mirror of
https://github.com/xinnan-tech/xiaozhi-esp32-server.git
synced 2026-07-26 00:53:54 +08:00
Sakura-RanChen
@@ -275,6 +275,35 @@ Memory:
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# https://app.mem0.ai/dashboard/api-keys
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# 每月有1000次免费调用
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api_key: 你的mem0ai api key
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powermem:
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# PowerMem是OceanBase开源的agent记忆组件，通过本地LLM进行记忆总结
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# 费用说明：PowerMem本身免费，实际费用取决于所选LLM和数据库
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# - 使用sqlite + 免费LLM(如glm-4-flash) = 完全免费
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# - 使用云端LLM或云端数据库 = 按对应服务收费
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# GitHub: https://github.com/oceanbase/powermem
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# 官网: https://www.powermem.ai/
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# 使用示例: https://github.com/oceanbase/powermem/tree/main/examples
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type: powermem
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# 是否启用用户画像功能（需要OceanBase作为存储后端）
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# - false: 使用普通记忆模式(AsyncMemory)，支持所有数据库
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# - true: 使用用户画像模式(UserMemory)，自动提取用户信息，仅支持OceanBase
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enable_user_profile: false
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# 数据库提供者: oceanbase(推荐,最佳性能), seekdb(推荐,AI应用存储一体), postgres, sqlite(轻量备选)
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# 资源充足时建议使用 oceanbase 或 seekdb
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# 注意：用户画像功能(enable_user_profile: true)仅支持oceanbase
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database_provider: sqlite
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# LLM提供者: qwen(默认), zhipu(免费glm-4-flash), openai, 等
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llm_provider: qwen
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# 嵌入模型提供者: qwen(默认), zhipu, openai, 等
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embedding_provider: qwen
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# LLM配置 (使用通义千问为例)
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# 通义千问: https://bailian.console.aliyun.com/?apiKey=1#/api-key
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# 智谱AI(免费): https://bigmodel.cn/usercenter/proj-mgmt/apikeys
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llm_api_key: 你的LLM API密钥
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llm_model: qwen-plus
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# 嵌入模型配置 (使用通义千问为例)
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embedding_api_key: 你的嵌入模型API密钥
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embedding_model: text-embedding-v3
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nomem:
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# 不想使用记忆功能，可以使用nomem
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type: nomem
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@@ -0,0 +1,299 @@
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#!/usr/bin/env python
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# -*- coding: UTF-8 -*-
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"""
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@time: 2026/01/08
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@file: powermem.py
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@desc: PowerMem memory provider for xiaozhi-esp32-server
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PowerMem is an open-source agent memory component from OceanBase
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GitHub: https://github.com/oceanbase/powermem
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Website: https://www.powermem.ai/
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@Author: wayyoungboy
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"""
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import traceback
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from typing import Optional, Dict, Any
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from ..base import MemoryProviderBase, logger
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TAG = __name__
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class MemoryProvider(MemoryProviderBase):
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"""
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PowerMem memory provider implementation.
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PowerMem is an open-source agent memory component that provides
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efficient memory management for AI agents.
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Supports multiple storage backends (sqlite, oceanbase, postgres),
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LLM providers (qwen, openai, etc.) and embedding providers.
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Config options:
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- enable_user_profile: bool - Enable UserMemory for user profiling (requires OceanBase)
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- database_provider: str - Storage backend (sqlite, oceanbase, postgres)
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- llm_provider: str - LLM provider (qwen, openai, etc.)
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- embedding_provider: str - Embedding provider (qwen, openai, etc.)
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"""
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def __init__(self, config: Dict[str, Any], summary_memory: Optional[str] = None):
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super().__init__(config)
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self.use_powermem = False
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self.memory_client = None
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self.enable_user_profile = False
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try:
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# Check if user profile mode is enabled
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self.enable_user_profile = config.get("enable_user_profile", False)
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# Get configuration parameters
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database_provider = config.get("database_provider", "sqlite")
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llm_provider = config.get("llm_provider", "qwen")
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embedding_provider = config.get("embedding_provider", "qwen")
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# UserMemory requires OceanBase
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if self.enable_user_profile and database_provider not in ["oceanbase"]:
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logger.bind(tag=TAG).warning(
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f"UserMemory requires OceanBase as storage backend, but got {database_provider}. "
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"Falling back to AsyncMemory mode."
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)
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self.enable_user_profile = False
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# Build powermem configuration dict
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# PowerMem supports two config styles:
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# 1. powermem style: database, llm, embedding
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# 2. mem0 style: vector_store, llm, embedder
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powermem_config = {}
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# Configure vector store / database
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if "vector_store" in config:
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powermem_config["vector_store"] = config["vector_store"]
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elif "database" in config:
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powermem_config["database"] = config["database"]
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else:
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powermem_config["vector_store"] = {
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"provider": database_provider,
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"config": {}
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}
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# Configure LLM
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if "llm" in config:
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powermem_config["llm"] = config["llm"]
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else:
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llm_config = {}
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if "llm_api_key" in config:
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llm_config["api_key"] = config["llm_api_key"]
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if "llm_model" in config:
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llm_config["model"] = config["llm_model"]
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if "llm_base_url" in config:
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llm_config["base_url"] = config["llm_base_url"]
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powermem_config["llm"] = {
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"provider": llm_provider,
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"config": llm_config
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}
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# Configure embedder
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if "embedder" in config:
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powermem_config["embedder"] = config["embedder"]
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else:
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embedder_config = {}
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if "embedding_api_key" in config:
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embedder_config["api_key"] = config["embedding_api_key"]
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if "embedding_model" in config:
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embedder_config["model"] = config["embedding_model"]
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if "embedding_base_url" in config:
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embedder_config["base_url"] = config["embedding_base_url"]
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powermem_config["embedder"] = {
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"provider": embedding_provider,
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"config": embedder_config
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}
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# Initialize memory client based on mode
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if self.enable_user_profile:
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from powermem import UserMemory
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self.memory_client = UserMemory(config=powermem_config)
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memory_mode = "UserMemory (用户画像模式)"
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else:
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from powermem import AsyncMemory
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self.memory_client = AsyncMemory(config=powermem_config)
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memory_mode = "AsyncMemory (普通记忆模式)"
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self.use_powermem = True
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logger.bind(tag=TAG).info(
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f"PowerMem initialized successfully: mode={memory_mode}, "
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f"database={database_provider}, llm={llm_provider}, embedding={embedding_provider}"
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)
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except ImportError as e:
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logger.bind(tag=TAG).error(
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f"PowerMem not installed. Please install with: pip install powermem. Error: {e}"
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)
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self.use_powermem = False
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except Exception as e:
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logger.bind(tag=TAG).error(f"Failed to initialize PowerMem: {str(e)}")
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logger.bind(tag=TAG).debug(f"Detailed error: {traceback.format_exc()}")
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self.use_powermem = False
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async def save_memory(self, msgs):
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"""
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Save conversation messages to PowerMem.
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Args:
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msgs: List of message objects with 'role' and 'content' attributes
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Returns:
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Result from PowerMem API or None if failed
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"""
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if not self.use_powermem or self.memory_client is None:
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logger.bind(tag=TAG).warning("PowerMem is not available, skipping save_memory")
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return None
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if len(msgs) < 2:
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logger.bind(tag=TAG).debug("Not enough messages to save (need at least 2)")
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return None
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try:
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# Format the content as a message list for PowerMem
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messages = [
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{"role": message.role, "content": message.content}
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for message in msgs
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if message.role != "system"
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]
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# Add memory using PowerMem SDK
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result = await self.memory_client.add(
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messages=messages,
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user_id=self.role_id
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)
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logger.bind(tag=TAG).debug(f"Save memory result: {result}")
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return result
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except Exception as e:
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logger.bind(tag=TAG).error(f"Error saving memory: {str(e)}")
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logger.bind(tag=TAG).debug(f"Detailed error: {traceback.format_exc()}")
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return None
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async def query_memory(self, query: str) -> str:
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"""
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Query memories from PowerMem based on similarity search.
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Args:
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query: The search query string
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Returns:
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Formatted string of relevant memories or empty string if none found
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"""
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if not self.use_powermem or self.memory_client is None:
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logger.bind(tag=TAG).warning("PowerMem is not available, skipping query_memory")
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return ""
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try:
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if not getattr(self, "role_id", None):
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logger.bind(tag=TAG).debug("No role_id set, returning empty memory")
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return ""
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result_parts = []
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# If user profile mode is enabled, include user profile in results
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if self.enable_user_profile:
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profile = await self.get_user_profile()
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if profile:
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result_parts.append(f"【用户画像】\n{profile}")
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# Search memories using PowerMem SDK
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results = await self.memory_client.search(
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query=query,
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user_id=self.role_id,
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limit=30
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)
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if results and "results" in results:
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# Format each memory entry with its update time
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memories = []
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for entry in results.get("results", []):
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# Get timestamp from updated_at or created_at
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timestamp = ""
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if "updated_at" in entry and entry["updated_at"]:
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timestamp = str(entry["updated_at"])
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elif "created_at" in entry and entry["created_at"]:
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timestamp = str(entry["created_at"])
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if timestamp:
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try:
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# Parse and reformat the timestamp (remove milliseconds if present)
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if "." in timestamp:
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dt = timestamp.split(".")[0]
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else:
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dt = timestamp
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formatted_time = dt.replace("T", " ")
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except Exception:
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formatted_time = timestamp
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else:
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formatted_time = ""
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memory = entry.get("memory", "") or entry.get("content", "")
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if memory:
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if formatted_time:
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# Store tuple of (timestamp, formatted_string) for sorting
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memories.append((timestamp, f"[{formatted_time}] {memory}"))
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else:
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memories.append(("", memory))
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# Sort by timestamp in descending order (newest first)
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memories.sort(key=lambda x: x[0], reverse=True)
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# Extract only the formatted strings
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if memories:
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memories_str = "\n".join(f"- {memory[1]}" for memory in memories)
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result_parts.append(f"【相关记忆】\n{memories_str}")
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final_result = "\n\n".join(result_parts)
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logger.bind(tag=TAG).debug(f"Query results: {final_result}")
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return final_result
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except Exception as e:
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logger.bind(tag=TAG).error(f"Error querying memory: {str(e)}")
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logger.bind(tag=TAG).debug(f"Detailed error: {traceback.format_exc()}")
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return ""
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async def get_user_profile(self) -> str:
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"""
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Get user profile from PowerMem (only available in UserMemory mode).
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Returns:
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Formatted user profile string or empty string if not available
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"""
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if not self.use_powermem or self.memory_client is None:
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return ""
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if not self.enable_user_profile:
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logger.bind(tag=TAG).debug("User profile mode is not enabled")
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return ""
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try:
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if not getattr(self, "role_id", None):
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return ""
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# Get user profile using UserMemory SDK
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profile = await self.memory_client.get_profile(user_id=self.role_id)
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if not profile:
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return ""
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# Format profile as readable string
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profile_parts = []
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for key, value in profile.items():
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if value:
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profile_parts.append(f"- {key}: {value}")
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return "\n".join(profile_parts)
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except Exception as e:
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logger.bind(tag=TAG).error(f"Error getting user profile: {str(e)}")
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logger.bind(tag=TAG).debug(f"Detailed error: {traceback.format_exc()}")
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return ""
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# Register the memory provider instance
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powermem = MemoryProvider({})
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@@ -23,6 +23,7 @@ loguru==0.7.3
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requests==2.32.5
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cozepy==0.20.0
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mem0ai==1.0.0
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powermem>=0.2.1
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bs4==0.0.2
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modelscope==1.32.0
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sherpa_onnx==1.12.17
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