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README.md

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@@ -25,7 +25,7 @@ pip install faim-sdk
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## Authentication
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Get your free API key at **[https://faim.it.com/](https://faim.it.com/)**
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Get your API key at **[https://faim.it.com/](https://faim.it.com/)**
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```python
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from faim_sdk import ForecastClient
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responses = asyncio.run(forecast_multiple_series())
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```
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## Configuration
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### Client Options
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```python
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from faim_sdk import ForecastClient
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# Basic configuration
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client = ForecastClient(
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timeout=120.0, # Request timeout in seconds (default: 120)
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verify_ssl=True, # SSL certificate verification (default: True)
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)
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# With API key authentication
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client = ForecastClient(
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api_key="your-secret-api-key",
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timeout=120.0
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)
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# Advanced configuration with custom httpx settings
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import httpx
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client = ForecastClient(
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api_key="your-api-key",
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timeout=120.0,
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limits=httpx.Limits(max_connections=10), # Connection pooling
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headers={"X-Custom-Header": "value"} # Custom headers
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)
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```
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### Request Options
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```python
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# Compression options for large payloads
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request = Chronos2ForecastRequest(
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x=data,
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horizon=24,
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compression="zstd" # Options: "zstd", "lz4", None (default: "zstd")
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)
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# Model version pinning
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request = FlowStateForecastRequest(
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x=data,
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horizon=24,
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model_version="1.2.3" # Pin to specific version (default: "latest")
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)
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```
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## Context Managers
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Use context managers for automatic resource cleanup:
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```python
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# Sync context manager
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with ForecastClient() as client:
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request = Chronos2ForecastRequest(x=data, horizon=24, quantiles=[0.1, 0.5, 0.9])
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response = client.forecast(request)
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print(response.quantiles)
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# Client automatically closed
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# Async context manager
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async with ForecastClient() as client:
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request = Chronos2ForecastRequest(x=data, horizon=24, quantiles=[0.1, 0.9])
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response = await client.forecast_async(request)
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print(response.quantiles)
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# Client automatically closed
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```
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## Examples
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See the `examples/` directory for complete Jupyter notebook examples:
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- **`flowstate_simple_example.ipynb`** - Point forecasting with FlowState on AirPassengers dataset
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- **`chronos2_probabilistic.ipynb`** - Probabilistic forecasting with quantiles (coming soon)
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- **`batch_processing.ipynb`** - Efficient batch processing patterns (coming soon)
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- **`model_comparison_simple.ipynb`** - Point forecasting with FlowState on AirPassengers dataset
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## Requirements
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## Support
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- **Documentation**: [docs.faim.example.com](https://docs.faim.example.com)
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- **Issues**: [GitHub Issues](https://github.com/your-org/faim-sdk/issues)
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- **Email**: support@faim.example.com
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- **Email**: support@faim.it.com
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## License
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title = {FAIM SDK: Foundation AI Models for Time Series Forecasting},
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author = {FAIM Team},
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year = {2024},
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url = {https://github.com/your-org/faim-sdk}
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url = {https://github.com/S-FM/faim-python-client}
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}
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```

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