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AgentDB Vector Search

Index document embeddings with HNSW indexing for semantic search.

Updated Mar 4, 2026

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

One-click install

npx skills add https://github.com/script-repo/002-NXClaw --skill agentdb-vector-search-script-repo

Or copy as Structured Prompt for Agent▼

Please help me install this Agent Skill.
Skill:  AgentDB Vector Search
Source: https://github.com/script-repo/002-NXClaw/tree/main/.claude/skills/agentdb-vector-search
Command: npx skills add https://github.com/script-repo/002-NXClaw --skill agentdb-vector-search-script-repo

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Semantic search over large document collections by indexing and querying embeddings with AgentDB for fast, scalable retrieval.

Core Features & Use Cases

HNSW-based vector indexing for rapid similarity search.
Quantization and optimizations enabling memory-efficient deployment.
Use cases include building RAG pipelines, knowledge bases, and intelligent search interfaces.

Quick Start

Index your documents with AgentDB and run a semantic search query to retrieve the most relevant results.

Frequently Asked Questions about AgentDB Vector Search

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

What is the best way to implement semantic search over a large document collection?▼

Drop this duplicate key.

How do I build a RAG pipeline with fast vector-based retrieval?▼

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Does this semantic search approach support memory-efficient deployment at scale?▼

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Can I use both CLI and API usage patterns for vector search queries?▼

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When do I need HNSW indexing for my knowledge base?▼

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Related Skills

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embedding-pipeline-builder

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qwen

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rag-implementation

Build RAG pipelines that ground LLM responses with retrieved external knowledge.

AgentDB Vector Search

Index embeddings and perform context-aware similarity queries for document retrieval.