README.md

---
language:
- fr
tags:
- france
- legislation
- law
- loi
- codes
- embeddings
- open-data
- government
- legifrance
pretty_name: French Consolidated Legislation Dataset (LEGI)
size_categories:
- 1M<n<10M
license: etalab-2.0
configs:
- config_name: latest
  data_files: "data/legi-20260102/*/*.parquet"
  default: true
---
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### 📢 Sondage 2026 : Utilisation des datasets publiques de MediaTech
Vous utilisez ce dataset ou d’autres datasets de notre collection [MediaTech](https://huggingface.co/collections/AgentPublic/mediatech) ? Votre avis compte ! 
Aidez-nous à améliorer nos datasets publiques en répondant à ce sondage rapide (5 min) : 👉 https://grist.numerique.gouv.fr/o/albert/forms/gF4hLaq9VvUog6c5aVDuMw/11 
Merci pour votre contribution ! 🙌

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# 🇫🇷 French Consolidated Legislation Dataset (LEGI)

This dataset contains a **semantic-ready and embedded version** of the French **full consolidated text of national legislation and regulations** as published in the official **LEGI** database by Légifrance.
The original data is downloaded from [the dedicated **DILA** open data repository](https://echanges.dila.gouv.fr/OPENDATA/LEGI) and is also published on [data.gouv.fr](https://www.data.gouv.fr/datasets/legi-codes-lois-et-reglements-consolides/).

The original and full **LEGI** dataset includes:
- All **laws, codes, decrees, circulars, deliberations, decree-laws, ordinances etc...** since 1945.
- A selection of consolidated **ministerial orders (arrêtés)**.
- All **official codes in force**, and **repealed codes**.
- And more ...
  
In this version, only the following articles are included :
- **In force** (`VIGUEUR`)
- **Modified** (`MODIFIE`) : content that has been modified, so not in force anymore
- **Abrogated deferred** (`ABROGE_DIFF`) : content still valid until the end date indicated.

Each article is chunked and vectorized using the [`BAAI/bge-m3`](https://huggingface.co/BAAI/bge-m3) embedding model, enabling use in **semantic search**, **retrieval-augmented generation (RAG)**, and **legal research** systems for example.

The dataset is splitted in subfolders by 'category' and 'code', so that the dataset is more usable for specifics use cases.

---

## 🗂️ Dataset Contents

The dataset is provided in **Parquet format** and includes the following columns:

| Column Name         | Type     | Description                                                                 |
|---------------------|----------|-----------------------------------------------------------------------------|
| `chunk_id`          | `str`    | Unique chunk identifier.                                                    |
| `doc_id`            | `str`    | LEGI article source identifier.                                             |
| `chunk_index`       | `int`    | Index of the chunk within its original document. Starting from 1.           |
| `chunk_xxh64`       | `str`    | XXH64 hash of the `chunk_text` value.                                       |
| `nature`            | `str`    | Nature of the text (e.g., `Article`).                                       |
| `category`          | `str`    | Type of legislative document (e.g., `LOI`, `DECRET`, `ARRETE`, etc.).       |
| `ministry`          | `str`    | Ministry responsible for the publication, if applicable.                    |
| `status`            | `str`    | Status of the article: `VIGUEUR` (in force), `MODIFIE` (modified) or `ABROGE_DIFF` (abrogated deferred).|
| `title`             | `str`    | Short title of the legislative document.                                    |
| `full_title`        | `str`    | Full formal title of the text.                                              |
| `subtitles`         | `str`    | Subtitle(s) indicating article grouping or section.                         |
| `number`            | `str`    | Article or section number.                                     |
| `start_date`        | `str`    | Date the article entered into force (format: YYYY-MM-DD).                   |
| `end_date`          | `str`    | End of validity date (or `2999-01-01` if still in force).                   |
| `nota`              | `str`    | Additional notes, if present.                                               |
| `links`             | `list[dict]` |  List of legal links associated with the text with metadata.            |
| `text`              | `str`    | Textual content chunk of the article (or subsection).                       |
| `chunk_text`        | `str`    | Formatted full chunk including `full_title`, `number`, formatted `subtitles` and `text`.|
| `embeddings_bge-m3` | `str`    | Embedding vector of `chunk_text` using `BAAI/bge-m3`, stored as JSON array string.     |

---

## 🛠️ Data Processing Methodology

### 📥 1. Field Extraction
The following fields were extracted and/or transformed from the original source:

- **Basic fields**:
  - `doc_id`, `nature`, `category`, ,`ministry`,`status`, `title`, `full_title`, `subtitles`, `number`, and `start_date`, `end_date` are extracted directly from the metadata of each article.

- **Generated fields**:
  - `chunk_id`: a generated unique identifier of the chunk, combining the `doc_id` and `chunk_index`.
  - `chunk_index`: is the index of the chunk of a same article. Each article has an unique `doc_id`.
  - `chunk_xxh64`: is the xxh64 hash of the `chunk_text` value. It is useful to determine if the `chunk_text` value has changed from a version to another.
    
- **Textual fields**:
  - `nota`: additional notes, also directly extracted from metadatas.
  - `text`: chunk of the main text content.
  - `chunk_text`: generated by concatenating `full_title`, article `number`, formatted `subtitles` and `text`.

- **List-based fields**:
  - `links`: list of legal relationships associated with the article, extracted from the LEGI metadata.
    Each element is a JSON object describing a link to another legal document (source or target), with fields such as:
    - `title`: official title of the related document.
    - `doc_id` / `text_doc_id`: LEGI or JORF identifier of the related document.
    - `number` / `text_number`: article or act number when applicable.
    - `category`: type of the related document (e.g. `LOI`, `DECRET`, etc.).
    - `link_type`: nature of the relationship (e.g. `TXT_SOURCE`, `CITATION`).
    - `link_direction`: direction of the relation, indicates whether the element carrying the link corresponds to the source or target of the link.
      (e.g. `source` when the current article cites another text, `cible` when it is cited by another text).
    - `nor`: NOR identifier of the related act, when available.
    - `text_signature_date`: signature date of the related text (YYYY-MM-DD), when available.

### ✂️ 2. Generation of 'chunk_text'

Each article is naturally isolated and has its own `cid` value. But in case the article is too long, we have to split it in several chunks.
The Langchain's `RecursiveCharacterTextSplitter` function was used to make these chunks, which correspond to the `text` value. The parameters used are :

- `chunk_size` = 5000
- `chunk_overlap` = 250
- `length_function` = len

For each chunk (`text`), a `chunk_text` is constructed as follows:

``` text
"{full_title} - Article {number}
{formatted subtitles}
{text}"
```

### 🧠 3. Embeddings Generation

Each `chunk_text` was embedded using the [**`BAAI/bge-m3`**](https://huggingface.co/BAAI/bge-m3) model.
The resulting embedding vector is stored in the `embeddings_bge-m3` column as a **string**, but can easily be parsed back into a `list[float]` or NumPy array.

## 📌 Embedding Use Notice

⚠️ The `embeddings_bge-m3` column is stored as a **stringified list** of floats (e.g., `"[-0.03062629,-0.017049594,...]"`). 
To use it as a vector, you need to parse it into a list of floats or NumPy array. For example, if you want to load the dataset into a dataframe by using the `datasets` library:

```python
import pandas as pd
import json
from datasets import load_dataset
# The Pyarrow library must be installed in your Python environment for this example. By doing => pip install pyarrow

dataset = load_dataset("AgentPublic/legi") # Loading the full dataset
df = pd.DataFrame(dataset['train'])
df["embeddings_bge-m3"] = df["embeddings_bge-m3"].apply(json.loads)
```

Otherwise, if you have already downloaded some parquet files from the `data/legi-latest/` folder : 
```python
import pandas as pd
import json
# The Pyarrow library must be installed in your Python environment for this example. By doing => pip install pyarrow

df = pd.read_parquet(path="legi-latest/") # Assuming that all parquet files are located into this folder
df["embeddings_bge-m3"] = df["embeddings_bge-m3"].apply(json.loads)
```

You can then use the dataframe as you wish, such as by inserting the data from the dataframe into the vector database of your choice.

## 🐱 GitHub repository : 
The project MediaTech is open source ! You are free to contribute or see the complete code used to build the dataset by checking the [GitHub repository](https://github.com/etalab-ia/mediatech)

## 📚 Source & License

### 🔗 Source : 
- [**DILA** open data repository](https://echanges.dila.gouv.fr/OPENDATA/LEGI)
- [Data.gouv.fr : LEGI: Codes, lois et règlements consolidés ](https://www.data.gouv.fr/datasets/legi-codes-lois-et-reglements-consolides/)

### 📄 Licence : 
**Open License (Etalab)** — This dataset is publicly available and can be reused under the conditions of the Etalab open license.