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Filipcsupka
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AgentDB Vector Search

Enable semantic vector search across large document collections with HNSW indexing.

Updated Sep 21, 2025
Search Tags:#embeddings#rag#agentdb#semantic-search#knowledge-base#vector-search
One-click install
npx skills add https://github.com/Filipcsupka/cv-web --skill agentdb-vector-search-filipcsupka
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Vector Search
Source: https://github.com/Filipcsupka/cv-web/tree/main/.agents/skills/agentdb-vector-search
Command: npx skills add https://github.com/Filipcsupka/cv-web --skill agentdb-vector-search-filipcsupka

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Vector-based semantic search across large document collections to enable rapid retrieval and contextual insights.

Core Features & Use Cases

  • Vector storage & indexing with HNSW for sub-millisecond lookups
  • Embeddings integration with common models and formats
  • RAG and knowledge bases workflows enabling context-aware retrieval and ranking

Quick Start

Run a basic vector search against a sample dataset to retrieve top 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 across large document collections?▼

Semantic vector search across large document collections is enabled by applying HNSW indexing and embeddings integration, providing sub-millisecond lookups, similarity ranking, and context-aware querying.

Can I use vector search for RAG pipelines and knowledge bases?▼

Vector search supports RAG pipelines and knowledge bases by enabling context-aware retrieval and ranking, allowing rapid extraction of relevant documents for generation workflows.

What is the best way to achieve sub-millisecond lookups in a knowledge base?▼

Sub-millisecond lookups in a knowledge base are achieved using HNSW indexing and quantization, optimizing vector storage and retrieval speed for enterprise document repositories.

Does AgentDB vector search support JSON or JSONL outputs for automation?▼

AgentDB vector search supports JSON and JSONL outputs, facilitating automation by structuring retrieved results and similarity rankings for downstream processing pipelines.

Do I need specific embeddings formats to run context-aware querying?▼

Context-aware querying requires embeddings integration with common models and formats, ensuring compatibility when generating vectors for semantic search and similarity ranking.

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