This repository provides a step-by-step guide to building a Retrieval-Augmented Generation (RAG) pipeline from scratch. The pipeline enables efficient document retrieval and AI-powered response generation using FastEmbed, SQLite, and Groq AI.
- Document Ingestion: Process and store text-based FAQs in a structured format.
- Vector Embeddings: Convert text into numerical embeddings for similarity search.
- Database Storage: Store and retrieve embeddings using SQLite.
- Semantic Search: Find the most relevant FAQ entries based on user queries.
- AI-Powered Answers: Generate customer support responses using Groq AI.
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Python 3.9+
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FastEmbed (for embedding generation)
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SQLite (for database storage)
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Groq AI (for response generation)
For a detailed explanation of the code in the repository, read the full article on How to Build Generative AI Applications