Einstein Search Perform semantic search on structure and unstructured content using Retrieval Augmented Generation.
Semantic search searches for meaning, not keyword.
How to use Data for Semantic search :
-> Select an embedding model
-> prepare the data
-> Convert that data into vectors
-> Store those vectors in a vector database
-> Index everything
-> Create an API for handling the semantic Searches
-> Keep refreshing the vector database as your data changes
Before you can create a retriever, you need to prepare the data.
Data preparation involves loading, chunking, vectorizing, and storing content in a search-optimized way.
A search index stores chunked and vectorized data. When a search index is created in Data Cloud, a default retriever is created automatically.
Default retrievers can’t be customized. However, in Einstein Studio within Data Cloud, you can create your own customized retrievers.
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