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AgentDB Vector Search

Enable semantic vector search over embedded documents with HNSW indexing.

1|Updated Nov 24, 2025
Search Tags:#semantic search#embeddings#RAG#node.js#vector search#AgentDB
One-click install
npx skills add https://github.com/Human-Frontier-Labs-Inc/parencylegal --skill agentdb-vector-search-human-frontier-labs-inc
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Vector Search
Source: https://github.com/Human-Frontier-Labs-Inc/parencylegal/tree/main/corn-docs/.claude/skills/agentdb-vector-search
Command: npx skills add https://github.com/Human-Frontier-Labs-Inc/parencylegal --skill agentdb-vector-search-human-frontier-labs-inc

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables fast and accurate semantic retrieval by leveraging AgentDB's vector search capabilities to power RAG systems, knowledge bases, and document repositories.

Core Features & Use Cases

  • High-performance vector storage: supports HNSW indexing, quantization, and sub-millisecond queries.
  • Flexible embeddings: works with OpenAI and alternative embedding models for semantic search and content discovery.
  • RAG integration and knowledge bases: ideal for building retrieval-augmented pipelines to fetch relevant documents and context.
  • Use Case: Build a document discovery tool that retrieves the most relevant policies and clauses from a legal corpus based on a user query.

Quick Start

Use the AgentDB vector search to initialize a database, insert a document embedding, and run a similarity query against a small corpus.

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 vector search in Node.js?▼

Build a RAG system with vector search in Node.js by inserting document embeddings into AgentDB and running similarity queries to fetch relevant context. The Skill implements HNSW indexing to enable fast, context-aware retrieval for augmented generation pipelines.

Can I use custom embedding models for semantic search or does it only support OpenAI?▼

Semantic search supports both OpenAI and custom embedding models. You can generate embeddings using your preferred model and insert them into AgentDB via the CLI or API workflow to enable context-aware document discovery.

How does HNSW indexing make document retrieval faster?▼

HNSW indexing accelerates document retrieval by organizing embeddings into a navigable small-world graph, enabling sub-millisecond similarity queries. Combined with quantization support, it ensures fast semantic search across large embedded document corpora.

What's the best way to store and query embeddings for a legal or policy document corpus?▼

Store and query embeddings for a legal corpus by initializing an AgentDB database, inserting document embeddings, and running similarity queries. This enables fast semantic document search to retrieve relevant policies and clauses based on user queries.

Does AgentDB vector search support quantization for large-scale embeddings?▼

Vector search in AgentDB supports quantization to optimize storage and query performance for large-scale embeddings. This feature reduces the memory footprint of vector data while maintaining fast semantic retrieval speeds.

Why use semantic vector search instead of keyword search for knowledge bases?▼

Use semantic vector search for knowledge bases instead of keyword search to retrieve contextually relevant documents based on meaning rather than exact matches. This approach leverages HNSW indexing and embeddings to deliver accurate, context-aware results.

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