AgentSkillsCN

agentdb-semantic-vector-search

使用 AgentDB 构建语义向量搜索系统，用于智能文档检索、RAG 应用及基于嵌入相似度匹配的知识库。

SKILL.md
--- frontmatter
skill_id: when-building-semantic-search-use-agentdb-vector-search
name: agentdb-semantic-vector-search
description: Build semantic vector search systems with AgentDB for intelligent document retrieval, RAG applications, and knowledge bases using embedding-based similarity matching
version: 1.0.0
category: agentdb
subcategory: semantic-search
trigger_pattern: "when-building-semantic-search"
agents:
  - ml-developer
  - backend-dev
  - tester
complexity: intermediate
estimated_duration: 6-8 hours
prerequisites:
  - AgentDB basics
  - Embedding models knowledge
  - REST API development
outputs:
  - Semantic search engine
  - Document retrieval system
  - RAG-ready infrastructure
  - Query API endpoints
validation_criteria:
  - Search returns relevant results
  - Retrieval accuracy > 90%
  - Query latency < 100ms
  - API functional and documented
evidence_based_techniques:
  - Relevance evaluation
  - Precision/recall metrics
  - User feedback testing
metadata:
  author: claude-flow
  created: 2025-10-30
  tags:
    - agentdb
    - semantic-search
    - rag
    - vector-search
    - embeddings

AgentDB Semantic Vector Search

Overview

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Build RAG systems, semantic search engines, and knowledge bases.

SOP Framework: 5-Phase Semantic Search

Phase 1: Setup Vector Database (1-2 hours)

  • •Initialize AgentDB
  • •Configure embedding model
  • •Setup database schema

Phase 2: Embed Documents (1-2 hours)

  • •Process document corpus
  • •Generate embeddings
  • •Store vectors with metadata

Phase 3: Build Search Index (1-2 hours)

  • •Create HNSW index
  • •Optimize search parameters
  • •Test retrieval accuracy

Phase 4: Implement Query Interface (1-2 hours)

  • •Create REST API endpoints
  • •Add filtering and ranking
  • •Implement hybrid search

Phase 5: Refine and Optimize (1-2 hours)

  • •Improve relevance
  • •Add re-ranking
  • •Performance tuning

Quick Start

typescript
import { AgentDB, EmbeddingModel } from 'agentdb-vector-search';

// Initialize
const db = new AgentDB({ name: 'semantic-search', dimensions: 1536 });
const embedder = new EmbeddingModel('openai/ada-002');

// Embed documents
for (const doc of documents) {
  const embedding = await embedder.embed(doc.text);
  await db.insert({
    id: doc.id,
    vector: embedding,
    metadata: { title: doc.title, content: doc.text }
  });
}

// Search
const query = 'machine learning tutorials';
const queryEmbedding = await embedder.embed(query);
const results = await db.search({
  vector: queryEmbedding,
  topK: 10,
  filter: { category: 'tech' }
});

Features

  • •Semantic Search: Meaning-based retrieval
  • •Hybrid Search: Vector + keyword search
  • •Filtering: Metadata-based filtering
  • •Re-ranking: Improve result relevance
  • •RAG Integration: Context for LLMs

Success Metrics

  • •Retrieval accuracy > 90%
  • •Query latency < 100ms
  • •Relevant results in top-10: > 95%
  • •API uptime > 99.9%

Additional Resources