Retrieval Augmented Generation (RAG)
Foundation models are usually trained offline, making the model agnostic to any
data that is created after the model was trained. Additionally, foundation models
are trained on very general domain corpora, making them less effective for
domain-specific tasks. You can use Retrieval Augmented Generation (RAG) to retrieve
data from outside a foundation model and augment your prompts by adding the relevant
retrieved data in context. For more information about RAG model architectures, see
Retrieval-Augmented Generation for
Knowledge-Intensive NLP Tasks
With RAG, the external data used to augment your prompts can come from multiple data sources, such as a document repositories, databases, or APIs. The first step is to convert your documents and any user queries into a compatible format to perform relevancy search. To make the formats compatible, a document collection, or knowledge library, and user-submitted queries are converted to numerical representations using embedding language models. Embedding is the process by which text is given numerical representation in a vector space. RAG model architectures compare the embeddings of user queries within the vector of the knowledge library. The original user prompt is then appended with relevant context from similar documents within the knowledge library. This augmented prompt is then sent to the foundation model. You can update knowledge libraries and their relevant embeddings asynchronously.
For more information, see the following example notebooks:
