This article provides a step-by-step guide on building a generative search engine for local files using Qdrant, NVidia NIM API, or Llama 3. It includes system design, indexing local files, and creating a user interface.
Simon Willison explains an accidental prompt injection attack on RAG applications, caused by concatenating user questions with documentation fragments in a Retrieval Augmented Generation (RAG) system.
The technology of retrieval-augmented generation, or RAG, could be pivotal in shaping the battle between large language models.
This article discusses the integration of Large Language Models (LLMs) into Vespa, a full-featured search engine and vector database. It explores the benefits of using LLMs for Retrieval-augmented Generation (RAG), demonstrating how Vespa can efficiently retrieve the most relevant data and enrich responses with up-to-date information.
This article discusses GNN-RAG, a new AI method that combines the language understanding abilities of LLMs with the reasoning abilities of GNNs for Retrieval-Augmented Generation (RAG) style. This approach improves KGQA performance by utilizing GNNs for retrieval and RAG for reasoning.
An article discussing a paper that proposes a new framework, MetRag, for retrieval augmented generation. The framework is designed to improve the performance of large language models in knowledge-intensive tasks.
In this tutorial, we will build a RAG system with a self-querying retriever in the LangChain framework. This will enable us to filter the retrieved movies using metadata, thus providing more meaningful movie recommendations.
This article discusses Retrieval-Augmented Generation (RAG) models, a new approach that addresses the limitations of traditional models in knowledge-intensive Natural Language Processing (NLP) tasks. RAG models combine parametric memory from pre-trained seq2seq models with non-parametric memory from a dense vector index of Wikipedia, enabling dynamic knowledge access and integration.
Verba is an open-source application designed to offer an end-to-end, streamlined, and user-friendly interface for Retrieval-Augmented Generation (RAG) out of the box. It supports various RAG techniques, data types, LLM providers, and offers Docker support and a fully-customizable frontend.
This is a local LLM chatbot project with RAG for processing PDF input files