perseus-client 1.0.0rc3
A Python client for Perseus API
Requires Python: >=3.8

<div align="center">

# Perseus Text-to-Graph

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Documentation](https://img.shields.io/badge/docs-available-blue.svg)](https://docs.perseus.lettria.net/)

[Documentation](https://docs.perseus.lettria.net/)

</div>

In today's world, a vast amount of valuable information is locked away in unstructured text—documents, articles, emails, and more. While AI and analytics tools are incredibly powerful, they struggle to make sense of this chaotic data. They need structured, connected information to reason effectively.

This is where the gap lies:

| **What Organizations Have** | **What AI Systems Need**        |
| :-------------------------- | :------------------------------ |
| 📄 **Unstructured Text**    | 🔗 **Connected Knowledge**      |
| Chaotic, disconnected data  | Structured, queryable graphs    |
| Implicit relationships      | Explicit entities and relations |
| Hard to query and analyze   | Ready for deep analysis         |

Without a way to bridge this gap, AI systems can't unlock the full potential of your data. They might miss critical insights, provide incomplete answers, or fail to see the bigger picture.

Lettria's Perseus service is designed to solve this problem. It transforms your raw text into a structured knowledge graph, making it instantly usable for AI applications, from advanced search to complex reasoning. Furthermore, the SDK empowers users to leverage their own ontologies, providing a flexible way to define the desired data schema. This greatly reduces data complexity and ensures the generated knowledge graph is precisely tailored to specific use cases.

## 🌟 Features

- **Asynchronous Client**: High-performance, non-blocking API calls using `asyncio` and `aiohttp`.
- **Simple Interface**: Easy-to-use methods for file operations, ontology management, and graph building.
- **Data Validation**: Robust data modeling with `pydantic`.
- **Neo4j Integration**: Directly save your graph data to a Neo4j instance.
- **Flexible Configuration**: Configure via environment variables or directly in code.

## 📦 Installation

```bash
pip install perseus-client==1.0.0-rc.3
```

## 🚀 Quick Start

To start using the SDK, you will need an API key from Lettria.

To create an API key, please visit our app [here](https://app.perseus.lettria.net/).

### Configuration

The SDK can be configured via environment variables. The `PerseusClient` will automatically load them. You can place them in a `.env` file in your project root.

| Variable          | Description                               | Required |
| ----------------- | ----------------------------------------- | -------- |
| `LETTRIA_API_KEY` | Your unique API key for the Lettria API.  | Yes      |
| `NEO4J_URI`       | The URI for your Neo4j database instance. | No       |
| `NEO4J_USER`      | The username for your Neo4j database.     | No       |
| `NEO4J_PASSWORD`  | The password for your Neo4j database.     | No       |

### Example: Build a Graph

This example shows how to build a graph from a text file.

```python
import asyncio
from perseus_client import PerseusClient

async def main():
    async with PerseusClient() as client:
        try:
            await client.build_graph(
                file_path="path/to/your/document.txt",
            )
            print("🎉 Graph built successfully!")
        except Exception as e:
            print(f"An error occurred: {e}")

if __name__ == "__main__":
    asyncio.run(main())
```

## 📚 API Reference

### `client.build_graph`

```python
async def build_graph(
    file_path: str,
    ontology_path: Optional[str] = None,
    output_path: Optional[str] = None,
    save_to_neo4j: bool = False,
    refresh_graph: bool = False,
) -> Job:
```

Processes a file by uploading it, optionally with an ontology, running a job, and downloading the output.

| Parameter       | Type            | Description                                                                   | Default |
| --------------- | --------------- | ----------------------------------------------------------------------------- | ------- |
| `file_path`     | `str`           | The path to the file to process.                                              |         |
| `ontology_path` | `Optional[str]` | The path to the ontology file to use.                                         | `None`  |
| `output_path`   | `Optional[str]` | The path to save the output to. If not provided, a default path will be used. | `None`  |
| `save_to_neo4j` | `bool`          | Whether to save the output to Neo4j.                                          | `False` |
| `refresh_graph` | `bool`          | Whether to force a new job to be created (refresh the graph).                 | `False` |

## 📂 Examples

For more detailed examples, check out the [`examples/`](./examples/) directory. Each example has its own README with instructions.

### Simple Examples

- **[Build Graph](./examples/simple/build_graph/)**: Build a knowledge graph from a text file.
- **[File Operations](./examples/simple/file_operations/)**: Upload and manage files.
- **[Ontology Operations](./examples/simple/ontology_operations/)**: Upload and manage ontologies.
- **[Delete Operations](./examples/simple/delete_operations/)**: Delete files and ontologies.

### Advanced Example

- **[Graph RAG Reporting](./examples/advanced/graph-rag-reporting/)**: A complete workflow to turn a PDF into a knowledge graph and generate a report.

## 🤝 Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request.

## 📧 Contact

For support or questions, please reach out at [hello@lettria.com](mailto:hello@lettria.com).

## 📄 License

This SDK is licensed under the MIT License.