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---
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
configs:
- config_name: latest
data_files: "data/local-administrations-directory-latest/*.parquet"
default: true
---
# 🇫🇷 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 |
| `doc_id` | `str` | Document identifier. Identical to chunk_id as each document only has 1 chunk. |
| `chunk_xxh64` | `str` | XXH64 hash of the `chunk_text` value. |
| `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`, `doc_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.
- `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.
### ✂️ 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, 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/local-administrations-directory")
df = pd.DataFrame(dataset['train'])
df["embeddings_bge-m3"] = df["embeddings_bge-m3"].apply(json.loads)
```
Otherwise, if you have already downloaded all parquet files from the `data/local-administrations-directory-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="local-administrations-directory-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.
## 📚 Source & License
## 🐱 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 :
- [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. |