fintom8

LiteLLM connector for Gemini / Vertex AI / OpenAI / Azure. Chat, stream, and document extract. Students install with pip and call a few methods — keys stay in .env.

Requires Python 3.14+.

Install from Git (recommended for private use)

GitHub Packages Python upload is currently unreliable (SSL issues). Install directly from this repo instead:

# HTTPS (use a PAT with repo read access if the repo is private)
pip install "git+https://github.com/NikolaienkoIgor/f8_templates.git#subdirectory=fintom8"

# Pin a tag / commit
pip install "git+https://github.com/NikolaienkoIgor/f8_templates.git@fintom8-v0.1.45#subdirectory=fintom8"

# SSH (no token in the URL if your SSH key is set up)
pip install "git+ssh://git@github.com/NikolaienkoIgor/f8_templates.git#subdirectory=fintom8"

Private HTTPS with an explicit token:

pip install "git+https://<GITHUB_USERNAME>:<GITHUB_PAT>@github.com/NikolaienkoIgor/f8_templates.git#subdirectory=fintom8"

Other install options

# Public PyPI (when published)
pip install fintom8

# Local editable install from this checkout
pip install -e ./fintom8
# or: pip install -e "./fintom8[dev]"

import asyncio
from fintom8 import LLM

async def main():
    llm = LLM()  # reads .env / environment
    print((await llm.achat("Summarize this invoice")).text)

asyncio.run(main())

Configuration

Resolution order: constructor kwargs / LLMConfig > environment > defaults.

Copy .env.example to .env in your project (never commit it).

For azure/<deployment>, missing api_key, api_base, or api_version raises Fintom8Error (set via constructor or AZURE_* env vars).

from fintom8 import LLM, LLMConfig

llm = LLM()  # env defaults
llm = LLM(model="gpt-4o", api_key="sk-...", temperature=0)
llm = LLM(LLMConfig(
    model="azure/my-deploy",
    api_key="...",
    api_base="https://....openai.azure.com",
    api_version="2024-10-21",
))

Switch provider

Contributors

• Igor Nikolaienko
• Harsh Bansal

Usage

import asyncio
from fintom8 import LLM, facturx_to_bt_json, zugferd_to_bt_json

async def main():
    llm = LLM()

    resp = await llm.achat("Hello")
    print(resp.text, resp.usage)

    # Structured output — file only; detect format → LiteLLM SO → cleanse dates
    invoice_rf = {
        "type": "json_schema",
        "json_schema": {
            "name": "Invoice",
            "strict": True,
            "schema": {
                "type": "object",
                "properties": {
                    "total": {"type": "number"},
                    "vendor": {"type": "string"},
                    "invoiceDate": {"type": ["date", "null"]},
                },
                "required": ["total", "vendor", "invoiceDate"],
                "additionalProperties": False,
            },
        },
    }
    data = await llm.astructured(
        "invoice.pdf",  # also: .png/.jpg, .txt/.csv/.xml/.xlsx, or bytes
        structuredOutput=invoice_rf,
        dateFormat="DD.MM.YYYY",
    )
    # {"total": 42.5, "vendor": "Acme", "invoiceDate": "08.08.2026"}

    # Invoice / EN16931 / chem ping-pong (keyword args)
    report = await llm.agenerate_and_validate(name="invoice", source="invoice.pdf")
    # {"correct": true, "name": "invoice", "format": "json", "artifact": {…}, …}

    en16931 = await llm.agenerate_and_validate(
        name="en16931", source="invoice.pdf", invoice_format="ubl"
    )
    # en16931["artifact"] is the last UBL XML string; requires fintom8[schematron]

    chem = await llm.agenerate_and_validate(name="chemical_composition", source="cert.pdf")
    # Prefer a path for source; pass filePath= only when source is raw bytes

    # Document → UBL / ZUGFeRD XML (LLM generate only; no Schematron loop)
    ubl_xml = await llm.aconvert("invoice.pdf", invoice_format="ubl")
    zugferd_xml = await llm.aconvert("invoice.csv", invoice_format="zugferd")
    # also: .json / .xml / images / .xlsx — or bytes with mime= / filePath=

    # ZUGFeRD / Factur-X CII XML → EN 16931 BT JSON (CPU; sync is fine)
    bt = zugferd_to_bt_json("invoice.xml")  # or XML text / bytes
    # bt["BT-1"] == {"description": "Invoice number", "value": "INV-1"}

    # Factur-X CII or UBL → factur-x field ids (no XSD check)
    fx = facturx_to_bt_json("invoice.xml")
    # fx["BT-1"] == {"description": "Invoice Number", "value": "INV-1"}

    # aprocess() is an alias of astructured()
    data = await llm.aprocess(
        "invoice.pdf",
        structuredOutput=invoice_rf,
        dateFormat="DD.MM.YYYY",
        instructions="Extract invoice vendor and total.",
    )

    async for chunk in llm.astream([{"role": "user", "content": "Write a haiku"}]):
        print(chunk, end="", flush=True)

    resp = await llm.aextract("invoice.pdf", response_format=invoice_rf)

asyncio.run(main())

LLM / network APIs are async-only: achat, astream, aextract, astructured, aprocess, aconvert, agenerate_and_validate, acorrect_and_validate, acorrect_facturx_from_pdf, acompare, adetect_pii, aanonymize.

CPU helpers (validate / PDF / XML mapping) stay sync, with async a* twins via asyncio.to_thread for FastAPI: validate/avalidate, create_pdfa3/acreate_pdfa3, extract_xml/aextract_xml, mask_pdf/amask_pdf, facturx_to_bt_json/afacturx_to_bt_json, zugferd_to_bt_json/azugferd_to_bt_json, …

Optional helpers: detect_format, fields_to_schema, compile_fields, prepare_response_format, apply_cleanse, json_schema_response_format, structured_output, enforce_strict, inline_refs, acompare.

Compare two sources

LLM-based dual-source comparison with built-in profiles or a custom schema:

import asyncio
from fintom8 import LLM

async def main():
    llm = LLM()

    # PDF/image/document vs invoice XML (payment-critical mismatches)
    report = await llm.acompare("invoice.pdf", "invoice.xml", profile="document_xml")
    # {"matches": true, "total_mismatches": 0, "mismatches": []}

    # Mapping document vs reference rules text (Peppol gap analysis step 1)
    findings = await llm.acompare(
        "mapping.html",
        "PEPPOL-EN16931-UBL.sch",
        profile="mapping_reference",
        max_findings=5,
        model="gemini/gemini-3.1-pro-preview",
    )

    # Two invoice XMLs — structural diffs + calculation fixes (e.g. BR-CO-15)
    xml_report = await llm.acompare(
        "invoice_original.xml",
        "invoice_corrected.xml",
        profile="xml-comparison",
        max_findings=10,
    )

    # Custom comparison
    custom = await llm.acompare(
        doc_a,
        doc_b,
        profile="custom",
        instructions="Compare these two contracts for clause differences.",
        structuredOutput={"differences": {"type": "array", "items": {"type": "string"}}},
    )

asyncio.run(main())

Register additional profiles with fintom8.comparison.register_compare_profile(). See examples/compare_document_xml.py and examples/compare_xml.py.

Bundled templates

import asyncio
from fintom8 import LLM
from fintom8.templates import invoice
# or: from fintom8 import templates; templates.invoice
# or: from fintom8 import use_template; use_template("invoice")

async def main():
    llm = LLM()
    data = await llm.astructured(
        "invoice.pdf",
        structuredOutput=invoice["structuredOutput"],
        systemPrompt=invoice["systemPrompt"],
        dateFormat=invoice.get("dateFormat", "YYYY-MM-DD"),
    )

asyncio.run(main())

list_templates() lists packaged names (invoice, chemical_composition, recipient_statement). Pass a path or dict to use_template for custom templates.

Validation

One engine, one envelope. Document types differ only by the pack registered for that name. ERP check is a second step on the same envelope — not a different API.

from fintom8 import validate, erp_check

report = validate("invoice", payload)
report = validate("chemical_composition", payload)
report = validate("recipient_statement", payload)

# EN16931 Schematron on UBL or CII XML (format is detected; requires fintom8[schematron])
en16931 = validate("en16931", {"xml": ubl_or_cii_xml})
# or: validate("en16931", {"xml_path": "invoice.xml"})

# Factur-X EN 16931 XSD on CII XML (ZUGFeRD Comfort successor; requires fintom8[xsd])
facturx = validate("facturx_en16931", {"xml": cii_xml})
# UBL 2.1 XSD on Invoice or CreditNote XML (same optional extra)
ubl = validate("ubl_2.1", {"xml": ubl_xml})

# ERP check is named + injected. qc55 is SAP vs chemical coils only.
qc55 = erp_check("qc55", chem_payload, reference=qc55_rows)  # bundled qc55.csv if omitted

Every call returns:

{
  "correct": false,
  "name": "invoice",
  "format": "json",
  "errors": [{"path": "lineItems[0].totalPriceWithTax", "message": "...", "code": "FORMULA"}],
  "warnings": [],
  "artifact": {},
  "units": null,
  "_debug": null
}

artifact is the document (invoice JSON, chemical extraction, recipient JSON, or EN16931 XML string). format is json, ubl, or cii. Chemical and QC55 put per-coil reports in units (including a single coil). Invoice rewritten totals live in artifact (the caller's payload is not mutated). QC55 match rows live in units[coil_id].artifact. EN16931 format is ubl or cii; Schematron rule ids (e.g. BR-CO-15) are in errors[].code and XPath in errors[].path. _debug is {attempts, exhausted, raw} when include_debug=True. agenerate_and_validate(..., include_debug=True) also adds _debug.history: one {attempt, correct, errors, warnings} per ping-pong iteration, so a later pass still shows why earlier attempts failed. Top-level errors is only the last attempt.

Install Schematron support with:

pip install "fintom8[schematron]"

PDF/A-3 (embed invoice XML into a PDF) and PDF text masking are available via:

pip install "fintom8[pdf]"

from pathlib import Path
from fintom8 import create_pdfa3, extract_xml, get_facturx_xml_from_pdf, mask_pdf

pdfa3_bytes = create_pdfa3("invoice.pdf", "invoice.xml")
# Re-embedding replaces any prior *.xml attachments (non-XML attachments kept).
xml = extract_xml(pdfa3_bytes)  # or extract_xml("invoice_zugferd.pdf")
# factur-x-style extract: (filename, xml_bytes) or (False, False)
name, xml_bytes = get_facturx_xml_from_pdf("invoice_zugferd.pdf")

masked = mask_pdf(
    "invoice.pdf",
    {"Acme GmbH": "Demo Corp", "DE123456789": "DE000000000"},
)
Path("masked.pdf").write_bytes(masked.pdf_bytes)

mask_pdf finds each original string on the page, paints a cover box with the replacement, and by default raster-flattens so the original text layer is destroyed. Hit counts are in masked.applied_counts.

To detect PII with the LLM and then mask (anonymizer endpoint), use detect_pii plus mask_pdf, or anonymize_pdf for both steps:

import asyncio
from pathlib import Path
from fintom8 import LLM, mask_pdf

async def main():
    llm = LLM()

    # LLM call only — same mapping the anonymizer endpoint feeds into mask_pdf
    items = await llm.adetect_pii("invoice.pdf")
    mapping = {item.original: item.replacement for item in items}
    masked = mask_pdf("invoice.pdf", mapping)

    # Or one shot: detect + mask
    result = await llm.aanonymize("invoice.pdf")
    Path("anonymized.pdf").write_bytes(result.pdf_bytes)
    # result.items, result.mapping, result.applied_counts

asyncio.run(main())

See examples/anonymize_pdf.py.

Bundled EN16931 XSLT files are licensed under EUPL 1.2 (CEN). Default pip install fintom8 does not require Saxon or pikepdf.

Chemical composition document validation does not call QC55; sequence them explicitly:

from fintom8 import validate, erp_check

chem = validate("chemical_composition", extracted)
qc55 = erp_check("qc55", extracted)  # or erp_check("qc55", extracted, reference=live_rows)

Optional aliases: validate_invoice, validate_recipient_statement, validate_en16931, validate_facturx_en16931, validate_ubl, erp_check_qc55 — same envelope as the named calls above.

Generate + validate (ping-pong)

One standard wrapper. name selects SO (JSON schema) or NON-SO (XML); source is the document path or bytes. Same Validation Wrapper loop: validate → on error, correction prompt + regenerate → until correct or max attempts. For en16931, the validate step runs Schematron and the matching XSD together: Factur-X EN 16931 when invoice_format is "zugferd" (or cii / factur-x), and UBL 2.1 when it is "ubl". The corrector receives both error lists and the XML once.

import asyncio
from fintom8 import LLM

async def main():
    llm = LLM()
    report = await llm.agenerate_and_validate(name="invoice", source="invoice.pdf", include_debug=True)

    report = await llm.agenerate_and_validate(
        name="en16931", source="invoice.pdf", invoice_format="ubl", include_debug=True
    )
    # report["artifact"], report["_debug"]["attempts"]

    report = await llm.agenerate_and_validate(name="chemical_composition", source="cert.pdf")

asyncio.run(main())

Packs stay pure (no LLM/retry inside validation/). Pass filePath= / mime= only when source is raw bytes.

Correct Factur-X PDF (extract → validate → fix → PDF/A-3)

For an existing Factur-X / ZUGFeRD PDF: extract the embedded XML, run the EN16931 cleanse / Schematron (+ XSD) / LLM correction loop on the XML only (PDF is not sent to the LLM), then create_pdfa3 (any prior *.xml attachments are removed and replaced). Attempt 1 uses the extracted XML (no generate-from-scratch).

import asyncio
from pathlib import Path
from fintom8 import LLM

async def main():
    llm = LLM()
    report = await llm.acorrect_facturx_from_pdf("invoice_facturx.pdf", include_debug=True)
    corrected_xml = report["artifact"]       # str
    Path("corrected.pdf").write_bytes(report["pdfa3"])  # PDF/A-3 bytes

asyncio.run(main())

See examples/correct_facturx_from_pdf.py.

Convert document → UBL / ZUGFeRD XML (generate only)

Same idea as the platform invoice-agent generate step without the Schematron loop. For generate + validate + fix, use agenerate_and_validate(name="en16931", source=…) instead.

import asyncio
from fintom8 import LLM, aconvert_to_xml

async def main():
    llm = LLM()
    ubl = await llm.aconvert("invoice.pdf", invoice_format="ubl")
    cii = await llm.aconvert("invoice.json", invoice_format="zugferd")  # aliases: cii, factur-x
    # or: await aconvert_to_xml(llm, "invoice.pdf", invoice_format="ubl")

asyncio.run(main())

See examples/convert_to_xml.py, examples/validate_en16931.py.

See examples/invoice_extraction_fintom8.py, examples/generate_and_validate.py, examples/validate_invoice.py, examples/validate_chemical_composition.py, examples/validate_recipient_statement.py, examples/validate_en16931.py, examples/erp_check_qc55.py.

Also exported: Invoice, LineItem, ValidationReport, FieldError, validation_payload_from_llm. Packs are pure (dict in, report out) — no LLM, HTTP, or retry loops.

Failures raise Fintom8Error.

If you see an authentication error (for example missing GEMINI_API_KEY, OPENAI_API_KEY, or Vertex setup), that means package import and retries are working; configure credentials for the selected LLM_MODEL.

See examples/chat.py and examples/extract.py.

Publish (maintainers)

1. Install dev extras and run tests:

   cd fintom8
   pip install -e ".[dev]"
   pytest
   python -c "from fintom8 import LLM"
   

2. Build:

   python -m build
   

3. Upload to TestPyPI first, then PyPI:

   python -m twine upload --repository testpypi dist/*
   python -m twine upload dist/*
   

4. Tag for CI Trusted Publishing (OIDC). Create the PyPI project once and add a GitHub environment pypi with Trusted Publisher pointing at .github/workflows/publish-fintom8.yml. Then:

   git tag fintom8-v0.1.45
   git push origin fintom8-v0.1.45