Create README.md

README.md

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+---
+language:
+- fr
+tags:
+- france
+- legislation
+- law
+- embeddings
+- open-data
+- government
+- parlement
+pretty_name: French Legislative Dossiers Dataset (DOLE)
+size_categories:
+- 10K<n<100K
+license: etalab-2.0
+---
+
+# 🇫🇷 French Legislative Dossiers Dataset (DOLE)
+
+This dataset provides a semantic-ready, chunked and embedded version of the **Dossiers Législatifs** ("DOLE") published by the French government. It includes all **laws promulgated since the XIIᵉ legislature (June 2002)**, **ordinances**, and **legislative proposals** under preparation.
+The original data is downloaded from [the dedicated **DILA** open data repository](https://echanges.dila.gouv.fr/OPENDATA/DOLE) and is also published on [data.gouv.fr](https://www.data.gouv.fr/datasets/dole-les-dossiers-legislatifs/).
+
+Each article is chunked and vectorized using the [`BAAI/bge-m3`](https://huggingface.co/BAAI/bge-m3) embedding model, enabling use in **semantic search**, **retrieval-augmented generation (RAG)**, and **legal research** systems for example.
+
+---
+
+## 🗂️ Dataset Contents
+
+The dataset is available in **Parquet format** and contains the following columns:
+
+| Column Name         | Type             | Description                                                                 |
+|---------------------|------------------|-----------------------------------------------------------------------------|
+| `chunk_id`          | `str`            | Unique identifier for each chunk.                                           |
+| `cid`               | `str`            | Legislative file identifier                                                 |
+| `chunk_number`      | `int`            | Index of the chunk within its parent document.                              |
+| `category`          | `str`            | Type of dossier (e.g., `LOI_PUBLIEE`, `PROJET_LOI`, etc.).                  |
+| `content_type`      | `str`            | Nature of the content: `article`, `dossier_content`, or `explanatory_memorandum`. |
+| `title`             | `str`            | Title summarizing the subject matter.                                       |
+| `number`            | `str`            | Internal document number.                                                   |
+| `wording`           | `str`            | Libelle, Legislature reference (e.g., `XIVème législature`).                |
+| `creation_date`     | `str`            | Creation or publication date (YYYY-MM-DD).                                  |
+| `article_number`    | `int` or `null`  | Article number if applicable.                                               |
+| `article_title`     | `str` or `null`  | Optional title of the article.                                              |
+| `article_synthesis` | `str` or `null`  | Optional synthesis of the article.                                          |
+| `text`              | `str` or `null`  | Text content of the explanatory_memorandum, article or file content (contenu du dossier) chunk.|
+| `chunk_text`        | `str`            | Concatenated text (`title` + `article_text` or related content).            |
+| `embeddings_bge-m3` | `str`            | Embedding vector of `chunk_text` using `BAAI/bge-m3`, stored as JSON string.|
+
+---
+
+## 🛠️ Data Processing Methodology
+
+### 🧩 1. Content Extraction
+
+Each **dossier législatif** was parsed, processed and standardized from his official XML structure. 
+Metadata, article blocks, and explanatory sections were normalized into a unified schema. 
+Specific rules applied per content type:
+
+- `explanatory_memorandum`: Includes the explanatory's introduction only. All articles synthesis that are in the explanatory are split by their `article_number` and added to `article_synthesis`.
+  Article fields are `null`.
+  An explanatory memorandum (exposé des motifs) is an official text that accompanies a draft or proposed law.
+  It is used to explain the reasons why the law is being proposed, the context in which it is set, and the objectives pursued by the legislator.
+- `dossier_content`: Includes dossier's textual content if the split by article didn't work.
+  The split may not work if there is no mention of article numbers in the dossier content or if the code was not adapted to a specific case in which the split wasn't possible.
+  Article metadata fields are `null`.
+- `article`: Structured content, where `article_number` and `text` are always present. `article_title` and `article_synthesis` may be missing.
+
+- **Basic fields**: `cid`, `category`, `title`, `number`, `wording`, `creation_date`, were taken directly from the source XML file.
+- **Generated fields**:
+  - `chunk_id`: A unique hash for each text chunk.
+  - `chunk_number`: Indicates the order of a chunk within a same deliberation.
+  - `content_type`: Nature of the content.
+  - `article_number`: Number of the article. Available only if `content_type` is `article`.
+  - `article_title`: Title of the article. Available only if `content_type` is `article`.
+  - `article_synthesis`: Synthesis of the article extracted from the explanatory memorandum. Available only if `content_type` is `article`.
+- **Textual fields**:
+  - `text`: Chunk of the main text content.
+    It can be an article text content extracted from the dossier content, a chunk of the explanatory memorandum's introduction or a chunk from the dossier content.
+  - `chunk_text`: Combines `title` and the main `text` body to maximize embedding relevance.
+    If `content_type` is `article`, then the article number is also added.
+    
+### ✂️ 2. Chunk Generation
+
+A `chunk_text` was built by combining the `title`, the `article_number` if applicable and its corresponding `text` content section. 
+Chunking ensures semantic granularity for embedding purposes.
+
+No recursive split was necessary as legal articles and memos are inherently structured and relatively short for `content_type` = `article`. 
+If needed, the Langchain's `RecursiveCharacterTextSplitter` function was used to make these chunks (`text` value). The parameters used are : 
+
+- `chunk_size` = 8000
+- `chunk_overlap` = 400
+- `length_function` = len
+
+---
+
+### 🧠 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.
+
+## 📌 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("dole-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/DOLE)
+- [Data.gouv.fr : DOLE : les dossiers législatifs ](https://www.data.gouv.fr/datasets/dole-les-dossiers-legislatifs/)
+
+### 📄 License:
+**Open License (Etalab)** — This dataset is publicly available and reusable under the Etalab open license.