支持昆仑万维天工大模型 (#2166)
--------- Co-authored-by: Eden <chuangqi.huang@ubtrobot.com> Co-authored-by: liunux4odoo <liunux@qq.com>
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README.md
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README.md
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@ -5,7 +5,7 @@
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📃 **LangChain-Chatchat** (原 Langchain-ChatGLM)
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基于 Langchain 与 ChatGLM 等大语言模型的本地知识库问答应用实现。
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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。
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---
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@ -42,21 +42,21 @@
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🚩 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。
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🌐 [AutoDL 镜像](https://www.codewithgpu.com/i/chatchat-space/Langchain-Chatchat/Langchain-Chatchat) 中 `v10` 版本所使用代码已更新至本项目 `v0.2.6` 版本。
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🌐 [AutoDL 镜像](https://www.codewithgpu.com/i/chatchat-space/Langchain-Chatchat/Langchain-Chatchat) 中 `v11` 版本所使用代码已更新至本项目 `v0.2.7` 版本。
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🐳 [Docker 镜像](registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.6) 已经更新到 ```0.2.6``` 版本。
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🐳 [Docker 镜像](registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.6) 已经更新到 ```0.2.7``` 版本。
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🌲 一行命令运行 Docker ：
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```shell
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docker run -d --gpus all -p 80:8501 registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.6
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docker run -d --gpus all -p 80:8501 registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.7
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```
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🧩 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，或者对相对本项目做出共享。请移步 [Wiki](https://github.com/chatchat-space/Langchain-Chatchat/wiki/) 界面
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🧩 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
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## 解决的痛点
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该项目是一个可以实现 __完全本地化__推理的知识库增强方案, 重点解决数数据安全保护，私域化部署的企业痛点。
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该项目是一个可以实现 __完全本地化__推理的知识库增强方案, 重点解决数据安全保护，私域化部署的企业痛点。
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本开源方案采用```Apache License```，可以免费商用，无需付费。
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我们支持市面上主流的本地大预言模型和Embedding模型，支持开源的本地向量数据库。
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@ -67,7 +67,7 @@ docker run -d --gpus all -p 80:8501 registry.cn-beijing.aliyuncs.com/chatchat/ch
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### 1. 环境配置
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+ 首先，确保你的机器安装了 Python 3.10
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+ 首先，确保你的机器安装了 Python 3.8 - 3.10
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```
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$ python --version
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Python 3.10.12
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@ -148,11 +148,12 @@ $ python startup.py -a
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[](https://t.me/+RjliQ3jnJ1YyN2E9)
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### 项目交流群
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<img src="img/qr_code_71.jpg" alt="二维码" width="300" />
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<img src="img/qr_code_74.jpg" alt="二维码" width="300" />
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🎉 Langchain-Chatchat 项目微信交流群，如果你也对本项目感兴趣，欢迎加入群聊参与讨论交流。
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### 公众号
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🎉 Langchain-Chatchat 项目官方公众号，欢迎扫码关注。
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<u>[Langchain-Chatchat](https://github.com/chatchat-space/Langchain-Chatchat)</u>: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
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### 公众号
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<img src="img/official_wechat_mp_account.png" alt="二维码" width="300" />
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🎉 Langchain-Chatchat 项目官方公众号，欢迎扫码关注。
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@ -40,7 +40,7 @@ ONLINE_LLM_MODEL = {
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# 线上模型。请在server_config中为每个在线API设置不同的端口
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"openai-api": {
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"model_name": "gpt-35-turbo",
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"model_name": "gpt-3.5-turbo",
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"api_base_url": "https://api.openai.com/v1",
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"api_key": "",
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"openai_proxy": "",
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@ -114,6 +114,14 @@ ONLINE_LLM_MODEL = {
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"provider": "AzureWorker",
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},
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# 昆仑万维天工 API https://model-platform.tiangong.cn/
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"tiangong-api": {
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"version":"SkyChat-MegaVerse",
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"api_key": "",
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"secret_key": "",
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"provider": "TianGongWorker",
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},
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}
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# 在以下字典中修改属性值，以指定本地embedding模型存储位置。支持3种设置方法：
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@ -120,6 +120,9 @@ FSCHAT_MODEL_WORKERS = {
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"azure-api": {
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"port": 21008,
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},
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"tiangong-api": {
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"port": 21009,
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},
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}
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# fastchat multi model worker server
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@ -7,7 +7,7 @@ xformers>=0.0.22.post4
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openai>=0.28.1
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sentence_transformers
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transformers>=4.34
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torch>=2.0.1 # 推荐2.1
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torch>=2.0.1 # suggest version 2.1
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torchvision
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torchaudio
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fastapi>=0.104
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@ -58,5 +58,5 @@ streamlit-option-menu>=0.3.6
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streamlit-antd-components>=0.1.11
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streamlit-chatbox>=1.1.11
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streamlit-aggrid>=0.3.4.post3
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httpx[brotli,http2,socks]>=0.25.0
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httpx~=0.24.0
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watchdog
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@ -7,7 +7,7 @@ xformers>=0.0.22.post4
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openai>=0.28.1
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sentence_transformers
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transformers>=4.34
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torch>=2.0.1 # 推荐2.1
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torch>=2.0.1 # suggest version 2.1
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torchvision
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torchaudio
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fastapi>=0.104
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@ -7,3 +7,4 @@ from .fangzhou import FangZhouWorker
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from .qwen import QwenWorker
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from .baichuan import BaiChuanWorker
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from .azure import AzureWorker
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from .tiangong import TianGongWorker
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import json
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import time
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import hashlib
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from fastchat.conversation import Conversation
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from server.model_workers.base import *
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from server.utils import get_httpx_client
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from fastchat import conversation as conv
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import sys
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import json
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from typing import List, Literal, Dict
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import requests
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class TianGongWorker(ApiModelWorker):
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def __init__(
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self,
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*,
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controller_addr: str = None,
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worker_addr: str = None,
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model_names: List[str] = ["tiangong-api"],
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version: Literal["SkyChat-MegaVerse"] = "SkyChat-MegaVerse",
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**kwargs,
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):
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kwargs.update(model_names=model_names, controller_addr=controller_addr, worker_addr=worker_addr)
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kwargs.setdefault("context_len", 32768)
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super().__init__(**kwargs)
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self.version = version
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def do_chat(self, params: ApiChatParams) -> Dict:
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params.load_config(self.model_names[0])
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url = 'https://sky-api.singularity-ai.com/saas/api/v4/generate'
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data = {
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"messages": params.messages,
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"model": "SkyChat-MegaVerse"
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}
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timestamp = str(int(time.time()))
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sign_content = params.api_key + params.secret_key + timestamp
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sign_result = hashlib.md5(sign_content.encode('utf-8')).hexdigest()
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headers={
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"app_key": params.api_key,
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"timestamp": timestamp,
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"sign": sign_result,
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"Content-Type": "application/json",
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"stream": "true" # or change to "false" 不处理流式返回内容
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}
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# 发起请求并获取响应
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response = requests.post(url, headers=headers, json=data, stream=True)
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text = ""
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# 处理响应流
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for line in response.iter_lines(chunk_size=None, decode_unicode=True):
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if line:
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# 处理接收到的数据
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# print(line.decode('utf-8'))
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resp = json.loads(line)
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if resp["code"] == 200:
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text += resp['resp_data']['reply']
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yield {
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"error_code": 0,
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"text": text
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}
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else:
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yield {
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"error_code": resp["code"],
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"text": resp["code_msg"]
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}
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def get_embeddings(self, params):
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# TODO: 支持embeddings
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print("embedding")
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print(params)
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def make_conv_template(self, conv_template: str = None, model_path: str = None) -> Conversation:
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# TODO: 确认模板是否需要修改
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return conv.Conversation(
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name=self.model_names[0],
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system_message="",
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messages=[],
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roles=["user", "system"],
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sep="\n### ",
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stop_str="###",
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)
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