Add runtime service links for lab endpoints

content/labs/lab-6-embedding-and-chunking.md

@@ -24,8 +24,8 @@ In this lab, we will:
 
 To start this lab, two web services have been preconfigured:
 
-- ChunkViz - http://<IP>:3000
-- Embedding Atlas - http://<IP>:5055
+- ChunkViz - {{service-url:chunkviz}}
+- Embedding Atlas - {{service-url:embedding-atlas}}
 
 ## Objective 1 Explore: Chunking Strategy
 
@@ -33,7 +33,7 @@ Chunking is the first step in any RAG pipeline. It is the process of dividing a
 
 Successful chunking is highly dependent on the type of document being processed. In production-grade RAG systems, teams often evaluate multiple strategies across different document types, then route content through the processing path that produces the strongest retrieval results. For this lab, we will use a visualization tool to build intuition for those trade-offs.
 
-In a web browser, navigate to http://<STUDENT ASSIGNED SYSTEM IP>:3000. Once loaded, you should see the ChunkViz homepage.
+In a web browser, navigate to {{service-url:chunkviz}}. Once loaded, you should see the ChunkViz homepage.
 
 <figure style="text-align: center;">
   <a href="https://i.imgur.com/PG6fp1V.png" target="_blank">
@@ -115,7 +115,7 @@ Now that we have seen some of the trade-offs involved in chunking, we can move t
 
 This allows a system to perform similarity search efficiently. When a user submits a query, the query is embedded into the same vector space, and the system retrieves the chunks whose embeddings are closest to it. This differs from how embeddings are used internally by an LLM for attention and transformation, but it is the key step that allows a RAG system to retrieve information based on meaning rather than simple keyword matching.
 
-Navigate to http://<STUDENT ASSIGNED SYSTEM IP>:5055. Here, we have started a project called Embedding Atlas. Embedding Atlas is a tool that provides interactive visualizations for datasets stored in parquet format. Each chunk in this case is one row in the dataset, allowing us to visualize, cross-filter, and search embeddings and metadata interactively.
+Navigate to {{service-url:embedding-atlas}}. Here, we have started a project called Embedding Atlas. Embedding Atlas is a tool that provides interactive visualizations for datasets stored in parquet format. Each chunk in this case is one row in the dataset, allowing us to visualize, cross-filter, and search embeddings and metadata interactively.
 
 <figure style="text-align: center;">
   <a href="https://i.imgur.com/8PvcZBP.png" target="_blank">