优化代码，提高解耦

01_agent_introduction/agent-demo.py

@@ -1,17 +1,11 @@
-import os  # 导入环境变量
-from dotenv import load_dotenv
-
-load_dotenv()
-api_key = os.getenv("OPENAI_API_KEY")
-
-
 # 让ai说一句话
+from config import OPENAI_API_KEY
 from langchain_openai import ChatOpenAI
 
 # 配置deepseek
 llm = ChatOpenAI(
     model="deepseek-chat",
-    api_key=api_key,
+    api_key=OPENAI_API_KEY,
     # 注：在.env里把OPENAI_API_KEY改成你自己的api-key即可
     base_url="https://api.deepseek.com"
 )

02_llm_fundamentals/01_basic_llm_invocation.py

@@ -1,14 +1,8 @@
-import os # 导入环境变量
+from config import OPENAI_API_KEY
-from dotenv import load_dotenv
-
-load_dotenv()
-api_key = os.getenv("OPENAI_API_KEY")
-
-
 from openai import OpenAI
 
 client = OpenAI(
-    api_key=api_key,
+    api_key=OPENAI_API_KEY,
     base_url="https://api.deepseek.com")
 
 response = client.chat.completions.create(

02_llm_fundamentals/02_conversational_agent.py

@@ -1,12 +1,8 @@
-import os # 导入环境变量
+from config import OPENAI_API_KEY
-from dotenv import load_dotenv
 from openai import OpenAI
 
-load_dotenv()
-
 def create_client():
-    api_key = os.getenv("OPENAI_API_KEY")
+    return OpenAI(api_key=OPENAI_API_KEY,base_url="https://api.deepseek.com")
-    return OpenAI(api_key=api_key,base_url="https://api.deepseek.com")
 
 def chat_loop(agent_client):
     messages = [

03_function_calling_tools/01_custom_function_calling.py

@@ -1,14 +1,10 @@
-import os
+from config import OPENAI_API_KEY
-from dotenv import load_dotenv
 from openai import OpenAI
 import json
 
-load_dotenv()
-
 
 def create_client():
-    api_key = os.getenv("OPENAI_API_KEY")
+    return OpenAI(api_key=OPENAI_API_KEY, base_url="https://api.deepseek.com")
-    return OpenAI(api_key=api_key, base_url="https://api.deepseek.com")
 
 def get_weather(location):
     # 模拟获得天气信息

03_function_calling_tools/02_api_invocation.py

@@ -1,14 +1,10 @@
-from dotenv import load_dotenv
+from config import OPENAI_API_KEY
 from openai import OpenAI
-import json
-import os
 import requests
 
-load_dotenv()
 
 def create_client():
-    api_key = os.getenv("OPENAI_API_KEY")
+    return OpenAI(api_key=OPENAI_API_KEY, base_url="https://api.deepseek.com")
-    return OpenAI(api_key=api_key, base_url="https://api.deepseek.com")
 
 # 通过api调用获得当前ip与位置
 def get_addr():

04_langchain_basics/01_models.py

@@ -1,15 +1,10 @@
-import os
+from config import OPENAI_API_KEY
-from dotenv import load_dotenv
-
-load_dotenv()
-api_key = os.getenv("OPENAI_API_KEY")
-
 from langchain_openai import ChatOpenAI
 
 # 初始化模型
 llm = ChatOpenAI(
     model="deepseek-chat",
-    api_key=api_key,
+    api_key=OPENAI_API_KEY,
     base_url="https://api.deepseek.com"
 )
 

04_langchain_basics/02_prompt.py

@@ -1,10 +1,3 @@
-import os
-from dotenv import load_dotenv
-
-load_dotenv()
-api_key = os.getenv("OPENAI_API_KEY")
-
-
 from langchain_core.prompts import ChatPromptTemplate
 
 # 定义提示词模板(推荐写法)

04_langchain_basics/03_chain.py

@@ -1,10 +1,4 @@
-import os
+from config import OPENAI_API_KEY
-from dotenv import load_dotenv
-
-load_dotenv()
-api_key = os.getenv("OPENAI_API_KEY")
-
-
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_openai import ChatOpenAI
 from langchain_core.output_parsers import StrOutputParser
@@ -18,7 +12,7 @@ prompt = ChatPromptTemplate.from_messages([
 # 初始化模型
 llm = ChatOpenAI(
     model="deepseek-chat",
-    api_key=api_key,
+    api_key=OPENAI_API_KEY,
     base_url="https://api.deepseek.com"
 )
 

04_langchain_basics/04_memory.py

@@ -1,18 +1,9 @@
-import os
-from dotenv import load_dotenv
-
-
-load_dotenv()
-api_key = os.getenv("OPENAI_API_KEY")
-
-
-
 from langchain_core.prompts import ChatPromptTemplate,MessagesPlaceholder
 from langchain_openai import ChatOpenAI
 from langchain_core.output_parsers import StrOutputParser
 from langchain_community.chat_message_histories import ChatMessageHistory
 from langchain_core.runnables import RunnableWithMessageHistory
-
+from config import OPENAI_API_KEY
 
 prompt = ChatPromptTemplate.from_messages([
     ("system", "你非常可爱，说话末尾会带个喵"),
@@ -21,7 +12,7 @@ prompt = ChatPromptTemplate.from_messages([
 ])
 llm = ChatOpenAI(
     model="deepseek-chat",
-    api_key=api_key,
+    api_key=OPENAI_API_KEY,
     base_url="https://api.deepseek.com"
 )
 parser = StrOutputParser()
@@ -58,32 +49,3 @@ while 1:
         config={"configurable":{"session_id":session_id}}
     )
     print(f'AI:{response}')
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
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-

04_langchain_basics/05_practice.py

@@ -1,13 +1,4 @@
-import os
+from config import OPENAI_API_KEY
-from dotenv import load_dotenv
-
-load_dotenv()
-
-def create_client():
-    api_key = os.getenv("OPENAI_API_KEY")
-    return api_key
-
-
 from langchain_core.prompts import ChatPromptTemplate,MessagesPlaceholder
 from langchain_openai import ChatOpenAI
 from langchain_core.output_parsers import StrOutputParser
@@ -44,7 +35,7 @@ def main():
     # 此处集中配置LLM
     llm = ChatOpenAI(
         model="deepseek-chat",
-        api_key=create_client(),
+        api_key=OPENAI_API_KEY,
         base_url="https://api.deepseek.com"
     )
     prompt = """你是‘小智’，一位专业、耐心且记忆力出色的 AI 助手。

05_langchain_advanced/01_define_toolbox.py

@@ -1,10 +1,3 @@
-import os
-from dotenv import load_dotenv
-
-load_dotenv()
-api_key = os.getenv("OPENAI_API_KEY")
-
-
 from langchain_core.tools import tool
 
 @tool

05_langchain_advanced/02_general_agent.py

@@ -1,20 +1,15 @@
-import os
+from config import OPENAI_API_KEY
-from dotenv import load_dotenv
-
-load_dotenv()
-api_key = os.getenv("OPENAI_API_KEY")
-
-
-import os
 from langchain_openai import ChatOpenAI
-from langchain.agents import AgentExecutor, create_tool_calling_agent
+# 在LangChain 1.0+版本中，以下俩组件移到了langchain-classic包中
+from langchain_classic.agents import AgentExecutor
+from langchain_classic.agents import create_tool_calling_agent
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain_core.tools import tool # 导入 @tool
 
 # 配置LLM
 llm = ChatOpenAI(
     model="deepseek-chat",
-    api_key=api_key,
+    api_key=OPENAI_API_KEY,
     base_url="https://api.deepseek.com"
 )
 

05_langchain_advanced/03_sql_agent.py

@@ -1,18 +1,14 @@
-import os
+from config import OPENAI_API_KEY
-from dotenv import load_dotenv
-
-load_dotenv()
-api_key = os.getenv("OPENAI_API_KEY")
-
 import sqlite3
 from langchain_openai import ChatOpenAI
 from langchain_community.agent_toolkits import create_sql_agent
 from langchain_community.utilities import SQLDatabase  # 导入 SQLDatabase
+import os
 
 # 配置llm
 llm = ChatOpenAI(
     model="deepseek-chat",
-    api_key=api_key,
+    api_key=OPENAI_API_KEY,
     base_url="https://api.deepseek.com"
 )
 

05_langchain_advanced/04_memory_general_agent.py

@@ -1,13 +1,7 @@
-import os
+from config import OPENAI_API_KEY
-from dotenv import load_dotenv
-
-load_dotenv()
-api_key = os.getenv("OPENAI_API_KEY")
-
-
-import os
 from langchain_openai import ChatOpenAI
-from langchain.agents import AgentExecutor, create_tool_calling_agent
+from langchain_classic.agents import AgentExecutor
+from langchain_classic.agents import create_tool_calling_agent
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain_core.tools import tool # 导入 @tool
 from langchain_community.chat_message_histories import ChatMessageHistory
@@ -17,7 +11,7 @@ from langchain_core.runnables import RunnableWithMessageHistory
 # 配置llm
 llm = ChatOpenAI(
     model="deepseek-chat",
-    api_key=api_key,
+    api_key=OPENAI_API_KEY,
     base_url="https://api.deepseek.com"
 )
 # 配置prompt(新增俩占位符 一个为对话历史记录，一个为agent的思考过程)

05_langchain_advanced/05_caching.py

@@ -1,19 +1,13 @@
-import os
+from config import OPENAI_API_KEY
-from dotenv import load_dotenv
-
-load_dotenv()
-api_key = os.getenv("OPENAI_API_KEY")
-
-
 import time
 from langchain_openai import ChatOpenAI
-from langchain.globals import set_llm_cache
+from langchain_core.globals import set_llm_cache
 from langchain_community.cache import InMemoryCache
 
 # 配置llm
 llm = ChatOpenAI(
     model="deepseek-chat",
-    api_key=api_key,
+    api_key=OPENAI_API_KEY,
     base_url="https://api.deepseek.com"
 )
 # 设置全局缓存

05_langchain_advanced/06_streaming.py

@@ -1,23 +1,18 @@
-import os
+from config import OPENAI_API_KEY
-from dotenv import load_dotenv
-
-load_dotenv()
-api_key = os.getenv("OPENAI_API_KEY")
-
-
 from langchain_openai import ChatOpenAI
-from langchain.agents import AgentExecutor, create_tool_calling_agent
+from langchain_classic.agents import AgentExecutor
+from langchain_classic.agents import create_tool_calling_agent
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain_core.tools import tool # 导入 @tool
 from langchain_community.chat_message_histories import ChatMessageHistory
 from langchain_core.runnables import RunnableWithMessageHistory
-from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain_core.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 
 
 # 配置llm
 llm = ChatOpenAI(
     model="deepseek-chat",
-    api_key=api_key,
+    api_key=OPENAI_API_KEY,
     base_url="https://api.deepseek.com",
     streaming=True,
     callbacks=[StreamingStdOutCallbackHandler()]

06_rag_basics/02_embedding.py

@@ -1,12 +1,11 @@
-from langchain_huggingface import HuggingFaceEmbeddings
+from embeddings import get_embeddings
 
-# 第一次运行可能时间较久
+
-print('---正在首次加载本地嵌入模型(bge-small-zh-v1.5)...---')
+# 首次运行可能时间较久 -- 同时运行本文件需要梯子，不然无法加载到本地
+print('---正在加载本地嵌入模型(bge-small-zh-v1.5)...---')
 
 # 理论：有embedding的向量模型
-embeddings_model = HuggingFaceEmbeddings(
+embeddings_model = get_embeddings("bge-small-zh-v1.5")
-    model_name="BAAI/bge-small-zh-v1.5" # 一个中英双语开源模型
-)
 print('嵌入模型载入完毕')
 
 # 演示：将文本转换为向量

06_rag_basics/03_build_index.py

@@ -1,10 +1,9 @@
 # 仅负责: 切片 -> 向量化 -> 构建索引 -> 保存到磁盘
 # 运行一次即可，无需每次检索都运行
-
+from embeddings import get_embeddings
 import os
 from langchain_community.document_loaders import TextLoader
 from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_huggingface import HuggingFaceEmbeddings
 from langchain_community.vectorstores import FAISS
 
 # 准备知识库内容
@@ -48,7 +47,7 @@ splits = text_splitter.split_documents(docs) # 运行切分器
 print(f'p1完成，文档已切分成{len(splits)}个片段\n')
 
 # 2. 向量化(Embedding)
-embeddings_model = HuggingFaceEmbeddings(model_name="BAAI/bge-small-zh-v1.5") # 载入向量化模型
+embeddings_model = get_embeddings() # 载入向量化模型
 print(f'p2完成，Embedding模型已准备\n') #
 
 # 3. 存储(Store)
@@ -61,17 +60,3 @@ print(f'p3完成，向量数据库{db}已构建')
 os.remove("knowledge_base.txt")
 
 print('---所有阶段已经完成!---')
-
-
-
-
-
-
-
-
-
-
-
-
-
-

06_rag_basics/04_rag_chain_full.py

@@ -1,12 +1,8 @@
+from embeddings import get_embeddings
+from config import OPENAI_API_KEY
 import os
-from dotenv import load_dotenv
-
-load_dotenv()
-api_key = os.getenv("OPENAI_API_KEY")
-
 from langchain_community.document_loaders import TextLoader
 from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_huggingface import HuggingFaceEmbeddings
 from langchain_community.vectorstores import FAISS
 from langchain_openai import ChatOpenAI
 from langchain_core.prompts import ChatPromptTemplate
@@ -43,8 +39,8 @@ loader = TextLoader('knowledge_base.txt',encoding='utf8')
 docs = loader.load()
 text_splitter = RecursiveCharacterTextSplitter(chunk_size=250,chunk_overlap=40)
 splits = text_splitter.split_documents(docs)
-embedding_model = HuggingFaceEmbeddings(model_name="BAAI/bge-small-zh-v1.5")
+embedding_model = get_embeddings("bge-small-zh-v1.5")
-db = FAISS.from_documents(splits,embedding_model)
+db = FAISS.from_documents(splits,embedding_model) # 在内存中构建向量索引，但不持久化到本地文件
 print('---模块A(Indexing)完成---\n')
 
 
@@ -75,7 +71,7 @@ prompt = ChatPromptTemplate.from_messages([
 # 3. G (Generation - 生成)
 llm = ChatOpenAI(
     model="deepseek-chat",
-    api_key=api_key,
+    api_key=OPENAI_API_KEY,
     base_url="https://api.deepseek.com"
 )
 
@@ -111,15 +107,3 @@ print(f'回答:{response}\n')
 
 # 清理临时文件
 os.remove("knowledge_base.txt")
-
-
-
-
-
-
-
-
-
-
-
-

07_rag_advanced/00_build_index.py

@@ -1,17 +1,14 @@
-# pip install chroma
-# pip install -U langchain-chroma
-
 import os
 from langchain_community.document_loaders import TextLoader
 from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_huggingface import HuggingFaceEmbeddings
 from langchain_chroma import Chroma
+from embeddings import get_embeddings
 
 
 knowledge_base_file = "war_and_peace.txt"
 # 持久化目录: Chroma会把所有数据(向量+文本+元数据)都存到这个文件夹
 persist_directory = './chroma_db_war_and_peace_bge_small_en_v1.5'
-embedding_model = 'BAAI/bge-small-en-v1.5' # 如果愿意等待，可以换成模型"BAAI/bge-m3"，效果更好更适合长文，但下载时间也更久(2.2G)
+model_name_str = 'BAAI/bge-small-en-v1.5' # 如果愿意等待，可以换成模型"BAAI/bge-m3"，效果更好更适合长文，但下载时间也更久(2.2G)
 chunk_size = 500
 chunk_overlap = 75
 
@@ -42,9 +39,10 @@ text_splitter = RecursiveCharacterTextSplitter(
 splits = text_splitter.split_documents(docs)
 print('分割完成...\n')
 # 3. 向量化 -- 第一次运行会下载模型,预计耗时2分钟
-embedding_model = HuggingFaceEmbeddings(
+print(f'正在加载/下载模型{model_name_str}...')
-    model_name=embedding_model,
+embedding_model = get_embeddings(
-    model_kwargs={'device':'cpu'}, # 强制模型在cpu上运行
+    model_name=model_name_str,
+    device='cpu', # 强制模型在cpu上运行
     encode_kwargs={'batch_size':64} # 每次处理64个文本片段
 )
 print('Embedding模型加载完成...\n')
@@ -63,10 +61,4 @@ for i in range(0, len(splits), batch_size):
     print(f"已插入 {min(i + batch_size, len(splits))} / {len(splits)} 条")
 
 
-print(f'✅ 索引构建完毕，共 {len(splits)} 条，已保存到 {persist_directory}')
+print(f'✅ 索引构建完毕，共 {len(splits)} 条，已保存到 {persist_directory}')
-
-
-
-
-
-

07_rag_advanced/01_load_from_chroma.py

@@ -1,45 +1,41 @@
-# pip install --upgrade langchain-openai
-# pip install --upgrade langchain-huggingface langchain-core langchain-community
-# pip install --upgrade langchain-core langchain-community
-import os
-from dotenv import load_dotenv
-from langchain_core.runnables import RunnablePassthrough
-
-load_dotenv()
-api_key = os.getenv("OPENAI_API_KEY")
-
-import os
-from langchain_huggingface import HuggingFaceEmbeddings
 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_openai import ChatOpenAI
+from embeddings import get_embeddings
+from config import OPENAI_API_KEY
+import os
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import RunnablePassthrough
 
 
 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---')
 
 # 模块A:链接本地Chroma向量数据库
 # 1. 加载 Embedding 模型
-embedding_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. 从本地目录加载Chroma DB
 db = Chroma(
     persist_directory=Persist_directory,
-    embedding_function=embedding_model
+    embedding_function=embeddings_model
 )
 print(f'Chroma数据库已从本地加载(共{db._collection.count()}条)\n')
 
 # 模块B:R-A-G Flow
 # 1. R-检索
-retriever = db.as_retriever(search_kwargs={"k": 3})  # 召回3条相关数据
+retriever = db.as_retriever(search_kwargs={"k": 5})  # 召回5条相关数据
 
 # 2. A-增强
 sys_prompt = """
@@ -58,7 +54,7 @@ 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"
 )
 
@@ -79,6 +75,4 @@ print('---正在运行RAG链条---')
 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？'
 response = rag_chain.invoke(question)
 print(f'提问:{question}')
-print(f'回答:{response}')
+print(f'回答:{response}')
-
-

07_rag_advanced/02_reranker.py

@@ -1,33 +1,31 @@
 import os
-from dotenv import load_dotenv
+from config import OPENAI_API_KEY
-
+from embeddings import get_embeddings
-load_dotenv()
-api_key = os.getenv("OPENAI_API_KEY")
-
-from langchain_huggingface import HuggingFaceEmbeddings
 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
-
+from langchain_classic.retrievers import ContextualCompressionRetriever
-# --- Reranker (02) 新增的 import ---
+from langchain_classic.retrievers.document_compressors import CrossEncoderReranker
-from langchain.retrievers import ContextualCompressionRetriever
 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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07_rag_advanced/03_rag_as_tool.py

@@ -1,25 +1,21 @@
 import os
-from dotenv import load_dotenv
+from config import OPENAI_API_KEY
-
+from embeddings import get_embeddings
-load_dotenv()
-api_key = os.getenv("OPENAI_API_KEY")
-
-from langchain_huggingface import HuggingFaceEmbeddings
 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
-from langchain.retrievers import ContextualCompressionRetriever
+from langchain_classic.retrievers import ContextualCompressionRetriever
 from langchain_community.cross_encoders import HuggingFaceCrossEncoder
-from langchain.retrievers.document_compressors import CrossEncoderReranker
+from langchain_classic.retrievers.document_compressors import CrossEncoderReranker
 from langchain_core.tools import tool
 
 
 # 全局 LLM (供Agent和Rag共用)
 llm = ChatOpenAI(
     model="deepseek-chat",
-    api_key=api_key,
+    api_key=OPENAI_API_KEY,
     base_url="https://api.deepseek.com"
 )
 
@@ -27,23 +23,26 @@ llm = ChatOpenAI(
 def build_rag_chain(llm_instance):
     print('---正在构建RAG链条...---\n')
 
-    Persist_directory = './chroma_db_war_and_peace_bge_small_en_v1.5'
+    persist_directory = './chroma_db_war_and_peace_bge_small_en_v1.5'
-    Embedding_model = 'BAAI/bge-small-en-v1.5'
+    embedding_model_name = 'BAAI/bge-small-en-v1.5'
-    Encoder_model = "BAAI/bge-reranker-base"
+    encoder_model_name = "BAAI/bge-reranker-base"
 
-    if not os.path.exists(Persist_directory):
+    if not os.path.exists(persist_directory):
-        raise FileNotFoundError(f'索引目录{Persist_directory}未找到，请先运行 build_index.py')
+        raise FileNotFoundError(f'索引目录{persist_directory}未找到，请先运行 build_index.py')
 
-    embeddings_model = HuggingFaceEmbeddings(model_name=Embedding_model)
+    print(f'正在加载/下载 Embedding模型：{embedding_model_name}')
+    embeddings_model = get_embeddings(model_name=embedding_model_name,device='cpu')
     db = Chroma(
-        persist_directory=Persist_directory,
+        persist_directory=persist_directory,
         embedding_function=embeddings_model
     )
 
     # 1. R-检索--强化版
-    base_retriever = db.as_retriever(search_kwargs={"k":5})
+    base_retriever = db.as_retriever(search_kwargs={"k":50})
-    encoder = HuggingFaceCrossEncoder(model_name=Encoder_model)
+
-    reranker = CrossEncoderReranker(model=encoder,top_n=2)
+    print(f'正在加载 Reranker模型:{encoder_model_name}...')
+    encoder = HuggingFaceCrossEncoder(model_name=encoder_model_name)
+    reranker = CrossEncoderReranker(model=encoder,top_n=6)
     compression_retriever=ContextualCompressionRetriever(
         base_retriever=base_retriever,
         base_compressor=reranker
@@ -59,6 +58,7 @@ def build_rag_chain(llm_instance):
     [上下文]: {context}
     [问题]: {question}
     """
+
     prompt = ChatPromptTemplate.from_messages([
         ('system',sys_prompt),
         ('human','{question}')
@@ -105,32 +105,4 @@ if __name__ == '__main__':
     question = "皮埃尔是共济会成员吗？他在其中扮演什么角色？"
     res = search_war_and_peace.invoke(question)
     print(f'问题:{question}')
-    print(f'回答:{res}')
+    print(f'回答:{res}')
-
-
-
-
-
-
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07_rag_advanced/04_memory_rag_agent.py

@@ -1,21 +1,17 @@
 import os
-from dotenv import load_dotenv
+from config import OPENAI_API_KEY
-
+from embeddings import get_embeddings
-load_dotenv()
-api_key = os.getenv("OPENAI_API_KEY")
-
-from langchain_huggingface import HuggingFaceEmbeddings
 from langchain_chroma import Chroma
 from langchain_openai import ChatOpenAI
 from langchain_core.prompts import ChatPromptTemplate,MessagesPlaceholder
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.runnables import RunnablePassthrough
-from langchain.retrievers import ContextualCompressionRetriever
+from langchain_classic.retrievers import ContextualCompressionRetriever
 from langchain_community.cross_encoders import HuggingFaceCrossEncoder
-from langchain.retrievers.document_compressors import CrossEncoderReranker
+from langchain_classic.retrievers.document_compressors import CrossEncoderReranker
 from langchain_core.tools import tool
-
+from langchain_classic.agents import AgentExecutor
-from langchain.agents import AgentExecutor, create_tool_calling_agent
+from langchain_classic.agents import create_tool_calling_agent
 from langchain_community.chat_message_histories import ChatMessageHistory
 from langchain_core.runnables import RunnableWithMessageHistory
 
@@ -25,23 +21,25 @@ from langchain_core.runnables import RunnableWithMessageHistory
 def build_rag_chain(llm_instance):
     print('---正在构建RAG链条---')
 
-    Persist_directory = './chroma_db_war_and_peace_bge_small_en_v1.5'
+    persist_directory = './chroma_db_war_and_peace_bge_small_en_v1.5'
-    Embedding_model = 'BAAI/bge-small-en-v1.5'
+    embedding_model_name = 'BAAI/bge-small-en-v1.5'
-    Encoder_model = "BAAI/bge-reranker-base"
+    encoder_model_name = "BAAI/bge-reranker-base"
 
-    if not os.path.exists(Persist_directory):
+    if not os.path.exists(persist_directory):
-        raise FileNotFoundError(f'索引目录{Persist_directory}未找到，请先运行 build_index.py')
+        raise FileNotFoundError(f'索引目录{persist_directory}未找到，请先运行 build_index.py')
 
     # 链接向量数据库
-    embedding_model = HuggingFaceEmbeddings(model_name=Embedding_model)
+    print(f'正在加载/下载 Embedding模型：{embedding_model_name}')
+    embeddings_model = get_embeddings(model_name=embedding_model_name,device='cpu')
     db = Chroma(
-        persist_directory=Persist_directory,
+        persist_directory=persist_directory,
-        embedding_function=embedding_model
+        embedding_function=embeddings_model
     )
     # R
-    base_retriever = db.as_retriever(search_kwargs={'k':5})
+    print(f'正在加载 Reranker模型:{encoder_model_name}...')
-    encoder = HuggingFaceCrossEncoder(model_name=Encoder_model)
+    base_retriever = db.as_retriever(search_kwargs={'k':50})
-    reranker = CrossEncoderReranker(model=encoder,top_n=2)
+    encoder = HuggingFaceCrossEncoder(model_name=encoder_model_name)
+    reranker = CrossEncoderReranker(model=encoder,top_n=6)
     compression_retriever = ContextualCompressionRetriever(
         base_retriever=base_retriever,
         base_compressor=reranker
@@ -80,7 +78,7 @@ def create_agent_with_memory():
     # LLm
     llm = ChatOpenAI(
         model="deepseek-chat",
-        api_key=api_key,
+        api_key=OPENAI_API_KEY,
         base_url="https://api.deepseek.com"
     )
     # Prompt

requirements.txt

@@ -1,12 +1,14 @@
-python-dotenv~=1.0.0
+huggingface_hub==0.36.0
-openai>=1.40.0,<2.0.0
+langchain==1.0.8
-requests~=2.32.4
+langchain_chroma==1.0.0
-langchain~=0.2.16
+langchain_classic==1.0.0
-langchain-community~=0.2.16
+langchain_community==0.4.1
-langchain-openai~=0.1.15
+langchain_core==1.0.6
-langchain-core~=0.2.38
+langchain_huggingface==1.0.1
-langgraph~=0.2.0
+langchain_openai==1.0.3
-langchain-huggingface==0.0.3
+langchain_text_splitters==1.0.0
-langchain-chroma==0.1.1
+langgraph==1.0.3
-chromadb==0.4.22
+langsmith==0.4.43
-sentence-transformers>=2.2.0
+openai==2.8.1
+python-dotenv==1.2.1
+Requests==2.32.5