What is RAG Corpus - A practical demo!
A common problem with LLMs is that they don't understand private knowledge, that is, your organization's data. With Vertex AI RAG Engine, you can enrich the LLM context with additional private information, because the model can reduce hallucination and answer questions more accurately.
By combining additional knowledge sources with the existing knowledge that LLMs have, a better context is provided. The improved context along with the query enhances the quality of the LLM's response.
Vector databases play a crucial role in enabling retrieval for RAG applications. Vector databases offer a specialized way to store and query vector embeddings, which are mathematical representations of text or other data that capture semantic meaning and relationships.
When creating a RAG corpus, Vertex AI RAG Engine offers RagManagedDb as the default choice of a vector database, which requires no additional provisioning or managing. For Vertex AI RAG Engine to automatically create and manage the vector database for you, see Create a RAG corpus.
In addition to the default RagManagedDb, Vertex AI RAG Engine lets you provision and bring your vector database for use within your RAG corpus. In this case, you are responsible for the lifecycle and scalability of your vector database.
Vertex AI RAG Engine: https://cloud.google.com/vertex-ai/ge...
Vector database choices in Vertex AI RAG Engine: https://cloud.google.com/vertex-ai/ge...
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