names = ['c-rag','self-rag','kg-rag']files = ['c-rag.pdf','self-rag.pdf','kg-rag.pdf']

#采用chroma向量数据库chroma = chromadb.HttpClient(host="localhost", port=8000)collection = chroma.get_or_create_collection(name="agentic_rag")   vector_store = ChromaVectorStore(chroma_collection=collection)
#创建针对某个文档的doc_agent  def create_doc_agent(file,name):  
#文档拆分    print(f'Starting to create doc agent for 【{name}】...\n')  docs =SimpleDirectoryReader(input_files = [file]).load_data()splitter = SentenceSplitter(chunk_size=500,chunk_overlap=50)nodes = splitter.get_nodes_from_documents(docs)
#创建向量索引，并做持久保存if not os.path.exists(f"./storage/{name}"):print('Creating vector index...\n')storage_context = StorageContext.from_defaults(vector_store=vector_store)vector_index = VectorStoreIndex(nodes,storage_context=storage_context)vector_index.storage_context.persist(persist_dir=f"./storage/{name}")else:print('Loading vector index...\n')storage_context = StorageContext.from_defaults(persist_dir=f"./storage/{name}",vector_store=vector_store)vector_index = load_index_from_storage(storage_context=storage_context)
#创建基于向量的查询引擎query_engine = vector_index.as_query_engine(similarity_top_k=5)
#创建摘要索引与对应的查询引擎summary_index = SummaryIndex(nodes)    summary_engine = summary_index.as_query_engine(response_mode="tree_summarize")  
#将RAG引擎转化为两个tool    query_tool = QueryEngineTool.from_defaults(query_engine=query_engine,name=f'query_tool',description=f'Use if you want to query details about {name}')      summary_tool = QueryEngineTool.from_defaults(query_engine=summary_engine,name=f'summary_tool',description=f'Use ONLY IF you want to get a holistic summary of the documents. DO NOT USE if you want to query some details about {name}.')  
    #创建一个doc agent      doc_agent =   **ReActAgent** .from_tools(  **[query_tool,summary_tool]** ,verbose=True,                                    system_prompt=f"""You are a specialized agent designed to answer queries about {name}.You must ALWAYS use at least one of the tools provided when answering a question; DO NOT rely on prior knowledge. DO NOT fabricate answer.""")      return doc_agent

#创建不同文档的agent  print('===============================================\n')print('Creating tool agents for different documents...\n')doc_agents_dict = {}  for name, file in zip(names, files):    doc_agent = create_doc_agent(file, name)      doc_agents_dict[name] = tool_agent

 #首先将Doc Agent进行“工具化”  print('===============================================\n')print('Creating tools from doc agents...\n')  all_tools = []
for name in names:agent_tool = QueryEngineTool.from_defaults(           #注意，Agent本身也是一种Query Engine，所以直接转为tool             query_engine=doc_agents_dict[name],             #这个工具的名字             name=f"tool\_{name.replace("-", "")}",             #描述这个工具的作用和使用方法             description=f"Use this tool if you want to answer any questions about {name}."  )
all_tools.append(agent_tool)
#创建Top Agentprint('Creating top agent...\n')top_agent =  **OpenAIAgent.** from_tools(  **tools=all_tools** ,verbose=True,system_prompt="""You are an agent designed to answer queries over a set of given papers.Please always use the tools provided to answer a question.Do not rely on prior knowledge.DO NOT fabricate answer""" ) 

==Entering Chat REPL
Type "exit" to exit.
Human: Please introduce Retrieval Evaluator in C-RAG pattern?
Added user message to memory: Please introduce Retrieval Evaluator in C-RAG pattern? 
== Callina Function ==
Calling function: tool crag with args: {"input": "Retrieval Evaluator in C-RAG pattern")
Thought: The user is asking about the "Retrieval Evaluator"in the C-RAG pattern. I need to use a tool to help me answer the question.
Action: query tool
Action Input: { 'input: Retrieval Evaluator in C-RAG pattern'}
Observation: The retrieval evaluator in the CRAG pattern is designed to evaluate the relevance of retrieved documents to the input questi egree based on the relevance scores calculated for each question-document pair. Depending on this confidence level, the evaluator trigger actions- Correct, Incorrect, or Ambiguous. This process helps in determining whether the retrieved documents are suitable for generatin 
Thought: I can answer without using any more tools. I'll use the user's lanquage to answer
Answer: The Retrieval Evaluator in the C-RAG pattern is responsible for evaluating the relevance of retrieved documents to the input ques e level based on relevance scores for each question-document pair, triggering actions like Correct, Incorrect, or Ambiquous. This evaluat Leved documents are appropriate for generating a response
Got output: The Retrieval Evaluator in the C-RAG pattern is responsible for evaluating the relevance of retrieved documents to the input dence level based on relevance scores for each question-document pair, triggering actions like Correct, Incorrect, or Ambiquous. This eva retrieved documents are appropriate for generating a response.