This repository serves as a comprehensive guide to implementing vector search in production environments, targeting developers and engineers seeking to leverage semantic search capabilities. It offers tutorials, best practices, and educational resources to understand and deploy vector search solutions effectively.

How It Works

The project focuses on the core concepts of vector-based information retrieval, explaining the differences between keyword and vector search. It provides tutorials for building both sparse and dense vector feature extraction engines, demonstrating how to integrate vector search into application architectures. The approach emphasizes practical implementation and understanding of underlying technologies like HNSW graphs and transformer models.

Quick Start & Requirements

This repository is primarily educational and does not provide a direct installable package or executable. It guides users through concepts and comparisons of existing vector search engines like MongoDB Atlas. Specific implementation details would depend on the chosen vector database or library.

Highlighted Details

• Covers foundational concepts from keyword vs. vector search to sparse and dense vector tutorials.
• Explores various use cases including sentence similarity, question answering, personalization, and file search.
• Provides architectural guidance on model deployment, hosting, versioning, and feedback loops.
• Includes links to key research papers and resources on transformer architectures and similarity search.

Maintenance & Community

The repository is a "living document" maintained by esteininger, with acknowledgments to Nick Gogan and Marcus Eagan. Users are encouraged to subscribe to changes for updates as the landscape evolves.

Licensing & Compatibility

The repository itself does not specify a license. Content is for educational purposes, and specific vector search engine integrations would be subject to their respective licenses.

Limitations & Caveats

This repository is a guide and does not offer a deployable solution. Users will need to select and integrate with specific vector search engines or libraries, which may have their own dependencies, performance characteristics, and licensing requirements.