AgentDB Vector Search

Implement semantic vector search with HNSW indexing in Node.js.

6|3|Updated Dec 3, 2025

Search Tags:#semantic search #RAG #vector search #AgentDB #MMR

Ready to run

npx skills add https://github.com/pacphi/ampel --skill agentdb-vector-search-pacphi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: AgentDB Vector Search
Source: https://github.com/pacphi/ampel/tree/main/.claude/skills/agentdb-vector-search
Command: npx skills add https://github.com/pacphi/ampel --skill agentdb-vector-search-pacphi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Implements vector-based semantic search for fast, accurate retrieval in knowledge bases, RAG pipelines, and intelligent document systems.

Core Features & Use Cases

HNSW indexing: Scales to large vector collections with fast lookup.
Hybrid search: Combines vector similarity with metadata filters.
MMR and context synthesis: Produces diverse, context-rich results for downstream tasks.

Quick Start

Instruction: "Index a few documents, then run a semantic search for 'quantum computing' and retrieve top 5 results."

Frequently Asked Questions about AgentDB Vector Search

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I implement semantic search for document retrieval?▼

Semantic search uses vector embeddings to find documents by meaning rather than keyword matching. AgentDB provides HNSW indexing for fast similarity matching, enabling you to index documents with OpenAI or custom embeddings and retrieve contextually relevant results in milliseconds.

Can I use vector search in a RAG pipeline?▼

Yes. Vector search is core to RAG systems—it retrieves relevant context from your knowledge base to ground LLM responses. AgentDB's hybrid search combines vector similarity with metadata filtering, and MMR diversity ensures you get varied, high-quality context for generation.

What's the difference between vector search and keyword search?▼

Keyword search matches exact terms; vector search captures semantic meaning. Vector embeddings let you find related documents even when they use different words, making it superior for knowledge bases and intelligent document systems where intent matters more than exact phrasing.

Do I need to manage embeddings myself with AgentDB?▼

No. AgentDB integrates with OpenAI embeddings and supports custom models in Node.js. You pass documents and queries; the Skill handles embedding generation, HNSW indexing, and quantization to deliver sub-millisecond latency at scale.

How does HNSW indexing improve search performance?▼

HNSW (Hierarchical Navigable Small World) is a graph-based indexing algorithm that accelerates nearest-neighbor lookup in high-dimensional spaces. It scales to large vector collections while maintaining sub-millisecond query latency, making it ideal for knowledge bases with thousands or millions of documents.