Update README.md

README.md

@@ -92,16 +92,24 @@ The value of `chunk_text` includes the `title` and the textual content chunk `te
 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.
 
-## 🔄 The chunking doesn't fit your use case?
+## 🎓 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**.
 
-## 📌 Embedding Use Notice
+### 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. For example, if you want to load the dataset into a dataframe by using the `datasets` library:
+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
@@ -113,8 +121,8 @@ 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:
 
-Otherwise, if you have already downloaded all parquet files from the `data/constit-latest/` folder : 
 ```python
 import pandas as pd
 import json