fixed links

Author: HeidiSteen

articles/search/cognitive-search-skill-azure-openai-embedding.md

@@ -30,7 +30,7 @@ Microsoft.Skills.Text.AzureOpenAIEmbeddingSkill
 
 ## Data limits
 
-The maximum size of a text input should be 8,000 tokens. If input exceeds the maximum allowed, the model throws an invalid request error. For more information, see the [tokens](/azure/ai-services/openai/overview#tokens) key concept in the Azure OpenAI documentation. Consider using the [Text Split skill](cognitive-search-skill-textplit.md) if you need data chunking.
+The maximum size of a text input should be 8,000 tokens. If input exceeds the maximum allowed, the model throws an invalid request error. For more information, see the [tokens](/azure/ai-services/openai/overview#tokens) key concept in the Azure OpenAI documentation. Consider using the [Text Split skill](cognitive-search-skill-textsplit.md) if you need data chunking.
 
 ## Skill parameters
 
@@ -102,7 +102,7 @@ For the given input text, a vectorized embedding output is produced.
 }
 ```
 
-The output resides in memory. To send this output to a field in the search index, you must define an [outputFieldMapping](cognitive-search-output-field-mapping.md) that maps the vectorized embedding output (which is an array) to a [vector field](vector-search-how-to-create.md). Assuming the skill output resides in the document's **embedding** node, and **content_vector** is the field in the search index, the outputFieldMapping in indexer should look like:
+The output resides in memory. To send this output to a field in the search index, you must define an [outputFieldMapping](cognitive-search-output-field-mapping.md) that maps the vectorized embedding output (which is an array) to a [vector field](vector-search-how-to-create-index.md). Assuming the skill output resides in the document's **embedding** node, and **content_vector** is the field in the search index, the outputFieldMapping in indexer should look like:
 
 ```json
   "outputFieldMappings": [

articles/search/vector-search-how-to-chunk-documents.md

@@ -64,7 +64,7 @@ The following examples demonstrate how chunking strategies are applied to [NASA'
 
 ### Text Split skill example
 
-Integrated data chunking through [Text Split skill](cognitive-search-skill-textsplit.dd) is in public preview. Use a preview REST API or an Azure SDK beta package for this scenario.
+Integrated data chunking through [Text Split skill](cognitive-search-skill-textsplit.md) is in public preview. Use a preview REST API or an Azure SDK beta package for this scenario.
 
 This section describes the built-in data chunking using a skills-driven approach and [Text Split skill parameters](cognitive-search-skill-textsplit.md#skill-parameters).