About

Imported from aiskillstore/marketplace (skills/dnyoussef/agentdb-semantic-vector-search/SKILL.md). Install upstream with npx skills add aiskillstore/marketplace --skill agentdb-semantic-vector-search. Copyright stays with the author.

AgentDB Semantic Vector Search

Name: agentdb-semantic-vector-search
Author: aiskillstore

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

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

Full docs: SKILL.md
AgentDB Vector Search: https://agentdb.dev/docs/vector-search

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml

targets:
  - https://api.opensmartroute.ai/api/v1/registry/aiskillstore-marketplace-agentdb-semantic-vector-search/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

aiskillstore-marketplace-agentdb-semantic-vector-search.ocm.jsonjson

{
  "ocm": "1",
  "id": "aiskillstore-marketplace-agentdb-semantic-vector-search",
  "kind": "skill",
  "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",
  "publisher": "aiskillstore",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "general"
    ],
    "tags": [
      "skill-md",
      "agentdb",
      "semantic-search",
      "rag",
      "vector-search",
      "embeddings",
      "skills-sh"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "Build semantic vector search systems with AgentDB for intelligent document retrieval, RAG applications, and knowledge bases using embedding-based similarity matching"
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "skills.sh",
      "repository": "https://github.com/aiskillstore/marketplace",
      "path": "skills/dnyoussef/agentdb-semantic-vector-search/SKILL.md",
      "ref": "HEAD",
      "url": "https://github.com/aiskillstore/marketplace/blob/HEAD/skills/dnyoussef/agentdb-semantic-vector-search/SKILL.md",
      "key": "aiskillstore/marketplace/skills/dnyoussef/agentdb-semantic-vector-search/SKILL.md"
    }
  },
  "instructions": "# AgentDB Semantic Vector Search\n\n## Overview\n\nImplement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Build RAG systems, semantic search engines, and knowledge bases.\n\n## SOP Framework: 5-Phase Semantic Search\n\n### Phase 1: Setup Vector Database (1-2 hours)\n- Initialize AgentDB\n- Configure embedding model\n- Setup database schema\n\n### Phase 2: Embed Documents (1-2 hours)\n- Process document corpus\n- Generate embeddings\n- Store vectors with metadata\n\n### Phase 3: Build Search Index (1-2 hours)\n- Create HNSW index\n- Optimi",
  "cost": {
    "context_tokens": 508
  }
}

Fetch it by URL: GET /api/v1/registry/aiskillstore-marketplace-agentdb-semantic-vector-search/manifest?version=1.0.0

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