Increase embedding TPM capacity and add note in cloud ingestion guide

Author: pamelafox

docs/data_ingestion.md

@@ -153,15 +153,21 @@ This project includes an optional feature to perform data ingestion in the cloud
 
 3. Open `azure.yaml` and un-comment the document-extractor, figure-processor, and text-processor sections. Those are the Azure Functions apps that will be deployed and serve as Azure AI Search skills.
 
-4. Provision the new Azure Functions resources, deploy the function apps, and update the search indexer with:
+4. (Recommended) Increase the capacity for the embedding model to the maximum quota allowed for your region/subscription, so that the Azure Functions can generate embeddings without hitting rate limits:
+
+    ```shell
+    azd env set AZURE_OPENAI_EMB_DEPLOYMENT_CAPACITY 400
+    ```
+
+5. Provision the new Azure Functions resources, deploy the function apps, and update the search indexer with:
 
     ```shell
     azd up
     ```
 
-5. That will upload the documents in the `data/` folder to the Blob storage container, create the indexer and skillset, and run the indexer to ingest the data. You can monitor the indexer status from the portal.
+6. That will upload the documents in the `data/` folder to the Blob storage container, create the indexer and skillset, and run the indexer to ingest the data. You can monitor the indexer status from the portal.
 
-6. When you have new documents to ingest, you can upload documents to the Blob storage container and run the indexer from the Azure Portal to ingest new documents.
+7. When you have new documents to ingest, you can upload documents to the Blob storage container and run the indexer from the Azure Portal to ingest new documents.
 
 ### Indexer architecture
 

infra/main.bicep

@@ -231,7 +231,7 @@ var embedding = {
   deploymentName: !empty(embeddingDeploymentName) ? embeddingDeploymentName : 'text-embedding-3-large'
   deploymentVersion: !empty(embeddingDeploymentVersion) ? embeddingDeploymentVersion : (embeddingModelName == 'text-embedding-ada-002' ? '2' : '1')
   deploymentSkuName: !empty(embeddingDeploymentSkuName) ? embeddingDeploymentSkuName : (embeddingModelName == 'text-embedding-ada-002' ? 'Standard' : 'GlobalStandard')
-  deploymentCapacity: embeddingDeploymentCapacity != 0 ? embeddingDeploymentCapacity : 30
+  deploymentCapacity: embeddingDeploymentCapacity != 0 ? embeddingDeploymentCapacity : 200
   dimensions: embeddingDimensions != 0 ? embeddingDimensions : 3072
 }