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Gala_Agent.py
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"""
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LangGraph RAG Agent 整合所有必要组件
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"""
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from typing import TypedDict, Annotated
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from langgraph.graph.message import add_messages
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from langchain_core.messages import AnyMessage, HumanMessage, AIMessage
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from langgraph.prebuilt import ToolNode
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from langgraph.graph import START, StateGraph
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from langgraph.prebuilt import tools_condition
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from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
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from langchain_openai import ChatOpenAI
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from tools import DuckDuckGoSearchRun, weather_info_tool, hub_stats_tool
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from retriever import guest_info_tool
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search_tool = DuckDuckGoSearchRun()
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DOUBAO_API_KEY = "cc556f72-0b09-4899-b9c3-ca2d2b4db8a5"
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DOUBAO_BASE_URL = "https://ark.cn-beijing.volces.com/api/v3"
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llm = ChatOpenAI(
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model="doubao-seed-1-8-251228",
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openai_api_key=DOUBAO_API_KEY,
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openai_api_base=DOUBAO_BASE_URL,
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)
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tools = [
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guest_info_tool, search_tool, weather_info_tool, hub_stats_tool,
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]
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chat_with_tools = llm.bind_tools(tools)
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class AgentState(TypedDict):
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messages: Annotated[list[AnyMessage], add_messages]
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def assistant(state: AgentState):
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return {
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"messages": [chat_with_tools.invoke(state["messages"])]
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}
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builder = StateGraph(AgentState)
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builder.add_node("assistant", assistant)
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builder.add_node("tools", ToolNode(tools))
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builder.add_edge(START, "assistant")
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builder.add_conditional_edges(
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"assistant",
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tools_condition
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)
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builder.add_edge("tools", "assistant")
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alfred = builder.compile()
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# 示例1:获取嘉宾信息
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# response = alfred.invoke({"messages": "告诉我关于 Ada Lovelace的信息"})
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# 组合多工具应用
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response = alfred.invoke({"messages":"I need to speak with 'Dr. Nikola Tesla' about recent advancements in wireless energy. Can you help me prepare for this conversation?"})
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print("🎩 Alfred's Response:")
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print(response['messages'][-1].content)
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