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.

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-v0.1.5#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]"

from fintom8 import LLM

llm = LLM()  # reads .env / environment
print(llm.chat("Summarize this invoice").text)

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

from fintom8 import LLM

llm = LLM()

resp = llm.chat("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 = llm.structured(
    "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 = llm.produce_and_validate(name="invoice", source="invoice.pdf")
# {"ok": true, "name": "invoice", "format": "json", "artifact": {…}, …}

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

chem = llm.produce_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 = llm.convert("invoice.pdf", invoice_format="ubl")
zugferd_xml = llm.convert("invoice.csv", invoice_format="zugferd")
# also: .json / .xml / images / .xlsx — or bytes with mime= / filePath=

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

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

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

Async twins: achat, astream, aextract, astructured, aprocess, aconvert, aproduce_and_validate.

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

Bundled templates

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

data = llm.structured(
    "invoice.pdf",
    structuredOutput=invoice["structuredOutput"],
    systemPrompt=invoice["systemPrompt"],
    dateFormat=invoice.get("dateFormat", "YYYY-MM-DD"),
)

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 cross-check is a second step on the same envelope — not a different API.

from fintom8 import validate, cross_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"})

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

Every call returns:

{
  "ok": 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} only when include_debug=True.

Install Schematron support with:

pip install "fintom8[schematron]"

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

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

from fintom8 import validate, cross_check

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

Optional aliases: validate_invoice, validate_recipient_statement, validate_en16931, cross_check_qc55 — same envelope as the named calls above.

Produce + 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 ok or max attempts.

from fintom8 import LLM

llm = LLM()
report = llm.produce_and_validate(name="invoice", source="invoice.pdf", include_debug=True)

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

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

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

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 produce_and_validate(name="en16931", source=…) instead.

from fintom8 import LLM, convert_to_xml

llm = LLM()
ubl = llm.convert("invoice.pdf", invoice_format="ubl")
cii = llm.convert("invoice.json", invoice_format="zugferd")  # aliases: cii, factur-x
# or: convert_to_xml(llm, "invoice.pdf", invoice_format="ubl")

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

See examples/invoice_extraction_fintom8.py, examples/produce_and_validate.py, examples/validate_invoice.py, examples/validate_chemical_composition.py, examples/validate_recipient_statement.py, examples/validate_en16931.py, examples/cross_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-v0.1.5
   git push origin fintom8-v0.1.5