official
https://python.langchain.com/v0.1/docs/modules/agents/agent_types/react/
https://python.langchain.com/v0.2/api_reference/langchain/agents/langchain.agents.react.agent.create_react_agent.html
from langchain import hub from langchain_community.llms import OpenAI from langchain.agents import AgentExecutor, create_react_agent prompt = hub.pull("hwchase17/react") model = OpenAI() tools = ... agent = create_react_agent(model, tools, prompt) agent_executor = AgentExecutor(agent=agent, tools=tools) agent_executor.invoke({"input": "hi"}) # Use with chat history from langchain_core.messages import AIMessage, HumanMessage agent_executor.invoke( { "input": "what's my name?", # Notice that chat_history is a string # since this prompt is aimed at LLMs, not chat models "chat_history": "Human: My name is Bob\nAI: Hello Bob!", } )
from langchain_core.prompts import PromptTemplate
template = '''Answer the following questions as best you can. You have access to the following tools:
{tools}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
Question: {input}
Thought:{agent_scratchpad}'''
prompt = PromptTemplate.from_template(template)
demo
https://zhuanlan.zhihu.com/p/685013312
# You need to set the environmental variable OPENAI_API_KEY from langchain_openai import ChatOpenAI from langchain.tools import tool from langchain import hub # Custom tool for the Agent @tool def get_employee_id(name): """ To get employee id, it takes employee name as arguments name(str): Name of the employee """ fake_employees = { "Alice": "E001", "Bob": "E002", "Charlie": "E003", "Diana": "E004", "Evan": "E005", "Fiona": "E006", "George": "E007", "Hannah": "E008", "Ian": "E009", "Jasmine": "E010"} return fake_employees.get(name,"Employee not found") # Custom tool for the Agent @tool def get_employee_salary(employee_id): """ To get the salary of an employee, it takes employee_id as input and return salary """ employee_salaries = { "E001": 56000, "E002": 47000, "E003": 52000, "E004": 61000, "E005": 45000, "E006": 58000, "E007": 49000, "E008": 53000, "E009": 50000, "E010": 55000 } return employee_salaries.get(employee_id,"Employee not found") # Saved React Prompt in langchain hub, we could manually type the prompt as well. prompt = hub.pull("hwchase17/react") model = ChatOpenAI(model='gpt-4-0125-preview') tools = [get_employee_salary, get_employee_id] agent = create_react_agent(model,tools, prompt) agent_executor = AgentExecutor(agent=agent,tools=tools,verbose=True) agent_executor.invoke({"input":"What is the Salary of Evan?"})
https://www.cnblogs.com/mangod/p/18230328
from langchain import hub from langchain.agents import create_structured_chat_agent, AgentExecutor from langchain.memory import ConversationBufferMemory from langchain.schema import HumanMessage from langchain.tools import BaseTool from langchain_openai import ChatOpenAI # 模型 model = ChatOpenAI(model="gpt-3.5-turbo", openai_api_key="sk-XXXXXXXXXX", openai_api_base="https://api.aigc369.com/v1") # 直接让模型计算数字,模型会算错 model.invoke([HumanMessage(content="你帮我算下,3.941592623412424+4.3434532535353的结果")]) # 下面开始使用ReAct机制,定义工具,让LLM使用工具做专业的事情。 # 定义工具,要继承自LangChain的BaseTool class SumNumberTool(BaseTool): name = "数字相加计算工具" description = "当你被要求计算2个数字相加时,使用此工具" def _run(self, a, b): return a.value + b.value # 工具合集 tools = [SumNumberTool()] # 提示词,直接从langchain hub上下载,因为写这个ReAct机制的prompt比较复杂,直接用现成的。 prompt = hub.pull("hwchase17/structured-chat-agent") # 定义AI Agent agent = create_structured_chat_agent( llm=model, tools=tools, prompt=prompt ) # 使用Memory记录上下文 memory = ConversationBufferMemory( memory_key='chat_history', return_messages=True ) # 定义AgentExecutor,必须使用AgentExecutor,才能执行代理定义的工具 agent_executor = AgentExecutor.from_agent_and_tools( agent=agent, tools=tools, memory=memory, verbose=True, handle_parsing_errors=True ) # 测试使用到工具的场景 agent_executor.invoke({"input": "你帮我算下3.941592623412424+4.3434532535353的结果"}) # 测试不使用工具的场景 agent_executor.invoke({"input": "请你充当稿件审核师,帮我看看'''号里的内容有没有错别字,如果有的话帮我纠正下。'''今天班级里的学生和老实要去哪里玩'''"})
autoCOT 理解
https://zhuanlan.zhihu.com/p/659102403
langchain react理解
https://github.com/Papakobina/Langchain-React-Agent/blob/main/react-langchain/main.py
https://zhuanlan.zhihu.com/p/686796330
from typing import Union, List from dotenv import load_dotenv from langchain.agents import tool from langchain.agents.format_scratchpad import format_log_to_str from langchain.agents.output_parsers import ReActSingleInputOutputParser from langchain_openai import ChatOpenAI from langchain.prompts import PromptTemplate from langchain.schema import AgentAction, AgentFinish from langchain.tools import Tool from langchain.tools.render import render_text_description load_dotenv() @tool def get_text_length(text: str) -> int: """Returns the length of a text by characters""" print(f"get_text_length enter with {text=}") text = text.strip("'\n").strip( '"' ) # stripping away non-alphabetic characters just in case return len(text) def find_tool_by_name(tools: List[Tool], tool_name: str) -> Tool: for tool in tools: if tool.name == tool_name: return tool raise ValueError(f"Tool wtih name {tool_name} not found") if __name__ == "__main__": print("Hello ReAct LangChain!") tools = [get_text_length] template = """ Answer the following questions as best you can. You have access to the following tools: {tools} Use the following format: Question: the input question you must answer Thought: you should always think about what to do Action: the action to take, should be one of [{tool_names}] Action Input: the input to the action Observation: the result of the action ... (this Thought/Action/Action Input/Observation can repeat N times) Thought: I now know the final answer Final Answer: the final answer to the original input question Begin! Question: {input} Thought: {agent_scratchpad} """ prompt = PromptTemplate.from_template(template=template).partial( tools=render_text_description(tools), tool_names=", ".join([t.name for t in tools]), ) llm = ChatOpenAI( temperature=0, model_kwargs={"stop": ["\nObservation", "Observation"]}, ) intermediate_steps = [] agent = ( { "input": lambda x: x["input"], "agent_scratchpad": lambda x: format_log_to_str(x["agent_scratchpad"]), } | prompt | llm | ReActSingleInputOutputParser() ) agent_step: Union[AgentAction, AgentFinish] = agent.invoke( { "input": "What is the length of the word: DOG", "agent_scratchpad": intermediate_steps, } ) print(agent_step) if isinstance(agent_step, AgentAction): tool_name = agent_step.tool tool_to_use = find_tool_by_name(tools, tool_name) tool_input = agent_step.tool_input observation = tool_to_use.func(str(tool_input)) print(f"{observation=}") intermediate_steps.append((agent_step, str(observation))) agent_step: Union[AgentAction, AgentFinish] = agent.invoke( { "input": "What is the length of the word: DOG", "agent_scratchpad": intermediate_steps, } ) print(agent_step) if isinstance(agent_step, AgentFinish): print("### AgentFinish ###") print(agent_step.return_values)
标签:tool,agent,langchain,ReAct,Langchain,input,import,tools From: https://www.cnblogs.com/lightsong/p/18538670