https://github.com/e-candeloro/credem_hack_2025
AI-powered document processing pipeline for Credem Hackathon 2025. Leverages Google Cloud AI services to intelligently extract, classify, and process HR documents through a robust ETL pipeline.
https://github.com/e-candeloro/credem_hack_2025
ai document-processing googlecloudplatform hackathon llm prompt-engineering python
Last synced: 5 months ago
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AI-powered document processing pipeline for Credem Hackathon 2025. Leverages Google Cloud AI services to intelligently extract, classify, and process HR documents through a robust ETL pipeline.
- Host: GitHub
- URL: https://github.com/e-candeloro/credem_hack_2025
- Owner: e-candeloro
- License: mit
- Created: 2025-06-19T00:42:15.000Z (6 months ago)
- Default Branch: master
- Last Pushed: 2025-06-22T21:37:03.000Z (6 months ago)
- Last Synced: 2025-06-22T22:20:23.446Z (6 months ago)
- Topics: ai, document-processing, googlecloudplatform, hackathon, llm, prompt-engineering, python
- Language: Jupyter Notebook
- Homepage:
- Size: 13.9 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# Credem Hack 2025 - AI Document Processing Pipeline
[](https://www.python.org/downloads/)
[](https://www.docker.com/)
[](https://cloud.google.com/)
[](LICENSE.md)
> **🏆 Hackathon Project**: Advanced AI-powered document processing solution for Credem Bank
This project is a sophisticated data processing pipeline built for the Credem Hackathon 2025. It leverages cutting-edge Google Cloud AI services, including Document AI and Gemini 2.5 Pro, to intelligently extract, classify, and process information from various document formats through a robust ETL pipeline.
## 📋 Table of Contents
- [🚦 What is this?](#-what-is-this)
- [📋 Challenge Information & Team](#-challenge-information--team)
- [🏆 Solution Strengths](#-solution-strengths)
- [🏁 Quick Start with Docker](#-quick-start-with-docker)
- [🛠️ Local Development Setup](#️-local-development-setup)
- [📊 Architecture Overview](#-architecture-overview)
- [🔧 Configuration](#-configuration)
- [📈 Performance & Results](#-performance--results)
- [📄 License](#-license)
## 🚦 What is this?
### Core Technologies
- **🤖 AI/OCR Engine**: Google Document AI + Google Gemini 2.5 Pro via GCP API
- **🐍 Core Engine**: Python 3.11 with modern async capabilities
- **📊 Data Processing**: Pandas for efficient data manipulation
- **🏗️ Architecture**: Microservices-ready with Docker containerization
- **🔧 Dev Experience**: VS Code Dev Container, pre-commit hooks, `uv` for Python dependency management
- **☁️ Cloud Native**: Built for Google Cloud Platform deployment
### Key Features
- **Intelligent Document Classification**: 22+ predefined document categories
- **Multi-format Support**: PDF, TIFF, JPEG, PNG, and more
- **Real-time Processing**: Streamlined pipeline for high-volume document processing
- **Error Resilience**: Robust error handling and fallback mechanisms
- **Scalable Architecture**: Designed for enterprise-grade scalability
## 📋 Challenge Information & Team
### Challenge Documents
The document challenge information and team pitch presentation can be found in the `documents/` folder:
- 📄 `Credemhack - Materiale per i team.pdf` - Challenge specifications and requirements
- 📋 `specifiche.pdf` - Technical specifications and evaluation criteria
- 🎯 `team_presentation.pdf` - Our team's solution presentation and pitch
### Team: CloudFunctions 🚀
Our diverse team combines expertise in cybersecurity, AI research, data science, and engineering:
- **🛡️ Daniele Di Battista** - Cybersecurity Expert at Leonardo - [LinkedIn Profile](https://www.linkedin.com/in/daniele-di-battista-883160266/)
- **💻 Luca Pedretti** - Data Scientist - [LinkedIn Profile](https://www.linkedin.com/in/luca-pedretti-re/)
- **🔧 Matteo Peroni** - Data Analyst/UX Design - [LinkedIn Profile](https://www.linkedin.com/in/matteo-peroni-049951237/)
- **🎓 Omar Carpentiero** - MSc Student in AI Engineering at UNIMORE - [LinkedIn Profile](https://www.linkedin.com/in/omar-carpentiero-6543992a5/)
- **🤖 Ettore Candeloro** - AI Researcher at AImagelab at UNIMORE - [LinkedIn Profile](https://linkedin.com/in/ettore-candeloro-900081162)
### 📊 Solution Presentation
📽️ **Canva Presentation**: [View our solution presentation](https://www.canva.com/design/DAGq6NnpBmQ/VWwyAapBeBnXo8b5S6CODQ/edit?utm_content=DAGq6NnpBmQ&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton)
## 🏆 Solution Strengths
Our AI-powered document processing solution offers several key advantages:
### 🔍 Advanced AI Integration
- **Dual AI Approach**: Combines Google Document AI for text extraction with Gemini 2.5 Pro for intelligent classification and data extraction
- **Multi-modal Processing**: Handles both text and image-based documents seamlessly
- **Context-Aware Analysis**: Understands document context for better classification accuracy
### 📊 High Accuracy & Performance
- **Smart Classification**: Automatically categorizes documents into 22+ predefined clusters with confidence scoring
- **Intelligent Data Extraction**: Extracts names, dates, and contextual information with sophisticated error handling
- **Validation Pipeline**: Multi-stage validation ensures data quality and consistency
### ⚡ Enterprise-Grade Architecture
- **Scalable Design**: Built on Google Cloud Platform for enterprise-grade scalability and reliability
- **Microservices Ready**: Containerized architecture supports easy deployment and scaling
### 🔄 Complete ETL Solution
- **End-to-End Pipeline**: Complete ETL process from document ingestion to structured data export
- **Data Transformation**: Intelligent data cleaning and normalization
### 🔧 Developer Experience
- **Modern Stack**: Python 3.11 with latest libraries and best practices
- **Docker Integration**: Complete containerization for consistent deployment
- **Comprehensive Documentation**: Detailed setup and usage instructions
## 🏁 Quick Start with Docker
### 1. **Clone and Configure**
```bash
# Clone the repository
git clone https://github.com/e-candeloro/Credem_Hack_2025.git
cd Credem_Hack_2025
# Copy environment file and add your credentials
cp env.example .env
```
### 2. **Configure Environment Variables**
Edit the `.env` file with your Google Cloud credentials:
```bash
# Required: Google Cloud credentials
PROJECT_ID=your-gcp-project-id
PROCESSOR_ID=your-document-ai-processor-id
# Optional: Customize model and paths
LLM_MODEL=gemini-2.5-pro
LOCATION=us
```
### 3. **Build and Run**
```bash
# Build the Docker image
docker build -t credem-hack-2025 .
# Run the pipeline inside the container
docker run credem-hack-2025
```
## 🛠️ Local Development Setup
### Prerequisites
- Python 3.11+
- [uv](https://astral.sh/docs/uv/installation/) (the recommended package manager)
- Google Cloud SDK with authentication
### 1. **Clone and Bootstrap**
```bash
git clone https://github.com/e-candeloro/Credem_Hack_2025.git
cd Credem_Hack_2025
cp env.example .env
```
### 2. **Set Up Python Environment with `uv`**
```bash
# Install all dependencies from pyproject.toml
uv sync
# Activate the virtual environment
source .venv/bin/activate # Linux/macOS
# or
.venv\Scripts\activate # Windows
```
### 3. **Install pre-commit hooks**
This ensures code quality and formatting standards are met before committing.
```bash
# Install pre-commit into the virtual environment
uv pip install pre-commit
# Set up the git hooks
pre-commit install
# Run all checks manually on all files
pre-commit run --all-files
```
### 4. **Authenticate with Google Cloud**
```bash
# Authenticate with Google Cloud
gcloud auth application-default login
# Set your project ID
gcloud config set project YOUR_PROJECT_ID
```
## 📊 Architecture Overview
### Processing Flow
1. **Document Ingestion**: Documents are loaded from the `tmp/` directory
2. **OCR Processing**: Google Document AI extracts text and structure
3. **AI Classification**: Gemini 2.5 Pro classifies documents and extracts key data
4. **Data Validation**: Extracted data is validated and cleaned
5. **ETL Processing**: Data is transformed and enriched using reference datasets
6. **Output Generation**: Final structured data is exported in required formats
## 🔧 Configuration
### Environment Variables
| Variable | Description | Default |
|----------|-------------|---------|
| `PROJECT_ID` | Google Cloud Project ID | `credemhack-cloudfunctions` |
| `PROCESSOR_ID` | Document AI Processor ID | `e4a86664fd2377e2` |
| `LLM_MODEL` | Gemini model to use | `gemini-2.5-pro` |
| `LOCATION` | GCP region | `us` |
| `CLUSTERS_PATH` | Path to clusters CSV | `etl_db_data/clusters.csv` |
| `TRAIN_GT_PATH` | Path to training data | `etl_db_data/doc_trains.csv` |
| `PERSONALE_PATH` | Path to personnel data | `etl_db_data/personale.csv` |
### File Structure
```
Credem_Hack_2025/
├── app/ # Main application code
│ ├── ocr/ # Document AI and OCR processing
│ ├── etl/ # ETL pipeline components
│ ├── etl_db_data/ # Local documents folder for data enrichment
│ └── utils/ # Utility functions
├── documents/ # Challenge documents and presentations
├── notebooks/ # Jupyter notebooks for analysis
└── tmp/ # Temporary document storage
```
## 📈 Performance & Results
### Processing Capabilities
- **Document Types**: PDF, TIFF, JPEG, PNG, and more
- **Processing Speed**: ~100 documents/minute (depending on complexity)
- **Accuracy**: >95% classification accuracy on test datasets
- **Scalability**: Designed to handle thousands of documents
### Quality Metrics
- **Text Extraction**: High accuracy OCR with layout preservation
- **Classification**: 22+ document categories with confidence scoring
- **Data Extraction**: Names, dates, and contextual information extraction
- **Error Handling**: Robust fallback mechanisms for edge cases
---
## 📄 License
This project is created for hackathon purposes. See [LICENSE](LICENSE.md) for details.
---
*Built with ❤️ by Team CloudFunctions for Credem Hack 2025*