172 lines
4.8 KiB
Python
172 lines
4.8 KiB
Python
import os
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from dotenv import load_dotenv
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load_dotenv()
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api_key = os.getenv("OPENAI_API_KEY")
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from langchain_huggingface import HuggingFaceEmbeddings
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from langchain_chroma import Chroma
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from langchain_openai import ChatOpenAI
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from langchain_core.prompts import ChatPromptTemplate,MessagesPlaceholder
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.runnables import RunnablePassthrough
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from langchain.retrievers import ContextualCompressionRetriever
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from langchain_community.cross_encoders import HuggingFaceCrossEncoder
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from langchain.retrievers.document_compressors import CrossEncoderReranker
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from langchain_core.tools import tool
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from langchain.agents import AgentExecutor, create_tool_calling_agent
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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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# 1. 构建一个可复用的 RAG链条
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def build_rag_chain(llm_instance):
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print('---正在构建RAG链条---')
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Persist_directory = './chroma_db_war_and_peace_bge_small_en_v1.5'
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Embedding_model = 'BAAI/bge-small-en-v1.5'
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Encoder_model = "BAAI/bge-reranker-base"
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if not os.path.exists(Persist_directory):
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raise FileNotFoundError(f'索引目录{Persist_directory}未找到，请先运行 build_index.py')
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embedding_model = HuggingFaceEmbeddings(model_name=Embedding_model)
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db = Chroma(
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persist_directory=Persist_directory,
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embedding_function=embedding_model
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)
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# R
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base_retriever = db.as_retriever(search_kwargs={'k':5})
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encoder = HuggingFaceCrossEncoder(model_name=Encoder_model)
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reranker = CrossEncoderReranker(model=encoder,top_n=2)
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compression_retriever = ContextualCompressionRetriever(
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base_retriever=base_retriever,
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base_compressor=reranker
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)
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retriever = compression_retriever
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# A
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sys_prompt = """
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你是一个博学的历史学家和文学评论家。
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请根据以下上下文回答问题。如果上下文**强烈暗示**了答案，即使未明说，也可推理回答。
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如果完全无关，请回答“对不起，根据所提供的上下文我不知道”。
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[上下文]: {context}
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[问题]: {question}
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"""
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prompt = ChatPromptTemplate.from_messages([
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('system',sys_prompt),
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('human','{question}')
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])
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def format_docs(docs):
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return '\n'.join(doc.page_content for doc in docs)
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# R-A-G
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rag_chain = (
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{'context':retriever | format_docs,'question':RunnablePassthrough()}
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| prompt
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| llm_instance
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| StrOutputParser()
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)
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print('---RAG链条构建完毕!---\n')
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return rag_chain
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# 2. 将 RAG链条 组装进Agent里
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def create_agent_with_memory():
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# LLm
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llm = ChatOpenAI(
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model="deepseek-chat",
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api_key=api_key,
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base_url="https://api.deepseek.com"
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)
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# Prompt
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prompt = ChatPromptTemplate.from_messages([
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('system','你是一个强大的助手。你能查天气，也能查《战争与和平》。请尽力回答用户所提的所有问题。'),
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MessagesPlaceholder(variable_name="history"), # 05篇所学:记忆占位符
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('human','{input}'),
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MessagesPlaceholder(variable_name="agent_scratchpad") # 05篇所学:ReAct 思考链，使其能够调用工具
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])
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# Tool
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rag_chain_instance = build_rag_chain(llm_instance=llm)
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@tool
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def search_war_and_peace(query):
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"""查询《战争与和平》小说中的内容，包括人物、情节、历史事件等"""
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print(f'\n正在检索《战争与和平》:{query}')
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return rag_chain_instance.invoke(query)
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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,search_war_and_peace]
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# 创建Agent
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agent = create_tool_calling_agent(llm=llm,tools=tools,prompt=prompt)
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agent_executor = AgentExecutor(agent=agent,tools=tools,verbose=False)
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# 封装Memory
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store = {}
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def get_session_history(session_id:int):
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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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return agent_with_memory
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# 测试
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if __name__ == '__main__':
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session_id = 'user123'
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agent = create_agent_with_memory()
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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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exit()
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response = agent.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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