📊RAG pipelines use indexing to load external documents for retrieval.
🔍Numeric representations of documents simplify the process of identifying relevancy to input questions.
🤖Advanced machine learning techniques enable effective generation of document embeddings.
🔗Embedding models must consider context window limitations when processing documents.
⚙️Vector similarity comparisons are key for efficient document retrieval.
What is indexing in RAG pipelines?
—Indexing in RAG pipelines involves loading external documents into a retriever, allowing the system to find relevant documents based on input questions.
How is document relevance determined?
—Document relevance is determined through numerical representations of documents, enabling easy comparison of vectors to free-form text.
What methods are used for numerical representation?
—Statistical methods like sparse vectors and machine learning-based embeddings are commonly used to represent documents numerically.
What are embeddings in the context of indexing?
—Embeddings are compressed vector representations of documents generated by machine learning models that capture semantic meaning.
How do you perform document retrieval using vector representation?
—Document retrieval is performed by comparing query embeddings to embedded document vectors using numerical methods like cosine similarity.
00:00Introduction to RAG and indexing.
00:12Overview of the RAG pipeline components.
00:25Definition and purpose of indexing in document retrieval.
00:48Importance of numerical representation in establishing document relevance.
01:23Introduction to statistical and embedding methods for document representation.
02:08Explanation on how embeddings work in indexing.
02:34Step-by-step process for computing the number of tokens.
03:30Demonstration of using embeddings for question and document pair.
