A cookiecutter template for deploying spaCy NLP models as FastAPI services compatible with Azure Search Custom Skills.
cookiecutter-spacy-fastapi is a Cookiecutter template that generates a ready-to-deploy FastAPI service for spaCy natural language processing models. It solves the problem of quickly turning NLP models into production APIs, specifically tailored for integration as Custom Cognitive Skills in Azure Search. The template provides a standardized structure, Docker support, and compliance with Azure's interface requirements.
Data scientists, ML engineers, and developers who need to deploy spaCy NLP models as scalable APIs, especially those working within the Azure ecosystem or building search-related cognitive services.
Developers choose this template because it drastically reduces the time and effort required to productionize spaCy models, offering a battle-tested, Azure-compatible setup with best practices like FastAPI for performance and Docker for deployment consistency.
Cookiecutter API for creating Custom Skills for Azure Search using Python and Docker
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
The template generates APIs compliant with Azure Search Custom Skill interface, enabling seamless integration as documented in the README for easy deployment in Azure pipelines.
Leverages FastAPI for high-performance, automatically documented endpoints with type safety, reducing development time and ensuring production readiness.
Provides a structured template to package and serve pre-trained or custom spaCy NLP models, minimizing boilerplate code for quick API generation.
Includes Docker configuration for consistent deployment across environments, facilitating cloud and local testing with minimal setup effort.
Primarily designed for Azure Search, making it less flexible for deployments on other platforms without significant code modifications and interface adjustments.
Only supports spaCy models out of the box, so integrating other NLP libraries like NLTK or TensorFlow requires extensive customization of the generated codebase.
Requires users to install and understand Cookiecutter, adding an extra step for those unfamiliar with template generators, which can slow down initial adoption.