dsProject/dsLightRag/Volcengine/chat.py

import logging
import os
import time

from volcenginesdkarkruntime import Ark

from Config.Config import VOLC_API_KEY

"""
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。
"""
import json
import time
from VikingDBMemoryService import initialize_services, ensure_collection_exists, search_relevant_memories
# 控制日志输出
logger = logging.getLogger('CollectionMemory')
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
logger.addHandler(handler)
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。"""
    logger.info("\n" + "=" * 60)
    logger.info(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。"

    logger.info("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:
        logger.info(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？"

    logger.info(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:
        logger.info("没有对话可以归档。")
        return False

    logger.info(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
        )
        logger.info(f"对话已成功归档，会话ID: {session_id}")
        logger.info("正在等待记忆索引更新...")
        return True
    except Exception as e:
        logger.info(f"归档对话失败: {e}")
        return False

def setup_memory_collection(collection_name="emotional_support"):
    """独立封装记忆体创建逻辑，返回memory_service供测试使用"""
    try:
        memory_service, _ = initialize_services()
        ensure_collection_exists(memory_service, collection_name)
        logger.info(f"记忆体 '{collection_name}' 创建/验证成功")
        
        # 添加集合就绪等待
        logger.info("等待集合准备就绪...")
        if wait_for_collection_ready(memory_service, collection_name):
            logger.info(f"集合 '{collection_name}' 已就绪")
            return memory_service
        else:
            logger.info(f"集合 '{collection_name}' 未能就绪")
            return None
    except Exception as e:
        logger.info(f"记忆体创建失败: {e}")
        return None

def wait_for_collection_ready(memory_service, collection_name, timeout=300, interval=10):
    """
    等待集合准备就绪
    :param memory_service: 记忆库服务实例
    :param collection_name: 集合名称
    :param timeout: 超时时间(秒)
    :param interval: 检查间隔(秒)
    :return: True if ready, False if timeout
    """
    start_time = time.time()
    while time.time() - start_time < timeout:
        try:
            collection_info = memory_service.get_collection(collection_name)
            # 根据Volcengine API文档，状态可能在Status字段中，值可能为"READY"、"CREATING"等
            status = collection_info.get("Status", "UNKNOWN")
            logger.info(f"集合 '{collection_name}' 当前状态: {status}")
            if status == "READY":
                return True
            time.sleep(interval)
        except Exception as e:
            logger.info(f"检查集合状态失败: {e}")
            time.sleep(interval)
    logger.info(f"集合 '{collection_name}' 在{timeout}秒内未就绪")
    return False

def main():
    logger.info("开始端到端记忆测试...")
    collection_name="emotional_support"

    try:
        # 调用封装的记忆体创建函数
        memory_service = setup_memory_collection()
        if not memory_service:
            return
        llm_client = Ark(
            base_url="https://ark.cn-beijing.volces.com/api/v3",
            api_key=VOLC_API_KEY
        )
        user_id = "xiaoming"  # 用户ID:小明
        assistant_id = "assistant1"  # 助手ID:助手1
    except Exception as e:
        logger.info(f"初始化失败: {e}")
        return

    logger.info("\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
    )

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

    logger.info("\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
    )

    logger.info("\n端到端记忆测试完成！")

if __name__ == "__main__":
    setup_memory_collection()
#    main()
"""
memory_service = setup_memory_collection()
if memory_service:
    is_ready = wait_for_collection_ready(memory_service, "emotional_support")
"""