优化代码，提高解耦

07_rag_advanced/02_reranker.py

@@ -1,33 +1,31 @@
 import os
-from dotenv import load_dotenv
-
-load_dotenv()
-api_key = os.getenv("OPENAI_API_KEY")
-
-from langchain_huggingface import HuggingFaceEmbeddings
+from config import OPENAI_API_KEY
+from embeddings import get_embeddings
 from langchain_chroma import Chroma
 from langchain_openai import ChatOpenAI
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.runnables import RunnablePassthrough
-
-# --- Reranker (02) 新增的 import ---
-from langchain.retrievers import ContextualCompressionRetriever
+from langchain_classic.retrievers import ContextualCompressionRetriever
+from langchain_classic.retrievers.document_compressors import CrossEncoderReranker
 from langchain_community.cross_encoders import HuggingFaceCrossEncoder
-from langchain.retrievers.document_compressors import CrossEncoderReranker
 
 Persist_directory = './chroma_db_war_and_peace_bge_small_en_v1.5'
-Embedding_model = 'BAAI/bge-small-en-v1.5'
+model_name_str = 'BAAI/bge-small-en-v1.5'
 
 if not os.path.exists(Persist_directory):
     print(f"错误: 知识库文件 {Persist_directory} 未找到。")
-    print("请先运行'build_index.py'生成向量数据库，再运行该文件")
+    print("请先运行'00_build_index.py'生成向量数据库，再运行该文件")
     exit()
 
 print('---加载本地向量数据库---\n')
 
 # 1. 加载 Embedding 模型
-embeddings_model = HuggingFaceEmbeddings(model_name=Embedding_model)
+print(f'正在加载/下载模型{model_name_str}...')
+embeddings_model = get_embeddings(
+    model_name=model_name_str,
+    device='cpu'
+)
 
 # 2. 加载 Chroma db
 db = Chroma(
@@ -40,11 +38,11 @@ print('---Chroma数据库已加载---\n')
 # --- 模块 B (R-A-G Flow) ---
 # 1. R-检索--强化版
 # 1.1 基础检索器(Base Retriever) - '粗召回'
-base_retriever = db.as_retriever(search_kwargs={"k":5}) # K调大到5
+base_retriever = db.as_retriever(search_kwargs={"k":50}) # K调大到60
 # 1.2 Reranker (重排器) - "精排序" -- 首次运行需要耗时下载
-print('正在加载Reranker模型 (bge-reranker-base)...')
+print('正在加载 Reranker模型 (bge-reranker-base)...')
 encoder = HuggingFaceCrossEncoder(model_name="BAAI/bge-reranker-base") # 加载Ranker模型
-reranker = CrossEncoderReranker(model=encoder,top_n=2) # 对检索结果进行精排
+reranker = CrossEncoderReranker(model=encoder,top_n=6) # 对检索结果进行精排
 # 1.3 创建管道封装器
 compression_retriever = ContextualCompressionRetriever(
     base_retriever=base_retriever, # 用Chroma做 海选
@@ -71,7 +69,7 @@ prompt = ChatPromptTemplate.from_messages([
 # 3. G-生成
 llm = ChatOpenAI(
     model="deepseek-chat",
-    api_key=api_key,
+    api_key=OPENAI_API_KEY,
     base_url="https://api.deepseek.com"
 )
 
@@ -94,17 +92,3 @@ question = '皮埃尔是共济会成员吗？他在其中扮演什么角色？'
 response = rag_chain.invoke(question)
 print(f'提问:{question}')
 print(f'回答:{response}')
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