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What is the main role of Amazon Titan Embeddings in a RAG pipeline?
A
Generating answers to user queries
B
Converting documents into vector embeddings
C
Running SQL queries
D
Rendering a UI interface
Explanation:
Amazon Titan Embeddings is a foundational model (FM) from AWS that specializes in text embeddings. In a RAG (Retrieval-Augmented Generation) pipeline:
Embeddings are numerical vector representations of text that capture semantic meaning
Amazon Titan Embeddings converts documents/text into these vector embeddings
These embeddings are then stored in a vector database
When a user query comes in, it's also converted to an embedding
The system performs a similarity search to find the most relevant documents
The retrieved documents are then used by a language model (like Amazon Titan Text) to generate answers
Why other options are incorrect:
A: Generating answers is typically done by language models (like Amazon Titan Text), not embedding models
C: SQL queries are unrelated to embedding models in RAG pipelines
D: UI rendering is a frontend concern, not the function of an embedding model
Key takeaway: Amazon Titan Embeddings is specifically designed for creating vector representations of text, which is a crucial step in RAG pipelines for semantic search and retrieval.