GenAI RAG System: Unlocking Q&A Over Company Documents
Companies’ internal reports, policies, and customer documents contain vast amounts of knowledge. However, this information often remains underutilized because employees struggle to search and retrieve the right details quickly.
Retrieval-Augmented Generation (RAG) offers a practical way to unlock this knowledge — allowing teams to ask questions in plain language and get accurate, context-aware answers directly from company documents.
In this article, we demonstrate how to build a simple, cost-free RAG system that runs locally, ensuring full data privacy. Using ChromaDB for document storage and SentenceTransformers for embeddings, we’ll walk through how to index your PDFs, perform semantic search, and integrate with a language model to generate answers tailored to your business’s specific content.
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