| --- |
| language: |
| - fr |
| tags: |
| - france |
| - constitution |
| - council |
| - conseil-constitutionnel |
| - decisions |
| - justice |
| - embeddings |
| - open-data |
| - government |
| pretty_name: French Constitutional Council Decisions Dataset |
| size_categories: |
| - 10K<n<100K |
| license: etalab-2.0 |
| configs: |
| - config_name: latest |
| data_files: "data/constit-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 Constitutional Council Decisions Dataset (Conseil constitutionnel) |
|
|
| This dataset is a processed and embedded version of all decisions issued by the **Conseil constitutionnel** (French Constitutional Council) since its creation in 1958. |
| It includes full legal texts of decisions, covering constitutional case law, electoral disputes, and other related matters. |
| The original data is downloaded from [the dedicated **DILA** open data repository](https://echanges.dila.gouv.fr/OPENDATA/CONSTIT) and is also published on [data.gouv.fr](https://www.data.gouv.fr/fr/datasets/les-decisions-du-conseil-constitutionnel/). |
|
|
| The dataset provides semantic-ready, structured and chunked content of constitutional decisions suitable for semantic search, AI legal assistants, or RAG pipelines for example. |
| Each chunk of text has been vectorized using the [`BAAI/bge-m3`](https://huggingface.co/BAAI/bge-m3) embedding model. |
|
|
| --- |
|
|
| ## 🗂️ Dataset Contents |
|
|
| The dataset is provided in **Parquet format** and includes the following columns: |
|
|
| | Column Name | Type | Description | |
| |--------------------|------------------|-----------------------------------------------------------------------------| |
| | `chunk_id` | `str` | Unique generated identifier for each text chunk. | |
| | `doc_id` | `str` | Document identifier from the source site. | |
| | `chunk_index` | `int` | Index of the chunk within the same document. Starting from 1. | |
| | `chunk_xxh64` | `str` | XXH64 hash of the `chunk_text` value. | |
| | `nature` | `str` | Nature of the decision (e.g., Non lieu à statuer, Conformité, etc.). | |
| | `solution` | `str` | Legal outcome or conclusion of the decision. | |
| | `title` | `str` | Title summarizing the subject matter of the decision. | |
| | `number` | `str` | Official number of the decision (e.g., 2019-790). | |
| | `decision_date` | `str` | Date of the decision (format: YYYY-MM-DD). | |
| | `text` | `str` | Raw full-text content of the chunk. | |
| | `chunk_text` | `str` | Formatted full chunk including `title` 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` (cid), `title`, `nature`, `solution`, `number`, and `decision_date` are extracted directly from the metadata of each decision record. |
|
|
| - **Generated fields**: |
| - `chunk_id`: a generated unique identifier combining the `doc_id` and `chunk_index`. |
| - `chunk_index`: is the index of the chunk of a same document. Each document 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**: |
| - `text`: chunk of the main text content. |
| - `chunk_text`: generated by concatenating `title` and `text`. |
|
|
| ### ✂️ 2. Generation of `chunk_text` |
| |
| The Langchain's `RecursiveCharacterTextSplitter` function was used to make these chunks, which correspond to the `text` value. The parameters used are : |
| |
| - `chunk_size` = 1500 |
| - `chunk_overlap` = 0 |
| - `length_function` = len |
|
|
| The value of `chunk_text` includes the `title` and the textual content chunk `text`. This strategy is designed to improve document search. |
|
|
| ### 🧠 3. Embeddings Generation |
|
|
| Each `chunk_text` was embedded using the [**`BAAI/bge-m3`**](https://huggingface.co/BAAI/bge-m3) model. |
| The resulting embedding is stored as a JSON stringified array of 1024 floating point numbers in the `embeddings_bge-m3` column. |
|
|
| ## 🎓 Tutorials |
|
|
| ### 🔄 1. The chunking doesn't fit your use case? |
|
|
| If you need to reconstitute the original, un-chunked dataset, you can follow [this tutorial notebook available on our GitHub repository](https://github.com/etalab-ia/mediatech/blob/main/docs/reconstruct_vector_database.ipynb). |
|
|
| ⚠️ The tutorial is only relevant for datasets that were chunked **without overlap**. |
|
|
| ### 🤖 2. 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) |
|
|
| ### 📌 3. 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/constit") |
| 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="constit-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/CONSTIT) |
| - [Data.gouv.fr : CONSTIT: les décisions du Conseil constitutionnel](https://www.data.gouv.fr/datasets/constit-les-decisions-du-conseil-constitutionnel/) |
|
|
| ### 📄 Licence : |
| **Open License (Etalab)** — This dataset is publicly available and can be reused under the conditions of the Etalab open license. |
