Metadata-Version: 2.1
Name: colivara-py
Version: 0.1.1
Summary: The official Python SDK for the ColiVara API
Author: tjmlabs
License: Apache-2.0
Project-URL: Homepage, https://github.com/tjmlabs/colivara-py
Project-URL: Changelog, https://github.com/tjmlabs/colivara-py/releases
Project-URL: Issues, https://github.com/tjmlabs/colivara-py/issues
Project-URL: CI, https://github.com/tjmlabs/colivara-py/actions
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pydantic ==2.9.2
Requires-Dist: requests ==2.32.3

# colivara-py

[![PyPI](https://img.shields.io/pypi/v/colivara-py.svg)](https://pypi.org/project/colivara-py/)
[![Changelog](https://img.shields.io/github/v/release/tjmlabs/colivara-py?include_prereleases&label=changelog)](https://github.com/tjmlabs/colivara-py/releases)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/tjmlabs/colivara-py/blob/main/LICENSE)
[![Tests](https://github.com/tjmlabs/colivara-py/actions/workflows/test.yml/badge.svg)](https://github.com/tjmlabs/colivara-py/actions/workflows/test.yml) [![codecov](https://codecov.io/gh/tjmlabs/ColiVara/branch/main/graph/badge.svg)](https://codecov.io/gh/tjmlabs/ColiVara)

The official Python SDK for the ColiVara API

## Installation

Install this library using `pip`:
```bash
pip install colivara-py
```
## Usage

Please see the [ColiVara API documentation](https://docs.colivara.com) for more information on how to use this library.

You will need to either self-host the API (see the [ColiVara API repo](https://github.com/tjmlabs/ColiVara)) or use the hosted version at https://colivara.com. You will also need an API key, which you can obtain by signing up at [ColiVara](https://colivara.com) or from your self-hosted API.

```python
import os
from colivara_py import ColiVara


rag_client = ColiVara(
     # This is the default and can be omitted
    api_key=os.environ.get("COLIVARA_API_KEY"),
    # This is the default and can be omitted
    base_url="https://api.colivara.com"
)
# Create a new collection
new_collection = rag_client.create_collection(name="my_collection", metadata={"description": "A sample collection"})
print(f"Created collection: {new_collection.name}")

# Upload a document to the collection
document = rag_client.upsert_document(
    name="sample_document",
    collection_name="my_collection",
    url="https://example.com/sample.pdf",
    metadata={"author": "John Doe"}
)
print(f"Uploaded document: {document.name}")

# Search for documents
search_results = rag_client.search(
    query="machine learning",
    collection_name="my_collection",
    top_k=3
)
for result in search_results.results:
    print(f"Page {result.page_number} of {result.document_name}: Score {result.normalized_score}")

# List documents in a collection
documents = client.list_documents(collection_name="my_collection")
for doc in documents:
    print(f"Document: {doc.name}, Pages: {doc.num_pages}")

# Generate embeddings
embeddings = rag_client.create_embedding(
    input_data=["This is a sample text for embedding"],
    task="query"
)
print(f"Generated {len(embeddings.data)} embeddings")

# Delete a document
rag_client.delete_document("sample_document", collection_name="my_collection")
print("Document deleted")
```

## Development

To contribute to this library, first checkout the code. Then create a new virtual environment:

We use uv, but you can use the pip interface if you prefer:

```bash
cd colivara-py
uv .venv
source .venv/bin/activate
```
Now install the dependencies and test dependencies:
```bash
uv sync --extra dev-dependencies
```
To run the tests:
```bash
pytest
```
