lei-enricher 0.1.0

Batch-validate and enrich LEI codes via GLEIF API — usable as a library, CLI, or web-app backend

LEI Enricher

Batch-validate and enrich LEI codes from Excel / CSV files using the GLEIF API, with optional fallback provider.

Built for KYC / AML / compliance teams who maintain spreadsheets with Legal Entity Identifiers.

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Features

• 🔍 GLEIF API — batched lookups (up to 200 LEIs per request), rate-limit friendly
• 💾 SQLite cache — configurable TTL (default 14 days), avoids redundant API calls
• 📊 Excel / CSV / ODS input & output
• 🖥️ Desktop GUI (PySide6) — optional, install with [gui]
• 🌐 Web-app ready — use enrich_dataframe() directly in Flask / FastAPI / Celery
• ⚡ CLI — lei-enrich input.xlsx --output enriched.xlsx
• 🔄 Fallback — optional HTML scrape of lei-lookup.com for GLEIF misses

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Installation

Core (no GUI — suitable for servers & web apps)

pip install lei-enricher

With Desktop GUI

pip install "lei-enricher[gui]"

With ODS (LibreOffice Calc) support

pip install "lei-enricher[ods]"

With Flask web backend

pip install "lei-enricher[web]"

Everything

pip install "lei-enricher[all]"

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Quickstart

As a Python library

import pandas as pd
from lei_enricher import enrich_dataframe

df = pd.read_excel("my_leis.xlsx")
result = enrich_dataframe(df)
result.to_excel("enriched.xlsx", index=False)

Single LEI lookup

from lei_enricher import GleifClient

client = GleifClient()
results = client.lookup_batch(["HWUPKR0MPOU8FGXBT394", "5493001KJTIIGC8Y1R12"])
for lei, info in results.items():
    print(lei, info.entity_status, info.next_renewal_date)

CLI

lei-enrich input.xlsx --output enriched.xlsx
lei-enrich input.xlsx --lei-col "Company LEI" --sheet "Sheet1" --fallback

Desktop GUI

lei-enricher          # requires: pip install "lei-enricher[gui]"

Flask web app (minimal example)

from flask import Flask, request, send_file
import pandas as pd
from lei_enricher import enrich_dataframe
import io

app = Flask(__name__)

@app.post("/enrich")
def enrich():
    file = request.files["file"]
    df = pd.read_excel(file)
    result = enrich_dataframe(df)
    buf = io.BytesIO()
    result.to_excel(buf, index=False)
    buf.seek(0)
    return send_file(buf, download_name="enriched.xlsx", as_attachment=True)

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enrich_dataframe() parameters

Parameter	Default	Description
df	—	Input DataFrame with LEI column
lei_col	auto-detect	Column name containing LEI codes
status_col	"Entity Status"	Output column for entity status
renewal_col	"Next Renewal Date"	Output column for next renewal date
cache_db	~/lei_cache.sqlite	SQLite cache path
cache_days	14	Cache TTL in days
gleif_batch_size	200	LEIs per GLEIF API request
gleif_throttle_s	0.2	Delay (seconds) between GLEIF batches
fallback_enabled	False	Use lei-lookup.com for GLEIF misses
fallback_throttle_s	1.0	Delay (seconds) between fallback requests
progress_callback	None	callback(done, total) for progress tracking

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Output columns

The enriched file adds two columns immediately to the right of the LEI column:

• Entity Status — e.g. ACTIVE, LAPSED, MERGED, RETIRED
• Next Renewal Date — ISO 8601 date string, e.g. 2025-12-31

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Project structure

src/lei_enricher/
  __init__.py     — public API
  core.py         — GleifClient, LeiLookupFallback, LeiResult
  cli.py          — enrich_dataframe() + CLI entry point
  cache.py        — SQLite cache
  io_excel.py     — Excel/CSV/ODS read & write
  gui.py          — PySide6 desktop GUI (optional)
  main.py         — GUI entry point (optional)
tests/

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License

GPL-3.0-or-later — see LICENSE.