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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 tools import search_tool, weather_info_tool, hub_stats_tool, guest_info_tool
from retriever import docs
from langchain_ollama import ChatOllama
# 生成聊天界面，包括工具
llm = ChatOllama(model="gpt-oss:20b", request_timeout=120.0)
tools = [guest_info_tool, search_tool, weather_info_tool, hub_stats_tool]
chat_with_tools = llm.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：执行具体工作
builder.add_node("assistant", assistant)
builder.add_node("tools", ToolNode(tools))
# 定义边：控制流程走向
builder.add_edge(START, "assistant")
builder.add_conditional_edges(
"assistant",
# 如果最新消息需要工具调用，则路由到 tools 节点
# 否则直接响应
tools_condition,
)
builder.add_edge("tools", "assistant")
alfred = builder.compile()
#示例1
# response = alfred.invoke({"messages": "Tell me about 'Lady Ada Lovelace' and translate output to chinese."})
# print("🎩 Alfred's Response:")
# print(response['messages'][-1].content)
#示例2
# response = alfred.invoke({"messages": "What's the weather like in Tokyo tonight? Will it be suitable for our fireworks display?and translate output to chinese."})
# print("🎩 Alfred's Response:")
# print(response['messages'][-1].content)
#示例 3：给 AI 研究者留下深刻印象
# response = alfred.invoke({"messages": "One of our guests is from Qwen. What can you tell me about their most popular model?请用中文回答"})
# print("🎩 Alfred's Response:")
# print(response['messages'][-1].content)
#示例 4：组合多工具应用
# response = alfred.invoke({"messages":"我需要与“尼古拉·特斯拉博士”讨论最近在无线能源方面的进展。你能帮我为这次对话做准备吗？"})
# print("🎩 Alfred's Response:")
# print(response['messages'][-1].content)
#高级功能：对话记忆
# 首次交互
response = alfred.invoke({"messages": [HumanMessage(content="Tell me about 'Lady Ada Lovelace'. What's her background and how is she related to me?请用中文回答")]})
print("🎩 Alfred's Response:")
print(response['messages'][-1].content)
print("以下是第二次对话内容")
# 二次交互（引用首次内容）
response = alfred.invoke({"messages": response["messages"] + [HumanMessage(content="What projects is she currently working on?请用中文回答")]})
print("🎩 Alfred's Response:")
print(response['messages'][-1].content) |