DocRevAI

DocRevAI is an AI-powered document analysis and question-answering system that enables users to upload PDF documents and ask questions about their content. The application extracts text from PDFs, processes the content, retrieves relevant information using TF-IDF similarity search, and generates context-aware responses using local Large Language Models (LLMs) through Ollama.

Features

• PDF document ingestion
• Text extraction and preprocessing
• Intelligent text chunking
• TF-IDF based document retrieval
• Context-aware question answering
• Local AI inference using Ollama
• Modular and scalable architecture
• Comprehensive logging and error handling

Project Workflow

PDF Upload
    ↓
Text Extraction
    ↓
Text Cleaning
    ↓
Chunk Creation
    ↓
TF-IDF Vectorization
    ↓
Similarity Search
    ↓
Context Retrieval
    ↓
Ollama LLM
    ↓
Generated Response

Tech Stack

Backend

• Python 3.12+
• Ollama
• Scikit-learn
• PyPDF2 / PDF Processing Libraries
• Logging Module

AI & Retrieval

• TF-IDF Vectorization
• Cosine Similarity Search
• Local Language Models via Ollama

Development Tools

• uv
• Git
• GitHub
• Jira
• Pytest

Dependency Management

This project uses uv for package management and virtual environment handling, providing faster dependency resolution and installation compared to traditional pip-based workflows.

Project Structure

DocRevAI/
│
├── docrevai/
│   ├── scripts/
│   │   ├── clean_text.py
│   │   ├── create_chunks.py
│   │   ├── similarity_finder.py
│   │   ├── tf_idf.py
│   │   └── ...
│   │
│   ├── logging/
│   │   └── logger.py
│   │
│   └── ...
│
├── tests/
│
├── logs/
│
├── pyproject.toml
│
├── uv.lock
│
└── README.md

Installation

Clone Repository

git clone https://github.com/Hanan-Nawaz/DocRevAI
cd DocRevAI

Install uv

curl -LsSf https://astral.sh/uv/install.sh | sh

or

pip3 install uv

Create Virtual Environment

uv venv

Activate environment:

macOS/Linux:

source .venv/bin/activate

Windows:

.venv\Scripts\activate

Install Dependencies

uv sync

Running the Project

uv run main.py

Testing

Run all tests:

uv run pytest

Run tests with coverage:

uv run pytest --cov=docrevai

Logging

DocRevAI includes centralized logging for:

• Error tracking
• Debugging
• System monitoring
• Runtime diagnostics

Logs are stored in the project's log directory.

Project Management

The project follows Agile development practices and uses Jira for:

• Sprint planning
• Task management
• Issue tracking
• Feature development tracking

Future Enhancements

• Semantic search using embeddings
• Vector databases (FAISS / ChromaDB)
• Multi-document support
• Web-based user interface
• Conversation memory
• Document summarization
• Citation and source highlighting
• Hybrid retrieval (TF-IDF + Embeddings)

Author

Abdul Hanan Nawaz

License

This project is intended for educational and portfolio purposes.