embedding-vector-search
CommunityEmbed text with gobed and vector search.
Authorgtrig
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Generate embeddings for text and enable fast vector-based similarity search using gobed and an HNSW index for scalable internal retrieval.
Core Features & Use Cases
- Pure Go embeddings using gobed for 1024-dim vectors
- HNSW-based nearest neighbor search for fast retrieval
- SavedGraph-based persistence for long-running indexes
- Use Case: index internal documents and retrieve top-k similar items to a query
Quick Start
Load the embedding model, build the vector index, and perform a similarity search for a query.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: embedding-vector-search Download link: https://github.com/gtrig/LaightDB/archive/main.zip#embedding-vector-search Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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