dsProject/dsLightRag/Volcengine/chat.py

import json
import os
import time
from dotenv import load_dotenv
from volcenginesdkarkruntime import Ark

from Config.Config import VOLC_ACCESSKEY, VOLC_SECRETKEY, VOLC_API_KEY
from VikingDBMemoryService import VikingDBMemoryService

"""
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 initialize_services():
    load_dotenv()
    ak = VOLC_ACCESSKEY
    sk = VOLC_SECRETKEY
    ark_api_key = VOLC_API_KEY

    if not all([ak, sk, ark_api_key]):
        raise ValueError("必须在环境变量中设置 VOLC_ACCESSKEY, VOLC_SECRETKEY, 和 ARK_API_KEY。")

    memory_service = VikingDBMemoryService(ak=ak, sk=sk)
    llm_client = Ark(
        base_url="https://ark.cn-beijing.volces.com/api/v3",
        api_key=ark_api_key,
    )
    return memory_service, llm_client


def ensure_collection_exists(memory_service, collection_name):
    """检查记忆集合是否存在，如果不存在则创建。"""
    try:
        memory_service.get_collection(collection_name)
        print(f"记忆集合 '{collection_name}' 已存在。")
    except Exception as e:
        error_message = str(e)
        if "collection not exist" in error_message:
            print(f"记忆集合 '{collection_name}' 未找到，正在创建...")
            try:
                memory_service.create_collection(
                    collection_name=collection_name,
                    description="中文情感陪伴场景测试",
                    builtin_event_types=["sys_event_v1", "sys_profile_collect_v1"],
                    builtin_entity_types=["sys_profile_v1"]
                )
                print(f"记忆集合 '{collection_name}' 创建成功。")
                print("等待集合准备就绪...")
            except Exception as create_e:
                print(f"创建集合失败: {create_e}")
                raise
        else:
            print(f"检查集合时出错: {e}")
            raise


def search_relevant_memories(memory_service, collection_name, user_id, query):
    """搜索与用户查询相关的记忆，并在索引构建中时重试。"""
    print(f"正在搜索与 '{query}' 相关的记忆...")
    retry_attempt = 0
    while True:
        try:
            filter_params = {
                "user_id": [user_id],
                "memory_type": ["sys_event_v1", "sys_profile_v1"]
            }
            response = memory_service.search_memory(
                collection_name=collection_name,
                query=query,
                filter=filter_params,
                limit=3
            )

            memories = []
            if response.get('data', {}).get('count', 0) > 0:
                for result in response['data']['result_list']:
                    if 'memory_info' in result and result['memory_info']:
                        memories.append({
                            'memory_info': result['memory_info'],
                            'score': result['score']
                        })

            if memories:
                if retry_attempt > 0:
                    print("重试后搜索成功。")
                print(f"找到 {len(memories)} 条相关记忆:")
                for i, memory in enumerate(memories, 1):
                    print(
                        f"  {i}. (相关度: {memory['score']:.3f}): {json.dumps(memory['memory_info'], ensure_ascii=False, indent=2)}")
            else:
                print("未找到相关记忆。")
            return memories

        except Exception as e:
            error_message = str(e)
            if "1000023" in error_message:
                retry_attempt += 1
                print(f"记忆索引正在构建中。将在60秒后重试... (尝试次数 {retry_attempt})")
                time.sleep(60)
            else:
                print(f"搜索记忆时出错 (不可重试): {e}")
                return []


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()