mirror of
https://github.com/xinnan-tech/xiaozhi-esp32-server.git
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feat: 添加 PowerMem 智能记忆支持
- 新增 PowerMem 配置选项和集成文档 - 更新 README 和多语言文档 - 添加 powermem 依赖包
This commit is contained in:
渠磊
@@ -242,7 +242,7 @@ Websocket接口地址: wss://2662r3426b.vicp.fun/xiaozhi/v1/
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| 智能对话 | 支持多种LLM(大语言模型)，实现智能对话 |
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| 视觉感知 | 支持多种VLLM(视觉大模型)，实现多模态交互 |
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| 意图识别 | 支持外挂的大模型意图识别、大模型自主函数调用，提供插件化意图处理机制 |
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| 记忆系统 | 支持本地短期记忆、mem0ai接口记忆，具备记忆总结功能 |
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| 记忆系统 | 支持本地短期记忆、mem0ai接口记忆、PowerMem智能记忆，具备记忆总结功能 |
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| 知识库 | 支持RAGFlow知识库，让大模型判断需要调度知识库后再回答 |
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| 工具调用 | 支持客户端IOT协议、客户MCP协议、服务端MCP协议、MCP接入点协议、自定义工具函数 |
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| 指令下发 | 依托MQTT协议，支持从智控台将MCP指令下发到ESP32设备 |
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@@ -330,6 +330,7 @@ Websocket接口地址: wss://2662r3426b.vicp.fun/xiaozhi/v1/
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| 类型 | 平台名称 | 使用方式 | 收费模式 | 备注 |
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|:------:|:---------------:|:----:|:---------:|:--:|
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| Memory | mem0ai | 接口调用 | 1000次/月额度 | |
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| Memory | [powermem](./docs/powermem-integration.md) | 本地总结 | 取决于LLM和DB | OceanBase开源，支持智能检索 |
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| Memory | mem_local_short | 本地总结 | 免费 | |
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| Memory | nomem | 无记忆模式 | 免费 | |
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+2
-1
@@ -240,7 +240,7 @@ Dieses Projekt bietet die folgenden Testwerkzeuge, um Ihnen bei der Überprüfun
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| Intelligenter Dialog | Unterstützt mehrere LLM (große Sprachmodelle), implementiert intelligenten Dialog |
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| Visuelle Wahrnehmung | Unterstützt mehrere VLLM (Vision Large Models), implementiert multimodale Interaktion |
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| Absichtserkennung | Unterstützt LLM-Absichtserkennung, Function Call-Funktionsaufruf, bietet plugin-basierten Absichtsverarbeitungsmechanismus |
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| Gedächtnissystem | Unterstützt lokales Kurzzeitgedächtnis, mem0ai-Schnittstellengedächtnis, mit Gedächtniszusammenfassungsfunktion |
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| Gedächtnissystem | Unterstützt lokales Kurzzeitgedächtnis, mem0ai-Schnittstellengedächtnis, PowerMem intelligentes Gedächtnis, mit Gedächtniszusammenfassungsfunktion |
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| Wissensdatenbank | Unterstützt RAGFlow-Wissensdatenbank, ermöglicht großem Modell die Bewertung, ob Wissensdatenbank benötigt wird, bevor geantwortet wird |
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| Werkzeugaufruf | Unterstützt Client-IOT-Protokoll, Client-MCP-Protokoll, Server-MCP-Protokoll, MCP-Endpunktprotokoll, benutzerdefinierte Werkzeugfunktionen |
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| Befehlsübermittlung | Basierend auf MQTT-Protokoll, unterstützt die Übermittlung von MCP-Befehlen von der intelligenten Steuerkonsole an ESP32-Geräte |
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@@ -328,6 +328,7 @@ Tatsächlich kann jedes VLLM, das OpenAI-Schnittstellenaufrufe unterstützt, int
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| Typ | Plattformname | Verwendungsmethode | Preismodell | Hinweise |
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|:------:|:---------------:|:----:|:---------:|:--:|
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| Memory | mem0ai | Schnittstellenaufrufe | 1000 Mal/Monat Kontingent | |
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| Memory | [powermem](./docs/powermem-integration.md) | Lokale Zusammenfassung | Abhängig von LLM und DB | OceanBase Open Source, unterstützt intelligente Abfrage |
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| Memory | mem_local_short | Lokale Zusammenfassung | Kostenlos | |
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| Memory | nomem | Kein Gedächtnismodus | Kostenlos | |
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+3
-1
@@ -238,7 +238,7 @@ This project provides the following testing tools to help you verify the system
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| Intelligent Dialogue | Supports multiple LLM(large language models), implements intelligent dialogue |
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| Visual Perception | Supports multiple VLLM(vision large models), implements multimodal interaction |
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| Intent Recognition | Supports LLM intent recognition, Function Call function calling, provides plugin-based intent processing mechanism |
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| Memory System | Supports local short-term memory, mem0ai interface memory, with memory summarization functionality |
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| Memory System | Supports local short-term memory, mem0ai interface memory, PowerMem intelligent memory, with memory summarization functionality |
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| Knowledge Base | Supports RAGFlow knowledge base, enabling LLM to judge whether to schedule the knowledge base after receiving the user's question, and then answer the question |
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| Command Delivery | Supports MCP command delivery to ESP32 devices via MQTT protocol from Smart Console |
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| Tool Calling | Supports client IOT protocol, client MCP protocol, server MCP protocol, MCP endpoint protocol, custom tool functions |
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@@ -330,7 +330,9 @@ In fact, any VLLM that supports OpenAI interface calls can be integrated and use
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| Type | Platform Name | Usage Method | Pricing Model | Notes |
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|:------:|:---------------:|:----:|:---------:|:--:|
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| Memory | mem0ai | Interface calls | 1000 times/month quota | |
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| Memory | [powermem](./docs/powermem-integration.md) | Local summarization | Depends on LLM and DB | OceanBase open source, supports intelligent retrieval |
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| Memory | mem_local_short | Local summarization | Free | |
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| Memory | nomem | No memory mode | Free | |
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---
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+2
-1
@@ -241,7 +241,7 @@ Dự án này cung cấp các công cụ kiểm tra sau để giúp bạn xác m
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| Đối thoại thông minh | Hỗ trợ nhiều LLM(Mô hình ngôn ngữ lớn), thực hiện đối thoại thông minh |
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| Cảm nhận thị giác | Hỗ trợ nhiều VLLM(Mô hình lớn thị giác), thực hiện tương tác đa phương thức |
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| Nhận dạng ý định | Hỗ trợ nhận dạng ý định mô hình lớn gắn ngoài, gọi hàm tự chủ mô hình lớn, cung cấp cơ chế xử lý ý định dạng plugin |
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| Hệ thống bộ nhớ | Hỗ trợ bộ nhớ ngắn hạn cục bộ, bộ nhớ giao diện mem0ai, có chức năng tóm tắt bộ nhớ |
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| Hệ thống bộ nhớ | Hỗ trợ bộ nhớ ngắn hạn cục bộ, bộ nhớ giao diện mem0ai, bộ nhớ thông minh PowerMem, có chức năng tóm tắt bộ nhớ |
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| Kho tri thức | Hỗ trợ kho tri thức RAGFlow, cho phép mô hình lớn đánh giá cần gọi kho tri thức trước khi trả lời |
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| Gọi công cụ | Hỗ trợ giao thức IOT phía client, giao thức MCP phía client, giao thức MCP phía server, giao thức điểm truy cập MCP, hàm công cụ tùy chỉnh |
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| Gửi lệnh | Dựa vào giao thức MQTT, hỗ trợ gửi lệnh MCP từ bảng điều khiển thông minh xuống thiết bị ESP32 |
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@@ -329,6 +329,7 @@ Trên thực tế, bất kỳ VLLM nào hỗ trợ gọi giao diện openai đ
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| Loại | Tên nền tảng | Phương pháp sử dụng | Mô hình thu phí | Ghi chú |
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|:------:|:---------------:|:----:|:---------:|:--:|
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| Memory | mem0ai | Gọi giao diện | Hạn mức 1000 lần/tháng | |
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| Memory | [powermem](./docs/powermem-integration.md) | Tóm tắt cục bộ | Phụ thuộc vào LLM và DB | OceanBase mã nguồn mở, hỗ trợ tìm kiếm thông minh |
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| Memory | mem_local_short | Tóm tắt cục bộ | Miễn phí | |
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| Memory | nomem | Chế độ không có bộ nhớ | Miễn phí | |
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@@ -82,6 +82,7 @@ VAD:
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8、[新闻插件源配置指南](./newsnow_plugin_config.md)<br/>
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9、[知识库ragflow集成指南](./ragflow-integration.md)<br/>
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10、[如何部署上下文源](./context-provider-integration.md)<br/>
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11、[如何集成PowerMem智能记忆](./powermem-integration.md)<br/>
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### 11、语音克隆、本地语音部署相关教程
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1、[如何在智控台克隆音色](./huoshan-streamTTS-voice-cloning.md)<br/>
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@@ -0,0 +1,253 @@
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# PowerMem 记忆组件集成指南
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## 简介
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[PowerMem](https://www.powermem.ai/) 是由 OceanBase 开源的 Agent 记忆组件，通过本地 LLM 进行记忆总结和智能检索，为 AI 代理提供高效的记忆管理功能。
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费用说明：PowerMem 本身开源免费，实际费用取决于您选择的 LLM 和数据库：
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- 使用 SQLite + 免费 LLM（如智谱 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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> 💡 **最佳性能提示**：PowerMem 配合 OceanBase 使用可实现最大性能释放，SQLite 仅建议在资源不足的情况下使用。
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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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## 功能特性
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- **本地总结**：通过 LLM 在本地进行记忆总结和提取
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- **多种存储后端**：支持 OceanBase（推荐，最佳性能）、SeekDB（推荐，AI应用存储一体）、PostgreSQL、SQLite（轻量备选）
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- **多种 LLM 支持**：通义千问、智谱（glm-4-flash 免费）、OpenAI 等
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- **智能检索**：基于向量搜索的语义检索能力
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- **私有部署**：完全支持本地私有化部署
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- **异步操作**：高效的异步记忆管理
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## 安装
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PowerMem 已添加到项目依赖中，如果需要手动安装：
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```bash
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pip install powermem
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```
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## 配置说明
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### 基础配置
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在 `config.yaml` 中配置 PowerMem：
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```yaml
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selected_module:
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Memory: powermem
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Memory:
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powermem:
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type: powermem
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# 数据库提供者: oceanbase(推荐,最佳性能), seekdb, postgres, sqlite(轻量备选)
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database_provider: sqlite # 资源充足时建议使用 oceanbase 或 seekdb
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# LLM提供者: qwen(默认), openai, 等
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llm_provider: qwen
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# 嵌入模型提供者: qwen(默认), openai, 等
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embedding_provider: qwen
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# LLM配置
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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
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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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```
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### 配置参数详解
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| 参数 | 说明 | 默认值 | 可选值 |
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|------|------|--------|--------|
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| `database_provider` | 存储后端类型 | `sqlite` | `oceanbase`(推荐), `seekdb`, `postgres`, `sqlite`(轻量) |
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| `llm_provider` | LLM 提供商 | `qwen` | `qwen`, `zhipu`(免费), `openai`, 等 |
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| `embedding_provider` | 嵌入模型提供商 | `qwen` | `qwen`, `zhipu`, `openai`, 等 |
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| `llm_api_key` | LLM API 密钥 | - | - |
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| `llm_model` | LLM 模型名称 | - | 根据提供商选择 |
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| `llm_base_url` | LLM API 地址（可选） | - | - |
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| `embedding_api_key` | 嵌入模型 API 密钥 | - | - |
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| `embedding_model` | 嵌入模型名称 | - | 根据提供商选择 |
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| `embedding_base_url` | 嵌入模型 API 地址（可选） | - | - |
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### 使用通义千问（推荐）
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1. 访问 [阿里云百炼平台](https://bailian.console.aliyun.com/) 注册账号
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2. 在 [API Key 管理](https://bailian.console.aliyun.com/?apiKey=1#/api-key) 页面获取 API 密钥
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3. 配置如下：
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```yaml
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Memory:
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powermem:
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type: powermem
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database_provider: sqlite
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llm_provider: qwen
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embedding_provider: qwen
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llm_api_key: sk-xxxxxxxxxxxxxxxx
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llm_model: qwen-plus
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embedding_api_key: sk-xxxxxxxxxxxxxxxx
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embedding_model: text-embedding-v3
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```
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### 使用智谱免费 LLM（完全免费方案）
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智谱提供免费的 glm-4-flash 模型，配合 SQLite 可实现完全免费使用：
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1. 访问 [智谱AI开放平台](https://bigmodel.cn/) 注册账号
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2. 在 [API Keys](https://bigmodel.cn/usercenter/proj-mgmt/apikeys) 页面获取 API 密钥
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3. 配置如下：
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```yaml
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Memory:
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powermem:
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type: powermem
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database_provider: sqlite
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llm_provider: zhipu
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embedding_provider: zhipu
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llm_api_key: xxxxxxxxxxxxxxxx.xxxxxxxxxxxxxxxx
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llm_model: glm-4-flash
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llm_base_url: https://open.bigmodel.cn/api/paas/v4/
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embedding_api_key: xxxxxxxxxxxxxxxx.xxxxxxxxxxxxxxxx
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embedding_model: embedding-3
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embedding_base_url: https://open.bigmodel.cn/api/paas/v4/
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```
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### 使用 OpenAI
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```yaml
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Memory:
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powermem:
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type: powermem
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database_provider: sqlite
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llm_provider: openai
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embedding_provider: openai
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llm_api_key: sk-xxxxxxxxxxxxxxxx
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llm_model: gpt-4o-mini
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llm_base_url: https://api.openai.com/v1
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embedding_api_key: sk-xxxxxxxxxxxxxxxx
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embedding_model: text-embedding-3-small
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embedding_base_url: https://api.openai.com/v1
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```
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### 使用 OceanBase（最佳性能方案）
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OceanBase 是 PowerMem 的最佳搭档，可实现最大性能释放：
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1. 部署 OceanBase 数据库（支持开源本地部署或使用云服务）
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- 开源部署：https://github.com/oceanbase/oceanbase
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- 云服务：https://www.oceanbase.com/
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2. 配置如下：
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```yaml
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Memory:
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powermem:
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type: powermem
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database_provider: oceanbase
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llm_provider: qwen
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embedding_provider: qwen
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llm_api_key: sk-xxxxxxxxxxxxxxxx
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llm_model: qwen-plus
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embedding_api_key: sk-xxxxxxxxxxxxxxxx
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embedding_model: text-embedding-v3
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# OceanBase 数据库连接配置
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vector_store:
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provider: oceanbase
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config:
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host: 127.0.0.1
|
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port: 2881
|
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user: root@test
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password: your_password
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database: powermem
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```
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### 高级配置
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如果需要更精细的控制，可以使用完整的配置结构：
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|
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```yaml
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Memory:
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powermem:
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type: powermem
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# 向量存储配置
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vector_store:
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provider: sqlite
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config:
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path: ./data/powermem.db
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# LLM 配置
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llm:
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provider: qwen
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config:
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api_key: sk-xxxxxxxxxxxxxxxx
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model: qwen-plus
|
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# 嵌入模型配置
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embedder:
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provider: qwen
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config:
|
||||
api_key: sk-xxxxxxxxxxxxxxxx
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model: text-embedding-v3
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||||
```
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## 设备记忆隔离
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||||
PowerMem 会自动使用设备 ID（`device_id`）作为 `user_id` 进行记忆隔离。这意味着：
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||||
- 每个设备拥有独立的记忆空间
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- 不同设备之间的记忆完全隔离
|
||||
- 同一设备的多次对话可以共享记忆上下文
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## 与其他记忆组件的对比
|
||||
|
||||
| 特性 | PowerMem | mem0ai | mem_local_short |
|
||||
|------|----------|--------|-----------------|
|
||||
| 工作方式 | 本地总结 | 云端接口 | 本地总结 |
|
||||
| 存储位置 | 本地/云端DB | 云端 | 本地YAML |
|
||||
| 费用 | 取决于LLM和DB | 1000次/月免费 | 完全免费 |
|
||||
| 智能检索 | ✅ 向量搜索 | ✅ 向量搜索 | ❌ 全量返回 |
|
||||
| 私有部署 | ✅ 支持 | ❌ 仅云端 | ✅ 支持 |
|
||||
| 数据库支持 | OceanBase(推荐)/SeekDB/PostgreSQL/SQLite | - | YAML 文件 |
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||||
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||||
## 常见问题
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||||
|
||||
### 1. API 密钥错误
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||||
|
||||
如果出现 `API key is required` 错误，请检查：
|
||||
- `llm_api_key` 和 `embedding_api_key` 是否正确填写
|
||||
- API 密钥是否有效
|
||||
|
||||
### 2. 模型不存在
|
||||
|
||||
如果出现模型不存在的错误，请确认：
|
||||
- `llm_model` 和 `embedding_model` 名称是否正确
|
||||
- 对应的模型服务是否已开通
|
||||
|
||||
### 3. 连接超时
|
||||
|
||||
如果出现连接超时，可以尝试：
|
||||
- 检查网络连接
|
||||
- 如果使用代理，配置 `llm_base_url` 和 `embedding_base_url`
|
||||
|
||||
## 测试验证
|
||||
|
||||
可以在虚拟环境中测试 PowerMem 是否正常工作：
|
||||
|
||||
```bash
|
||||
# 激活虚拟环境
|
||||
source .venv/bin/activate
|
||||
|
||||
# 测试 PowerMem 导入
|
||||
python -c "from powermem import AsyncMemory; print('PowerMem 导入成功')"
|
||||
```
|
||||
|
||||
## 更多资源
|
||||
|
||||
- [PowerMem 官方文档](https://www.powermem.ai/)
|
||||
- [PowerMem GitHub 仓库](https://github.com/oceanbase/powermem)
|
||||
- [PowerMem 使用示例](https://github.com/oceanbase/powermem/tree/main/examples)
|
||||
- [OceanBase 官网](https://www.oceanbase.com/)
|
||||
- [OceanBase GitHub](https://github.com/oceanbase/oceanbase)
|
||||
- [SeekDB GitHub](https://github.com/oceanbase/seekdb)（AI原生搜索数据库）
|
||||
- [阿里云百炼平台](https://bailian.console.aliyun.com/)
|
||||
|
||||
@@ -275,6 +275,30 @@ Memory:
|
||||
# https://app.mem0.ai/dashboard/api-keys
|
||||
# 每月有1000次免费调用
|
||||
api_key: 你的mem0ai api key
|
||||
powermem:
|
||||
# PowerMem是OceanBase开源的agent记忆组件，通过本地LLM进行记忆总结
|
||||
# 费用说明：PowerMem本身免费，实际费用取决于所选LLM和数据库
|
||||
# - 使用sqlite + 免费LLM(如glm-4-flash) = 完全免费
|
||||
# - 使用云端LLM或云端数据库 = 按对应服务收费
|
||||
# GitHub: https://github.com/oceanbase/powermem
|
||||
# 官网: https://www.powermem.ai/
|
||||
# 使用示例: https://github.com/oceanbase/powermem/tree/main/examples
|
||||
type: powermem
|
||||
# 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)
|
||||
# 资源充足时建议使用 oceanbase 或 seekdb
|
||||
database_provider: sqlite
|
||||
# LLM提供者: qwen(默认), zhipu(免费glm-4-flash), openai, 等
|
||||
llm_provider: qwen
|
||||
# 嵌入模型提供者: qwen(默认), zhipu, openai, 等
|
||||
embedding_provider: qwen
|
||||
# LLM配置 (使用通义千问为例)
|
||||
# 通义千问: https://bailian.console.aliyun.com/?apiKey=1#/api-key
|
||||
# 智谱AI(免费): https://bigmodel.cn/usercenter/proj-mgmt/apikeys
|
||||
llm_api_key: 你的LLM API密钥
|
||||
llm_model: qwen-plus
|
||||
# 嵌入模型配置 (使用通义千问为例)
|
||||
embedding_api_key: 你的嵌入模型API密钥
|
||||
embedding_model: text-embedding-v3
|
||||
nomem:
|
||||
# 不想使用记忆功能，可以使用nomem
|
||||
type: nomem
|
||||
|
||||
@@ -0,0 +1,227 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: UTF-8 -*-
|
||||
"""
|
||||
@time: 2026/01/08
|
||||
@file: powermem.py
|
||||
@desc: PowerMem memory provider for xiaozhi-esp32-server
|
||||
PowerMem is an open-source agent memory component from OceanBase
|
||||
GitHub: https://github.com/oceanbase/powermem
|
||||
Website: https://www.powermem.ai/
|
||||
"""
|
||||
|
||||
import traceback
|
||||
from typing import Optional, Dict, Any
|
||||
|
||||
from ..base import MemoryProviderBase, logger
|
||||
from powermem import AsyncMemory
|
||||
|
||||
TAG = __name__
|
||||
|
||||
|
||||
class MemoryProvider(MemoryProviderBase):
|
||||
"""
|
||||
PowerMem memory provider implementation.
|
||||
|
||||
PowerMem is an open-source agent memory component that provides
|
||||
efficient memory management for AI agents.
|
||||
|
||||
Supports multiple storage backends (sqlite, oceanbase, postgres),
|
||||
LLM providers (qwen, openai, etc.) and embedding providers.
|
||||
"""
|
||||
|
||||
def __init__(self, config: Dict[str, Any], summary_memory: Optional[str] = None):
|
||||
super().__init__(config)
|
||||
self.use_powermem = False
|
||||
self.memory_client = None
|
||||
|
||||
try:
|
||||
# Get configuration parameters
|
||||
database_provider = config.get("database_provider", "sqlite")
|
||||
llm_provider = config.get("llm_provider", "qwen")
|
||||
embedding_provider = config.get("embedding_provider", "qwen")
|
||||
|
||||
# Build powermem configuration dict
|
||||
# PowerMem supports two config styles:
|
||||
# 1. powermem style: database, llm, embedding
|
||||
# 2. mem0 style: vector_store, llm, embedder
|
||||
powermem_config = {}
|
||||
|
||||
# Configure vector store / database
|
||||
if "vector_store" in config:
|
||||
powermem_config["vector_store"] = config["vector_store"]
|
||||
elif "database" in config:
|
||||
powermem_config["database"] = config["database"]
|
||||
else:
|
||||
powermem_config["vector_store"] = {
|
||||
"provider": database_provider,
|
||||
"config": {}
|
||||
}
|
||||
|
||||
# Configure LLM
|
||||
if "llm" in config:
|
||||
powermem_config["llm"] = config["llm"]
|
||||
else:
|
||||
llm_config = {}
|
||||
if "llm_api_key" in config:
|
||||
llm_config["api_key"] = config["llm_api_key"]
|
||||
if "llm_model" in config:
|
||||
llm_config["model"] = config["llm_model"]
|
||||
if "llm_base_url" in config:
|
||||
llm_config["base_url"] = config["llm_base_url"]
|
||||
powermem_config["llm"] = {
|
||||
"provider": llm_provider,
|
||||
"config": llm_config
|
||||
}
|
||||
|
||||
# Configure embedder
|
||||
if "embedder" in config:
|
||||
powermem_config["embedder"] = config["embedder"]
|
||||
else:
|
||||
embedder_config = {}
|
||||
if "embedding_api_key" in config:
|
||||
embedder_config["api_key"] = config["embedding_api_key"]
|
||||
if "embedding_model" in config:
|
||||
embedder_config["model"] = config["embedding_model"]
|
||||
if "embedding_base_url" in config:
|
||||
embedder_config["base_url"] = config["embedding_base_url"]
|
||||
powermem_config["embedder"] = {
|
||||
"provider": embedding_provider,
|
||||
"config": embedder_config
|
||||
}
|
||||
|
||||
# Initialize AsyncMemory client
|
||||
self.memory_client = AsyncMemory(config=powermem_config)
|
||||
self.use_powermem = True
|
||||
|
||||
logger.bind(tag=TAG).info(
|
||||
f"PowerMem initialized successfully with database={database_provider}, "
|
||||
f"llm={llm_provider}, embedding={embedding_provider}"
|
||||
)
|
||||
|
||||
except ImportError as e:
|
||||
logger.bind(tag=TAG).error(
|
||||
f"PowerMem not installed. Please install with: pip install powermem. Error: {e}"
|
||||
)
|
||||
self.use_powermem = False
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"Failed to initialize PowerMem: {str(e)}")
|
||||
logger.bind(tag=TAG).debug(f"Detailed error: {traceback.format_exc()}")
|
||||
self.use_powermem = False
|
||||
|
||||
async def save_memory(self, msgs):
|
||||
"""
|
||||
Save conversation messages to PowerMem.
|
||||
|
||||
Args:
|
||||
msgs: List of message objects with 'role' and 'content' attributes
|
||||
|
||||
Returns:
|
||||
Result from PowerMem API or None if failed
|
||||
"""
|
||||
if not self.use_powermem or self.memory_client is None:
|
||||
logger.bind(tag=TAG).warning("PowerMem is not available, skipping save_memory")
|
||||
return None
|
||||
|
||||
if len(msgs) < 2:
|
||||
logger.bind(tag=TAG).debug("Not enough messages to save (need at least 2)")
|
||||
return None
|
||||
|
||||
try:
|
||||
# Format the content as a message list for PowerMem
|
||||
messages = [
|
||||
{"role": message.role, "content": message.content}
|
||||
for message in msgs
|
||||
if message.role != "system"
|
||||
]
|
||||
|
||||
# Add memory using PowerMem SDK
|
||||
result = await self.memory_client.add(
|
||||
messages=messages,
|
||||
user_id=self.role_id
|
||||
)
|
||||
|
||||
logger.bind(tag=TAG).debug(f"Save memory result: {result}")
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"Error saving memory: {str(e)}")
|
||||
logger.bind(tag=TAG).debug(f"Detailed error: {traceback.format_exc()}")
|
||||
return None
|
||||
|
||||
async def query_memory(self, query: str) -> str:
|
||||
"""
|
||||
Query memories from PowerMem based on similarity search.
|
||||
|
||||
Args:
|
||||
query: The search query string
|
||||
|
||||
Returns:
|
||||
Formatted string of relevant memories or empty string if none found
|
||||
"""
|
||||
if not self.use_powermem or self.memory_client is None:
|
||||
logger.bind(tag=TAG).warning("PowerMem is not available, skipping query_memory")
|
||||
return ""
|
||||
|
||||
try:
|
||||
if not getattr(self, "role_id", None):
|
||||
logger.bind(tag=TAG).debug("No role_id set, returning empty memory")
|
||||
return ""
|
||||
|
||||
# Search memories using PowerMem SDK
|
||||
results = await self.memory_client.search(
|
||||
query=query,
|
||||
user_id=self.role_id,
|
||||
limit=30
|
||||
)
|
||||
|
||||
if not results or "results" not in results:
|
||||
logger.bind(tag=TAG).debug("No memory results found")
|
||||
return ""
|
||||
|
||||
# Format each memory entry with its update time
|
||||
memories = []
|
||||
for entry in results.get("results", []):
|
||||
# Get timestamp from updated_at or created_at
|
||||
timestamp = ""
|
||||
if "updated_at" in entry and entry["updated_at"]:
|
||||
timestamp = str(entry["updated_at"])
|
||||
elif "created_at" in entry and entry["created_at"]:
|
||||
timestamp = str(entry["created_at"])
|
||||
|
||||
if timestamp:
|
||||
try:
|
||||
# Parse and reformat the timestamp (remove milliseconds if present)
|
||||
if "." in timestamp:
|
||||
dt = timestamp.split(".")[0]
|
||||
else:
|
||||
dt = timestamp
|
||||
formatted_time = dt.replace("T", " ")
|
||||
except Exception:
|
||||
formatted_time = timestamp
|
||||
else:
|
||||
formatted_time = ""
|
||||
|
||||
memory = entry.get("memory", "") or entry.get("content", "")
|
||||
if memory:
|
||||
if formatted_time:
|
||||
# Store tuple of (timestamp, formatted_string) for sorting
|
||||
memories.append((timestamp, f"[{formatted_time}] {memory}"))
|
||||
else:
|
||||
memories.append(("", memory))
|
||||
|
||||
# Sort by timestamp in descending order (newest first)
|
||||
memories.sort(key=lambda x: x[0], reverse=True)
|
||||
|
||||
# Extract only the formatted strings
|
||||
memories_str = "\n".join(f"- {memory[1]}" for memory in memories)
|
||||
logger.bind(tag=TAG).debug(f"Query results: {memories_str}")
|
||||
return memories_str
|
||||
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"Error querying memory: {str(e)}")
|
||||
logger.bind(tag=TAG).debug(f"Detailed error: {traceback.format_exc()}")
|
||||
return ""
|
||||
|
||||
|
||||
# Register the memory provider instance
|
||||
powermem = MemoryProvider({})
|
||||
@@ -23,6 +23,7 @@ loguru==0.7.3
|
||||
requests==2.32.5
|
||||
cozepy==0.20.0
|
||||
mem0ai==1.0.0
|
||||
powermem>=0.2.1
|
||||
bs4==0.0.2
|
||||
modelscope==1.32.0
|
||||
sherpa_onnx==1.12.17
|
||||
|
||||
Reference in New Issue
Block a user