docs(fix): categories

Author: bene2k1

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

@@ -9,7 +9,7 @@ dates:
   validation: 2024-10-10
   posted: 2018-10-10
 categories:
-    - inference
+  - managed-inference
 ---
 
 Retrieval-Augmented Generation (RAG) enhances language models by incorporating relevant information from your own datasets. This hybrid approach improves both the accuracy and contextual relevance of the model's outputs, making it ideal for advanced AI applications.

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

@@ -6,7 +6,7 @@ content:
     h1: Implementing Retrieval-Augmented Generation (RAG) with LangChain and Scaleway Managed Inference
 tags: inference managed postgresql pgvector object storage RAG langchain machine learning AI language models
 categories:
-    - inference
+  - managed-inference
 ---
 
 Retrieval-Augmented Generation (RAG) supercharges language models by enabling real-time retrieval of relevant information from external datasets. This hybrid approach boosts both the accuracy and contextual relevance of model outputs, making it essential for advanced AI applications.

tutorials/processing-images-structured-outputs-pixtral/index.mdx

@@ -7,8 +7,7 @@ content:
   paragraph: Discover how to leverage Mistral's Pixtral vision model to analyze images and generate comprehensive structured outputs.
 tags: AI vision-model image-processing Pixtral Mistral structured-data
 categories:
-  - artificial-intelligence
-  - computer-vision
+  - managed-inference
 hero: assets/Pixtral-Structured-Outputs.webp
 dates:
   validation: 2024-10-09