** 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*
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：
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。
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）
3.3. 生成Google Search API的KEY
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 ChatOpenAI
os.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 result
tools = [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 answer
Thought: consider previous and subsequent steps
Action:
$JSON_BLOB
Observation: action result
... (repeat Thought/Action/Observation N times)
Thought: I know what to respond
Action:
{{
"action": "Final Answer",
"action_input": "Final response to human"
}}
Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:$JSON_BLOBthen 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.content
if 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 ChatOpenAI
os.environ["OPENAI_API_KEY"] = 'sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx' # 你的Open AI Key
os.environ["SERPAPI_API_KEY"] = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
from langchain import hub
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 result
tools = [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.content
if 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 os
from 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 SerpAPIWrapper
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
os.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 result
def get_prompt():
prompt = hub.pull("hwchase17/structured-chat-agent")
return prompt
async def chat(query):
global llm,tools
agent = 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.content
if content and len(content) > 0:
print(content)
yield content
@app.websocket("/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：
WebSocket Chat
<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><button onclick="connect(event)">Connect</button>
<label>Message: <input type="text" id="messageText" autocomplete="off"/></label> <button>Send</button> </form>
调用结果：
用户输入：你好
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:
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。
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。
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