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Tags: retrieval augmented generation* + rag*

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1. Make room for RAG: How Gen AI's balance of power is shifting

   The technology of retrieval-augmented generation, or RAG, could be pivotal in shaping the battle between large language models.

   2024-06-04 Tags: retrieval-augmented generation, large language models by klotz
2. Vespa and LLMs

   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.

   2024-06-03 Tags: vespa, search, information retrieval, llm, retrieval-augmented generation by klotz
3. GNN-RAG: A Novel AI Method for Combining Language Understanding Abilities of LLMs with the Reasoning Abilities of GNNs in a Retrieval-Augmented Generation (RAG) Style

   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.

   2024-06-03 Tags: gnn-rag, llm, graph neural networks, retrieval-augmented generation, knowledge graph by klotz
4. Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts

   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.

   2024-06-01 Tags: retrieval augmented generation, large language models, metrag, similarity, task adaptive summarizer by klotz
5. Retrieving and Filtering with Metadata

   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.

   2024-05-28 Tags: recommender systems, llm, langchain, rag, pinecone, python by klotz
6. Combining the Best of Both Worlds: Retrieval-Augmented Generation for Knowledge-Intensive Natural Language Processing

   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.

   2024-05-28 Tags: retrieval-augmented generation, nlp, llm, parametric memory by klotz
7. Verba: A Retrieval-Augmented Generation (RAG) chatbot powered by Weaviate

   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.

   2024-05-20 Tags: verba, retrieval-augmented generation, rag, weaviate, open-source, chatbot, python, llm, github by klotz
8. llm-chatbot-rag - A local LLM chatbot with RAG for PDF input files

   This is a local LLM chatbot project with RAG for processing PDF input files

   2024-05-17 Tags: llm, chatbot, rag, github, python by klotz
9. How to Use Re-Ranking for Better LLM RAG Retrieval

   In this article, Dr. Leon Eversberg explains how to build an advanced Local Language Model (LLM) Retrieval-Augmented Generation (RAG) pipeline using open-source bi-encoders and cross-encoders for better chatbot performance.

   2024-05-17 Tags: llm, rag, chatbots, bi-encoders, cross-encoders by klotz
10. Open-Source Models, Temperature Scaling, Re-Ranking, and More: Don’t Miss Our Recent LLM Must-Reads

    The Towards Data Science team highlights recent articles on the rise of open-source LLMs, ethical considerations with chatbots, potential manipulation of LLM recommendations, and techniques for temperature scaling and re-ranking in generative AI.

    2024-05-17 Tags: temperature scaling, re-ranking, llm, retrieval-augmented generation, retrieval, bi-encoders, cross-encoders, customer lifetime value, predictive methods, marketers, data scientists, statistical convergence, 19th-century shipwreck, transformer architecture, multi-classifier model, molecular biology, bioinformatics, ai, career transition, physics, data science by klotz

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