Building a Smarter Retrieval-Augmented Generation System with OpenAI, LanceDB & Phidata

Build intelligent systems that look up relevant information in enterprise documents (text, PDFs, documentation) before responding to specific customer prompts and requests.

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Prerequisites

To build our sample retrieval-based agent in this tutorial, we need an API key from providers like , , , and to access an LLM. We will use OpenAI; however, you can choose your preferred model provider. We also need a vector database for information retrieval, a Python framework to build the RAG agent, and a service to store data in documents.

• OpenAI API account: Create an and export your API keys to access LLMs like gpt-4o-mini.
• Vector Database: This example uses for an accurate similarity search for data in PDF documents. You can use other vector databases like or .
• : For building the agentic RAG system
• : For a PDF storage.

What is Retrieval Augmented Generation (RAG)?

RAG (Retrieval-Augmented Generation) is an AI framework that combines the strengths of traditional information retrieval systems (such as search…