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Build a Custom AI Document Analyzer with RAG – Step-by-Step Tutorial

1. What We’re Building & Why It Matters

• Overview of a Retrieval-Augmented Generation (RAG) pipeline that lets you upload PDFs and ask questions in natural language.
• Real-world use cases: contract review, research paper summarization, internal knowledge base queries.
• Key tech stack: OpenAI embeddings + GPT-4o-mini, ChromaDB for vector storage, and Streamlit for the UI.

2. Setting Up Your Environment & Dependencies

• Create a Python virtual environment and install required packages: openai, chromadb, langchain, streamlit, pypdf.
• Set your OpenAI API key as an environment variable and test connectivity with a simple embedding call.
• Organize your project folder: app.py, ingest.py, <
  

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