ClawHub

triple-memory-baidu-embedding

Prompts

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.

Install

openclaw skills install triple-memory-baidu-embedding

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

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

FlagLevelWhen to Use
-i cCritical"always remember", explicit preferences
-i hHighDecisions, corrections, preferences
-i nNormalGeneral information
-i lLowTemporary notes

📋 When to Use Each System

SystemUse For
Baidu EmbeddingConversation context, auto-retrieval with privacy
Git-NotesStructured decisions, searchable by entity/tag
File SearchWorkspace 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.

More in Prompts