Merge pull request #5743 from EnterpriseDB/docs/josh/DOCS-705-color-f…

…ont-analytics-diagram

advocacy_docs/edb-postgres-ai/analytics/concepts.mdx

@@ -14,7 +14,7 @@ operational data on the EDB Postgres® AI platform.
 
 Here's how it fits together:
 
-![Level 50 basic architecture](./images/level-50-architecture.png)
+[![Level 50 basic architecture](/svg/edb-postgres-ai/analytics/level-50.svg)](/svg/edb-postgres-ai/analytics/level-50.svg)
 
 ### Lakehouse node
 
@@ -103,7 +103,7 @@ its attached hard drives. Instead, you can keep data in object storage (and also
 in highly compressible formats), and only provision the compute needed to query
 it when necessary.
 
-![Level 100 Architecture](./images/level-100-architecture.png)
+[![Level 100 Architecture](/svg/edb-postgres-ai/analytics/level-100.svg)](/svg/edb-postgres-ai/analytics/level-100.svg)
 
 On the compute side, a Vectorized Query Engine is optimized to query Lakehouse
 Tables, but still fall back to Postgres for full compatibility.
@@ -115,10 +115,10 @@ columnar storage formats optimzied for Analytics.
 
 Here's a slightly more comprehensive diagram of how these services fit together:
 
-![Level 200 Marketecture](./images/level-200-marketecture.png)
+[![Level 200 Architecture](/svg/edb-postgres-ai/analytics/level-200.svg)](/svg/edb-postgres-ai/analytics/level-200.svg)
 
 ### Level 300
 
 Here's the more detailed, zoomed-in view of "what's in the box":
 
-![Level 300 Architecture](./images/level-300-architecture.png)
+[![Level 200 Architecture](/svg/edb-postgres-ai/analytics/level-300.svg)](/svg/edb-postgres-ai/analytics/level-300.svg)