RAG System from Scratch In LlamaIndex

Building a RAG system from scratch using LlamaIndex enables combining state-of-the-art retrieval and generation techniques efficiently. It covers:

1. Preparing datasets.
2. Creating and indexing documents.
3. Generating and evaluating responses.

What is RAG?

It consists of two main components:

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• Retriever Models: Fetch relevant documents from a knowledge base.
• Generator Models: Generate responses using the retrieved documents as context.

This architecture is useful for tasks like question answering, summarization, and more.

Why Use LlamaIndex?

LlamaIndex (formerly known as GPT Index) simplifies the implementation of RAG systems by:

• Providing an easy interface to index and retrieve documents.
• Supporting various embedding models for efficient document search.
• Enabling seamless integration with large language models (LLMs).
• Providing multiple metrics to evaluate the response.

How To Implement?

1. Setup:

!pip install llama_index…