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🇫🇷 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.
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 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 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:

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 :

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

🔗 Source :

📄 Licence :

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