Agent skill · data analytics · aiskillstore
agentdb-semantic-vector-search
Build semantic vector search systems with AgentDB for intelligent document retrieval, RAG applications, and knowledge bases using embedding-based similarity matching
Why this skill is useful
Provides specific implementation patterns and commands for building semantic vector search systems with AgentDB that the AI wouldn't reliably generate on its own.
What it needs
Requires agentdb installed locally. About 2k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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 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
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/agentdb-semantic-vector-search