AgentDB Vector Search

Name: AgentDB Vector Search
Availability: InStock
Author: adebold

Perform vector-based semantic search against an AgentDB index.

Updated Dec 14, 2025

Search Tags:#RAG #retrieval #AgentDB #semantic #embedding #vector-search

Ready to run

npx skills add https://github.com/adebold/warehouse-network --skill agentdb-vector-search-adebold

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Implements vector-based semantic search using AgentDB's high-performance vector store to enable fast document retrieval, high-precision similarity matching, and context-aware querying for RAG workflows.

Core Features & Use Cases

Vector Storage & Embedding: Store documents with embeddings automatically.
Similarity Search: Retrieve highly similar documents quickly.
Hybrid Search: Combine vector similarity with metadata filters for precise results.
RAG & Retrieval Augmented Generation: Build systems that reference relevant context.

Quick Start

Initialize the vector store, index documents with embeddings, then run semantic queries against the dataset.

Frequently Asked Questions about AgentDB Vector Search

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

FAQPage Schema

How do I perform semantic search on documents with vector embeddings?▼

Semantic vector search retrieves documents based on meaning rather than keyword matching by converting text to embeddings and finding similar vectors in an index. AgentDB enables this through high-performance vector storage and HNSW indexing for sub-millisecond retrieval against large document collections.

Can I use AgentDB vector search with OpenAI embeddings or custom embedding models?▼

Yes, AgentDB vector search supports both OpenAI embeddings and custom embedding models, allowing flexibility in how you generate document representations before indexing and querying.

What's the best way to build a retrieval system for Retrieval-Augmented Generation?▼

Build RAG systems by storing documents with embeddings in a vector index, then retrieving contextually relevant documents at query time to augment language model responses. AgentDB provides the indexing, similarity matching, and fast retrieval required for this workflow.

How do I combine vector similarity with metadata filtering for precise search results?▼

Hybrid search combines vector similarity scoring with metadata filters to narrow results by document attributes alongside semantic relevance. AgentDB supports this approach to balance precision and recall in retrieval workflows.

Does AgentDB vector search support batch operations and data import/export?▼

Yes, AgentDB provides batch operations and import/export capabilities via CLI and API surfaces, enabling bulk document indexing and integration with external data pipelines.

What indexing method does AgentDB use to achieve sub-millisecond search latency?▼

AgentDB uses HNSW (Hierarchical Navigable Small World) indexing with quantization support to optimize search speed, enabling sub-millisecond retrieval even across large document collections.