AgentDB Vector Search

Name: AgentDB Vector Search
Availability: InStock
Author: harshaldhaduk

Indexes and queries high-dimensional vectors with HNSW, quantization, and metadata filtering.

1|1|Updated Apr 11, 2026

Search Tags:#semantic search #embeddings #rag #knowledge base #agentdb #vector search #hnsw

Ready to run

npx skills add https://github.com/harshaldhaduk/Lattice --skill agentdb-vector-search-harshaldhaduk

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Traditional vector search solutions are slow, memory-intensive, and difficult to scale for use cases like RAG systems and intelligent knowledge bases, leading to poor user experience and high infrastructure costs.

Core Features & Use Cases

Sub-millisecond semantic search: HNSW indexing delivers <100µs query latency, 150x-12,500x faster than traditional vector databases for large-scale document retrieval.
Memory-efficient vector storage: Binary, scalar, and product quantization options reduce memory usage by 4-32x, making it cost-effective to store millions of embedding vectors.
Use Case: A developer building a RAG-powered customer support chatbot can use this skill to index 50k support tickets and retrieve the most relevant solutions for user queries in real time.

Quick Start

Use the AgentDB Vector Search skill to index your team's internal wiki documents and find the 5 most relevant pages for a query about remote work policy.

Frequently Asked Questions about AgentDB Vector Search

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

FAQPage Schema

How do I speed up semantic vector search for large-scale document retrieval?▼

Speed up semantic vector search using HNSW indexing, which delivers sub-millisecond query latency for large-scale document retrieval. This approach reduces search times significantly compared to traditional vector databases by optimizing the indexing layer for high throughput.

What is the best way to reduce memory usage when storing millions of embedding vectors?▼

Reduce memory usage for millions of embedding vectors by applying binary, scalar, or product quantization. These quantization options compress vector storage by 4 to 32 times, making large-scale knowledge bases cost-effective to maintain.

How does HNSW indexing work for RAG system development?▼

HNSW indexing works for RAG system development by creating a navigable small-world graph for your embeddings, enabling fast similarity matching. It allows retrieval-augmented generation chatbots to fetch relevant documents in real time with low latency.

Can I filter search results by metadata and ensure diversity in my knowledge base queries?▼

Filter search results by metadata and ensure diversity using hybrid metadata filtering and MMR diversity. These features restrict results to specific categories while removing redundant matches, returning relevant and non-redundant documents from your knowledge base.

Does vector quantization impact the accuracy of semantic search results?▼

Vector quantization optimizes memory usage without fundamentally breaking semantic search accuracy. By using binary, scalar, or product quantization, you balance memory efficiency with retrieval precision, keeping large-scale querying cost-effective.