AgentDB Vector Search
Skill Verifiziert AktivImplement 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.
To enable intelligent document retrieval and similarity matching by implementing advanced semantic vector search with AgentDB, supporting the creation of RAG systems and knowledge bases.
Funktionen
- Implement semantic vector search with AgentDB
- Support for HNSW indexing and quantization
- Sub-millisecond search (<100µs) and fast batch operations
- Provide CLI and API for vector database operations
- Enable RAG systems, semantic search engines, and knowledge bases
Anwendungsfälle
- Building RAG systems for enhanced LLM context
- Creating intelligent document retrieval systems
- Implementing similarity matching for large datasets
- Developing context-aware querying interfaces
Nicht-Ziele
- Replacing general-purpose databases
- Providing natural language querying without vector embeddings
- Handling non-vector data without conversion
Workflow
- Initialize vector database with specified dimensions or presets.
- Store documents with their computed embeddings.
- Perform similarity searches using vector queries, optionally with thresholds, metrics, or MMR.
- Combine vector search with metadata filtering for hybrid search.
- Export and import vectors for backup or migration.
Voraussetzungen
- Node.js 18+
- AgentDB v1.0.7+ (via agentic-flow or standalone)
- OpenAI API key (for embeddings) or custom embedding model
Installation
npx skills add ruvnet/rufloFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
VerifiziertVertrauenssignale
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