108 lines
3.5 KiB
Python
108 lines
3.5 KiB
Python
import os
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from config import OPENAI_API_KEY
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from embeddings import get_embeddings
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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
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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_classic.retrievers import ContextualCompressionRetriever
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from langchain_community.cross_encoders import HuggingFaceCrossEncoder
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from langchain_classic.retrievers.document_compressors import CrossEncoderReranker
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from langchain_core.tools import tool
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# 全局 LLM (供Agent和Rag共用)
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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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# (1) 构建一个可复用的 RAG链条 (P1+P2)
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def build_rag_chain(llm_instance):
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print('---正在构建RAG链条...---\n')
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persist_directory = './chroma_db_war_and_peace_bge_small_en_v1.5'
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embedding_model_name = 'BAAI/bge-small-en-v1.5'
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encoder_model_name = "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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print(f'正在加载/下载 Embedding模型：{embedding_model_name}')
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embeddings_model = get_embeddings(model_name=embedding_model_name,device='cpu')
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db = Chroma(
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persist_directory=persist_directory,
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embedding_function=embeddings_model
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)
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# 1. R-检索--强化版
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base_retriever = db.as_retriever(search_kwargs={"k":50})
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print(f'正在加载 Reranker模型:{encoder_model_name}...')
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encoder = HuggingFaceCrossEncoder(model_name=encoder_model_name)
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reranker = CrossEncoderReranker(model=encoder,top_n=6)
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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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# 2. A-增强
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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**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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# 3.G-生成(llm已在全局生成)
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# 4. 辅助函数
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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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# 5.组装RAG链条
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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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# 初始化RAG链
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rag_chain_instance = build_rag_chain(llm)
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# (2) 封装为标准 Langchain Tool
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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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# 也可以与其他工具并列使用
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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 = [search_war_and_peace,get_weather]
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# 运行
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if __name__ == '__main__':
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question = "皮埃尔是共济会成员吗？他在其中扮演什么角色？"
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res = search_war_and_peace.invoke(question)
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print(f'问题:{question}')
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print(f'回答:{res}') |