vector-db

What problem does it solve?

This Skill addresses the complexities of building and optimizing systems for semantic search, recommendation engines, and knowledge augmentation for LLMs, ensuring efficient and accurate retrieval of information.

Core Features & Use Cases

• Embedding Strategy: Guidance on selecting and using embedding models for various domains.
• Indexing and Search: Expertise in choosing and configuring vector indexes (HNSW, IVF) and distance metrics.
• RAG Implementation: Detailed patterns and techniques for Retrieval-Augmented Generation pipelines.
• Use Case: Implement a RAG system for your company's internal documentation, allowing employees to ask natural language questions and receive accurate answers sourced directly from the documents.

Quick Start

Consult the skill for advice on selecting an embedding model and configuring an HNSW index for a new vector database.