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Real-World Use Cases

Real-World Applications of RAG Pipeline Utils​

This project goes beyond traditional RAG tools — it’s a developer-focused modular framework. Here's how it’s used:

1. Customizable LLM Workflows​

Use Case: A team wants to test three different retrievers (Pinecone, Weaviate, Redis) and switch LLMs dynamically during eval.

rag-utils ingest sample.pdf --retriever pinecone --llm openai

2. Plugin-Based Evaluation Benchmarks​

Use Case: You want to run BLEU/ROUGE scoring across prompt templates or documents using CLI:

rag-utils evaluate --dataset tests/eval.json --llm anthropic

3. Internal LLM System for SaaS​

Use Case: Embed RAG processing into a backend:

import { PluginRegistry, runPipeline } from 'rag-pipeline-utils';

const registry = new PluginRegistry();
registry.register('embedder', 'openai', new OpenAIEmbedder());

const output = await runPipeline({
  loader: 'pdf',
  retriever: 'pinecone',
  llm: 'openai',
  query: 'How does this work?'
});

4. GitHub + NPM Automation for ML Pipelines​

Use Case: You want a release blog post + versioned package published automatically:

  • Commit code
  • Push to main
  • GitHub Action triggers:
    • Semantic release
    • CHANGELOG update
    • Blog post generation
    • NPM publish

Benefits​

  • Pluggable components via clean interfaces
  • CLI + programmatic access for flexible DX
  • CI-validated plugin contract enforcement
  • Docs-first developer onboarding
  • Production-ready for real ML teams

Want to contribute your use case? PRs welcome on GitHub.