合并分支，支持 (#1808)
* 北京黑客松更新 知识库支持： 支持zilliz数据库 Agent支持： 支持以下工具调用 1. 支持互联网Agent调用 2. 支持知识库Agent调用 3. 支持旅游助手工具(未上传） 知识库更新 1. 支持知识库简介，用于Agent选择 2. UI对应知识库简介 提示词选择 1. UI 和模板支持提示词模板更换选择 * 数据库更新介绍问题解决 * 关于Langchain自己支持的模型 1. 修复了Openai无法调用的bug 2. 支持了Azure Openai Claude模型 （在模型切换界面由于优先级问题，显示的会是其他联网模型) 3. 422问题被修复，用了另一种替代方案。 4. 更新了部分依赖
This commit is contained in:
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parent
83e25f8011
commit
e920cd0064
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@ -1,6 +1,5 @@
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import os
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# 默认使用的知识库
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DEFAULT_KNOWLEDGE_BASE = "samples"
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@ -57,14 +56,11 @@ KB_INFO = {
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"知识库名称": "知识库介绍",
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"samples": "关于本项目issue的解答",
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}
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# 通常情况下不需要更改以下内容
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# 知识库默认存储路径
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KB_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "knowledge_base")
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if not os.path.exists(KB_ROOT_PATH):
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os.mkdir(KB_ROOT_PATH)
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# 数据库默认存储路径。
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# 如果使用sqlite，可以直接修改DB_ROOT_PATH；如果使用其它数据库，请直接修改SQLALCHEMY_DATABASE_URI。
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DB_ROOT_PATH = os.path.join(KB_ROOT_PATH, "info.db")
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@ -112,7 +112,8 @@ TEMPERATURE = 0.7
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# TOP_P = 0.95 # ChatOpenAI暂不支持该参数
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ONLINE_LLM_MODEL = {
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LANGCHAIN_LLM_MODEL = {
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# 不需要走Fschat封装的，Langchain直接支持的模型。
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# 调用chatgpt时如果报出： urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='api.openai.com', port=443):
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# Max retries exceeded with url: /v1/chat/completions
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# 则需要将urllib3版本修改为1.25.11
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@ -128,11 +129,29 @@ ONLINE_LLM_MODEL = {
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# 4.0 seconds as it raised APIConnectionError: Error communicating with OpenAI.
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# 需要添加代理访问(正常开的代理软件可能会拦截不上)需要设置配置openai_proxy 或者 使用环境遍历OPENAI_PROXY 进行设置
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# 比如: "openai_proxy": 'http://127.0.0.1:4780'
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"gpt-3.5-turbo": {
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# 这些配置文件的名字不能改动
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"Azure-OpenAI": {
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"deployment_name": "your Azure deployment name",
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"model_version": "0701",
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"openai_api_type": "azure",
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"api_base_url": "https://your Azure point.azure.com",
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"api_version": "2023-07-01-preview",
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"api_key": "your Azure api key",
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"openai_proxy": "",
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},
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"OpenAI": {
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"model_name": "your openai model name(such as gpt-4)",
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"api_base_url": "https://api.openai.com/v1",
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"api_key": "your OPENAI_API_KEY",
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"openai_proxy": "your OPENAI_PROXY",
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"openai_proxy": "",
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},
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"Anthropic": {
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"model_name": "your claude model name(such as claude2-100k)",
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"api_key":"your ANTHROPIC_API_KEY",
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}
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}
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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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# 具体注册及api key获取请前往 http://open.bigmodel.cn
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"zhipu-api": {
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@ -1,3 +1,5 @@
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import sys
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sys.path.append(".")
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from server.knowledge_base.migrate import create_tables, reset_tables, folder2db, prune_db_docs, prune_folder_files
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from configs.model_config import NLTK_DATA_PATH
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import nltk
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@ -1,14 +1,15 @@
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langchain==0.0.317
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langchain-experimental==0.0.30
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langchain>=0.0.319
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langchain-experimental>=0.0.30
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fschat[model_worker]==0.2.31
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openai
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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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torchvision
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torchaudio
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fastapi>=0.103.2
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nltk~=3.8.1
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fastapi>=0.104
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nltk>=3.8.1
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uvicorn~=0.23.1
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starlette~=0.27.0
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pydantic~=1.10.11
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@ -1,13 +1,14 @@
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langchain==0.0.317
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langchain-experimental==0.0.30
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langchain>=0.0.319
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langchain-experimental>=0.0.30
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fschat[model_worker]==0.2.31
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openai
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xformers>=0.0.22.post4
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openai>=0.28.1
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sentence_transformers>=2.2.2
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transformers>=4.34
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torch>=2.0.1
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torch>=2.1
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torchvision
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torchaudio
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fastapi>=0.103.1
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fastapi>=0.104
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nltk~=3.8.1
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uvicorn~=0.23.1
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starlette~=0.27.0
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@ -1,11 +1,11 @@
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numpy~=1.24.4
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pandas~=2.0.3
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streamlit>=1.26.0
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streamlit>=1.27.2
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streamlit-option-menu>=0.3.6
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streamlit-antd-components>=0.1.11
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streamlit-antd-components>=0.2.3
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streamlit-chatbox==1.1.10
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streamlit-aggrid>=0.3.4.post3
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httpx~=0.24.1
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nltk
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httpx>=0.25.0
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nltk>=3.8.1
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watchdog
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websockets
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@ -97,7 +97,6 @@ class CustomAsyncIteratorCallbackHandler(AsyncIteratorCallbackHandler):
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llm_token="",
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)
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self.queue.put_nowait(dumps(self.cur_tool))
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async def on_chat_model_start(
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self,
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serialized: Dict[str, Any],
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@ -4,7 +4,6 @@ from langchain.prompts import StringPromptTemplate
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from typing import List
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from langchain.schema import AgentAction, AgentFinish
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from server.agent import model_container
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begin = False
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class CustomPromptTemplate(StringPromptTemplate):
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# The template to use
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template: str
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def parse(self, llm_output: str) -> AgentFinish | tuple[dict[str, str], str] | AgentAction:
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# Check if agent should finish
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support_agent = ["gpt","Qwen","qwen-api","baichuan-api"]
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support_agent = ["Azure-OpenAI", "OpenAI", "Anthropic", "Qwen", "qwen-api", "baichuan-api"] # 目前支持agent的模型
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if not any(agent in model_container.MODEL for agent in support_agent) and self.begin:
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self.begin = False
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stop_words = ["Observation:"]
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@ -2,7 +2,6 @@
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from .search_knowledge_simple import knowledge_search_simple
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from .search_all_knowledge_once import knowledge_search_once
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from .search_all_knowledge_more import knowledge_search_more
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# from .travel_assistant import travel_assistant
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from .calculate import calculate
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from .translator import translate
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from .weather import weathercheck
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@ -1,7 +1,5 @@
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import os
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from transformers import AutoTokenizer
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from configs import (
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EMBEDDING_MODEL,
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KB_ROOT_PATH,
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@ -1,5 +1,5 @@
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from fastapi import Body
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from configs import logger, log_verbose, LLM_MODEL, HTTPX_DEFAULT_TIMEOUT
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from configs import logger, log_verbose, LLM_MODEL, HTTPX_DEFAULT_TIMEOUT,LANGCHAIN_LLM_MODEL
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from server.utils import (BaseResponse, fschat_controller_address, list_config_llm_models,
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get_httpx_client, get_model_worker_config)
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@ -52,7 +52,6 @@ def get_model_config(
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获取LLM模型配置项（合并后的）
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'''
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config = get_model_worker_config(model_name=model_name)
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# 删除ONLINE_MODEL配置中的敏感信息
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del_keys = set(["worker_class"])
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for k in config:
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@ -5,12 +5,11 @@ from fastapi import FastAPI
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from pathlib import Path
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import asyncio
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from configs import (LLM_MODEL, LLM_DEVICE, EMBEDDING_DEVICE,
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MODEL_PATH, MODEL_ROOT_PATH, ONLINE_LLM_MODEL,
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logger, log_verbose,
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MODEL_PATH, MODEL_ROOT_PATH, ONLINE_LLM_MODEL, LANGCHAIN_LLM_MODEL, logger, log_verbose,
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FSCHAT_MODEL_WORKERS, HTTPX_DEFAULT_TIMEOUT)
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import os
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from langchain.chat_models import ChatOpenAI
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from langchain.chat_models import ChatOpenAI, AzureChatOpenAI, ChatAnthropic
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import httpx
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from typing import Literal, Optional, Callable, Generator, Dict, Any, Awaitable, Union
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verbose: bool = True,
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**kwargs: Any,
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) -> ChatOpenAI:
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config = get_model_worker_config(model_name)
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model = ChatOpenAI(
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streaming=streaming,
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verbose=verbose,
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callbacks=callbacks,
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openai_api_key=config.get("api_key", "EMPTY"),
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openai_api_base=config.get("api_base_url", fschat_openai_api_address()),
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model_name=model_name,
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temperature=temperature,
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max_tokens=max_tokens,
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openai_proxy=config.get("openai_proxy"),
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**kwargs
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)
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## 以下模型是Langchain原生支持的模型，这些模型不会走Fschat封装
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config_models = list_config_llm_models()
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if model_name in config_models.get("langchain", {}):
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config = config_models["langchain"][model_name]
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if model_name == "Azure-OpenAI":
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model = AzureChatOpenAI(
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streaming=streaming,
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verbose=verbose,
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callbacks=callbacks,
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deployment_name=config.get("deployment_name"),
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model_version=config.get("model_version"),
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openai_api_type=config.get("openai_api_type"),
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openai_api_base=config.get("api_base_url"),
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openai_api_version=config.get("api_version"),
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openai_api_key=config.get("api_key"),
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openai_proxy=config.get("openai_proxy"),
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temperature=temperature,
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max_tokens=max_tokens,
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)
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elif model_name == "OpenAI":
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model = ChatOpenAI(
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streaming=streaming,
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verbose=verbose,
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callbacks=callbacks,
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model_name=config.get("model_name"),
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openai_api_base=config.get("api_base_url"),
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openai_api_key=config.get("api_key"),
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openai_proxy=config.get("openai_proxy"),
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temperature=temperature,
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max_tokens=max_tokens,
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)
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elif model_name == "Anthropic":
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model = ChatAnthropic(
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streaming=streaming,
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verbose=verbose,
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callbacks=callbacks,
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model_name=config.get("model_name"),
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anthropic_api_key=config.get("api_key"),
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)
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## TODO 支持其他的Langchain原生支持的模型
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else:
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## 非Langchain原生支持的模型，走Fschat封装
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config = get_model_worker_config(model_name)
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model = ChatOpenAI(
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streaming=streaming,
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verbose=verbose,
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callbacks=callbacks,
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openai_api_key=config.get("api_key", "EMPTY"),
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openai_api_base=config.get("api_base_url", fschat_openai_api_address()),
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model_name=model_name,
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temperature=temperature,
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max_tokens=max_tokens,
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openai_proxy=config.get("openai_proxy"),
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**kwargs
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)
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return model
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@ -249,8 +293,9 @@ def MakeFastAPIOffline(
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redoc_favicon_url=favicon,
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)
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# 从model_config中获取模型信息
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# 从model_config中获取模型信息
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def list_embed_models() -> List[str]:
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'''
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get names of configured embedding models
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@ -266,9 +311,9 @@ def list_config_llm_models() -> Dict[str, Dict]:
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workers = list(FSCHAT_MODEL_WORKERS)
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if LLM_MODEL not in workers:
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workers.insert(0, LLM_MODEL)
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return {
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"local": MODEL_PATH["llm_model"],
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"langchain": LANGCHAIN_LLM_MODEL,
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"online": ONLINE_LLM_MODEL,
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"worker": workers,
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}
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return str(path)
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return path_str # THUDM/chatglm06b
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# 从server_config中获取服务信息
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# 从server_config中获取服务信息
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def get_model_worker_config(model_name: str = None) -> dict:
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'''
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加载model worker的配置项。
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config.update(FSCHAT_MODEL_WORKERS.get(model_name, {}))
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# 在线模型API
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if model_name in LANGCHAIN_LLM_MODEL:
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config["langchain_model"] = True
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config["worker_class"] = ""
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if model_name in ONLINE_LLM_MODEL:
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config["online_api"] = True
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if provider := config.get("provider"):
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@ -389,7 +439,7 @@ def webui_address() -> str:
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return f"http://{host}:{port}"
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def get_prompt_template(type:str,name: str) -> Optional[str]:
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def get_prompt_template(type: str, name: str) -> Optional[str]:
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'''
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从prompt_config中加载模板内容
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type: "llm_chat","agent_chat","knowledge_base_chat","search_engine_chat"的其中一种，如果有新功能，应该进行加入。
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@ -459,8 +509,9 @@ def set_httpx_config(
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import urllib.request
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urllib.request.getproxies = _get_proxies
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# 自动检查torch可用的设备。分布式部署时，不运行LLM的机器上可以不装torch
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# 自动检查torch可用的设备。分布式部署时，不运行LLM的机器上可以不装torch
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def detect_device() -> Literal["cuda", "mps", "cpu"]:
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try:
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import torch
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14
startup.py
14
startup.py
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@ -68,7 +68,9 @@ def create_model_worker_app(log_level: str = "INFO", **kwargs) -> FastAPI:
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controller_address:
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worker_address:
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对于Langchain支持的模型：
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langchain_model:True
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不会使用fschat
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对于online_api:
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online_api:True
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worker_class: `provider`
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@ -85,9 +87,11 @@ def create_model_worker_app(log_level: str = "INFO", **kwargs) -> FastAPI:
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for k, v in kwargs.items():
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setattr(args, k, v)
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if worker_class := kwargs.get("langchain_model"): #Langchian支持的模型不用做操作
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from fastchat.serve.base_model_worker import app
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worker = ""
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# 在线模型API
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if worker_class := kwargs.get("worker_class"):
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elif worker_class := kwargs.get("worker_class"):
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from fastchat.serve.base_model_worker import app
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worker = worker_class(model_names=args.model_names,
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@ -127,8 +131,8 @@ def create_model_worker_app(log_level: str = "INFO", **kwargs) -> FastAPI:
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args.engine_use_ray = False
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args.disable_log_requests = False
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# 0.2.0 vllm后要加的参数
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args.max_model_len = 8192 # 模型可以处理的最大序列长度。请根据你的大模型设置，
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# 0.2.0 vllm后要加的参数, 但是这里不需要
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args.max_model_len = None
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args.revision = None
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args.quantization = None
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args.max_log_len = None
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@ -4,10 +4,9 @@ from streamlit_chatbox import *
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from datetime import datetime
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import os
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from configs import (LLM_MODEL, TEMPERATURE, HISTORY_LEN, PROMPT_TEMPLATES,
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DEFAULT_KNOWLEDGE_BASE, DEFAULT_SEARCH_ENGINE)
|
||||
DEFAULT_KNOWLEDGE_BASE, DEFAULT_SEARCH_ENGINE,LANGCHAIN_LLM_MODEL)
|
||||
from typing import List, Dict
|
||||
|
||||
|
||||
chat_box = ChatBox(
|
||||
assistant_avatar=os.path.join(
|
||||
"img",
|
||||
|
|
@ -42,7 +41,6 @@ def get_default_llm_model(api: ApiRequest) -> (str, bool):
|
|||
返回类型为（model_name, is_local_model）
|
||||
'''
|
||||
running_models = api.list_running_models()
|
||||
|
||||
if not running_models:
|
||||
return "", False
|
||||
|
||||
|
|
@ -52,7 +50,6 @@ def get_default_llm_model(api: ApiRequest) -> (str, bool):
|
|||
local_models = [k for k, v in running_models.items() if not v.get("online_api")]
|
||||
if local_models:
|
||||
return local_models[0], True
|
||||
|
||||
return list(running_models)[0], False
|
||||
|
||||
|
||||
|
|
@ -82,7 +79,7 @@ def dialogue_page(api: ApiRequest):
|
|||
"搜索引擎问答",
|
||||
"自定义Agent问答",
|
||||
],
|
||||
index=3,
|
||||
index=0,
|
||||
on_change=on_mode_change,
|
||||
key="dialogue_mode",
|
||||
)
|
||||
|
|
@ -100,16 +97,18 @@ def dialogue_page(api: ApiRequest):
|
|||
return x
|
||||
|
||||
running_models = list(api.list_running_models())
|
||||
running_models += LANGCHAIN_LLM_MODEL.keys()
|
||||
available_models = []
|
||||
config_models = api.list_config_models()
|
||||
worker_models = list(config_models.get("worker", {})) # 仅列出在FSCHAT_MODEL_WORKERS中配置的模型
|
||||
for m in worker_models:
|
||||
if m not in running_models and m != "default":
|
||||
available_models.append(m)
|
||||
for k, v in config_models.get("online", {}).items(): # 列出ONLINE_MODELS中直接访问的模型（如GPT）
|
||||
for k, v in config_models.get("online", {}).items(): # 列出ONLINE_MODELS中直接访问的模型
|
||||
if not v.get("provider") and k not in running_models:
|
||||
print(k, v)
|
||||
available_models.append(k)
|
||||
for k, v in config_models.get("langchain", {}).items(): # 列出LANGCHAIN_LLM_MODEL支持的模型
|
||||
available_models.append(k)
|
||||
llm_models = running_models + available_models
|
||||
index = llm_models.index(st.session_state.get("cur_llm_model", get_default_llm_model(api)[0]))
|
||||
llm_model = st.selectbox("选择LLM模型：",
|
||||
|
|
@ -119,10 +118,10 @@ def dialogue_page(api: ApiRequest):
|
|||
on_change=on_llm_change,
|
||||
key="llm_model",
|
||||
)
|
||||
if (llm_model
|
||||
and st.session_state.get("prev_llm_model") != llm_model
|
||||
and not api.get_model_config(llm_model).get("online_api")
|
||||
and llm_model not in running_models):
|
||||
if (st.session_state.get("prev_llm_model") != llm_model
|
||||
and not llm_model in config_models.get("online", {})
|
||||
and not llm_model in config_models.get("langchain", {})
|
||||
and llm_model not in running_models):
|
||||
with st.spinner(f"正在加载模型： {llm_model}，请勿进行操作或刷新页面"):
|
||||
prev_model = st.session_state.get("prev_llm_model")
|
||||
r = api.change_llm_model(prev_model, llm_model)
|
||||
|
|
@ -228,9 +227,9 @@ def dialogue_page(api: ApiRequest):
|
|||
])
|
||||
text = ""
|
||||
ans = ""
|
||||
support_agent = ["gpt", "Qwen", "qwen-api", "baichuan-api"] # 目前支持agent的模型
|
||||
support_agent = ["Azure-OpenAI", "OpenAI", "Anthropic", "Qwen", "qwen-api", "baichuan-api"] # 目前支持agent的模型
|
||||
if not any(agent in llm_model for agent in support_agent):
|
||||
ans += "正在思考... \n\n <span style='color:red'>该模型并没有进行Agent对齐，无法正常使用Agent功能！</span>\n\n\n<span style='color:red'>请更换 GPT4或Qwen-14B等支持Agent的模型获得更好的体验！ </span> \n\n\n"
|
||||
ans += "正在思考... \n\n <span style='color:red'>该模型并没有进行Agent对齐，请更换支持Agent的模型获得更好的体验！</span>\n\n\n"
|
||||
chat_box.update_msg(ans, element_index=0, streaming=False)
|
||||
for d in api.agent_chat(prompt,
|
||||
history=history,
|
||||
|
|
|
|||
Loading…
Reference in New Issue