resolve build issues

Author: laujan

articles/ai-services/content-understanding/concepts/analyzers-overview.md

@@ -27,8 +27,6 @@ Analyzers are the core processing units in Content Understanding that define how
 * Content extraction configurations - determining what foundational elements to extract.
 * Field extraction schemas - specifying how to get the fields(extract/generate/classify) from the content.
 
-:::image type="content" source="../concepts/analyzer-architecture.png" alt-text="Screenshot of Analyzer architecture.":::
-
 Key benefits of analyzers include:
 
 * **Consistency**: Analyzers ensure uniform processing across all content by applying the same extraction rules and schemas, delivering reliable and predictable results.
@@ -85,7 +83,7 @@ The value lies in its ability to handle multiple content types (text, audio, vid
 
 Each modality supports specific generation approaches optimized for that content type. We support the following methods across modalities.
 > [!NOTE]
-> Extract method is only supported for documents for now. To learn more about modality support, *see* [field schema limits](service-limits.md#Field-schema-limits) page.
+> Extract method is only supported for documents for now. To learn more about modality support, *see* [field schema limits](../service-limits.md#Field-schema-limits) page.
 >
 > There's a distinction between digital documents (PDFs, DOCX, etc.) and text documents (plain text, markdown, HTML) in terms of content extraction capabilities.
 
@@ -139,7 +137,7 @@ Azure AI Content Understanding supports three processing locations:
 
 1. **Video Content Analysis**: A media company with a global audience wants to analyze video content for metadata extraction and tagging. Choosing a global processing location enables optimization of performance and scalability, ensuring efficient processing of large volumes of video data.
 
-To learn more, *see* [Azure OpenAI Deployment Data processing Locations](../../openai/how-to/deployment-types.ms#azure-openai-deployment-data-processing-locations).
+To learn more, *see* [Azure OpenAI Deployment Data processing Locations](../../openai/how-to/deployment-types.md#azure-openai-deployment-data-processing-locations).
 
 ## Next steps
 

articles/ai-services/content-understanding/concepts/retrieval-augmented-generation.md

@@ -62,7 +62,7 @@ Content extraction forms the foundation of effective **RAG** systems by transfor
 * **Audio:** Generate speaker-aware transcriptions that accurately capture spoken content while automatically detecting and processing multiple languages.
 * **Video:** Video data is segmented into meaningful units, transcribe spoken content, and provide scene descriptions while addressing context window limitations in generative AI models.
 
-While content extraction provides a strong foundation for indexing and retrieval, it may not fully address domain-specific needs or provide deeper contextual insights. Learn more about [content extraction](capabilities.md)
+While content extraction provides a strong foundation for indexing and retrieval, it may not fully address domain-specific needs or provide deeper contextual insights. Learn more about [content extraction](analyzers-overview.md)
 
 ### Field Extraction: Enhance knowledge bases for better retrieval
 
@@ -75,7 +75,7 @@ Field extraction complements content extraction by generating targeted metadata
 
 Combining content extraction with field extraction enables organizations to create a contextually rich knowledge base optimized for indexing, retrieval, and **RAG** scenarios, ensuring more accurate and meaningful responses to user queries.
 
-Learn more about [field extraction](capabilities.md#field-extraction).
+Learn more about [field extraction](analyzers-overview.md#field-extraction).
 
 #### Analyzer and schema configuration
 
@@ -510,5 +510,5 @@ Content Understanding supports the following development options:
 * Try our **RAG** [code samples.](https://github.com/Azure-Samples/azure-ai-search-with-content-understanding-python#samples)
 * Follow our [**RAG** Tutorial](../tutorial/build-rag-solution.md)
 * Learn more about [document](../document/overview.md), [image](../image/overview.md), [audio](../audio/overview.md), [video](../video/overview.md) capabilities.
-* Learn more about Content Understanding [**best practices**](../concepts/best-practices.md) and [**capabilities**](../concepts/capabilities.md).
+* Learn more about Content Understanding [**best practices**](../concepts/best-practices.md) and [**capabilities**](../concepts/analyzers-overview.md).
 * Review Content Understanding [**code samples**](https://github.com/Azure-Samples/azure-ai-content-understanding-python/tree/main)

articles/ai-services/content-understanding/toc.yml

@@ -38,7 +38,7 @@ items:
   items:
   - name: Overview
     displayName: content understanding capabilities, document, text, images, video, audio, visual, structured, content, field, extraction
-    href: concepts/capabilities.md
+    href: concepts/analyzers-overview.md
   - name: Analyzer templates
     displayName: analyzer, templates, document, text, images, video, audio, multimodal, visual, structured, content, field, extraction
     href: concepts/analyzer-templates.md

articles/ai-services/content-understanding/tutorial/build-rag-solution.md

@@ -42,7 +42,7 @@ To get started, you need **An active Azure subscription**. If you don't have an
 * **API Version:** This tutorial uses the latest preview [API version](/rest/api/contentunderstanding/analyzers?view=rest-contentunderstanding-2024-12-01-preview&preview&preserve-view=true): `2024-12-01-preview`.
 * **Python Environment:** Install [Python 3.11](https://www.python.org/downloads/) to execute the provided code samples and scripts.
 * This tutorial follows this sample code can be found in our [Python notebook](https://github.com/Azure-Samples/azure-ai-search-with-content-understanding-python#samples). Follow the [README](https://github.com/Azure-Samples/azure-ai-search-with-content-understanding-python/blob/main/README.md) to create essential resources, grant resources the right Access control(IAM) roles and install all packages needed for this tutorial.
-* The [multimodal data](../concepts/capabilities.md) used in this tutorial consists of documents, images, audio, and video. They're designed to guide you through the process of building a robust RAG solution with Azure AI Content Understanding.
+* The [multimodal data](../concepts/analyzers-overview.md) used in this tutorial consists of documents, images, audio, and video. They're designed to guide you through the process of building a robust RAG solution with Azure AI Content Understanding.
 
 ## Extract data
 
@@ -793,4 +793,4 @@ while True:
 
 * [Try a multimodal content solution accelerator](https://github.com/microsoft/content-processing-solution-accelerator)
 
-* [Learn more Content Understanding capabilities](../concepts/capabilities.md)
+* [Learn more Content Understanding capabilities](../concepts/analyzers-overview.md)