Cleaned up links

Closes #108
Azure · May 28, 2024 · 5aec1ff · 5aec1ff
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diff --git a/labs/03-orchestration/02-Embeddings/embeddings.ipynb b/labs/03-orchestration/02-Embeddings/embeddings.ipynb
@@ -433,13 +433,11 @@
     "\n",
     "Choose one or more of the following Vector Store and AI Orchestration options:\n",
     "\n",
-    "📣 [Qdrant with Langchain](../03-Qdrant/qdrant.ipynb)\n",
+    "📣 [Implement Retrieval Augmented Generation with Qdrant as vector store](../03-VectorStore/qdrant.ipynb)\n",
     "\n",
-    "📣 [Azure AI Search with Semantic Kernel and C#](../04-ACS/acs-sk-csharp.ipynb)\n",
+    "📣 [Implement Retrieval Augmented Generation with Azure CosmosDB as vector store and as semantic cache](../03-VectorStore/mongo.ipynb)\n",
     "\n",
-    "📣 [Azure AI Search with Semantic Kernel and Python](../04-ACS/acs-sk-python.ipynb)\n",
-    "\n",
-    "📣 [Azure AI Search with Langchain and Python](../04-ACS/acs-lc-python.ipynb)"
+    "📣 [Implement Retrieval Augmented Generation with Azure AI Search as vector store with semantic ranking](../03-VectorStore/aisearch.ipynb)"
    ]
   }
  ],
@@ -459,7 +457,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.3"
   },
   "orig_nbformat": 4
  },

diff --git a/labs/03-orchestration/03-VectorStore/mongo.ipynb b/labs/03-orchestration/03-VectorStore/mongo.ipynb
@@ -454,11 +454,7 @@
    "source": [
     "## Next Section\n",
     "\n",
-    "📣 [Azure AI Search with Semantic Kernel and C#](../04-ACS/acs-sk-csharp.ipynb)\n",
-    "\n",
-    "📣 [Azure AI Search with Semantic Kernel and Python](../04-ACS/acs-sk-python.ipynb)\n",
-    "\n",
-    "📣 [Azure AI Search with Langchain and Python](../04-ACS/acs-lc-python.ipynb)"
+    "📣 [Implement Retrieval Augmented Generation with Azure AI Search as vector store with semantic ranking](./aisearch.ipynb)"
    ]
   }
  ],

diff --git a/labs/03-orchestration/03-VectorStore/qdrant.ipynb b/labs/03-orchestration/03-VectorStore/qdrant.ipynb
@@ -307,9 +307,9 @@
    "source": [
     "## Next Section\n",
     "\n",
-    "📣 [Azure AI Search with Semantic Kernel and C#](../04-ACS/acs-sk-csharp.ipynb)\n",
+    "📣 [Implement Retrieval Augmented Generation with Azure CosmosDB as vector store and as semantic cache](./mongo.ipynb)\n",
     "\n",
-    "📣 [Azure AI Search with Langchain and Python](../04-ACS/acs-lc-python.ipynb)"
+    "📣 [Implement Retrieval Augmented Generation with Azure AI Search as vector store with semantic ranking](./aisearch.ipynb)"
    ]
   }
  ],

diff --git a/labs/03-orchestration/README.md b/labs/03-orchestration/README.md
@@ -20,7 +20,7 @@ In this lab, we'll walk through the basics of creating Vectors using Embeddings
 
 [Qdrant](https://github.com/Azure/intro-to-intelligent-apps/blob/main/labs/03-orchestration/03-VectorStore/qdrant.ipynb )
 
-In this lab, we'll walk through using an open source vector store called Qdrant.
+In this lab, we'll walk through using an open source vector store called Qdrant to understand the basics.
 
 [Mongo](https://github.com/Azure/intro-to-intelligent-apps/blob/main/labs/03-orchestration/03-VectorStore/mongo.ipynb)