Optimizing RAG Pipelines: Indexing, Query, Retrieval, Document Selection, and Context
1 years ago
Retrieval Augmented Generation (RAG) encodes data into embeddings and indexes it in a vector database. When a user queries, it searches for similar embeddings to construct a prompt for an LLM. The RAG pipeline includes indexing, querying, retrieval, document selection, and context optimization. Strategies for optimization include indexing by small data chunks, questions the document answers, and document summaries.
