Customized Elasticsearch

This project serves as a demonstration of a custom Elasticsearch service built on Python using the FastMCP and FastAPI frameworks. It provides a robust interface for interacting with an Elasticsearch backend, specifically designed to expose common data retrieval operations like keyword searches, secondary filtering, and ID-based lookups as standardized FastMCP tools. The service is production-ready with integrated Prometheus monitoring, Redis-backed session storage, comprehensive Docker support, and a structured architecture including unit and integration tests, making it a solid foundation for developing custom data services.

Key Features

Use Cases