![\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/08\/20250816024448-689ff0a0e57af.png\")
<\/p>\nAgentic RAG（Agent-based Retrieval-Augmented Generation）\u662f\u6307\u5728\u4f20\u7edf\u7684 RAG（Retrieval-Augmented Generation）\u6846\u67b6\u4e2d\u5f15\u5165\u4e86 Agent（\u667a\u80fd\u4f53）\u4f5c\u4e3a\u6838\u5fc3\u7ec4\u4ef6\u7684\u53d8\u4f53\u3002\u5728\u6807\u51c6\u7684 RAG \u7cfb\u7edf\u4e2d，\u4e3b\u8981\u662f\u901a\u8fc7\u68c0\u7d22\u76f8\u5173\u6587\u6863\u6216\u4fe1\u606f\u6765\u589e\u5f3a\u751f\u6210\u6a21\u578b\u7684\u80fd\u529b，\u800c Agentic RAG \u5219\u901a\u8fc7\u96c6\u6210\u4e00\u4e2a\u667a\u80fd\u4f53，\u5e2e\u52a9\u7cfb\u7edf\u5728\u641c\u7d22\u548c\u751f\u6210\u8fc7\u7a0b\u4e2d\u8fdb\u884c\u66f4\u667a\u80fd\u7684\u51b3\u7b56\u548c\u4ea4\u4e92\u3002<\/p>\n
<\/p>\n
\u5177\u4f53\u6765\u8bf4：\u5b83\u5c06 AI \u4ee3\u7406\u5408\u5e76\u5230 RAG \u7ba1\u9053\u4e2d，\u4ee5\u7f16\u6392\u5176\u7ec4\u4ef6\u5e76\u6267\u884c\u7b80\u5355\u4fe1\u606f\u68c0\u7d22\u548c\u751f\u6210\u4e4b\u5916\u7684\u5176\u4ed6\u64cd\u4f5c，\u4ee5\u514b\u670d\u975e\u4ee3\u7406\u7ba1\u9053\u7684\u9650\u5236\u3002\u4ee3\u7406 RAG \u6700\u5e38\u89c1\u7684\u662f\u6307\u5728\u68c0\u7d22\u7ec4\u4ef6\u4e2d\u4f7f\u7528\u4ee3\u7406\u3002\u901a\u8fc7\u4f7f\u7528\u53ef\u8bbf\u95ee\u4e0d\u540c\u68c0\u7d22\u5668\u5de5\u5177\u7684\u68c0\u7d22\u4ee3\u7406\u800c\u53d8\u5f97\u4ee3\u7406，\u4f8b\u5982：<\/p>\n
\u8fd9\u91cc\u6211\u4eec\u7528 Agentic RAG \u53bb\u6574\u5408 Web \u641c\u7d22\u548c\u4f20\u7edf\u7684 RAG\u3002\u9996\u5148 \u6784\u5efa\u4f20\u7edf RAG \u7684Agent\u8282\u70b9:<\/p>\n
from<\/span> langchain_openai import<\/span> ChatOpenAI llm =<\/span> ChatOpenAI(<\/span>model_name=<\/span>"gpt-4o-mini"<\/span>,<\/span> temperature=<\/span>0.1<\/span>)<\/span><\/p>\n @tool<\/span> """ prompt =<\/span> PromptTemplate(<\/span> # \u6784\u5efa\u4f20\u7edf\u7684RAG Chain<\/span> # \u6784\u5efa\u68c0\u7d22\u5668<\/span> # \u6267\u884c\u68c0\u7d22<\/span> return<\/span> generation<\/p>\n tools =<\/span> [<\/span>fetch_real_time_info,<\/span> vec_kg]<\/span><\/p>\n model =<\/span> llm.<\/span>bind_tools(<\/span>tools)<\/span><\/p>\n tools<\/p>\n [StructuredTool(name='fetch_real_time_info', description='Get real-time Internet information', args_schema=<class '__main__.SearchQuery'>, func=<function fetch_real_time_info at 0x15cb50ae0>), import<\/span> json tools_by_name =<\/span> {<\/span>tool.<\/span>name:<\/span> tool for<\/span> tool in<\/span> tools}<\/span><\/p>\n # \u5b9a\u4e49\u5de5\u5177\u8282\u70b9<\/span> # \u5b9a\u4e49\u95ee\u7b54\u6a21\u578b<\/span> # \u5b9a\u4e49\u8def\u7531\u8282\u70b9<\/span> if<\/span> not<\/span> last_message.<\/span>tool_calls:<\/span> else<\/span>:<\/span> from<\/span> langgraph.<\/span>graph import<\/span> StateGraph,<\/span> END<\/p>\n # \u5b9a\u4e49\u4e00\u4e2a\u56fe\u7ed3\u6784<\/span> # \u5728\u56fe\u7ed3\u6784\u4e2d\u6dfb\u52a0\u8282\u70b9<\/span>
\nfrom<\/span> langchain.<\/span>prompts import<\/span> PromptTemplate
\nfrom<\/span> langchain.<\/span>schema.<\/span>output_parser import<\/span> StrOutputParser
\nfrom<\/span> langchain.<\/span>tools import<\/span> tool<\/p>\n
\ndef<\/span> vec_kg<\/span>(<\/span>question:<\/span>str<\/span>)<\/span>:<\/span><\/p>\n
\n \u4e2a\u4eba\u77e5\u8bc6\u5e93，\u5b58\u50a8AI Agent\u9879\u76ee\u6982\u5ff5\u7684\u89e3\u8bfb
\n """<\/span><\/p>\n
\n template=<\/span>"""\u4f60\u662f\u4e00\u540d\u95ee\u7b54\u4efb\u52a1\u7684\u52a9\u624b\u3002
\n \u8bf7\u4f7f\u7528\u4ee5\u4e0b\u68c0\u7d22\u5230\u7684\u4e0a\u4e0b\u6587\u6765\u56de\u7b54\u95ee\u9898\u3002\u5982\u679c\u4f60\u4e0d\u77e5\u9053\u7b54\u6848，\u5c31\u76f4\u63a5\u8bf4\u4f60\u4e0d\u77e5\u9053\u3002
\n \u6700\u591a\u4f7f\u75285\u53e5\u8bdd，\u5e76\u4fdd\u6301\u7b54\u6848\u7b80\u6d01：
\n Question: {question}
\n Context: {context}
\n Answer:
\n """<\/span>,<\/span>
\n input_variables=<\/span>[<\/span>"question"<\/span>,<\/span> "document"<\/span>]<\/span>,<\/span>
\n )<\/span><\/p>\n
\n rag_chain =<\/span> prompt |<\/span> llm |<\/span> StrOutputParser(<\/span>)<\/span><\/p>\n
\n retriever =<\/span> vectorstore.<\/span>as_retriever(<\/span>search_kwargs=<\/span>{<\/span>"k"<\/span>:<\/span> 1<\/span>}<\/span>)<\/span><\/p>\n
\n docs =<\/span> retriever.<\/span>invoke(<\/span>"question"<\/span>)<\/span>
\n generation =<\/span> rag_chain.<\/span>invoke(<\/span>{<\/span>"context"<\/span>:<\/span> docs,<\/span> "question"<\/span>:<\/span> question}<\/span>)<\/span><\/p>\n
\n StructuredTool(name='vec_kg', description='\u4e2a\u4eba\u77e5\u8bc6\u5e93，\u5b58\u50a8AI Agent\u9879\u76ee\u6982\u5ff5\u7684\u89e3\u8bfb', args_schema=<class 'langchain_core.utils.pydantic.vec_kg'>, func=<function vec_kg at 0x15d79df80>)]<\/p>\n
\nfrom<\/span> langchain_core.<\/span>messages import<\/span> ToolMessage,<\/span> SystemMessage,<\/span> HumanMessage
\nfrom<\/span> langchain_core.<\/span>runnables import<\/span> RunnableConfig<\/p>\n
\ndef<\/span> tool_node<\/span>(<\/span>state:<\/span> AgentState)<\/span>:<\/span>
\n outputs =<\/span> [<\/span>]<\/span>
\n for<\/span> tool_call in<\/span> state[<\/span>"messages"<\/span>]<\/span>[<\/span>–<\/span>1<\/span>]<\/span>.<\/span>tool_calls:<\/span>
\n tool_result =<\/span> tools_by_name[<\/span>tool_call[<\/span>"name"<\/span>]<\/span>]<\/span>.<\/span>invoke(<\/span>tool_call[<\/span>"args"<\/span>]<\/span>)<\/span>
\n outputs.<\/span>append(<\/span>
\n ToolMessage(<\/span>
\n content=<\/span>json.<\/span>dumps(<\/span>tool_result)<\/span>,<\/span>
\n name=<\/span>tool_call[<\/span>"name"<\/span>]<\/span>,<\/span>
\n tool_call_id=<\/span>tool_call[<\/span>"id"<\/span>]<\/span>,<\/span>
\n )<\/span>
\n )<\/span>
\n return<\/span> {<\/span>"messages"<\/span>:<\/span> outputs}<\/span><\/p>\n
\ndef<\/span> call_model<\/span>(<\/span>
\n state:<\/span> AgentState,<\/span>
\n)<\/span>:<\/span>
\n system_prompt =<\/span> SystemMessage(<\/span>
\n "\u60a8\u662f\u4e00\u4f4d\u4e50\u4e8e\u52a9\u4eba\u7684AI\u52a9\u624b，\u8bf7\u5c3d\u529b\u56de\u590d\u7528\u6237\u7684\u67e5\u8be2！"<\/span>
\n )<\/span>
\n response =<\/span> model.<\/span>invoke(<\/span>[<\/span>system_prompt]<\/span> +<\/span> state[<\/span>"messages"<\/span>]<\/span>)<\/span>
\n return<\/span> {<\/span>"messages"<\/span>:<\/span> [<\/span>response]<\/span>}<\/span><\/p>\n
\ndef<\/span> should_continue<\/span>(<\/span>state:<\/span> AgentState)<\/span>:<\/span>
\n messages =<\/span> state[<\/span>"messages"<\/span>]<\/span>
\n last_message =<\/span> messages[<\/span>–<\/span>1<\/span>]<\/span><\/p>\n
\n return<\/span> "end"<\/span><\/p>\n
\n return<\/span> "continue"<\/span><\/p>\n
\nworkflow =<\/span> StateGraph(<\/span>AgentState)<\/span><\/p>\n
\nworkflow.<\/span>add_node(<\/span>"agent"<\/span>,<\/span> call_model)<\/span>
\nworkflow.<\/span>add_node(<\/span>"tools"<\/span>