Text embeddings

RAG Pipeline: Semantic Search and Text Embeddings

• Goal of Retrieval: After text chunks are extracted from a source document, the RAG pipeline must find text chunks related to the user's query to use as context in the prompt.
• The Problem: Finding relevant chunks is a complex search problem.
• The Solution: This is commonly implemented using Semantic Search.
• Text Embeddings:
• A text embedding is a numerical representation of the meaning contained within a piece of text.
• They are generated by an Embedding Model (e. g. , Titan Embed Text V2).
• The output is a long list of numbers (the embedding).
• Understanding Embedding Values:
• Each number within an embedding represents a score of a certain quality or aspect of the input text (e. g. , topic, sentiment).
• Key Concept: The exact meaning of each individual number is unknown; the interpretation is conceptual and helpful for understanding the text's overall meaning.
• Function in RAG: Embeddings allow the system to understand the meaning of the text, enabling semantic search to match the user's query to the most relevant chunks, regardless of exact keyword matches.

Takeaways

• The RAG pipeline uses Semantic Search to identify text chunks relevant to a user's query.
• Text embeddings are numerical representations of the meaning contained within a piece of text.
• An Embedding Model generates these embeddings, which are long lists of numbers.
• Embeddings allow the system to match the meaning of the query to the text, rather than relying on exact keyword matches.