A Python backend library for integrating RNet OAuth and AI Provider services. This library allows users to authenticate via RNet and pay for AI model token costs directly using their RNet account.
- OAuth2 PKCE Support: Secure authorization code flow with automatic code verifier and challenge generation.
- Token Management: Exchange authorization codes for tokens and refresh expired tokens.
- UserInfo Endpoint: Fetch the authenticated user's RNet profile with an access token.
- AI Integration: Easy methods to chat with AI models using standard or streaming responses.
pip install rnet-oauthfrom rnet_oauth import RNetAuth, RNetAi
auth = RNetAuth(
client_id='client-id',
client_secret='client-secret',
redirect_uri='redirect-uri'
)
ai = RNetAi()# 1. Generate PKCE
pkce = auth.generate_pkce()
verifier = pkce['verifier']
challenge = pkce['challenge']
# 2. Get Authorization URL
# challenge: PKCE code challenge (optional)
# state: An optional string to maintain state between the request and callback (recommended for security)
auth_url = auth.get_authorization_url(challenge, state='optional-state')# 3. Exchange code for tokens
tokens = auth.exchange_code_for_token(code, verifier)
access_token = tokens['access_token']user_info = auth.get_user_info(access_token)
print(user_info['email'])
print(user_info['name'])The UserInfo response comes from RNet's /userinfo endpoint and may include:
sub, email, email_verified, name, preferred_username, user_id, role, and status.
response = ai.chat({
"contents": [
{
"role": "user",
"parts": [{"text": "Hello!"}]
}
]
}, access_token, "gemini-2.5-flash-lite")for chunk in ai.chat_stream({
"contents": [
{
"role": "user",
"parts": [{"text": "Hello!"}]
}
]
}, access_token, "gemini-2.5-flash-lite"):
print(chunk)MIT