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Category: Retrieval Augmented

  • Introducing Google’s File Search Tool

    Introducing Google’s File Search Tool The search giant fires its latest salvo against traditional RAG processing. The post Introducing Google’s File Search Tool appeared first on Towards Data Science. Thomas Reid Go to original source

  • Introducing Google’s LangExtract tool

    Introducing Google’s LangExtract tool Do RAG without doing RAG with this powerful new NLP and data extraction library The post Introducing Google’s LangExtract tool appeared first on Towards Data Science. Thomas Reid Go to original source

  • Hitchhiker’s Guide to RAG with ChatGPT API and LangChain

    Hitchhiker’s Guide to RAG with ChatGPT API and LangChain Build a simple Python RAG pipeline using your local files as context The post Hitchhiker’s Guide to RAG with ChatGPT API and LangChain appeared first on Towards Data Science. Maria Mouschoutzi Go to original source

  • How to Train a Chatbot Using RAG and Custom Data

    How to Train a Chatbot Using RAG and Custom Data Retrieval-Augmented Generation made easy with Llama The post How to Train a Chatbot Using RAG and Custom Data appeared first on Towards Data Science. Haden Pelletier Go to original source

  • Connecting the Dots for Better Movie Recommendations

    Connecting the Dots for Better Movie Recommendations Connecting the Dots for Better Movie Recommendations: Lightweight graph RAG on Rotten Tomatoes movie reviews The post Connecting the Dots for Better Movie Recommendations appeared first on Towards Data Science. Brian Godsey Go to original source

  • Government Funding Graph RAG

    Government Funding Graph RAG In this article, I present my latest open-source project — Government Funding Graph. The inspiration for this project came from a desire to make better tooling for grant writing, namely to suggest research topics, funding bodies, research institutions, and researchers. I have made Innovate UK grant applications in the past, so I have…

  • Overcome Failing Document Ingestion & RAG Strategies with Agentic Knowledge Distillation

    Overcome Failing Document Ingestion & RAG Strategies with Agentic Knowledge Distillation Introduction Many generative AI use cases still revolve around Retrieval Augmented Generation (RAG), yet consistently fall short of user expectations. Despite the growing body of research on RAG improvements and even adding Agents into the process, many solutions still fail to return exhaustive results,…

  • LLM + RAG: Creating an AI-Powered File Reader Assistant

    LLM + RAG: Creating an AI-Powered File Reader Assistant Introduction AI is everywhere.  It is hard not to interact at least once a day with a Large Language Model (LLM). The chatbots are here to stay. They’re in your apps, they help you write better, they compose emails, they read emails…well, they do a lot.…

  • Enhancing RAG: Beyond Vanilla Approaches

    Enhancing RAG: Beyond Vanilla Approaches Retrieval-Augmented Generation (RAG) is a powerful technique that enhances language models by incorporating external information retrieval mechanisms. While standard RAG implementations improve response relevance, they often struggle in complex retrieval scenarios. This article explores the limitations of a vanilla RAG setup and introduces advanced techniques to enhance its accuracy and…

  • 6 Common LLM Customization Strategies Briefly Explained

    6 Common LLM Customization Strategies Briefly Explained Why Customize LLMs? Large Language Models (Llms) are deep learning models pre-trained based on self-supervised learning, requiring a vast amount of resources on training data, training time and holding a large number of parameters. LLM have revolutionized natural language processing especially in the last 2 years, demonstrating remarkable…

  • How to Measure the Reliability of a Large Language Model’s Response

    How to Measure the Reliability of a Large Language Model’s Response The basic principle of Large Language Models (LLMs) is very simple: to predict the next word (or token) in a sequence of words based on statistical patterns in their training data. However, this seemingly simple capability turns out to be incredibly sophisticated when it…

  • Synthetic Data Generation with LLMs

    Synthetic Data Generation with LLMs Popularity of RAG Over the past two years while working with financial firms, I’ve observed firsthand how they identify and prioritize Generative AI use cases, balancing complexity with potential value. Retrieval-Augmented Generation (RAG) often stands out as a foundational capability across many LLM-driven solutions, striking a balance between ease of implementation…

  • Supercharge Your RAG with Multi-Agent Self-RAG

    Supercharge Your RAG with Multi-Agent Self-RAG Introduction Many of us might have tried to build a RAG application and noticed it falls significantly short of addressing real-life needs. Why is that? It’s because many real-world problems require multiple steps of information retrieval and reasoning. We need our agent to perform those as humans normally do,…