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RAG-Based Document Search Application

Develop an end-to-end Retrieval-Augmented Generation (RAG) system to create a 'Document Search' application, enabling users to chat with their own data from PDFs and text files. This project emphasizes modular coding for production-ready AI applications, utilizing UV for environment setup and comprehensive data lifecycle management.

8 lectures

What You Will Learn

Mastering production-grade modular coding for GenAI projects.
Implementing advanced RAG architecture including Semantic Chunking and Vector Stores.
Utilizing UV for modern Python package management.
Applying LangGraph and Chains for LLM data connectivity.
Building interactive user interfaces for document upload and querying.
Optimizing data ingestion, preprocessing, and retrieval pipelines.
Implementing source citation for transparency in AI applications.

System Architecture

RAG-Based Document Search Application Architecture Diagram

High-level architecture overview of the RAG-Based Document Search Application .

What You'll Build

  • A modular RAG pipeline for document processing.
  • A semantic search engine using Vector Databases.
  • An interactive web UI for querying documents.
  • A system providing accurate answers with source citations.
RAG-Based Document Search Application
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