Update documentation references to GPT-6 Astra

0_RAG_Overview.md

@@ -33,7 +33,7 @@
 |----------------|----------------|-------------------------------|
 | **Retrieval**  | Retrieval involves searching and extracting relevant documents or data from a knowledge base or external data source based on the input query. | Use Azure AI Search to index and query documents stored in Azure Storage Blob Containers. Configure the search index to perform semantic search and return the most relevant results. |
 | **Augmentation** | Augmentation involves enhancing the input query with the retrieved information to provide additional context and details. | Use Azure AI Search skillsets to preprocess the retrieved data, extracting key phrases, entities, and contextual information. This augmented input is then used to inform the generative model. |
-| **Generation** | Generation involves using a generative model to process the augmented input and produce a coherent and contextually relevant response. | Deploy a generative model like GPT-4 on Azure AI Foundry. Use an Azure Function App to orchestrate the data flow, calling the Azure AI Foundry model API to generate responses based on the augmented input. |
+| **Generation** | Generation involves using a generative model to process the augmented input and produce a coherent and contextually relevant response. | Deploy a generative model like GPT-6 Astra on Azure AI Foundry. Use an Azure Function App to orchestrate the data flow, calling the Azure AI Foundry model API to generate responses based on the augmented input. |
 
 > Implementing RAG Pattern with Azure AI:
 
@@ -44,7 +44,7 @@ graph LR
 
 1. **Set Up a Knowledge Base**: Store your documents in Azure Storage Blob Containers or another accessible data source.
 2. **Configure a Retrieval System**: Use Azure AI Search to index and retrieve relevant documents based on user queries.
-3. **Integrate with a Generative Model**: Use a generative model like GPT-4 to process the retrieved documents and generate responses.
+3. **Integrate with a Generative Model**: Use a generative model like GPT-6 Astra to process the retrieved documents and generate responses.
 
 > Traditional methods, `Retrieval-Augmented Generation (RAG)`, and `Agentic RAG`:
 
@@ -74,7 +74,7 @@ graph LR
     E --> F[Contextual Info]
     E --> G[Enhanced Query]
     A --> H[Generation]
-    H --> I[LLM: e.g GPT-4]
+    H --> I[LLM: e.g. GPT-6 Astra]
     H --> J[Coherent Response]
     A --> K[Applications]
     K --> L[Question Answering]

GPT-RAG_SolutionAccelerator/docs/AUTOMATED_INSTALLATION.md

@@ -88,7 +88,7 @@ Here is the complete list of resources for a standard Zero Trust deployment, inc
     <BR>Generates responses and vector embeddings.
     - SKU: Standard
     - Deployments:
-        - Regional gpt-4o, 40 TPM.
+        - Regional gpt-6-astra, 40 TPM.
         - text-embedding-3-large, 40 TPM.
 - **Search Service**
     <BR>Provides vector indexes for the retrieval step.

GPT-RAG_SolutionAccelerator/docs/CUSTOMIZATIONS_BYOR.md

@@ -103,7 +103,7 @@ If you're using a different name for the Embedding model, you'll need to set the
 | Embeddings Deployment Name | AZURE_EMBEDDINGS_DEPLOYMENT_NAME        |
 | Embeddings Vector Size     | AZURE_EMBEDDINGS_VECTOR_SIZE            |
 
-The default GPT model is **gpt-4o** version **2024-11-20** and the default deployment name is **chat**. If you're using the pre-created service with these default settings, no further modifications are required.
+The default GPT model is **gpt-6-astra** and the default deployment name is **chat**. If you're using the pre-created service with these default settings, no further modifications are required.
 
 However, if you're using a different name for the GPT, or a different model, you'll need to set the corresponding environment variables as shown in the table below.
 

GPT-RAG_SolutionAccelerator/docs/GUIDE.md

@@ -344,7 +344,7 @@ As of December 19, 2024, the solution has been tested in the following regions w
 
 #### Azure OpenAI Model Support
 
-By default, the solution deploys a **Global Deployment** of the GPT-4o model (version 2024-11-20) and a **Standard Deployment** of the text-embedding-3-large model for embedding generation. To ensure compatibility, refer to the Azure OpenAI model summary table and region availability at the following link:  
+By default, the solution deploys a **Global Deployment** of the GPT-6 Astra model and a **Standard Deployment** of the text-embedding-3-large model for embedding generation. To ensure compatibility, refer to the Azure OpenAI model summary table and region availability at the following link:
 [Azure OpenAI Model Availability](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models?tabs=global-standard%2Cstandard-chat-completions#model-summary-table-and-region-availability).
 
 If your selected region does not support these default models, you have two options:
@@ -355,7 +355,7 @@ Before proceeding with the [Provision Infrastructure Components](#9-provision-in
 
    For the Chat Completions model:
    ```bash
-   azd env set AZURE_CHAT_GPT_MODEL_NAME gpt-4o
+   azd env set AZURE_CHAT_GPT_MODEL_NAME gpt-6-astra
    azd env set AZURE_CHAT_GPT_MODEL_VERSION 2024-11-20
    azd env set AZURE_CHAT_GPT_DEPLOYMENT_TYPE GlobalStandard
    ```
@@ -1045,7 +1045,7 @@ To learn how this scenario works and configure it, check the [NL2SQL and Fabric
 
 ## Enabling Multimodality
 
-To enable GPT-RAG to use multimodal capabilities, such as those provided by GPT-4o, set the `MULTIMODAL` environment variable to `true` in the data ingestion and set the `multimodal_rag` agent strategy in agentic orchestration Function Apps. For more details on how multimodality works and image data is ingested, refer to [Multimodal RAG Overview](MULTIMODAL_RAG.md) and the documentation in the data ingestion repository: [Multimodal Ingestion](https://github.com/Azure/gpt-rag-ingestion?tab=readme-ov-file#multimodal-ingestion) respectively.
+To enable GPT-RAG to use multimodal capabilities, such as those provided by GPT-6 Astra, set the `MULTIMODAL` environment variable to `true` in the data ingestion and set the `multimodal_rag` agent strategy in agentic orchestration Function Apps. For more details on how multimodality works and image data is ingested, refer to [Multimodal RAG Overview](MULTIMODAL_RAG.md) and the documentation in the data ingestion repository: [Multimodal Ingestion](https://github.com/Azure/gpt-rag-ingestion?tab=readme-ov-file#multimodal-ingestion) respectively.
 
 > [!NOTE]
 > Currently, only the Agentic Orchestrator supports this feature.
@@ -1219,7 +1219,7 @@ Here is the complete list of resources for a standard Zero Trust deployment, inc
     <BR>Generates responses and vector embeddings.
     - SKU: Standard
     - Deployments:
-        - Regional gpt-4o, 40 TPM.
+        - Regional gpt-6-astra, 40 TPM.
         - text-embedding-3-large, 40 TPM.
 - **Search Service**
     <BR>Provides vector indexes for the retrieval step.

GPT-RAG_SolutionAccelerator/docs/MANUAL_ENVIRONMENT.md

@@ -23,7 +23,7 @@ az functionapp config appsettings set --name <FUNCTION_APP_NAME> --resource-grou
 
 az functionapp config appsettings set --name <FUNCTION_APP_NAME> --resource-group <RESOURCE_GROUP> --settings AZURE_OPENAI_CHATGPT_LLM_MONITORING="true"
 
-az functionapp config appsettings set --name <FUNCTION_APP_NAME> --resource-group <RESOURCE_GROUP> --settings AZURE_OPENAI_CHATGPT_MODEL="gpt-4o"
+az functionapp config appsettings set --name <FUNCTION_APP_NAME> --resource-group <RESOURCE_GROUP> --settings AZURE_OPENAI_CHATGPT_MODEL="gpt-6-astra"
 
 az functionapp config appsettings set --name <FUNCTION_APP_NAME> --resource-group <RESOURCE_GROUP> --settings AZURE_OPENAI_EMBEDDING_DEPLOYMENT="text-embedding-3-large"
 

GPT-RAG_SolutionAccelerator/docs/MANUAL_INSTALLATION.md

@@ -66,7 +66,7 @@ Here is the complete list of resources for a standard Zero Trust deployment, inc
     <BR>Generates responses and vector embeddings.
     - SKU: Standard
     - Deployments:
-        - Regional gpt-4o, 40 TPM.
+        - Regional gpt-6-astra, 40 TPM.
         - text-embedding-3-large, 40 TPM.
 - **Search Service**
     <BR>Provides vector indexes for the retrieval step.
@@ -227,7 +227,7 @@ Gather Necessary Information:
    - **Azure OpenAI**
      - Create an Azure OpenAI service
      - Create deployments:
-        - Regional gpt-4o, 40 TPM.
+        - Regional gpt-6-astra, 40 TPM.
         - text-embedding-3-large, 40 TPM.
      - Disable public network access.
 

GPT-RAG_SolutionAccelerator/docs/MULTIMODAL_RAG.md

@@ -41,9 +41,9 @@ This document outlines the architecture and workflow for our **multimodal Retrie
    - Perform retrieval, searching both `contentVector` and `captionVector` fields.
    - Results include both textual context and references to relevant images.
 
-6. **Response Generation (GPT-4o)**  
+6. **Response Generation (GPT-6 Astra)**
    - Build a multimodal prompt that includes retrieved text, image descriptions, and image URLs.
-   - GPT-4 generates a final enriched response, referencing both textual and visual elements.
+   - GPT-6 Astra generates a final enriched response, referencing both textual and visual elements.
 
 7. **Document & Image Lifecycle Management**  
    - Deleting a document from the index triggers an Azure Function to remove associated images from Blob Storage.

GPT-RAG_SolutionAccelerator/docs/RAG_CONCEPTS.md

@@ -16,7 +16,7 @@ Reference implementation of the Retrieval-Augmented Generation (RAG) pattern.
 
 * [Retrieval Augmented Generation (RAG) in Azure AI Search](https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview)
 
-* [Build and maintain your company Copilot with Azure ML and GPT-4](https://www.youtube.com/watch?si=B2tjsq4z4R7rksEw&v=2meEvuWAyXs)
+* [GPT-6 Astra in Microsoft Foundry](https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/models-sold-directly-by-azure)
 
 * [Revolutionize your Enterprise Data with ChatGPT: Next-gen Apps w/ Azure OpenAI and AI Search](https://aka.ms/entgptsearchblog)
 

README.md

@@ -22,8 +22,7 @@
 - [Azure Developer CLI (azd)](https://learn.microsoft.com/en-us/azure/developer/azure-developer-cli/)
 - [What is PowerShell?](https://learn.microsoft.com/en-us/powershell/scripting/overview?view=powershell-7.5)
 - [Integrate Azure services with virtual networks for network isolation](https://learn.microsoft.com/en-us/azure/virtual-network/vnet-integration-for-azure-services)
-- [GPT-4 and GPT-4 Turbo models max tokens](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models?tabs=global-standard%2Cstandard-chat-completions#gpt-4-and-gpt-4-turbo-models)
-- [GPT-4 and GPT-4 Turbo model availability](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models?tabs=standard%2Cstandard-chat-completions#gpt-4-and-gpt-4-turbo-model-availability)
+- [GPT-6 Astra model limits and availability](https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/models-sold-directly-by-azure)
 - [View the service principal for a managed identity using the Azure portal](https://learn.microsoft.com/en-us/entra/identity/managed-identities-azure-resources/how-to-view-managed-identity-service-principal?pivots=identity-mi-service-principal-portal#view-the-service-principal-for-a-managed-identity-using-the-azure-portal)
 - [Microsoft.Search searchServices bicep template](https://learn.microsoft.com/en-us/azure/templates/microsoft.search/searchservices?pivots=deployment-language-bicep)
 - [Recover/Purge a deleted resource](https://learn.microsoft.com/en-us/azure/ai-services/recover-purge-resources?tabs=azure-portal#recover-a-deleted-resource)