I Built a Grounded AI Document Analyzer with Python + Azure OpenAI
Practical AI Engineering — AI Document Analyzer V1.0
In this video, I walk through how I built a grounded AI Document Analyzer with Python, Streamlit, PyMuPDF, Pydantic, and Azure OpenAI.
The project is designed to go beyond a simple LLM demo and focus on practical AI engineering concerns such as structured outputs, grounding, application boundaries, state management, testing, and provider configuration.
Topics covered:
PDF parsing and metadata extraction
Typed data models with Pydantic
Structured AI outputs
Grounded document Q&A
Prompt-injection safeguards
Streamlit session-state handling
Offline testing with mocked Azure calls
Python version compatibility debugging
Azure OpenAI 404 debugging
Security and privacy boundaries
Current V1 limitations
Roadmap toward semantic search and Retrieval-Augmented Generation (RAG)
Full technical write-up:
https://practicalaiengineering.hashnode.de...
V1 intentionally does not include OCR, embeddings, vector search, RAG, authentication, or persistent storage. Those are planned as future extensions.
Technologies: Python, Azure OpenAI, Streamlit, PyMuPDF, Pydantic, pytest, Ruff
Practical AI Engineering focuses on building useful AI applications with Python, Azure, LLMs, RAG, agents, automation, testing, and production-oriented software engineering.