Minor release adding support for DataSynth 3.0 features: scenario packs,
fingerprint synthesis, adversarial ONNX probing, AI-assisted config tuning,
and the dashboard co-pilot. All features verified end-to-end against the live API.

New Endpoints

Scenario Packs (client.scenarios.packs())

Eleven built-in counterfactual simulations across four categories:

packs = client.scenarios.packs()
scenario = client.scenarios.create(
    name="Q3 revenue stress",
    generation_config={
        "sector": "retail", "rows": 10000,
        "scenarios": {"enabled": True, "packs": ["channel_stuffing"]},
    },
)
client.scenarios.run(scenario.id)
diff = client.scenarios.diff(scenario.id)

AI Tuning (client.jobs.tune(), Scale+)

suggestion = client.jobs.tune(job_id, target_scores={"overall": 0.95})
print(suggestion.explanation)
# -> {original_config, suggested_config, explanation, quality_summary}

Dashboard Co-pilot (client.ai.chat(), Scale+)

reply = client.ai.chat("Which fraud packs are right for audit training?")

Fingerprint Synthesis (client.fingerprint.synthesize(), Team+)

# Privacy-preserving synthesis from a .dsf fingerprint
submission = client.fingerprint.synthesize(
    "./private_data.dsf",
    rows=10000,
    backend="statistical",  # or "neural"/"hybrid" (Scale+)
)

Adversarial Probing (client.adversarial.probe(), Enterprise)

# Probe an ONNX fraud detector for decision-boundary weaknesses
probe = client.adversarial.probe("./model.onnx", n_probes=10000)
results = client.adversarial.results(probe.id)

Config-side DS 3.0 features (no SDK changes needed)

• Neural diffusion: diffusion.backend = "neural" | "hybrid" with neural.* subsection (Scale+)
• Quality gates: qualityGates.profile = "standard" | "strict" | "audit" (Team+)
• Custom interventions: scenarios.interventions[].target/value/timing (Scale+)

Upstream DataSynth fixes now live

• OCPM fields populated on JE headers — ocpm_event_ids, ocpm_object_ids, ocpm_case_id now carry full process mining metadata (was empty in 2.3.x). Verified 209/300 entries on a sample retail job.
• is_fraud on document flow records, display_name on banking customers, numericMode: native, analytics/labels/process_mining output dirs — all confirmed.

Still upstream

• exportLayout: flat hangs the DataSynth binary — use the default nested layout until upstream fix lands.

Other changes

• Default client timeout bumped 30s → 60s (generate_quick server-side limit is 30s; 30s default was too tight with network latency).
• Scenarios.create() contract updated to DS 3.0 shape ({name, generation_config}). Legacy template_id/interventions kwargs still work — auto-folded into config.

Four new examples

• scenario_packs.py
• ai_tune.py
• fingerprint_synthesis.py
• neural_diffusion.py

Full Changelog

v1.3.0...v1.4.0

Major release adding support for DataSynth 2.3 + VynFi API 2.0 features.
All features verified end-to-end against the live API.

New Endpoints

# Pre-built statistical analytics for a completed job
a = client.jobs.analytics(job_id)
print(f"Benford MAD: {a.benford_analysis.mad:.4f}")
print(f"AML coverage: {a.banking_evaluation.aml.typology_coverage:.2%}")

# Rate-controlled NDJSON streaming for TB-scale jobs (Scale tier+)
for envelope in client.jobs.stream_ndjson(job_id, rate=500, progress_interval=1000):
    if envelope.get("type") == "_progress":
        print(f"  {envelope['lines_emitted']:,} lines emitted")
    else:
        my_pipeline.send(envelope)

# Storage quota validation for TB-scale jobs
size = client.configs.estimate_size(config=my_config)
print(f"~{size.estimated_files} files, ~{size.estimated_bytes / 1e9:.1f} GB")
print(f"Tier quota: {size.tier_quota_bytes / 1e12:.1f} TB")

# Raw DataSynth YAML config submission (Scale tier+)
result = client.configs.submit_raw(yaml="rows: 1000\nsector: retail")

Transparent Archive Backends

JobArchive now seamlessly handles both legacy zip archives and the new TB-scale managed_blob manifests with presigned URLs:

archive = client.jobs.download_archive(job_id)
print(archive.backend)          # "zip" or "managed_blob"
entries = archive.json("journal_entries.json")  # lazy fetch via presigned URL if blob

DataSynth 2.3 Output Modes

job = client.jobs.generate_config(config={
    "sector": "retail",
    "rows": 1000,
    "output": {
        "exportLayout": "flat",    # one row per line, header merged ✓ verified live
        "numericMode": "native",   # JSON numbers (upstream DataSynth bug pending)
    },
})

New Models

• Analytics (15 models): JobAnalytics, BenfordAnalysis, AmountDistributionAnalysis, VariantAnalysis, BankingEvaluation, KycCompletenessAnalysis, AmlDetectabilityAnalysis, CrossLayerCoherenceAnalysis, VelocityQualityAnalysis, FalsePositiveAnalysis, TypologyDetection
• Sizing: EstimateSizeResponse, SizeBucket
• Raw config: RawConfigResponse

Bug Fixes

• CamelCase deserialization for JobFileList, JobFile, EstimateSizeResponse — were silently returning defaults when API actually had data
• Download timeout extended from 30s → 5min (was breaking on large archive downloads)

Process Mining Notebook Enhanced

05_process_mining_ocel.ipynb now covers:

• All 8 DataSynth processes (O2C, P2P, S2C, H2R, MFG, Banking, Audit, BankRecon)
• OCEL 2.0 readiness section
• Cross-process traceability via cross_process_links.json

New Examples

• analytics_export.py — pre-built analytics workflow
• ndjson_streaming.py — rate-controlled streaming for TB-scale
• native_mode.py — DataSynth 2.3 native + flat layout

New Output Categories (DataSynth 2.3)

Verification

10 of 11 server-side fixes verified live. See docs/v1.3.0-verification-report.md for details.

Full Changelog

v1.2.0...v1.3.0

What's New

Ships support for 3 API features deployed today by the API team.

File listing with schemas

List all files in a completed job's archive without downloading the full zip:

file_list = client.jobs.list_files(job_id)
print(f"{file_list.total_files} files, {file_list.total_size_bytes / 1e6:.0f} MB")

for f in file_list.files:
    cols = ", ".join(s.name for s in f.schema_[:3])
    print(f"  {f.path} ({f.size_bytes:,} bytes) [{cols}, ...]")

Output size estimates

estimate_cost() now returns expected output dimensions before you run a job:

est = client.configs.estimate_cost(config=my_config)
print(f"Credits: {est.total_credits}")
print(f"Output: ~{est.output.estimated_files} files, ~{est.output.estimated_size_bytes / 1e6:.0f} MB")
print(f"Note: {est.output.note}")

Per-file download (now working)

Download individual files from a job without pulling the full archive:

data = client.jobs.download_file(job_id, "journal_entries.json")
# Also supports subdirectory paths:
data = client.jobs.download_file(job_id, "banking/banking_customers.json")

New types

• JobFileList, JobFile, FileSchema -- file listing response models
• OutputEstimate -- output size estimate on EstimateCostResponse.output

Full Changelog

v1.1.0...v1.2.0

What's New

JobArchive — ergonomic archive access

Downloaded job archives are now wrapped in a JobArchive class for easy file access:

archive = client.jobs.download_archive(job_id)
archive.files()            # list all 80+ files
archive.categories()       # ['banking', 'document_flows', 'esg', ...]
archive.json("journal_entries.json")  # parse JSON directly
archive.find("esg/*")     # glob-style search
archive.summary()          # file counts and sizes by category
archive.extract_to("./output")  # extract to disk

pandas: archive_to_dataframes()

Convert all JSON files in an archive to DataFrames in one call, with automatic header/lines flattening for journal entries:

from vynfi.integrations.pandas import archive_to_dataframes

frames = archive_to_dataframes(archive)
# {'journal_entries.json': DataFrame(95881 rows), 'banking/banking_customers.json': DataFrame(620 rows), ...}

14 examples — notebooks + scripts

Plus 7 standalone scripts: quickstart, streaming, pandas workflow, config management, multi-period sessions, what-if scenarios, quality monitoring.

Bug fixes

• Config endpoints used wrong URL path (/v1/configs/... → /v1/config/... for validate, estimate-cost, compose)
• Sessions generate_next() used wrong URL path (/generate-next → /generate)

All fixes confirmed against the Rust SDK reference and live API.

Full Changelog

v1.0.0...v1.1.0

VynFi Python SDK v1.0.0

First stable release. Full API parity with the VynFi Rust SDK reference implementation.

New Resources

• Configs — save, validate, estimate cost, and compose generation configs
• Credits — purchase prepaid packs, check balance, view history
• Sessions — multi-period generation sessions
• Scenarios — what-if scenarios with causal graph templates
• Notifications — list and mark-read

New Methods on Existing Resources

• jobs.generate_config(), jobs.download_file(), jobs.wait()
• catalog.list_templates()
• billing.checkout(), billing.portal()

Ecosystem Integrations

• pip install vynfi[pandas] — download job output as DataFrames
• pip install vynfi[polars] — Polars DataFrame support

Other

• 12 resources, 52 tests, all verified against the live API
• ForbiddenError (403), dedicated QuickJobResponse / CancelJobResponse types
• All Pydantic models aligned with actual API response shapes

See CHANGELOG.md for full details.

Initial release

First public release of the VynFi Python SDK.

Features

• Full coverage of VynFi API resources: Jobs, Catalog, Usage, API Keys, Quality, Webhooks, Billing
• Automatic retry on 429/5xx with exponential backoff
• SSE streaming for job progress
• Typed responses via Pydantic v2
• Python 3.9–3.13 support

Installation

pip install vynfi

Quick start

from vynfi import VynFi

client = VynFi(api_key="vf_live_...")
job = client.jobs.generate(sector="banking", tables={"transactions": 1000})