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Verisim

Context-aware synthetic data for Python. The name comes from “verisimilitude,” meaning “the appearance of being real.”

Verisim generates coherent Pydantic domain objects with shared context. A person's name, username, email, address, job, company, and bio fit together, making the data useful for demos, seed data, tests, and synthetic datasets. Generation runs offline by default, supports reproducible seeds, and uses non-routable synthetic contact details.

Status: early prototype (alpha). See the feature guides below for usage and current limits.

Install

Requires Python 3.11 or newer.

uv add verisim
# Or:
python -m pip install verisim

Quickstart

from verisim import PersonRecord, Verisim

record = Verisim(locale="en_US", seed=123).generate(PersonRecord)

print(record.person.name)
print(record.contact.email)
print(record.model_dump_json())
verisim person-record --seed 123

Feature Guides

Feature README
Core models, context, related datasets, schema inference, statistical profiles, QA modes, CLI, and activity streams Core generation
JSON/JSONL, CSV, SQL, SQLite, Parquet, Arrow, Avro, and schema contracts Exports
Locale, script, lite/full data packs, and data provenance Data packs
Coherent replacements for existing tabular PII Masking
SQLAlchemy, Django, and pytest factories and fixtures Framework integrations
Offline instruction pairs, classification, NER, chat datasets, and custom adapters AI training
Measured API branch scenarios and replayable fixture bundles API fixtures
Runnable examples and a comparison with independent random fields Examples

Contributing

See CONTRIBUTING.md for source setup and local checks, CHANGELOG.md for changes, and RELEASING.md for release instructions.

License

MIT.

Metadata

Release files for verisim 0.2.1

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