Make Learn links relative (#52950)

Author: gewarren

docs/ai/vector-stores/overview.md

@@ -22,7 +22,7 @@ For example, you can use a vector database to:
 
 Vector databases provide vector search capabilities to find similar items based on their data characteristics rather than by exact matches on a property field. Vector search works by analyzing the vector representations of your data that you created using an AI embedding model such as the [Azure OpenAI embedding models](/azure/ai-services/openai/concepts/models#embeddings-models). The search process measures the distance between the data vectors and your query vector. The data vectors that are closest to your query vector are the ones that are most similar semantically.
 
-Most modern database products support vector search alongside traditional querying; this is the case with [Azure SQL/SQL Server](https://learn.microsoft.com/sql/sql-server/ai/vectors), [Azure Cosmos DB](https://learn.microsoft.com/azure/cosmos-db/vector-database), [PostgreSQL](https://github.com/pgvector/pgvector-dotnet) and many other major products. As an alternative, a wide area of dedicated, specialized vector database products exist; these are highly optimized to perform vector search, and are typically installed alongside a traditional database exclusively to handle vector search workloads.
+Most modern database products support vector search alongside traditional querying; this is the case with [Azure SQL/SQL Server](/sql/sql-server/ai/vectors), [Azure Cosmos DB](/azure/cosmos-db/vector-database), [PostgreSQL](https://github.com/pgvector/pgvector-dotnet), and many other major products. As an alternative, a wide range of dedicated, specialized vector database products exist. These products are highly optimized to perform vector search, and are typically installed alongside a traditional database exclusively to handle vector search workloads.
 
 ## Vector search workflows with .NET and OpenAI