https://github.com/cherninlab/vertex-rag-flask
Flask application for building RAG systems using Google Vertex AI.
https://github.com/cherninlab/vertex-rag-flask
flask vertex-ai
Last synced: 11 months ago
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Flask application for building RAG systems using Google Vertex AI.
- Host: GitHub
- URL: https://github.com/cherninlab/vertex-rag-flask
- Owner: cherninlab
- License: mit
- Created: 2024-11-05T10:43:47.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-15T09:28:53.000Z (over 1 year ago)
- Last Synced: 2024-12-27T14:44:30.391Z (over 1 year ago)
- Topics: flask, vertex-ai
- Language: HTML
- Homepage:
- Size: 43 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Vertex RAG Flask
[](https://www.python.org/downloads/)
[](https://github.com/psf/black)
[](https://opensource.org/licenses/MIT)
Flask application for building RAG (Retrieval Augmented Generation) systems using Google Vertex AI. Process PDFs with layout-aware parsing, chat with documents in multiple languages, and build AI-powered document analysis systems.
## Installation
1. Clone the repository:
```bash
git clone https://github.com/cherninlab/vertex-rag-flask.git
cd vertex-rag-flask
```
2. Set up Python environment:
```bash
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
pip install --upgrade pip
```
3. Install the project:
```bash
pip install -e ".[dev]"
```
4. Configure environment:
```bash
cp .env.example .env
# Edit .env with your configuration:
# - Add your Google Cloud Project ID
# - Set path to service account credentials
```
5. Run the application:
```bash
flask run
```
## Google Cloud Setup
1. Create a new project in [Google Cloud Console](https://console.cloud.google.com/)
2. Enable required APIs:
```bash
# Enable Vertex AI API
gcloud services enable aiplatform.googleapis.com
# Enable Cloud Storage API
gcloud services enable storage.googleapis.com
# Enable IAM API
gcloud services enable iam.googleapis.com
```
3. Create and configure service account:
```bash
# Create service account
gcloud iam service-accounts create vertex-rag-sa --display-name="Vertex RAG Service Account"
# Grant Vertex AI user role
gcloud projects add-iam-policy-binding YOUR_PROJECT_ID --member="serviceAccount:vertex-rag-sa@YOUR_PROJECT_ID.iam.gserviceaccount.com" --role="roles/aiplatform.user"
# Grant Storage Admin role (for bucket and file management)
gcloud projects add-iam-policy-binding YOUR_PROJECT_ID --member="serviceAccount:vertex-rag-sa@YOUR_PROJECT_ID.iam.gserviceaccount.com" --role="roles/storage.admin"
# Download credentials
gcloud iam service-accounts keys create credentials.json --iam-account=vertex-rag-sa@YOUR_PROJECT_ID.iam.gserviceaccount.com
```
**Important Notes:**
- Keep your credentials.json secure and never commit it to version control
- The service account needs both Vertex AI and Storage permissions to function properly
- You can use more granular permissions instead of storage.admin if needed
## Usage
1. Start the application:
```bash
flask run
# FLASK_DEBUG=1 FLASK_APP=src/app flask run --debug
```
2. Open http://localhost:5000 in your browser
3. Upload a document
4. After processing, you'll be redirected to the chat interface
## Development
### Project Structure
```
vertex-rag-flask/
├── src/
│ ├── app/
│ │ ├── routes/ # API and web routes
│ │ ├── services/ # Business logic
│ │ ├── templates/ # HTML templates
│ │ └── utils/ # Helper functions
│ └── config/ # Configuration
├── tests/ # Test files
├── uploads/ # Temporary upload directory
├── credentials.json # GCP service account key
├── pyproject.toml # Project dependencies
└── README.md
```
### System Requirements
- Python 3.11+
- Google Cloud Project with enabled APIs
- Service account with appropriate permissions
### Running Tests
```bash
# Run pytest
pytest
# Run type checking
mypy .
# Run linting
flake8 .
```
### Code Style
The project uses several tools to maintain code quality:
- **black**: Code formatting
- **isort**: Import sorting
- **flake8**: Style guide enforcement
- **mypy**: Static type checking
These are automatically run as pre-commit hooks when you commit changes.
### Using Dev Containers
If you use VS Code:
1. Install the "Remote - Containers" extension
2. Open the project
3. Click "Reopen in Container" when prompted
4. VS Code will set up the development environment automatically
## Troubleshooting
### Common Issues
1. **Permission Denied Errors**
- Verify that your service account has all required roles
- Check if credentials.json is properly configured
- Ensure APIs are enabled in your project
2. **Upload Failures**
- Verify the file format is supported
- Check if the selected bucket exists
- Ensure your service account has storage permissions
3. **Chat Not Working**
- Verify Vertex AI API is enabled
- Check if the model has access to your document
- Ensure proper network connectivity
## Contributing
Contributions are welcome!
## License
Distributed under the MIT License. See `LICENSE` for more information.
---
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