- name
- local-vector-store
- description
- Implements semantic search using local vector embeddings for knowledge base indexing and similarity matching. Use when you need to search documents by meaning rather than keywords, or build a searchable knowledge base.
Local Vector Store
A lightweight semantic search engine that indexes documents as vectors and enables similarity-based retrieval without external APIs.
Features
- Document indexing with vector embeddings
- Semantic similarity search
- Local storage (no external dependencies)
- Batch indexing support
- Configurable embedding dimensions
- Cosine similarity matching
Usage
const vectorStore = require('./local-vector-store');
// Initialize store
const store = await vectorStore.create({
dimension: 384,
storePath: '/tmp/vector-store'
});
// Index documents
await store.index({
id: 'doc1',
content: 'Machine learning is a subset of artificial intelligence',
metadata: { source: 'wiki' }
});
// Search by semantic similarity
const results = await store.search({
query: 'AI and deep learning',
topK: 5,
threshold: 0.7
});
// Batch operations
await store.indexBatch([
{ id: 'doc2', content: 'Neural networks process data' },
{ id: 'doc3', content: 'Algorithms solve computational problems' }
]);Configuration
Set environment variables:
VECTOR_DIMENSION: Embedding dimension (default: 384)STORE_PATH: Local storage directory (default: /tmp/vector-store)SIMILARITY_THRESHOLD: Minimum similarity score (default: 0.5)
Output Format
{
"query": "semantic search",
"results": [
{
"id": "doc1",
"content": "...",
"similarity": 0.92,
"metadata": {}
}
],
"searchTime": 45
}