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triple-memory-baidu-embedding 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.
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INTEGRATION_GUIDE.md 5.0 KB 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 storageChinese Optimized: Better understanding of Chinese language semanticsTools: 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 branchEntity extraction: Auto-extracts topics, names, conceptsImportance levels: critical, high, normal, lowNo external API calls
3. File Search (Workspace)
Searches: MEMORY.md, memory/*.md, any workspace fileScript: scripts/file-search.sh
🛠️ Setup
Install Dependencies clawdhub install git-notes-memory
clawdhub install memory-baidu-embedding-db
Configure Baidu API Set environment variables:
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) python3 skills/git-notes-memory/memory.py -p $WORKSPACE sync --start
Store Important Decisions python3 skills/git-notes-memory/memory.py -p $WORKSPACE remember \
'{"decision": "Use PostgreSQL", "reason": "Team expertise"}' \
-t architecture,database -i h
Search Workspace Files ./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 cCritical "always remember", explicit preferences -i hHigh Decisions, corrections, preferences -i nNormal General information -i lLow 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:
The memory-boot-loader hook can be configured to run /root/clawd/session-init-triple-baidu.sh
This ensures all three memory layers are initialized on startup
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
Install this skill in your skills/ directoryConfigure your Baidu API credentials Disable original triple-memory if desiredUpdate your bot configuration to use this memory systemVerify data integrity and performance
📈 Performance Benefits
Enhanced Privacy : All vector storage local with Baidu APIBetter Chinese Support : Baidu Embedding optimized for ChineseReduced Costs : Potentially lower API costs compared to OpenAISame 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.
