@WMorath.SA Vector Search is what enables searching large troves of unstructured data by semantic understanding. While not strictly necessary, in many cases you may see better results, especially when the input prompt by your users does not have the exact keywords as in your data.
Vector Databases still rely on using embeddings output from models like ada, but provide an index that speeds up searches compared to you manually computing distances for each chunk of your data set on each query.
And in your case, Azure Cognitive Search is a good option since it includes the necessary processing pipelines to automatically handle PDFs, saving time on implementing your own pipeline if you don't already have one.
Overall, you don't really need to use Vector Databases for every scenario but usually for knowledgebase scenarios, it might be worth considering it.