// the take
A collection of reference RAG pipelines, notebooks, and microservice examples built around NVIDIA's NIM ecosystem (NeMo, TensorRT, Triton). Aimed at teams who already have GPU infrastructure and want to wire NVIDIA's stack into LangChain, LlamaIndex, or Haystack without starting from scratch. This is not a library — it's a demo catalog.
The Docker Compose setup for basic RAG genuinely works end-to-end in a few commands, which is rarer than it should be for vendor example repos. The knowledge graph RAG example with RAPIDS is technically interesting and not something you'll find in the generic LangChain tutorials. The observability tooling (OpenTelemetry + Jaeger) is included rather than left as an exercise. The data flywheel section — fine-tune, evaluate, guardrail in one loop — reflects how production ML teams actually work.
Heavy NVIDIA API key dependency means most examples silently fail or require paid NIM endpoints before you can evaluate whether the architecture is even right for your use case. The repo is more catalog than codebase: dozens of notebooks with no shared abstractions, so you copy-paste rather than import. Notebook-first means there's no obvious path from 'ran the demo' to 'deployed this properly' — production hardening is entirely on you. The community examples (Azure, SQL Server) are one-offs with no maintenance signal; some already look stale relative to the main examples.
We read the README, the directory tree and a key source file in July 2026. Nobody pays to be listed. How we judge →