#AI-powered search

Learn about using Elasticsearch as a vector database for advanced search capabilities with AI/ML. Access resources such as Python notebooks and sample apps to explore use cases like retrieval augmented generation and question answering. Stay informed on Elastic's latest features such as Elastic Learned Sparse Encoder and reciprocal rank fusion, and see how to integrate Elasticsearch with OpenAI and Hugging Face. Utilize Elasticsearch to support LLM-based applications by leveraging a strong search infrastructure. Visit Elasticsearch Labs on GitHub for up-to-date articles and guides.

Explore Redis' powerful vector database capabilities for semantic search of arXiv papers. This application leverages AI-driven vector similarity using HuggingFace, OpenAI, and Cohere embeddings. Key technologies include FastAPI, React, and Docker, forming an efficient Single Page Application. It's designed for researchers needing customizable and rapid access to scientific literature, whether via local or cloud Redis setups.