Capturas de pantalla del proyecto
Resumen
The STACKIT RAG Template provides a comprehensive architecture for developing and deploying AI-driven applications. It includes multiple microservices for document extraction, administration, and RAG-backed chatting, alongside a frontend interface. The system integrates with vector databases for semantic search, S3 for object storage, and Langfuse for monitoring. It supports both local development environments using Tilt and k3d, and production deployments on Kubernetes clusters.
Características principales
- Document management with automatic fallback chain across Docling, MarkItDown, and custom extractors
- Support for multiple LLM and embedder providers (STACKIT, Ollama)
- Model Context Protocol (MCP) server for standardized AI assistant integration
- Langfuse integration for tracing, monitoring, and analytics
- S3 object storage integration
- Basic authentication for secure access
- Kubernetes-ready deployment with Helm charts
- Local development setup using Tilt and k3d with debug mode support
Requisitos, instalación y guía rápida
2. Clone the repository.
3. Copy `.env.template` to `.env` and fill in the required values (Basic Auth, S3 credentials, Langfuse keys, optional LLM API keys).
4. Build the helm chart dependencies: `cd infrastructure/rag; helm dependency update; cd ../..`
5. Start the local cluster using Tilt: `tilt up` (for production mode) or `tilt up -- --dev=true` (for development mode with live code updates).
Uso
Compatibilidad de modelos y casos de uso
Supports OpenAI-compatible LLMs via the STACKIT model serving service and Ollama as an LLM provider.
Notas sobre la licencia y los riesgos
Apache-2.0
Editorial verification 2026-08-02: repository URL, owner, description, license and repository statistics were reviewed. License metadata: Apache-2.0. README was fetched for the channel draft; re-check repository dependencies, releases and model terms before production use.
Lanzamiento y mantenimiento
Not stated in the repository metadata