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Omnimem retrieval augmented generation

Retrieval-Augmented Generation (RAG) is a technology that combines artificial intelligence with an information retrieval system. It uses large language models (LLMs) to generate text and accesses a knowledge base to find relevant, up-to-date information.

This combination allows the AI to give more accurate and relevant responses without needing to be retrained or fine-tuned. As a result, the AI becomes more adaptable and capable of handling new information or specific topics.

The smart advantage of AI with knowledge retrieval

RAG is unique because it relies on both user prompts and responses generated by a large language model, which works alongside a system that retrieves information from a knowledge base. In the past, AI systems like ChatGPT could only provide answers based on the data they were trained on or the specific details users provided. If the AI needed more general knowledge, it had to be retrained, which is expensive and takes time. RAG solves this by allowing the AI to search a knowledge base, helping it deliver more accurate and up-to-date responses without needing retraining.

How it works

Data Collection and Embedding (A-D)

(A) Data can be collected from various sources, such as the internet, or (B) specific document pools.
(C) This collected data is processed and prepared for analysis. An embedding model is used to convert the data into vectorized representations (numeric form), making it easier for AI to understand and process.
(D)  The vectorized data is stored in a vector database, a special type of database optimized for searching and retrieving this kind of information efficiently.
(E) Over time, the large language model can be fine-tuned using the data from the vector database to improve its accuracy and relevance in generating responses.

Query and Response Generation (1-5)

(1) A user inputs a query, and the system first analyzes and understands the question.
(2) The system then converts the query into an embedding (a vectorized form), similar to the data stored in the vector database.
(3) The query embedding is used to search the vector database and retrieve relevant information based on similarity to the user’s question.
(4) The retrieved data is combined with a large language model (LLM) to provide deeper insights and context (5).