AgentDB Vector Search

Name: AgentDB Vector Search
Availability: InStock
Author: bjpl

Perform semantic vector search to retrieve relevant documents from AgentDB.

Updated Aug 23, 2026

Search Tags:#embeddings #RAG #retrieval #AgentDB #semantic-search #knowledge-base #vector-search

Ready to run

npx skills add https://github.com/bjpl/hablas --skill agentdb-vector-search

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

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

Core Features & Use Cases

Vector Storage: Store documents with embeddings for fast retrieval.
Similarity Search: Retrieve top-k similar results quickly.
Hybrid Search: Combine vector similarity with metadata filters for refined results.
RAG Pipelines: Build Retrieval Augmented Generation workflows with contextual results.

Quick Start

Initialize the AgentDB vector store, index documents with embeddings, and run a query to retrieve relevant 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 perform semantic vector search over large document collections?▼

Semantic vector search retrieves documents based on meaning rather than keyword matching. AgentDB's vector search stores document embeddings and finds top-k similar results using HNSW indexing, enabling sub-millisecond queries across knowledge bases and RAG systems.

What's the fastest way to build a RAG pipeline with semantic retrieval?▼

Build RAG pipelines by indexing documents with embeddings in AgentDB, then querying for semantically relevant context to feed into language models. The Skill handles vector storage, similarity matching, and metadata filtering for context-aware retrieval.

Can I combine vector similarity with metadata filters in my searches?▼

Yes, hybrid search combines vector similarity scoring with metadata filters to refine results. AgentDB supports filtering on document metadata alongside semantic similarity, letting you narrow results by domain, date, category, or custom attributes.

Do I need an embedding model to use semantic vector search?▼

Yes, semantic vector search requires an embedding model to convert text into vectors. The Skill supports OpenAI's API or custom embedding models; you provide the embeddings, and AgentDB indexes and searches them for retrieval.

What are the performance characteristics of AgentDB vector search?▼

AgentDB achieves sub-millisecond search performance using HNSW indexing and optional quantization for memory efficiency. It's designed for fast retrieval across large document collections without sacrificing accuracy.