VinceGx33/legi-french-law-2025

{
    'article_id': str,              # Unique LEGI identifier
    'article_num': str,             # Article number (e.g., "1836")
    'article_contenu_text': str,    # Article text content
    'article_contenu_markdown': str, # Article content in Markdown
    'texte_titre': str,             # Legislative text title (e.g., "Code civil")
    'texte_nature': str,            # Type (CODE, LOI, ORDONNANCE, DÉCRET)
    'texte_contexte': str,          # Hierarchical path (Book > Title > Section > ...)
    'texte_date_publi': str,        # Publication date
    'article_etat': str             # Legal status (always "VIGUEUR" in this dataset)
}

{
    "article_id": "LEGIARTI000006419282",
    "article_num": "1",
    "article_contenu_text": "Laws and regulations are enforceable...",
    "texte_titre": "Code civil",
    "texte_nature": "CODE",
    "texte_contexte": "Preliminary Title: On the publication..."
}

import pandas as pd
from datasets import load_dataset

# Option 1: Using Hugging Face datasets
dataset = load_dataset("VinceGx33/legi-french-law-2025", split="train")

# Option 2: Using pandas
df = pd.read_parquet("hf://datasets/VinceGx33/legi-french-law-2025/legi_french_law.parquet")

from llama_index.core import VectorStoreIndex, Document
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
import pandas as pd

# Load dataset
df = pd.read_parquet("hf://datasets/VinceGx33/legi-french-law-2025/legi_french_law.parquet")

# Create documents
documents = [
    Document(
        text=f"Article {row['article_num']}\n{row['article_contenu_text']}",
        metadata={
            "article_id": row['article_id'],
            "title": row['texte_titre'],
            "nature": row['texte_nature']
        }
    )
    for _, row in df.iterrows()
]

# French legal-specialized embeddings
embed_model = HuggingFaceEmbedding(
    model_name="OrdalieTech/Solon-embeddings-large-0.1"  # Recommended for French law
)

# Create vector index
index = VectorStoreIndex.from_documents(documents, embed_model=embed_model)

# Query
query_engine = index.as_query_engine()
response = query_engine.query("What are the conditions for divorce?")
print(response)

# Script: 1_download_legi.py
import requests

DILA_BASE_URL = "https://echanges.dila.gouv.fr/OPENDATA/LEGI"
DUMP_FILE = "Freemium_legi_global_YYYYMMDD-HHMMSS.tar.gz"  # ~1.1 GB

# Download with progress tracking
response = requests.get(f"{DILA_BASE_URL}/{DUMP_FILE}", stream=True)
# Total: 1.1 GB compressed, ~15 GB extracted (1M+ XML files)

# Script: 2b_parse_legi_xml_custom.py
import tarfile

with tarfile.open(dump_path, 'r:gz') as tar:
    tar.extractall(path=extract_dir)
# Extracts 1M+ XML files (article/*.xml, texte/code/*.xml, etc.)

<!-- texte/code/code_CODE_TEXTE_ID/article_ARTICLE_ID.xml -->
<ARTICLE>
  <META>
    <META_COMMUN>
      <ID>LEGIARTI000006419282</ID>
      <NUM>1</NUM>
      <ETAT>VIGUEUR</ETAT>  <!-- Filter criterion -->
    </META_COMMUN>
  </META>

  <CONTEXTE>
    <TEXTE>
      <TITRE>Code civil</TITRE>
      <DATE_PUBLI>1804-03-15</DATE_PUBLI>
    </TEXTE>
    <TM pos="1">
      <TITRE_TM>Titre préliminaire : De la publication...</TITRE_TM>
    </TM>
    <TM pos="2">
      <TITRE_TM>Chapitre Ier : De la promulgation...</TITRE_TM>
    </TM>
  </CONTEXTE>

  <BLOC_TEXTUEL>
    <CONTENU>Les lois et les règlements...</CONTENU>
  </BLOC_TEXTUEL>
</ARTICLE>

import xml.etree.ElementTree as ET
from pathlib import Path

def parse_article_xml(xml_path):
    tree = ET.parse(xml_path)
    root = tree.getroot()

    # Extract metadata
    article_id = root.find('.//ID').text
    article_num = root.find('.//NUM').text
    article_etat = root.find('.//ETAT').text

    # CRITICAL FILTER: Only "VIGUEUR" (in force)
    if article_etat != "VIGUEUR":
        return None  # Skip abrogated/modified articles

    # Extract legislative text info
    texte = root.find('.//TEXTE')
    texte_titre = texte.find('TITRE').text  # e.g., "Code civil"
    texte_date_publi = texte.find('DATE_PUBLI').text

    # Build hierarchy from TM elements (Livre > Titre > Chapitre > Section...)
    hierarchy_parts = []
    for tm in root.findall('.//TM'):
        titre_tm = tm.find('TITRE_TM')
        if titre_tm is not None:
            hierarchy_parts.append(titre_tm.text.strip())
    hierarchy = " > ".join(hierarchy_parts)

    # Extract article content
    contenu = root.find('.//CONTENU')
    article_content = contenu.text if contenu is not None else ""

    return {
        'article_id': article_id,
        'article_num': article_num,
        'article_contenu_text': article_content,
        'texte_titre': texte_titre,
        'texte_contexte': hierarchy,  # Full hierarchical path
        'texte_date_publi': texte_date_publi,
        'article_etat': article_etat
    }

# Process all XML files recursively
articles = []
for xml_file in Path(extract_dir).rglob('*.xml'):
    article_data = parse_article_xml(xml_file)
    if article_data:  # Only EN VIGUEUR
        articles.append(article_data)

def detect_text_nature(xml_path):
    # Classify legislative text type from file path and content
    path_str = str(xml_path)

    if '/code/' in path_str:
        return 'CODE'
    elif '/loi/' in path_str:
        return 'LOI'
    elif '/ordonnance/' in path_str:
        return 'ORDONNANCE'
    elif '/decret/' in path_str:
        return 'DECRET'
    else:
        return 'AUTRE'

import pandas as pd

df = pd.DataFrame(articles)

# Optimize data types
df['article_id'] = df['article_id'].astype('string')
df['article_num'] = df['article_num'].astype('string')
df['texte_titre'] = df['texte_titre'].astype('string')
df['texte_nature'] = df['texte_nature'].astype('category')

# Save to Parquet with compression
df.to_parquet(
    'legi_french_law.parquet',
    engine='pyarrow',
    compression='snappy',
    index=False
)

@dataset{legi_french_law_2025,
  author = {DILA - Direction de l'information légale et administrative},
  title = {LEGI - French Codes, Laws and Consolidated Regulations},
  year = {2025},
  month = {July},
  url = {https://www.data.gouv.fr/fr/datasets/legi-codes-lois-et-reglements-consolides/},
  note = {Prepared and published by @VinceGx33 on Hugging Face}
}