feat(inference): add tutorial how to implement RAG with managed inference

Author: Laure-di

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

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+---
+meta:
+   title: How to implement RAG with managed inference
+   description:
+---
+
+RAG (Retrieval-Augmented Generation) is a powerful approach for enhancing a model's knowledge by leveraging your own dataset.
+Scaleway's robust infrastructure makes it easier than ever to implement RAG, as our products are fully compatible with LangChain, especially the OpenAI integration.
+By utilizing our managed inference services, managed databases, and object storage, you can effortlessly build and deploy a customized model tailored to your specific needs.
+
+<Macro id="requirements" />
+- A Scaleway account logged into the [console](https://console.scaleway.com)
+- [Owner](/identity-and-access-management/iam/concepts/#owner) status or [IAM permissions](/identity-and-access-management/iam/concepts/#permission) allowing you to perform actions in the intended Organization
+- [Inference Deployment](/ai-data/managed-inference/how-to/create-deployment/): Set up an inference deployment using [sentence-transformers/sentence-t5-xxl](/ai-data/managed-inference/reference-content/sentence-t5-xxl/) on an L4 instance to efficiently process embeddings.
+- [Inference Deployment](/ai-data/managed-inference/how-to/create-deployment/) with the model of your choice.
+- [Object Storage Bucket](/storage/object/how-to/create-a-bucket/) to store all the data you want to inject into your LLM model.
+- [Managed Database](/managed-databases/postgresql-and-mysql/how-to/create-a-database/) to securely store all your embeddings.