An AI-powered document processing platform that extracts text from uploaded PDF and DOCX files, generates intelligent summaries using Google's Gemini model, and stores processed results in PostgreSQL.
The application combines containerized services with serverless event processing, using Amazon ECS Fargate to host the FastAPI application while AWS Lambda asynchronously processes uploaded documents through an event-driven pipeline.
Watch the application in action:
IntelliDoc follows a hybrid cloud architecture that combines long-running containerized services with serverless event processing.
The user uploads a document through the FastAPI application running on Amazon ECS Fargate. The application stores the file in Amazon S3, which automatically triggers an AWS Lambda function. Lambda extracts the document text, sends it to the Google Gemini API for analysis, stores the processed result in Amazon RDS PostgreSQL, and the FastAPI application later retrieves the result when the client polls for completion.
- Upload PDF and DOCX documents
- Automatic text extraction
- AI-powered document summarization
- Key point extraction
- Basic document classification
- PostgreSQL persistence
- Dockerized deployment
- Event-driven document processing
- Infrastructure as Code using Terraform
- CloudWatch monitoring and logging
- Python
- FastAPI
- SQLAlchemy
- PostgreSQL
- Uvicorn
- Google Gemini 2.5 Flash
- Amazon ECS (Fargate)
- Amazon ECR
- Amazon S3
- AWS Lambda
- Amazon RDS (PostgreSQL)
- Amazon CloudWatch
- IAM
- Amazon VPC
- Docker
- Terraform
Detailed technical documentation is available inside the docs directory.
| Document | Description |
|---|---|
| Backend Documentation | Backend architecture, extraction pipeline, Gemini integration, frontend implementation, database design, and local development workflow. |
| Infrastructure Documentation | AWS architecture, networking, Terraform infrastructure, deployment process, monitoring, and operational considerations. |
| Challenges & Debugging | Major technical challenges encountered during development and how they were diagnosed and resolved. |
Clone the repository:
git clone https://github.com/clemcloud/intellidoc.git
cd intellidocCreate a virtual environment:
python -m venv venv
source venv/bin/activateInstall dependencies:
pip install -r requirements.txtCreate a .env file:
GEMINI_API_KEY=your-api-key
DATABASE_URL=postgresql://user:password@localhost:5432/intellidocRun the application:
uvicorn main:app --reloadOpen:
http://127.0.0.1:8000
- Integrate Amazon Bedrock as an alternative AI provider
- HTTPS using AWS Certificate Manager
- Route 53 custom domain
- ECS Service Auto Scaling
- AWS Secrets Manager integration
- CloudWatch dashboards and alarms
- OCR support for scanned PDF documents
Umoru Clement
Cloud Engineer | Backend Developer | AWS Solutions Architect Associate Candidate
LinkedIn: www.linkedin.com/in/clementcloud
This project is provided for educational and portfolio purposes.