联入SQLite

app/ai/ai.py

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+
+# pip install -q langgraph-checkpoint-sqlite
+
+import sqlite3
+from google.colab import userdata
+from typing import Literal, TypedDict, Annotated
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_core.messages import SystemMessage, HumanMessage, RemoveMessage, AnyMessage
+from langgraph.graph import StateGraph, START, END
+from langgraph.checkpoint.sqlite import SqliteSaver
+from langgraph.graph.message import add_messages
+from langchain_core.runnables import RunnableConfig
+
+# --- 1. 状态定义 ---
+class State(TypedDict):
+    messages: Annotated[list[AnyMessage], add_messages]
+    summary: str  # 永久存储在数据库中的摘要内容
+
+# --- 2. 核心逻辑 ---
+llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", temperature = 0.7, google_api_key='AIzaSyCfkiIgq4FmH5siBp3Iw6MRCml5zeSURnY')
+
+def call_model(state: State, config: RunnableConfig):
+    """对话节点：融合了动态 Prompt 和 长期摘要"""
+
+    # 获取当前 session 特有的 System Prompt（如果没传则使用默认）
+    configurable = config.get("configurable", {})
+    system_base_prompt = configurable.get("system_prompt", "你是一个通用的 AI 助手。")
+
+    # 构造当前上下文
+    prompt = f"{system_base_prompt}"
+    if state.get("summary"):
+        prompt += f"\n\n[之前的对话核心摘要]: {state['summary']}"
+
+    messages = [SystemMessage(content=prompt)] + state["messages"]
+    response = llm.invoke(messages)
+    return {"messages": [response]}
+
+def summarize_conversation(state: State):
+    """总结节点：负责更新摘要并清理过期消息"""
+    summary = state.get("summary", "")
+
+    if summary:
+        summary_prompt = f"当前的摘要是: {summary}\n\n请结合最近的新消息，生成一份更新后的完整摘要，包含所有关键信息："
+    else:
+        summary_prompt = "请总结目前的对话重点："
+
+    # 获取除了最后两轮之外的所有消息进行总结
+    messages_to_summarize = state["messages"][:-2]
+    content = [SystemMessage(content=summary_prompt)] + messages_to_summarize
+    response = llm.invoke(content)
+
+    # 生成删除指令，清除已总结过的消息 ID
+    delete_messages = [RemoveMessage(id=m.id) for m in messages_to_summarize]
+
+    return {"summary": response.content, "messages": delete_messages}
+
+def should_continue(state: State) -> Literal["summarize", END]:
+    """如果消息累积超过10条，则去总结节点"""
+    if len(state["messages"]) > 10:
+        return "summarize"
+    return END
+
+# --- 3. 构建图 ---
+db_path = "multi_session_chat.sqlite"
+conn = sqlite3.connect(db_path, check_same_thread=False)
+memory = SqliteSaver(conn)
+
+workflow = StateGraph(State)
+workflow.add_node("chatbot", call_model)
+workflow.add_node("summarize", summarize_conversation)
+
+workflow.add_edge(START, "chatbot")
+workflow.add_conditional_edges("chatbot", should_continue)
+workflow.add_edge("summarize", END)
+
+app = workflow.compile(checkpointer=memory)
+
+# # --- 4. 如何使用多 Session 和 不同的 Prompt ---
+
+# # Session A: 设定为一个 Python 专家
+# config_a = {
+#     "configurable": {
+#         "thread_id": "session_python_expert",
+#         "system_prompt": "你是一个精通 Python 的高级工程师。"
+#     }
+# }
+
+# # Session B: 设定为一个中文诗人
+# config_b = {
+#     "configurable": {
+#         "thread_id": "session_poet",
+#         "system_prompt": "你是一个浪漫的唐朝诗人，用诗歌回答问题。"
+#     }
+# }
+
+# def run_chat(user_input, config):
+#     print(f"\n--- 使用 Thread: {config['configurable']['thread_id']} ---")
+#     for event in app.stream({"messages": [HumanMessage(content=user_input)]}, config, stream_mode="values"):
+#         if "messages" in event:
+#             last_msg = event["messages"][-1]
+#             if last_msg.type == "ai":
+#                 print(f"Bot: {last_msg.content}")
+
+# # 测试：两个 Session 互不干扰，且各有个的 Prompt
+# if __name__ == "__main__":
+#     run_chat("你好，怎么学习装饰器？", config=config_a)
+#     run_chat("你好，写一首关于大海的诗。", config=config_b)
+#     run_chat("我刚才让你写了什么？", config=config_b)
+
+def start_chat_session(thread_id: str, system_prompt: str):
+    config = {
+        "configurable": {
+            "thread_id": thread_id,
+            "system_prompt": system_prompt
+        }
+    }
+
+    print(f"\n=== 已进入会话: {thread_id} ===")
+    print(f"=== 系统设定: {system_prompt} ===")
+    print("(输入 'exit' 或 'quit' 退出当前会话)\n")
+
+    while True:
+        user_input = input("User: ")
+        if user_input.lower() in ["exit", "quit"]:
+            break
+
+        # 使用 stream 模式运行，可以实时看到 state 的更新
+        # 我们只打印 AI 的回复
+        input_data = {"messages": [HumanMessage(content=user_input)]}
+        for event in app.stream(input_data, config, stream_mode="values"):
+            if "messages" in event:
+                last_msg = event["messages"][-1]
+                if last_msg.type == "ai":
+                    print(f"Bot: {last_msg.content}")
+
+if __name__ == "__main__":
+    # 模拟场景 1: Python 专家会话
+    # 即使你关掉程序再运行，只要 thread_id 还是 'py_expert_001'，记忆就会从 sqlite 读取
+    start_chat_session(
+        thread_id="py_expert_001",
+        system_prompt="你是一个精通 Python 的架构师。"
+    )
+
+# from IPython.display import Image, display
+# display(Image(app.get_graph().draw_mermaid_png()))