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Enable optional cloud services (Azure, IBM) while maintaining local-first operation.
---
## Quick Start
### 1. Install Cloud Support
```bash
pip install flask openai ibm-cloud-sdk-core ibm-cloud-sdk-watsonx requests
```
### 2. Start Cloud Endpoint
```bash
python cloud_endpoint.py
```
The dashboard opens at: `http://localhost:5000`
### 3. Configure Your Cloud Services
#### Azure OpenAI
1. Go to dashboard → **Azure** tab
2. Enable Azure OpenAI
3. Enter:
- **API Key**: Your Azure OpenAI key (from portal)
- **API Endpoint**: `https://{resource-name}.openai.azure.com/`
- **Deployment Name**: Name of your deployed model (e.g., `gpt-4`)
- **Model Blob**: Reference name (e.g., `gpt-4-vision`)
4. Click **Test Connection**
#### IBM Watsonx
1. Go to dashboard → **IBM** tab
2. Enable IBM Watsonx
3. Enter:
- **API Key**: Your IBM Cloud API key
- **API Endpoint**: Your Watsonx endpoint URL
- **Model Name**: Model ID (e.g., `granite-13b-chat-v2`)
- **Model Blob**: Reference (e.g., `ibm/granite`)
4. Click **Test Connection**
---
## Architecture
```
┌─────────────────────────────────────┐
│ Genesis_Engine (Local COSMOS) │
│ - Ollama (default) │
│ - Hebbian learning │
│ - Quantum heart │
└──────────────────┬──────────────────┘
│
┌──────────────┼──────────────┐
▼ ▼ ▼
┌────────┐ ┌──────────┐ ┌────────┐
│ Ollama │ │ Azure │ │ IBM │
│ (Local)│ │ OpenAI │ │ Watsonx│
└────────┘ └──────────┘ └────────┘
Cloud Router (cloud_endpoint.py)
- Config management
- Credential handling
- Request routing
- Vision processing
```
---
## API Endpoints
### Configuration
**GET /api/config**
Get current configuration (sanitized).
```bash
curl http://localhost:5000/api/config
```
**POST /api/config/default**
Set default provider.
```bash
curl -X POST http://localhost:5000/api/config/default \
-H "Content-Type: application/json" \
-d '{"provider": "azure"}'
```
**POST /api/config/provider/{provider}**
Update provider settings.
```bash
curl -X POST http://localhost:5000/api/config/provider/azure \
-H "Content-Type: application/json" \
-d '{
"endpoint": "https://myresource.openai.azure.com/",
"model_name": "gpt-4",
"model_blob": "gpt-4-vision"
}'
```
### Credentials
**POST /api/credentials/{provider}**
Set API credentials (from environment variable or request body).
```bash
curl -X POST http://localhost:5000/api/credentials/azure \
-H "Content-Type: application/json" \
-d '{"api_key": "YOUR_AZURE_KEY"}'
```
**POST /api/credentials/test/{provider}**
Test provider connectivity.
```bash
curl -X POST http://localhost:5000/api/credentials/test/azure
```
### Generation
**POST /api/generate**
Generate response from selected provider.
```bash
curl -X POST http://localhost:5000/api/generate \
-H "Content-Type: application/json" \
-d '{
"prompt": "Hello, what is your name?",
"provider": "azure"
}'
```
**POST /api/vision**
Analyze image with vision model.
```bash
curl -X POST http://localhost:5000/api/vision \
-F "image=@photo.jpg" \
-F "prompt=Describe this image" \
-F "provider=azure"
```
### Status
**GET /api/status**
Get system status and provider info.
```bash
curl http://localhost:5000/api/status
```
**GET /api/health**
Health check.
```bash
curl http://localhost:5000/api/health
```
---
## Environment Variables
For security, use environment variables instead of hardcoding keys:
```bash
# Azure
export COSMOS_AZURE_KEY="your-azure-key-here"
# IBM
export COSMOS_IBM_KEY="your-ibm-key-here"
# Start endpoint
python cloud_endpoint.py
```
The dashboard will automatically load credentials from environment.
---
## Integration with Genesis_Engine
Add cloud routing to your Genesis_Engine:
```python
# In soul/loop.py or serve.py
from cloud_router import CloudConfig, CloudRouter
# Initialize
config = CloudConfig()
router = CloudRouter(config)
# Generate with cloud (or local fallback)
response = router.generate(
prompt="Your message",
provider="azure" # or "ibm", "ollama"
)
# Handle vision
image_analysis = router.vision(
image_path="/path/to/image.jpg",
prompt="Describe this",
provider="azure" # gpt-4-vision
)
```
---
## Configuration File
Cloud settings are stored in `cloud_config.json` (credentials not persisted):
```json
{
"enabled": true,
"default_provider": "ollama",
"providers": {
"azure": {
"enabled": false,
"api_key": "[SET_VIA_ENV]",
"api_endpoint": "https://myresource.openai.azure.com/",
"deployment_name": "gpt-4",
"model_blob": "gpt-4-vision",
"temperature": 0.7,
"timeout": 30
},
"ibm": {
"enabled": false,
"api_key": "[SET_VIA_ENV]",
"api_endpoint": "https://api.us-south.watson-platform.net/instances/...",
"model_name": "granite-13b-chat-v2",
"model_blob": "ibm/granite",
"temperature": 0.7,
"timeout": 30
},
"ollama": {
"enabled": true,
"api_endpoint": "http://localhost:11434",
"model_name": "cosmos-q4:latest",
"timeout": 60
}
}
}
```
---
## Example: Full Cloud Setup
### Setup Script
```bash
#!/bin/bash
# Install dependencies
pip install flask openai ibm-cloud-sdk-core ibm-cloud-sdk-watsonx requests
# Set credentials
export COSMOS_AZURE_KEY="your-azure-key"
export COSMOS_IBM_KEY="your-ibm-key"
# Start endpoint
python cloud_endpoint.py
```
### Client Code
```python
import requests
import json
# Configure Azure as default
requests.post('http://localhost:5000/api/config/default',
json={'provider': 'azure'})
# Generate response
response = requests.post('http://localhost:5000/api/generate',
json={'prompt': 'Hello!'})
print(response.json()['response'])
```
---
## Fallback Behavior
If cloud service fails, routes to:
1. Default provider (if enabled)
2. Ollama (if available)
3. Error
```python
# Graceful fallback
try:
response = router.generate(prompt, provider='azure')
except Exception as e:
print(f"Azure failed, falling back to Ollama: {e}")
response = router.generate(prompt, provider='ollama')
```
---
## Vision Support
Currently supported:
- ✅ Azure OpenAI (gpt-4-vision)
- ⏳ IBM Watsonx (coming soon)
- ✅ Ollama (with multimodal models)
```python
# Azure vision
analysis = router.vision(
image_path="photo.jpg",
prompt="What's in this image?",
provider="azure"
)
```
---
## Troubleshooting
### "Azure not enabled or API key not set"
- Ensure `COSMOS_AZURE_KEY` environment variable is set
- Or use dashboard to set credentials
### "Connection timeout"
- Check endpoint URL is correct
- Verify network access to cloud service
- Increase timeout in `cloud_config.json`
### "Invalid deployment name"
- Check deployment exists in Azure portal
- Name is case-sensitive
- Use "gpt-4" not "GPT-4"
### "Model not found"
- For IBM: check model ID in Watsonx console
- For Azure: verify deployment is created
---
## Security Best Practices
1. **Never commit API keys** to version control
2. **Use environment variables** for credentials
3. **Rotate keys regularly** in cloud portals
4. **Use API key with minimal permissions** if possible
5. **Monitor usage** in cloud provider dashboards
6. **Set rate limits** on endpoint if exposed
---
## Performance
Typical latencies:
- **Ollama (local)**: 50-500ms
- **Azure OpenAI**: 500-2000ms (network + model)
- **IBM Watsonx**: 500-2000ms (network + model)
For best performance:
- Use Ollama for interactive chat
- Use Azure/IBM for heavy lifting (vision, reasoning)
- Implement caching for repeated prompts
---
## Advanced: Custom Providers
Extend `CloudRouter` to add custom providers:
```python
class CustomRouter(CloudRouter):
def _generate_custom(self, prompt: str, **kwargs) -> str:
"""Add your custom provider here."""
# Implementation
pass
def generate(self, prompt, provider=None, **kwargs):
if provider == "custom":
return self._generate_custom(prompt, **kwargs)
return super().generate(prompt, provider, **kwargs)
```
---
## Deployment
### Docker
```dockerfile
FROM python:3.9
WORKDIR /cosmos
COPY cloud_*.py .
COPY templates/ templates/
RUN pip install flask openai requests
EXPOSE 5000
CMD ["python", "cloud_endpoint.py"]
```
### Systemd Service
```ini
[Unit]
Description=COSMOS Cloud Router
After=network.target
[Service]
Type=simple
User=cosmos
WorkingDirectory=/opt/cosmos
EnvironmentFile=/etc/cosmos/cloud.env
ExecStart=/usr/bin/python3 cloud_endpoint.py
Restart=always
[Install]
WantedBy=multi-user.target
```
---
## License & Citation
COSMOS Cloud Integration is part of the COSMOS project.
```bibtex
@misc{phera2026cosmos,
title={COSMOS: A 54D Quantum-Inspired Transformer with Cloud Integration},
author={Phera},
year={2026},
url={https://zenodo.org/records/17574447}
}
```
---
**Questions?** Check `/docs` endpoint or review `cloud_router.py` source code.
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