Openai-pdf-assistant :robot:

Laveraging OpenAI's Assistant API to create a PDF assistant that can help you with your PDFs.

Features :sparkles:

• [X] User authentication
• [X] Assistant creation and management
• [X] PDF upload
• [x] Ask questions about the PDFs
• [ ] Hanlde conversations

Contribute :raised_hands:

Contributions are more than welcome using the "fork and pull request" workflow. Briefly:

1. Fork the project and clone it locally.
2. Create a new branch following the pattern <placeholder>-issueNumber. Where placeholders can be develop, bugfix, documentation only.
3. Make your changes and push them to your fork.
4. Create a pull request to the main branch.
5. Your pull request will be reviewed and merged.

Installation :wrench:

1. Clone the repository
2. Create a .env file in the root directory with the following content:

OPENAI_API_KEY=<your_openai_api_key>
ENVIRONMENT=<development|production>
FLASK_SECRET_KEY=<flask_secret>     # You can generate this from the terminal (see below)
FLASK_DEBUG=<True|False>
FLASK_APP=main.py
FLASK_RUN_PORT=<port>
FLASK_SQLALCHEMY_DATABASE_URI=<database_uri>
FLASK_SQLALCHEMY_ECHO=<True|False>
FLASK_JWT_SECRET_KEY=<jwt_secret_key> # Generate this from flask shell (see below)

1. Create a virtual environment: python -m venv venv
2. Activate the virtual environment: source venv/bin/activate or .\venv\Scripts\activate on Windows
3. Install the requirements: pip install -r requirements.txt
4. Set the FLASK_APP environment variable: cd src && <export|set> FLASK_APP=main.py
5. Finally, run the application: flask run

Local database setup (SQLAlchemy) :floppy_disk:

# Generate FLASK_SECRET_KEY
python -c 'import secrets; print(secrets.token_urlsafe(32))'

# Generate FLASK_JWT_SECRET_KEY
python -c 'import secrets; print(secrets.token_hex(12))'

# Setup the DB (SQLAlchemy)
flask shell
db # Access the SQLAlchemy database instance
from data.user_model import User
db.create_all()

Flask shell useful commands :shell:

app             # Access the Flask application instance.
current_app     # Access the current active Flask application.
g               # Access the application’s context globals.
request         # Access the incoming request object (only available during a request).
session         # Access the user session data (only available during a request).

db                              # The SQLAlchemy database instance if you are using Flask-SQLAlchemy.
db.create_all()                 # Create all database tables defined in your models.
db.drop_all()                   # Drop all database tables.
db.session.add(instance)        # Add an instance to the database session.
db.session.commit()             # Commit the current transaction.
db.session.rollback()           # Roll back the current transaction.
db.session.query(Model).all()   # Query all records of a model.
db.session.delete(instance)     # Delete an instance from the database.

Model.query.all()                                   # Retrieve all records from the table associated with the model.
Model.query.filter_by(attribute=value).first()      # Query records with specific criteria.
Model.query.get(id)                                 # Retrieve a record by its primary key.
model_instance.save()                               # Save an instance (requires custom method in model).
model_instance.delete()                             # Delete an instance (requires custom method in model).

# Example
from data.user_model import User
user = User.query.filter_by(username='fatjonfreskina').first()