chatan 0.2.0

Create synthetic datasets with LLM generators and samplers

Chatan

Create diverse, synthetic datasets. Start from scratch or augment an existing dataset. Simply define your dataset schema as a set of generators, typically being LLMs with a prompt describing what kind of examples you want.

Installation

Basic installation (includes OpenAI, Anthropic, and core functionality):

pip install chatan

With optional features:

# For local model support (transformers + PyTorch)
pip install chatan[local]

# For advanced evaluation features (semantic similarity, BLEU score)
pip install chatan[eval]

# For all optional features
pip install chatan[all]

Getting Started

import chatan

# Create a generator
gen = chatan.generator("openai", "YOUR_API_KEY")

# Define a dataset schema
ds = chatan.dataset({
    "topic": chatan.sample.choice(["Python", "JavaScript", "Rust"]),
    "prompt": gen("write a programming question about {topic}"),
    "response": gen("answer this question: {prompt}")
})

# Generate the data with a progress bar
df = ds.generate(n=10)

Generator Options

API-based Generators (included in base install)

# OpenAI
gen = chatan.generator("openai", "YOUR_OPENAI_API_KEY")

# Anthropic
gen = chatan.generator("anthropic", "YOUR_ANTHROPIC_API_KEY")

Local Model Support (requires pip install chatan[local])

# HuggingFace Transformers
gen = chatan.generator("transformers", model="microsoft/DialoGPT-medium")

Examples

Create Data Mixes

from chatan import dataset, generator, sample
import uuid

gen = generator("openai", "YOUR_API_KEY")

mix = [
    "san antonio, tx",
    "marfa, tx",
    "paris, fr"
]

ds = dataset({
    "id": sample.uuid(),
    "topic": sample.choice(mix),
    "prompt": gen("write an example question about the history of {topic}"),
    "response": gen("respond to: {prompt}"),
})

Augment datasets

from chatan import generator, dataset, sample
from datasets import load_dataset

gen = generator("openai", "YOUR_API_KEY")
hf_data = load_dataset("some/dataset")

ds = dataset({
    "original_prompt": sample.from_dataset(hf_data, "prompt"),
    "variation": gen("rewrite this prompt: {original_prompt}"),
    "response": gen("respond to: {variation}")
})

Evaluation

Evaluate rows inline or compute aggregate metrics:

from chatan import dataset, eval, sample

ds = dataset({
    "col1": sample.choice(["a", "a", "b"]),
    "col2": "b",
    "score": eval.exact_match("col1", "col2")
})

df = ds.generate()
aggregate = ds.evaluate({
    "exact_match": ds.eval.exact_match("col1", "col2")
})

Advanced Evaluation (requires pip install chatan[eval])

# Semantic similarity using sentence transformers
aggregate = ds.evaluate({
    "semantic_sim": ds.eval.semantic_similarity("col1", "col2")
})

# BLEU score evaluation
aggregate = ds.evaluate({
    "bleu": ds.eval.bleu_score("col1", "col2")
})

Installation Options Summary

Feature	Install Command	What's Included
Basic	pip install chatan	OpenAI, Anthropic, core sampling, basic evaluation
Local Models	pip install chatan[local]	+ HuggingFace Transformers, PyTorch
Advanced Eval	pip install chatan[eval]	+ Semantic similarity, BLEU scores, NLTK
Everything	pip install chatan[all]	All features above

Citation

If you use this code in your research, please cite:

@software{reetz2025chatan,
  author = {Reetz, Christian},
  title = {chatan: Create synthetic datasets with LLM generators.},
  url = {https://github.com/cdreetz/chatan},
  year = {2025}
}

Contributing

Community contributions are more than welcome, bug reports, bug fixes, feature requests, feature additions, please refer to the Issues tab.