Zhipu AI & Zilliz`.
+ `April 2023`: `Langchain-ChatGLM 0.1.0` released, supporting local knowledge base question and answer based on the
+  ChatGLM-6B model.
+ `August 2023`: `Langchain-ChatGLM` was renamed to `Langchain-Chatchat`, `0.2.0` was released, using `fastchat` as the
+  model loading solution, supporting more models and databases.
+ `October 2023`: `Langchain-Chachat 0.2.5` was released, Agent content was launched, and the open source project won
+  the third prize in the hackathon held by `Founder Park & Zhipu AI & Zilliz`.
 + `December 2023`: `Langchain-Chachat` open source project received more than **20K** stars.
-+ `January 2024`: `LangChain 0.1.x` is launched, `Langchain-Chatchat 0.2.x` will stop updating and technical support, and all efforts will be made to develop `Langchain-Chatchat 0.3.x` with stronger applicability.
+ `January 2024`: `LangChain 0.1.x` is launched, `Langchain-Chachat 0.2.x` is released. After the stable
+  version `0.2.10` is released, updates and technical support will be stopped, and all efforts will be made to
+  develop `Langchain with stronger applicability -Chat 0.3.x`.
+

 + 🔥 Let’s look forward to the future Chatchat stories together···
+
 ---

 ## Contact Us
@ -176,7 +190,7 @@ please refer to the [Wiki](https://github.com/chatchat-space/Langchain-Chatchat/

 ### WeChat Group

-<img src="img/qr_code_85.jpg" alt="二维码" width="300" height="300" />
+<img src="img/qr_code_87.jpg" alt="二维码" width="300" height="300" />

 ### WeChat Official Account

--- a/configs/model_config.py.example
+++ b/configs/model_config.py.example
@ -120,7 +120,7 @@ ONLINE_LLM_MODEL = {
        "secret_key": "",
        "provider": "TianGongWorker",
    },
-    # Gemini API (开发组未测试，由社群提供，只支持pro）
+    # Gemini API https://makersuite.google.com/app/apikey
    "gemini-api": {
        "api_key": "",
        "provider": "GeminiWorker",
--- a/configs/server_config.py.example
+++ b/configs/server_config.py.example
@ -92,11 +92,10 @@ FSCHAT_MODEL_WORKERS = {
        # 'disable_log_requests': False

    },
-    # 可以如下示例方式更改默认配置
-    # "Qwen-1_8B-Chat": { # 使用default中的IP和端口
-    #    "device": "cpu",
-    # },
-    "chatglm3-6b": {  # 使用default中的IP和端口
+    "Qwen-1_8B-Chat": {
+        "device": "cpu",
+    },
+    "chatglm3-6b": {
        "device": "cuda",
    },

@ -129,16 +128,10 @@ FSCHAT_MODEL_WORKERS = {
        "port": 21009,
    },
    "gemini-api": {
-        "port": 21012,
+        "port": 21010,
    },
 }

-# fastchat multi model worker server
-FSCHAT_MULTI_MODEL_WORKERS = {
-    # TODO:
-}
-
-# fastchat controller server
 FSCHAT_CONTROLLER = {
    "host": DEFAULT_BIND_HOST,
    "port": 20001,
--- a/document_loaders/mypdfloader.py
+++ b/document_loaders/mypdfloader.py
@ -16,11 +16,8 @@ class RapidOCRPDFLoader(UnstructuredFileLoader):

            b_unit = tqdm.tqdm(total=doc.page_count, desc="RapidOCRPDFLoader context page index: 0")
            for i, page in enumerate(doc):
-                # 更新描述
                b_unit.set_description("RapidOCRPDFLoader context page index: {}".format(i))
-                # 立即显示进度条更新结果
                b_unit.refresh()
-                # TODO: 依据文本与图片顺序调整处理方式
                text = page.get_text("")
                resp += text + "\n"

--- a/img/qr_code_85.jpg
+++ b/img/qr_code_85.jpg
--- a/img/qr_code_87.jpg
+++ b/img/qr_code_87.jpg
--- a/requirements.txt
+++ b/requirements.txt
@ -1,79 +1,61 @@
-# API requirements
-
 torch~=2.1.2
 torchvision~=0.16.2
 torchaudio~=2.1.2
-xformers==0.0.23.post1
-transformers==4.36.2
-sentence_transformers==2.2.2
-
+xformers~=0.0.23.post1
+transformers~=4.36.2
+sentence_transformers~=2.2.2
 langchain==0.0.354
 langchain-experimental==0.0.47
 pydantic==1.10.13
-fschat==0.2.35
-openai~=1.7.1
-fastapi~=0.108.0
-sse_starlette==1.8.2
-nltk>=3.8.1
-uvicorn>=0.24.0.post1
+fschat~=0.2.35
+openai~=1.9.0
+fastapi~=0.109.0
+sse_starlette~=1.8.2
+nltk~=3.8.1
+uvicorn~=0.24.0.post1
 starlette~=0.32.0
-unstructured[all-docs]==0.11.0
-python-magic-bin; sys_platform == 'win32'
-SQLAlchemy==2.0.19
-faiss-cpu~=1.7.4  # `conda install faiss-gpu -c conda-forge` if you want to accelerate with gpus
+unstructured[all-docs]~=0.12.0
+python-magic-bin; sys_platform ~= 'win32'
+SQLAlchemy~=2.0.25
+faiss-cpu~=1.7.4
 accelerate~=0.24.1
 spacy~=3.7.2
-PyMuPDF~=1.23.8
-rapidocr_onnxruntime==1.3.8
+PyMuPDF~=1.23.16
+rapidocr_onnxruntime~=1.3.8
 requests~=2.31.0
 pathlib~=1.0.1
 pytest~=7.4.3
-numexpr~=2.8.6 # max version for py38
+numexpr~=2.8.6
 strsimpy~=0.2.1
 markdownify~=0.11.6
 tiktoken~=0.5.2
-tqdm>=4.66.1
-websockets>=12.0
+tqdm~=4.66.1
+websockets~=12.0
 numpy~=1.24.4
 pandas~=2.0.3
-einops>=0.7.0
-transformers_stream_generator==0.0.4
-vllm==0.2.7; sys_platform == "linux"
-
-# flash-attn>=2.4.3 # For Orion-14B-Chat and Qwen-14B-Chat
-
-
-# optional document loaders
-
-#rapidocr_paddle[gpu]>=1.3.0.post5 # gpu accelleration for ocr of pdf and image files
-jq==1.6.0 # for .json and .jsonl files. suggest `conda install jq` on windows
-beautifulsoup4~=4.12.2 # for .mhtml files
+einops~=0.7.0
+transformers_stream_generator~=0.0.4
+vllm~=0.2.7; sys_platform ~= "linux"
+jq~=1.6.0
+beautifulsoup4~=4.12.2
 pysrt~=1.1.2
-
-# Online api libs dependencies
-# zhipuAI sdk is not supported on our platform, so use http instead
-dashscope==1.13.6 # qwen
-# volcengine>=1.0.119 # fangzhou
-
+dashscope~=1.13.6 # qwen
+# volcengine~=1.0.119 # fangzhou
 # uncomment libs if you want to use corresponding vector store
-# pymilvus>=2.3.4
-# psycopg2==2.9.9
-# pgvector>=0.2.4
-
-# Agent and Search Tools
-
+# pymilvus~=2.3.4
+# psycopg2~=2.9.9
+# pgvector~=0.2.4
+# flash-attn~=2.4.3 # For Orion-14B-Chat and Qwen-14B-Chat
+#rapidocr_paddle[gpu]~=1.3.0.post5 # gpu accelleration for ocr of pdf and image files
 arxiv~=2.1.0
 youtube-search~=2.1.2
 duckduckgo-search~=3.9.9
 metaphor-python~=0.1.23
-
-# WebUI requirements
-
-streamlit==1.30.0
-streamlit-option-menu==0.3.6
-streamlit-antd-components==0.3.1
-streamlit-chatbox==1.1.11
-streamlit-modal==0.1.0
-streamlit-aggrid==0.3.4.post3
-httpx==0.26.0
-watchdog==3.0.0
+streamlit~=1.30.0
+streamlit-option-menu~=0.3.12
+streamlit-antd-components~=0.3.1
+streamlit-chatbox~=1.1.11
+streamlit-modal~=0.1.0
+streamlit-aggrid~=0.3.4.post3
+httpx~=0.26.0
+watchdog~=3.0.0
--- a/requirements_api.txt
+++ b/requirements_api.txt
@ -1,15 +1,14 @@
 torch~=2.1.2
 torchvision~=0.16.2
 torchaudio~=2.1.2
-xformers==0.0.23.post1
+xformers>=0.0.23.post1
 transformers==4.36.2
 sentence_transformers==2.2.2
-
 langchain==0.0.354
 langchain-experimental==0.0.47
 pydantic==1.10.13
 fschat==0.2.35
-openai~=1.7.1
+openai~=1.9.0
 fastapi~=0.108.0
 sse_starlette==1.8.2
 nltk>=3.8.1
@ -18,7 +17,7 @@ starlette~=0.32.0
 unstructured[all-docs]==0.11.0
 python-magic-bin; sys_platform == 'win32'
 SQLAlchemy==2.0.19
-faiss-cpu~=1.7.4  # `conda install faiss-gpu -c conda-forge` if you want to accelerate with gpus
+faiss-cpu~=1.7.4
 accelerate~=0.24.1
 spacy~=3.7.2
 PyMuPDF~=1.23.8
@ -26,7 +25,7 @@ rapidocr_onnxruntime==1.3.8
 requests~=2.31.0
 pathlib~=1.0.1
 pytest~=7.4.3
-numexpr~=2.8.6 # max version for py38
+numexpr~=2.8.6
 strsimpy~=0.2.1
 markdownify~=0.11.6
 tiktoken~=0.5.2
@ -39,29 +38,18 @@ transformers_stream_generator==0.0.4
 vllm==0.2.7; sys_platform == "linux"
 httpx==0.26.0
 llama-index
-# flash-attn>=2.4.3 # For Orion-14B-Chat and Qwen-14B-Chat
-
-# optional document loaders
-
-# rapidocr_paddle[gpu]>=1.3.0.post5 # gpu accelleration for ocr of pdf and image files
-jq==1.6.0 # for .json and .jsonl files. suggest `conda install jq` on windows
-beautifulsoup4~=4.12.2 # for .mhtml files
+jq==1.6.0
+beautifulsoup4~=4.12.2
 pysrt~=1.1.2
-
-# Online api libs dependencies
-
-# zhipuAI sdk is not supported on our platform, so use http instead
-dashscope==1.13.6 # qwen
-# volcengine>=1.0.119 # fangzhou
-
-# uncomment libs if you want to use corresponding vector store
-# pymilvus>=2.3.4
-# psycopg2==2.9.9
-# pgvector>=0.2.4
-
-# Agent and Search Tools
-
+dashscope==1.13.6
 arxiv~=2.1.0
 youtube-search~=2.1.2
 duckduckgo-search~=3.9.9
-metaphor-python~=0.1.23
+metaphor-python~=0.1.23
+
+# volcengine>=1.0.119
+# pymilvus>=2.3.4
+# psycopg2==2.9.9
+# pgvector>=0.2.4
+# flash-attn>=2.4.3 # For Orion-14B-Chat and Qwen-14B-Chat
+# rapidocr_paddle[gpu]>=1.3.0.post5
--- a/requirements_lite.txt
+++ b/requirements_lite.txt
@ -1,64 +1,32 @@
-# API requirements
-
 langchain==0.0.354
 langchain-experimental==0.0.47
 pydantic==1.10.13
-fschat==0.2.35
-openai~=1.7.1
-fastapi~=0.108.0
-sse_starlette==1.8.2
-nltk>=3.8.1
-uvicorn>=0.24.0.post1
+fschat~=0.2.35
+openai~=1.9.0
+fastapi~=0.109.0
+sse_starlette~=1.8.2
+nltk~=3.8.1
+uvicorn~=0.24.0.post1
 starlette~=0.32.0
-unstructured[all-docs]==0.11.0
-python-magic-bin; sys_platform == 'win32'
-SQLAlchemy==2.0.19
+unstructured[all-docs]~=0.12.0
+python-magic-bin; sys_platform ~= 'win32'
+SQLAlchemy~=2.0.25
 faiss-cpu~=1.7.4
+accelerate~=0.24.1
+spacy~=3.7.2
+PyMuPDF~=1.23.16
+rapidocr_onnxruntime~=1.3.8
 requests~=2.31.0
 pathlib~=1.0.1
 pytest~=7.4.3
-numexpr~=2.8.6 # max version for py38
-strsimpy~=0.2.1
-markdownify~=0.11.6
-tiktoken~=0.5.2
-tqdm>=4.66.1
-websockets>=12.0
-numpy~=1.24.4
-pandas~=2.0.3
-einops>=0.7.0
-transformers_stream_generator==0.0.4
-vllm==0.2.7; sys_platform == "linux"
-httpx[brotli,http2,socks]==0.25.2
-requests
-pathlib
-pytest
-
-
-# Online api libs dependencies
-
-# zhipuAI sdk is not supported on our platform, so use http instead
 dashscope==1.13.6
-# volcengine>=1.0.119
-
-# uncomment libs if you want to use corresponding vector store
-# pymilvus>=2.3.4
-# psycopg2==2.9.9
-# pgvector>=0.2.4
-
-# Agent and Search Tools
-
 arxiv~=2.1.0
 youtube-search~=2.1.2
 duckduckgo-search~=3.9.9
 metaphor-python~=0.1.23
-
-# WebUI requirements
-
-streamlit==1.30.0
-streamlit-option-menu==0.3.6
-streamlit-antd-components==0.3.1
-streamlit-chatbox==1.1.11
-streamlit-modal==0.1.0
-streamlit-aggrid==0.3.4.post3
-httpx==0.26.0
-watchdog==3.0.0
+watchdog~=3.0.0
+# volcengine>=1.0.119
+# pymilvus>=2.3.4
+# psycopg2==2.9.9
+# pgvector>=0.2.4
+# flash-attn>=2.4.3 # For Orion-14B-Chat and Qwen-14B-Chat
--- a/requirements_webui.txt
+++ b/requirements_webui.txt
@ -1,10 +1,8 @@
-# WebUI requirements
-
-streamlit==1.30.0
-streamlit-option-menu==0.3.6
-streamlit-antd-components==0.3.1
-streamlit-chatbox==1.1.11
-streamlit-modal==0.1.0
-streamlit-aggrid==0.3.4.post3
-httpx==0.26.0
-watchdog==3.0.0
+streamlit~=1.30.0
+streamlit-option-menu~=0.3.12
+streamlit-antd-components~=0.3.1
+streamlit-chatbox~=1.1.11
+streamlit-modal~=0.1.0
+streamlit-aggrid~=0.3.4.post3
+httpx~=0.26.0
+watchdog~=3.0.0
--- a/server/embeddings_api.py
+++ b/server/embeddings_api.py
@ -16,7 +16,6 @@ def embed_texts(
 ) -> BaseResponse:
    '''
    对文本进行向量化。返回数据格式：BaseResponse(data=List[List[float]])
-    TODO: 也许需要加入缓存机制，减少 token 消耗
    '''
    try:
        if embed_model in list_embed_models():  # 使用本地Embeddings模型
--- a/server/knowledge_base/kb_api.py
+++ b/server/knowledge_base/kb_api.py
@ -13,9 +13,9 @@ def list_kbs():


 def create_kb(knowledge_base_name: str = Body(..., examples=["samples"]),
-            vector_store_type: str = Body("faiss"),
-            embed_model: str = Body(EMBEDDING_MODEL),
-            ) -> BaseResponse:
+              vector_store_type: str = Body("faiss"),
+              embed_model: str = Body(EMBEDDING_MODEL),
+              ) -> BaseResponse:
    # Create selected knowledge base
    if not validate_kb_name(knowledge_base_name):
        return BaseResponse(code=403, msg="Don't attack me")
@ -39,8 +39,8 @@ def create_kb(knowledge_base_name: str = Body(..., examples=["samples"]),


 def delete_kb(
-    knowledge_base_name: str = Body(..., examples=["samples"])
-    ) -> BaseResponse:
+        knowledge_base_name: str = Body(..., examples=["samples"])
+) -> BaseResponse:
    # Delete selected knowledge base
    if not validate_kb_name(knowledge_base_name):
        return BaseResponse(code=403, msg="Don't attack me")
--- a/server/knowledge_base/kb_cache/faiss_cache.py
+++ b/server/knowledge_base/kb_cache/faiss_cache.py
@ -55,8 +55,6 @@ class _FaissPool(CachePool):
        embed_model: str = EMBEDDING_MODEL,
        embed_device: str = embedding_device(),
    ) -> FAISS:
-        # TODO: 整个Embeddings加载逻辑有些混乱，待清理
-        # create an empty vector store
        embeddings = EmbeddingsFunAdapter(embed_model)
        doc = Document(page_content="init", metadata={})
        vector_store = FAISS.from_documents([doc], embeddings, normalize_L2=True,distance_strategy="METRIC_INNER_PRODUCT")
--- a/server/knowledge_base/kb_doc_api.py
+++ b/server/knowledge_base/kb_doc_api.py
@ -95,7 +95,6 @@ def _save_files_in_thread(files: List[UploadFile],
                    and not override
                    and os.path.getsize(file_path) == len(file_content)
            ):
-                # TODO: filesize 不同后的处理
                file_status = f"文件 {filename} 已存在。"
                logger.warn(file_status)
                return dict(code=404, msg=file_status, data=data)
@ -116,7 +115,6 @@ def _save_files_in_thread(files: List[UploadFile],
        yield result


-# TODO: 等langchain.document_loaders支持内存文件的时候再开通
 # def files2docs(files: List[UploadFile] = File(..., description="上传文件，支持多文件"),
 #                 knowledge_base_name: str = Form(..., description="知识库名称", examples=["samples"]),
 #                 override: bool = Form(False, description="覆盖已有文件"),
--- a/server/knowledge_base/kb_service/base.py
+++ b/server/knowledge_base/kb_service/base.py
@ -191,7 +191,6 @@ class KBService(ABC):
        '''
        传入参数为： {doc_id: Document, ...}
        如果对应 doc_id 的值为 None，或其 page_content 为空，则删除该文档
-        TODO：是否要支持新增 docs ？
        '''
        self.del_doc_by_ids(list(docs.keys()))
        docs = []
--- a/server/knowledge_base/kb_service/milvus_kb_service.py
+++ b/server/knowledge_base/kb_service/milvus_kb_service.py
@ -70,7 +70,6 @@ class MilvusKBService(KBService):
        return score_threshold_process(score_threshold, top_k, docs)

    def do_add_doc(self, docs: List[Document], **kwargs) -> List[Dict]:
-        # TODO: workaround for bug #10492 in langchain
        for doc in docs:
            for k, v in doc.metadata.items():
                doc.metadata[k] = str(v)
--- a/server/knowledge_base/kb_service/pg_kb_service.py
+++ b/server/knowledge_base/kb_service/pg_kb_service.py
@ -32,8 +32,6 @@ class PGKBService(KBService):
            results = [Document(page_content=row[0], metadata=row[1]) for row in
                       session.execute(stmt, {'ids': ids}).fetchall()]
            return results
-
-    # TODO:
    def del_doc_by_ids(self, ids: List[str]) -> bool:
        return super().del_doc_by_ids(ids)

--- a/server/knowledge_base/kb_summary/base.py
+++ b/server/knowledge_base/kb_summary/base.py
@ -13,7 +13,6 @@ from server.db.repository.knowledge_metadata_repository import add_summary_to_db
 from langchain.docstore.document import Document


-# TODO 暂不考虑文件更新，需要重新删除相关文档，再重新添加
 class KBSummaryService(ABC):
    kb_name: str
    embed_model: str
--- a/server/knowledge_base/kb_summary/summary_chunk.py
+++ b/server/knowledge_base/kb_summary/summary_chunk.py
@ -112,12 +112,6 @@ class SummaryAdapter:
                         docs: List[DocumentWithVSId] = []) -> List[Document]:

        logger.info("start summary")
-        # TODO 暂不处理文档中涉及语义重复、上下文缺失、document was longer than the context length 的问题
-        # merge_docs = self._drop_overlap(docs)
-        # # 将merge_docs中的句子合并成一个文档
-        # text = self._join_docs(merge_docs)
-        # 根据段落于句子的分隔符，将文档分成chunk，每个chunk长度小于token_max长度
-
        """
        这个过程分成两个部分：
        1. 对每个文档进行处理，得到每个文档的摘要
--- a/server/knowledge_base/utils.py
+++ b/server/knowledge_base/utils.py
@ -174,7 +174,6 @@ def get_loader(loader_name: str, file_path: str, loader_kwargs: Dict = None):
            if encode_detect is None:
                encode_detect = {"encoding": "utf-8"}
            loader_kwargs["encoding"] = encode_detect["encoding"]
-        ## TODO：支持更多的自定义CSV读取逻辑

    elif loader_name == "JSONLoader":
        loader_kwargs.setdefault("jq_schema", ".")
--- a/server/model_workers/azure.py
+++ b/server/model_workers/azure.py
@ -67,12 +67,10 @@ class AzureWorker(ApiModelWorker):
                        self.logger.error(f"请求 Azure API 时发生错误：{resp}")

    def get_embeddings(self, params):
-        # TODO: 支持embeddings
        print("embedding")
        print(params)

    def make_conv_template(self, conv_template: str = None, model_path: str = None) -> Conversation:
-        # TODO: 确认模板是否需要修改
        return conv.Conversation(
            name=self.model_names[0],
            system_message="You are a helpful, respectful and honest assistant.",
--- a/server/model_workers/baichuan.py
+++ b/server/model_workers/baichuan.py
@ -88,12 +88,10 @@ class BaiChuanWorker(ApiModelWorker):
                        yield data

    def get_embeddings(self, params):
-        # TODO: 支持embeddings
        print("embedding")
        print(params)

    def make_conv_template(self, conv_template: str = None, model_path: str = None) -> Conversation:
-        # TODO: 确认模板是否需要修改
        return conv.Conversation(
            name=self.model_names[0],
            system_message="",
--- a/server/model_workers/base.py
+++ b/server/model_workers/base.py
@ -125,8 +125,6 @@ class ApiModelWorker(BaseModelWorker):


    def count_token(self, params):
-        # TODO：需要完善
-        # print("count token")
        prompt = params["prompt"]
        return {"count": len(str(prompt)), "error_code": 0}

--- a/server/model_workers/fangzhou.py
+++ b/server/model_workers/fangzhou.py
@ -12,16 +12,16 @@ class FangZhouWorker(ApiModelWorker):
    """

    def __init__(
-        self,
-        *,
-        model_names: List[str] = ["fangzhou-api"],
-        controller_addr: str = None,
-        worker_addr: str = None,
-        version: Literal["chatglm-6b-model"] = "chatglm-6b-model",
-        **kwargs,
+            self,
+            *,
+            model_names: List[str] = ["fangzhou-api"],
+            controller_addr: str = None,
+            worker_addr: str = None,
+            version: Literal["chatglm-6b-model"] = "chatglm-6b-model",
+            **kwargs,
    ):
        kwargs.update(model_names=model_names, controller_addr=controller_addr, worker_addr=worker_addr)
-        kwargs.setdefault("context_len", 16384) # TODO: 不同的模型有不同的大小
+        kwargs.setdefault("context_len", 16384)
        super().__init__(**kwargs)
        self.version = version

@ -53,15 +53,15 @@ class FangZhouWorker(ApiModelWorker):
            if error := resp.error:
                if error.code_n > 0:
                    data = {
-                            "error_code": error.code_n,
-                            "text": error.message,
-                            "error": {
-                                "message": error.message,
-                                "type": "invalid_request_error",
-                                "param": None,
-                                "code": None,
-                            }
+                        "error_code": error.code_n,
+                        "text": error.message,
+                        "error": {
+                            "message": error.message,
+                            "type": "invalid_request_error",
+                            "param": None,
+                            "code": None,
                        }
+                    }
                    self.logger.error(f"请求方舟 API 时发生错误：{data}")
                    yield data
                elif chunk := resp.choice.message.content:
@ -77,7 +77,6 @@ class FangZhouWorker(ApiModelWorker):
                break

    def get_embeddings(self, params):
-        # TODO: 支持embeddings
        print("embedding")
        print(params)

--- a/server/model_workers/gemini.py
+++ b/server/model_workers/gemini.py
@ -3,7 +3,7 @@ from fastchat.conversation import Conversation
 from server.model_workers.base import *
 from server.utils import get_httpx_client
 from fastchat import conversation as conv
-import json,httpx
+import json, httpx
 from typing import List, Dict
 from configs import logger, log_verbose

@ -14,14 +14,14 @@ class GeminiWorker(ApiModelWorker):
            *,
            controller_addr: str = None,
            worker_addr: str = None,
-            model_names: List[str] = ["Gemini-api"],
+            model_names: List[str] = ["gemini-api"],
            **kwargs,
    ):
        kwargs.update(model_names=model_names, controller_addr=controller_addr, worker_addr=worker_addr)
        kwargs.setdefault("context_len", 4096)
        super().__init__(**kwargs)

-    def create_gemini_messages(self,messages) -> json:
+    def create_gemini_messages(self, messages) -> json:
        has_history = any(msg['role'] == 'assistant' for msg in messages)
        gemini_msg = []

@ -42,11 +42,11 @@ class GeminiWorker(ApiModelWorker):

        msg = dict(contents=gemini_msg)
        return msg
-          
+
    def do_chat(self, params: ApiChatParams) -> Dict:
        params.load_config(self.model_names[0])
        data = self.create_gemini_messages(messages=params.messages)
-        generationConfig=dict(
+        generationConfig = dict(
            temperature=params.temperature,
            topK=1,
            topP=1,
@ -54,8 +54,8 @@ class GeminiWorker(ApiModelWorker):
            stopSequences=[]
        )

-        data['generationConfig'] = generationConfig         
-        url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent"+ '?key=' + params.api_key
+        data['generationConfig'] = generationConfig
+        url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent" + '?key=' + params.api_key
        headers = {
            'Content-Type': 'application/json',
        }
@ -67,7 +67,7 @@ class GeminiWorker(ApiModelWorker):
        text = ""
        json_string = ""
        timeout = httpx.Timeout(60.0)
-        client=get_httpx_client(timeout=timeout)
+        client = get_httpx_client(timeout=timeout)
        with client.stream("POST", url, headers=headers, json=data) as response:
            for line in response.iter_lines():
                line = line.strip()
@ -89,13 +89,12 @@ class GeminiWorker(ApiModelWorker):
                                    "error_code": 0,
                                    "text": text
                                }
-                        print(text)       
+                        print(text)
            except json.JSONDecodeError as e:
                print("Failed to decode JSON:", e)
                print("Invalid JSON string:", json_string)

    def get_embeddings(self, params):
-        # TODO: 支持embeddings
        print("embedding")
        print(params)

--- a/server/model_workers/minimax.py
+++ b/server/model_workers/minimax.py
@ -37,7 +37,6 @@ class MiniMaxWorker(ApiModelWorker):

    def do_chat(self, params: ApiChatParams) -> Dict:
        # 按照官网推荐，直接调用abab 5.5模型
-        # TODO: 支持指定回复要求，支持指定用户名称、AI名称
        params.load_config(self.model_names[0])

        url = 'https://api.minimax.chat/v1/text/chatcompletion{pro}?GroupId={group_id}'
@ -55,7 +54,7 @@ class MiniMaxWorker(ApiModelWorker):
            "temperature": params.temperature,
            "top_p": params.top_p,
            "tokens_to_generate": params.max_tokens or 1024,
-            # TODO: 以下参数为minimax特有，传入空值会出错。
+            # 以下参数为minimax特有，传入空值会出错。
            # "prompt": params.system_message or self.conv.system_message,
            # "bot_setting": [],
            # "role_meta": params.role_meta,
@ -143,12 +142,10 @@ class MiniMaxWorker(ApiModelWorker):
            return {"code": 200, "data": result}

    def get_embeddings(self, params):
-        # TODO: 支持embeddings
        print("embedding")
        print(params)

    def make_conv_template(self, conv_template: str = None, model_path: str = None) -> Conversation:
-        # TODO: 确认模板是否需要修改
        return conv.Conversation(
            name=self.model_names[0],
            system_message="你是MiniMax自主研发的大型语言模型，回答问题简洁有条理。",
--- a/server/model_workers/qianfan.py
+++ b/server/model_workers/qianfan.py
@ -187,14 +187,11 @@ class QianFanWorker(ApiModelWorker):
                i += batch_size
            return {"code": 200, "data": result}

-    # TODO: qianfan支持续写模型
    def get_embeddings(self, params):
-        # TODO: 支持embeddings
        print("embedding")
        print(params)

    def make_conv_template(self, conv_template: str = None, model_path: str = None) -> Conversation:
-        # TODO: 确认模板是否需要修改
        return conv.Conversation(
            name=self.model_names[0],
            system_message="你是一个聪明的助手，请根据用户的提示来完成任务",
--- a/server/model_workers/qwen.py
+++ b/server/model_workers/qwen.py
@ -100,12 +100,10 @@ class QwenWorker(ApiModelWorker):
        return {"code": 200, "data": result}

    def get_embeddings(self, params):
-        # TODO: 支持embeddings
        print("embedding")
        print(params)

    def make_conv_template(self, conv_template: str = None, model_path: str = None) -> Conversation:
-        # TODO: 确认模板是否需要修改
        return conv.Conversation(
            name=self.model_names[0],
            system_message="你是一个聪明、对人类有帮助的人工智能，你可以对人类提出的问题给出有用、详细、礼貌的回答。",
--- a/server/model_workers/tiangong.py
+++ b/server/model_workers/tiangong.py
@ -70,12 +70,10 @@ class TianGongWorker(ApiModelWorker):
                    yield data

    def get_embeddings(self, params):
-        # TODO: 支持embeddings
        print("embedding")
        print(params)

    def make_conv_template(self, conv_template: str = None, model_path: str = None) -> Conversation:
-        # TODO: 确认模板是否需要修改
        return conv.Conversation(
            name=self.model_names[0],
            system_message="",
--- a/server/model_workers/xinghuo.py
+++ b/server/model_workers/xinghuo.py
@ -42,7 +42,6 @@ class XingHuoWorker(ApiModelWorker):
        self.version = version

    def do_chat(self, params: ApiChatParams) -> Dict:
-        # TODO: 当前每次对话都要重新连接websocket，确认是否可以保持连接
        params.load_config(self.model_names[0])

        version_mapping = {
@ -73,12 +72,10 @@ class XingHuoWorker(ApiModelWorker):
                yield {"error_code": 0, "text": text}

    def get_embeddings(self, params):
-        # TODO: 支持embeddings
        print("embedding")
        print(params)

    def make_conv_template(self, conv_template: str = None, model_path: str = None) -> Conversation:
-        # TODO: 确认模板是否需要修改
        return conv.Conversation(
            name=self.model_names[0],
            system_message="你是一个聪明的助手，请根据用户的提示来完成任务",
--- a/server/utils.py
+++ b/server/utils.py
@ -36,7 +36,6 @@ async def wrap_done(fn: Awaitable, event: asyncio.Event):
        await fn
    except Exception as e:
        logging.exception(e)
-        # TODO: handle exception
        msg = f"Caught exception: {e}"
        logger.error(f'{e.__class__.__name__}: {msg}',
                     exc_info=e if log_verbose else None)
@ -404,7 +403,7 @@ def fschat_controller_address() -> str:


 def fschat_model_worker_address(model_name: str = LLM_MODELS[0]) -> str:
-    if model := get_model_worker_config(model_name):  # TODO: depends fastchat
+    if model := get_model_worker_config(model_name):
        host = model["host"]
        if host == "0.0.0.0":
            host = "127.0.0.1"
@ -449,7 +448,7 @@ def get_prompt_template(type: str, name: str) -> Optional[str]:

    from configs import prompt_config
    import importlib
-    importlib.reload(prompt_config)  # TODO: 检查configs/prompt_config.py文件有修改再重新加载
+    importlib.reload(prompt_config)
    return prompt_config.PROMPT_TEMPLATES[type].get(name)


@ -550,7 +549,7 @@ def run_in_thread_pool(
            thread = pool.submit(func, **kwargs)
            tasks.append(thread)

-        for obj in as_completed(tasks):  # TODO: Ctrl+c无法停止
+        for obj in as_completed(tasks):
            yield obj.result()


--- a/startup.py
+++ b/startup.py
@ -418,7 +418,7 @@ def run_openai_api(log_level: str = "INFO", started_event: mp.Event = None):
    set_httpx_config()

    controller_addr = fschat_controller_address()
-    app = create_openai_api_app(controller_addr, log_level=log_level)  # TODO: not support keys yet.
+    app = create_openai_api_app(controller_addr, log_level=log_level)
    _set_app_event(app, started_event)

    host = FSCHAT_OPENAI_API["host"]
--- a/webui_pages/dialogue/dialogue.py
+++ b/webui_pages/dialogue/dialogue.py
@ -12,7 +12,6 @@ from server.knowledge_base.utils import LOADER_DICT
 import uuid
 from typing import List, Dict

-
 chat_box = ChatBox(
    assistant_avatar=os.path.join(
        "img",
@ -127,7 +126,6 @@ def dialogue_page(api: ApiRequest, is_lite: bool = False):
        chat_box.use_chat_name(conversation_name)
        conversation_id = st.session_state["conversation_ids"][conversation_name]

-        # TODO: 对话模型与会话绑定
        def on_mode_change():
            mode = st.session_state.dialogue_mode
            text = f"已切换到 {mode} 模式。"
@ -138,11 +136,11 @@ def dialogue_page(api: ApiRequest, is_lite: bool = False):
            st.toast(text)

        dialogue_modes = ["LLM 对话",
-                        "知识库问答",
-                        "文件对话",
-                        "搜索引擎问答",
-                        "自定义Agent问答",
-                        ]
+                          "知识库问答",
+                          "文件对话",
+                          "搜索引擎问答",
+                          "自定义Agent问答",
+                          ]
        dialogue_mode = st.selectbox("请选择对话模式：",
                                     dialogue_modes,
                                     index=0,
@ -166,9 +164,9 @@ def dialogue_page(api: ApiRequest, is_lite: bool = False):
        available_models = []
        config_models = api.list_config_models()
        if not is_lite:
-            for k, v in config_models.get("local", {}).items(): # 列出配置了有效本地路径的模型
+            for k, v in config_models.get("local", {}).items():  # 列出配置了有效本地路径的模型
                if (v.get("model_path_exists")
-                    and k not in running_models):
+                        and k not in running_models):
                    available_models.append(k)
        for k, v in config_models.get("online", {}).items():  # 列出ONLINE_MODELS中直接访问的模型
            if not v.get("provider") and k not in running_models:
@ -250,14 +248,14 @@ def dialogue_page(api: ApiRequest, is_lite: bool = False):
        elif dialogue_mode == "文件对话":
            with st.expander("文件对话配置", True):
                files = st.file_uploader("上传知识文件：",
-                                        [i for ls in LOADER_DICT.values() for i in ls],
-                                        accept_multiple_files=True,
-                                        )
+                                         [i for ls in LOADER_DICT.values() for i in ls],
+                                         accept_multiple_files=True,
+                                         )
                kb_top_k = st.number_input("匹配知识条数：", 1, 20, VECTOR_SEARCH_TOP_K)

                ## Bge 模型会超过1
                score_threshold = st.slider("知识匹配分数阈值：", 0.0, 2.0, float(SCORE_THRESHOLD), 0.01)
-                if st.button("开始上传", disabled=len(files)==0):
+                if st.button("开始上传", disabled=len(files) == 0):
                    st.session_state["file_chat_id"] = upload_temp_docs(files, api)
        elif dialogue_mode == "搜索引擎问答":
            search_engine_list = api.list_search_engines()
@ -279,9 +277,9 @@ def dialogue_page(api: ApiRequest, is_lite: bool = False):
    chat_input_placeholder = "请输入对话内容，换行请使用Shift+Enter。输入/help查看自定义命令 "

    def on_feedback(
-        feedback,
-        message_id: str = "",
-        history_index: int = -1,
+            feedback,
+            message_id: str = "",
+            history_index: int = -1,
    ):
        reason = feedback["text"]
        score_int = chat_box.set_feedback(feedback=feedback, history_index=history_index)
@ -296,7 +294,7 @@ def dialogue_page(api: ApiRequest, is_lite: bool = False):
    }

    if prompt := st.chat_input(chat_input_placeholder, key="prompt"):
-        if parse_command(text=prompt, modal=modal): # 用户输入自定义命令
+        if parse_command(text=prompt, modal=modal):  # 用户输入自定义命令
            st.rerun()
        else:
            history = get_messages_history(history_len)
@ -306,11 +304,11 @@ def dialogue_page(api: ApiRequest, is_lite: bool = False):
                text = ""
                message_id = ""
                r = api.chat_chat(prompt,
-                                history=history,
-                                conversation_id=conversation_id,
-                                model=llm_model,
-                                prompt_name=prompt_template_name,
-                                temperature=temperature)
+                                  history=history,
+                                  conversation_id=conversation_id,
+                                  model=llm_model,
+                                  prompt_name=prompt_template_name,
+                                  temperature=temperature)
                for t in r:
                    if error_msg := check_error_msg(t):  # check whether error occured
                        st.error(error_msg)
@ -321,12 +319,12 @@ def dialogue_page(api: ApiRequest, is_lite: bool = False):

                metadata = {
                    "message_id": message_id,
-                    }
+                }
                chat_box.update_msg(text, streaming=False, metadata=metadata)  # 更新最终的字符串，去除光标
                chat_box.show_feedback(**feedback_kwargs,
-                                    key=message_id,
-                                    on_submit=on_feedback,
-                                    kwargs={"message_id": message_id, "history_index": len(chat_box.history) - 1})
+                                       key=message_id,
+                                       on_submit=on_feedback,
+                                       kwargs={"message_id": message_id, "history_index": len(chat_box.history) - 1})

            elif dialogue_mode == "自定义Agent问答":
                if not any(agent in llm_model for agent in SUPPORT_AGENT_MODEL):
@ -373,13 +371,13 @@ def dialogue_page(api: ApiRequest, is_lite: bool = False):
                ])
                text = ""
                for d in api.knowledge_base_chat(prompt,
-                                                knowledge_base_name=selected_kb,
-                                                top_k=kb_top_k,
-                                                score_threshold=score_threshold,
-                                                history=history,
-                                                model=llm_model,
-                                                prompt_name=prompt_template_name,
-                                                temperature=temperature):
+                                                 knowledge_base_name=selected_kb,
+                                                 top_k=kb_top_k,
+                                                 score_threshold=score_threshold,
+                                                 history=history,
+                                                 model=llm_model,
+                                                 prompt_name=prompt_template_name,
+                                                 temperature=temperature):
                    if error_msg := check_error_msg(d):  # check whether error occured
                        st.error(error_msg)
                    elif chunk := d.get("answer"):
@ -397,13 +395,13 @@ def dialogue_page(api: ApiRequest, is_lite: bool = False):
                ])
                text = ""
                for d in api.file_chat(prompt,
-                                        knowledge_id=st.session_state["file_chat_id"],
-                                        top_k=kb_top_k,
-                                        score_threshold=score_threshold,
-                                        history=history,
-                                        model=llm_model,
-                                        prompt_name=prompt_template_name,
-                                        temperature=temperature):
+                                       knowledge_id=st.session_state["file_chat_id"],
+                                       top_k=kb_top_k,
+                                       score_threshold=score_threshold,
+                                       history=history,
+                                       model=llm_model,
+                                       prompt_name=prompt_template_name,
+                                       temperature=temperature):
                    if error_msg := check_error_msg(d):  # check whether error occured
                        st.error(error_msg)
                    elif chunk := d.get("answer"):
@ -455,4 +453,4 @@ def dialogue_page(api: ApiRequest, is_lite: bool = False):
        file_name=f"{now:%Y-%m-%d %H.%M}_对话记录.md",
        mime="text/markdown",
        use_container_width=True,
-    )
+    )
--- a/webui_pages/knowledge_base/knowledge_base.py
+++ b/webui_pages/knowledge_base/knowledge_base.py
@ -7,15 +7,12 @@ from server.knowledge_base.utils import get_file_path, LOADER_DICT
 from server.knowledge_base.kb_service.base import get_kb_details, get_kb_file_details
 from typing import Literal, Dict, Tuple
 from configs import (kbs_config,
-                    EMBEDDING_MODEL, DEFAULT_VS_TYPE,
-                    CHUNK_SIZE, OVERLAP_SIZE, ZH_TITLE_ENHANCE)
+                     EMBEDDING_MODEL, DEFAULT_VS_TYPE,
+                     CHUNK_SIZE, OVERLAP_SIZE, ZH_TITLE_ENHANCE)
 from server.utils import list_embed_models, list_online_embed_models
 import os
 import time

-
-# SENTENCE_SIZE = 100
-
 cell_renderer = JsCode("""function(params) {if(params.value==true){return '✓'}else{return '×'}}""")


@ -32,7 +29,7 @@ def config_aggrid(
    gb.configure_selection(
        selection_mode=selection_mode,
        use_checkbox=use_checkbox,
-        # pre_selected_rows=st.session_state.get("selected_rows", [0]),
+        pre_selected_rows=st.session_state.get("selected_rows", [0]),
    )
    gb.configure_pagination(
        enabled=True,
@ -59,7 +56,8 @@ def knowledge_base_page(api: ApiRequest, is_lite: bool = None):
    try:
        kb_list = {x["kb_name"]: x for x in get_kb_details()}
    except Exception as e:
-        st.error("获取知识库信息错误，请检查是否已按照 `README.md` 中 `4 知识库初始化与迁移` 步骤完成初始化或迁移，或是否为数据库连接错误。")
+        st.error(
+            "获取知识库信息错误，请检查是否已按照 `README.md` 中 `4 知识库初始化与迁移` 步骤完成初始化或迁移，或是否为数据库连接错误。")
        st.stop()
    kb_names = list(kb_list.keys())

@ -150,7 +148,8 @@ def knowledge_base_page(api: ApiRequest, is_lite: bool = None):
                                 [i for ls in LOADER_DICT.values() for i in ls],
                                 accept_multiple_files=True,
                                 )
-        kb_info = st.text_area("请输入知识库介绍:", value=st.session_state["selected_kb_info"], max_chars=None, key=None,
+        kb_info = st.text_area("请输入知识库介绍:", value=st.session_state["selected_kb_info"], max_chars=None,
+                               key=None,
                               help=None, on_change=None, args=None, kwargs=None)

        if kb_info != st.session_state["selected_kb_info"]:
@ -200,8 +199,8 @@ def knowledge_base_page(api: ApiRequest, is_lite: bool = None):
            doc_details = doc_details[[
                "No", "file_name", "document_loader", "text_splitter", "docs_count", "in_folder", "in_db",
            ]]
-            # doc_details["in_folder"] = doc_details["in_folder"].replace(True, "✓").replace(False, "×")
-            # doc_details["in_db"] = doc_details["in_db"].replace(True, "✓").replace(False, "×")
+            doc_details["in_folder"] = doc_details["in_folder"].replace(True, "✓").replace(False, "×")
+            doc_details["in_db"] = doc_details["in_db"].replace(True, "✓").replace(False, "×")
            gb = config_aggrid(
                doc_details,
                {
@ -252,7 +251,8 @@ def knowledge_base_page(api: ApiRequest, is_lite: bool = None):
            st.write()
            # 将文件分词并加载到向量库中
            if cols[1].button(
-                    "重新添加至向量库" if selected_rows and (pd.DataFrame(selected_rows)["in_db"]).any() else "添加至向量库",
+                    "重新添加至向量库" if selected_rows and (
+                            pd.DataFrame(selected_rows)["in_db"]).any() else "添加至向量库",
                    disabled=not file_exists(kb, selected_rows)[0],
                    use_container_width=True,
            ):
@ -285,39 +285,39 @@ def knowledge_base_page(api: ApiRequest, is_lite: bool = None):

        st.divider()

-        # cols = st.columns(3)
+        cols = st.columns(3)

-        # if cols[0].button(
-        #         "依据源文件重建向量库",
-        #         # help="无需上传文件，通过其它方式将文档拷贝到对应知识库content目录下，点击本按钮即可重建知识库。",
-        #         use_container_width=True,
-        #         type="primary",
-        # ):
-        #     with st.spinner("向量库重构中，请耐心等待，勿刷新或关闭页面。"):
-        #         empty = st.empty()
-        #         empty.progress(0.0, "")
-        #         for d in api.recreate_vector_store(kb,
-        #                                         chunk_size=chunk_size,
-        #                                         chunk_overlap=chunk_overlap,
-        #                                         zh_title_enhance=zh_title_enhance):
-        #             if msg := check_error_msg(d):
-        #                 st.toast(msg)
-        #             else:
-        #                 empty.progress(d["finished"] / d["total"], d["msg"])
-        #         st.rerun()
+        if cols[0].button(
+                "依据源文件重建向量库",
+                help="无需上传文件，通过其它方式将文档拷贝到对应知识库content目录下，点击本按钮即可重建知识库。",
+                use_container_width=True,
+                type="primary",
+        ):
+            with st.spinner("向量库重构中，请耐心等待，勿刷新或关闭页面。"):
+                empty = st.empty()
+                empty.progress(0.0, "")
+                for d in api.recreate_vector_store(kb,
+                                                   chunk_size=chunk_size,
+                                                   chunk_overlap=chunk_overlap,
+                                                   zh_title_enhance=zh_title_enhance):
+                    if msg := check_error_msg(d):
+                        st.toast(msg)
+                    else:
+                        empty.progress(d["finished"] / d["total"], d["msg"])
+                st.rerun()

-        # if cols[2].button(
-        #         "删除知识库",
-        #         use_container_width=True,
-        # ):
-        #     ret = api.delete_knowledge_base(kb)
-        #     st.toast(ret.get("msg", " "))
-        #     time.sleep(1)
-        #     st.rerun()
+        if cols[2].button(
+                "删除知识库",
+                use_container_width=True,
+        ):
+            ret = api.delete_knowledge_base(kb)
+            st.toast(ret.get("msg", " "))
+            time.sleep(1)
+            st.rerun()

-        # with st.sidebar:
-        #     keyword = st.text_input("查询关键字")
-        #     top_k = st.slider("匹配条数", 1, 100, 3)
+        with st.sidebar:
+            keyword = st.text_input("查询关键字")
+            top_k = st.slider("匹配条数", 1, 100, 3)

        st.write("文件内文档列表。双击进行修改，在删除列填入 Y 可删除对应行。")
        docs = []
@ -325,11 +325,12 @@ def knowledge_base_page(api: ApiRequest, is_lite: bool = None):
        if selected_rows:
            file_name = selected_rows[0]["file_name"]
            docs = api.search_kb_docs(knowledge_base_name=selected_kb, file_name=file_name)
-            data = [{"seq": i+1, "id": x["id"], "page_content": x["page_content"], "source": x["metadata"].get("source"),
-                    "type": x["type"],
-                    "metadata": json.dumps(x["metadata"], ensure_ascii=False),
-                    "to_del": "",
-                    } for i, x in enumerate(docs)]
+            data = [
+                {"seq": i + 1, "id": x["id"], "page_content": x["page_content"], "source": x["metadata"].get("source"),
+                 "type": x["type"],
+                 "metadata": json.dumps(x["metadata"], ensure_ascii=False),
+                 "to_del": "",
+                 } for i, x in enumerate(docs)]
            df = pd.DataFrame(data)

            gb = GridOptionsBuilder.from_dataframe(df)
@ -343,22 +344,24 @@ def knowledge_base_page(api: ApiRequest, is_lite: bool = None):
            edit_docs = AgGrid(df, gb.build())

            if st.button("保存更改"):
-                # origin_docs = {x["id"]: {"page_content": x["page_content"], "type": x["type"], "metadata": x["metadata"]} for x in docs}
+                origin_docs = {
+                    x["id"]: {"page_content": x["page_content"], "type": x["type"], "metadata": x["metadata"]} for x in
+                    docs}
                changed_docs = []
                for index, row in edit_docs.data.iterrows():
-                    # origin_doc = origin_docs[row["id"]]
-                    # if row["page_content"] != origin_doc["page_content"]:
-                    if row["to_del"] not in ["Y", "y", 1]:
-                        changed_docs.append({
-                            "page_content": row["page_content"],
-                            "type": row["type"],
-                            "metadata": json.loads(row["metadata"]),
-                        })
+                    origin_doc = origin_docs[row["id"]]
+                    if row["page_content"] != origin_doc["page_content"]:
+                        if row["to_del"] not in ["Y", "y", 1]:
+                            changed_docs.append({
+                                "page_content": row["page_content"],
+                                "type": row["type"],
+                                "metadata": json.loads(row["metadata"]),
+                            })

                if changed_docs:
                    if api.update_kb_docs(knowledge_base_name=selected_kb,
-                                        file_names=[file_name],
-                                        docs={file_name: changed_docs}):
+                                          file_names=[file_name],
+                                          docs={file_name: changed_docs}):
                        st.toast("更新文档成功")
                    else:
                        st.toast("更新文档失败")