Cloud database-as-a-service provider Couchbase Inc. today added some powerful new capabilities to its platform that should enhance its ability to support more advanced generative artificial ...

Endee.io launches Endee, an open source vector database delivering fast, accurate, and cost-efficient AI and semantic ...

Retrieval-augmented generation, or RAG, integrates external data sources to reduce hallucinations and improve the response accuracy of large language models. Retrieval-augmented generation (RAG) is a ...

High-performance open-source vector database Qdrant today announced the launch of BM42, a new pure vector-based hybrid search approach for modern artificial intelligence and retrieval-augmented ...

SurrealDB 3.0 launches with $23M in new funding and a pitch to replace multi-database RAG stacks with a single engine that handles vectors, graphs, and agent memory transactionally.

How to implement a local RAG system using LangChain, SQLite-vss, Ollama, and Meta’s Llama 2 large language model. In “Retrieval-augmented generation, step by step,” we walked through a very simple RAG ...

AI teams are discovering that the most expensive part of an agent is not the model, it is the memory strategy wrapped around it. Retrieval-Augmented Generation has become the default pattern for ...

Native integrations reduce setup time and ongoing maintenance by making it easy to ingest, index, and continuously ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Vivek Yadav, an engineering manager from ...

Retrieval-Augmented Generation (RAG) systems have emerged as a powerful approach to significantly enhance the capabilities of language models. By seamlessly integrating document retrieval with text ...