Merge pull request #3437 from MicrosoftDocs/repo_sync_working_branch

Confirm merge from repo_sync_working_branch to main to sync with https://github.com/MicrosoftDocs/azure-ai-docs (branch main)

Author: Taojunshen

articles/ai-services/openai/overview.md

@@ -20,7 +20,7 @@ Azure OpenAI Service provides REST API access to OpenAI's powerful language mode
 
 | Feature | Azure OpenAI |
 | --- | --- |
-| Models available | [**o3-mini & o1**](./how-to/reasoning.md) - (Limited Access - [**Request Access**](https://aka.ms/OAI/o1access))<br>[**o1-mini**](./how-to/reasoning.md)<br>**GPT-4o & GPT-4o mini**<br> **GPT-4 series (including GPT-4 Turbo with Vision)** <br>**GPT-3.5-Turbo series**<br> Embeddings series <br> Learn more in our [Models](./concepts/models.md) page.|
+| Models available | [**o3-mini & o1**](./how-to/reasoning.md) <br>[**o1-mini**](./how-to/reasoning.md)<br>**GPT-4o & GPT-4o mini**<br> **GPT-4 series (including GPT-4 Turbo with Vision)** <br>**GPT-3.5-Turbo series**<br> Embeddings series <br> Learn more in our [Models](./concepts/models.md) page.|
 | Fine-tuning | `GPT-4o-mini` (preview) <br> `GPT-4` (preview) <br>`GPT-3.5-Turbo` (0613).|
 | Price | [Available here](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) <br> For details on vision-enabled chat models, see the [special pricing information](../openai/concepts/gpt-with-vision.md#special-pricing-information).|
 | Virtual network support & private link support | Yes.  |

articles/search/vector-search-how-to-create-index.md

@@ -189,7 +189,7 @@ Be sure to have a strategy for [vectorizing your content](vector-search-how-to-g
 
    + `vectorSearch.algorithms.efConstruction` is the number of nearest neighbors used during indexing. Default is 400. The range is 100 to 1,000.
 
-   + `"vectorSearch.algorithms.fSearch` is the number of nearest neighbors used during search. Default is 500. The range is 100 to 1,000.
+   + `"vectorSearch.algorithms.efSearch` is the number of nearest neighbors used during search. Default is 500. The range is 100 to 1,000.
 
    + `vectorSearch.algorithms.metric` should be "cosine" if you're using Azure OpenAI, otherwise use the similarity metric associated with the embedding model you're using. Supported values are `cosine`, `dotProduct`, `euclidean`, `hamming` (used for [indexing binary data](vector-search-how-to-index-binary-data.md)).