remove unsued deployment files

README.md

@@ -1,5 +1,5 @@
 ---
-name: Contoso Chat Retail with Azure AI Studio and Promptflow
+name: Contoso Chat Retail with Azure AI Studio and Prompty
 description: A retail copilot that answers customer queries with responses grounded in retailer's product and customer data.
 languages:
 - python
@@ -15,7 +15,7 @@ page_type: sample
 urlFragment: contoso-chat
 ---
 
-# Contoso Chat Retail with Azure AI Studio and Promptflow
+# Contoso Chat Retail with Azure AI Studio and Prompty
 
 This sample creates a customer support chat agent for an online retailer called Contoso Outdoors. The solution uses a _retrieval-augmented generation pattern_ to ground responses in the company's product and customer data. Customers can ask questions about the retailer's product catalog, and also get recommendations based on their prior purchases.
 
@@ -32,8 +32,8 @@ The sample uses [Azure AI Search](https://learn.microsoft.com/azure/search/) to
 
 By exploring and deploying this sample, you will learn to:
 - Build a retail copilot application using the [_RAG pattern_](https://learn.microsoft.com/azure/ai-studio/concepts/retrieval-augmented-generation).
-- Define and engineer prompts using the [_Prompty_ asset](https://microsoft.github.io/promptflow/tutorials/prompty-quickstart.html?highlight=prompty#).
-- Design, run & evaluate a copilot using the [_Promptflow_ framework](https://microsoft.github.io/promptflow/tutorials/flex-flow-quickstart.html).
+- Define and engineer prompts using the Prompty
+- Design, run & evaluate a copilot
 - Provision and deploy the solution to Azure using the [_Azure Developer CLI_](https://learn.microsoft.com/azure/developer/azure-developer-cli/).
 - Understand and apply Responsible AI practices like [_evaluation and content safety_](https://learn.microsoft.com/en-us/azure/ai-services/responsible-use-of-ai-overview?context=%2Fazure%2Fai-studio%2Fcontext%2Fcontext).
 
@@ -103,7 +103,6 @@ This has been the signature sample used to showcase end-to-end development of a
 * [Visual Studio Code](https://code.visualstudio.com) - recommended IDE for local development.
 * [Azure Developer CLI (azd)](https://aka.ms/install-azd) - to manage Azure deployment.
 * [Python 3.10+](https://www.python.org/downloads/) - to run, test & evaluate application.
-* [Promptflow 1.10+](https://microsoft.github.io/promptflow/) - to build, evaluate, and deploy application flows.
 
 You will also need:
 * [Azure Subscription](https://azure.microsoft.com/free/) - sign up for a free account.
@@ -222,26 +221,7 @@ The [contoso_chat](./contoso_chat) sample contains an example [chat.prompty](./c
     - `question` section to embed user query
     - `Instructions` section to reference related product recommendations
 
-This specific prompty takes 3 inputs: a `customer` object, a `documentation` object (that could be chat history) and a `question` string that represents the user query. You can now _load_, _execute_, and _trace_ individual prompty assets for a more granular prompt engineering solution.
-
- * See the [prompty specification](https://microsoft.github.io/promptflow/how-to-guides/develop-a-prompty/index.html#prompty-specification) for more details on the format.
- * Read the [prompty examples](https://github.com/microsoft/promptflow/tree/main/examples/prompty) for usage guidance from SDK or CLI.
-
-### Testing the Application Flow
-
-This sample uses a [flex-flow](https://microsoft.github.io/promptflow/how-to-guides/develop-a-flex-flow/index.html) feature that lets you "create LLM apps using a Python class or function as the entry point" - making it easier to test and run them using a code-first experience.
-  - This sample implements a _Function based flow_ 
-  - The entry point is the _get_response_ functionin `chat_request.py`
-
-You can now [test the flow](https://microsoft.github.io/promptflow/how-to-guides/develop-a-flex-flow/function-based-flow.html#flow-test) in different ways:
- - Run it directly, like any Python script
- - Convert it to a flow, then use `pf flow test --flow ...`
- - Start a UI to chat with the flow using `pf flow test --flow ... --ui`
-
-🌟 | Watch this space for more testing guidance.
-
-
-## Guidance
+This specific prompty takes 3 inputs: a `customer` object, a `documentation` object (that could be chat history) and a `question` string that represents the user query. You can now _load_, _execute_, and _trace_ individual prompty assets for a more granular prompt 
 
 ### Region Availability
 
@@ -270,14 +250,6 @@ This template uses [Managed Identity](https://learn.microsoft.com/entra/identity
 
 Additionally, we have added a [GitHub Action tool](https://github.com/microsoft/security-devops-action) that scans the infrastructure-as-code files and generates a report containing any detected issues. To ensure best practices we recommend anyone creating solutions based on our templates ensure that the [Github secret scanning](https://docs.github.com/code-security/secret-scanning/about-secret-scanning) setting is enabled in your repo.
 
-
-## Resources
-
-* [Azure AI Studio Documentation](https://learn.microsoft.com/azure/ai-studio/)
-* [Promptflow/Prompty Documentation](https://microsoft.github.io/promptflow/reference/python-library-reference/promptflow-core/promptflow.core.html?highlight=prompty#promptflow.core.Prompty)
-* [Develop Python apps that use Azure AI services](https://learn.microsoft.com/azure/developer/python/azure-ai-for-python-developers)
-* Related Sample: [Process Automation: Speech to Text and Summarization with ACA](https://github.com/Azure-Samples/summarization-openai-python-promptflow/blob/main/README.md)
-
 ## Troubleshooting
 
 Have questions or issues to report? Please [open a new issue](https://github.com/Azure-Samples/contoso-chat/issues) after first verifying that the same question or issue has not already been reported. In the latter case, please add any additional comments you may have, to the existing issue.