chore: rename multiple files to improve importability and module structure
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from langchain_chroma import Chroma
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from langchain_core.output_parsers import StrOutputParser
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from langchain_openai import ChatOpenAI
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from embeddings import get_embeddings
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from config import OPENAI_API_KEY
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import os
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.runnables import RunnablePassthrough
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Persist_directory = './chroma_db_war_and_peace_bge_small_en_v1.5'
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model_name_str = 'BAAI/bge-small-en-v1.5'
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if not os.path.exists(Persist_directory):
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print(f"错误: 知识库文件 {Persist_directory} 未找到。")
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print("请先运行'build_index.py'生成向量数据库,再运行该文件")
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exit()
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print('---加载本地向量数据库---')
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# 模块A:链接本地Chroma向量数据库
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# 1. 加载 Embedding 模型
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print(f'正在加载/下载模型{model_name_str}...')
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embeddings_model = get_embeddings(
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model_name=model_name_str,
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device='cpu'
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)
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# 2. 从本地目录加载Chroma DB
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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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print(f'Chroma数据库已从本地加载(共{db._collection.count()}条)\n')
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# 模块B:R-A-G Flow
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# 1. R-检索
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retriever = db.as_retriever(search_kwargs={"k": 5}) # 召回5条相关数据
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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**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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# 3. G-生成
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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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# 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(LCEL)
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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
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| StrOutputParser()
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)
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# 运行RAG链
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print('---正在运行RAG链条---')
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question = '莫斯科大火发生在小说的哪一部分?有哪些角色亲历了这场灾难?'
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response = rag_chain.invoke(question)
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print(f'提问:{question}')
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print(f'回答:{response}')
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