Merge pull request #2753 from zRzRzRzRzRzRzR/dev

细节更新和补充

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)
+    * [1. 环境配置](README.md#1-环境配置)
-  * [2. 模型下载](README.md#2-模型下载)
+    * [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)
+    * [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)
+    * [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)
+    * [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。
 

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
+🌐 [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.
-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.
++ `April 2023`: `Langchain-ChatGLM 0.1.0` released, supporting local knowledge base question and answer based on the
-+ `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.
+  ChatGLM-6B model.
-+ `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`.
++ `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
 

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",

configs/server_config.py.example

@@ -132,12 +132,6 @@ FSCHAT_MODEL_WORKERS = {
     },
 }
 
-# fastchat multi model worker server
-FSCHAT_MULTI_MODEL_WORKERS = {
-    # TODO:
-}
-
-# fastchat controller server
 FSCHAT_CONTROLLER = {
     "host": DEFAULT_BIND_HOST,
     "port": 20001,

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"
 

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
+xformers~=0.0.23.post1
-transformers==4.36.2
+transformers~=4.36.2
-sentence_transformers==2.2.2
+sentence_transformers~=2.2.2
-
 langchain==0.0.354
 langchain-experimental==0.0.47
 pydantic==1.10.13
-fschat==0.2.35
+fschat~=0.2.35
-openai~=1.7.1
+openai~=1.9.0
-fastapi~=0.108.0
+fastapi~=0.109.0
-sse_starlette==1.8.2
+sse_starlette~=1.8.2
-nltk>=3.8.1
+nltk~=3.8.1
-uvicorn>=0.24.0.post1
+uvicorn~=0.24.0.post1
 starlette~=0.32.0
-unstructured[all-docs]==0.11.0
+unstructured[all-docs]~=0.12.0
-python-magic-bin; sys_platform == 'win32'
+python-magic-bin; sys_platform ~= 'win32'
-SQLAlchemy==2.0.19
+SQLAlchemy~=2.0.25
-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
+PyMuPDF~=1.23.16
-rapidocr_onnxruntime==1.3.8
+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
+tqdm~=4.66.1
-websockets>=12.0
+websockets~=12.0
 numpy~=1.24.4
 pandas~=2.0.3
-einops>=0.7.0
+einops~=0.7.0
-transformers_stream_generator==0.0.4
+transformers_stream_generator~=0.0.4
-vllm==0.2.7; sys_platform == "linux"
+vllm~=0.2.7; sys_platform ~= "linux"
-
+jq~=1.6.0
-# flash-attn>=2.4.3 # For Orion-14B-Chat and Qwen-14B-Chat
+beautifulsoup4~=4.12.2
-
-
-# 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
 pysrt~=1.1.2
-
+dashscope~=1.13.6 # qwen
-# Online api libs dependencies
+# volcengine~=1.0.119 # fangzhou
-# 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
+# pymilvus~=2.3.4
-# psycopg2==2.9.9
+# psycopg2~=2.9.9
-# pgvector>=0.2.4
+# pgvector~=0.2.4
-
+# flash-attn~=2.4.3 # For Orion-14B-Chat and Qwen-14B-Chat
-# Agent and Search Tools
+#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
-
+streamlit~=1.30.0
-# WebUI requirements
+streamlit-option-menu~=0.3.12
-
+streamlit-antd-components~=0.3.1
-streamlit==1.30.0
+streamlit-chatbox~=1.1.11
-streamlit-option-menu==0.3.6
+streamlit-modal~=0.1.0
-streamlit-antd-components==0.3.1
+streamlit-aggrid~=0.3.4.post3
-streamlit-chatbox==1.1.11
+httpx~=0.26.0
-streamlit-modal==0.1.0
+watchdog~=3.0.0
-streamlit-aggrid==0.3.4.post3
-httpx==0.26.0
-watchdog==3.0.0

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
+jq==1.6.0
-
+beautifulsoup4~=4.12.2
-# 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
 pysrt~=1.1.2
-
+dashscope==1.13.6
-# 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
-
 arxiv~=2.1.0
 youtube-search~=2.1.2
 duckduckgo-search~=3.9.9
 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

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
+fschat~=0.2.35
-openai~=1.7.1
+openai~=1.9.0
-fastapi~=0.108.0
+fastapi~=0.109.0
-sse_starlette==1.8.2
+sse_starlette~=1.8.2
-nltk>=3.8.1
+nltk~=3.8.1
-uvicorn>=0.24.0.post1
+uvicorn~=0.24.0.post1
 starlette~=0.32.0
-unstructured[all-docs]==0.11.0
+unstructured[all-docs]~=0.12.0
-python-magic-bin; sys_platform == 'win32'
+python-magic-bin; sys_platform ~= 'win32'
-SQLAlchemy==2.0.19
+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
-
+watchdog~=3.0.0
-# WebUI requirements
+# volcengine>=1.0.119
-
+# pymilvus>=2.3.4
-streamlit==1.30.0
+# psycopg2==2.9.9
-streamlit-option-menu==0.3.6
+# pgvector>=0.2.4
-streamlit-antd-components==0.3.1
+# flash-attn>=2.4.3 # For Orion-14B-Chat and Qwen-14B-Chat
-streamlit-chatbox==1.1.11
-streamlit-modal==0.1.0
-streamlit-aggrid==0.3.4.post3
-httpx==0.26.0
-watchdog==3.0.0

requirements_webui.txt

@@ -1,10 +1,8 @@
-# WebUI requirements
+streamlit~=1.30.0
-
+streamlit-option-menu~=0.3.12
-streamlit==1.30.0
+streamlit-antd-components~=0.3.1
-streamlit-option-menu==0.3.6
+streamlit-chatbox~=1.1.11
-streamlit-antd-components==0.3.1
+streamlit-modal~=0.1.0
-streamlit-chatbox==1.1.11
+streamlit-aggrid~=0.3.4.post3
-streamlit-modal==0.1.0
+httpx~=0.26.0
-streamlit-aggrid==0.3.4.post3
+watchdog~=3.0.0
-httpx==0.26.0
-watchdog==3.0.0

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

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")

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"),

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 = []

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)

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)
 

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

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

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", ".")

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.",

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="",

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}
 

server/model_workers/fangzhou.py

@@ -12,16 +12,16 @@ class FangZhouWorker(ApiModelWorker):
     """
 
     def __init__(
-        self,
+            self,
-        *,
+            *,
-        model_names: List[str] = ["fangzhou-api"],
+            model_names: List[str] = ["fangzhou-api"],
-        controller_addr: str = None,
+            controller_addr: str = None,
-        worker_addr: str = None,
+            worker_addr: str = None,
-        version: Literal["chatglm-6b-model"] = "chatglm-6b-model",
+            version: Literal["chatglm-6b-model"] = "chatglm-6b-model",
-        **kwargs,
+            **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,
+                        "error_code": error.code_n,
-                            "text": error.message,
+                        "text": error.message,
-                            "error": {
+                        "error": {
-                                "message": error.message,
+                            "message": error.message,
-                                "type": "invalid_request_error",
+                            "type": "invalid_request_error",
-                                "param": None,
+                            "param": None,
-                                "code": 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)
 

server/model_workers/gemini.py

@@ -95,7 +95,6 @@ class GeminiWorker(ApiModelWorker):
                 print("Invalid JSON string:", json_string)
 
     def get_embeddings(self, params):
-        # TODO: 支持embeddings
         print("embedding")
         print(params)
 

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。",

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",

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。",

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="",

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",

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()
 
 

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"]

webui_pages/dialogue/dialogue.py

@@ -126,7 +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。"