Combining traditional keyword filters with vector similarity search to improve precision and control.

An overview of vector databases, how they store embeddings, and enable similarity search in AI and RAG systems.

Understanding cosine similarity, dot product, and Euclidean distance for comparing vector embeddings.

Tools and methods to compare vector DBs on latency, recall, indexing time, and scalability.

FAISS is a fast library for similarity search developed by Meta AI, useful for local vector storage and benchmarking.

A fully managed vector database optimized for production-scale similarity search and real-time retrieval.

Open-source, modular vector database supporting hybrid search and schema-driven semantic indexing.

A Rust-based vector engine for structured and unstructured search with REST and gRPC APIs.

Scalable open-source vector database built for billion-scale vector search workloads.

How to plug vector databases into retrieval-augmented generation (RAG) pipelines for grounding LLM outputs.

Swift Lessons: Vector Databases

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