libvector

libvector - Vector similarity search. VectorIndex stores embeddings with metadata and performs cosine similarity search. VectorProcessor handles embedding generation and indexing. Supports filtering by metadata and threshold-based retrieval. Use for semantic search, RAG retrieval, and similarity matching.

libvector Skill

When to Use

Building semantic search functionality
Implementing RAG retrieval pipelines
Finding similar documents by embedding
Filtering vector results by metadata

Key Concepts

VectorIndex: Storage-backed index for vectors with cosine similarity search and metadata filtering.

VectorProcessor: Processes documents into embeddings and indexes them.

calculateDotProduct: Utility function for computing dot product (cosine similarity for normalized vectors) with loop unrolling for performance.

Usage Patterns

Pattern 1: Search by vector

import { VectorIndex } from "@copilot-ld/libvector/index.js";

const index = new VectorIndex(storage, "content");
const results = await index.search(queryVector, {
  limit: 10,
  threshold: 0.7,
  filter: { type: "document" },
});

Pattern 2: Add vectors

await index.add({
  id: "doc-123",
  vector: embedding,
  metadata: { type: "document", title: "Example" },
});

Pattern 3: Calculate similarity directly

import { calculateDotProduct } from "@copilot-ld/libvector";

// For normalized vectors, dot product equals cosine similarity
const similarity = calculateDotProduct(vectorA, vectorB);

Integration

Used by Vector service. Embeddings generated via LLM service. Stored in data/vectors/.