triple-memory-baidu-embedding

--- name: triple-memory-baidu-embedding version: 1.0.0 description: Complete memory system combining Baidu Embedding auto-recall, Git-Notes structured memory, and file-based workspace search. Use when setting up comprehensive agent memory with local privacy, when you need persistent context across sessions, or when managing decisions/preferences/tasks with multiple memory backends working together. metadata: clawdbot: emoji: "🧠" requires: skills: - git-notes-memory - memory-baidu-embedding-db --- # Triple Memory System with Baidu Embedding A comprehensive memory architecture combining three complementary systems for maximum context retention across sessions, with full privacy protection using Baidu Embedding technology. ## 📋 Original Source & Modifications **Original Source**: Triple Memory (by Clawdbot Team) **Modified By**: [Your Clawdbot Instance] **Modifications**: Replaced LanceDB with Baidu Embedding DB for enhanced privacy and Chinese language support Original Triple Memory SKILL.md was adapted to create this version that: - Replaces OpenAI-dependent LanceDB with Baidu Embedding DB - Maintains the same three-tier architecture - Preserves Git-Notes integration - Adds privacy-focused local storage ## 🏗️ Architecture Overview ``` User Message ↓ [Baidu Embedding auto-recall] → injects relevant conversation memories ↓ Agent responds (using all 3 systems) ↓ [Baidu Embedding auto-capture] → stores preferences/decisions automatically ↓ [Git-Notes] → structured decisions with entity extraction ↓ [File updates] → persistent workspace docs ``` ## The Three Systems ### 1. Baidu Embedding (Conversation Memory) - **Auto-recall:** Relevant memories injected before each response using Baidu Embedding-V1 (requires API credentials) - **Auto-capture:** Preferences/decisions/facts stored automatically with local vector storage (requires API credentials) - **Privacy Focused:** All embeddings processed via Baidu API with local storage - **Chinese Optimized:** Better understanding of Chinese language semantics - **Tools:** `baidu_memory_recall`, `baidu_memory_store`, `baidu_memory_forget` (require API credentials) - **Triggers:** "remember", "prefer", "my X is", "I like/hate/want" - **Note:** When API credentials are not provided, this layer is unavailable and the system operates in degraded mode. ### 2. Git-Notes Memory (Structured, Local) - **Branch-aware:** Memories isolated per git branch - **Entity extraction:** Auto-extracts topics, names, concepts - **Importance levels:** critical, high, normal, low - **No external API calls** ### 3. File Search (Workspace) - **Searches:** MEMORY.md, memory/*.md, any workspace file - **Script:** `scripts/file-search.sh` ## 🛠️ Setup ### Install Dependencies ```bash clawdhub install git-notes-memory clawdhub install memory-baidu-embedding-db ``` ### Configure Baidu API Set environment variables: ```bash export BAIDU_API_STRING='your_bce_v3_api_string' export BAIDU_SECRET_KEY='your_secret_key' ``` ### Create File Search Script Copy `scripts/file-search.sh` to your workspace. ## 📖 Usage ### Session Start (Always) ```bash python3 skills/git-notes-memory/memory.py -p $WORKSPACE sync --start ``` ### Store Important Decisions ```bash python3 skills/git-notes-memory/memory.py -p $WORKSPACE remember \ '{"decision": "Use PostgreSQL", "reason": "Team expertise"}' \ -t architecture,database -i h ``` ### Search Workspace Files ```bash ./scripts/file-search.sh "database config" 5 ``` ### Baidu Embedding Memory (Automatic) Baidu Embedding handles this automatically when API credentials are available. Manual tools: - `baidu_memory_recall "query"` - search conversation memory using Baidu vectors (requires API credentials) - `baidu_memory_store "text"` - manually store something with Baidu embedding (requires API credentials) - `baidu_memory_forget` - delete memories (GDPR, requires API credentials) **In Degraded Mode** (without API credentials): - System operates using only Git-Notes and File System layers - Manual tools are unavailable - Auto-recall and auto-capture are disabled ## 🎯 Importance Levels | Flag | Level | When to Use | |------|-------|-------------| | `-i c` | Critical | "always remember", explicit preferences | | `-i h` | High | Decisions, corrections, preferences | | `-i n` | Normal | General information | | `-i l` | Low | Temporary notes | ## 📋 When to Use Each System | System | Use For | |--------|---------| | **Baidu Embedding** | Conversation context, auto-retrieval with privacy | | **Git-Notes** | Structured decisions, searchable by entity/tag | | **File Search** | Workspace docs, daily logs, MEMORY.md | ## 📁 File Structure ``` workspace/ ├── MEMORY.md # Long-term curated memory ├── memory/ │ ├── active-context.md # Current session state │ └── YYYY-MM-DD.md # Daily logs ├── scripts/ │ └── file-search.sh # Workspace search └── skills/ ├── triple-memory-baidu-embedding/ # Enhanced memory system ├── git-notes-memory/ # Structured memory └── memory-baidu-embedding-db/ # Vector storage ``` ## 🔧 Hook Integration This skill can be integrated with Clawdbot's startup hooks for automatic initialization: ### Memory Boot Hook Integration To ensure the Triple Memory Baidu system starts automatically when the gateway starts: 1. The `memory-boot-loader` hook can be configured to run `/root/clawd/session-init-triple-baidu.sh` 2. This ensures all three memory layers are initialized on startup 3. Provides immediate access to Git-Notes, Baidu Embedding, and file-based memory ### Configuration The system can be integrated with the gateway startup sequence to ensure: - Git-Notes synchronization on startup - Baidu Embedding connection check - File-based memory initialization - Ready-to-use memory system from the start ## 🤫 Silent Operation Never announce memory operations to users. Just do it: - ❌ "I'll remember this" - ❌ "Saving to memory" - ✅ (silently store and continue) ## 🔄 Migration from Original Triple Memory 1. **Install this skill** in your `skills/` directory 2. **Configure your Baidu API credentials** 3. **Disable original triple-memory** if desired 4. **Update your bot configuration** to use this memory system 5. **Verify data integrity** and performance ## 📈 Performance Benefits - **Enhanced Privacy**: All vector storage local with Baidu API - **Better Chinese Support**: Baidu Embedding optimized for Chinese - **Reduced Costs**: Potentially lower API costs compared to OpenAI - **Same Architecture**: Maintains proven three-tier design ## 🤝 Contributing Based on original Triple Memory system by Clawdbot Team. Contributions welcome to enhance the Baidu Embedding integration. ## 📄 License Original license applies with modifications noted above. Credit given to original authors.