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1. New Technique Makes RAG Systems Much Better at Retrieving the Right Documents

   Researchers from Cornell University developed a technique called 'contextual document embeddings' to improve the performance of Retrieval-Augmented Generation (RAG) systems, enhancing the retrieval of relevant documents by making embedding models more context-aware.
   
   Standard methods like bi-encoders often fail to account for context-specific details, leading to poor performance in application-specific datasets. Contextual document embeddings address this by enhancing the sensitivity of the embedding model to subtle differences in documents, particularly in specialized domains.
   
   The researchers proposed two complementary methods to improve bi-encoders:
   
   - Modifying the training process using contrastive learning to distinguish between similar documents.
   - Modifying the bi-encoder architecture to incorporate corpus context during the embedding process.
   
   These modifications allow the model to capture both the general context and specific details of documents, leading to better performance, especially in out-of-domain scenarios. The new technique has shown consistent improvements over standard bi-encoders and can be adapted for various applications beyond text-based models.

   2024-10-10 Tags: rag, embedding, document retrieval, llm by klotz
2. Advanced RAG Techniques

   This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.

   2024-08-01 Tags: rag, nlp, machine learning, information retrieval, natural language processing, llm, embeddings, semantic search by klotz
3. A Step-by-Step Guide to Building and Distributing a Sleek RAG Pipeline

   Walkthrough on building a Q and A pipeline using various tools, and distributing it with ModelKits for collaboration.

   2024-07-10 Tags: llm, rag, kitops, python, machine learning, mlops, chromadb by klotz
4. The Challenges of Retrieving Relevant Context for RAG

   Case study on measuring context relevance in retrieval-augmented generation systems using Ragas, TruLens, and DeepEval. Develop practical strategies to evaluate the accuracy and relevance of generated context.

   2024-06-11 Tags: natural language processing, rag, machine learning, llm by klotz
5. Overcoming the Limits of RAG with ColBERT

   ColBERT is a new way of scoring passage relevance using a BERT language model that substantially solves the problems with dense passage retrieval.

   2024-03-12 Tags: llm, rag, embedding, bert, colbert, cosine distance, concept expansion by klotz
6. From RAGs to Riches 10 Applications of vector search to deeply understand your data and models

   Image Similarity Search
   Reverse Image Search
   Object Similarity Search
   Robust OCR Document Search
   Semantic Search
   Cross-modal Retrieval
   Probing Perceptual Similarity
   Comparing Model Representations
   Concept Interpolation
   Concept Space Traversal
   Image Similarity Search

   2023-10-26 Tags: llm, rag, similarity, vision, document, search, machine learning by klotz

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