Metadata-Version: 2.1
Name: orca_sdk
Version: 0.0.88
Summary: SDK for interacting with Orca Services
License: Apache-2.0
Author: Orca DB Inc.
Author-email: dev-rel@orcadb.ai
Requires-Python: >=3.10,<3.13
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: attrs (>=22.2.0)
Requires-Dist: datasets (>=3.1.0,<4.0.0)
Requires-Dist: gradio (==5.13.0)
Requires-Dist: httpx (>=0.20.0,<0.29.0)
Requires-Dist: networkx (>=3.4.2,<4.0.0)
Requires-Dist: pandas (>=2.2.3,<3.0.0)
Requires-Dist: pyarrow (>=18.0.0,<19.0.0)
Requires-Dist: python-dateutil (>=2.8.0,<3.0.0)
Requires-Dist: python-dotenv (>=1.1.0,<2.0.0)
Requires-Dist: torch (>=2.5.1,<3.0.0)
Description-Content-Type: text/markdown

<!--
IMPORTANT NOTE:
- This file will get rendered in the public facing PyPi page here: https://pypi.org/project/orca_sdk/
- Only content suitable for public consumption should be placed in this file everything else should go into CONTRIBUTING.md
-->

# OrcaSDK

OrcaSDK is a Python library for building and using retrieval-augmented models with [OrcaCloud](https://orcadb.ai). It enables you to create, deploy, and maintain models that can adapt to changing circumstances without retraining by accessing external data called "memories."

## Documentation

You can find the documentation for all things Orca at [docs.orcadb.ai](https://docs.orcadb.ai). This includes tutorials, how-to guides, and the full interface reference for OrcaSDK.

## Features

- **Labeled Memorysets**: Store and manage labeled examples that your models can use to guide predictions
- **Classification Models**: Build retrieval-augmented classification models that adapt to new data without retraining
- **Embedding Models**: Use pre-trained or fine-tuned embedding models to represent your data
- **Telemetry**: Collect feedback and monitor memory usage to optimize model performance
- **Datasources**: Easily ingest data from various sources into your memorysets

## Installation

Orca SDK is compatible with Python 3.10 or higher and is available on [PyPI](https://pypi.org/project/orca_sdk/). You can install it with your favorite python package manager:

- Pip: `pip install orca_sdk`
- Conda: `conda install orca_sdk`
- Poetry: `poetry add orca_sdk`

## Quick Start

```python
from dotenv import load_dotenv
from orca_sdk import OrcaCredentials, LabeledMemoryset, ClassificationModel

# Load your API key from environment variables
load_dotenv()
assert OrcaCredentials.is_authenticated()

# Create a labeled memoryset
memoryset = LabeledMemoryset.from_disk("my_memoryset", "./data.jsonl")

# Create a classification model using the memoryset
model = ClassificationModel("my_model", memoryset)

# Make predictions
prediction = model.predict("my input")
```

For a more detailed walkthrough, check out our [Quick Start Guide](https://docs.orcadb.ai/quickstart/).

## Support

If you have any questions, please reach out to us at support@orcadb.ai.

