[Examples] use gpt-4o instead of gpt-35-turbo-0125

examples/dev_requirements.txt

@@ -11,3 +11,4 @@ black-nb
 pypandoc # for markdown reader
 pypandoc_binary # pypandoc pandoc backend
 panflute # for pandoc filters
+numpy<2.0.0 # for compatibility

examples/flex-flows/basic/flex-flow-quickstart-azure.ipynb

@@ -195,7 +195,7 @@
     "\n",
     "model_config = AzureOpenAIModelConfiguration(\n",
     "    connection=\"open_ai_connection\",\n",
-    "    azure_deployment=\"gpt-35-turbo\",\n",
+    "    azure_deployment=\"gpt-4o\",\n",
     ")"
    ]
   },

examples/flex-flows/basic/flex-flow-quickstart.ipynb

@@ -68,7 +68,7 @@
    "outputs": [],
    "source": [
     "# control the AOAI deployment (model) used in this example\n",
-    "deployment_name = \"gpt-35-turbo\""
+    "deployment_name = \"gpt-4o\""
    ]
   },
   {

examples/flex-flows/basic/llm.py

@@ -59,6 +59,6 @@ def my_llm_tool(
 if __name__ == "__main__":
     result = my_llm_tool(
         prompt="Write a simple Hello, world! program that displays the greeting message.",
-        deployment_name="gpt-35-turbo",
+        deployment_name="gpt-4o",
     )
     print(result)

examples/flex-flows/basic/programmer.py

@@ -25,7 +25,7 @@ def load_prompt(jinja2_template: str, text: str) -> str:
 
 @trace
 def write_simple_program(
-    text: str = "Hello World!", deployment_name="gpt-35-turbo"
+    text: str = "Hello World!", deployment_name="gpt-4o"
 ) -> Result:
     """Ask LLM to write a simple program."""
     prompt = load_prompt("hello.jinja2", text)
@@ -37,5 +37,5 @@ def write_simple_program(
     from promptflow.tracing import start_trace
 
     start_trace()
-    result = write_simple_program("Hello, world!", "gpt-35-turbo")
+    result = write_simple_program("Hello, world!", "gpt-4o")
     print(result)

examples/flex-flows/chat-async-stream/chat-stream-with-async-flex-flow.ipynb

@@ -141,7 +141,7 @@
     "\n",
     "# create a chatFlow obj with connection\n",
     "config = AzureOpenAIModelConfiguration(\n",
-    "    connection=\"open_ai_connection\", azure_deployment=\"gpt-35-turbo\"\n",
+    "    connection=\"open_ai_connection\", azure_deployment=\"gpt-4o\"\n",
     ")\n",
     "chat_flow = ChatFlow(config)\n",
     "\n",

examples/flex-flows/chat-async-stream/chat.prompty

@@ -5,7 +5,7 @@ model:
   api: chat
   configuration:
     type: azure_openai
-    azure_deployment: gpt-35-turbo
+    azure_deployment: gpt-4o
   parameters:
     temperature: 0.2
     stream: true

examples/flex-flows/chat-async-stream/flow.py

@@ -56,7 +56,7 @@ async def __call__(
 
     start_trace()
     config = AzureOpenAIModelConfiguration(
-        connection="open_ai_connection", azure_deployment="gpt-35-turbo"
+        connection="open_ai_connection", azure_deployment="gpt-4o"
     )
     flow = ChatFlow(model_config=config)
     result = flow("What's Azure Machine Learning?", [])

examples/flex-flows/chat-async-stream/init.json

@@ -1,6 +1,6 @@
 {
     "model_config": {
         "connection": "open_ai_connection",
-        "azure_deployment": "gpt-35-turbo"
+        "azure_deployment": "gpt-4o"
     }
 }

examples/flex-flows/chat-basic/chat-with-class-based-flow-azure.ipynb

@@ -130,7 +130,7 @@
     "\n",
     "# create the model config to be used in below flow calls\n",
     "config = AzureOpenAIModelConfiguration(\n",
-    "    connection=\"open_ai_connection\", azure_deployment=\"gpt-35-turbo\"\n",
+    "    connection=\"open_ai_connection\", azure_deployment=\"gpt-4o\"\n",
     ")"
    ]
   },
@@ -199,7 +199,7 @@
    "source": [
     "eval_flow = \"../eval-checklist/flow.flex.yaml\"\n",
     "config = AzureOpenAIModelConfiguration(\n",
-    "    connection=\"open_ai_connection\", azure_deployment=\"gpt-35-turbo\"\n",
+    "    connection=\"open_ai_connection\", azure_deployment=\"gpt-4o\"\n",
     ")\n",
     "eval_run = pf.run(\n",
     "    flow=eval_flow,\n",

examples/flex-flows/chat-basic/chat-with-class-based-flow.ipynb

@@ -109,7 +109,7 @@
     "\n",
     "# create the model config to be used in below flow calls\n",
     "config = AzureOpenAIModelConfiguration(\n",
-    "    connection=\"open_ai_connection\", azure_deployment=\"gpt-35-turbo\"\n",
+    "    connection=\"open_ai_connection\", azure_deployment=\"gpt-4o\"\n",
     ")"
    ]
   },

examples/flex-flows/chat-basic/chat.prompty

@@ -4,7 +4,7 @@ model:
   api: chat
   configuration:
     type: azure_openai
-    azure_deployment: gpt-35-turbo
+    azure_deployment: gpt-4o
   parameters:
     temperature: 0.2
     max_tokens: 1024

examples/flex-flows/chat-basic/flow.flex.yaml

@@ -6,7 +6,7 @@ sample:
   init: 
     model_config: 
       connection: open_ai_connection
-      azure_deployment: gpt-35-turbo
+      azure_deployment: gpt-4o
     max_total_token: 1024
 environment:
   # image: mcr.microsoft.com/azureml/promptflow/promptflow-python

examples/flex-flows/chat-basic/flow.py

@@ -59,7 +59,7 @@ def __call__(
 
     start_trace()
     config = AzureOpenAIModelConfiguration(
-        connection="open_ai_connection", azure_deployment="gpt-35-turbo"
+        connection="open_ai_connection", azure_deployment="gpt-4o"
     )
     flow = ChatFlow(config)
     result = flow("What's Azure Machine Learning?", [])

examples/flex-flows/chat-basic/init.json

@@ -1,7 +1,7 @@
 {
     "model_config": {
         "connection": "open_ai_connection",
-        "azure_deployment": "gpt-35-turbo"
+        "azure_deployment": "gpt-4o"
     },
     "max_total_token": 2048
 }

examples/flex-flows/chat-basic/run.yml

@@ -4,6 +4,6 @@ data: data.jsonl
 init:
   model_config:
     connection: open_ai_connection
-    azure_deployment: gpt-35-turbo
+    azure_deployment: gpt-4o
 column_mapping:
   question: ${data.question}

examples/flex-flows/chat-minimal/chat.prompty

@@ -4,7 +4,7 @@ model:
   api: chat
   configuration:
     type: azure_openai
-    azure_deployment: gpt-35-turbo
+    azure_deployment: gpt-4o
   parameters:
     temperature: 0.2
     max_tokens: 1024

examples/flex-flows/chat-stream/chat-stream-with-flex-flow.ipynb

@@ -141,7 +141,7 @@
     "\n",
     "# create a chatFlow obj with connection\n",
     "config = AzureOpenAIModelConfiguration(\n",
-    "    connection=\"open_ai_connection\", azure_deployment=\"gpt-35-turbo\"\n",
+    "    connection=\"open_ai_connection\", azure_deployment=\"gpt-4o\"\n",
     ")\n",
     "chat_flow = ChatFlow(config)\n",
     "\n",

examples/flex-flows/chat-stream/chat.prompty

@@ -5,7 +5,7 @@ model:
   api: chat
   configuration:
     type: azure_openai
-    azure_deployment: gpt-35-turbo
+    azure_deployment: gpt-4o
   parameters:
     temperature: 0.2
     stream: true

examples/flex-flows/chat-stream/flow.py

@@ -57,7 +57,7 @@ def __call__(
 
     start_trace()
     config = AzureOpenAIModelConfiguration(
-        connection="open_ai_connection", azure_deployment="gpt-35-turbo"
+        connection="open_ai_connection", azure_deployment="gpt-4o"
     )
     flow = ChatFlow(model_config=config)
     result = flow("What's Azure Machine Learning?", [])

examples/flex-flows/chat-stream/init.json

@@ -1,6 +1,6 @@
 {
     "model_config": {
         "connection": "open_ai_connection",
-        "azure_deployment": "gpt-35-turbo"
+        "azure_deployment": "gpt-4o"
     }
 }

examples/flex-flows/chat-with-functions/chat_with_tools.prompty

@@ -5,7 +5,7 @@ model:
     api: chat
     configuration:
       type: azure_openai
-      azure_deployment: gpt-35-turbo
+      azure_deployment: gpt-4o
     parameters:
       max_tokens: 128
       temperature: 0.2

examples/flex-flows/eval-checklist/check_list.py

@@ -75,7 +75,7 @@ def __aggregate__(self, line_results: list) -> dict:
     }
 
     config = AzureOpenAIModelConfiguration(
-        connection="open_ai_connection", azure_deployment="gpt-35-turbo"
+        connection="open_ai_connection", azure_deployment="gpt-4o"
     )
     flow = EvalFlow(config)
 

examples/flex-flows/eval-checklist/eval.prompty

@@ -6,7 +6,7 @@ model:
   api: chat
   configuration:
     type: azure_openai
-    azure_deployment: gpt-35-turbo
+    azure_deployment: gpt-4o
   parameters:
     max_tokens: 256
     temperature: 0.7

examples/flex-flows/eval-checklist/init.json

@@ -1,6 +1,6 @@
 {
     "model_config": {
         "connection": "open_ai_connection",
-        "azure_deployment": "gpt-35-turbo-0125"
+        "azure_deployment": "gpt-4o"
     }
 }

examples/flex-flows/eval-code-quality/code_quality.py

@@ -60,7 +60,7 @@ def __aggregate__(self, line_results: list) -> dict:
     start_trace()
     model_config = AzureOpenAIModelConfiguration(
         connection="open_ai_connection",
-        azure_deployment="gpt-35-turbo",
+        azure_deployment="gpt-4o",
     )
     evaluator = CodeEvaluator(model_config)
     result = evaluator('print("Hello, world!")')

examples/flex-flows/eval-code-quality/eval_code_quality.prompty

@@ -5,7 +5,7 @@ model:
   api: chat
   configuration:
     type: azure_openai
-    azure_deployment: gpt-35-turbo
+    azure_deployment: gpt-4o
   parameters:
     temperature: 0.2
 inputs: 

examples/flex-flows/eval-code-quality/init.json

@@ -1,6 +1,6 @@
 {
     "model_config": {
         "connection": "open_ai_connection",
-        "azure_deployment": "gpt-35-turbo"
+        "azure_deployment": "gpt-4o"
     }
 }

examples/flex-flows/eval-criteria-with-langchain/eval_conciseness.py

@@ -27,7 +27,7 @@ def __init__(self, custom_connection: CustomConnection):
             )
         elif "openai_api_key" in self.custom_connection.secrets:
             self.llm = AzureChatOpenAI(
-                deployment_name="gpt-35-turbo",
+                deployment_name="gpt-4o",
                 openai_api_key=self.custom_connection.secrets["openai_api_key"],
                 azure_endpoint=self.custom_connection.configs["azure_endpoint"],
                 openai_api_type="azure",

examples/flows/standard/basic-with-connection/flow.dag.yaml

@@ -22,7 +22,7 @@ nodes:
     path: hello.py
   inputs:
     connection: basic_custom_connection
-    deployment_name: text-davinci-003
+    deployment_name: gpt-35-turbo-instruct
     max_tokens: "120"
     prompt: ${hello_prompt.output}
 environment:

examples/flows/standard/basic/flow.dag.yaml

@@ -24,5 +24,5 @@ nodes:
     path: hello.py
   inputs:
     prompt: ${hello_prompt.output}
-    deployment_name: text-davinci-003
+    deployment_name: gpt-35-turbo-instruct
     max_tokens: "120"

examples/prompty/basic/basic.prompty

@@ -5,7 +5,7 @@ model:
     api: chat
     configuration:
         type: azure_openai
-        azure_deployment: gpt-35-turbo
+        azure_deployment: gpt-4o
     parameters:
         max_tokens: 128
         temperature: 0.2

examples/prompty/basic/prompty-quickstart.ipynb

@@ -112,7 +112,7 @@
     "configuration = AzureOpenAIModelConfiguration(\n",
     "    # azure_endpoint=\"${env:AZURE_OPENAI_ENDPOINT}\",  # Use ${env:<ENV_NAME>} to surround the environment variable name.\n",
     "    # api_key=\"${env:AZURE_OPENAI_API_KEY}\",\n",
-    "    azure_deployment=\"gpt-35-turbo-0125\",\n",
+    "    azure_deployment=\"gpt-4o\",\n",
     ")\n",
     "\n",
     "# override configuration with OpenAIModelConfiguration\n",
@@ -177,7 +177,7 @@
    "source": [
     "### Eval the result \n",
     "\n",
-    "Note: the eval flow returns a `json_object`. You need a new model version like [gpt-35-turbo (0125)](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models#gpt-35-models) to use the `json_object` response_format feature."
+    "Note: the eval flow returns a `json_object`."
    ]
   },
   {

examples/prompty/chat-basic/chat-with-prompty.ipynb

@@ -146,7 +146,7 @@
     "\n",
     "# override configuration with created connection in AzureOpenAIModelConfiguration\n",
     "configuration = AzureOpenAIModelConfiguration(\n",
-    "    connection=\"open_ai_connection\", azure_deployment=\"gpt-35-turbo-0125\"\n",
+    "    connection=\"open_ai_connection\", azure_deployment=\"gpt-4o\"\n",
     ")\n",
     "\n",
     "# override openai connection with OpenAIModelConfiguration\n",
@@ -229,7 +229,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note: the eval flow returns a `json_object`. You need a new model version like [gpt-35-turbo (0125)](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models#gpt-35-models) to use the `json_object` response_format feature."
+    "Note: the eval flow returns a `json_object`."
    ]
   },
   {
@@ -369,7 +369,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.9.17"
   },
   "resources": "examples/requirements.txt, examples/prompty/chat-basic, examples/prompty/eval-apology"
  },

examples/prompty/chat-basic/chat.prompty

@@ -6,7 +6,7 @@ model:
     configuration:
         type: azure_openai
         connection: open_ai_connection
-        azure_deployment: gpt-35-turbo
+        azure_deployment: gpt-4o
     parameters:
         max_tokens: 256
         temperature: 0.2

examples/prompty/eval-apology/README.md

@@ -13,8 +13,6 @@ Go to "Prompt flow" "Connections" tab. Click on "Create" button, select one of L
 
 Currently, there are two connection types supported by LLM tool: "AzureOpenAI" and "OpenAI". If you want to use "AzureOpenAI" connection type, you need to create an Azure OpenAI service first. Please refer to [Azure OpenAI Service](https://azure.microsoft.com/en-us/products/cognitive-services/openai-service/) for more details. If you want to use "OpenAI" connection type, you need to create an OpenAI account first. Please refer to [OpenAI](https://platform.openai.com/) for more details.
 
-Note: you need the new [gpt-35-turbo (0125) version](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models#gpt-35-models) to use the json_object response_format feature.
-
 ```bash
 # Override keys with --set to avoid yaml file changes
 pf connection create --file ../../connections/azure_openai.yml --set api_key=<your_api_key> api_base=<your_api_base>

examples/prompty/eval-apology/apology.prompty

@@ -6,7 +6,7 @@ model:
   configuration:
     type: azure_openai
     connection: open_ai_connection
-    azure_deployment: gpt-35-turbo-0125
+    azure_deployment: gpt-4o
   parameters:
     temperature: 0.2
     response_format: { "type": "json_object" }

examples/prompty/eval-basic/README.md

@@ -11,7 +11,6 @@ pip install promptflow-devkit
 ## Run prompty
 
 - Prepare your Azure Open AI resource follow this [instruction](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal) and get your `api_key` if you don't have one.
-- Note: you need the new [gpt-35-turbo (0125) version](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models#gpt-35-models) to use the json_object response_format feature.
 - Setup environment variables
 
 Ensure you have put your azure open ai endpoint key in [.env](../.env) file. You can create one refer to this [example file](../.env.example).

examples/prompty/eval-basic/eval.prompty

@@ -5,7 +5,7 @@ model:
   api: chat
   configuration:
     type: azure_openai
-    azure_deployment: gpt-35-turbo-0125
+    azure_deployment: gpt-4o
     api_key: ${env:AZURE_OPENAI_API_KEY}
     azure_endpoint: ${env:AZURE_OPENAI_ENDPOINT}
   parameters:

examples/prompty/format-output/README.md

@@ -21,8 +21,6 @@ Go to "Prompt flow" "Connections" tab. Click on "Create" button, select one of L
 
 Currently, there are two connection types supported by LLM tool: "AzureOpenAI" and "OpenAI". If you want to use "AzureOpenAI" connection type, you need to create an Azure OpenAI service first. Please refer to [Azure OpenAI Service](https://azure.microsoft.com/en-us/products/cognitive-services/openai-service/) for more details. If you want to use "OpenAI" connection type, you need to create an OpenAI account first. Please refer to [OpenAI](https://platform.openai.com/) for more details.
 
-- Note: you need the new [gpt-35-turbo (0125) version](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models#gpt-35-models) to use the json_object response_format feature.
-
 
 ```bash
 # Override keys with --set to avoid yaml file changes