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How to scaffold a RAG evaluation project in one command with Ragas

Use the Ragas quickstart CLI to generate a complete example project for evaluating a RAG system.

Short answer: Install Ragas with pip install ragas; then run ragas quickstart to list the available templates; run ragas quickstart rag_eval -o ./my-project to create the RAG evaluation project. All steps.

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Who it is forA developer who has a RAG app and no evaluation setup yet.

The problemStarting an eval harness from a blank file is slow and easy to get wrong.

The outcomeA ready-to-edit RAG evaluation project in a folder you choose.

What you need first

  • Python with pip
  • An LLM provider key for LLM-based metrics, for example OPENAI_API_KEY in the README example
  • uv or another way to run the quickstart CLI is optional

Steps

  1. Install Ragas with pip install ragas
  2. Run ragas quickstart to list the available templates
  3. Run ragas quickstart rag_eval -o ./my-project to create the RAG evaluation project
  4. Open the project, point it at your own RAG pipeline and run it

Prompt for your agent

Using vibrantlabsai/ragas, install it with `pip install ragas`, run `ragas quickstart rag_eval -o ./my-project`, then wire the generated project to <my RAG pipeline> and explain each file it created.

Caveat. Only the rag_eval template is available today; agent, benchmark, prompt and workflow templates are listed as coming soon.

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Using the Deduped MCP, get the setup steps for vibrantlabsai/ragas and "Scaffold a RAG evaluation project in one command"

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