---
language:
- fr
tags:
- france
- public-sector
- embeddings
- directory
- open-data
- government
- etalab
pretty_name: French Local Administrations Directory
size_categories:
- 10K<n<100K
license: etalab-2.0
---

# 🇫🇷 French Local Administrations Directory Dataset

This dataset is a processed and embedded version of the public data **Annuaire de l’administration - Base de données locales** (French Local Administrations Directory), published on [data.gouv.fr](https://www.data.gouv.fr/datasets/service-public-fr-annuaire-de-l-administration-base-de-donnees-locales/).  
This information is also available on the official directory website of Service-Public.fr: https://lannuaire.service-public.fr/

The dataset provides semantic-ready, structured and chunked data of French **local** public entities, including organizational details, missions, contact information, and hierarchical links. Each chunk of text is vectorized using the [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) embedding model to enable semantic search and retrieval tasks.

---

## 🗂️ Dataset Contents

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

| Column Name             | Type                        | Description                                                                 |
|------------------------|-----------------------------|-----------------------------------------------------------------------------|
| `chunk_id`             | `str`                       | Unique source based identifier of the chunk                                 |
| `types`                | `str`                       | Type(s) of administrative entity.                                           |
| `name`                 | `str`                       | Name of the organization or service.                                        |
| `mission_description`  | `str`                       | Description of the entity's mission.                                        |
| `addresses`            | `list[dict]`                | List of address objects (street, postal code, city, etc.).                  |
| `phone_numbers`        | `list[str]`                 | List of telephone numbers.                                                  |
| `mails`                | `list[str]`                 | List of contact email addresses.                                            |
| `urls`                 | `list[str]`                 | List of related URLs.                                                       |
| `social_medias`        | `list[str]`                 | Social media accounts.                                                      |
| `mobile_applications`  | `list[str]`                 | Related mobile applications.                                                |
| `opening_hours`        | `str`                       | Opening hours.                                                              |
| `contact_forms`        | `list[str]`                 | Contact form URLs.                                                          |
| `additional_information` | `str`                     | Additional information.                                                     |
| `modification_date`    | `str`                       | Last update date.                                                           |
| `siret`                | `str`                       | SIRET number.                                                               |
| `siren`                | `str`                       | SIREN number.                                                               |
| `people_in_charge`     | `list[dict]`                | List of responsible persons.                                                |
| `organizational_chart` | `list[str]`                 | Organization chart references.                                              |
| `hierarchy`            | `list[dict]`                | Links to parent or child entities.                                          |
| `directory_url`        | `str`                       | Source URL from the official state directory website.                       |
| `chunk_text`           | `str`                       | Textual content of the administrative chunk.                                |
| `embeddings_bge-m3`    | `str` (stringified list)    | Embeddings of `chunk_text` using `BAAI/bge-m3`. Stored as a JSON array string. |

---

## 🛠️ Data Processing Methodology

### 📥 1. Field Extraction

The following fields were extracted and/or transformed from the original JSON:

- **Basic fields**: `chunk_id`, `name`, `types`, `mission_description`, `additional_information`, `siret`, `siren`, `directory_url`, `modification_date` are directly extracted from JSON attributes.
- **Structured lists**:
  - `addresses`: list of dictionaries with `adresse`, `code_postal`, `commune`, `pays`, `longitude`, and `latitude`.
  - `phone_numbers`, `mails`, `urls`, `social_medias`, `mobile_applications`, `contact_forms`: derived from their respective fields with formatting.
- **People and structure**:
  - `people_in_charge`: list of dictionaries representing staff members or leadership (title, name, rank, etc.).
  - `organizational_chart`, `hierarchy`: structural information within the administration.
- **Other fields**:
  - `opening_hours`: built using a custom function that parses declared time slots into readable strings.

### ✂️ 2. Generation of `chunk_text`

A synthetic text field called `chunk_text` was created to summarize key aspects of each administrative body. This field is designed for semantic search and embedding generation. It includes:

- The entity’s name : `name`
- Its mission statement (if available) : `mission_description`
- Key responsible individuals (formatted using role, title, name, and rank) : `people_in_charge`

There was no need here to split characters here.

### 🧠 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.

## 📌 Embeddings Notice

⚠️ The `embeddings_bge-m3` column is stored as a stringified list (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:

```python
import pandas as pd
import json

df = pd.read_parquet("local-administrations-directory-latest.parquet")
df["embeddings_bge-m3"] = df["embeddings_bge-m3"].apply(json.loads)
```

## 📚 Source & License

## 🔗 Source : 
- [Lannuaire.Service-Public.fr](https://lannuaire.service-public.fr/)
- [Data.Gouv.fr : Service-public.fr - Annuaire de l’administration - Base de données locales](https://www.data.gouv.fr/datasets/service-public-fr-annuaire-de-l-administration-base-de-donnees-locales/)

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