AgentDB Vector Search
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
Profile →npx skills add ruvnet/ruflo --skill agentdb-vector-search --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# AgentDB Vector Search ## What This Skill Does Implements vector-based semantic search using AgentDB's high-performance vector database with **150x-12,500x faster** operations than traditional solutions. Features HNSW indexing, quantization, and sub-millisecond search (<100µs). ## Prerequisites - Node.js 18+ - AgentDB v1.0.7+ (via agentic-flow or standalone) - OpenAI API key (for embeddings) or custom embedding model ## Quick Start with CLI ### Initialize Vector Database ```bash # Initialize with default dimensions (1536 for OpenAI ada-002) npx agentdb@latest init .$vectors.db # Custom dimensions for different embedding models npx agentdb@latest init .$vectors.db --dimension 768 # sentence-transformers npx agentdb@latest init .$vectors.db --dimension 384 # all-MiniLM-L6-v2 # Use preset configurations npx agentdb@latest init .$vectors.db --preset small # <10K vectors npx agentdb@latest init .$vectors.db --preset medium # 10K-100K vectors npx agentdb@latest init .$vectors.db --preset large # >100K vectors # In-memory database for testing npx agentdb@latest init .$vectors.db --in-memory ``` ### Query Vector Database ```bash # Basic similarity search npx agentdb@latest query .$vectors
- What This Skill Does
- Prerequisites
- Quick Start with CLI
- Initialize Vector Database
- Query Vector Database
- Import/Export Vectors
- Quick Start with API
- Core Features
- 1. Vector Storage
- 2. Similarity Search
- 3. Hybrid Search (Vector + Metadata)
- Advanced Usage
- RAG (Retrieval Augmented Generation)
- Batch Operations
Initialize with default dimensions (1536 for OpenAI ada-002) npx agentdb@latest init .$vectors.db Custom dimensions for different embedding models npx agentdb@latest init .$vectors.db --dimension 768 # sentence-transformers npx agentdb@latest init .$vectors.db --dimension 384 # all-MiniLM-L6-v2 Use preset configurations npx agentdb@latest init .$vectors.db --preset small # <10K vectors npx agentdb@latest init .$vectors.db --preset medium # 10K-100K vectors npx agentdb@latest init .$vectors.db --preset large # >100K vectors In-memory database for testing
What does the AgentDB Vector Search skill do?
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
How do I install it?
Run `npx skills add ruvnet/ruflo --skill agentdb-vector-search --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ruvnet/ruflo, a repository with 67,015 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.