Implementing RAG with Amazon Bedrock
Prior to responding to user queries, the system must ingest and index the provided documents. This process can be considered as step 0, and consists of these sub-steps:
- Ingest the raw text documents into the knowledge base.
- Preprocess the documents by splitting them into smaller chunks to enable more granular retrieval.
- Generate dense vector representations for each passage using an embedding model such as Amazon Bedrock’s Titan Text Embeddings model. This encodes the semantic meaning of each passage into a high-dimensional vector space.
- Index the passages and their corresponding vector embeddings into a specialized search index optimized for efficient nearest neighbor (NN) search. These are also referred to as vector databases, which store numerical representations of text in the form of vectors. This index powers fast retrieval of the most relevant passages in response to user queries.
By completing this workflow...
