Introduction to RAG with LlamaIndex

The field of generative AI is constantly evolving, and one of the most exciting recent advancements is the rise of the retrieval-augmented generation (RAG).

What is RAG?

Retrieval-augmented generation (RAG) is a technique that combines traditional machine learning methods (like information retrieval) with LLMs to improve the quality and relevance of generated text. Instead of relying solely on pre-existing knowledge encoded within its parameters, a RAG system actively retrieves relevant information from an external knowledge base before generating a response. This two-step process allows for enhanced factuality as RAG models are less prone to “hallucinating” information. Since their responses are grounded in retrieved facts, they provide more accurate and trustworthy output. The information they provide can also be more up-to-date as RAG systems can access constantly updated databases.

Here’s a breakdown of the illustration above:

1. User query: A user submits a query or question.

2. Embedding model: An embedding model converts the query into a numerical representation (embedding).

3. Retrieval: The embedding is compared to vectors in a vector database to find the most relevant context or information. Vector databases are optimized for efficient similarity search, making them ideal for retrieval in RAG. They are created using data that can come from various other databases.

4. Context: The retrieved context is passed to the LLM.

5. LLM: The LLM processes the query and the retrieved context to generate a response.

Why is it needed?

RAG addresses the crucial limitations of standard LLMs that might hinder their real-world ...