Local Vector Store

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.

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
}