Update index.mdx

tutorials/how-to-implement-rag-generativeapis/index.mdx

@@ -123,7 +123,7 @@ vector_store = PGVector(connection=connection_string, embeddings=embeddings)
 
 6. At this stage, you need to have data (e.g. PDF files) stored in your Scaleway Object storage bucket. As examples, you can download our [Instance CLI cheatsheet](https://www-uploads.scaleway.com/Instances_CLI_Cheatsheet_7ae4ed5564.pdf) and our [Kubernetes cheatsheets](https://www.scaleway.com/en/docs/static/be9a6e5821a4e8e268c7c5bd3624e256/scaleway-kubernetes-cheatsheet.pdf) and store them into your [Object Storage bucket](https://console.scaleway.com/object-storage/buckets).
 
-Below we will use LangChain's [`S3FileLoader`](https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.s3_file.S3FileLoader.html) to load documents, and split them into chunks. 
+Below we will use LangChain's [`S3DirectoryLoader`](https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.s3_file.S3FileLoader.html) to load documents, and split them into chunks. 
 Then, we will embed them as vectors and store these vectors in your PostgreSQL database.
 
 ### Import required modules