Module 9: Building a PDF Chatbot (RAG Project)
Build a real PDF chatbot end-to-end with Python, ChromaDB, OpenAI, and Streamlit. Upload PDFs, chunk, embed, retrieve, generate, and ship to the public internet.
Module 9: Building a PDF Chatbot (RAG Project)
What this module gives you
Ship the flagship project of this course: a working "chat with any PDF" app that uses real RAG, retrieves citations, and lives at a public URL. By the end you can rebuild this in any future job interview from memory.
Skills you will pick up
- Parsing and chunking PDFs
- Building a persistent vector index
- End-to-end RAG with citations
- Streamlit UI for upload + chat
- Evaluating and improving RAG quality
- Free deployment with secrets
Why it matters in production
PDF chatbots are 2026's most common AI feature request: legal teams, students, founders, support orgs all want them. Knowing how to build one professionally is hireable on its own.
Lessons in this module
- 1
Lesson 9.1
Project setup and architecture review
2 min - 2
Lesson 9.2
Parsing PDFs into clean text
2 min - 3
Lesson 9.3
Chunking strategy that actually works
2 min - 4
Lesson 9.4
Building the vector index
2 min - 5
Lesson 9.5
The retrieve-and-answer flow with citations
2 min - 6
Lesson 9.6
Streamlit UI: upload, chat, citations
2 min - 7
Lesson 9.7
Evaluation: knowing when RAG is "good enough"
2 min - 8
Lesson 9.8
Deployment and stretch goals
2 min
Ready to start?
Open Lesson 9.1: Project setup and architecture review