Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
memory_baidu_embedding_db
Semantic memory system using Baidu Embedding-V1 for secure, local vector storage and retrieval in Clawdbot with SQLite persistence.
skill openclawclawhub Free
Semantic memory system using Baidu Embedding-V1 for secure, local vector storage and retrieval in Clawdbot with SQLite persistence.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- ERROR_HANDLING_BEST_PRACTICES.md, DISABLE_LANCEDB.md, memory_skill_full_verification.sh, memory_baidu_embedding_db.py, memory_system_comprehensive_guide.md, API_REFERENCE.md
Validation
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- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 2.0.1
Provenance
- Publisher
- xqicxx
- Source page
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Documentation
Memory Baidu Embedding DB - Semantic Memory for Clawdbot
Vector-Based Memory Storage and Retrieval Using Baidu Embedding Technology A semantic memory system for Clawdbot that uses Baidu's Embedding-V1 model to store and retrieve memories based on meaning rather than keywords. Designed as a secure, locally-stored replacement for traditional vector databases like LanceDB.
π Features
Semantic Memory Search - Find memories based on meaning, not just keywords Baidu Embedding Integration - Uses Baidu's powerful Embedding-V1 model SQLite Persistence - Local, secure storage without external dependencies Zero Data Leakage - All processing happens locally with your API credentials Flexible Tagging System - Organize memories with custom tags and metadata High Performance - Optimized vector similarity calculations Easy Migration - Drop-in replacement for memory-lancedb systems
π― Use Cases
Conversational Context - Remember user preferences and conversation history Knowledge Management - Store and retrieve information semantically Personalization - Maintain user-specific settings and preferences Information Retrieval - Find related information based on meaning Data Organization - Structure memories with tags and metadata
π Requirements
Clawdbot installation Baidu Qianfan API credentials (API Key and Secret Key) Python 3.8+ Internet connection for initial API calls
Manual Installation
Place the skill files in your ~/clawd/skills/ directory Install dependencies (if any Python packages are needed) Configure your Baidu API credentials
Configuration
Set environment variables: export BAIDU_API_STRING='${BAIDU_API_STRING}' export BAIDU_SECRET_KEY='${BAIDU_SECRET_KEY}'
Basic Usage
from memory_baidu_embedding_db import MemoryBaiduEmbeddingDB # Initialize the memory system memory_db = MemoryBaiduEmbeddingDB() # Add a memory memory_db.add_memory( content="The user prefers concise responses and enjoys technical discussions", tags=["user-preference", "communication-style"], metadata={"importance": "high"} ) # Search for related memories using natural language related_memories = memory_db.search_memories("What does the user prefer?", limit=3)
Advanced Usage
# Add multiple memories with rich metadata memory_db.add_memory( content="User's favorite programming languages are Python and JavaScript", tags=["tech-preference", "programming"], metadata={"confidence": 0.95, "source": "conversation-2026-01-30"} ) # Search with tag filtering filtered_memories = memory_db.search_memories( query="programming languages", tags=["tech-preference"], limit=5 )
π§ Integration
This skill integrates seamlessly with Clawdbot's memory system as a drop-in replacement for memory-lancedb. Simply update your configuration to use this memory system instead of the traditional one.
π Performance
Vector Dimension: 384 (Baidu Embedding-V1 output) Storage: SQLite database (~1MB per 1000 memories) Search Speed: ~50ms for 1000 memories (on typical hardware) API Latency: Depends on Baidu API response time (typically <500ms)
π Security
Local Storage: All memories stored in local SQLite database Encrypted API Keys: Credentials stored securely in environment variables No External Sharing: Memories never leave your system Selective Access: Granular control over what gets stored
π Migration from memory-lancedb
Install this skill in your skills/ directory Configure your Baidu API credentials Initialize the new system Update your bot configuration to use the new memory system Verify data integrity and performance
π€ Contributing
We welcome contributions! Feel free to submit issues, feature requests, or pull requests to improve this skill.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
4 Docs2 Scripts
- API_REFERENCE.md
- DISABLE_LANCEDB.md
- ERROR_HANDLING_BEST_PRACTICES.md
- memory_system_comprehensive_guide.md
- memory_baidu_embedding_db.py
- memory_skill_full_verification.sh