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.

🚀 Features

• Three-Tier Memory Architecture - Combines Baidu Embedding, Git-Notes, and File Search
• Privacy Focused - All vector storage local with Baidu API calls
• Chinese Language Optimized - Better semantic understanding for Chinese
• Branch-Aware Storage - Git-based memory isolation per branch
• Auto-Recall & Capture - Automatic memory injection and storage
• Entity Extraction - Smart topic and concept recognition

🎯 Use Cases

• Persistent Context - Maintain conversation history across sessions
• Decision Tracking - Remember user decisions and preferences
• Knowledge Management - Store and retrieve information semantically
• Task Management - Track ongoing tasks and progress
• Learning Retention - Remember learned information from conversations

📋 Prerequisites

• Clawdbot installation
• Baidu Qianfan API credentials (API Key and Secret Key) - Optional, without these the system will operate in degraded mode
• Git for branch-aware memory
• Python 3.8+

🛠️ Installation

Method 1: Using ClawdHub

clawdhub install triple-memory-baidu-embedding

Method 2: Manual Installation

# Copy the skill to your skills directory
cp -r /path/to/triple-memory-baidu-embedding ~/clawd/skills/

Configuration

Set your Baidu API credentials (optional, but required for full functionality):

export BAIDU_API_STRING='your_bce_v3_api_string'
export BAIDU_SECRET_KEY='your_secret_key'

Note: Without API credentials, the system operates in degraded mode using only Git-Notes and file system search.

📚 Usage

Initialization

The system can be integrated with Clawdbot's startup hooks for automatic initialization.

Hook Integration (Recommended)

To integrate with Clawdbot's startup hook system, configure the memory-boot-loader hook to use the session initialization script:

1. The hook will automatically run /root/clawd/session-init-triple-baidu.sh on gateway startup
2. This initializes all three memory layers simultaneously
3. Ensures memory system is ready when the gateway starts

Session Start (Automatic)

The system automatically syncs at session start:

# This runs automatically
python3 skills/git-notes-memory/memory.py -p $WORKSPACE sync --start

Manual Session Initialization

# Run manually to initialize the session
bash /root/clawd/session-init-triple-baidu.sh

Memory Operations

The system handles memory operations automatically:

• Auto-Store: When you say "remember this" or "I prefer..."
• Auto-Recall: When relevant past information is needed
• Manual Operations: Use Git-Notes directly for advanced features

Manual Git-Notes Operations

# Remember something important
python3 skills/git-notes-memory/memory.py -p $WORKSPACE remember \
  '{"decision": "Use PostgreSQL", "reason": "Team expertise"}' \
  -t architecture,database -i h

# Search for information
python3 skills/git-notes-memory/memory.py -p $WORKSPACE search "database choice"

# Get information about a topic
python3 skills/git-notes-memory/memory.py -p $WORKSPACE get "preferences"

🏗️ Architecture

Three-Tier Design

1. Baidu Embedding Layer - Semantic search and auto-recall (requires API credentials)
2. Git-Notes Layer - Structured, branch-aware storage (always available)
3. File System Layer - Persistent workspace documents (always available)

Degraded Mode: When API credentials are not provided, the system operates using only Git-Notes and File System layers.

Data Flow

User Input
    ↓
Baidu Embedding Auto-Recall (relevant memories, if API credentials available)
    ↓
Response Generation (with available memory systems)
    ↓
Baidu Embedding Auto-Capture (new memories, if API credentials available)
    ↓
Git-Notes Storage (structured data)
    ↓
File System (persistent docs)

In Degraded Mode (without API credentials):

User Input
    ↓
Git-Notes and File System Search (relevant memories)
    ↓
Response Generation (with available memory systems)
    ↓
Git-Notes Storage (structured data)
    ↓
File System (persistent docs)

🔧 Configuration

Environment Variables

# Baidu Qianfan API credentials
export BAIDU_API_STRING='your_bce_v3_api_string'
export BAIDU_SECRET_KEY='your_secret_key'

Memory Settings

The skill works with Clawdbot's built-in memory configuration.

📊 Performance

• Search Speed: ~50-100ms for semantic searches
• Storage: Local SQLite database (~1MB per 1000 memories)
• API Latency: Depends on Baidu API response time
• Chinese Accuracy: Enhanced semantic understanding for Chinese

🔐 Privacy & Security

• Local Storage: All memories stored in local SQLite
• Controlled API Calls: Only Baidu API for embeddings
• No External Sharing: Memories never leave your system
• Branch Isolation: Memories separated by git branch

🔄 Migration from Original Triple Memory

If migrating from the original Triple Memory:

1. Install this skill
2. Configure Baidu API credentials
3. Disable original if desired
4. The system will continue to work with existing Git-Notes memories

🤝 Contributing

Contributions are welcome! Please submit issues and pull requests to improve this skill.

Development Setup

# Clone the repository
git clone <repository-url>

# Install for development
cd triple-memory-baidu-embedding
clawdhub install --dev

📄 License

Based on the original Triple Memory system by Clawdbot Team. Modified to use Baidu Embedding for enhanced privacy and Chinese language support.

🆘 Support

For support, please:

1. Check the documentation
2. Review the original Triple Memory documentation
3. Ensure Baidu API credentials are properly configured
4. Verify Git is properly installed for branch-aware features

🙏 Acknowledgments

• Original Triple Memory system by Clawdbot Team
• Baidu Qianfan API for embedding services
• Git-Notes Memory system for structured storage
• Memory-Baidu-Embedding-DB for vector storage