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AgentDB Vector Search

Implement semantic vector search with AgentDB for document retrieval and similarity matching.

1|Updated Jun 3, 2026
Search Tags:#semantic search#embeddings#rag#agentdb#vector search#similarity search#hnsw
One-click install
npx skills add https://github.com/KentwareDemo/RuView --skill agentdb-vector-search-kentwaredemo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Vector Search
Source: https://github.com/KentwareDemo/RuView/tree/main/.claude/skills/agentdb-vector-search
Command: npx skills add https://github.com/KentwareDemo/RuView --skill agentdb-vector-search-kentwaredemo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires agentdb, agentic-flow.

What problem does it solve? Building semantic search and RAG systems requires a fast vector database, but traditional solutions are slow at scale and consume excessive memory. This Skill provides CLI commands and API patterns for storing embeddings, running similarity queries, and building retrieval pipelines with AgentDB's HNSW-indexed vector store. ## Core Features & Use Cases - Vector Storage & Search: Initialize databases with preset dimensions, insert embeddings, and run top-k similarity queries with cosine, euclidean, or dot-product metrics. - Memory-Efficient Quantization: Reduce memory usage 4-32x with binary, scalar, or product quantization for large vector collections. - RAG & Hybrid Search: Combine vector similarity with metadata filters and MMR diversification to build retrieval-augmented generation pipelines. - Use Case: Build a knowledge base where support documents are embedded and stored, then retrieve the most relevant passages to ground LLM answers to customer questions. ## Quick Start Initialize an AgentDB vector database and run a semantic similarity query against my document embeddings.

Frequently Asked Questions about AgentDB Vector Search

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

FAQPage Schema
How do I set up vector search with AgentDB?▼

Initialize a database with npx agentdb@latest init ./vectors.db, specifying the embedding dimension for your model. Then insert vectors via the CLI or the createAgentDBAdapter API and query with top-k similarity search.

How to build a RAG pipeline with vector search?▼

Embed your documents and store them in AgentDB, then at query time embed the question and retrieve the top-k similar chunks. Pass the retrieved context into your LLM prompt to generate grounded answers.

What embedding dimensions does AgentDB support?▼

AgentDB supports any dimension configured at initialization. Common presets are 1536 for OpenAI ada-002, 768 for sentence-transformers, and 384 for all-MiniLM-L6-v2, set via the --dimension flag.

Does AgentDB support metadata filtering with vector search?▼

Yes, hybrid search combines vector similarity with metadata filters such as category or date ranges. You can also enable MMR to diversify results and avoid redundant matches.

Why is my vector search using too much memory?▼

Large vector collections consume significant memory at full precision. Enable binary quantization for 32x reduction, scalar for 4x, or product quantization for 8-16x reduction in the adapter configuration.

Can I use AgentDB with Claude Code via MCP?▼

Yes, run npx agentdb@latest mcp to start the MCP server and register it with claude mcp add. This exposes tools for querying, storing documents, and viewing database statistics inside Claude Code.

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