dsProject/dsLightRag/Volcengine/chat.py

import json
from VikingDBMemoryService import VikingDBMemoryService, initialize_services, ensure_collection_exists, search_relevant_memories

"""
在记忆库准备好后，我们先模拟一段包含两轮的完整对话。
对话结束后，把这段对话历史消息写入记忆库。然后再开启一个新话题，提出和刚才相关的问题，
AI 就能用刚写入的记忆来回答。
注意：首次写入需要 3–5 分钟建立索引，这段时间内检索会报错。
"""

def handle_conversation_turn(memory_service, llm_client, collection_name, user_id, user_message, conversation_history):
    """处理一轮对话，包括记忆搜索和LLM响应。"""
    print("\n" + "=" * 60)
    print(f"用户: {user_message}")

    relevant_memories = search_relevant_memories(memory_service, collection_name, user_id, user_message)

    system_prompt = "你是一个富有同情心、善于倾听的AI伙伴，拥有长期记忆能力。你的目标是为用户提供情感支持和温暖的陪伴。"
    if relevant_memories:
        memory_context = "\n".join(
            [f"- {json.dumps(mem['memory_info'], ensure_ascii=False)}" for mem in relevant_memories])
        system_prompt += f"\n\n这是我们过去的一些对话记忆，请参考：\n{memory_context}\n\n请利用这些信息来更好地理解和回应用户。"

    print("AI正在思考...")

    try:
        messages = [{"role": "system", "content": system_prompt}] + conversation_history + [
            {"role": "user", "content": user_message}]
        completion = llm_client.chat.completions.create(
            model="doubao-seed-1-6-flash-250715",
            messages=messages
        )
        assistant_reply = completion.choices[0].message.content
    except Exception as e:
        print(f"LLM调用失败: {e}")
        assistant_reply = "抱歉，我现在有点混乱，无法回应。我们可以稍后再聊吗？"

    print(f"伙伴: {assistant_reply}")

    conversation_history.extend([
        {"role": "user", "content": user_message},
        {"role": "assistant", "content": assistant_reply}
    ])
    return assistant_reply

def archive_conversation(memory_service, collection_name, user_id, assistant_id, conversation_history, topic_name):
    """将对话历史归档到记忆数据库。"""
    if not conversation_history:
        print("没有对话可以归档。")
        return False

    print(f"\n正在归档关于 '{topic_name}' 的对话...")
    session_id = f"{topic_name}_{int(time.time())}"
    metadata = {
        "default_user_id": user_id,
        "default_assistant_id": assistant_id,
        "time": int(time.time() * 1000)
    }

    try:
        memory_service.add_session(
            collection_name=collection_name,
            session_id=session_id,
            messages=conversation_history,
            metadata=metadata
        )
        print(f"对话已成功归档，会话ID: {session_id}")
        print("正在等待记忆索引更新...")
        return True
    except Exception as e:
        print(f"归档对话失败: {e}")
        return False

def main():
    print("开始端到端记忆测试...")

    try:
        memory_service, llm_client = initialize_services()
        collection_name = "emotional_support"
        user_id = "xiaoming" # 用户ID:小明
        assistant_id = "assistant1" # 助手ID:助手1
        ensure_collection_exists(memory_service, collection_name)
    except Exception as e:
        print(f"初始化失败: {e}")
        return

    print("\n--- 阶段 1: 初始对话 ---")
    initial_conversation_history = []
    handle_conversation_turn(
        memory_service, llm_client, collection_name, user_id,
        "你好，我是小明，今年18岁，但压力好大。",
        initial_conversation_history
    )
    handle_conversation_turn(
        memory_service, llm_client, collection_name, user_id,
        "马上就要高考了，家里人的期待好高。",
        initial_conversation_history
    )

    print("\n--- 阶段 2: 归档记忆 ---")
    archive_conversation(
        memory_service, collection_name, user_id, assistant_id,
        initial_conversation_history, "study_stress_discussion"
    )

    print("\n--- 阶段 3: 验证记忆 ---")
    verification_conversation_history = []
    handle_conversation_turn(
        memory_service, llm_client, collection_name, user_id,
        "我最近很焦虑，不知道该怎么办。",
        verification_conversation_history
    )

    print("\n端到端记忆测试完成！")

if __name__ == "__main__":
    main()