Naive RAG Pipeline

Overview

The simplest RAG pipeline involves three sequential steps:

  1. Indexing

    • Documents are split into chunks.

    • Each chunk is encoded into a vector using an embedding model.

    • These vectors are stored in a vector database.

  2. Retrieval

    • User query is also converted into a vector.

    • The top-K similar vectors (document chunks) are retrieved using similarity metrics.

  3. Generation

    • Retrieved content is concatenated with the query.

    • The combined input is passed into an LLM for generation.

naive-rag

Limitations of Naive RAG:

  • May retrieve irrelevant content.

  • Doesn’t prioritize or summarize results.

  • Might cause hallucinations if bad content is retrieved.

  • Lacks control mechanisms and adaptability.