Customized Elasticsearch
Provides a custom Elasticsearch service built on Python, offering tailored data retrieval tools via the FastMCP protocol.
Acerca de
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.
Características Principales
- Integrates Prometheus for monitoring
- Supports keyword search, secondary filtering, and ID-based queries
- Provides FastMCP protocol tools for news retrieval
- Utilizes Redis for server-side session storage
- 0 GitHub stars
- Includes Docker and Docker Compose support
Casos de Uso
- Rapidly prototyping custom data retrieval services with Elasticsearch
- Integrating Elasticsearch data with FastMCP clients
- Providing a standardized API for news search and retrieval
