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Gala_Agent.py

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

retriever.py

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+from langchain_community.retrievers import BM25Retriever
+from langchain_core.tools import Tool
+from importlib.metadata import metadata
+
+# 加载数据集
+import datasets
+from langchain_core.documents import Document
+
+guest_dataset = datasets.load_dataset("agents-course/unit3-invitees", split="train")
+
+# 转换为 Document 对象
+docs = [
+    Document(
+        page_content="\n".join([
+            f"Name: {guest['name']}",
+            f"Relation: {guest['relation']}",
+            f"Description: {guest['description']}",
+            f"Email: {guest['email']}",
+        ]),
+        metadata={"name": guest["name"]}
+    )
+    for guest in guest_dataset
+]
+
+bm25_retriever = BM25Retriever.from_documents(docs)
+
+def extract_text(query:str) -> str:
+    """
+    Retrieves detailed information about gala guests based on their name or relation
+    """
+    results = bm25_retriever.invoke(query)
+    if results:
+        return "\n\n".join([doc.page_content for doc in results[:3]])
+    else:
+        return "No matching guest information found."
+
+guest_info_tool = Tool(
+    name="guest_info_retriever",
+    func=extract_text,
+    description="Retrieves detailed information about gala guests based on their name or relation."
+)

tools.py

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+from langchain_core.tools import Tool
+from huggingface_hub import list_models
+
+def get_hub_stats(author:str) -> str:
+    """Fetches the most downloaded model from a specific author on the Hugging Face Hub"""
+    try:
+        models = list(list_models(author=author, sort="downloads", direction=-1, limit=1))
+
+        if models:
+            model = models[0]
+            return f"The most downloaded model by {author} is {model.id} with {model.downloads:,} downloads."
+        else:
+            return f"No models found for author {author}."
+    except Exception as e:
+        return f"Error fetching models for {author}: {str(e)}"
+
+# 初始化工具
+hub_stats_tool = Tool(
+    name="get_hub_stats",
+    func=get_hub_stats,
+    description="Fetches the most downloaded model from a specific author on the Hugging Face Hub."
+)
+
+
+
+from langchain_core.tools import Tool
+import random
+
+def get_weather_info(location: str) -> str:
+    """Fetches dummy weather information for a give location"""
+    # mock data
+    weather_conditions = [
+        {"condition": "Rainy", "temp_c": 15},
+        {"condition": "Clear", "temp_c": 25},
+        {"condition": "Windy", "temp_c": 20}
+    ]
+    data = random.choice(weather_conditions)
+    return f"Weather in {location}: {data['condition']}, {data['temp_c']}°C"
+
+weather_info_tool = Tool(
+    name="get_weather_info",
+    func=get_weather_info,
+    description="Fetches dummy weather information for a give location"
+)
+
+from langchain_community.tools import DuckDuckGoSearchRun
+
+search_tool = DuckDuckGoSearchRun()