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MarcoDava
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AgentDB Vector Search

Enable semantic vector search with HNSW indexing and MCP integration.

1|Updated Feb 7, 2026
Search Tags:#rag#agentdb#embedding#semantic-search#vector-database#hybrid-search#document-retrieval
One-click install
npx skills add https://github.com/MarcoDava/MockCortex --skill agentdb-vector-search-marcodava
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Vector Search
Source: https://github.com/MarcoDava/MockCortex/tree/main/.agents/skills/agentdb-vector-search
Command: npx skills add https://github.com/MarcoDava/MockCortex --skill agentdb-vector-search-marcodava

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Vector-based semantic search over large document collections enables fast, context-aware retrieval for knowledge bases, RAG pipelines, and enterprise search.

Core Features & Use Cases

  • Vector storage and similarity search with HNSW indexing and quantization for memory efficiency
  • Hybrid search combining vector similarity with metadata filters to refine results
  • MCP server integration for Codex workflows and developer tooling

Quick Start

Initialize the AgentDB vector store and run a sample query to verify 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 build a RAG system with semantic vector search for enterprise document retrieval?▼

To build a RAG system with semantic vector search, you need vector storage and similarity search capabilities. This Skill enables context-aware document retrieval across large corpora using HNSW indexing and optional hybrid search for refined results.

What is the best way to perform hybrid search combining vector similarity and metadata filters?▼

Hybrid search combines vector similarity with metadata filters to refine retrieval results. You can apply this approach to narrow down context-aware searches across large knowledge bases by filtering documents based on specific attributes.

How does HNSW indexing and quantization improve memory efficiency in a vector database?▼

HNSW indexing and quantization improve memory efficiency in a vector database by optimizing how vectors are stored and retrieved. This enables fast similarity search across large document collections without exhausting memory resources.

Can I integrate vector search into Codex workflows using MCP server integration?▼

Yes, you can integrate vector search into Codex workflows using MCP server integration. This provides developer tooling support for building intelligent retrieval systems directly within your existing pipelines.

Does AgentDB vector search support batching for large-scale embedding processing?▼

AgentDB vector search supports batching for large-scale embedding processing. This allows you to handle high volumes of vector storage and similarity search operations efficiently when building enterprise knowledge bases.

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