The AI Document Assistant is an end-to-end, production-style AI system designed to ingest, process, and intelligently retrieve information from unstructured documents. This project demonstrates the full lifecycle of an applied AI solution — from data ingestion and preprocessing to model inference, API serving, and user interaction.
It is built to showcase real-world data engineering and AI system design capabilities, rather than isolated model experimentation.
Organisations across industries manage large volumes of unstructured documents (e.g., policies, reports, contracts) that are difficult to search, analyse, and extract insights from efficiently.
This solution addresses that challenge by:
- Structuring unstructured document data
- Enabling semantic retrieval of relevant information
- Providing an interactive interface for querying document content
User Interface (Streamlit)
↓
API Layer (FastAPI)
↓
Retrieval Engine (NLP / ML)
↓
Document Store (Processed Text Data)
The system follows a modular architecture, separating:
- Presentation layer (UI)
- Inference layer (API)
- Processing and retrieval logic
- Data storage
- Python — core programming language
- FastAPI — high-performance API for model serving
- Streamlit — interactive UI for demonstration
- scikit-learn / NLP techniques — text processing and retrieval
- Docker — containerisation for reproducibility
- GitHub Actions — CI/CD pipeline
- End-to-end document ingestion and preprocessing
- Text chunking and indexing for efficient retrieval
- Query-based document search and response generation
- RESTful API for model inference
- Interactive front-end for user queries
- Containerised deployment setup
ai-document-assistant/
│
├── app/ # API and Streamlit UI
├── src/ # Core processing and retrieval logic
├── data/ # Sample documents
├── tests/ # Unit tests
├── Dockerfile
├── docker-compose.yml
├── requirements.txt
└── README.md
git clone https://github.com/YOUR_USERNAME/ai-document-assistant.git
cd ai-document-assistant
pip install -r requirements.txt
uvicorn app.main:app --reload
streamlit run app/streamlit_app.py
- Load or upload sample documents
- System processes and indexes the content
- User submits a query
- System retrieves relevant information and returns a response
- Integration with LLMs (e.g., OpenAI-compatible APIs)
- Vector database implementation (FAISS / Pinecone)
- Cloud deployment (AWS / GCP / Azure)
- Advanced monitoring and logging
- Authentication and role-based access
- Designed and implemented a modular AI system architecture
- Built a scalable data processing and retrieval pipeline
- Exposed model inference via a production-ready API
- Integrated backend and frontend components into a cohesive system
- Packaged and deployed the application using Docker and CI/CD practices
Ramafa Makhafola Senior Data Engineer | Aspiring AI Engineer
This project is intended as a portfolio-grade demonstration of applied AI engineering, highlighting the integration of data pipelines, machine learning, and system deployment in a real-world context.