4 posts tagged with "rag"

RAG is a groundbreaking approach that combines the strengths of information retrieval (IR) techniques with the creative capabilities of LLMs. This improvement transforms LLMs from being merely conversationalists to experts capable of engaging in in-depth and contextually rich dialogues on specialized topics, significantly enhancing their use and applicability across various domains.

Large Language Models (LLMs) are typically trained to converse on a wide range of topics with relative ease. However, their responses often lack depth and specificity and they might struggle to engage in detailed discussions on specialized subjects due to a lack of domain-specific knowledge. To overcome this, RAG fetches relevant information from different data sources in real-time and incorporates it into its responses; With it, the RAG model acts as an expert that evolves a general LLM into a specialized one, capable of retrieving and utilizing relevant information to provide precise responses, even to queries that require knowledge beyond its initial training data.

Querying your SQL database purely in human language​

Unless you live under a rock, you must have heard the buzzword “LLMs”.

It’s the talk around town.

LLM models, as we all know, have so much potential. But they have the issue of hallucinating and a knowledge cut-off.

The need to mitigate these two significant issues when using LLMs has led to the rise of RAGs and the implementation of RAGs in your existing database.

Despite the huge surge in popularity in building AI applications with LLMs and vector search, we haven't seen any walkthroughs boil this down to a super-simple, few-command process. With SuperDuperDB together with MongoDB Atlas, it's easier and more flexible than ever before.

Implementing a (RAG) chat application like a question-your-documents service can be a tedious and complex process. There are several steps involved in doing this:

Imagine effortlessly infusing AI into your data repositories—databases, data warehouses, or data lakes—without breaking a sweat. With SuperDuperDB, we aim to make this dream a reality. We want to provide everyone with the tools to build AI applications directly on top of their data stores, with just a pinch of Python magic sprinkled on top! 🐍✨

In this latest blog post we take a dive into one such example - a Retrieval Augmented Generation (RAG) app we built directly on top of our MongoDB store.