AgentSkillsCN

libvector

libvector——向量相似度搜索工具。VectorIndex 存储嵌入向量及其元数据，并执行余弦相似度搜索。VectorProcessor 负责嵌入向量的生成与索引。支持基于元数据的过滤，以及基于阈值的检索。适用于语义搜索、RAG 检索，以及相似度匹配。

SKILL.md
--- frontmatter
name: libvector
description: >
  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

javascript
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

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

Pattern 3: Calculate similarity directly

javascript
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/.