Test data generation for function calling

[alexHeu]

Hey, I was wondering if you think it would be possible to create a synthetic dataset for function calling tasks? I would like to use that dataset for a finetuning experiment.

Thanks for any guidance that you might have!

[dosubot[bot]]

Hey @alexHeu! I'm a bot designed to assist you with bugs, questions, and contributions. How can I help you today?

Yes, it is possible to create a synthetic dataset for function calling tasks to use in a finetuning experiment. Ragas provides tools for synthetic test data generation, which can be applied to function calling tasks. Here is a relevant code snippet for generating a synthetic test dataset:

from ragas.testset.generator import TestsetGenerator
from ragas.testset.evolutions import simple, reasoning, multi_context
from langchain_openai import ChatOpenAI, OpenAIEmbeddings

TEST_SIZE = 25

# generator with openai models
generator_llm = ChatOpenAI(model="gpt-3.5-turbo-16k")
critic_llm = ChatOpenAI(model="gpt-4")
embeddings = OpenAIEmbeddings()

generator = TestsetGenerator.from_langchain(generator_llm, critic_llm, embeddings)

# set question type distribution
distribution = {simple: 0.5, reasoning: 0.25, multi_context: 0.25}
# generate testset
testset = generator.generate_with_llamaindex_docs(
    documents, test_size=TEST_SIZE, distributions=distribution
)
test_df = testset.to_pandas()
test_df.head()

Ragas employs an evolutionary generation paradigm to create diverse and complex questions from provided documents. This approach ensures comprehensive coverage of various question types, including reasoning, conditioning, and multi-context, which are crucial for robust evaluation and finetuning experiments ^[1][2].

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