from langchain_core.documents import Document

# 加载数据集
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
]

from llama_index.core.schema import Document

# 加载数据集
guest_dataset = datasets.load_dataset("agents-course/unit3-invitees", split="train")

# 转换为 Document 对象
docs = [
    Document(
        text="\n".join([
            f"Name: {guest_dataset['name'][i]}",
            f"Relation: {guest_dataset['relation'][i]}",
            f"Description: {guest_dataset['description'][i]}",
            f"Email: {guest_dataset['email'][i]}"
        ]),
        metadata={"name": guest_dataset['name'][i]}
    )
    for i in range(len(guest_dataset))
]

from langchain_core.documents import Document

# 加载数据集
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
]

from smolagents import Tool
from langchain_community.retrievers import BM25Retriever

class GuestInfoRetrieverTool(Tool):
    name = "guest_info_retriever"
    description = "Retrieves detailed information about gala guests based on their name or relation."
    inputs = {
        "query": {
            "type": "string",
            "description": "The name or relation of the guest you want information about."
        }
    }
    output_type = "string"

    def __init__(self, docs):
        self.is_initialized = False
        self.retriever = BM25Retriever.from_documents(docs)

    def forward(self, query: str):
        results = self.retriever.get_relevant_documents(query)
        if results:
            return "\n\n".join([doc.page_content for doc in results[:3]])
        else:
            return "No matching guest information found."

# 初始化工具
guest_info_tool = GuestInfoRetrieverTool(docs)

from llama_index.core.tools import FunctionTool
from llama_index.retrievers.bm25 import BM25Retriever

bm25_retriever = BM25Retriever.from_defaults(nodes=docs)

def get_guest_info_retriever(query: str) -> str:
    """Retrieves detailed information about gala guests based on their name or relation."""
    results = bm25_retriever.retrieve(query)
    if results:
        return "\n\n".join([doc.text for doc in results[:3]])
    else:
        return "No matching guest information found."

# 初始化工具
guest_info_tool = FunctionTool.from_defaults(get_guest_info_retriever)

from langchain_community.retrievers import BM25Retriever
from langchain_core.tools import Tool

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."
)

from smolagents import CodeAgent, InferenceClientModel

# 初始化 Hugging Face 模型
model = InferenceClientModel()

# 使用宾客信息工具创建我们的晚会智能体 Alfred
alfred = CodeAgent(tools=[guest_info_tool], model=model)

# Example query Alfred might receive during the gala
response = alfred.run("Tell me about our guest named 'Lady Ada Lovelace'.")

print("🎩 Alfred's Response:")
print(response)

from llama_index.core.agent.workflow import AgentWorkflow
from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI

# 初始化 Hugging Face 模型
llm = HuggingFaceInferenceAPI(model_name="Qwen/Qwen2.5-Coder-32B-Instruct")

# 使用宾客信息工具创建我们的晚会智能体 Alfred
alfred = AgentWorkflow.from_tools_or_functions(
    [guest_info_tool],
    llm=llm,
)

# Example query Alfred might receive during the gala
response = await alfred.run("Tell me about our guest named 'Lady Ada Lovelace'.")

print("🎩 Alfred's Response:")
print(response)

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

# 生成聊天界面，包括工具
llm = HuggingFaceEndpoint(
    repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
    huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
)

chat = ChatHuggingFace(llm=llm, verbose=True)
tools = [guest_info_tool]
chat_with_tools = chat.bind_tools(tools)

# 生成 AgentState 和 Agent 图
class AgentState(TypedDict):
    messages: Annotated[list[AnyMessage], add_messages]

def assistant(state: AgentState):
    return {
        "messages": [chat_with_tools.invoke(state["messages"])],
    }

## 构建流程图
builder = StateGraph(AgentState)

# 定义节点：这些节点完成工作
builder.add_node("assistant", assistant)
builder.add_node("tools", ToolNode(tools))

# 定义边：这些决定了控制流如何移动
builder.add_edge(START, "assistant")
builder.add_conditional_edges(
    "assistant",
    # If the latest message requires a tool, route to tools
    # Otherwise, provide a direct response
    tools_condition,
)
builder.add_edge("tools", "assistant")
alfred = builder.compile()

messages = [HumanMessage(content="Tell me about our guest named 'Lady Ada Lovelace'.")]
response = alfred.invoke({"messages": messages})

print("🎩 Alfred's Response:")
print(response['messages'][-1].content)