Core Components

1. Retriever

   • Searches a database to find relevant documents based on the user query.

   • Uses similarity search (e.g., cosine similarity between vector embeddings).

   • Types:

     • Sparse retrievers: Traditional (e.g., BM25).

     • Dense retrievers: Modern (e.g., dual encoder models).

     • Hybrid: Combines both.

2. Generator

   • Receives the query and the retrieved content.

   • Produces a final answer using natural language.

   • Usually a pretrained model like GPT, LLaMA, or T5.

3. Knowledge Base (Corpus)

   • The source of truth (e.g., documents, web pages, PDFs).

   • Preprocessed into small “chunks” and embedded as vectors.

   • Stored in vector databases for efficient similarity search.

4. Augmentation Layer

   • Optional enhancements to improve quality:

     • Query rewriting

     • Reranking

     • Chunk compression or repacking

     • Metadata filtering