Tags: retrieval-augmented generation*

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  1. pgai brings AI workflows to your PostgreSQL database. It simplifies the process of building search and Retrieval Augmented Generation (RAG) AI applications with PostgreSQL by bringing embedding and generation AI models closer to the database.
  2. This article guides you through the process of building a local RAG (Retrieval-Augmented Generation) system using Llama 3, Ollama for model management, and LlamaIndex as the RAG framework. The tutorial demonstrates how to get a basic local RAG system up and running with just a few lines of code.
    2024-06-21 Tags: , , , , , by klotz
  3. Learn about how to prompt Command R: Understand the structured prompts used for RAG, formatting chat history and tool outputs, and changing sections of the prompt for different tasks.
    2024-06-19 Tags: , , , by klotz
  4. A CLI tool for interacting with local or remote LLMs to retrieve information about files, execute queries, and perform other tasks in a Retrieval-Augmented Generation (RAG) fashion.
    2024-06-21 Tags: , , , by klotz
  5. LlamaIndex comes with a built-in indexing feature, which allows developers to index large datasets efficiently. This makes it easier to search and retrieve information from these datasets, ultimately improving the overall performance of LLM-based applications.
    2024-06-18 Tags: , , by klotz
  6. This article discusses how to overcome limitations of retrieval-augmented generation (RAG) models by creating an AI assistant using advanced SQL vector queries. The author uses tools such as MyScaleDB, OpenAI, LangChain, Hugging Face and the HackerNews API to develop an application that enhances the accuracy and efficiency of data retrieval process.
  7. This article discusses the potential of unstructured data in software development and how it can be leveraged using Retrieval-Augmented Generation (RAG). The article highlights various types of unstructured data on GitHub, the value of unstructured data, and how RAG can be used to extract insights from this data. The article also explains how RAG works, its applications, and its benefits for developers and organizations.
    2024-06-13 Tags: , , , , by klotz
  8. 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.
  9. 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.
  10. 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.
    2024-06-06 Tags: , , , by klotz

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