Tags: rag*

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  1. 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.
    2024-04-27 Tags: , , , , by klotz
  2. Retrieval-Augmented Generation (RAG) is the concept of providing large language models (LLMs) with additional information from an external knowledge source. This allows them to generate more accurate and contextual answers while reducing hallucinations. In this article, we will provide a step-by-step guide to building a complete RAG application using the latest open-source LLM by Google Gemma 7B and Upstash serverless vector database.
    2024-03-12 Tags: , , , by klotz
  3. ColBERT is a new way of scoring passage relevance using a BERT language model that substantially solves the problems with dense passage retrieval.
  4. In this notebook, we will explore a typical RAG solution where we will utilize an open-source model and the vector database Chroma DB. However, we will integrate a semantic cache system that will store various user queries and decide whether to generate the prompt enriched with information from the vector database or the cache.
    2024-03-12 Tags: , , , , by klotz
  5. A step-by-step guide on deploying LlamaIndex RAGs to AWS ECS fargate
    2024-01-15 Tags: , , , , by klotz
  6. A deep dive into model quantization with GGUF and llama.cpp and model evaluation with LlamaIndex
  7. PDFwhisper allows you to have a conversation with your PDF docs. Finding info on your PDF files is now easier than ever.
    2024-01-12 Tags: , , , , , , , by klotz
  8. Windows only
    2024-01-11 Tags: , , , by klotz
  9. 2024-01-11 Tags: , , , by klotz

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