This repository contains a lightweight demonstration of LLMOps concepts using a toy retrieval‑augmented generation (RAG) evaluation. The goal is to provide a reproducible example for running an evaluation pipeline, producing reports, and integrating with continuous integration (CI) workflows.
code/llmops_trend_demo.py– a standalone Python script that runs a toy RAG evaluation. It reads a small in‑memory corpus, answers a set of questions using simple heuristics, and measures groundedness, latency and cost.reports/– this directory is created when the demo script runs. Each execution writes a markdown report with a filename of the form<date>-ragops-eval.mdcontaining summary statistics and detailed results.
To run the evaluation, execute the script from the project root:
python code/llmops_trend_demo.pyThis will create a report in the reports/ directory. The script requires
only the Python standard library and should run on any modern Python 3
interpreter.
In a production environment, you would replace the toy retrieval and heuristic answer generation with calls to real vector databases and language models. The evaluation metrics could be extended to include factual accuracy, toxicity, and cost tracking based on actual API usage. The resulting markdown report can be uploaded as a build artifact, emailed to stakeholders, or displayed in dashboards.