Update links and clean up tasks (#2124)

* Clean up links and task

* Newline

* Add absolute path

* Update settings URL, replace aka with full URL

Author: pamelafox

.vscode/tasks.json

@@ -3,14 +3,6 @@
     "tasks": [
         {
             "label": "Start App",
-            "type": "dotenv",
-            "targetTasks": [
-                "Start App (Script)"
-            ],
-            "file": "${input:dotEnvFilePath}"
-        },
-        {
-            "label": "Start App (Script)",
             "type": "shell",
             "command": "${workspaceFolder}/app/start.sh",
             "windows": {
@@ -24,12 +16,5 @@
             },
             "problemMatcher": []
         }
-    ],
-    "inputs": [
-        {
-            "id": "dotEnvFilePath",
-            "type": "command",
-            "command": "azure-dev.commands.getDotEnvFilePath"
-        }
     ]
 }

README.md

@@ -288,11 +288,11 @@ Here are the most common failure scenarios and solutions:
 ### Resources
 
 - [Additional documentation for this app](docs/README.md)
-- [📖 Revolutionize your Enterprise Data with ChatGPT: Next-gen Apps w/ Azure OpenAI and AI Search](https://aka.ms/entgptsearchblog)
+- [📖 Revolutionize your Enterprise Data with ChatGPT: Next-gen Apps w/ Azure OpenAI and AI Search](https://techcommunity.microsoft.com/blog/azure-ai-services-blog/revolutionize-your-enterprise-data-with-chatgpt-next-gen-apps-w-azure-openai-and/3762087)
 - [📖 Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search)
 - [📖 Azure OpenAI Service](https://learn.microsoft.com/azure/cognitive-services/openai/overview)
 - [📖 Comparing Azure OpenAI and OpenAI](https://learn.microsoft.com/azure/cognitive-services/openai/overview#comparing-azure-openai-and-openai/)
-- [📖 Access Control in Generative AI applications with Azure Cognitive Search](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/access-control-in-generative-ai-applications-with-azure/ba-p/3956408)
+- [📖 Access Control in Generative AI applications with Azure AI Search](https://techcommunity.microsoft.com/blog/azure-ai-services-blog/access-control-in-generative-ai-applications-with-azure-ai-search/3956408)
 - [📺 Quickly build and deploy OpenAI apps on Azure, infused with your own data](https://www.youtube.com/watch?v=j8i-OM5kwiY)
 - [📺 AI Chat App Hack series](https://www.youtube.com/playlist?list=PL5lwDBUC0ag6_dGZst5m3G72ewfwXLcXV)
 

docs/customization.md

@@ -117,7 +117,7 @@ You can also try changing the ChatCompletion parameters, like temperature, to se
 
 ### Improving Azure AI Search results
 
-If the problem is with Azure AI Search (step 2 above), the first step is to check what search parameters you're using. Generally, the best results are found with hybrid search (text + vectors) plus the additional semantic re-ranking step, and that's what we've enabled by default. There may be some domains where that combination isn't optimal, however. Check out this blog post which [evaluates AI search strategies](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/azure-ai-search-outperforming-vector-search-with-hybrid/ba-p/3929167) for a better understanding of the differences.
+If the problem is with Azure AI Search (step 2 above), the first step is to check what search parameters you're using. Generally, the best results are found with hybrid search (text + vectors) plus the additional semantic re-ranking step, and that's what we've enabled by default. There may be some domains where that combination isn't optimal, however. Check out this blog post which [evaluates AI search strategies](https://techcommunity.microsoft.com/blog/azure-ai-services-blog/azure-ai-search-outperforming-vector-search-with-hybrid-retrieval-and-ranking-ca/3929167) for a better understanding of the differences.
 
 #### Configuring parameters in the app
 

docs/data_ingestion.md

@@ -57,7 +57,7 @@ If needed, you can modify the chunking algorithm in `scripts/prepdocslib/textspl
 
 To enhance search functionality, categorize data during the ingestion process with the `--category` argument, for example `scripts/prepdocs.ps1 --category ExampleCategoryName`. This argument specifies the category to which the data belongs, enabling you to filter search results based on these categories.
 
-After running the script with the desired category, ensure these categories are added to the 'Include Category' dropdown list. This can be found in the developer settings in [`Settings.tsx`](/app/frontend/src/components/settings/Settings.tsx). The default option for this dropdown is "All". By including specific categories, you can refine your search results more effectively.
+After running the script with the desired category, ensure these categories are added to the 'Include Category' dropdown list. This can be found in the developer settings in [`Settings.tsx`](https://github.com/Azure-Samples/azure-search-openai-demo/blob/main/app/frontend/src/components/Settings/Settings.tsx). The default option for this dropdown is "All". By including specific categories, you can refine your search results more effectively.
 
 ### Indexing additional documents
 
@@ -75,7 +75,7 @@ You can also remove individual documents by using the `--remove` flag. Open eith
 
 ## Integrated Vectorization
 
-Azure AI Search includes an [integrated vectorization feature](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/announcing-the-public-preview-of-integrated-vectorization-in/ba-p/3960809#:~:text=Integrated%20vectorization%20is%20a%20new%20feature%20of%20Azure,pull-indexers%2C%20and%20vectorization%20of%20text%20queries%20through%20vectorizers), a cloud-based approach to data ingestion. Integrated vectorization takes care of document format cracking, data extraction, chunking, vectorization, and indexing, all with Azure technologies.
+Azure AI Search includes an [integrated vectorization feature](https://techcommunity.microsoft.com/blog/azure-ai-services-blog/announcing-the-public-preview-of-integrated-vectorization-in-azure-ai-search/3960809), a cloud-based approach to data ingestion. Integrated vectorization takes care of document format cracking, data extraction, chunking, vectorization, and indexing, all with Azure technologies.
 
 See [this notebook](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/integrated-vectorization/azure-search-integrated-vectorization-sample.ipynb) to understand the process of setting up integrated vectorization.
 We have integrated that code into our `prepdocs` script, so you can use it without needing to understand the details.

docs/deploy_features.md

@@ -206,7 +206,7 @@ azd env set USE_SPEECH_OUTPUT_BROWSER true
 
 ## Enabling Integrated Vectorization
 
-Azure AI search recently introduced an [integrated vectorization feature in preview mode](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/announcing-the-public-preview-of-integrated-vectorization-in/ba-p/3960809#:~:text=Integrated%20vectorization%20is%20a%20new%20feature%20of%20Azure,pull-indexers%2C%20and%20vectorization%20of%20text%20queries%20through%20vectorizers). This feature is a cloud-based approach to data ingestion, which takes care of document format cracking, data extraction, chunking, vectorization, and indexing, all with Azure technologies.
+Azure AI search recently introduced an [integrated vectorization feature in preview mode](https://techcommunity.microsoft.com/blog/azure-ai-services-blog/announcing-the-public-preview-of-integrated-vectorization-in-azure-ai-search/3960809). This feature is a cloud-based approach to data ingestion, which takes care of document format cracking, data extraction, chunking, vectorization, and indexing, all with Azure technologies.
 
 To enable integrated vectorization with this sample:
 

docs/deploy_lowcost.md

@@ -40,7 +40,7 @@ However, if your goal is to minimize costs while prototyping your application, f
     If you have one already, either delete that service or follow instructions to
     reuse your [existing search service](../README.md#existing-azure-ai-search-resource).
     2. The free tier does not support semantic ranker, so the app UI will no longer display
-    the option to use the semantic ranker. Note that will generally result in [decreased search relevance](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/azure-ai-search-outperforming-vector-search-with-hybrid/ba-p/3929167).
+    the option to use the semantic ranker. Note that will generally result in [decreased search relevance](https://techcommunity.microsoft.com/blog/azure-ai-services-blog/azure-ai-search-outperforming-vector-search-with-hybrid-retrieval-and-ranking-ca/3929167).
 
 1. Use the free tier of Azure Document Intelligence (used in analyzing files):
 

docs/productionizing.md

@@ -83,7 +83,7 @@ and scale up the maximum/minimum based on load.
 * **Networking**: We recommend [deploying inside a Virtual Network](./deploy_private.md). If the app is only for
   internal enterprise use, use a private DNS zone. Also consider using Azure API Management (APIM)
   for firewalls and other forms of protection.
-  For more details, read [Azure OpenAI Landing Zone reference architecture](https://techcommunity.microsoft.com/t5/azure-architecture-blog/azure-openai-landing-zone-reference-architecture/ba-p/3882102).
+  For more details, read [Azure OpenAI Landing Zone reference architecture](https://techcommunity.microsoft.com/blog/azurearchitectureblog/azure-openai-landing-zone-reference-architecture/3882102).
 
 ## Load testing