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AgentDB Vector Search

Store embeddings and query vectors for semantic document retrieval.

43|12|Updated Jul 26, 2025
Search Tags:#rag#retrieval#embedding#semantic-search#knowledge-base#vector-search
One-click install
npx skills add https://github.com/proffesor-for-testing/sentinel-api-testing --skill agentdb-vector-search-proffesor-for-testing
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Vector Search
Source: https://github.com/proffesor-for-testing/sentinel-api-testing/tree/main/.claude/skills/agentdb-vector-search
Command: npx skills add https://github.com/proffesor-for-testing/sentinel-api-testing --skill agentdb-vector-search-proffesor-for-testing

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enables rapid semantic retrieval over large document collections by storing embeddings in a purpose-built vector store and providing fast similarity search.

Core Features & Use Cases

  • Vector storage and fast similarity search with HNSW indexing
  • Embedding-based querying, metadata filtering, and retrieval-augmented generation (RAG) workflows
  • Use cases include knowledge bases, document search, and intelligent assistants

Quick Start

Initialize the vector store, index embeddings, and perform a semantic search with a sample input.

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 knowledge base with semantic vector search?▼

Build a knowledge base using semantic vector search by encoding documents into embeddings, storing them in a purpose-built vector store, and querying through fast similarity search with metadata filtering.

What is the best way to retrieve documents for a RAG system?▼

Retrieve documents for a RAG system by querying a vector store with HNSW indexing to find semantically similar embeddings, enabling rapid retrieval-augmented generation workflows.

Do I need an embedding model to perform semantic document search?▼

Yes, you need an embedding model and an embedding workflow to generate vectors from documents and queries before storing and searching them in the vector database.

Can I filter search results by metadata during vector retrieval?▼

Yes, vector retrieval supports metadata filtering, allowing you to narrow down similarity search results by specific document attributes during the query process.

How does HNSW indexing work for fast similarity search?▼

HNSW indexing enables fast similarity search by organizing stored embeddings into a navigable graph structure, allowing rapid retrieval of the closest matching vectors for semantic queries.

Does AgentDB Vector Search require AgentDB integration to function?▼

Yes, AgentDB integration is required alongside an embedding model and workflow to store and query vectors for semantic retrieval across your document collections.

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