Create README.md

README.md

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+---
+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
+---
+
+# 🇫🇷 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.                            |
+| `cid`              | `str`            | Unique identifier of the decision (e.g., `CONSTEXT...`).                    |
+| `chunk_number`     | `int`            | Index of the chunk within the same decision.                                |
+| `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**:
+  - `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 `cid` and `chunk_number`.
+  - `chunk_number`: index of the chunk from the original decision.
+
+- **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 (in order to maximize the compability of most LLMs context windows)
+- `chunk_overlap` = 200
+- `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.
+
+## 📌 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. For example, if you want to load the dataset into a dataframe :
+
+```python
+import pandas as pd
+import json
+
+df = pd.read_parquet("constit-latest.parquet")
+df["embeddings_bge-m3"] = df["embeddings_bge-m3"].apply(json.loads)
+```
+
+## 📚 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.