| --- |
| 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 |
| --- |
| --------------------------------------------------------------------------------------------------- |
| ### 📢 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 ! 🙌 |
|
|
| --------------------------------------------------------------------------------------------------- |
| # 🇫🇷 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. |
|
|
| ## 🎓 Tutorials |
|
|
| ### 🤖 1. How to load MediaTech's datasets from Hugging Face and use them in a RAG pipeline ? |
|
|
| To learn how to load MediaTech's datasets from Hugging Face and integrate them into a Retrieval-Augmented Generation (RAG) pipeline, check out our [step-by-step RAG tutorial available on our GitHub repository !](https://github.com/etalab-ia/mediatech/blob/main/docs/hugging_face_rag_tutorial.ipynb) |
|
|
| ### 📌 2. 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. |
|
|
| #### 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) |
| ``` |
| #### Using downloaded local Parquet files: |
|
|
| ```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. |
