安装依赖
pip install -qU langchain-core langchain-openai
编写代码
定义一个工具
# 定义工具 @tool def get_word_length(word: str) -> int: """Returns the length of a word.""" return len(word)
创建一个Agent
# 创建Agent agent = ( { "input": lambda x: x["input"], "agent_scratchpad": lambda x: format_to_openai_tool_messages( x["intermediate_steps"] ), } | prompt | llm_with_tools | OpenAIToolsAgentOutputParser() )
推荐使用GPT-4，GPT3.5任务表现上并不是很好。
完整的代码如下
from langchain_openai import ChatOpenAI from langchain.agents import tool from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder from langchain.agents.format_scratchpad.openai_tools import ( format_to_openai_tool_messages, ) from langchain.agents.output_parsers.openai_tools import OpenAIToolsAgentOutputParser from langchain.agents import AgentExecutor llm = ChatOpenAI(model="gpt-4-turbo-preview", temperature=0) # llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0) # 定义工具 @tool def get_word_length(word: str) -> int: """Returns the length of a word.""" return len(word) # print(get_word_length.invoke("abc")) # 定义一个工具集 tools = [get_word_length] # 提示词 prompt = ChatPromptTemplate.from_messages( [ ( "system", "You are very powerful assistant, but don't know current events", ), ( "user", "{input}" ), MessagesPlaceholder(variable_name="agent_scratchpad"), ] ) # 绑定工具集 llm_with_tools = llm.bind_tools(tools) # 创建Agent agent = ( { "input": lambda x: x["input"], "agent_scratchpad": lambda x: format_to_openai_tool_messages( x["intermediate_steps"] ), } | prompt | llm_with_tools | OpenAIToolsAgentOutputParser() ) # 执行器 agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True) agent_executor_stream = list(agent_executor.stream({"input": "How many letters in the word eudca"})) print(f"agent_executor_stream: {agent_executor_stream}")
执行结果
➜ python3 test27.py > Entering new AgentExecutor chain... Invoking: `get_word_length` with `{'word': 'eudca'}` 5The word "eudca" has 5 letters. > Finished chain. agent_executor_stream: [{'actions': [OpenAIToolAgentAction(tool='get_word_length', tool_input={'word': 'eudca'}, log="\nInvoking: `get_word_length` with `{'word': 'eudca'}`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_Oo2mhYvyOHkw4YvzaL1Gz0tb', 'function': {'arguments': '{"word":"eudca"}', 'name': 'get_word_length'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls'})], tool_call_id='call_Oo2mhYvyOHkw4YvzaL1Gz0tb')], 'messages': [AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_Oo2mhYvyOHkw4YvzaL1Gz0tb', 'function': {'arguments': '{"word":"eudca"}', 'name': 'get_word_length'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls'})]}, {'steps': [AgentStep(action=OpenAIToolAgentAction(tool='get_word_length', tool_input={'word': 'eudca'}, log="\nInvoking: `get_word_length` with `{'word': 'eudca'}`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_Oo2mhYvyOHkw4YvzaL1Gz0tb', 'function': {'arguments': '{"word":"eudca"}', 'name': 'get_word_length'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls'})], tool_call_id='call_Oo2mhYvyOHkw4YvzaL1Gz0tb'), observation=5)], 'messages': [FunctionMessage(content='5', name='get_word_length')]}, {'output': 'The word "eudca" has 5 letters.', 'messages': [AIMessage(content='The word "eudca" has 5 letters.')]}]
