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Plexe Documentation

For agents: Plexe documentation: build ML models from natural language with the Plexe Python library, covering quickstart, callbacks, distributed training with Ray and I/O

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# Plexe Documentation

## Docs

- [Welcome to Plexe](https://docs.plexe.ai/pages/introduction/welcome.md): Build ML models from natural language in minutes
- [Quickstart](https://docs.plexe.ai/pages/library/tutorials/quickstart.md): Build your first ML model using the Plexe Python library in minutes.
- [Using Callbacks](https://docs.plexe.ai/pages/library/tutorials/using_callbacks.md): Instrument the model building process using built-in and custom callbacks
- [Distributed Training with Ray](https://docs.plexe.ai/pages/library/tutorials/distributed_training.md): Leverage Ray for distributed and parallel model building with Plexe.
- [Installation](https://docs.plexe.ai/pages/library/how-to/install_plexe.md): Learn how to install the Plexe Python library with different dependency sets.
- [Provide I/O Schemas](https://docs.plexe.ai/pages/library/how-to/provide_schemas.md): Define the expected structure of your model inputs and outputs using Pydantic models or dictionaries.
- [Use Datasets for Building Models](https://docs.plexe.ai/pages/library/how-to/use_datasets.md): Provide training and validation data to the model build process using Pandas DataFrames or DatasetGenerator.
- [Configure LLM Providers](https://docs.plexe.ai/pages/library/how-to/configure_llm_provider.md): Specify which Large Language Model providers and models Plexe should use for its agent system.
- [Save and Load Models](https://docs.plexe.ai/pages/library/how-to/save_load_models.md): Persist trained Plexe models to disk and load them back for later use.
- [Core Concepts](https://docs.plexe.ai/pages/library/explanation/concepts.md): Understand the fundamental concepts behind the Plexe Python library.
- [LLM Providers](https://docs.plexe.ai/pages/library/explanation/providers.md): Understanding the role of LLM providers in the Plexe Python library.
- [Datasets and Schemas](https://docs.plexe.ai/pages/library/explanation/datasets_schemas.md): How Plexe handles data inputs and defines the structure of ML models.
- [Callbacks and Logging](https://docs.plexe.ai/pages/library/explanation/callbacks_logging.md): Monitor and interact with the Plexe model building process through callbacks and logging.
- [API Reference](https://docs.plexe.ai/pages/library/reference/api.md): Complete reference documentation for the Plexe Python library API.
- [Callbacks Reference](https://docs.plexe.ai/pages/library/reference/callbacks.md): Detailed reference documentation for the callback system in the Plexe Python library.
- [Datasets Reference](https://docs.plexe.ai/pages/library/reference/datasets.md): Detailed reference documentation for dataset handling in the Plexe Python library.
- [Exceptions Reference](https://docs.plexe.ai/pages/library/reference/exceptions.md): Comprehensive guide to exception handling in the Plexe Python library.
- [Quickstart](https://docs.plexe.ai/pages/platform/tutorials/quickstart_api.md): Build, deploy, and use your first model using the Plexe Platform REST API.
- [Manage
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