diff --git a/README.md b/README.md index 283cfbd31..3182dbe66 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,5 @@ ![](img/logo-long-chatchat-trans-v2.png) - 🌍 [READ THIS IN ENGLISH](README_en.md) 🌍 [日本語で読む](README_ja.md) @@ -8,6 +7,8 @@ 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 @@ -15,23 +16,31 @@ * [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) @@ -43,7 +52,8 @@ 🚩 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。 @@ -53,7 +63,10 @@ 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 @@ -63,17 +76,19 @@ 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 @@ -89,33 +104,44 @@ $ 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 @@ -134,19 +160,32 @@ $ 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` 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 @@ -158,4 +197,4 @@ $ python startup.py -a 二维码 -🎉 Langchain-Chatchat 项目官方公众号，欢迎扫码关注。 +🎉 Langchain-Chatchat 项目官方公众号，欢迎扫码关注。 \ No newline at end of file diff --git a/README_en.md b/README_en.md index 62ca1853d..fca63ea9a 100644 --- a/README_en.md +++ b/README_en.md @@ -8,6 +8,10 @@ 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 @@ -25,7 +29,8 @@ 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) @@ -56,10 +61,9 @@ 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 @@ -99,7 +103,9 @@ $ 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 @@ -159,15 +165,33 @@ please refer to the [Wiki](https://github.com/chatchat-space/Langchain-Chatchat/ --- +## 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`. ++ `December 2023`: `Langchain-Chachat` open source project received more than **20K** stars. ++ `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 ### Telegram [![Telegram](https://img.shields.io/badge/Telegram-2CA5E0?style=for-the-badge&logo=telegram&logoColor=white "langchain-chatglm")](https://t.me/+RjliQ3jnJ1YyN2E9) -### WeChat Group、 +### WeChat Group -二维码 +二维码 ### WeChat Official Account diff --git a/configs/__init__.py b/configs/__init__.py index e1c210109..e4f5f1d04 100644 --- a/configs/__init__.py +++ b/configs/__init__.py @@ -5,4 +5,4 @@ from .prompt_config import * -VERSION = "v0.2.9" +VERSION = "v0.2.10" diff --git a/configs/kb_config.py.example b/configs/kb_config.py.example index 731148a3e..23e06bdcb 100644 --- a/configs/kb_config.py.example +++ b/configs/kb_config.py.example @@ -21,10 +21,9 @@ OVERLAP_SIZE = 50 # 知识库匹配向量数量 VECTOR_SEARCH_TOP_K = 3 -# 知识库匹配的距离阈值，取值范围在0-1之间，SCORE越小，距离越小从而相关度越高， -# 取到1相当于不筛选，实测bge-large的距离得分大部分在0.01-0.7之间， -# 相似文本的得分最高在0.55左右，因此建议针对bge设置得分为0.6 -SCORE_THRESHOLD = 0.6 +# 知识库匹配的距离阈值，一般取值范围在0-1之间，SCORE越小，距离越小从而相关度越高。 +# 但有用户报告遇到过匹配分值超过1的情况，为了兼容性默认设为1，在WEBUI中调整范围为0-2 +SCORE_THRESHOLD = 1.0 # 默认搜索引擎。可选：bing, duckduckgo, metaphor DEFAULT_SEARCH_ENGINE = "duckduckgo" @@ -49,12 +48,17 @@ BING_SUBSCRIPTION_KEY = "" # metaphor搜索需要KEY METAPHOR_API_KEY = "" +# 心知天气 API KEY，用于天气Agent。申请：https://www.seniverse.com/ +SENIVERSE_API_KEY = "" # 是否开启中文标题加强，以及标题增强的相关配置 # 通过增加标题判断，判断哪些文本为标题，并在metadata中进行标记； # 然后将文本与往上一级的标题进行拼合，实现文本信息的增强。 ZH_TITLE_ENHANCE = False +# PDF OCR 控制：只对宽高超过页面一定比例（图片宽/页面宽，图片高/页面高）的图片进行 OCR。 +# 这样可以避免 PDF 中一些小图片的干扰，提高非扫描版 PDF 处理速度 +PDF_OCR_THRESHOLD = (0.6, 0.6) # 每个知识库的初始化介绍，用于在初始化知识库时显示和Agent调用，没写则没有介绍，不会被Agent调用。 KB_INFO = { diff --git a/configs/model_config.py.example b/configs/model_config.py.example index b203e9337..8746f098b 100644 --- a/configs/model_config.py.example +++ b/configs/model_config.py.example @@ -6,9 +6,9 @@ import os MODEL_ROOT_PATH = "" # 选用的 Embedding 名称 -EMBEDDING_MODEL = "bge-large-zh" +EMBEDDING_MODEL = "bge-large-zh-v1.5" -# Embedding 模型运行设备。设为"auto"会自动检测，也可手动设定为"cuda","mps","cpu"其中之一。 +# Embedding 模型运行设备。设为 "auto" 会自动检测(会有警告)，也可手动设定为 "cuda","mps","cpu","xpu" 其中之一。 EMBEDDING_DEVICE = "auto" # 选用的reranker模型 @@ -26,44 +26,33 @@ EMBEDDING_MODEL_OUTPUT_PATH = "output" # 在这里，我们使用目前主流的两个离线模型，其中，chatglm3-6b 为默认加载模型。 # 如果你的显存不足，可使用 Qwen-1_8B-Chat, 该模型 FP16 仅需 3.8G显存。 -# chatglm3-6b输出角色标签<|user|>及自问自答的问题详见项目wiki->常见问题->Q20. - -LLM_MODELS = ["chatglm3-6b", "zhipu-api", "openai-api"] # "Qwen-1_8B-Chat", - -# AgentLM模型的名称 (可以不指定，指定之后就锁定进入Agent之后的Chain的模型，不指定就是LLM_MODELS[0]) +LLM_MODELS = ["chatglm3-6b", "zhipu-api", "openai-api"] Agent_MODEL = None -# LLM 运行设备。设为"auto"会自动检测，也可手动设定为"cuda","mps","cpu"其中之一。 +# LLM 模型运行设备。设为"auto"会自动检测(会有警告)，也可手动设定为 "cuda","mps","cpu","xpu" 其中之一。 LLM_DEVICE = "auto" -# 历史对话轮数 HISTORY_LEN = 3 -# 大模型最长支持的长度，如果不填写，则使用模型默认的最大长度，如果填写，则为用户设定的最大长度 -MAX_TOKENS = None +MAX_TOKENS = 2048 -# LLM通用对话参数 TEMPERATURE = 0.7 -# TOP_P = 0.95 # ChatOpenAI暂不支持该参数 ONLINE_LLM_MODEL = { - # 线上模型。请在server_config中为每个在线API设置不同的端口 - "openai-api": { - "model_name": "gpt-3.5-turbo", + "model_name": "gpt-4", "api_base_url": "https://api.openai.com/v1", "api_key": "", "openai_proxy": "", }, - # 具体注册及api key获取请前往 http://open.bigmodel.cn + # 智谱AI API,具体注册及api key获取请前往 http://open.bigmodel.cn "zhipu-api": { "api_key": "", - "version": "chatglm_turbo", # 可选包括 "chatglm_turbo" + "version": "glm-4", "provider": "ChatGLMWorker", }, - # 具体注册及api key获取请前往 https://api.minimax.chat/ "minimax-api": { "group_id": "", @@ -72,13 +61,12 @@ ONLINE_LLM_MODEL = { "provider": "MiniMaxWorker", }, - # 具体注册及api key获取请前往 https://xinghuo.xfyun.cn/ "xinghuo-api": { "APPID": "", "APISecret": "", "api_key": "", - "version": "v1.5", # 你使用的讯飞星火大模型版本，可选包括 "v3.0", "v1.5", "v2.0" + "version": "v3.0", # 你使用的讯飞星火大模型版本，可选包括 "v3.0", "v2.0", "v1.5" "provider": "XingHuoWorker", }, @@ -93,8 +81,8 @@ ONLINE_LLM_MODEL = { # 火山方舟 API，文档参考 https://www.volcengine.com/docs/82379 "fangzhou-api": { - "version": "chatglm-6b-model", # 当前支持 "chatglm-6b-model"， 更多的见文档模型支持列表中方舟部分。 - "version_url": "", # 可以不填写version，直接填写在方舟申请模型发布的API地址 + "version": "chatglm-6b-model", + "version_url": "", "api_key": "", "secret_key": "", "provider": "FangZhouWorker", @@ -102,15 +90,15 @@ ONLINE_LLM_MODEL = { # 阿里云通义千问 API，文档参考 https://help.aliyun.com/zh/dashscope/developer-reference/api-details "qwen-api": { - "version": "qwen-turbo", # 可选包括 "qwen-turbo", "qwen-plus" - "api_key": "", # 请在阿里云控制台模型服务灵积API-KEY管理页面创建 + "version": "qwen-max", + "api_key": "", "provider": "QwenWorker", - "embed_model": "text-embedding-v1" # embedding 模型名称 + "embed_model": "text-embedding-v1" # embedding 模型名称 }, # 百川 API，申请方式请参考 https://www.baichuan-ai.com/home#api-enter "baichuan-api": { - "version": "Baichuan2-53B", # 当前支持 "Baichuan2-53B"， 见官方文档。 + "version": "Baichuan2-53B", "api_key": "", "secret_key": "", "provider": "BaiChuanWorker", @@ -132,6 +120,11 @@ ONLINE_LLM_MODEL = { "secret_key": "", "provider": "TianGongWorker", }, + # Gemini API https://makersuite.google.com/app/apikey + "gemini-api": { + "api_key": "", + "provider": "GeminiWorker", + } } @@ -143,6 +136,7 @@ ONLINE_LLM_MODEL = { # - GanymedeNil/text2vec-large-chinese # - text2vec-large-chinese # 2.2 如果以上本地路径不存在，则使用huggingface模型 + MODEL_PATH = { "embed_model": { "ernie-tiny": "nghuyong/ernie-3.0-nano-zh", @@ -169,55 +163,59 @@ MODEL_PATH = { }, "llm_model": { - # 以下部分模型并未完全测试，仅根据fastchat和vllm模型的模型列表推定支持 "chatglm2-6b": "THUDM/chatglm2-6b", "chatglm2-6b-32k": "THUDM/chatglm2-6b-32k", - "chatglm3-6b": "THUDM/chatglm3-6b", "chatglm3-6b-32k": "THUDM/chatglm3-6b-32k", - "chatglm3-6b-base": "THUDM/chatglm3-6b-base", - "Qwen-1_8B": "Qwen/Qwen-1_8B", - "Qwen-1_8B-Chat": "Qwen/Qwen-1_8B-Chat", - "Qwen-1_8B-Chat-Int8": "Qwen/Qwen-1_8B-Chat-Int8", - "Qwen-1_8B-Chat-Int4": "Qwen/Qwen-1_8B-Chat-Int4", + "Orion-14B-Chat": "OrionStarAI/Orion-14B-Chat", + "Orion-14B-Chat-Plugin": "OrionStarAI/Orion-14B-Chat-Plugin", + "Orion-14B-LongChat": "OrionStarAI/Orion-14B-LongChat", - "Qwen-7B": "Qwen/Qwen-7B", - "Qwen-7B-Chat": "Qwen/Qwen-7B-Chat", + "Llama-2-7b-chat-hf": "meta-llama/Llama-2-7b-chat-hf", + "Llama-2-13b-chat-hf": "meta-llama/Llama-2-13b-chat-hf", + "Llama-2-70b-chat-hf": "meta-llama/Llama-2-70b-chat-hf", - "Qwen-14B": "Qwen/Qwen-14B", + "Qwen-1_8B-Chat": "Qwen/Qwen-1_8B-Chat", + "Qwen-7B-Chat": "Qwen/Qwen-7B-Chat", "Qwen-14B-Chat": "Qwen/Qwen-14B-Chat", - - "Qwen-14B-Chat-Int8": "Qwen/Qwen-14B-Chat-Int8", - # 在新版的transformers下需要手动修改模型的config.json文件，在quantization_config字典中 - # 增加`disable_exllama:true` 字段才能启动qwen的量化模型 - "Qwen-14B-Chat-Int4": "Qwen/Qwen-14B-Chat-Int4", - - "Qwen-72B": "Qwen/Qwen-72B", "Qwen-72B-Chat": "Qwen/Qwen-72B-Chat", - "Qwen-72B-Chat-Int8": "Qwen/Qwen-72B-Chat-Int8", - "Qwen-72B-Chat-Int4": "Qwen/Qwen-72B-Chat-Int4", - - "baichuan2-13b": "baichuan-inc/Baichuan2-13B-Chat", - "baichuan2-7b": "baichuan-inc/Baichuan2-7B-Chat", - "baichuan-7b": "baichuan-inc/Baichuan-7B", - "baichuan-13b": "baichuan-inc/Baichuan-13B", + "baichuan-7b-chat": "baichuan-inc/Baichuan-7B-Chat", "baichuan-13b-chat": "baichuan-inc/Baichuan-13B-Chat", - - "aquila-7b": "BAAI/Aquila-7B", - "aquilachat-7b": "BAAI/AquilaChat-7B", + "baichuan2-7b-chat": "baichuan-inc/Baichuan2-7B-Chat", + "baichuan2-13b-chat": "baichuan-inc/Baichuan2-13B-Chat", "internlm-7b": "internlm/internlm-7b", "internlm-chat-7b": "internlm/internlm-chat-7b", + "internlm2-chat-7b": "internlm/internlm2-chat-7b", + "internlm2-chat-20b": "internlm/internlm2-chat-20b", + + "BlueLM-7B-Chat": "vivo-ai/BlueLM-7B-Chat", + "BlueLM-7B-Chat-32k": "vivo-ai/BlueLM-7B-Chat-32k", + + "Yi-34B-Chat": "https://huggingface.co/01-ai/Yi-34B-Chat", + + "agentlm-7b": "THUDM/agentlm-7b", + "agentlm-13b": "THUDM/agentlm-13b", + "agentlm-70b": "THUDM/agentlm-70b", "falcon-7b": "tiiuae/falcon-7b", "falcon-40b": "tiiuae/falcon-40b", "falcon-rw-7b": "tiiuae/falcon-rw-7b", + "aquila-7b": "BAAI/Aquila-7B", + "aquilachat-7b": "BAAI/AquilaChat-7B", + "open_llama_13b": "openlm-research/open_llama_13b", + "vicuna-13b-v1.5": "lmsys/vicuna-13b-v1.5", + "koala": "young-geng/koala", + "mpt-7b": "mosaicml/mpt-7b", + "mpt-7b-storywriter": "mosaicml/mpt-7b-storywriter", + "mpt-30b": "mosaicml/mpt-30b", + "opt-66b": "facebook/opt-66b", + "opt-iml-max-30b": "facebook/opt-iml-max-30b", "gpt2": "gpt2", "gpt2-xl": "gpt2-xl", - "gpt-j-6b": "EleutherAI/gpt-j-6b", "gpt4all-j": "nomic-ai/gpt4all-j", "gpt-neox-20b": "EleutherAI/gpt-neox-20b", @@ -225,63 +223,51 @@ MODEL_PATH = { "oasst-sft-4-pythia-12b-epoch-3.5": "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5", "dolly-v2-12b": "databricks/dolly-v2-12b", "stablelm-tuned-alpha-7b": "stabilityai/stablelm-tuned-alpha-7b", - - "Llama-2-13b-hf": "meta-llama/Llama-2-13b-hf", - "Llama-2-70b-hf": "meta-llama/Llama-2-70b-hf", - "open_llama_13b": "openlm-research/open_llama_13b", - "vicuna-13b-v1.3": "lmsys/vicuna-13b-v1.3", - "koala": "young-geng/koala", - - "mpt-7b": "mosaicml/mpt-7b", - "mpt-7b-storywriter": "mosaicml/mpt-7b-storywriter", - "mpt-30b": "mosaicml/mpt-30b", - "opt-66b": "facebook/opt-66b", - "opt-iml-max-30b": "facebook/opt-iml-max-30b", - - "agentlm-7b": "THUDM/agentlm-7b", - "agentlm-13b": "THUDM/agentlm-13b", - "agentlm-70b": "THUDM/agentlm-70b", - - "Yi-34B-Chat": "01-ai/Yi-34B-Chat", }, - "reranker":{ - "bge-reranker-large":"BAAI/bge-reranker-large", - "bge-reranker-base":"BAAI/bge-reranker-base", - #TODO 增加在线reranker，如cohere + + "reranker": { + "bge-reranker-large": "BAAI/bge-reranker-large", + "bge-reranker-base": "BAAI/bge-reranker-base", } } - # 通常情况下不需要更改以下内容 # nltk 模型存储路径 NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data") +# 使用VLLM可能导致模型推理能力下降，无法完成Agent任务 VLLM_MODEL_DICT = { - "aquila-7b": "BAAI/Aquila-7B", - "aquilachat-7b": "BAAI/AquilaChat-7B", - - "baichuan-7b": "baichuan-inc/Baichuan-7B", - "baichuan-13b": "baichuan-inc/Baichuan-13B", - "baichuan-13b-chat": "baichuan-inc/Baichuan-13B-Chat", - "chatglm2-6b": "THUDM/chatglm2-6b", "chatglm2-6b-32k": "THUDM/chatglm2-6b-32k", "chatglm3-6b": "THUDM/chatglm3-6b", "chatglm3-6b-32k": "THUDM/chatglm3-6b-32k", + "Llama-2-7b-chat-hf": "meta-llama/Llama-2-7b-chat-hf", + "Llama-2-13b-chat-hf": "meta-llama/Llama-2-13b-chat-hf", + "Llama-2-70b-chat-hf": "meta-llama/Llama-2-70b-chat-hf", + + "Qwen-1_8B-Chat": "Qwen/Qwen-1_8B-Chat", + "Qwen-7B-Chat": "Qwen/Qwen-7B-Chat", + "Qwen-14B-Chat": "Qwen/Qwen-14B-Chat", + "Qwen-72B-Chat": "Qwen/Qwen-72B-Chat", + + "baichuan-7b-chat": "baichuan-inc/Baichuan-7B-Chat", + "baichuan-13b-chat": "baichuan-inc/Baichuan-13B-Chat", + "baichuan2-7b-chat": "baichuan-inc/Baichuan-7B-Chat", + "baichuan2-13b-chat": "baichuan-inc/Baichuan-13B-Chat", + "BlueLM-7B-Chat": "vivo-ai/BlueLM-7B-Chat", "BlueLM-7B-Chat-32k": "vivo-ai/BlueLM-7B-Chat-32k", - # 注意：bloom系列的tokenizer与model是分离的，因此虽然vllm支持，但与fschat框架不兼容 - # "bloom": "bigscience/bloom", - # "bloomz": "bigscience/bloomz", - # "bloomz-560m": "bigscience/bloomz-560m", - # "bloomz-7b1": "bigscience/bloomz-7b1", - # "bloomz-1b7": "bigscience/bloomz-1b7", - "internlm-7b": "internlm/internlm-7b", "internlm-chat-7b": "internlm/internlm-chat-7b", + "internlm2-chat-7b": "internlm/Models/internlm2-chat-7b", + "internlm2-chat-20b": "internlm/Models/internlm2-chat-20b", + + "aquila-7b": "BAAI/Aquila-7B", + "aquilachat-7b": "BAAI/AquilaChat-7B", + "falcon-7b": "tiiuae/falcon-7b", "falcon-40b": "tiiuae/falcon-40b", "falcon-rw-7b": "tiiuae/falcon-rw-7b", @@ -294,8 +280,6 @@ VLLM_MODEL_DICT = { "oasst-sft-4-pythia-12b-epoch-3.5": "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5", "dolly-v2-12b": "databricks/dolly-v2-12b", "stablelm-tuned-alpha-7b": "stabilityai/stablelm-tuned-alpha-7b", - "Llama-2-13b-hf": "meta-llama/Llama-2-13b-hf", - "Llama-2-70b-hf": "meta-llama/Llama-2-70b-hf", "open_llama_13b": "openlm-research/open_llama_13b", "vicuna-13b-v1.3": "lmsys/vicuna-13b-v1.3", "koala": "young-geng/koala", @@ -305,37 +289,14 @@ VLLM_MODEL_DICT = { "opt-66b": "facebook/opt-66b", "opt-iml-max-30b": "facebook/opt-iml-max-30b", - "Qwen-1_8B": "Qwen/Qwen-1_8B", - "Qwen-1_8B-Chat": "Qwen/Qwen-1_8B-Chat", - "Qwen-1_8B-Chat-Int8": "Qwen/Qwen-1_8B-Chat-Int8", - "Qwen-1_8B-Chat-Int4": "Qwen/Qwen-1_8B-Chat-Int4", - - "Qwen-7B": "Qwen/Qwen-7B", - "Qwen-7B-Chat": "Qwen/Qwen-7B-Chat", - - "Qwen-14B": "Qwen/Qwen-14B", - "Qwen-14B-Chat": "Qwen/Qwen-14B-Chat", - "Qwen-14B-Chat-Int8": "Qwen/Qwen-14B-Chat-Int8", - "Qwen-14B-Chat-Int4": "Qwen/Qwen-14B-Chat-Int4", - - "Qwen-72B": "Qwen/Qwen-72B", - "Qwen-72B-Chat": "Qwen/Qwen-72B-Chat", - "Qwen-72B-Chat-Int8": "Qwen/Qwen-72B-Chat-Int8", - "Qwen-72B-Chat-Int4": "Qwen/Qwen-72B-Chat-Int4", - - "agentlm-7b": "THUDM/agentlm-7b", - "agentlm-13b": "THUDM/agentlm-13b", - "agentlm-70b": "THUDM/agentlm-70b", - } -# 你认为支持Agent能力的模型，可以在这里添加，添加后不会出现可视化界面的警告 -# 经过我们测试，原生支持Agent的模型仅有以下几个 SUPPORT_AGENT_MODEL = [ - "azure-api", - "openai-api", - "qwen-api", - "Qwen", - "chatglm3", - "xinghuo-api", + "openai-api", # GPT4 模型 + "qwen-api", # Qwen Max模型 + "zhipu-api", # 智谱AI GLM4模型 + "Qwen", # 所有Qwen系列本地模型 + "chatglm3-6b", + "internlm2-chat-20b", + "Orion-14B-Chat-Plugin", ] diff --git a/configs/server_config.py.example b/configs/server_config.py.example index 7fa0c412b..eea9c34df 100644 --- a/configs/server_config.py.example +++ b/configs/server_config.py.example @@ -40,8 +40,6 @@ FSCHAT_MODEL_WORKERS = { "device": LLM_DEVICE, # False,'vllm',使用的推理加速框架,使用vllm如果出现HuggingFace通信问题，参见doc/FAQ # vllm对一些模型支持还不成熟，暂时默认关闭 - # fschat=0.2.33的代码有bug, 如需使用，源码修改fastchat.server.vllm_worker， - # 将103行中sampling_params = SamplingParams的参数stop=list(stop)修改为stop= [i for i in stop if i!=""] "infer_turbo": False, # model_worker多卡加载需要配置的参数 @@ -92,11 +90,10 @@ 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", }, @@ -128,14 +125,11 @@ FSCHAT_MODEL_WORKERS = { "tiangong-api": { "port": 21009, }, + "gemini-api": { + "port": 21010, + }, } -# fastchat multi model worker server -FSCHAT_MULTI_MODEL_WORKERS = { - # TODO: -} - -# fastchat controller server FSCHAT_CONTROLLER = { "host": DEFAULT_BIND_HOST, "port": 20001, diff --git a/document_loaders/__init__.py b/document_loaders/__init__.py index a4d6b28df..88cfeae81 100644 --- a/document_loaders/__init__.py +++ b/document_loaders/__init__.py @@ -1,2 +1,4 @@ from .mypdfloader import RapidOCRPDFLoader -from .myimgloader import RapidOCRLoader \ No newline at end of file +from .myimgloader import RapidOCRLoader +from .mydocloader import RapidOCRDocLoader +from .mypptloader import RapidOCRPPTLoader diff --git a/document_loaders/mydocloader.py b/document_loaders/mydocloader.py new file mode 100644 index 000000000..7f5462a2e --- /dev/null +++ b/document_loaders/mydocloader.py @@ -0,0 +1,71 @@ +from langchain.document_loaders.unstructured import UnstructuredFileLoader +from typing import List +import tqdm + + +class RapidOCRDocLoader(UnstructuredFileLoader): + def _get_elements(self) -> List: + def doc2text(filepath): + from docx.table import _Cell, Table + from docx.oxml.table import CT_Tbl + from docx.oxml.text.paragraph import CT_P + from docx.text.paragraph import Paragraph + from docx import Document, ImagePart + from PIL import Image + from io import BytesIO + import numpy as np + from rapidocr_onnxruntime import RapidOCR + ocr = RapidOCR() + doc = Document(filepath) + resp = "" + + def iter_block_items(parent): + from docx.document import Document + if isinstance(parent, Document): + parent_elm = parent.element.body + elif isinstance(parent, _Cell): + parent_elm = parent._tc + else: + raise ValueError("RapidOCRDocLoader parse fail") + + for child in parent_elm.iterchildren(): + if isinstance(child, CT_P): + yield Paragraph(child, parent) + elif isinstance(child, CT_Tbl): + yield Table(child, parent) + + b_unit = tqdm.tqdm(total=len(doc.paragraphs)+len(doc.tables), + desc="RapidOCRDocLoader block index: 0") + for i, block in enumerate(iter_block_items(doc)): + b_unit.set_description( + "RapidOCRDocLoader block index: {}".format(i)) + b_unit.refresh() + if isinstance(block, Paragraph): + resp += block.text.strip() + "\n" + images = block._element.xpath('.//pic:pic') # 获取所有图片 + for image in images: + for img_id in image.xpath('.//a:blip/@r:embed'): # 获取图片id + part = doc.part.related_parts[img_id] # 根据图片id获取对应的图片 + if isinstance(part, ImagePart): + image = Image.open(BytesIO(part._blob)) + result, _ = ocr(np.array(image)) + if result: + ocr_result = [line[1] for line in result] + resp += "\n".join(ocr_result) + elif isinstance(block, Table): + for row in block.rows: + for cell in row.cells: + for paragraph in cell.paragraphs: + resp += paragraph.text.strip() + "\n" + b_unit.update(1) + return resp + + text = doc2text(self.file_path) + from unstructured.partition.text import partition_text + return partition_text(text=text, **self.unstructured_kwargs) + + +if __name__ == '__main__': + loader = RapidOCRDocLoader(file_path="../tests/samples/ocr_test.docx") + docs = loader.load() + print(docs) diff --git a/document_loaders/mypdfloader.py b/document_loaders/mypdfloader.py index 51778b893..faaf63dd4 100644 --- a/document_loaders/mypdfloader.py +++ b/document_loaders/mypdfloader.py @@ -1,5 +1,6 @@ from typing import List from langchain.document_loaders.unstructured import UnstructuredFileLoader +from configs import PDF_OCR_THRESHOLD from document_loaders.ocr import get_ocr import tqdm @@ -15,23 +16,25 @@ def pdf2text(filepath): 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" - img_list = page.get_images() + img_list = page.get_image_info(xrefs=True) for img in img_list: - pix = fitz.Pixmap(doc, img[0]) - img_array = np.frombuffer(pix.samples, dtype=np.uint8).reshape(pix.height, pix.width, -1) - result, _ = ocr(img_array) - if result: - ocr_result = [line[1] for line in result] - resp += "\n".join(ocr_result) + if xref := img.get("xref"): + bbox = img["bbox"] + # 检查图片尺寸是否超过设定的阈值 + if ((bbox[2] - bbox[0]) / (page.rect.width) < PDF_OCR_THRESHOLD[0] + or (bbox[3] - bbox[1]) / (page.rect.height) < PDF_OCR_THRESHOLD[1]): + continue + pix = fitz.Pixmap(doc, xref) + img_array = np.frombuffer(pix.samples, dtype=np.uint8).reshape(pix.height, pix.width, -1) + result, _ = ocr(img_array) + if result: + ocr_result = [line[1] for line in result] + resp += "\n".join(ocr_result) # 更新进度 b_unit.update(1) diff --git a/document_loaders/mypptloader.py b/document_loaders/mypptloader.py new file mode 100644 index 000000000..f14d0728e --- /dev/null +++ b/document_loaders/mypptloader.py @@ -0,0 +1,59 @@ +from langchain.document_loaders.unstructured import UnstructuredFileLoader +from typing import List +import tqdm + + +class RapidOCRPPTLoader(UnstructuredFileLoader): + def _get_elements(self) -> List: + def ppt2text(filepath): + from pptx import Presentation + from PIL import Image + import numpy as np + from io import BytesIO + from rapidocr_onnxruntime import RapidOCR + ocr = RapidOCR() + prs = Presentation(filepath) + resp = "" + + def extract_text(shape): + nonlocal resp + if shape.has_text_frame: + resp += shape.text.strip() + "\n" + if shape.has_table: + for row in shape.table.rows: + for cell in row.cells: + for paragraph in cell.text_frame.paragraphs: + resp += paragraph.text.strip() + "\n" + if shape.shape_type == 13: # 13 表示图片 + image = Image.open(BytesIO(shape.image.blob)) + result, _ = ocr(np.array(image)) + if result: + ocr_result = [line[1] for line in result] + resp += "\n".join(ocr_result) + elif shape.shape_type == 6: # 6 表示组合 + for child_shape in shape.shapes: + extract_text(child_shape) + + b_unit = tqdm.tqdm(total=len(prs.slides), + desc="RapidOCRPPTLoader slide index: 1") + # 遍历所有幻灯片 + for slide_number, slide in enumerate(prs.slides, start=1): + b_unit.set_description( + "RapidOCRPPTLoader slide index: {}".format(slide_number)) + b_unit.refresh() + sorted_shapes = sorted(slide.shapes, + key=lambda x: (x.top, x.left)) # 从上到下、从左到右遍历 + for shape in sorted_shapes: + extract_text(shape) + b_unit.update(1) + return resp + + text = ppt2text(self.file_path) + from unstructured.partition.text import partition_text + return partition_text(text=text, **self.unstructured_kwargs) + + +if __name__ == '__main__': + loader = RapidOCRPPTLoader(file_path="../tests/samples/ocr_test.pptx") + docs = loader.load() + print(docs) diff --git a/embeddings/add_embedding_keywords.py b/embeddings/add_embedding_keywords.py index 622a4cac5..f46dee296 100644 --- a/embeddings/add_embedding_keywords.py +++ b/embeddings/add_embedding_keywords.py @@ -7,31 +7,35 @@ 保存的模型的位置位于原本嵌入模型的目录下，模型的名称为原模型名称+Merge_Keywords_时间戳 ''' import sys + sys.path.append("..") +import os +import torch + from datetime import datetime from configs import ( MODEL_PATH, EMBEDDING_MODEL, EMBEDDING_KEYWORD_FILE, ) -import os -import torch + from safetensors.torch import save_model from sentence_transformers import SentenceTransformer +from langchain_core._api import deprecated +@deprecated( + since="0.3.0", + message="自定义关键词 Langchain-Chatchat 0.3.x 重写, 0.2.x中相关功能将废弃", + removal="0.3.0" + ) def get_keyword_embedding(bert_model, tokenizer, key_words): tokenizer_output = tokenizer(key_words, return_tensors="pt", padding=True, truncation=True) - - # No need to manually convert to tensor as we've set return_tensors="pt" input_ids = tokenizer_output['input_ids'] - - # Remove the first and last token for each sequence in the batch input_ids = input_ids[:, 1:-1] keyword_embedding = bert_model.embeddings.word_embeddings(input_ids) keyword_embedding = torch.mean(keyword_embedding, 1) - return keyword_embedding @@ -47,14 +51,11 @@ def add_keyword_to_model(model_name=EMBEDDING_MODEL, keyword_file: str = "", out bert_model = word_embedding_model.auto_model tokenizer = word_embedding_model.tokenizer key_words_embedding = get_keyword_embedding(bert_model, tokenizer, key_words) - # key_words_embedding = st_model.encode(key_words) embedding_weight = bert_model.embeddings.word_embeddings.weight embedding_weight_len = len(embedding_weight) tokenizer.add_tokens(key_words) bert_model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=32) - - # key_words_embedding_tensor = torch.from_numpy(key_words_embedding) embedding_weight = bert_model.embeddings.word_embeddings.weight with torch.no_grad(): embedding_weight[embedding_weight_len:embedding_weight_len + key_words_len, :] = key_words_embedding @@ -76,46 +77,3 @@ def add_keyword_to_embedding_model(path: str = EMBEDDING_KEYWORD_FILE): output_model_name = "{}_Merge_Keywords_{}".format(EMBEDDING_MODEL, current_time) output_model_path = os.path.join(model_parent_directory, output_model_name) add_keyword_to_model(model_name, keyword_file, output_model_path) - - -if __name__ == '__main__': - add_keyword_to_embedding_model(EMBEDDING_KEYWORD_FILE) - - # input_model_name = "" - # output_model_path = "" - # # 以下为加入关键字前后tokenizer的测试用例对比 - # def print_token_ids(output, tokenizer, sentences): - # for idx, ids in enumerate(output['input_ids']): - # print(f'sentence={sentences[idx]}') - # print(f'ids={ids}') - # for id in ids: - # decoded_id = tokenizer.decode(id) - # print(f' {decoded_id}->{id}') - # - # sentences = [ - # '数据科学与大数据技术', - # 'Langchain-Chatchat' - # ] - # - # st_no_keywords = SentenceTransformer(input_model_name) - # tokenizer_without_keywords = st_no_keywords.tokenizer - # print("===== tokenizer with no keywords added =====") - # output = tokenizer_without_keywords(sentences) - # print_token_ids(output, tokenizer_without_keywords, sentences) - # print(f'-------- embedding with no keywords added -----') - # embeddings = st_no_keywords.encode(sentences) - # print(embeddings) - # - # print("--------------------------------------------") - # print("--------------------------------------------") - # print("--------------------------------------------") - # - # st_with_keywords = SentenceTransformer(output_model_path) - # tokenizer_with_keywords = st_with_keywords.tokenizer - # print("===== tokenizer with keyword added =====") - # output = tokenizer_with_keywords(sentences) - # print_token_ids(output, tokenizer_with_keywords, sentences) - # - # print(f'-------- embedding with keywords added -----') - # embeddings = st_with_keywords.encode(sentences) - # print(embeddings) \ No newline at end of file diff --git a/img/qr_code_85.jpg b/img/qr_code_85.jpg deleted file mode 100644 index d294f22f4..000000000 Binary files a/img/qr_code_85.jpg and /dev/null differ diff --git a/img/qr_code_87.jpg b/img/qr_code_87.jpg index f12adc9d6..ce8fc77af 100644 Binary files a/img/qr_code_87.jpg and b/img/qr_code_87.jpg differ diff --git a/knowledge_base/samples/content/wiki b/knowledge_base/samples/content/wiki index 2f24adb21..28f664aa0 160000 --- a/knowledge_base/samples/content/wiki +++ b/knowledge_base/samples/content/wiki @@ -1 +1 @@ -Subproject commit 2f24adb218f23eab00d7fcd7ccf5072f2f35cb3c +Subproject commit 28f664aa08f8191a70339c9ecbe7a89b35a1032a diff --git a/requirements.txt b/requirements.txt index b041742e2..adf32a76f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,77 +1,66 @@ -# API requirements - -torch~=2.1.2 -torchvision~=0.16.2 -torchaudio~=2.1.2 +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 - langchain==0.0.354 langchain-experimental==0.0.47 pydantic==1.10.13 -fschat==0.2.34 -openai~=1.7.1 -fastapi~=0.108.0 +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 +nltk==3.8.1 +uvicorn==0.24.0.post1 +starlette==0.35.0 +unstructured[all-docs] # ==0.11.8 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 -accelerate~=0.24.1 -spacy~=3.7.2 -PyMuPDF~=1.23.8 +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 +requests==2.31.0 +pathlib==1.0.1 +pytest==7.4.3 +numexpr==2.8.6 +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==0.2.6; sys_platform == "linux" -httpx[brotli,http2,socks]==0.25.2 -llama-index - -# 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 - -# Online api libs dependencies - -zhipuai==1.0.7 # zhipu -dashscope==1.13.6 # qwen -# volcengine>=1.0.119 # fangzhou +vllm==0.2.7; sys_platform == "linux" +llama-index==0.9.35 +#jq==1.6.0 +# beautifulsoup4==4.12.2 +# pysrt==1.1.2 +# 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 -# 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 +# pgvector==0.2.4 +#flash-attn==2.4.2 # For Orion-14B-Chat and Qwen-14B-Chat +#autoawq==0.1.8 # For Int4 +#rapidocr_paddle[gpu]==1.3.11 # gpu accelleration for ocr of pdf and image files -streamlit~=1.29.0 -streamlit-option-menu>=0.3.6 +arxiv==2.1.0 +youtube-search==2.1.2 +duckduckgo-search==3.9.9 +metaphor-python==0.1.23 +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 -watchdog>=3.0.0 +streamlit-modal==0.1.0 +streamlit-aggrid==0.3.4.post3 +httpx==0.26.0 +watchdog==3.0.0 +jwt==1.3.1 \ No newline at end of file diff --git a/requirements_api.txt b/requirements_api.txt index 5cd1048fb..4d2ef0181 100644 --- a/requirements_api.txt +++ b/requirements_api.txt @@ -1,24 +1,23 @@ 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==0.2.34 -openai~=1.7.1 -fastapi~=0.108.0 +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 +starlette~=0.35.0 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 @@ -36,31 +35,23 @@ numpy~=1.24.4 pandas~=2.0.3 einops>=0.7.0 transformers_stream_generator==0.0.4 -vllm==0.2.6; sys_platform == "linux" -httpx[brotli,http2,socks]==0.25.2 -llama-index - -# 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 - -# Online api libs dependencies - -zhipuai==1.0.7 # zhipu -dashscope==1.13.6 # qwen -# volcengine>=1.0.119 # fangzhou - -# uncomment libs if you want to use corresponding vector store +vllm==0.2.7; sys_platform == "linux" +httpx==0.26.0 +llama-index==0.9.35 + +# jq==1.6.0 +# beautifulsoup4~=4.12.2 +# pysrt~=1.1.2 +# dashscope==1.13.6 +# 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 - -# Agent and Search Tools - -arxiv~=2.1.0 -youtube-search~=2.1.2 -duckduckgo-search~=3.9.9 -metaphor-python~=0.1.23 \ No newline at end of file +#flash-attn==2.4.2 # For Orion-14B-Chat and Qwen-14B-Chat +#autoawq==0.1.8 # For Int4 +#rapidocr_paddle[gpu]==1.3.11 # gpu accelleration for ocr of pdf and image files \ No newline at end of file diff --git a/requirements_lite.txt b/requirements_lite.txt index 584d0c752..6019cefbf 100644 --- a/requirements_lite.txt +++ b/requirements_lite.txt @@ -1,64 +1,33 @@ -# API requirements - langchain==0.0.354 langchain-experimental==0.0.47 pydantic==1.10.13 -fschat==0.2.34 -openai~=1.7.1 -fastapi~=0.108.0 -sse_starlette==1.8.2 -nltk>=3.8.1 -uvicorn>=0.24.0.post1 -starlette~=0.32.0 -unstructured[all-docs]==0.11.0 +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~=0.35.0 +unstructured[all-docs]~=0.12.0 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==0.2.6; sys_platform == "linux" -httpx[brotli,http2,socks]==0.25.2 -requests -pathlib -pytest - +llama-index==0.9.35 -# Online api libs dependencies - -zhipuai==1.0.7 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>=1.29.0 -streamlit-option-menu>=0.3.6 -streamlit-antd-components>=0.3.0 -streamlit-chatbox>=1.1.11 -streamlit-modal>=0.1.0 -streamlit-aggrid>=0.3.4.post3 -httpx[brotli,http2,socks]>=0.25.2 -watchdog>=3.0.0 \ No newline at end of file +watchdog~=3.0.0 +# volcengine>=1.0.119 +# pymilvus>=2.3.4 +# psycopg2==2.9.9 +# pgvector>=0.2.4 \ No newline at end of file diff --git a/requirements_webui.txt b/requirements_webui.txt index 288102741..9dae4620e 100644 --- a/requirements_webui.txt +++ b/requirements_webui.txt @@ -1,10 +1,8 @@ -# WebUI requirements - -streamlit>=1.29.0 -streamlit-option-menu>=0.3.6 -streamlit-antd-components>=0.3.0 -streamlit-chatbox>=1.1.11 -streamlit-modal>=0.1.0 -streamlit-aggrid>=0.3.4.post3 -httpx[brotli,http2,socks]>=0.25.2 -watchdog>=3.0.0 \ No newline at end of file +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 \ No newline at end of file diff --git a/server/agent/custom_agent/ChatGLM3Agent.py b/server/agent/custom_agent/ChatGLM3Agent.py index 92ddc28b4..65f57567e 100644 --- a/server/agent/custom_agent/ChatGLM3Agent.py +++ b/server/agent/custom_agent/ChatGLM3Agent.py @@ -1,22 +1,19 @@ """ -This file is a modified version for ChatGLM3-6B the original ChatGLM3Agent.py file from the langchain repo. +This file is a modified version for ChatGLM3-6B the original glm3_agent.py file from the langchain repo. """ from __future__ import annotations -import yaml +import json +import logging +from typing import Any, List, Sequence, Tuple, Optional, Union +from pydantic.schema import model_schema + + from langchain.agents.structured_chat.output_parser import StructuredChatOutputParser from langchain.memory import ConversationBufferWindowMemory -from typing import Any, List, Sequence, Tuple, Optional, Union -import os from langchain.agents.agent import Agent from langchain.chains.llm import LLMChain -from langchain.prompts.chat import ( - ChatPromptTemplate, - HumanMessagePromptTemplate, - SystemMessagePromptTemplate, MessagesPlaceholder, -) -import json -import logging +from langchain.prompts.chat import ChatPromptTemplate, SystemMessagePromptTemplate from langchain.agents.agent import AgentOutputParser from langchain.output_parsers import OutputFixingParser from langchain.pydantic_v1 import Field @@ -43,12 +40,18 @@ def parse(self, text: str) -> Union[AgentAction, AgentFinish]: first_index = min([text.find(token) if token in text else len(text) for token in special_tokens]) text = text[:first_index] if "tool_call" in text: - tool_name_end = text.find("```") - tool_name = text[:tool_name_end].strip() - input_para = text.split("='")[-1].split("'")[0] + action_end = text.find("```") + action = text[:action_end].strip() + params_str_start = text.find("(") + 1 + params_str_end = text.rfind(")") + params_str = text[params_str_start:params_str_end] + + params_pairs = [param.split("=") for param in params_str.split(",") if "=" in param] + params = {pair[0].strip(): pair[1].strip().strip("'\"") for pair in params_pairs} + action_json = { - "action": tool_name, - "action_input": input_para + "action": action, + "action_input": params } else: action_json = { @@ -109,10 +112,6 @@ def _construct_scratchpad( else: return agent_scratchpad - @classmethod - def _validate_tools(cls, tools: Sequence[BaseTool]) -> None: - pass - @classmethod def _get_default_output_parser( cls, llm: Optional[BaseLanguageModel] = None, **kwargs: Any @@ -121,7 +120,7 @@ def _get_default_output_parser( @property def _stop(self) -> List[str]: - return ["```"] + return ["<|observation|>"] @classmethod def create_prompt( @@ -131,44 +130,25 @@ def create_prompt( input_variables: Optional[List[str]] = None, memory_prompts: Optional[List[BasePromptTemplate]] = None, ) -> BasePromptTemplate: - def tool_config_from_file(tool_name, directory="server/agent/tools/"): - """search tool yaml and return simplified json format""" - file_path = os.path.join(directory, f"{tool_name.lower()}.yaml") - try: - with open(file_path, 'r', encoding='utf-8') as file: - tool_config = yaml.safe_load(file) - # Simplify the structure if needed - simplified_config = { - "name": tool_config.get("name", ""), - "description": tool_config.get("description", ""), - "parameters": tool_config.get("parameters", {}) - } - return simplified_config - except FileNotFoundError: - logger.error(f"File not found: {file_path}") - return None - except Exception as e: - logger.error(f"An error occurred while reading {file_path}: {e}") - return None - tools_json = [] tool_names = [] for tool in tools: - tool_config = tool_config_from_file(tool.name) - if tool_config: - tools_json.append(tool_config) - tool_names.append(tool.name) - - # Format the tools for output + tool_schema = model_schema(tool.args_schema) if tool.args_schema else {} + simplified_config_langchain = { + "name": tool.name, + "description": tool.description, + "parameters": tool_schema.get("properties", {}) + } + tools_json.append(simplified_config_langchain) + tool_names.append(tool.name) formatted_tools = "\n".join([ f"{tool['name']}: {tool['description']}, args: {tool['parameters']}" for tool in tools_json ]) formatted_tools = formatted_tools.replace("'", "\\'").replace("{", "{{").replace("}", "}}") - template = prompt.format(tool_names=tool_names, tools=formatted_tools, - history="{history}", + history="None", input="{input}", agent_scratchpad="{agent_scratchpad}") @@ -225,7 +205,6 @@ def initialize_glm3_agent( tools: Sequence[BaseTool], llm: BaseLanguageModel, prompt: str = None, - callback_manager: Optional[BaseCallbackManager] = None, memory: Optional[ConversationBufferWindowMemory] = None, agent_kwargs: Optional[dict] = None, *, @@ -238,14 +217,12 @@ def initialize_glm3_agent( llm=llm, tools=tools, prompt=prompt, - callback_manager=callback_manager, **agent_kwargs + **agent_kwargs ) return AgentExecutor.from_agent_and_tools( agent=agent_obj, tools=tools, - callback_manager=callback_manager, memory=memory, tags=tags_, **kwargs, - ) - + ) \ No newline at end of file diff --git a/server/agent/model_contain.py b/server/agent/model_contain.py index 1927c88fc..0141ad037 100644 --- a/server/agent/model_contain.py +++ b/server/agent/model_contain.py @@ -1,5 +1,3 @@ - -## 由于工具类无法传参，所以使用全局变量来传递模型和对应的知识库介绍 class ModelContainer: def __init__(self): self.MODEL = None diff --git a/server/agent/tools/__init__.py b/server/agent/tools/__init__.py index 43d57343b..21dfd973a 100644 --- a/server/agent/tools/__init__.py +++ b/server/agent/tools/__init__.py @@ -3,7 +3,7 @@ from .search_knowledgebase_once import search_knowledgebase_once, KnowledgeSearchInput from .search_knowledgebase_complex import search_knowledgebase_complex, KnowledgeSearchInput from .calculate import calculate, CalculatorInput -from .weather_check import weathercheck, WhetherSchema +from .weather_check import weathercheck, WeatherInput from .shell import shell, ShellInput from .search_internet import search_internet, SearchInternetInput from .wolfram import wolfram, WolframInput diff --git a/server/agent/tools/arxiv.yaml b/server/agent/tools/arxiv.yaml deleted file mode 100644 index 5da8a9f95..000000000 --- a/server/agent/tools/arxiv.yaml +++ /dev/null @@ -1,10 +0,0 @@ -name: arxiv -description: A wrapper around Arxiv.org for searching and retrieving scientific articles in various fields. -parameters: - type: object - properties: - query: - type: string - description: The search query title -required: - - query \ No newline at end of file diff --git a/server/agent/tools/calculate.yaml b/server/agent/tools/calculate.yaml deleted file mode 100644 index 2976c0118..000000000 --- a/server/agent/tools/calculate.yaml +++ /dev/null @@ -1,10 +0,0 @@ -name: calculate -description: Useful for when you need to answer questions about simple calculations -parameters: - type: object - properties: - query: - type: string - description: The formula to be calculated -required: - - query \ No newline at end of file diff --git a/server/agent/tools/search_internet.yaml b/server/agent/tools/search_internet.yaml deleted file mode 100644 index 57608e7e3..000000000 --- a/server/agent/tools/search_internet.yaml +++ /dev/null @@ -1,10 +0,0 @@ -name: search_internet -description: Use this tool to surf internet and get information -parameters: - type: object - properties: - query: - type: string - description: Query for Internet search -required: - - query \ No newline at end of file diff --git a/server/agent/tools/search_knowledgebase_complex.yaml b/server/agent/tools/search_knowledgebase_complex.yaml deleted file mode 100644 index bccbfd8c3..000000000 --- a/server/agent/tools/search_knowledgebase_complex.yaml +++ /dev/null @@ -1,10 +0,0 @@ -name: search_knowledgebase_complex -description: Use this tool to search local knowledgebase and get information -parameters: - type: object - properties: - query: - type: string - description: The query to be searched -required: - - query \ No newline at end of file diff --git a/server/agent/tools/search_youtube.yaml b/server/agent/tools/search_youtube.yaml deleted file mode 100644 index ce4460681..000000000 --- a/server/agent/tools/search_youtube.yaml +++ /dev/null @@ -1,10 +0,0 @@ -name: search_youtube -description: Use this tools to search youtube videos -parameters: - type: object - properties: - query: - type: string - description: Query for Videos search -required: - - query \ No newline at end of file diff --git a/server/agent/tools/shell.yaml b/server/agent/tools/shell.yaml deleted file mode 100644 index 22b94180a..000000000 --- a/server/agent/tools/shell.yaml +++ /dev/null @@ -1,10 +0,0 @@ -name: shell -description: Use Linux Shell to execute Linux commands -parameters: - type: object - properties: - query: - type: string - description: The command to execute -required: - - query \ No newline at end of file diff --git a/server/agent/tools/weather_check.py b/server/agent/tools/weather_check.py index db88b874f..20f009a5f 100644 --- a/server/agent/tools/weather_check.py +++ b/server/agent/tools/weather_check.py @@ -1,338 +1,29 @@ -from __future__ import annotations - -## 单独运行的时候需要添加 -import sys -import os - -sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) - -import re -import warnings -from typing import Dict - -from langchain.callbacks.manager import ( - AsyncCallbackManagerForChainRun, - CallbackManagerForChainRun, -) -from langchain.chains.base import Chain -from langchain.chains.llm import LLMChain -from langchain.pydantic_v1 import Extra, root_validator -from langchain.schema import BasePromptTemplate -from langchain.schema.language_model import BaseLanguageModel -import requests -from typing import List, Any, Optional -from datetime import datetime -from langchain.prompts import PromptTemplate -from server.agent import model_container -from pydantic import BaseModel, Field - -## 使用和风天气API查询天气 -KEY = "ac880e5a877042809ac7ffdd19d95b0d" -# key长这样，这里提供了示例的key，这个key没法使用，你需要自己去注册和风天气的账号，然后在这里填入你的key - - -_PROMPT_TEMPLATE = """ -用户会提出一个关于天气的问题，你的目标是拆分出用户问题中的区，市 并按照我提供的工具回答。 -例如 用户提出的问题是: 上海浦东未来1小时天气情况？ -则 提取的市和区是: 上海 浦东 -如果用户提出的问题是: 上海未来1小时天气情况？ -则 提取的市和区是: 上海 None -请注意以下内容: -1. 如果你没有找到区的内容,则一定要使用 None 替代，否则程序无法运行 -2. 如果用户没有指定市 则直接返回缺少信息 - -问题: ${{用户的问题}} - -你的回答格式应该按照下面的内容，请注意，格式内的```text 等标记都必须输出，这是我用来提取答案的标记。 -```text - -${{拆分的市和区，中间用空格隔开}} -``` -... weathercheck(市 区)... -```output - -${{提取后的答案}} -``` -答案: ${{答案}} - - - -这是一个例子： -问题: 上海浦东未来1小时天气情况？ - - -```text -上海 浦东 -``` -...weathercheck(上海 浦东)... - -```output -预报时间: 1小时后 -具体时间: 今天 18:00 -温度: 24°C -天气: 多云 -风向: 西南风 -风速: 7级 -湿度: 88% -降水概率: 16% - -Answer: 上海浦东一小时后的天气是多云。 - -现在，这是我的问题： - -问题: {question} """ -PROMPT = PromptTemplate( - input_variables=["question"], - template=_PROMPT_TEMPLATE, -) - - -def get_city_info(location, adm, key): - base_url = 'https://geoapi.qweather.com/v2/city/lookup?' - params = {'location': location, 'adm': adm, 'key': key} - response = requests.get(base_url, params=params) - data = response.json() - return data - - -def format_weather_data(data, place): - hourly_forecast = data['hourly'] - formatted_data = f"\n 这是查询到的关于{place}未来24小时的天气信息: \n" - for forecast in hourly_forecast: - # 将预报时间转换为datetime对象 - forecast_time = datetime.strptime(forecast['fxTime'], '%Y-%m-%dT%H:%M%z') - # 获取预报时间的时区 - forecast_tz = forecast_time.tzinfo - # 获取当前时间（使用预报时间的时区） - now = datetime.now(forecast_tz) - # 计算预报日期与当前日期的差值 - days_diff = (forecast_time.date() - now.date()).days - if days_diff == 0: - forecast_date_str = '今天' - elif days_diff == 1: - forecast_date_str = '明天' - elif days_diff == 2: - forecast_date_str = '后天' - else: - forecast_date_str = str(days_diff) + '天后' - forecast_time_str = forecast_date_str + ' ' + forecast_time.strftime('%H:%M') - # 计算预报时间与当前时间的差值 - time_diff = forecast_time - now - # 将差值转换为小时 - hours_diff = time_diff.total_seconds() // 3600 - if hours_diff < 1: - hours_diff_str = '1小时后' - elif hours_diff >= 24: - # 如果超过24小时，转换为天数 - days_diff = hours_diff // 24 - hours_diff_str = str(int(days_diff)) + '天' - else: - hours_diff_str = str(int(hours_diff)) + '小时' - # 将预报时间和当前时间的差值添加到输出中 - formatted_data += '预报时间: ' + forecast_time_str + ' 距离现在有: ' + hours_diff_str + '\n' - formatted_data += '温度: ' + forecast['temp'] + '°C\n' - formatted_data += '天气: ' + forecast['text'] + '\n' - formatted_data += '风向: ' + forecast['windDir'] + '\n' - formatted_data += '风速: ' + forecast['windSpeed'] + '级\n' - formatted_data += '湿度: ' + forecast['humidity'] + '%\n' - formatted_data += '降水概率: ' + forecast['pop'] + '%\n' - # formatted_data += '降水量: ' + forecast['precip'] + 'mm\n' - formatted_data += '\n' - return formatted_data - - -def get_weather(key, location_id, place): - url = "https://devapi.qweather.com/v7/weather/24h?" - params = { - 'location': location_id, - 'key': key, - } - response = requests.get(url, params=params) - data = response.json() - return format_weather_data(data, place) - - -def split_query(query): - parts = query.split() - adm = parts[0] - if len(parts) == 1: - return adm, adm - location = parts[1] if parts[1] != 'None' else adm - return location, adm - - -def weather(query): - location, adm = split_query(query) - key = KEY - if key == "": - return "请先在代码中填入和风天气API Key" - try: - city_info = get_city_info(location=location, adm=adm, key=key) - location_id = city_info['location'][0]['id'] - place = adm + "市" + location + "区" - - weather_data = get_weather(key=key, location_id=location_id, place=place) - return weather_data + "以上是查询到的天气信息，请你查收\n" - except KeyError: - try: - city_info = get_city_info(location=adm, adm=adm, key=key) - location_id = city_info['location'][0]['id'] - place = adm + "市" - weather_data = get_weather(key=key, location_id=location_id, place=place) - return weather_data + "重要提醒：用户提供的市和区中，区的信息不存在，或者出现错别字，因此该信息是关于市的天气，请你查收\n" - except KeyError: - return "输入的地区不存在，无法提供天气预报" - - -class LLMWeatherChain(Chain): - llm_chain: LLMChain - llm: Optional[BaseLanguageModel] = None - """[Deprecated] LLM wrapper to use.""" - prompt: BasePromptTemplate = PROMPT - """[Deprecated] Prompt to use to translate to python if necessary.""" - input_key: str = "question" #: :meta private: - output_key: str = "answer" #: :meta private: - - class Config: - """Configuration for this pydantic object.""" - - extra = Extra.forbid - arbitrary_types_allowed = True - - @root_validator(pre=True) - def raise_deprecation(cls, values: Dict) -> Dict: - if "llm" in values: - warnings.warn( - "Directly instantiating an LLMWeatherChain with an llm is deprecated. " - "Please instantiate with llm_chain argument or using the from_llm " - "class method." - ) - if "llm_chain" not in values and values["llm"] is not None: - prompt = values.get("prompt", PROMPT) - values["llm_chain"] = LLMChain(llm=values["llm"], prompt=prompt) - return values - - @property - def input_keys(self) -> List[str]: - """Expect input key. - - :meta private: - """ - return [self.input_key] - - @property - def output_keys(self) -> List[str]: - """Expect output key. - - :meta private: - """ - return [self.output_key] - - def _evaluate_expression(self, expression: str) -> str: - try: - output = weather(expression) - except Exception as e: - output = "输入的信息有误，请再次尝试" - return output - - def _process_llm_result( - self, llm_output: str, run_manager: CallbackManagerForChainRun - ) -> Dict[str, str]: - - run_manager.on_text(llm_output, color="green", verbose=self.verbose) - - llm_output = llm_output.strip() - text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL) - if text_match: - expression = text_match.group(1) - output = self._evaluate_expression(expression) - run_manager.on_text("\nAnswer: ", verbose=self.verbose) - run_manager.on_text(output, color="yellow", verbose=self.verbose) - answer = "Answer: " + output - elif llm_output.startswith("Answer:"): - answer = llm_output - elif "Answer:" in llm_output: - answer = "Answer: " + llm_output.split("Answer:")[-1] - else: - return {self.output_key: f"输入的格式不对: {llm_output},应该输入 (市 区)的组合"} - return {self.output_key: answer} - - async def _aprocess_llm_result( - self, - llm_output: str, - run_manager: AsyncCallbackManagerForChainRun, - ) -> Dict[str, str]: - await run_manager.on_text(llm_output, color="green", verbose=self.verbose) - llm_output = llm_output.strip() - text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL) - - if text_match: - expression = text_match.group(1) - output = self._evaluate_expression(expression) - await run_manager.on_text("\nAnswer: ", verbose=self.verbose) - await run_manager.on_text(output, color="yellow", verbose=self.verbose) - answer = "Answer: " + output - elif llm_output.startswith("Answer:"): - answer = llm_output - elif "Answer:" in llm_output: - answer = "Answer: " + llm_output.split("Answer:")[-1] - else: - raise ValueError(f"unknown format from LLM: {llm_output}") - return {self.output_key: answer} - - def _call( - self, - inputs: Dict[str, str], - run_manager: Optional[CallbackManagerForChainRun] = None, - ) -> Dict[str, str]: - _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() - _run_manager.on_text(inputs[self.input_key]) - llm_output = self.llm_chain.predict( - question=inputs[self.input_key], - stop=["```output"], - callbacks=_run_manager.get_child(), - ) - return self._process_llm_result(llm_output, _run_manager) - - async def _acall( - self, - inputs: Dict[str, str], - run_manager: Optional[AsyncCallbackManagerForChainRun] = None, - ) -> Dict[str, str]: - _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager() - await _run_manager.on_text(inputs[self.input_key]) - llm_output = await self.llm_chain.apredict( - question=inputs[self.input_key], - stop=["```output"], - callbacks=_run_manager.get_child(), - ) - return await self._aprocess_llm_result(llm_output, _run_manager) - - @property - def _chain_type(self) -> str: - return "llm_weather_chain" +更简单的单参数输入工具实现，用于查询现在天气的情况 +""" +from pydantic import BaseModel, Field +import requests +from configs.kb_config import SENIVERSE_API_KEY - @classmethod - def from_llm( - cls, - llm: BaseLanguageModel, - prompt: BasePromptTemplate = PROMPT, - **kwargs: Any, - ) -> LLMWeatherChain: - llm_chain = LLMChain(llm=llm, prompt=prompt) - return cls(llm_chain=llm_chain, **kwargs) +def weather(location: str, api_key: str): + url = f"https://api.seniverse.com/v3/weather/now.json?key={api_key}&location={location}&language=zh-Hans&unit=c" + response = requests.get(url) + if response.status_code == 200: + data = response.json() + weather = { + "temperature": data["results"][0]["now"]["temperature"], + "description": data["results"][0]["now"]["text"], + } + return weather + else: + raise Exception( + f"Failed to retrieve weather: {response.status_code}") -def weathercheck(query: str): - model = model_container.MODEL - llm_weather = LLMWeatherChain.from_llm(model, verbose=True, prompt=PROMPT) - ans = llm_weather.run(query) - return ans +def weathercheck(location: str): + return weather(location, SENIVERSE_API_KEY) -class WhetherSchema(BaseModel): - location: str = Field(description="应该是一个地区的名称，用空格隔开，例如：上海 浦东，如果没有区的信息，可以只输入上海") -if __name__ == '__main__': - result = weathercheck("苏州姑苏区今晚热不热？") +class WeatherInput(BaseModel): + location: str = Field(description="City name,include city and county") diff --git a/server/agent/tools/weather_check.yaml b/server/agent/tools/weather_check.yaml deleted file mode 100644 index e72676ace..000000000 --- a/server/agent/tools/weather_check.yaml +++ /dev/null @@ -1,10 +0,0 @@ -name: weather_check -description: Use Weather API to get weather information -parameters: - type: object - properties: - query: - type: string - description: City name,include city and county,like "厦门市思明区" -required: - - query \ No newline at end of file diff --git a/server/agent/tools/wolfram.yaml b/server/agent/tools/wolfram.yaml deleted file mode 100644 index 532f82488..000000000 --- a/server/agent/tools/wolfram.yaml +++ /dev/null @@ -1,10 +0,0 @@ -name: wolfram -description: Useful for when you need to calculate difficult math formulas -parameters: - type: object - properties: - query: - type: string - description: The formula to be calculated -required: - - query \ No newline at end of file diff --git a/server/agent/tools_select.py b/server/agent/tools_select.py index 821d324e1..237c20b6d 100644 --- a/server/agent/tools_select.py +++ b/server/agent/tools_select.py @@ -1,8 +1,6 @@ from langchain.tools import Tool from server.agent.tools import * -## 请注意，如果你是为了使用AgentLM，在这里，你应该使用英文版本。 - tools = [ Tool.from_function( func=calculate, @@ -20,7 +18,7 @@ func=weathercheck, name="weather_check", description="", - args_schema=WhetherSchema, + args_schema=WeatherInput, ), Tool.from_function( func=shell, diff --git a/server/callback_handler/conversation_callback_handler.py b/server/callback_handler/conversation_callback_handler.py index 6642c1e26..8f09b40df 100644 --- a/server/callback_handler/conversation_callback_handler.py +++ b/server/callback_handler/conversation_callback_handler.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, List, Union, Optional +from typing import Any, Dict, List from langchain.callbacks.base import BaseCallbackHandler from langchain.schema import LLMResult diff --git a/server/chat/agent_chat.py b/server/chat/agent_chat.py index 03c0c2b91..41bf5baba 100644 --- a/server/chat/agent_chat.py +++ b/server/chat/agent_chat.py @@ -1,22 +1,23 @@ -from langchain.memory import ConversationBufferWindowMemory +import json +import asyncio -from server.agent.custom_agent.ChatGLM3Agent import initialize_glm3_agent -from server.agent.tools_select import tools, tool_names -from server.agent.callbacks import CustomAsyncIteratorCallbackHandler, Status -from langchain.agents import LLMSingleActionAgent, AgentExecutor -from server.agent.custom_template import CustomOutputParser, CustomPromptTemplate from fastapi import Body from sse_starlette.sse import EventSourceResponse from configs import LLM_MODELS, TEMPERATURE, HISTORY_LEN, Agent_MODEL -from server.utils import wrap_done, get_ChatOpenAI, get_prompt_template + from langchain.chains import LLMChain -from typing import AsyncIterable, Optional -import asyncio -from typing import List +from langchain.memory import ConversationBufferWindowMemory +from langchain.agents import LLMSingleActionAgent, AgentExecutor +from typing import AsyncIterable, Optional, List + +from server.utils import wrap_done, get_ChatOpenAI, get_prompt_template +from server.knowledge_base.kb_service.base import get_kb_details +from server.agent.custom_agent.ChatGLM3Agent import initialize_glm3_agent +from server.agent.tools_select import tools, tool_names +from server.agent.callbacks import CustomAsyncIteratorCallbackHandler, Status from server.chat.utils import History -import json from server.agent import model_container -from server.knowledge_base.kb_service.base import get_kb_details +from server.agent.custom_template import CustomOutputParser, CustomPromptTemplate async def agent_chat(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]), @@ -33,7 +34,6 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples max_tokens: Optional[int] = Body(None, description="限制LLM生成Token数量，默认None代表模型最大值"), prompt_name: str = Body("default", description="使用的prompt模板名称(在configs/prompt_config.py中配置)"), - # top_p: float = Body(TOP_P, description="LLM 核采样。勿与temperature同时设置", gt=0.0, lt=1.0), ): history = [History.from_data(h) for h in history] @@ -55,12 +55,10 @@ async def agent_chat_iterator( callbacks=[callback], ) - ## 传入全局变量来实现agent调用 kb_list = {x["kb_name"]: x for x in get_kb_details()} model_container.DATABASE = {name: details['kb_info'] for name, details in kb_list.items()} if Agent_MODEL: - ## 如果有指定使用Agent模型来完成任务 model_agent = get_ChatOpenAI( model_name=Agent_MODEL, temperature=temperature, @@ -79,23 +77,17 @@ async def agent_chat_iterator( ) output_parser = CustomOutputParser() llm_chain = LLMChain(llm=model, prompt=prompt_template_agent) - # 把history转成agent的memory memory = ConversationBufferWindowMemory(k=HISTORY_LEN * 2) for message in history: - # 检查消息的角色 if message.role == 'user': - # 添加用户消息 memory.chat_memory.add_user_message(message.content) else: - # 添加AI消息 memory.chat_memory.add_ai_message(message.content) - - if "chatglm3" in model_container.MODEL.model_name: + if "chatglm3" in model_container.MODEL.model_name or "zhipu-api" in model_container.MODEL.model_name: agent_executor = initialize_glm3_agent( llm=model, tools=tools, callback_manager=None, - # Langchain Prompt is not constructed directly here, it is constructed inside the GLM3 agent. prompt=prompt_template, input_variables=["input", "intermediate_steps", "history"], memory=memory, @@ -155,7 +147,6 @@ async def agent_chat_iterator( answer = "" final_answer = "" async for chunk in callback.aiter(): - # Use server-sent-events to stream the response data = json.loads(chunk) if data["status"] == Status.start or data["status"] == Status.complete: continue @@ -181,7 +172,7 @@ async def agent_chat_iterator( await task return EventSourceResponse(agent_chat_iterator(query=query, - history=history, - model_name=model_name, - prompt_name=prompt_name), - ) + history=history, + model_name=model_name, + prompt_name=prompt_name), + ) diff --git a/server/chat/search_engine_chat.py b/server/chat/search_engine_chat.py index db00dfa4a..42bef3c23 100644 --- a/server/chat/search_engine_chat.py +++ b/server/chat/search_engine_chat.py @@ -1,23 +1,23 @@ from langchain.utilities.bing_search import BingSearchAPIWrapper from langchain.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper from configs import (BING_SEARCH_URL, BING_SUBSCRIPTION_KEY, METAPHOR_API_KEY, - LLM_MODELS, SEARCH_ENGINE_TOP_K, TEMPERATURE, - TEXT_SPLITTER_NAME, OVERLAP_SIZE) + LLM_MODELS, SEARCH_ENGINE_TOP_K, TEMPERATURE, OVERLAP_SIZE) +from langchain.chains import LLMChain +from langchain.callbacks import AsyncIteratorCallbackHandler + +from langchain.prompts.chat import ChatPromptTemplate +from langchain.text_splitter import RecursiveCharacterTextSplitter +from langchain.docstore.document import Document from fastapi import Body -from sse_starlette import EventSourceResponse from fastapi.concurrency import run_in_threadpool +from sse_starlette import EventSourceResponse from server.utils import wrap_done, get_ChatOpenAI from server.utils import BaseResponse, get_prompt_template -from langchain.chains import LLMChain -from langchain.callbacks import AsyncIteratorCallbackHandler +from server.chat.utils import History from typing import AsyncIterable import asyncio -from langchain.prompts.chat import ChatPromptTemplate -from langchain.text_splitter import RecursiveCharacterTextSplitter -from typing import List, Optional, Dict -from server.chat.utils import History -from langchain.docstore.document import Document import json +from typing import List, Optional, Dict from strsimpy.normalized_levenshtein import NormalizedLevenshtein from markdownify import markdownify @@ -38,11 +38,11 @@ def duckduckgo_search(text, result_len=SEARCH_ENGINE_TOP_K, **kwargs): def metaphor_search( - text: str, - result_len: int = SEARCH_ENGINE_TOP_K, - split_result: bool = False, - chunk_size: int = 500, - chunk_overlap: int = OVERLAP_SIZE, + text: str, + result_len: int = SEARCH_ENGINE_TOP_K, + split_result: bool = False, + chunk_size: int = 500, + chunk_overlap: int = OVERLAP_SIZE, ) -> List[Dict]: from metaphor_python import Metaphor @@ -58,13 +58,13 @@ def metaphor_search( # metaphor 返回的内容都是长文本，需要分词再检索 if split_result: docs = [Document(page_content=x.extract, - metadata={"link": x.url, "title": x.title}) + metadata={"link": x.url, "title": x.title}) for x in contents] text_splitter = RecursiveCharacterTextSplitter(["\n\n", "\n", ".", " "], chunk_size=chunk_size, chunk_overlap=chunk_overlap) splitted_docs = text_splitter.split_documents(docs) - + # 将切分好的文档放入临时向量库，重新筛选出TOP_K个文档 if len(splitted_docs) > result_len: normal = NormalizedLevenshtein() @@ -74,13 +74,13 @@ def metaphor_search( splitted_docs = splitted_docs[:result_len] docs = [{"snippet": x.page_content, - "link": x.metadata["link"], - "title": x.metadata["title"]} + "link": x.metadata["link"], + "title": x.metadata["title"]} for x in splitted_docs] else: docs = [{"snippet": x.extract, - "link": x.url, - "title": x.title} + "link": x.url, + "title": x.title} for x in contents] return docs @@ -113,25 +113,27 @@ async def lookup_search_engine( docs = search_result2docs(results) return docs - async def search_engine_chat(query: str = Body(..., description="用户输入", examples=["你好"]), - search_engine_name: str = Body(..., description="搜索引擎名称", examples=["duckduckgo"]), - top_k: int = Body(SEARCH_ENGINE_TOP_K, description="检索结果数量"), - history: List[History] = Body([], - description="历史对话", - examples=[[ - {"role": "user", + search_engine_name: str = Body(..., description="搜索引擎名称", examples=["duckduckgo"]), + top_k: int = Body(SEARCH_ENGINE_TOP_K, description="检索结果数量"), + history: List[History] = Body([], + description="历史对话", + examples=[[ + {"role": "user", "content": "我们来玩成语接龙，我先来，生龙活虎"}, - {"role": "assistant", + {"role": "assistant", "content": "虎头虎脑"}]] - ), - stream: bool = Body(False, description="流式输出"), - model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"), - temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0), - max_tokens: Optional[int] = Body(None, description="限制LLM生成Token数量，默认None代表模型最大值"), - prompt_name: str = Body("default",description="使用的prompt模板名称(在configs/prompt_config.py中配置)"), - split_result: bool = Body(False, description="是否对搜索结果进行拆分（主要用于metaphor搜索引擎）") - ): + ), + stream: bool = Body(False, description="流式输出"), + model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"), + temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0), + max_tokens: Optional[int] = Body(None, + description="限制LLM生成Token数量，默认None代表模型最大值"), + prompt_name: str = Body("default", + description="使用的prompt模板名称(在configs/prompt_config.py中配置)"), + split_result: bool = Body(False, + description="是否对搜索结果进行拆分（主要用于metaphor搜索引擎）") + ): if search_engine_name not in SEARCH_ENGINES.keys(): return BaseResponse(code=404, msg=f"未支持搜索引擎 {search_engine_name}") @@ -198,9 +200,9 @@ async def search_engine_chat_iterator(query: str, await task return EventSourceResponse(search_engine_chat_iterator(query=query, - search_engine_name=search_engine_name, - top_k=top_k, - history=history, - model_name=model_name, - prompt_name=prompt_name), - ) + search_engine_name=search_engine_name, + top_k=top_k, + history=history, + model_name=model_name, + prompt_name=prompt_name), + ) diff --git a/server/db/repository/knowledge_file_repository.py b/server/db/repository/knowledge_file_repository.py index 0a6b782dc..4388e7ad6 100644 --- a/server/db/repository/knowledge_file_repository.py +++ b/server/db/repository/knowledge_file_repository.py @@ -83,7 +83,7 @@ def add_file_to_db(session, kb_file: KnowledgeFile, docs_count: int = 0, custom_docs: bool = False, - doc_infos: List[str] = [], # 形式：[{"id": str, "metadata": dict}, ...] + doc_infos: List[Dict] = [], # 形式：[{"id": str, "metadata": dict}, ...] ): kb = session.query(KnowledgeBaseModel).filter_by(kb_name=kb_file.kb_name).first() if kb: diff --git a/server/embeddings_api.py b/server/embeddings_api.py index 440bb7740..e907de075 100644 --- 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 diff --git a/server/knowledge_base/kb_api.py b/server/knowledge_base/kb_api.py index f50d8a732..0d2fbce9d 100644 --- 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") diff --git a/server/knowledge_base/kb_cache/faiss_cache.py b/server/knowledge_base/kb_cache/faiss_cache.py index ed48b5ddb..60c550ee4 100644 --- a/server/knowledge_base/kb_cache/faiss_cache.py +++ b/server/knowledge_base/kb_cache/faiss_cache.py @@ -55,8 +55,6 @@ def new_vector_store( 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") diff --git a/server/knowledge_base/kb_doc_api.py b/server/knowledge_base/kb_doc_api.py index 09a264f96..e58ea41f0 100644 --- a/server/knowledge_base/kb_doc_api.py +++ b/server/knowledge_base/kb_doc_api.py @@ -95,7 +95,6 @@ def save_file(file: UploadFile, knowledge_base_name: str, override: bool) -> dic 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_file(file: UploadFile, knowledge_base_name: str, override: bool) -> dic 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"), diff --git a/server/knowledge_base/kb_service/base.py b/server/knowledge_base/kb_service/base.py index 86b990544..bd5a54ebd 100644 --- a/server/knowledge_base/kb_service/base.py +++ b/server/knowledge_base/kb_service/base.py @@ -24,7 +24,7 @@ list_kbs_from_folder, list_files_from_folder, ) -from typing import List, Union, Dict, Optional +from typing import List, Union, Dict, Optional, Tuple from server.embeddings_api import embed_texts, aembed_texts, embed_documents from server.knowledge_base.model.kb_document_model import DocumentWithVSId @@ -191,7 +191,6 @@ def update_doc_by_ids(self, docs: Dict[str, Document]) -> bool: ''' 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 = [] @@ -261,7 +260,7 @@ def do_search(self, query: str, top_k: int, score_threshold: float, - ) -> List[Document]: + ) -> List[Tuple[Document, float]]: """ 搜索知识库子类实自己逻辑 """ diff --git a/server/knowledge_base/kb_service/es_kb_service.py b/server/knowledge_base/kb_service/es_kb_service.py index 4a408cbe7..2b4c8ad30 100644 --- a/server/knowledge_base/kb_service/es_kb_service.py +++ b/server/knowledge_base/kb_service/es_kb_service.py @@ -6,6 +6,7 @@ from langchain.vectorstores.elasticsearch import ElasticsearchStore from configs import KB_ROOT_PATH, EMBEDDING_MODEL, EMBEDDING_DEVICE, CACHED_VS_NUM from server.knowledge_base.kb_service.base import KBService, SupportedVSType +from server.knowledge_base.utils import KnowledgeFile from server.utils import load_local_embeddings from elasticsearch import Elasticsearch,BadRequestError from configs import logger @@ -15,7 +16,7 @@ class ESKBService(KBService): def do_init(self): self.kb_path = self.get_kb_path(self.kb_name) - self.index_name = self.kb_path.split("/")[-1] + self.index_name = os.path.split(self.kb_path)[-1] self.IP = kbs_config[self.vs_type()]['host'] self.PORT = kbs_config[self.vs_type()]['port'] self.user = kbs_config[self.vs_type()].get("user",'') @@ -38,7 +39,16 @@ def do_init(self): raise e try: # 首先尝试通过es_client_python创建 - self.es_client_python.indices.create(index=self.index_name) + mappings = { + "properties": { + "dense_vector": { + "type": "dense_vector", + "dims": self.dims_length, + "index": True + } + } + } + self.es_client_python.indices.create(index=self.index_name, mappings=mappings) except BadRequestError as e: logger.error("创建索引失败,重新") logger.error(e) @@ -80,9 +90,9 @@ def do_init(self): except Exception as e: logger.error("创建索引失败...") logger.error(e) - # raise e - - + # raise e + + @staticmethod def get_kb_path(knowledge_base_name: str): @@ -220,7 +230,12 @@ def do_drop_kb(self): shutil.rmtree(self.kb_path) - +if __name__ == '__main__': + esKBService = ESKBService("test") + #esKBService.clear_vs() + #esKBService.create_kb() + esKBService.add_doc(KnowledgeFile(filename="README.md", knowledge_base_name="test")) + print(esKBService.search_docs("如何启动api服务")) diff --git a/server/knowledge_base/kb_service/faiss_kb_service.py b/server/knowledge_base/kb_service/faiss_kb_service.py index f073b4edb..c05f61360 100644 --- a/server/knowledge_base/kb_service/faiss_kb_service.py +++ b/server/knowledge_base/kb_service/faiss_kb_service.py @@ -7,7 +7,7 @@ from server.knowledge_base.utils import KnowledgeFile, get_kb_path, get_vs_path from server.utils import torch_gc from langchain.docstore.document import Document -from typing import List, Dict, Optional +from typing import List, Dict, Optional, Tuple class FaissKBService(KBService): @@ -61,7 +61,7 @@ def do_search(self, query: str, top_k: int, score_threshold: float = SCORE_THRESHOLD, - ) -> List[Document]: + ) -> List[Tuple[Document, float]]: embed_func = EmbeddingsFunAdapter(self.embed_model) embeddings = embed_func.embed_query(query) with self.load_vector_store().acquire() as vs: diff --git a/server/knowledge_base/kb_service/milvus_kb_service.py b/server/knowledge_base/kb_service/milvus_kb_service.py index f08b62b83..43b616e2c 100644 --- a/server/knowledge_base/kb_service/milvus_kb_service.py +++ b/server/knowledge_base/kb_service/milvus_kb_service.py @@ -18,13 +18,10 @@ def get_collection(milvus_name): from pymilvus import Collection return Collection(milvus_name) - # def save_vector_store(self): - # if self.milvus.col: - # self.milvus.col.flush() - def get_doc_by_ids(self, ids: List[str]) -> List[Document]: result = [] if self.milvus.col: + # ids = [int(id) for id in ids] # for milvus if needed #pr 2725 data_list = self.milvus.col.query(expr=f'pk in {ids}', output_fields=["*"]) for data in data_list: text = data.pop("text") @@ -73,7 +70,6 @@ def do_search(self, query: str, top_k: int, score_threshold: float): 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) diff --git a/server/knowledge_base/kb_service/pg_kb_service.py b/server/knowledge_base/kb_service/pg_kb_service.py index 8a4afcbed..46efe7d8e 100644 --- a/server/knowledge_base/kb_service/pg_kb_service.py +++ b/server/knowledge_base/kb_service/pg_kb_service.py @@ -11,25 +11,27 @@ score_threshold_process from server.knowledge_base.utils import KnowledgeFile import shutil +import sqlalchemy +from sqlalchemy.engine.base import Engine +from sqlalchemy.orm import Session class PGKBService(KBService): - pg_vector: PGVector + engine: Engine = sqlalchemy.create_engine(kbs_config.get("pg").get("connection_uri"), pool_size=10) def _load_pg_vector(self): self.pg_vector = PGVector(embedding_function=EmbeddingsFunAdapter(self.embed_model), collection_name=self.kb_name, distance_strategy=DistanceStrategy.EUCLIDEAN, + connection=PGKBService.engine, connection_string=kbs_config.get("pg").get("connection_uri")) def get_doc_by_ids(self, ids: List[str]) -> List[Document]: - with self.pg_vector.connect() as connect: + with Session(PGKBService.engine) as session: stmt = text("SELECT document, cmetadata FROM langchain_pg_embedding WHERE collection_id in :ids") results = [Document(page_content=row[0], metadata=row[1]) for row in - connect.execute(stmt, parameters={'ids': ids}).fetchall()] + 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) @@ -43,8 +45,8 @@ def vs_type(self) -> str: return SupportedVSType.PG def do_drop_kb(self): - with self.pg_vector.connect() as connect: - connect.execute(text(f''' + with Session(PGKBService.engine) as session: + session.execute(text(f''' -- 删除 langchain_pg_embedding 表中关联到 langchain_pg_collection 表中 的记录 DELETE FROM langchain_pg_embedding WHERE collection_id IN ( @@ -53,11 +55,10 @@ def do_drop_kb(self): -- 删除 langchain_pg_collection 表中 记录 DELETE FROM langchain_pg_collection WHERE name = '{self.kb_name}'; ''')) - connect.commit() + session.commit() shutil.rmtree(self.kb_path) def do_search(self, query: str, top_k: int, score_threshold: float): - self._load_pg_vector() embed_func = EmbeddingsFunAdapter(self.embed_model) embeddings = embed_func.embed_query(query) docs = self.pg_vector.similarity_search_with_score_by_vector(embeddings, top_k) @@ -69,13 +70,13 @@ def do_add_doc(self, docs: List[Document], **kwargs) -> List[Dict]: return doc_infos def do_delete_doc(self, kb_file: KnowledgeFile, **kwargs): - with self.pg_vector.connect() as connect: + with Session(PGKBService.engine) as session: filepath = kb_file.filepath.replace('\\', '\\\\') - connect.execute( + session.execute( text( ''' DELETE FROM langchain_pg_embedding WHERE cmetadata::jsonb @> '{"source": "filepath"}'::jsonb;'''.replace( "filepath", filepath))) - connect.commit() + session.commit() def do_clear_vs(self): self.pg_vector.delete_collection() diff --git a/server/knowledge_base/kb_service/zilliz_kb_service.py b/server/knowledge_base/kb_service/zilliz_kb_service.py index 5d00a49e9..753225a0b 100644 --- a/server/knowledge_base/kb_service/zilliz_kb_service.py +++ b/server/knowledge_base/kb_service/zilliz_kb_service.py @@ -16,13 +16,10 @@ def get_collection(zilliz_name): from pymilvus import Collection return Collection(zilliz_name) - # def save_vector_store(self): - # if self.zilliz.col: - # self.zilliz.col.flush() - def get_doc_by_ids(self, ids: List[str]) -> List[Document]: result = [] if self.zilliz.col: + # ids = [int(id) for id in ids] # for zilliz if needed #pr 2725 data_list = self.zilliz.col.query(expr=f'pk in {ids}', output_fields=["*"]) for data in data_list: text = data.pop("text") @@ -50,8 +47,7 @@ def vs_type(self) -> str: def _load_zilliz(self): zilliz_args = kbs_config.get("zilliz") self.zilliz = Zilliz(embedding_function=EmbeddingsFunAdapter(self.embed_model), - collection_name=self.kb_name, connection_args=zilliz_args) - + collection_name=self.kb_name, connection_args=zilliz_args) def do_init(self): self._load_zilliz() @@ -95,9 +91,7 @@ def do_clear_vs(self): if __name__ == '__main__': - from server.db.base import Base, engine Base.metadata.create_all(bind=engine) zillizService = ZillizKBService("test") - diff --git a/server/knowledge_base/kb_summary/base.py b/server/knowledge_base/kb_summary/base.py index 00dcea6f9..6d095fee7 100644 --- a/server/knowledge_base/kb_summary/base.py +++ b/server/knowledge_base/kb_summary/base.py @@ -13,7 +13,6 @@ 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 diff --git a/server/knowledge_base/kb_summary/summary_chunk.py b/server/knowledge_base/kb_summary/summary_chunk.py index 0b88f2337..7c2aaf47a 100644 --- a/server/knowledge_base/kb_summary/summary_chunk.py +++ b/server/knowledge_base/kb_summary/summary_chunk.py @@ -112,12 +112,6 @@ async def asummarize(self, 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 diff --git a/server/knowledge_base/utils.py b/server/knowledge_base/utils.py index 85d0f0c59..3a8c701b0 100644 --- a/server/knowledge_base/utils.py +++ b/server/knowledge_base/utils.py @@ -91,9 +91,14 @@ def process_entry(entry): "JSONLoader": [".json"], "JSONLinesLoader": [".jsonl"], "CSVLoader": [".csv"], - # "FilteredCSVLoader": [".csv"], # 需要自己指定，目前还没有支持 + # "FilteredCSVLoader": [".csv"], 如果使用自定义分割csv "RapidOCRPDFLoader": [".pdf"], + "RapidOCRDocLoader": ['.docx', '.doc'], + "RapidOCRPPTLoader": ['.ppt', '.pptx', ], "RapidOCRLoader": ['.png', '.jpg', '.jpeg', '.bmp'], + "UnstructuredFileLoader": ['.eml', '.msg', '.rst', + '.rtf', '.txt', '.xml', + '.epub', '.odt','.tsv'], "UnstructuredEmailLoader": ['.eml', '.msg'], "UnstructuredEPubLoader": ['.epub'], "UnstructuredExcelLoader": ['.xlsx', '.xls', '.xlsd'], @@ -109,7 +114,6 @@ def process_entry(entry): "UnstructuredXMLLoader": ['.xml'], "UnstructuredPowerPointLoader": ['.ppt', '.pptx'], "EverNoteLoader": ['.enex'], - "UnstructuredFileLoader": ['.txt'], } SUPPORTED_EXTS = [ext for sublist in LOADER_DICT.values() for ext in sublist] @@ -141,15 +145,14 @@ def get_LoaderClass(file_extension): if file_extension in extensions: return LoaderClass - -# 把一些向量化共用逻辑从KnowledgeFile抽取出来，等langchain支持内存文件的时候，可以将非磁盘文件向量化 def get_loader(loader_name: str, file_path: str, loader_kwargs: Dict = None): ''' 根据loader_name和文件路径或内容返回文档加载器。 ''' loader_kwargs = loader_kwargs or {} try: - if loader_name in ["RapidOCRPDFLoader", "RapidOCRLoader","FilteredCSVLoader"]: + if loader_name in ["RapidOCRPDFLoader", "RapidOCRLoader", "FilteredCSVLoader", + "RapidOCRDocLoader", "RapidOCRPPTLoader"]: document_loaders_module = importlib.import_module('document_loaders') else: document_loaders_module = importlib.import_module('langchain.document_loaders') @@ -171,7 +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", ".") @@ -259,6 +261,10 @@ def make_text_splitter( text_splitter_module = importlib.import_module('langchain.text_splitter') TextSplitter = getattr(text_splitter_module, "RecursiveCharacterTextSplitter") text_splitter = TextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) + + # If you use SpacyTextSplitter you can use GPU to do split likes Issue #1287 + # text_splitter._tokenizer.max_length = 37016792 + # text_splitter._tokenizer.prefer_gpu() return text_splitter diff --git a/server/minx_chat_openai.py b/server/minx_chat_openai.py new file mode 100644 index 000000000..b5362bba0 --- /dev/null +++ b/server/minx_chat_openai.py @@ -0,0 +1,51 @@ +from typing import ( + TYPE_CHECKING, + Any, + Tuple +) +import sys +import logging + +logger = logging.getLogger(__name__) + +if TYPE_CHECKING: + import tiktoken + + +class MinxChatOpenAI: + + @staticmethod + def import_tiktoken() -> Any: + try: + import tiktoken + except ImportError: + raise ValueError( + "Could not import tiktoken python package. " + "This is needed in order to calculate get_token_ids. " + "Please install it with `pip install tiktoken`." + ) + return tiktoken + + @staticmethod + def get_encoding_model(self) -> Tuple[str, "tiktoken.Encoding"]: + tiktoken_ = MinxChatOpenAI.import_tiktoken() + if self.tiktoken_model_name is not None: + model = self.tiktoken_model_name + else: + model = self.model_name + if model == "gpt-3.5-turbo": + # gpt-3.5-turbo may change over time. + # Returning num tokens assuming gpt-3.5-turbo-0301. + model = "gpt-3.5-turbo-0301" + elif model == "gpt-4": + # gpt-4 may change over time. + # Returning num tokens assuming gpt-4-0314. + model = "gpt-4-0314" + # Returns the number of tokens used by a list of messages. + try: + encoding = tiktoken_.encoding_for_model(model) + except Exception as e: + logger.warning("Warning: model not found. Using cl100k_base encoding.") + model = "cl100k_base" + encoding = tiktoken_.get_encoding(model) + return model, encoding diff --git a/server/model_workers/__init__.py b/server/model_workers/__init__.py index 67c9aa63b..d0320f418 100644 --- a/server/model_workers/__init__.py +++ b/server/model_workers/__init__.py @@ -8,3 +8,4 @@ from .baichuan import BaiChuanWorker from .azure import AzureWorker from .tiangong import TianGongWorker +from .gemini import GeminiWorker \ No newline at end of file diff --git a/server/model_workers/azure.py b/server/model_workers/azure.py index 70959325f..f0835ae15 100644 --- a/server/model_workers/azure.py +++ b/server/model_workers/azure.py @@ -67,12 +67,10 @@ def do_chat(self, params: ApiChatParams) -> Dict: 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.", diff --git a/server/model_workers/baichuan.py b/server/model_workers/baichuan.py index 5e9cbbb03..75cfad4ec 100644 --- a/server/model_workers/baichuan.py +++ b/server/model_workers/baichuan.py @@ -88,12 +88,10 @@ def do_chat(self, params: ApiChatParams) -> Dict: 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="", diff --git a/server/model_workers/base.py b/server/model_workers/base.py index 88affb438..234ab47aa 100644 --- a/server/model_workers/base.py +++ b/server/model_workers/base.py @@ -125,8 +125,6 @@ def __init__( def count_token(self, params): - # TODO：需要完善 - # print("count token") prompt = params["prompt"] return {"count": len(str(prompt)), "error_code": 0} diff --git a/server/model_workers/fangzhou.py b/server/model_workers/fangzhou.py index ddbad4abe..fdb50a1c3 100644 --- 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 @@ def do_chat(self, params: ApiChatParams) -> Dict: 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 @@ def do_chat(self, params: ApiChatParams) -> Dict: break def get_embeddings(self, params): - # TODO: 支持embeddings print("embedding") print(params) diff --git a/server/model_workers/gemini.py b/server/model_workers/gemini.py new file mode 100644 index 000000000..e9175b6ee --- /dev/null +++ b/server/model_workers/gemini.py @@ -0,0 +1,123 @@ +import sys +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 +from typing import List, Dict +from configs import logger, log_verbose + + +class GeminiWorker(ApiModelWorker): + def __init__( + self, + *, + controller_addr: str = None, + worker_addr: str = None, + 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: + has_history = any(msg['role'] == 'assistant' for msg in messages) + gemini_msg = [] + + for msg in messages: + role = msg['role'] + content = msg['content'] + if role == 'system': + continue + if has_history: + if role == 'assistant': + role = "model" + transformed_msg = {"role": role, "parts": [{"text": content}]} + else: + if role == 'user': + transformed_msg = {"parts": [{"text": content}]} + + gemini_msg.append(transformed_msg) + + 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( + temperature=params.temperature, + topK=1, + topP=1, + maxOutputTokens=4096, + stopSequences=[] + ) + + data['generationConfig'] = generationConfig + url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent" + '?key=' + params.api_key + headers = { + 'Content-Type': 'application/json', + } + if log_verbose: + logger.info(f'{self.__class__.__name__}:url: {url}') + logger.info(f'{self.__class__.__name__}:headers: {headers}') + logger.info(f'{self.__class__.__name__}:data: {data}') + + text = "" + json_string = "" + timeout = httpx.Timeout(60.0) + 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() + if not line or "[DONE]" in line: + continue + + json_string += line + + try: + resp = json.loads(json_string) + if 'candidates' in resp: + for candidate in resp['candidates']: + content = candidate.get('content', {}) + parts = content.get('parts', []) + for part in parts: + if 'text' in part: + text += part['text'] + yield { + "error_code": 0, + "text": 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): + print("embedding") + print(params) + + def make_conv_template(self, conv_template: str = None, model_path: str = None) -> Conversation: + return conv.Conversation( + name=self.model_names[0], + system_message="You are a helpful, respectful and honest assistant.", + messages=[], + roles=["user", "assistant"], + sep="\n### ", + stop_str="###", + ) + + +if __name__ == "__main__": + import uvicorn + from server.utils import MakeFastAPIOffline + from fastchat.serve.base_model_worker import app + + worker = GeminiWorker( + controller_addr="http://127.0.0.1:20001", + worker_addr="http://127.0.0.1:21012", + ) + sys.modules["fastchat.serve.model_worker"].worker = worker + MakeFastAPIOffline(app) + uvicorn.run(app, port=21012) diff --git a/server/model_workers/minimax.py b/server/model_workers/minimax.py index deae07ea0..79d24514f 100644 --- a/server/model_workers/minimax.py +++ b/server/model_workers/minimax.py @@ -28,7 +28,7 @@ def __init__( def validate_messages(self, messages: List[Dict]) -> List[Dict]: role_maps = { - "user": self.user_role, + "USER": self.user_role, "assistant": self.ai_role, "system": "system", } @@ -37,7 +37,6 @@ def validate_messages(self, messages: List[Dict]) -> List[Dict]: 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 @@ def do_chat(self, params: ApiChatParams) -> Dict: "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, @@ -140,15 +139,13 @@ def do_embeddings(self, params: ApiEmbeddingsParams) -> Dict: self.logger.error(f"请求 MiniMax API 时发生错误：{data}") return data i += batch_size - return {"code": 200, "data": embeddings} + 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。", diff --git a/server/model_workers/qianfan.py b/server/model_workers/qianfan.py index 2bcce94e1..da362ec6d 100644 --- a/server/model_workers/qianfan.py +++ b/server/model_workers/qianfan.py @@ -84,30 +84,6 @@ def __init__( def do_chat(self, params: ApiChatParams) -> Dict: params.load_config(self.model_names[0]) - # import qianfan - - # comp = qianfan.ChatCompletion(model=params.version, - # endpoint=params.version_url, - # ak=params.api_key, - # sk=params.secret_key,) - # text = "" - # for resp in comp.do(messages=params.messages, - # temperature=params.temperature, - # top_p=params.top_p, - # stream=True): - # if resp.code == 200: - # if chunk := resp.body.get("result"): - # text += chunk - # yield { - # "error_code": 0, - # "text": text - # } - # else: - # yield { - # "error_code": resp.code, - # "text": str(resp.body), - # } - BASE_URL = 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat' \ '/{model_version}?access_token={access_token}' @@ -190,19 +166,19 @@ def do_embeddings(self, params: ApiEmbeddingsParams) -> Dict: i = 0 batch_size = 10 while i < len(params.texts): - texts = params.texts[i:i+batch_size] + texts = params.texts[i:i + batch_size] resp = client.post(url, json={"input": texts}).json() if "error_code" in resp: data = { - "code": resp["error_code"], - "msg": resp["error_msg"], - "error": { - "message": resp["error_msg"], - "type": "invalid_request_error", - "param": None, - "code": None, - } - } + "code": resp["error_code"], + "msg": resp["error_msg"], + "error": { + "message": resp["error_msg"], + "type": "invalid_request_error", + "param": None, + "code": None, + } + } self.logger.error(f"请求千帆 API 时发生错误：{data}") return data else: @@ -211,14 +187,11 @@ def do_embeddings(self, params: ApiEmbeddingsParams) -> Dict: 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", diff --git a/server/model_workers/qwen.py b/server/model_workers/qwen.py index 58d1bcd19..2741b74d6 100644 --- a/server/model_workers/qwen.py +++ b/server/model_workers/qwen.py @@ -100,12 +100,10 @@ def do_embeddings(self, params: ApiEmbeddingsParams) -> Dict: 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。", diff --git a/server/model_workers/tiangong.py b/server/model_workers/tiangong.py index 85a763fe4..88010a154 100644 --- a/server/model_workers/tiangong.py +++ b/server/model_workers/tiangong.py @@ -11,16 +11,15 @@ import requests - class TianGongWorker(ApiModelWorker): def __init__( - self, - *, - controller_addr: str = None, - worker_addr: str = None, - model_names: List[str] = ["tiangong-api"], - version: Literal["SkyChat-MegaVerse"] = "SkyChat-MegaVerse", - **kwargs, + self, + *, + controller_addr: str = None, + worker_addr: str = None, + model_names: List[str] = ["tiangong-api"], + version: Literal["SkyChat-MegaVerse"] = "SkyChat-MegaVerse", + **kwargs, ): kwargs.update(model_names=model_names, controller_addr=controller_addr, worker_addr=worker_addr) kwargs.setdefault("context_len", 32768) @@ -34,18 +33,18 @@ def do_chat(self, params: ApiChatParams) -> Dict: data = { "messages": params.messages, "model": "SkyChat-MegaVerse" - } - timestamp = str(int(time.time())) - sign_content = params.api_key + params.secret_key + timestamp - sign_result = hashlib.md5(sign_content.encode('utf-8')).hexdigest() - headers={ + } + timestamp = str(int(time.time())) + sign_content = params.api_key + params.secret_key + timestamp + sign_result = hashlib.md5(sign_content.encode('utf-8')).hexdigest() + headers = { "app_key": params.api_key, "timestamp": timestamp, "sign": sign_result, "Content-Type": "application/json", - "stream": "true" # or change to "false" 不处理流式返回内容 + "stream": "true" # or change to "false" 不处理流式返回内容 } - + # 发起请求并获取响应 response = requests.post(url, headers=headers, json=data, stream=True) @@ -56,27 +55,25 @@ def do_chat(self, params: ApiChatParams) -> Dict: # 处理接收到的数据 # print(line.decode('utf-8')) resp = json.loads(line) - if resp["code"] == 200: + if resp["code"] == 200: text += resp['resp_data']['reply'] yield { "error_code": 0, "text": text - } + } else: data = { "error_code": resp["code"], "text": resp["code_msg"] - } + } self.logger.error(f"请求天工 API 时出错：{data}") 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="", @@ -85,5 +82,3 @@ def make_conv_template(self, conv_template: str = None, model_path: str = None) sep="\n### ", stop_str="###", ) - - diff --git a/server/model_workers/xinghuo.py b/server/model_workers/xinghuo.py index 72db73897..de38308b5 100644 --- a/server/model_workers/xinghuo.py +++ b/server/model_workers/xinghuo.py @@ -37,12 +37,11 @@ def __init__( **kwargs, ): kwargs.update(model_names=model_names, controller_addr=controller_addr, worker_addr=worker_addr) - kwargs.setdefault("context_len", 8000) # TODO: V1模型的最大长度为4000，需要自行修改 + kwargs.setdefault("context_len", 8000) super().__init__(**kwargs) 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 @@ def get_version_details(version_key): 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", diff --git a/server/model_workers/zhipu.py b/server/model_workers/zhipu.py index 0005c7d3c..0ed3bdcdb 100644 --- a/server/model_workers/zhipu.py +++ b/server/model_workers/zhipu.py @@ -4,93 +4,89 @@ import sys from typing import List, Dict, Iterator, Literal from configs import logger, log_verbose +import requests +import jwt +import time +import json + + +def generate_token(apikey: str, exp_seconds: int): + try: + id, secret = apikey.split(".") + except Exception as e: + raise Exception("invalid apikey", e) + + payload = { + "api_key": id, + "exp": int(round(time.time() * 1000)) + exp_seconds * 1000, + "timestamp": int(round(time.time() * 1000)), + } + + return jwt.encode( + payload, + secret, + algorithm="HS256", + headers={"alg": "HS256", "sign_type": "SIGN"}, + ) class ChatGLMWorker(ApiModelWorker): - DEFAULT_EMBED_MODEL = "text_embedding" - def __init__( - self, - *, - model_names: List[str] = ["zhipu-api"], - controller_addr: str = None, - worker_addr: str = None, - version: Literal["chatglm_turbo"] = "chatglm_turbo", - **kwargs, + self, + *, + model_names: List[str] = ["zhipu-api"], + controller_addr: str = None, + worker_addr: str = None, + version: Literal["chatglm_turbo"] = "chatglm_turbo", + **kwargs, ): kwargs.update(model_names=model_names, controller_addr=controller_addr, worker_addr=worker_addr) - kwargs.setdefault("context_len", 32768) + kwargs.setdefault("context_len", 4096) super().__init__(**kwargs) self.version = version def do_chat(self, params: ApiChatParams) -> Iterator[Dict]: - # TODO: 维护request_id - import zhipuai - params.load_config(self.model_names[0]) - zhipuai.api_key = params.api_key - - if log_verbose: - logger.info(f'{self.__class__.__name__}:params: {params}') - - response = zhipuai.model_api.sse_invoke( - model=params.version, - prompt=params.messages, - temperature=params.temperature, - top_p=params.top_p, - incremental=False, - ) - for e in response.events(): - if e.event == "add": - yield {"error_code": 0, "text": e.data} - elif e.event in ["error", "interrupted"]: - data = { - "error_code": 500, - "text": e.data, - "error": { - "message": e.data, - "type": "invalid_request_error", - "param": None, - "code": None, - } - } - self.logger.error(f"请求智谱 API 时发生错误：{data}") - yield data - - def do_embeddings(self, params: ApiEmbeddingsParams) -> Dict: - import zhipuai - - params.load_config(self.model_names[0]) - zhipuai.api_key = params.api_key - - embeddings = [] - try: - for t in params.texts: - response = zhipuai.model_api.invoke(model=params.embed_model or self.DEFAULT_EMBED_MODEL, prompt=t) - if response["code"] == 200: - embeddings.append(response["data"]["embedding"]) - else: - self.logger.error(f"请求智谱 API 时发生错误：{response}") - return response # dict with code & msg - except Exception as e: - self.logger.error(f"请求智谱 API 时发生错误：{data}") - data = {"code": 500, "msg": f"对文本向量化时出错：{e}"} - return data - - return {"code": 200, "data": embeddings} + token = generate_token(params.api_key, 60) + headers = { + "Content-Type": "application/json", + "Authorization": f"Bearer {token}" + } + data = { + "model": params.version, + "messages": params.messages, + "max_tokens": params.max_tokens, + "temperature": params.temperature, + "stream": False + } + url = "https://open.bigmodel.cn/api/paas/v4/chat/completions" + response = requests.post(url, headers=headers, json=data) + # for chunk in response.iter_lines(): + # if chunk: + # chunk_str = chunk.decode('utf-8') + # json_start_pos = chunk_str.find('{"id"') + # if json_start_pos != -1: + # json_str = chunk_str[json_start_pos:] + # json_data = json.loads(json_str) + # for choice in json_data.get('choices', []): + # delta = choice.get('delta', {}) + # content = delta.get('content', '') + # yield {"error_code": 0, "text": content} + ans = response.json() + content = ans["choices"][0]["message"]["content"] + yield {"error_code": 0, "text": content} def get_embeddings(self, params): - # TODO: 支持embeddings + # 临时解决方案，不支持embedding print("embedding") - # print(params) + print(params) def make_conv_template(self, conv_template: str = None, model_path: str = None) -> Conversation: - # 这里的是chatglm api的模板，其它API的conv_template需要定制 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", + system_message="你是智谱AI小助手，请根据用户的提示来完成任务", messages=[], - roles=["Human", "Assistant", "System"], + roles=["user", "assistant", "system"], sep="\n###", stop_str="###", ) diff --git a/server/static/swagger-ui-bundle.js b/server/static/swagger-ui-bundle.js index 3eacadc71..aae2a1b39 100644 --- a/server/static/swagger-ui-bundle.js +++ b/server/static/swagger-ui-bundle.js @@ -5,5 +5,5 @@ * Do NOT use SRI with dynamically generated files! 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Ub}}}),Rw=Xs(Tw,{props:{timeout:5e3,redirects:5,withCredentials:!1},init({timeout:e=this.timeout,redirects:t=this.redirects,withCredentials:n=this.withCredentials}={}){this.timeout=e,this.redirects=t,this.withCredentials=n},methods:{canRead:e=>gb(e.uri),async read(){throw new Ub},getHttpClient(){throw new Ub}}}).compose({props:{name:"http-swagger-client",swaggerHTTPClient:ct,swaggerHTTPClientConfig:{}},init(){let{swaggerHTTPClient:e=this.swaggerHTTPClient}=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};this.swaggerHTTPClient=e},methods:{getHttpClient(){return this.swaggerHTTPClient},async read(e){const t=this.getHttpClient(),n=new AbortController,{signal:r}=n,o=setTimeout((()=>{n.abort()}),this.timeout),s=this.getHttpClient().withCredentials||this.withCredentials?"include":"same-origin",i=0===this.redirects?"error":"follow",a=this.redirects>0?this.redirects:void 0;try{return(await t(f()({url:e.uri,signal:r,userFetch:async(e,t)=>{let n=await 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d=Qw({reference:s,namespace:this.namespace,indirections:[...this.indirections],options:this.options,useCircularStructures:this.useCircularStructures,allowMetaPatches:this.allowMetaPatches,ancestors:t,basePath:this.basePath??[...Hw([...o,n,e]),"$ref"]});if(f=await Xw(f,d,{keyMap:Jv,nodeTypeGetter:Wv}),r.delete(e),this.indirections.pop(),iy(f)){const t=f.clone();return t.setMetaProperty("ref-fields",{$ref:e.$ref?.toValue()}),t.setMetaProperty("ref-origin",i),t}const m=new Ym([...f.content],f.meta.clone(),f.attributes.clone());if(e.forEach(((e,t,n)=>{m.remove(t.toValue()),m.content.push(n)})),m.remove("$ref"),m.setMetaProperty("ref-fields",{$ref:e.$ref?.toValue()}),m.setMetaProperty("ref-origin",i),this.allowMetaPatches&&void 0===m.get("$$ref")){const e=Eb(i,a);m.set("$$ref",e)}return m}catch(t){const r=Gw(t),s=new Zw(`Could not resolve reference: ${r.message}`,{baseDoc:this.reference.uri,$ref:e.$ref.toValue(),fullPath:this.basePath??[...Hw([...o,n,e]),"$ref"]},r);return void this.options.dereference.dereferenceOpts?.errors?.push?.(s)}},async LinkElement(){},async ExampleElement(e,t,n,r,o){try{return await Vw.compose.methods.ExampleElement.call(this,e,t,n,r,o)}catch(t){const r=Gw(t),s=Yw(r,{baseDoc:this.reference.uri,externalValue:e.externalValue?.toValue(),fullPath:this.basePath??[...Hw([...o,n,e]),"externalValue"]});return void this.options.dereference.dereferenceOpts?.errors?.push?.(s)}}}}),eE=Qw,tE=Kw.compose.bind(),nE=tE({init(e){let{parameterMacro:t,options:n}=e;this.parameterMacro=t,this.options=n},props:{parameterMacro:null,options:null,macroOperation:null,OperationElement:{enter(e){this.macroOperation=e},leave(){this.macroOperation=null}},ParameterElement:{leave(e,t,n,r,o){const s=null===this.macroOperation?null:Ri(this.macroOperation),i=Ri(e);try{const t=this.parameterMacro(s,i);e.set("default",t)}catch(e){const t=new 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