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Dorpeer95
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AgentDB Vector Search

Search large document collections semantically via AgentDB's vector database.

Updated Mar 1, 2026
Search Tags:#embeddings#rag#search#mcp#agentdb#vector#semantic
One-click install
npx skills add https://github.com/Dorpeer95/stocks-trading --skill agentdb-vector-search-dorpeer95
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Vector Search
Source: https://github.com/Dorpeer95/stocks-trading/tree/main/.claude/skills/agentdb-vector-search
Command: npx skills add https://github.com/Dorpeer95/stocks-trading --skill agentdb-vector-search-dorpeer95

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enables fast, accurate semantic search over large document collections by leveraging AgentDB's vector database and optimized indexing.

Core Features & Use Cases

  • High-performance vector search using HNSW indexing with sub-millisecond latency.
  • Memory-efficient quantization to reduce footprint while preserving retrieval quality.
  • Hybrid search capabilities combining vector similarity with metadata filters for precise results.
  • RAG-ready workflows and knowledge-base retrieval with context-aware querying.
  • CLI and API integration for embedding, storing, and querying documents, plus MCP support for Claude Code.

Quick Start

Install AgentDB, initialize the vector store, and run a semantic search against your document collection.

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 over large document collections for a RAG workflow?▼

Semantic search for RAG workflows is enabled by applying embedding-based similarity and HNSW indexing to large document collections via AgentDB. It utilizes quantization and caching to enforce vector dimension requirements while maintaining sub-millisecond retrieval latency.

Can I use vector similarity search with metadata filters for enterprise document retrieval?▼

Yes, vector similarity search supports hybrid search capabilities by combining semantic embeddings with metadata filters. This allows context-aware querying for precise results within enterprise document retrieval and knowledge-base workflows.

Does AgentDB vector search integrate with Claude Code via MCP?▼

Yes, AgentDB vector search integrates with Claude Code through MCP support. This allows you to initialize the vector store, embed documents, and run semantic queries directly through API or CLI endpoints.

How does HNSW indexing and quantization affect vector search performance?▼

HNSW indexing delivers sub-millisecond latency for high-performance vector search, while memory-efficient quantization reduces the overall footprint. This combination preserves retrieval quality and enforces vector dimension requirements during embedding-based similarity queries.

What is the best way to reduce memory footprint during knowledge-base retrieval without losing accuracy?▼

The best way to reduce memory footprint during knowledge-base retrieval is applying memory-efficient quantization alongside HNSW indexing. This approach preserves retrieval quality and enforces vector dimension requirements while maintaining sub-millisecond search latency.

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