Implementing RAG with LlamaIndex

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating an external knowledge retrieval process. Unlike standalone LLMs, which generate responses based purely on pretrained knowledge, RAG dynamically fetches relevant information from external sources before generating a response. This process involves three key components: indexing, retrieval, and augmented generation.

One of the core technologies enabling efficient retrieval in RAG is the vector database (vector DB). Vector databases store document embeddings—high-dimensional numerical text representations—allowing fast and accurate similarity searches. Instead of keyword-based searches like traditional databases, vector DBs use approximate nearest neighbor (ANN) search to quickly find the most relevant information based on semantic meaning.

Popular vector databases include FAISS, Chroma, Pinecone, and Weaviate, each optimized for handling large-scale embedding-based search. Using a vector DB ensures:

• Efficient retrieval: Rapid similarity-based search across millions of embeddings.

• Scalability: Ability to handle continuously growing datasets with minimal latency.

• Persistent storage: Unlike in-memory indexes, vector DBs allow persisting and reloading embeddings without reprocessing them every time.

Combining RAG with vector databases can significantly enhance retrieval accuracy and efficiency, ensuring that responses are grounded in factual and up-to-date information.

Setting up a RAG pipeline with LlamaIndex

Imagine we’re developing a research assistant that helps users find relevant academic papers. A researcher asks, “What are the key contributions of the transformer architecture?” To provide a well-informed answer, we need more than just a pretrained language model—we need access to relevant papers.

Instead of relying solely on the model’s pre-existing knowledge, we retrieve relevant research papers, extract key information, and use it to generate accurate responses.

LlamaIndex simplifies this process by helping us structure and retrieve data efficiently. In this lesson, we’ll walk through:

• Indexing documents to make external data searchable.

• Retrieving relevant information to ensure real sources back responses.

• Generating informed answers by combining retrieval with a language model.

Now, let’s get started by setting up our environment.

Installing dependencies

Before proceeding, ensure you have installed the necessary dependencies for building a retrieval-augmented generation (RAG) pipeline using LlamaIndex with Groq and Ollama:

The above command installs:

• llama-index: It is the core framework for ...