chore: rename multiple files to improve importability and module structure
This commit is contained in:
Annyfee
@@ -1,16 +0,0 @@
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from langchain_core.tools import tool
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@tool
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def get_weather(location):
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"""模拟获得天气信息"""
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return f"{location}当前天气：23℃，晴，风力2级"
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@tool
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def get_user_name(user):
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"""模拟获得用户名字"""
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return f'用户名字是:{user}'
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# 封装好要用的工具
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tools = [get_weather,get_user_name]
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print('工具箱已封装完毕!')
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@@ -1,49 +0,0 @@
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from config import OPENAI_API_KEY
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from langchain_openai import ChatOpenAI
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# 在LangChain 1.0+版本中，以下俩组件移到了langchain-classic包中
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from langchain_classic.agents import AgentExecutor
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from langchain_classic.agents import create_tool_calling_agent
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain_core.tools import tool # 导入 @tool
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# 配置LLM
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llm = ChatOpenAI(
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model="deepseek-chat",
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api_key=OPENAI_API_KEY,
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base_url="https://api.deepseek.com"
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)
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# 配置prompt
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prompt = ChatPromptTemplate.from_messages([
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("system","你是一个聪明的智能助手。当你遇到解决不了的问题时，会调用工具来解决问题。"),
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("human","{input}"),
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MessagesPlaceholder(variable_name="agent_scratchpad") # 必加，Agent的思考过程
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])
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# 配置tool
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@tool
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def get_weather(location):
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"""模拟获得天气信息"""
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return f"{location}当前天气：23℃，晴，风力2级"
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@tool
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def get_user_name(user):
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"""模拟获得用户名字"""
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return f'用户名字是:{user}'
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tools = [get_weather,get_user_name]
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# 创建Agent(大脑)
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agent = create_tool_calling_agent(llm=llm,prompt=prompt,tools=tools)
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# 创建AgentExecutor(执行器)--负责运行ReAct循环
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agent_executor = AgentExecutor(agent=agent,tools=tools,verbose=True) # 开启verbose以看到ai思考链
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# 运行
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response = agent_executor.invoke({
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'input':"今天北京的天气怎么样？"
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})
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print(response)
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print()
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print(response['output'])
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@@ -1,46 +0,0 @@
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from config import OPENAI_API_KEY
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import sqlite3
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from langchain_openai import ChatOpenAI
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from langchain_community.agent_toolkits import create_sql_agent
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from langchain_community.utilities import SQLDatabase # 导入 SQLDatabase
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import os
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# 配置llm
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llm = ChatOpenAI(
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model="deepseek-chat",
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api_key=OPENAI_API_KEY,
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base_url="https://api.deepseek.com"
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)
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# 创建一个临时的数据库--用于演示
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db_file = "test_sql.db"
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if os.path.exists(db_file):
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os.remove(db_file)
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conn = sqlite3.connect(db_file)
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cursor = conn.cursor()
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cursor.execute("CREATE TABLE users (id INT,name TEXT,age INT);")
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cursor.execute("INSERT INTO users (id,name,age) VALUES (1,'Alice',30);")
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cursor.execute("INSERT INTO users (id,name,age) VALUES (2,'Bob',25);")
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conn.commit()
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conn.close()
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# 连接数据库 -- LangChain 使用 SQLAlchemy URI (连接方式)
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db_uri = f'sqlite:///{db_file}'
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db = SQLDatabase.from_uri(db_uri)
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# 创建sqlAgent -- 一键完成，无需定义tools，仅告诉它使用openai-tools,即Tool Calling(工具调用)模式
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agent_executor = create_sql_agent(
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llm=llm,
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db=db,
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agent_type="openai-tools",
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verbose=True
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)
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# 运行
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response = agent_executor.invoke({"input":"告诉我Alice多大了？"})
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print(response['output'])
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# 清理
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db._engine.dispose() # 关闭连接池，避免文件被占用
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if os.path.exists(db_file):
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os.remove(db_file)
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@@ -1,76 +0,0 @@
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from config import OPENAI_API_KEY
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from langchain_openai import ChatOpenAI
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from langchain_classic.agents import AgentExecutor
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from langchain_classic.agents import create_tool_calling_agent
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain_core.tools import tool # 导入 @tool
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from langchain_community.chat_message_histories import ChatMessageHistory
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from langchain_core.runnables import RunnableWithMessageHistory
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# 配置llm
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llm = ChatOpenAI(
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model="deepseek-chat",
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api_key=OPENAI_API_KEY,
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base_url="https://api.deepseek.com"
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)
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# 配置prompt(新增俩占位符 一个为对话历史记录，一个为agent的思考过程)
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prompt = ChatPromptTemplate.from_messages([
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('system','你是小智，一个帮助他人的智能助手。当你无法解答当前问题时，会调用工具来解决问题。'),
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MessagesPlaceholder(variable_name="history"),
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('human','{input}'),
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MessagesPlaceholder(variable_name="agent_scratchpad")
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])
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# 配置tool
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@tool
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def get_weather(location):
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"""模拟获得天气信息"""
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return f"{location}当前天气：23℃，晴，风力2级"
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tools = [get_weather]
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# 配置agent
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agent = create_tool_calling_agent(llm=llm,prompt=prompt,tools=tools)
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# 配置AgentExecutor
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agent_executor = AgentExecutor(agent=agent,tools=tools) # 这里没加verbose=True，想打印日志看思考链的可以自行打印
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# 记忆存储--包装agent_executor
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store = {}
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def get_session_history(session_id:str):
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if session_id not in store:
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store[session_id] = ChatMessageHistory()
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return store[session_id]
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# 添加记忆功能
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agent_with_memory = RunnableWithMessageHistory(
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runnable=agent_executor,
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get_session_history=get_session_history,
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input_messages_key="input",
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history_messages_key="history"
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)
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# 打印测试
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session_id = 'user123'
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if __name__ == '__main__':
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while 1:
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user_input = input('\n你:')
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if user_input == 'quit':
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print('拜拜~')
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break
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response = agent_with_memory.invoke(
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{'input': user_input},
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config={'configurable': {'session_id': session_id}}
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)
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print(f"AI:{response['output']}")
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@@ -1,46 +0,0 @@
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from config import OPENAI_API_KEY
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import time
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from langchain_openai import ChatOpenAI
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from langchain_core.globals import set_llm_cache
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from langchain_community.cache import InMemoryCache
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# 配置llm
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llm = ChatOpenAI(
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model="deepseek-chat",
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api_key=OPENAI_API_KEY,
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base_url="https://api.deepseek.com"
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)
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# 设置全局缓存
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set_llm_cache(InMemoryCache())
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# 第一次调用llm(会远程请求)
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query = "用中文写一句关于猫的五言诗。"
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start_time = time.time()
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response1 = llm.invoke(query).content
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print(f"第一次调用结果: {response1}")
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print(f"第一次运行时间: {time.time() - start_time:.4f} 秒")
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print('')
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# 第二次调用llm(会命中缓存)
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start_time = time.time()
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response2 = llm.invoke(query).content
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print(f"第二次调用结果: {response2}")
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print(f"第二次运行时间 (已缓存): {time.time() - start_time:.4f} 秒")
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# 清理
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set_llm_cache(None) # 关闭缓存，以免影响后续实例
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print('缓存清理完成')
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@@ -1,73 +0,0 @@
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from config import OPENAI_API_KEY
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from langchain_openai import ChatOpenAI
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from langchain_classic.agents import AgentExecutor
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from langchain_classic.agents import create_tool_calling_agent
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain_core.tools import tool # 导入 @tool
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from langchain_community.chat_message_histories import ChatMessageHistory
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from langchain_core.runnables import RunnableWithMessageHistory
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from langchain_core.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
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# 配置llm
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llm = ChatOpenAI(
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model="deepseek-chat",
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api_key=OPENAI_API_KEY,
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base_url="https://api.deepseek.com",
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streaming=True,
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callbacks=[StreamingStdOutCallbackHandler()]
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)
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# 配置prompt(新增俩占位符 一个为对话历史记录，一个为agent的思考过程)
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prompt = ChatPromptTemplate.from_messages([
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('system','你是小智，一个帮助他人的智能助手。当你无法解答当前问题时，会调用工具来解决问题。'),
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MessagesPlaceholder(variable_name="history"),
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('human','{input}'),
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MessagesPlaceholder(variable_name="agent_scratchpad")
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])
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# 配置tool
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@tool
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def get_weather(location):
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"""模拟获得天气信息"""
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return f"{location}当前天气：23℃，晴，风力2级"
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tools = [get_weather]
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# 配置agent
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agent = create_tool_calling_agent(llm=llm,prompt=prompt,tools=tools)
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# 配置AgentExecutor
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agent_executor = AgentExecutor(agent=agent,tools=tools) # 这里没加verbose=True，想打印日志看思考链的可以自行打印
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# 记忆存储--包装agent_executor
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store = {}
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def get_session_history(session_id:str):
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if session_id not in store:
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store[session_id] = ChatMessageHistory()
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return store[session_id]
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agent_with_memory = RunnableWithMessageHistory(
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runnable=agent_executor,
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get_session_history=get_session_history,
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input_messages_key="input",
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history_messages_key="history"
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)
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# 打印测试
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session_id = 'user123'
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if __name__ == '__main__':
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while 1:
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user_input = input('\n你:')
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if user_input == 'quit':
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print('拜拜~')
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break
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# 用于标记"AI:"这个内容
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# flush=True保证"AI:"立即输出，而不是等缓存区存满再输出
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print("AI: ", end="", flush=True)
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response = agent_with_memory.invoke(
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{'input': user_input},
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config={'configurable': {'session_id': session_id}}
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)
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print()
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