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# GitHub Models - Limited Public Beta

Welcome to your shiny new Codespace for interacting with GitHub Models! We've got everything fired up and ready for you to explore AI Models hosted on Azure AI.

The git history is a nearly-blank canvas; there's a single initial commit with the contents you're seeing right now - where you go from here is up to you!

Everything you do here is contained within this one codespace. There is no repository on GitHub yet. When you’re ready, you can click "Publish Branch" and we’ll create your repository and push up your project. If you were just exploring and have no further need for this code, you can simply delete your codespace and it's gone forever.

For more information about the Models available on GitHub Models, check out the [Marketplace](https://github.com/marketplace/models).

When bringing your application to scale, you must provision resources and authenticate from Azure, not GitHub. Learn more about deploying models to meet your use case with Azure AI.

## Getting Started

There are a few basic examples that are ready for you to run. You can find them in the [samples directory](samples/README.md). If you want to jump straight to your favorite language, you can find the examples in the following directories:

- [JavaScript](samples/js/README.md)
- [Python](samples/python/README.md)
- [cURL](samples/curl/README.md)

If you are already familiar with the GitHub Models service, you can start by running our Cookbook examples. You can find them in the [cookbooks directory](cookbooks/README.md). Here are the direct links to the available languages (at this point only Python):

- [Python](cookbooks/python/README.md)

## Disclosures

Remember when interacting with a model you are experimenting with AI, so content mistakes are possible.

The feature is subject to various limits (including requests per minute, requests per day, tokens per request, and concurrent requests) and is not designed for production use cases.

GitHub Models uses [Azure AI Content Safety](https://azure.microsoft.com/en-us/products/ai-services/ai-content-safety). These filters cannot be turned off as part of the GitHub Models experience. If you decide to employ models through a paid service, please configure your content filters to meet your requirements.

This service is under GitHub’s [Pre-release Terms](https://docs.github.com/en/site-policy/github-terms/github-pre-release-license-terms). Your use of the GitHub Models is subject to the following [Product Terms](https://www.microsoft.com/licensing/terms/productoffering/MicrosoftAzure/allprograms) and [Privacy Statement](https://www.microsoft.com/licensing/terms/product/PrivacyandSecurityTerms/MCA). Content within this Repository may be subject to additional license terms.
# proyecto locot - Orquestador de Modelos de IA y APIs
Este proyecto integra y orquesta múltiples modelos de IA (OpenAI, Gemini) y APIs de Google Cloud, permitiendo comparar, fusionar y enriquecer respuestas para asistentes inteligentes, chatbots y automatización avanzada.

Mejoras y características recientes
Orquestador de IA: Script que consulta OpenAI y Gemini, compara y fusiona respuestas usando lógica propia o IA.
Fusión inteligente: Las respuestas de ambos modelos se combinan para ofrecer una respuesta más clara y completa.
Seguridad: Las credenciales y claves API se gestionan mediante .env y están protegidas por .gitignore.
Compatibilidad: Entorno preparado para Node.js, Python y cURL, con ejemplos y cookbooks en cada lenguaje.
Integración con Google Cloud: Listo para ampliar con APIs como Dialogflow, Translation, Vision, Speech-to-Text, Natural Language, etc.
Estructura organizada: Directorios separados para ejemplos, cookbooks, subproyectos y documentación.
Documentación ampliada: README actualizado con propósito, ejemplos, buenas prácticas, aspectos legales, troubleshooting y arquitectura para nuevas integraciones.
Estructura del proyecto
samples/: Ejemplos en JS, Python y cURL para modelos y APIs.
cookbooks/: Recetarios avanzados para flujos complejos.
locot/: Subproyecto y punto de entrada para personalización y despliegue.
.env: Variables de entorno (no se sube a git).
Archivos de configuración: package.json, tsconfig.json, .eslintrc.json, .prettierrc.json.
Próximos pasos sugeridos
Agregar interfaz de usuario web o CLI interactiva.
Integrar nuevas APIs de Google Cloud (Dialogflow, Vision, etc.).
Documentar cada integración y flujo de trabajo relevante.
Publicar el proyecto en GitHub y mantener la documentación actualizada.
Este README debe mantenerse actualizado con cada mejora, integración o cambio relevante en el proyecto.