131 lines
4.5 KiB
Python
131 lines
4.5 KiB
Python
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!pip install -q langgraph-checkpoint-sqlite langchain_google_genai
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import sqlite3
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from google.colab import userdata
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from typing import Literal, TypedDict, Annotated
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from langchain_google_genai import ChatGoogleGenerativeAI
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from langchain_core.messages import SystemMessage, HumanMessage, RemoveMessage, AnyMessage
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from langgraph.graph import StateGraph, START, END
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from langgraph.checkpoint.sqlite import SqliteSaver
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from langgraph.graph.message import add_messages
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from langchain_core.runnables import RunnableConfig
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from typing import Union, List, Dict
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# --- 1. 状态定义 ---
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class State(TypedDict):
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messages: Annotated[list[AnyMessage], add_messages]
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summary: str # 永久存储在数据库中的摘要内容
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# --- 2. 核心逻辑 ---
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llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", temperature = 0.7, google_api_key='') #在这里倒入API key
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def call_model(state: State, config: RunnableConfig):
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"""对话节点：融合了动态 Prompt 和 长期摘要"""
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# 获取当前 session 特有的 System Prompt（如果没传则使用默认）
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configurable = config.get("configurable", {})
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system_base_prompt = configurable.get("system_prompt", "你是一个通用的 AI 助手。")
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# 构造当前上下文
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summary = state.get("summary", "")
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if summary:
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system_base_prompt += f"\n\n<context_summary>\n{summary}\n</context_summary>"
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messages = [SystemMessage(content=system_base_prompt)] + state["messages"]
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response = llm.invoke(messages)
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return {"messages": [response]}
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def summarize_conversation(state: State):
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"""总结节点：负责更新摘要并清理过期消息"""
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summary = state.get("summary", "")
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messages_to_summarize = state["messages"][:-1]
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# If there's nothing to summarize yet, just END
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if not messages_to_summarize:
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return {"summary": summary}
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system_prompt = (
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"你是一个记忆管理专家。请更新摘要，合并新旧信息。"
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"1. 保持简练，仅保留事实（姓名、偏好、核心议题）。"
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"2. 如果新消息包含对旧信息的修正，请更新它。"
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"3. 如果对话中包含图片描述，请将图片的关键视觉信息也记录在摘要中"
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)
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summary_input = f"现有摘要: {summary}\n\n待加入的新信息: {messages_to_summarize}"
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# Invoke model to get new condensed summary
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response = llm.invoke([
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SystemMessage(content=system_prompt),
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HumanMessage(content=summary_input)
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])
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# Important: Create RemoveMessage objects for all messages that were summarized
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delete_messages = [RemoveMessage(id=m.id) for m in messages_to_summarize if m.id]
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return {
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"summary": response.content,
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"messages": delete_messages
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}
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def should_continue(state: State) -> Literal["summarize", END]:
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"""如果消息累积超过3条，则去总结节点"""
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if len(state["messages"]) > 3: #changed
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return "summarize"
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return END
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# --- 3. 构建图 ---
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db_path = "multi_session_chat.sqlite"
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conn = sqlite3.connect(db_path, check_same_thread=False)
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memory = SqliteSaver(conn)
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workflow = StateGraph(State)
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workflow.add_node("chatbot", call_model)
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workflow.add_node("summarize", summarize_conversation)
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workflow.add_edge(START, "chatbot")
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workflow.add_conditional_edges("chatbot", should_continue)
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workflow.add_edge("summarize", END)
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app = workflow.compile(checkpointer=memory)
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def chat(thread_id: str, system_prompt: str, user_content: Union[str, List[Dict]]):
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"""
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Processes a single user message and returns the AI response,
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persisting memory via the thread_id.
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"""
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config = {
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"configurable": {
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"thread_id": thread_id,
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"system_prompt": system_prompt
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}
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}
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# Prepare the input for this specific turn
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input_data = {"messages": [HumanMessage(content=user_content)]}
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ai_response = ""
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# Stream the values to get the final AI message
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for event in app.stream(input_data, config, stream_mode="values"):
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if "messages" in event:
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last_msg = event["messages"][-1]
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if last_msg.type == "ai":
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ai_response = last_msg.content
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return ai_response
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# 使用范例
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# if __name__ == "__main__":
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# tid = "py_expert_001"
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# sys_p = "你是个善解人意的机器人。"
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# # Call 1: Establish context
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# resp1 = chat(tid, sys_p, "你好，我叫小明。")
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# print(f"Bot: {resp1}")
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# # Call 2: Test memory (The model should remember the name '小明')
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# resp2 = chat(tid, sys_p, "我今天很开心")
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# print(f"Bot: {resp2}") |