chore: rename multiple files to improve importability and module structure

2025-11-28 11:25:58 +08:00
parent 7d10f27951
commit 5bc33d2584
41 changed files with 4 additions and 4 deletions
@@ -0,0 +1,170 @@
+import os
+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,MessagesPlaceholder
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+from langchain_classic.retrievers import ContextualCompressionRetriever
+from langchain_community.cross_encoders import HuggingFaceCrossEncoder
+from langchain_classic.retrievers.document_compressors import CrossEncoderReranker
+from langchain_core.tools import tool
+from langchain_classic.agents import AgentExecutor
+from langchain_classic.agents import create_tool_calling_agent
+from langchain_community.chat_message_histories import ChatMessageHistory
+from langchain_core.runnables import RunnableWithMessageHistory
+
+
+
+# 1. 构建一个可复用的 RAG链条
+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'
+    embedding_model_name = 'BAAI/bge-small-en-v1.5'
+    encoder_model_name = "BAAI/bge-reranker-base"
+
+    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')
+
+    # 链接向量数据库
+    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,
+        embedding_function=embeddings_model
+    )
+    # R
+    print(f'正在加载 Reranker模型:{encoder_model_name}...')
+    base_retriever = db.as_retriever(search_kwargs={'k':50})
+    encoder = HuggingFaceCrossEncoder(model_name=encoder_model_name)
+    reranker = CrossEncoderReranker(model=encoder,top_n=6)
+    compression_retriever = ContextualCompressionRetriever(
+        base_retriever=base_retriever,
+        base_compressor=reranker
+    )
+    retriever = compression_retriever
+    # A
+    sys_prompt = """
+    你是一个博学的历史学家和文学评论家。
+    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。
+    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”。
+
+    [上下文]: {context}
+    [问题]: {question}
+    """
+    prompt = ChatPromptTemplate.from_messages([
+        ('system',sys_prompt),
+        ('human','{question}')
+    ])
+
+    def format_docs(docs):
+        return '\n'.join(doc.page_content for doc in docs)
+
+    # R-A-G
+    rag_chain = (
+        {'context':retriever | format_docs,'question':RunnablePassthrough()}
+        | prompt
+        | llm_instance
+        | StrOutputParser()
+    )
+    print('---RAG链条构建完毕!---\n')
+    return rag_chain
+
+
+# 2. 将 RAG链条 组装进Agent里
+def create_agent_with_memory():
+    # LLm
+    llm = ChatOpenAI(
+        model="deepseek-chat",
+        api_key=OPENAI_API_KEY,
+        base_url="https://api.deepseek.com"
+    )
+    # Prompt
+    prompt = ChatPromptTemplate.from_messages([
+        ('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。'),
+        MessagesPlaceholder(variable_name="history"), # 05篇所学:记忆占位符
+        ('human','{input}'),
+        MessagesPlaceholder(variable_name="agent_scratchpad") # 05篇所学:ReAct 思考链，使其能够调用工具
+    ])
+
+    # Tool
+    rag_chain_instance = build_rag_chain(llm_instance=llm)
+
+
+    @tool
+    def search_war_and_peace(query):
+        """查询《战争与和平》小说中的内容，包括人物、情节、历史事件等"""
+        print(f'\n正在检索《战争与和平》:{query}')
+        return rag_chain_instance.invoke(query)
+
+    @tool
+    def get_weather(location):
+        """模拟获得天气信息"""
+        return f"{location}当前天气：23℃，晴，风力2级"
+
+    tools = [get_weather,search_war_and_peace]
+
+    # 创建Agent
+    agent = create_tool_calling_agent(llm=llm,tools=tools,prompt=prompt)
+    agent_executor = AgentExecutor(agent=agent,tools=tools,verbose=False)
+
+    # 封装Memory
+    store = {}
+
+    def get_session_history(session_id:int):
+        if session_id not in store:
+            store[session_id] = ChatMessageHistory()
+        return store[session_id]
+
+
+    # 添加记忆功能
+    agent_with_memory = RunnableWithMessageHistory(
+        runnable=agent_executor,
+        get_session_history=get_session_history,
+        input_messages_key="input",
+        history_messages_key="history"
+    )
+    return agent_with_memory
+
+
+# 测试
+
+if __name__ == '__main__':
+    session_id = 'user123'
+    agent = create_agent_with_memory()
+    while 1:
+        user_input = input('\n你:')
+        if user_input=='quit':
+            print('拜拜~')
+            exit()
+        response = agent.invoke(
+            {'input':user_input},
+            config={'configurable':{'session_id':session_id}}
+        )
+        print(f"AI:{response['output']}")
+
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