fiddlecube 0.1.7

Generate ideal question-answer dataset for testing your LLM.

FiddleCube - Generate ideal question-answers for testing RAG

FiddleCube generates an ideal question-answer dataset for testing your LLM. Run tests on this dataset before pushing any prompt or RAG upgrades.

Quickstart

Install FiddleCube

pip3 install fiddlecube

API Key

Get the API key here.

Usage

from fiddlecube import FiddleCube

fc = FiddleCube(api_key="<api-key>")
dataset = fc.generate(
    [
        "The cat did not want to be petted.",
        "The cat was not happy with the owner's behavior.",
    ],
    10,
)
dataset

{
  "results": [
    {
      "query": "Question: Why did the cat not want to be petted?",
      "contexts": ["The cat did not want to be petted."],
      "answer": "The cat did not want to be petted because it was not in the mood for physical affection at that moment.",
      "score": 0.8,
      "question_type": "SIMPLE"
    },
    {
      "query": "Was the cat pleased with the owner's actions?",
      "contexts": ["The cat was not happy with the owner's behavior."],
      "answer": "No, the cat was not pleased with the owner's actions.",
      "score": 0.8,
      "question_type": "NEGATIVE"
    }
  ],
  "status": "COMPLETED",
  "num_tokens_generated": 44,
  "rate_limited": false
}

Ideal QnA datasets for testing, eval and training LLMs

Testing, evaluation or training LLMs requires an ideal QnA dataset aka the golden dataset.

This dataset needs to be diverse, covering a wide range of queries with accurate responses.

Creating such a dataset takes significant manual effort.

As the prompt or RAG contexts are updated, which is nearly all the time for early applications, the dataset needs to be updated to match.

FiddleCube generates ideal QnA from vector embeddings

• The questions cover the entire RAG knowledge corpus.
• Complex reasoning, safety alignment and 5 other question types are generated.
• Filtered for correctness, context relevance and style.
• Auto-updated with prompt and RAG updates.

Roadmap

• Question-answers, complex reasoning from RAG
• Multi-turn conversations
• Evaluation Setup - Integrate metrics
• CI setup - Run as part of CI/CD pipeline
• Diagnose failures - step-by-step analysis of failed queries

More Questions?

Book a demo
Contact us at [e-mail] for any feature requests, feedback or questions.