RAG takes large language models a step further by drawing on trusted sources of domain-specific information. This brings clear benefits to healthcare, where access to technical and medical data is ...

Retrieval-Augmented Generation (RAG) is a machine learning framework that combines the advantages of both retrieval-based and generation-based models. The RAG framework is highly regarded for its ...

All winning or stakes-placed progeny are listed for North American performances within the previous seven days. Winners are updated on the list only when the information on new winners is ...

This repository implements a Retrieval-Augmented Generation (RAG) system to assess the impact of various RAG components and configurations individually. The framework expands user queries, retrieves ...

RAG can improve the efficacy of large language model (LLM) applications by leveraging custom data AIChat has a built-in vector database and full-text search engine, eliminating reliance on third-party ...

With the launch of the Contextual AI Platform, enterprises will be able to create specialized “RAG agents” that can automate knowledge work on behalf of users. The new platform is based on an ...

Learn More Popular AI orchestration framework LlamaIndex has introduced Agent Document Workflow (ADW), a new architecture that the company says goes beyond retrieval-augmented generation (RAG ...

For RAG systems specifically, RAGAS and LlamaIndex emerged as evaluation tools, with RAGAS focusing on response accuracy and relevance using human evaluators, while LlamaIndex employs GPT-4 for ...