Retrieval Augmented Generation (RAG)

One of the most powerful applications enabled by Large Language Models (LLMs) is sophisticated Question-Answering (Q&A) Chatbots. These chatbots can answer questions based on specific source information, offering more relevant and accurate responses.

To achieve this, these applications use a technique known as Retrieval Augmented Generation (RAG). RAG enhances the model's ability to generate responses by retrieving relevant information from a database or document, allowing the chatbot to answer questions with greater precision and context.

What is RAG?

RAG is a technique used to augment the knowledge of Large Language Models (LLMs) by providing additional, relevant data.

While LLMs are capable of reasoning about a wide range of topics, their knowledge is restricted to the public data available up until the point they were trained. This means they may not have up-to-date information or may lack knowledge of private or specialized data. To build AI applications that can reason about new data or private information, it's essential to augment the model’s knowledge with the specific information required.

This process of incorporating external data into the model's prompt, allowing it to generate more informed and accurate responses, is known as Retrieval Augmented Generation (RAG).

LangChain offers a variety of tools and components designed to help build Q&A applications and general RAG applications by efficiently retrieving relevant data and feeding it into the LLM, making it a powerful framework for integrating external information into AI models.

Rivalz RAG

Rivalz streamlines the process of creating Retrieval Augmented Generation (RAG) applications by offering an easy-to-use API.

• RAG APIs for Python

• RAG APIs for Nodejs