Iain Read - Rittman Mead

Tip Tuesday | Choosing a Chunk Size for RAG

A key step in the Retrieval Augmented Generation (RAG) pipeline is chunking your unstructured source data, the process of dividing a large body of information into small and manageable pieces. These chunks are the elements which will be searched through for relevance to the user query and used as the

GenAI

Oracle Powered RAG - Revisiting our Chatbot

Back in March, we demonstrated a chatbot that uses RAG to allow users to query the content of our blog using natural language. It made use of OpenAI’s API to access their embedding models and the GPT 3.5 turbo Large Language Model (LLM), and used Weaviate as the

GenAI

RAG Chatbot Demo Video - CloudWorld 2024

In a recent blog post we gave an overview of Retrieval Augmented Generation (RAG) as a way to leverage the natural language querying capabilities of Large Language Models (LLMs) on private or proprietary stores of unstructured data. A major use case for RAG is in the creation of chatbots which

machine learning

Custom Machine Learning Models in Oracle DV

In a previous post, we’ve covered how to upload and execute custom Python scripts in Oracle Analytics Server (OAS), allowing greater flexibility in your data flows and more control over how your data are manipulated. However, executable scripts aren’t the only way to augment your OAS workflows using

GenAI

Retrieval Augmented Generation - A Primer

Retrieval augmented generation, or RAG, is rapidly gaining traction as the next disruptive evolution for generative AI models. Promising a reduction in the hallucinations sometimes experienced by large language models such as GPT-4, greater accuracy of responses, access to up-to-date information, and the capability to use private or proprietary data

data science

4 Behaviours Separating Good Data Science From Great Data Science

For the interest of data scientists and data owners looking to improve their approach to data science projects, this post looks at a few ways we can take good data science practices and elevate them to great ones. Data science, at its heart, is a methodology for extracting insights and