Скриншоты проекта
Обзор
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.
Основные функции
- 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
Требования, установка и быстрый старт
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).
Использование
Совместимость моделей и варианты использования
Supports OpenAI-compatible LLMs via the STACKIT model serving service and Ollama as an LLM provider.
Лицензия и примечания о рисках
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.
Релиз и сопровождение
Not stated in the repository metadata