ArthurSrz/open_codes

Open Codes

Open dataset of French legal code articles with embeddings.

Chunked articles from French legal codes sourced from Legifrance via the PISTE API, with 1024-dimensional embeddings generated by Mistral AI.

Each row is a text chunk enriched with full article metadata from the parent legal code article.

Dataset Description

• —Source: PISTE Legifrance API (official French government legal database)
• —License: Licence Ouverte / Etalab 2.0
• —Embeddings: Mistral AI mistral-embed (1024 dimensions)
• —Update frequency: Daily (nightly sync at 02:00 UTC, dataset push at 07:00 UTC)
• —Codes: Dynamically sourced from LEX_codes_piste (active codes only)
• —Quality: Dedup + stale-chunk filtering applied before every push

Legal codes included

The list of legal codes is dynamic and managed in the LEX_codes_piste table. Adding a new code there (with actif=true) automatically includes it in the next sync and export.

Schema

Chunk fields

Article metadata fields (prefixed article_)

Identifiers

Content

Dates & Status

Date format: article_dateDebut and article_dateFin are stored as Unix timestamps in milliseconds (string type). This is the raw format returned by the Legifrance PISTE API.

To convert to a human-readable date in Python:

from datetime import datetime, timezone

timestamp_ms = "1301529600000"
dt = datetime.fromtimestamp(int(timestamp_ms) / 1000, tz=timezone.utc)
print(dt)  # 2011-03-31 00:00:00+00:00

Special value: 32472144000000 (year 2999) means "no end date" — the article is in force indefinitely.

Hierarchy

Extras

Configs

Usage

from datasets import load_dataset
from datetime import datetime, timezone

# Load default config (code articles)
ds = load_dataset("ArthurSrz/open_codes", split="train")

# Load jurisprudence config (court decisions)
juris = load_dataset("ArthurSrz/open_codes", "jurisprudence", split="train")

# Access a chunk with its embedding and article metadata
row = ds[0]
print(row["code_name"])                # e.g. "Code civil"
print(row["chunk_text"][:200])
print(len(row["embedding"]))           # 1024

# Convert dates from Unix ms to datetime
date_debut = datetime.fromtimestamp(int(row["article_dateDebut"]) / 1000, tz=timezone.utc)
print(date_debut)                      # e.g. 2011-03-31 00:00:00+00:00

# Filter by legal code
code_civil = ds.filter(lambda x: x["code_name"] == "Code civil")

# Filter active articles only
en_vigueur = ds.filter(lambda x: x["article_etat"] == "VIGUEUR")

# Use embeddings for semantic search
import numpy as np
query_emb = np.array(ds[0]["embedding"])

Provenance

Built by the marIAnne project. Sync pipeline fetches articles nightly from PISTE Legifrance, chunks them, and generates embeddings via Mistral AI.