diff --git a/libraries/radpdfprocessing/features/gen-ai-powered-document-insights/complete-context-question-processor.md b/libraries/radpdfprocessing/features/gen-ai-powered-document-insights/complete-context-question-processor.md
index 888ca426..b6a4dff8 100644
--- a/libraries/radpdfprocessing/features/gen-ai-powered-document-insights/complete-context-question-processor.md
+++ b/libraries/radpdfprocessing/features/gen-ai-powered-document-insights/complete-context-question-processor.md
@@ -1,10 +1,11 @@
 ---
 title: CompleteContextQuestionProcessor
+description: CompleteContextQuestionProcessor class enables you to ask questions about a PDF document and receive answers based on the entire document content.
 page_title: CompleteContextQuestionProcessor
 slug: radpdfprocessing-features-gen-ai-powered-document-insights-complete-context-question-processor
 tags: ai, document, analysis, question, processor, complete, context
 published: True
-position: 4
+position: 5
 ---
 <style>
 table, th, td {
@@ -22,8 +23,6 @@ table th:nth-of-type(2) {
 
 The **CompleteContextQuestionProcessor** class enables you to ask questions about a PDF document and receive answers based on the entire document content. This processor sends the complete document text to the AI model, which is suitable for smaller documents or when you need to ensure that the AI model has access to all the information in the document. This class inherits from the abstract **AIProcessorBase** class, which provides common functionality for all AI processors.
 
-## When to Use CompleteContextQuestionProcessor
-
 The **CompleteContextQuestionProcessor** is ideal for the following scenarios:
 
 1. **Small Documents**: When the document is small enough to fit within the token limit of the AI model.
@@ -36,7 +35,7 @@ However, if you're working with larger documents or want to optimize token usage
 
 |Property|Description|
 |---|---|
-|**Settings**|Gets the settings for the AI question-answering process. Returns [CompleteContextProcessorSettings]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-complete-context-question-processor%}#completectextprocessorsettings).|
+|**Settings**|Gets the settings for the AI question-answering process. Returns [CompleteContextProcessorSettings]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-complete-context-question-processor%}#completecontextprocessorsettings).|
 
 |Method|Description|
 |---|---|
@@ -58,114 +57,7 @@ The following example demonstrates how to use the **CompleteContextQuestionProce
 
 #### __[C#] Example 1: Using CompleteContextQuestionProcessor__
 
-```csharp
-private async void AskQuestionUsingCompleteContext()
-{
-    // Load the PDF document
-    string filePath = @"path\to\your\document.pdf";
-    PdfFormatProvider formatProvider = new PdfFormatProvider();
-    RadFixedDocument fixedDocument;
-    
-    using (FileStream fs = File.OpenRead(filePath))
-    {
-        fixedDocument = formatProvider.Import(fs);
-    }
-    
-    // Set up the AI client (Azure OpenAI in this example)
-    string key = Environment.GetEnvironmentVariable("AZUREOPENAI_KEY");
-    string endpoint = Environment.GetEnvironmentVariable("AZUREOPENAI_ENDPOINT");
-    string model = "gpt-4o-mini";
-    
-    AzureOpenAIClient azureClient = new(
-        new Uri(endpoint),
-        new Azure.AzureKeyCredential(key),
-        new AzureOpenAIClientOptions());
-    ChatClient chatClient = azureClient.GetChatClient(model);
-    
-    IChatClient iChatClient = new OpenAIChatClient(chatClient);
-    int maxTokenCount = 128000;
-    
-    // Create the processor
-    using (CompleteContextQuestionProcessor processor = 
-           new CompleteContextQuestionProcessor(iChatClient, maxTokenCount))
-    {
-        try
-        {
-            // Customize settings if needed
-            processor.Settings.TokenizationEncoding = "cl100k_base";
-            processor.Settings.ModelId = "gpt-4o-mini";
-            
-            // Example 1: Process full document
-            // Convert the document to a simple text representation
-            ISimpleTextDocument plainDoc = fixedDocument.ToSimpleTextDocument();
-            
-            // Ask a question about the full document
-            string question = "What is the main subject of this document?";
-            string answer = await processor.AnswerQuestion(plainDoc, question);
-            
-            Console.WriteLine($"Question: {question}");
-            Console.WriteLine($"Answer: {answer}");
-            
-            // Ask another question
-            string question2 = "What are the key conclusions drawn in this document?";
-            string answer2 = await processor.AnswerQuestion(plainDoc, question2);
-            
-            Console.WriteLine($"Question: {question2}");
-            Console.WriteLine($"Answer: {answer2}");
-            
-            // Example 2: Process specific pages
-            // Convert only pages 5-10 to a simple text document (0-based index)
-            ISimpleTextDocument partialDoc = fixedDocument.ToSimpleTextDocument(4, 9);
-            
-            // Ask a question about the specific pages
-            string pageQuestion = "Summarize the content of pages 5-10 of the document.";
-            string pageAnswer = await processor.AnswerQuestion(partialDoc, pageQuestion);
-            
-            Console.WriteLine($"Question: {pageQuestion}");
-            Console.WriteLine($"Answer: {pageAnswer}");
-        }
-        catch (ArgumentException ex) when (ex.Message.Contains("The text is too long"))
-        {
-            Console.WriteLine("The document is too large to process with CompleteContextQuestionProcessor.");
-            Console.WriteLine("Consider using PartialContextQuestionProcessor instead.");
-        }
-    }
-}
-```
-
-## Token Limit Considerations
-
-The **CompleteContextQuestionProcessor** sends the entire document to the AI model, which means the document must fit within the model's token limit. If the document exceeds this limit, the **AnswerQuestion** method will throw an **ArgumentException**. This is a key difference from the [SummarizationProcessor]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-summarization-processor%}#handling-large-documents), which can handle documents of any size.
-
-Here's how to check if a document is suitable for processing with **CompleteContextQuestionProcessor**:
-
-#### __[C#] Example 2: Checking Document Size__
-
-```csharp
-private bool IsDocumentSuitableForCompleteContext(RadFixedDocument document, int modelMaxInputTokenLimit)
-{
-    ISimpleTextDocument textDoc = document.ToSimpleTextDocument();
-    
-    if (textDoc is ISimpleTextDocumentInternal internalDoc)
-    {
-        string text = internalDoc.Text;
-        
-        // Create an encoding to count tokens
-        GptEncoding encoding = GptEncoding.GetEncoding("cl100k_base");
-        
-        // Estimate the token count for the document text + prompt + typical question
-        const string prompt = "You are a helpful assistant. Use the following context to answer the question.";
-        const string typicalQuestion = "What is this document about?";
-        
-        int estimatedTokens = encoding.Encode(prompt + text + typicalQuestion).Count;
-        
-        // Allow for a safety margin
-        return estimatedTokens <= (int)(modelMaxInputTokenLimit * 0.9);
-    }
-    
-    return false;
-}
-```
+<snippet id='libraries-pdf-features-gen-ai-ask-questions-using-complete-context'/>
 
 ## See Also
 
diff --git a/libraries/radpdfprocessing/features/gen-ai-powered-document-insights/getting-started.md b/libraries/radpdfprocessing/features/gen-ai-powered-document-insights/getting-started.md
new file mode 100644
index 00000000..71bd00ad
--- /dev/null
+++ b/libraries/radpdfprocessing/features/gen-ai-powered-document-insights/getting-started.md
@@ -0,0 +1,31 @@
+---
+title: Getting Started
+description: Learn how to use the GenAI-powered Document Insights functionality to summarize a PDF document with PdfProcessing.
+page_title: Overview
+slug: radpdfprocessing-features-gen-ai-powered-document-insights-getting-started
+tags: ai, document, analysis, overview, pdf, processing, genai, powered, insights
+published: True
+position: 2
+---
+
+# Getting Started
+
+The following example demonstrates how to use the GenAI-powered Document Insights functionality to summarize a PDF document and ask questions about it:
+
+>note The following code snippet is valid for Azure Open AI 9.3. The specific **IChatClient** initialization may be different according to the specific version.
+
+>important For **.NET Framework** and **.NET Standard** an [IEmbeddingsStorage]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-partial-context-question-processor%}#implementing-custom-iembeddingsstorage) implementation is required for the [PartialContextQuestionProcessor]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-partial-context-question-processor%}).
+
+#### __[C#] Example 1: Using GenAI-powered Document Insights__
+
+<snippet id='libraries-pdf-features-gen-ai-getting-started'/>
+
+When you run this code, the AI will process your document, generate a summary, and answer your questions.
+
+## See Also
+
+* [Prerequisites]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-prerequisites%})
+* [SummarizationProcessor]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-summarization-processor%})
+* [PartialContextQuestionProcessor]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-partial-context-question-processor%})
+* [Custom IEmbeddingsStorage Implementation]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-partial-context-question-processor%}#implementing-custom-iembeddingsstorage)
+* [CompleteContextQuestionProcessor]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-complete-context-question-processor%})
diff --git a/libraries/radpdfprocessing/features/gen-ai-powered-document-insights/overview.md b/libraries/radpdfprocessing/features/gen-ai-powered-document-insights/overview.md
index b0484c5e..07dfb581 100644
--- a/libraries/radpdfprocessing/features/gen-ai-powered-document-insights/overview.md
+++ b/libraries/radpdfprocessing/features/gen-ai-powered-document-insights/overview.md
@@ -1,5 +1,6 @@
 ---
 title: Overview
+description: Learn more about the GenAI-powered Document Insights feature of the PdfProcessing library. 
 page_title: Overview
 slug: radpdfprocessing-features-gen-ai-powered-document-insights-overview
 tags: ai, document, analysis, overview, pdf, processing, genai, powered, insights
@@ -11,12 +12,6 @@ position: 0
 
 The GenAI-powered Document Insights feature enables you to easily extract insights from PDF documents using Large Language Models (LLMs). This functionality allows you to summarize document content and ask questions about the document, with the AI providing relevant answers based on the document's content.
 
-The GenAI-powered Document Insights feature includes three main components:
-
-* **[SummarizationProcessor]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-summarization-processor%})**: Generates concise summaries of PDF documents.
-* **[CompleteContextQuestionProcessor]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-complete-context-question-processor%})**: Answers questions by providing the entire document content to the AI model.
-* **[PartialContextQuestionProcessor]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-partial-context-question-processor%})**: Answers questions by providing only the relevant portions of the document to the AI model.
-
 ## Key Features
 
 * **Extract Document Insights**: Quickly understand the key points of lengthy documents.
@@ -24,102 +19,18 @@ The GenAI-powered Document Insights feature includes three main components:
 * **Token Optimization**: Reduce token usage by only sending relevant portions of the document to the AI model as shown in the [PartialContextQuestionProcessor]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-partial-context-question-processor%}#when-to-use-partialcontextquestionprocessor) section.
 * **Multiple LLM Support**: Compatible with different AI providers including Azure OpenAI, OpenAI, and Ollama as described in the [Prerequisites]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-prerequisites%}#ai-provider-setup).
 
-## Complete Example
-
-The following example demonstrates how to use the GenAI-powered Document Insights functionality to summarize a PDF document and ask questions about it:
-
-#### __[C#] Example 1: Using GenAI-powered Document Insights__
-
-```csharp
-private async void ProcessPdfWithAI()
-{
-    // Load the PDF document
-    string filePath = @"path\to\your\document.pdf";
-    PdfFormatProvider formatProvider = new PdfFormatProvider();
-    RadFixedDocument fixedDocument;
-    
-    using (FileStream fs = File.OpenRead(filePath))
-    {
-        fixedDocument = formatProvider.Import(fs);
-    }
-    
-    // Convert the document to a simple text representation
-    ISimpleTextDocument plainDoc = fixedDocument.ToSimpleTextDocument();
-    
-    // Set up the AI client (Azure OpenAI in this example)
-    string key = Environment.GetEnvironmentVariable("AZUREOPENAI_KEY");
-    string endpoint = Environment.GetEnvironmentVariable("AZUREOPENAI_ENDPOINT");
-    string model = "gpt-4o-mini";
-    
-    AzureOpenAIClient azureClient = new(
-        new Uri(endpoint),
-        new Azure.AzureKeyCredential(key),
-        new AzureOpenAIClientOptions());
-    ChatClient chatClient = azureClient.GetChatClient(model);
-    
-    IChatClient iChatClient = new OpenAIChatClient(chatClient);
-    int maxTokenCount = 128000;
-    
-    // 1. Summarize the document
-    using (SummarizationProcessor summarizationProcessor = new SummarizationProcessor(iChatClient, maxTokenCount))
-    {
-        // Handle resources calculation event to control token usage
-        summarizationProcessor.SummaryResourcesCalculated += (object sender, SummaryResourcesCalculatedEventArgs e) =>
-        {
-            Console.WriteLine($"Estimated calls required: {e.EstimatedCallsRequired}");
-            Console.WriteLine($"Estimated tokens required: {e.EstimatedTokensRequired}");
-            
-            // Confirm if the operation should continue
-            e.ShouldContinueExecution = true;
-        };
-        
-        string summary = await summarizationProcessor.Summarize(plainDoc);
-        Console.WriteLine("Document Summary:");
-        Console.WriteLine(summary);
-    }
-    
-    // 2. Answer questions using partial context (recommended for efficiency)
-#if NET7_0_OR_GREATER
-    using (PartialContextQuestionProcessor partialContextQuestionProcessor = 
-           new PartialContextQuestionProcessor(iChatClient, maxTokenCount, plainDoc))
-    {
-        string question = "What are the main findings in the document?";
-        string answer = await partialContextQuestionProcessor.AnswerQuestion(question);
-        
-        Console.WriteLine($"Question: {question}");
-        Console.WriteLine($"Answer: {answer}");
-    }
-#else
-    IEmbeddingsStorage embeddingsStorage = new OllamaEmbeddingsStorage();
-    using (PartialContextQuestionProcessor partialContextQuestionProcessor = 
-           new PartialContextQuestionProcessor(iChatClient, embeddingsStorage, maxTokenCount, plainDoc))
-    {
-        string question = "What are the main findings in the document?";
-        string answer = await partialContextQuestionProcessor.AnswerQuestion(question);
-        
-        Console.WriteLine($"Question: {question}");
-        Console.WriteLine($"Answer: {answer}");
-    }
-#endif
-    
-    // 3. Answer questions using complete context (for smaller documents)
-    using (CompleteContextQuestionProcessor completeContextQuestionProcessor = 
-           new CompleteContextQuestionProcessor(iChatClient, maxTokenCount))
-    {
-        string question = "What is the conclusion of the document?";
-        string answer = await completeContextQuestionProcessor.AnswerQuestion(plainDoc, question);
-        
-        Console.WriteLine($"Question: {question}");
-        Console.WriteLine($"Answer: {answer}");
-    }
-}
-```
+The GenAI-powered Document Insights feature includes three main components:
 
-When you run this code, the AI will process your document, generate a summary, and answer your questions.
+|Processor|Description|
+|----|----|
+|**[SummarizationProcessor]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-summarization-processor%})**|Generates concise summaries of PDF documents.|
+|**[CompleteContextQuestionProcessor]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-complete-context-question-processor%})**|Answers questions by providing the entire document content to the AI model.|
+|**[PartialContextQuestionProcessor]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-partial-context-question-processor%})**|Answers questions by providing only the relevant portions of the document to the AI model.|
 
 ## See Also
 
 * [Prerequisites]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-prerequisites%})
+* [Getting Started]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-getting-started%})
 * [SummarizationProcessor]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-summarization-processor%})
 * [PartialContextQuestionProcessor]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-partial-context-question-processor%})
 * [CompleteContextQuestionProcessor]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-complete-context-question-processor%})
diff --git a/libraries/radpdfprocessing/features/gen-ai-powered-document-insights/partial-context-question-processor.md b/libraries/radpdfprocessing/features/gen-ai-powered-document-insights/partial-context-question-processor.md
index e747c6cd..14dcf039 100644
--- a/libraries/radpdfprocessing/features/gen-ai-powered-document-insights/partial-context-question-processor.md
+++ b/libraries/radpdfprocessing/features/gen-ai-powered-document-insights/partial-context-question-processor.md
@@ -1,10 +1,11 @@
 ---
 title: PartialContextQuestionProcessor
+description: PartialContextQuestionProcessor class enables you to ask questions about a PDF document and receive answers based on the most relevant parts of the document content.
 page_title: PartialContextQuestionProcessor
 slug: radpdfprocessing-features-gen-ai-powered-document-insights-partial-context-question-processor
 tags: ai, document, analysis, question, processor, partial, context, embeddings
 published: True
-position: 3
+position: 4
 ---
 <style>
 table, th, td {
@@ -25,8 +26,6 @@ table th:nth-of-type(3) {
 
 The **PartialContextQuestionProcessor** class enables you to ask questions about a PDF document and receive answers based on the most relevant parts of the document content. This processor uses embeddings to identify and send only the relevant portions of the document to the AI model, making it more efficient for token usage and more suitable for large documents. This class inherits from the abstract **AIProcessorBase** class, which provides common functionality for all AI processors.
 
-## When to Use PartialContextQuestionProcessor
-
 The **PartialContextQuestionProcessor** is ideal for the following scenarios:
 
 1. **Large Documents**: When the document exceeds the token limit of the AI model and cannot be processed in a single call.
@@ -37,10 +36,10 @@ The **PartialContextQuestionProcessor** is ideal for the following scenarios:
 
 |Constructor|Platform|Description|
 |---|---|---|
-|**PartialContextQuestionProcessor(IChatClient chatClient, int modelMaxInputTokenLimit, ISimpleTextDocument document)**|.NET 7+|Creates an instance with built-in embeddings storage|
+|**PartialContextQuestionProcessor(IChatClient chatClient, int modelMaxInputTokenLimit, ISimpleTextDocument document)**|.NET {{site.mindotnetversion}}|Creates an instance with built-in embeddings storage|
 |**PartialContextQuestionProcessor(IChatClient chatClient, IEmbeddingsStorage embeddingsStorage, int modelMaxInputTokenLimit, ISimpleTextDocument document)**|Any|Creates an instance with custom embeddings storage|
 
-> The .NET 7+ constructor uses **DefaultEmbeddingsStorage** internally, while the cross-platform constructor requires a custom implementation of **IEmbeddingsStorage** as shown in the [Prerequisites]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-prerequisites%}#iembeddingsstorage-setup-for-net-standard-and-net-framework).
+> The .NET {{site.mindotnetversion}} constructor uses **DefaultEmbeddingsStorage** internally, while the cross-platform constructor requires a custom implementation of **IEmbeddingsStorage** as shown in the [Prerequisites]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-prerequisites%}#iembeddingsstorage-setup-for-net-standard-and-net-framework).
 
 ### Properties and Methods
 
@@ -61,225 +60,38 @@ The settings class provides configuration options for the question-answering pro
 
 ## Usage Examples
 
-### Example 1: Using PartialContextQuestionProcessor in .NET 7 and Later
+#### Example 1: Using PartialContextQuestionProcessor in .NET {{site.mindotnetversion}} and Later
 
-This example demonstrates how to use the PartialContextQuestionProcessor with the built-in embeddings storage on .NET 7 and later. For setting up the AI client, see the [AI Provider Setup]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-prerequisites%}#ai-provider-setup) section:
+This example demonstrates how to use the PartialContextQuestionProcessor with the built-in embeddings storage on .NET {{site.mindotnetversion}} and later. For setting up the AI client, see the [AI Provider Setup]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-prerequisites%}#ai-provider-setup) section:
 
-```csharp
-private async void AskQuestionsUsingPartialContext()
-{
-    // Load the PDF document
-    string filePath = @"path\to\your\document.pdf";
-    PdfFormatProvider formatProvider = new PdfFormatProvider();
-    RadFixedDocument fixedDocument;
-    
-    using (FileStream fs = File.OpenRead(filePath))
-    {
-        fixedDocument = formatProvider.Import(fs);
-    }
-    
-    // Convert the document to a simple text representation
-    ISimpleTextDocument plainDoc = fixedDocument.ToSimpleTextDocument();
-    
-    // Set up the AI client (Azure OpenAI in this example)
-    string key = Environment.GetEnvironmentVariable("AZUREOPENAI_KEY");
-    string endpoint = Environment.GetEnvironmentVariable("AZUREOPENAI_ENDPOINT");
-    string model = "gpt-4o-mini";
-    
-    AzureOpenAIClient azureClient = new(
-        new Uri(endpoint),
-        new Azure.AzureKeyCredential(key),
-        new AzureOpenAIClientOptions());
-    ChatClient chatClient = azureClient.GetChatClient(model);
-    
-    IChatClient iChatClient = new OpenAIChatClient(chatClient);
-    int maxTokenCount = 128000;
-    
-    // Create the processor with built-in DefaultEmbeddingsStorage (.NET 7+ only)
-    using (PartialContextQuestionProcessor processor = 
-           new PartialContextQuestionProcessor(iChatClient, maxTokenCount, plainDoc))
-    {
-        // Customize settings if needed
-        processor.Settings.MaxNumberOfEmbeddingsSent = 20;
-        processor.Settings.EmbeddingTokenSize = 500;
-        
-        // Ask a question
-        string question = "What are the key findings in the document?";
-        string answer = await processor.AnswerQuestion(question);
-        
-        Console.WriteLine($"Question: {question}");
-        Console.WriteLine($"Answer: {answer}");
-        
-        // Ask additional questions using the same processor
-        string question2 = "What methodology was used in the research?";
-        string answer2 = await processor.AnswerQuestion(question2);
-        
-        Console.WriteLine($"\nQuestion: {question2}");
-        Console.WriteLine($"Answer: {answer2}");
-    }
-}
-```
+<snippet id='libraries-pdf-features-gen-ai-ask-questions-using-partial-context'/>
 
-### Example 2: Using PartialContextQuestionProcessor with Custom Embeddings (.NET Standard/.NET Framework)
+#### Example 2: Using PartialContextQuestionProcessor with Custom Embeddings (.NET Standard/.NET Framework)
 
 This example demonstrates how to use the PartialContextQuestionProcessor with a custom embeddings storage implementation as described in the [IEmbeddingsStorage Setup]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-prerequisites%}#iembeddingsstorage-setup-for-net-standard-and-net-framework) section:
 
-```csharp
-private async void AskQuestionsUsingPartialContext()
-{
-    // Load the PDF document and convert to simple text (same as Example 1)
-    string filePath = @"path\to\your\document.pdf";
-    PdfFormatProvider formatProvider = new PdfFormatProvider();
-    RadFixedDocument fixedDocument;
-    
-    using (FileStream fs = File.OpenRead(filePath))
-    {
-        fixedDocument = formatProvider.Import(fs);
-    }
-    
-    ISimpleTextDocument plainDoc = fixedDocument.ToSimpleTextDocument();
-    
-    // Set up the AI client (Azure OpenAI in this example)
-    string key = Environment.GetEnvironmentVariable("AZUREOPENAI_KEY");
-    string endpoint = Environment.GetEnvironmentVariable("AZUREOPENAI_ENDPOINT");
-    string model = "gpt-4o-mini";
-    
-    AzureOpenAIClient azureClient = new(
-        new Uri(endpoint),
-        new Azure.AzureKeyCredential(key),
-        new AzureOpenAIClientOptions());
-    ChatClient chatClient = azureClient.GetChatClient(model);
-    
-    IChatClient iChatClient = new OpenAIChatClient(chatClient);
-    int maxTokenCount = 128000;
-    
-    // Create an embeddings storage implementation
-    // Uses Ollama for embeddings and SQLite for storage
-    IEmbeddingsStorage embeddingsStorage = new OllamaEmbeddingsStorage();
-    
-    // Create the processor with custom embeddings storage
-    using (PartialContextQuestionProcessor processor = 
-           new PartialContextQuestionProcessor(iChatClient, embeddingsStorage, maxTokenCount, plainDoc))
-    {
-        try
-        {
-            // Ask a question
-            string question = "What are the main conclusions of the document?";
-            string answer = await processor.AnswerQuestion(question);
-            
-            Console.WriteLine($"Question: {question}");
-            Console.WriteLine($"Answer: {answer}");
-        }
-        catch (Exception ex)
-        {
-            Console.WriteLine($"Error processing question: {ex.Message}");
-        }
-    }
-}
-
-// IEmbeddingsStorage interface defines:
-// - SetText(string text, PartialContextProcessorSettings settings): Processes text and creates embeddings
-// - GetQuestionContext(string question): Returns relevant context for the question
-// - Dispose(): Cleans up resources
+<snippet id='libraries-pdf-features-gen-ai-ask-questions-using-partial-context-iembeddingsstorage'/>
 
-// Example implementation of IEmbeddingsStorage using Ollama and SQLite
-internal class OllamaEmbeddingsStorage : IEmbeddingsStorage
-{
-    // Configuration constants for Ollama embeddings model
-    private const string AllMinilmEmbeddingModelName = "all-minilm";
-    private const string DBName = "vectors.db";
-    private const int DimensionsForAllMinilm = 384; // Dimension size for all-minilm model
-    private static readonly string defaultCollectionName = "defaultName";
+### Implementing custom IEmbeddingsStorage
 
-    private readonly SqLiteVectorDatabase vectorDatabase;
-    private readonly OllamaEmbeddingModel embeddingModel;
-    private IVectorCollection vectorCollection;
+A sample custom implementation for the OllamaEmbeddingsStorage is shown in the below code snippet:
 
-    public OllamaEmbeddingsStorage()
-    {
-        // Initialize Ollama provider and SQLite vector database
-        OllamaProvider provider = new OllamaProvider();
-        this.embeddingModel = new OllamaEmbeddingModel(provider, id: AllMinilmEmbeddingModelName);
-        this.vectorDatabase = new SqLiteVectorDatabase(dataSource: DBName);
-    }
+>note Requires installing the following NuGet packages:
+> * **LangChain**
+> * **LangChain.Databases.Sqlite**
+> * **Microsoft.Extensions.AI.Ollama**
+> * **Telerik.Windows.Documents.AIConnector**
+> * **Telerik.Windows.Documents.Fixed**  
 
-    // Retrieve similar documents for a question using embeddings similarity
-    public async Task<string> GetQuestionContext(string question)
-    {
-        IReadOnlyCollection<Document> similarDocuments = await this.vectorCollection.GetSimilarDocuments(
-            this.embeddingModel, question, amount: 5);
+<snippet id='libraries-pdf-features-gen-ai-ask-questions-using-partial-context-ollama-embeddings-storage'/>
 
-        return similarDocuments.AsString();
-    }
+#### Example 3: Processing Specific Pages
 
-    public void SetText(string text, PartialContextProcessorSettings settings)
-    {
-        MemoryStream memoryStream = new MemoryStream();
-        StreamWriter writer = new StreamWriter(memoryStream);
-        writer.Write(text);
-        writer.Flush();
-        memoryStream.Position = 0;
+<snippet id='libraries-pdf-features-gen-ai-summarize-process-specific-pages'/>
 
-        if (this.vectorDatabase.IsCollectionExistsAsync(defaultCollectionName).Result)
-        {
-            this.vectorDatabase.DeleteCollectionAsync(defaultCollectionName).Wait();
-        }
-
-        this.vectorCollection = this.vectorDatabase.AddDocumentsFromAsync<TextLoader>(
-            this.embeddingModel,
-            dimensions: DimensionsForAllMinilm,
-            dataSource: DataSource.FromBytes(memoryStream.ToArray()),
-            textSplitter: null,
-            collectionName: defaultCollectionName,
-            behavior: AddDocumentsToDatabaseBehavior.JustReturnCollectionIfCollectionIsAlreadyExists).Result;
-    }
-
-    public void Dispose()
-    {
-        this.vectorDatabase.Dispose();
-    }
-}
-```
+#### Example 4: Optimizing Embeddings Settings
 
-### Example 3: Processing Specific Pages
-
-```csharp
-// Convert only pages 5-10 to a simple text document (0-based index)
-ISimpleTextDocument partialDoc = fixedDocument.ToSimpleTextDocument(4, 9);
-
-using (PartialContextQuestionProcessor processor = 
-       new PartialContextQuestionProcessor(iChatClient, maxTokenCount, partialDoc))
-{
-    // Ask a question about the specific pages
-    string question = "What information is presented on pages 5-10 of the document?";
-    string answer = await processor.AnswerQuestion(question);
-    
-    Console.WriteLine($"Question: {question}");
-    Console.WriteLine($"Answer: {answer}");
-}
-```
-
-### Example 4: Optimizing Embeddings Settings
-
-```csharp
-using (PartialContextQuestionProcessor processor = 
-       new PartialContextQuestionProcessor(iChatClient, maxTokenCount, plainDoc))
-{
-    // For more comprehensive answers at the cost of more tokens
-    processor.Settings.MaxNumberOfEmbeddingsSent = 50;   // Increase the number of context chunks
-    processor.Settings.EmbeddingTokenSize = 400;         // Increase the size of each context chunk
-    
-    // For more precise answers and fewer tokens
-    // processor.Settings.MaxNumberOfEmbeddingsSent = 15;  // Decrease for fewer chunks
-    // processor.Settings.EmbeddingTokenSize = 250;        // Decrease for smaller chunks
-    
-    string question = "What are the key recommendations in the document?";
-    string answer = await processor.AnswerQuestion(question);
-    
-    Console.WriteLine($"Question: {question}");
-    Console.WriteLine($"Answer: {answer}");
-}
-```
+<snippet id='libraries-pdf-features-gen-ai-summarize-optimize-embeddings-storage'/>
 
 ## See Also
 
diff --git a/libraries/radpdfprocessing/features/gen-ai-powered-document-insights/prerequisites.md b/libraries/radpdfprocessing/features/gen-ai-powered-document-insights/prerequisites.md
index 3f1ee4cd..774a5936 100644
--- a/libraries/radpdfprocessing/features/gen-ai-powered-document-insights/prerequisites.md
+++ b/libraries/radpdfprocessing/features/gen-ai-powered-document-insights/prerequisites.md
@@ -1,5 +1,6 @@
 ---
 title: Prerequisites
+description: Get familiar with the requirements for using the GenAI-powered Document Insights functionality in the PdfProcessing library. 
 page_title: Prerequisites
 slug: radpdfprocessing-features-gen-ai-powered-document-insights-prerequisites
 tags: ai, document, analysis, prerequisites, setup, installation
@@ -32,20 +33,17 @@ In addition to the [standard RadPdfProcessing assemblies]({%slug radpdfprocessin
 
 |.NET Framework|.NET Standard-compatible|
 |---|---|
-|**Telerik.Windows.Documents.AIConnector.dll**|**Telerik.Documents.AIConnector.dll**|
+|**Telerik.Windows.Documents.AIConnector.dll** * |**Telerik.Documents.AIConnector.dll** *|
 
-## NuGet Packages
-
-You will also need to install the following NuGet packages:
+The **Documents.AIConnector** assembly internally depends on **Microsoft.Extensions.AI.Abstractions**  which is currently available only in **preview** version.*
 
-* **Microsoft.Extensions.AI.Abstractions** - Core abstractions for AI functionality
-* **SharpToken** - For token counting and text chunking
+## NuGet Packages
 
-Additionally, you'll need a package for your specific AI provider:
+You will also need to install a package for your specific AI provider:
 
-* **Azure.AI.OpenAI** - For using Azure OpenAI
-* **Microsoft.Extensions.AI.OpenAI** - For using OpenAI
-* **OllamaSharp** - For using Ollama (local AI models)
+* **Microsoft.Extensions.AI.OpenAI** and **Azure.AI.OpenAI** - For using Azure OpenAI
+* **Microsoft.Extensions.AI.OpenAI** and **OpenAI** - For using OpenAI
+* **Microsoft.Extensions.AI.Ollama** - For using Ollama (local AI models)
 
 ## AI Provider Setup
 
@@ -61,6 +59,8 @@ Before using the GenAI-powered Document Insights functionality, you need to set
 2. Deploy a model in your Azure OpenAI resource.
 3. Get your Azure OpenAI endpoint and key.
 
+>note The following code snippet is valid for Azure Open AI 9.3. The specific **IChatClient** initialization may be different according to the specific version.
+
 #### __[C#] Example 1: Setting up Azure OpenAI__
 
 ```csharp
@@ -125,72 +125,6 @@ IChatClient iChatClient = new OllamaChatClient(new Uri("http://localhost:11434/"
 int maxTokenLimit = 4096; // Adjust based on your model
 ```
 
-## IEmbeddingsStorage Setup for .NET Standard and .NET Framework
-
-When using the [PartialContextQuestionProcessor]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-partial-context-question-processor%}#constructors-and-platform-support)  in .NET Standard or .NET Framework, you need to provide an implementation of the **IEmbeddingsStorage** interface.
-
-For this sample Ollama implementation, you'll need to add references to the following NuGet packages:
-* **LangChain.Ollama**
-* **LangChain.Databases.Sqlite**
-
-#### __[C#] Example 4: OllamaEmbeddingsStorage Implementation__
-
-```csharp
-internal class OllamaEmbeddingsStorage : IEmbeddingsStorage
-{
-    private const string AllMinilmEmbeddingModelName = "all-minilm";
-    private const string DBName = "vectors.db";
-    private const int DimensionsForAllMinilm = 384; // Should be 384 for all-minilm
-    private static readonly string defaultCollectionName = "defaultName";
-
-    private readonly SqLiteVectorDatabase vectorDatabase;
-    private readonly OllamaEmbeddingModel embeddingModel;
-    private IVectorCollection vectorCollection;
-
-    public OllamaEmbeddingsStorage()
-    {
-        OllamaProvider provider = new OllamaProvider();
-        this.embeddingModel = new OllamaEmbeddingModel(provider, id: AllMinilmEmbeddingModelName);
-        this.vectorDatabase = new SqLiteVectorDatabase(dataSource: DBName);
-    }
-
-    public async Task<string> GetQuestionContext(string question)
-    {
-        IReadOnlyCollection<Document> similarDocuments = await this.vectorCollection.GetSimilarDocuments(
-            this.embeddingModel, question, amount: 5);
-
-        return similarDocuments.AsString();
-    }
-
-    public void SetText(string text, PartialContextProcessorSettings settings)
-    {
-        MemoryStream memoryStream = new MemoryStream();
-        StreamWriter writer = new StreamWriter(memoryStream);
-        writer.Write(text);
-        writer.Flush();
-        memoryStream.Position = 0;
-
-        if (this.vectorDatabase.IsCollectionExistsAsync(defaultCollectionName).Result)
-        {
-            this.vectorDatabase.DeleteCollectionAsync(defaultCollectionName).Wait();
-        }
-
-        this.vectorCollection = this.vectorDatabase.AddDocumentsFromAsync<TextLoader>(
-            this.embeddingModel,
-            dimensions: DimensionsForAllMinilm,
-            dataSource: DataSource.FromBytes(memoryStream.ToArray()),
-            textSplitter: null,
-            collectionName: defaultCollectionName,
-            behavior: AddDocumentsToDatabaseBehavior.JustReturnCollectionIfCollectionIsAlreadyExists).Result;
-    }
-
-    public void Dispose()
-    {
-        this.vectorDatabase.Dispose();
-    }
-}
-```
-
 ## See Also
 
 * [GenAI-powered Document Insights Overview]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-overview%})
diff --git a/libraries/radpdfprocessing/features/gen-ai-powered-document-insights/summarization-processor.md b/libraries/radpdfprocessing/features/gen-ai-powered-document-insights/summarization-processor.md
index 052bfb8e..6fc2d8f5 100644
--- a/libraries/radpdfprocessing/features/gen-ai-powered-document-insights/summarization-processor.md
+++ b/libraries/radpdfprocessing/features/gen-ai-powered-document-insights/summarization-processor.md
@@ -1,10 +1,11 @@
 ---
 title: SummarizationProcessor
+description: SummarizationProcessor class enables you to generate concise summaries of PDF documents using Large Language Models.
 page_title: SummarizationProcessor
 slug: radpdfprocessing-features-gen-ai-powered-document-insights-summarization-processor
 tags: ai, document, analysis, summarization, processor, summary
 published: True
-position: 2
+position: 3
 ---
 <style>
 table, th, td {
@@ -28,12 +29,10 @@ The **SummarizationProcessor** class enables you to generate concise summaries o
 |---|---|
 |**Settings**|Gets or sets the settings that will be used for summarization.|
 
-
 |Method|Description|
 |---|---|
 |**Task<string> Summarize(ISimpleTextDocument document)**|Generates a summary of the provided document. The parameter **document** is an **ISimpleTextDocument** containing the text to be summarized.|
 
-
 |Event|Description|
 |---|---|
 |**EventHandler<SummaryResourcesCalculatedEventArgs> SummaryResourcesCalculated**|Triggered before the actual summarization process begins, providing information about the estimated resource usage. The **SummaryResourcesCalculatedEventArgs** provides properties: **EstimatedCallsRequired** (number of API calls required), **EstimatedTokensRequired** (number of tokens to be processed), and **ShouldContinueExecution** (boolean flag indicating whether to proceed with summarization, default is **true** for single-call and **false** for multi-call operations).|
@@ -46,92 +45,15 @@ The **SummarizationProcessorSettings** class provides configuration options for
 
 #### __[C#] Example 1: Configuring SummarizationProcessorSettings__
 
-```csharp
-// Create a summarization processor with settings
-using (SummarizationProcessor summarizationProcessor = new SummarizationProcessor(iChatClient, maxTokenCount))
-{
-    // Configure the summarization settings
-    summarizationProcessor.Settings.PromptAddition = "Focus on the key points and main arguments. ";
-    
-    // Rest of the code...
-}
-```
+<snippet id='libraries-pdf-features-gen-ai-summirize-configure'/>
 
 ## Usage Example
 
-The following example demonstrates how to use the **SummarizationProcessor** to generate a summary of a PDF document. To set up the AI client as shown in this example, see the [AI Provider Setup]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-prerequisites%}#ai-provider-setup) section:
+The following example demonstrates how to use the **SummarizationProcessor** to generate a summary of a PDF document. To set up the AI client as shown in this example, see the [AI Provider Setup]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-prerequisites%}#ai-provider-setup) section.
 
-#### __[C#] Example 2: Using SummarizationProcessor__
+### Handling Large Documents
 
-```csharp
-private async void SummarizeDocument()
-{
-    // Load the PDF document
-    string filePath = @"path\to\your\document.pdf";
-    PdfFormatProvider formatProvider = new PdfFormatProvider();
-    RadFixedDocument fixedDocument;
-    
-    using (FileStream fs = File.OpenRead(filePath))
-    {
-        fixedDocument = formatProvider.Import(fs);
-    }
-    
-    // Convert the document to a simple text representation
-    ISimpleTextDocument plainDoc = fixedDocument.ToSimpleTextDocument();
-    
-    // Set up the AI client (Azure OpenAI in this example)
-    string key = Environment.GetEnvironmentVariable("AZUREOPENAI_KEY");
-    string endpoint = Environment.GetEnvironmentVariable("AZUREOPENAI_ENDPOINT");
-    string model = "gpt-4o-mini";
-    
-    AzureOpenAIClient azureClient = new(
-        new Uri(endpoint),
-        new Azure.AzureKeyCredential(key),
-        new AzureOpenAIClientOptions());
-    ChatClient chatClient = azureClient.GetChatClient(model);
-    
-    IChatClient iChatClient = new OpenAIChatClient(chatClient);
-    int maxTokenCount = 128000;
-    
-    using (SummarizationProcessor summarizationProcessor = new SummarizationProcessor(iChatClient, maxTokenCount))
-    {
-        // Configure the summarization settings (optional)
-        summarizationProcessor.Settings.PromptAddition = "Focus on the key points and main arguments. ";
-        
-        // Subscribe to the SummaryResourcesCalculated event to monitor token usage
-        summarizationProcessor.SummaryResourcesCalculated += (object sender, SummaryResourcesCalculatedEventArgs e) =>
-        {
-            Console.WriteLine($"This summarization will require approximately {e.EstimatedTokensRequired} tokens " +
-                             $"and {e.EstimatedCallsRequired} API calls.");
-            
-            // For large documents, you need to explicitly approve the operation
-            // to avoid unexpected API usage and costs
-            if (e.EstimatedCallsRequired > 1)
-            {
-                Console.WriteLine("Document is large and will require multiple API calls.");
-                
-                // Set to true to proceed with summarization, or leave as false to cancel
-                e.ShouldContinueExecution = false;
-            }
-        };
-        
-        try
-        {
-            // Generate the summary
-            string summary = await summarizationProcessor.Summarize(plainDoc);
-            Console.WriteLine("Document Summary:");
-            Console.WriteLine(summary);
-        }
-        catch (OperationCanceledException)
-        {
-            Console.WriteLine("Summarization was cancelled.");
-        }
-    }
-}
-```
-## Handling Large Documents
-
-For large documents that exceed the token limit of the model, **SummarizationProcessor** automatically splits the document into smaller chunks and processes them separately. This approach is similar to how [PartialContextQuestionProcessor]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-partial-context-question-processor%}) handles large documents. The process is as follows:
+For large documents that exceed the token limit of the model, **SummarizationProcessor** automatically splits the document into smaller chunks and processes them separately:
 
 1. The document is split into chunks that fit within the model's token limit.
 2. Each chunk is summarized individually.
@@ -139,28 +61,10 @@ For large documents that exceed the token limit of the model, **SummarizationPro
 
 This approach allows the processor to efficiently handle documents of any size, but it increases the number of API calls required. The **SummaryResourcesCalculated** event provides information about the expected resource usage, allowing you to decide whether to proceed with the operation.
 
-#### __[C#] Example 4: Configuring Resource Limits__
-
-```csharp
-summarizationProcessor.SummaryResourcesCalculated += (object sender, SummaryResourcesCalculatedEventArgs e) =>
-{
-    // Set limits based on your requirements
-    int maxTokens = 500000; // Maximum tokens to process
-    int maxCalls = 5;      // Maximum API calls to make
-    
-    if (e.EstimatedTokensRequired > maxTokens || e.EstimatedCallsRequired > maxCalls)
-    {
-        Console.WriteLine("Document exceeds resource limits. Cancelling summarization.");
-        e.ShouldContinueExecution = false;
-    }
-    else
-    {
-        Console.WriteLine($"Proceeding with summarization. Estimated tokens: {e.EstimatedTokensRequired}, " +
-                         $"Estimated calls: {e.EstimatedCallsRequired}");
-        e.ShouldContinueExecution = true;
-    }
-};
-```
+#### __[C#] Example 2: Using SummarizationProcessor__
+
+<snippet id='libraries-pdf-features-gen-ai-summarize-document'/>
+
 ## See Also
 
 * [GenAI-powered Document Insights Overview]({%slug radpdfprocessing-features-gen-ai-powered-document-insights-overview%})