Vector Search and Information Retrieval: A Comprehensive Guide¶

A complete educational resource covering vector search technologies from foundational concepts to production deployment, designed for developers, architects, and practitioners building modern search systems.

Technical Specifications¶

Covered Technologies¶

AWS Services: OpenSearch Service, OpenSearch Serverless, Kendra
Vector Databases: Pinecone, OpenSearch
Algorithms: HNSW, IVF, Product Quantization, LSH

Implementation Topics¶

Core Components: Embeddings, similarity metrics, indexing strategies
Search Types: Semantic search, hybrid search, multi-modal search
Performance: Optimization, monitoring, troubleshooting
Production: Scaling, security, cost management

Use Case Coverage¶

Semantic Search: Document search, knowledge bases
Recommendation Systems: Content discovery, personalization
Multi-Modal Search: Text-to-image, cross-modal retrieval
Enterprise Search: Internal knowledge management

🚀 Start at Introduction to Search ¶

About¶

Content in this repository is created for educational and informational purposes. Articles are researched and curated with the assistance of LLMs.

Contributing¶

If you find errors or have suggestions for improvements, please open an issue at: https://github.com/shreedharn/vector_search

License¶

This project is licensed under the MIT License.

See important disclaimers.