开源模型应用落地-FastAPI-助力模型交互-WebSocket篇（六）

 一、前言

    使用 FastAPI 可以帮助我们更简单高效地部署 AI 交互业务。FastAPI 提供了快速构建 API 的能力,开发者可以轻松地定义模型需要的输入和输出格式,并编写好相应的业务逻辑。

    FastAPI 的异步高性能架构,可以有效支持大量并发的预测请求,为用户提供流畅的交互体验。此外,FastAPI 还提供了容器化部署能力,开发者可以轻松打包 AI 模型为 Docker 镜像,实现跨环境的部署和扩展。

    总之,使用 FastAPI 可以大大提高 AI 应用程序的开发效率和用户体验,为 AI 模型的部署和交互提供全方位的支持。

    本篇在开源模型应用落地-FastAPI-助力模型交互-WebSocket篇（五）基础上，学习如何集成Tool获取实时数据，并以流式方式返回

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二、术语

2.1.Tool

    Tool（工具）是为了增强其语言模型的功能和实用性而设计的一系列辅助手段，用于扩展模型的能力。例如代码解释器（Code Interpreter）和知识检索（Knowledge Retrieval）等都属于其工具。

2.2.langchain预置的tools

    https://github.com/langchain-ai/langchain/tree/v0.1.16/docs/docs/integrations/tools

   基本这些工具能满足大部分需求，具体使用参见：

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2.3.LangChain支持流式输出的方法

• stream：基本的流式传输方式，能逐步给出代理的动作和观察结果。
• astream：异步的流式传输，用于异步处理需求的情况。
• astream_events：更细致的流式传输，能流式传输代理的每个具体事件，如工具调用和结束、模型启动和结束等，便于深入了解和监控代理执行的详细过程。

2.4.langchainhub

    是 LangChain 相关工具的集合中心，其作用在于方便开发者发现和共享常用的提示（Prompt）、链、代理等。

    它受 Hugging Face Hub 启发，促进社区交流与协作，推动 LangChain 生态发展。当前，它在新架构中被置于 LangSmith 里，主要聚焦于 Prompt。

2.5.asyncio

    是一个用于编写并发代码的标准库,它提供了构建异步应用程序的基础框架。

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三、前置条件

3.1. 创建虚拟环境&安装依赖

  增加Google Search以及langchainhub的依赖包

conda create -n fastapi_test python=3.10
conda activate fastapi_test
pip install fastapi websockets uvicorn
pip install --quiet  langchain-core langchain-community langchain-openai
pip install google-search-results langchainhub

3.2. 注册Google Search API账号

参见：开源模型应用落地-FastAPI-助力模型交互-WebSocket篇（五）

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3.3. 生成Google Search API的KEY

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四、技术实现

4.1. 使用Tool&流式输出

# -*- coding: utf-8 -*-
import asyncio
import os
from langchain.agents import  create_structured_chat_agent, AgentExecutor
from langchain_community.utilities.serpapi import SerpAPIWrapper
from langchain_core.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate
from langchain_core.tools import tool
from langchain_openai import ChatOpenAIos.environ["OPENAI_API_KEY"] = 'sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'  # 你的Open AI Key
os.environ["SERPAPI_API_KEY"] = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"llm = ChatOpenAI(model="gpt-3.5-turbo",temperature=0,max_tokens=512)@tool
def search(query:str):"""只有需要了解实时信息或不知道的事情的时候才会使用这个工具，需要传入要搜索的内容。"""serp = SerpAPIWrapper()result = serp.run(query)print("实时搜索结果:", result)return resulttools = [search]template='''
Respond to the human as helpfully and accurately as possible. You have access to the following tools:{tools}Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).Valid "action" values: "Final Answer" or {tool_names}Provide only ONE action per $JSON_BLOB, as shown:```{{"action": $TOOL_NAME,"action_input": $INPUT}}```Follow this format:Question: input question to answerThought: consider previous and subsequent stepsAction:```$JSON_BLOB```Observation: action result... (repeat Thought/Action/Observation N times)Thought: I know what to respondAction:```{{"action": "Final Answer","action_input": "Final response to human"}}Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation
'''
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template='''
{input}{agent_scratchpad}(reminder to respond in a JSON blob no matter what)
'''
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])print(prompt)agent = create_structured_chat_agent(llm, tools, prompt
)agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True, handle_parsing_errors=True)async def chat(params):events = agent_executor.astream_events(params,version="v2")async for event in events:type = event['event']if 'on_chat_model_stream' == type:data = event['data']chunk =  data['chunk']content =  chunk.contentif content and len(content) > 0:print(content)asyncio.run(chat({"input": "广州现在天气如何?"}))

调用结果：

说明：

流式输出的数据结构为：

{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='天', id='run-92515b63-4b86-4af8-8515-2f84def9dfab')}, 'run_id': '92515b63-4b86-4af8-8515-2f84def9dfab', 'name': 'ChatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 512, 'ls_stop': ['\nObservation']}}
type: on_chat_model_stream
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='气', id='run-92515b63-4b86-4af8-8515-2f84def9dfab')}, 'run_id': '92515b63-4b86-4af8-8515-2f84def9dfab', 'name': 'ChatOpenAI', 'tags': ['seq:step:3'], 'metadata': {'ls_provider': 'openai', 'ls_model_name': 'gpt-3.5-turbo', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 512, 'ls_stop': ['\nObservation']}}

4.2. 通过langchainhub使用公共prompt

   在4.1使用Tool&流式输出的代码基础上进行调整

# -*- coding: utf-8 -*-
import asyncio
import os
from langchain.agents import  create_structured_chat_agent, AgentExecutor
from langchain_community.utilities.serpapi import SerpAPIWrapper
from langchain_core.tools import tool
from langchain_openai import ChatOpenAIos.environ["OPENAI_API_KEY"] = 'sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'  # 你的Open AI Key
os.environ["SERPAPI_API_KEY"] = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"from langchain import hubllm = ChatOpenAI(model="gpt-3.5-turbo",temperature=0,max_tokens=512)@tool
def search(query:str):"""只有需要了解实时信息或不知道的事情的时候才会使用这个工具，需要传入要搜索的内容。"""serp = SerpAPIWrapper()result = serp.run(query)print("实时搜索结果:", result)return resulttools = [search]prompt = hub.pull("hwchase17/structured-chat-agent")print(prompt)agent = create_structured_chat_agent(llm, tools, prompt
)agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True, handle_parsing_errors=True)async def chat(params):events = agent_executor.astream_events(params,version="v2")async for event in events:type = event['event']if 'on_chat_model_stream' == type:data = event['data']chunk =  data['chunk']content =  chunk.contentif content and len(content) > 0:print(content)asyncio.run(chat({"input": "广州现在天气如何?"}))

调用结果：

4.3. 整合代码

在开源模型应用落地-FastAPI-助力模型交互-WebSocket篇（五）的代码基础上进行调整

import uvicorn
import osfrom typing import Annotated
from fastapi import (Depends,FastAPI,WebSocket,WebSocketException,WebSocketDisconnect,status,
)
from langchain import hub
from langchain.agents import create_structured_chat_agent, AgentExecutor
from langchain_community.utilities import SerpAPIWrapperfrom langchain_core.tools import tool
from langchain_openai import ChatOpenAIos.environ["OPENAI_API_KEY"] = 'sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'  # 你的Open AI Key
os.environ["SERPAPI_API_KEY"] = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"class ConnectionManager:def __init__(self):self.active_connections: list[WebSocket] = []async def connect(self, websocket: WebSocket):await websocket.accept()self.active_connections.append(websocket)def disconnect(self, websocket: WebSocket):self.active_connections.remove(websocket)async def send_personal_message(self, message: str, websocket: WebSocket):await websocket.send_text(message)async def broadcast(self, message: str):for connection in self.active_connections:await connection.send_text(message)manager = ConnectionManager()app = FastAPI()async def authenticate(websocket: WebSocket,userid: str,secret: str,
):if userid is None or secret is None:raise WebSocketException(code=status.WS_1008_POLICY_VIOLATION)print(f'userid: {userid},secret: {secret}')if '12345' == userid and 'xxxxxxxxxxxxxxxxxxxxxxxxxx' == secret:return 'pass'else:return 'fail'@tool
def search(query:str):"""只有需要了解实时信息或不知道的事情的时候才会使用这个工具，需要传入要搜索的内容。"""serp = SerpAPIWrapper()result = serp.run(query)print("实时搜索结果:", result)return resultdef get_prompt():prompt = hub.pull("hwchase17/structured-chat-agent")return promptasync def chat(query):global llm,toolsagent = create_structured_chat_agent(llm, tools, get_prompt())agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True, handle_parsing_errors=True)events = agent_executor.astream_events({"input": query}, version="v1")async for event in events:type = event['event']if 'on_chat_model_stream' == type:data = event['data']chunk = data['chunk']content = chunk.contentif content and len(content) > 0:print(content)yield [e-mail]("/ws")
async def websocket_endpoint(*,websocket: WebSocket,userid: str,permission: Annotated[str, Depends(authenticate)],):await manager.connect(websocket)try:while True:text = await websocket.receive_text()if 'fail' == permission:await manager.send_personal_message(f"authentication failed", websocket)else:if text is not None and len(text) > 0:async for msg in chat(text):await manager.send_personal_message(msg, websocket)except WebSocketDisconnect:manager.disconnect(websocket)print(f"Client #{userid} left the chat")await manager.broadcast(f"Client #{userid} left the chat")if __name__ == '__main__':tools = [search]llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0, max_tokens=512)uvicorn.run(app, host='0.0.0.0',port=7777)

客户端：

<!DOCTYPE html>
<html><head><title>Chat</title></head><body><h1>WebSocket Chat</h1><form action="" onsubmit="sendMessage(event)"><label>USERID: <input type="text" id="userid" autocomplete="off" value="12345"/></label><label>SECRET: <input type="text" id="secret" autocomplete="off" value="xxxxxxxxxxxxxxxxxxxxxxxxxx"/></label><br/><button onclick="connect(event)">Connect</button><hr><label>Message: <input type="text" id="messageText" autocomplete="off"/></label><button>Send</button></form><ul id='messages'></ul><script>var ws = null;function connect(event) {var userid = document.getElementById("userid")var secret = document.getElementById("secret")ws = new WebSocket("ws://localhost:7777/ws?userid="+userid.value+"&secret=" + secret.value);ws.onmessage = function(event) {var messages = document.getElementById('messages')var message = document.createElement('li')var content = document.createTextNode(event.data)message.appendChild(content)messages.appendChild(message)};event.preventDefault()}function sendMessage(event) {var input = document.getElementById("messageText")ws.send(input.value)input.value = ''event.preventDefault()}</script></body>
</html>

调用结果：

用户输入：你好

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不需要触发工具调用

模型输出：

用户输入：广州现在天气如何？

​

需要调用工具

模型输出：

```
Action:
```
{"action": "Final Answer","action_input": "广州现在的天气是多云，温度为87华氏度，降水概率为7%，湿度为76%，风力为7英里/小时。"
}
```

PS:

1. 上面仅用于演示流式输出的效果，里面包含一些冗余的信息，例如："action": "Final Answer"，要根据实际情况过滤。

2. 页面输出的样式可以根据实际需要进行调整，此处仅用于演示效果。