AgentDB Vector Search

Enable high-speed semantic vector search with HNSW indexing and metadata filtering.

1|Updated Jun 9, 2020

Search Tags:#embeddings #rag #agentdb #quantization #hnsw #semantic-search #vector-search

Ready to run

npx skills add https://github.com/dalager/jernkorsetbreve --skill agentdb-vector-search-dalager

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enables extremely fast and scalable semantic vector search to retrieve relevant documents and context for downstream generation, similarity matching, and knowledge retrieval workflows, removing the latency and scalability bottlenecks of traditional indexes.

Core Features & Use Cases

High-performance retrieval: HNSW indexing and caching for sub-millisecond nearest-neighbor queries.
Memory-efficient storage: Multiple quantization strategies (binary, scalar, product) to reduce footprint at scale.
Hybrid and RAG: Combine vector similarity with metadata filtering for Retrieval-Augmented Generation, semantic search engines, and context assembly for LLM prompts.
Use Case: Power a RAG pipeline that finds the top K supporting documents for a user question, then synthesizes a concise answer using those contexts.

Quick Start

Find the top 5 most semantically similar documents to "quantum computing advances" in the AgentDB vectors.db and return their texts and metadata.

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 pipeline with sub-millisecond semantic vector search?▼

Semantic vector search enables RAG pipelines by using HNSW indexing to retrieve the top K supporting documents for a user question in under a millisecond. You can then synthesize a concise answer using those retrieved contexts.

How does HNSW indexing and quantization reduce vector search latency at scale?▼

HNSW indexing and caching provide sub-millisecond nearest-neighbor query latency, while multiple quantization strategies (binary, scalar, product) reduce the memory footprint to maintain high-speed semantic vector search at scale.

Can I combine metadata filtering with vector similarity search for document retrieval?▼

Yes, hybrid vector-plus-metadata filtering allows you to combine vector similarity with metadata filtering. This supports precise document retrieval for semantic search engines and context assembly for LLM prompts.

What is the best way to import and query large-scale document embeddings for a knowledge base?▼

Batch import/export capabilities allow you to load large-scale multi-dimension embeddings into an indexed database. You can then query the knowledge base using sub-millisecond semantic vector search to find matching documents.

Does AgentDB vector search support multi-dimension embeddings and MCP integration?▼

Yes, the system provides multi-dimension embedding support for various document formats and includes MCP integration to connect the vector search capabilities directly with your broader agent workflows and tooling.