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80 changes: 4 additions & 76 deletions README.md
Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@ pip install faim-sdk

## Authentication

Get your free API key at **[https://faim.it.com/](https://faim.it.com/)**
Get your API key at **[https://faim.it.com/](https://faim.it.com/)**

```python
from faim_sdk import ForecastClient
Expand Down Expand Up @@ -459,81 +459,11 @@ async def forecast_multiple_series():
responses = asyncio.run(forecast_multiple_series())
```

## Configuration

### Client Options

```python
from faim_sdk import ForecastClient

# Basic configuration
client = ForecastClient(
timeout=120.0, # Request timeout in seconds (default: 120)
verify_ssl=True, # SSL certificate verification (default: True)
)

# With API key authentication
client = ForecastClient(
api_key="your-secret-api-key",
timeout=120.0
)

# Advanced configuration with custom httpx settings
import httpx

client = ForecastClient(
api_key="your-api-key",
timeout=120.0,
limits=httpx.Limits(max_connections=10), # Connection pooling
headers={"X-Custom-Header": "value"} # Custom headers
)
```

### Request Options

```python
# Compression options for large payloads
request = Chronos2ForecastRequest(
x=data,
horizon=24,
compression="zstd" # Options: "zstd", "lz4", None (default: "zstd")
)

# Model version pinning
request = FlowStateForecastRequest(
x=data,
horizon=24,
model_version="1.2.3" # Pin to specific version (default: "latest")
)
```

## Context Managers

Use context managers for automatic resource cleanup:

```python
# Sync context manager
with ForecastClient() as client:
request = Chronos2ForecastRequest(x=data, horizon=24, quantiles=[0.1, 0.5, 0.9])
response = client.forecast(request)
print(response.quantiles)
# Client automatically closed

# Async context manager
async with ForecastClient() as client:
request = Chronos2ForecastRequest(x=data, horizon=24, quantiles=[0.1, 0.9])
response = await client.forecast_async(request)
print(response.quantiles)
# Client automatically closed
```

## Examples

See the `examples/` directory for complete Jupyter notebook examples:

- **`flowstate_simple_example.ipynb`** - Point forecasting with FlowState on AirPassengers dataset
- **`chronos2_probabilistic.ipynb`** - Probabilistic forecasting with quantiles (coming soon)
- **`batch_processing.ipynb`** - Efficient batch processing patterns (coming soon)
- **`model_comparison_simple.ipynb`** - Point forecasting with FlowState on AirPassengers dataset

## Requirements

Expand Down Expand Up @@ -562,9 +492,7 @@ See the `examples/` directory for complete Jupyter notebook examples:

## Support

- **Documentation**: [docs.faim.example.com](https://docs.faim.example.com)
- **Issues**: [GitHub Issues](https://github.com/your-org/faim-sdk/issues)
- **Email**: support@faim.example.com
- **Email**: support@faim.it.com

## License

Expand All @@ -579,6 +507,6 @@ If you use FAIM in your research, please cite:
title = {FAIM SDK: Foundation AI Models for Time Series Forecasting},
author = {FAIM Team},
year = {2024},
url = {https://github.com/your-org/faim-sdk}
url = {https://github.com/S-FM/faim-python-client}
}
```
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