Update README.md

README.md

@@ -99,8 +99,18 @@ There was no need here to split characters here.
 
 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.
 
-## 📌 Embeddings Notice
-⚠️ The `embeddings_bge-m3` column is stored as a stringified list (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:
+## 🎓 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
@@ -112,8 +122,8 @@ dataset = load_dataset("AgentPublic/state-administrations-directory")
 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/state-administrations-directory-latest/` folder : 
 ```python
 import pandas as pd
 import json