# 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()))