Azure · YusakuNo1 · Mar 17, 2025 · Mar 17, 2025
diff --git a/.vscode/cspell.json b/.vscode/cspell.json
@@ -375,6 +375,7 @@
     "prebuilts",
     "premf",
     "prevsnapshot",
+    "prompty",
     "pschema",
     "PSECRET",
     "pydantic",

@@ -9,6 +9,7 @@
 ### Bugs Fixed
 
 * Fix for a bug in agent tracing causing event handler return values to not be returned when tracing is enabled.
+* Add Prompty support from AI Foundry Inference SDK into AI Foundry Projects SDK
 
 ### Breaking Changes
 

@@ -0,0 +1,15 @@
+# ------------------------------------
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ------------------------------------
+# pylint: disable=unused-import
+try:
+    import prompty  # pylint: disable=unused-import
+except ImportError:
+    raise ImportError(
+        "The 'prompty' package is required to use the 'azure.ai.projects.prompts' module. "
+        "Please install it by running 'pip install prompty'."
+    )
+
+from ._patch import patch_sdk as _patch_sdk, PromptTemplate
+_patch_sdk()
@@ -0,0 +1,121 @@
+# ------------------------------------
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ------------------------------------
+# pylint: disable=line-too-long,R
+"""Customize generated code here.
+
+Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize
+"""
+
+import traceback
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+from typing_extensions import Self
+from prompty import headless, load, prepare
+from prompty.core import Prompty
+from ._utils import remove_leading_empty_space
+
+
+class PromptTemplate:
+    """The helper class which takes variant of inputs, e.g. Prompty format or string, and returns the parsed prompt in an array."""
+
+    @classmethod
+    def from_prompty(cls, file_path: str) -> Self:
+        """Initialize a PromptTemplate object from a prompty file.
+
+        :param file_path: The path to the prompty file.
+        :type file_path: str
+        :return: The PromptTemplate object.
+        :rtype: PromptTemplate
+        """
+        if not file_path:
+            raise ValueError("Please provide file_path")
+
+        # Get the absolute path of the file by `traceback.extract_stack()`, it's "-2" because:
+        #  In the stack, the last function is the current function.
+        #  The second last function is the caller function, which is the root of the file_path.
+        stack = traceback.extract_stack()
+        caller = Path(stack[-2].filename)
+        abs_file_path = Path(caller.parent / Path(file_path)).resolve().absolute()
+
+        prompty = load(str(abs_file_path))
+        prompty.template.type = "mustache"  # For Azure, default to mustache instead of Jinja2
+        return cls(prompty=prompty)
+
+    @classmethod
+    def from_string(cls, prompt_template: str, api: str = "chat", model_name: Optional[str] = None) -> Self:
+        """Initialize a PromptTemplate object from a message template.
+
+        :param prompt_template: The prompt template string.
+        :type prompt_template: str
+        :param api: The API type, e.g. "chat" or "completion".
+        :type api: str
+        :param model_name: The model name, e.g. "gpt-4o-mini".
+        :type model_name: str
+        :return: The PromptTemplate object.
+        :rtype: PromptTemplate
+        """
+        prompt_template = remove_leading_empty_space(prompt_template)
+        prompty = headless(api=api, content=prompt_template)
+        prompty.template.type = "mustache"  # For Azure, default to mustache instead of Jinja2
+        prompty.template.parser = "prompty"
+        return cls(
+            api=api,
+            model_name=model_name,
+            prompty=prompty,
+        )
+
+    def __init__(
+        self,
+        *,
+        api: str = "chat",
+        prompty: Optional[Prompty] = None,
+        prompt_template: Optional[str] = None,
+        model_name: Optional[str] = None,
+    ) -> None:
+        self.prompty = prompty
+        if self.prompty is not None:
+            self.model_name = (
+                self.prompty.model.configuration["azure_deployment"]
+                if "azure_deployment" in self.prompty.model.configuration
+                else None
+            )
+            self.parameters = self.prompty.model.parameters
+            self._config = {}
+        elif prompt_template is not None:
+            self.model_name = model_name
+            self.parameters = {}
+            # _config is a dict to hold the internal configuration
+            self._config = {
+                "api": api if api is not None else "chat",
+                "prompt_template": prompt_template,
+            }
+        else:
+            raise ValueError("Please pass valid arguments for PromptTemplate")
+
+    def create_messages(self, data: Optional[Dict[str, Any]] = None, **kwargs) -> List[Dict[str, Any]]:
+        """Render the prompt template with the given data.
+
+        :param data: The data to render the prompt template with.
+        :type data: Optional[Dict[str, Any]]
+        :return: The rendered prompt template.
+        :rtype: List[Dict[str, Any]]
+        """
+        if data is None:
+            data = kwargs
+
+        if self.prompty is not None:
+            parsed = prepare(self.prompty, data)
+            return parsed
+        else:
+            raise ValueError("Please provide valid prompt template")
+
+
+def patch_sdk():
+    """Do not remove from this file.
+
+    `patch_sdk` is a last resort escape hatch that allows you to do customizations
+    you can't accomplish using the techniques described in
+    https://aka.ms/azsdk/python/dpcodegen/python/customize
+    """
@@ -0,0 +1,39 @@
+# ------------------------------------
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ------------------------------------
+import sys
+
+
+def remove_leading_empty_space(multiline_str: str) -> str:
+    """
+    Processes a multiline string by:
+    1. Removing empty lines
+    2. Finding the minimum leading spaces
+    3. Indenting all lines to the minimum level
+
+    :param multiline_str: The input multiline string.
+    :type multiline_str: str
+    :return: The processed multiline string.
+    :rtype: str
+    """
+    lines = multiline_str.splitlines()
+    start_index = 0
+    while start_index < len(lines) and lines[start_index].strip() == "":
+        start_index += 1
+
+    # Find the minimum number of leading spaces
+    min_spaces = sys.maxsize
+    for line in lines[start_index:]:
+        if len(line.strip()) == 0:
+            continue
+        spaces = len(line) - len(line.lstrip())
+        spaces += line.lstrip().count("\t") * 2  # Count tabs as 2 spaces
+        min_spaces = min(min_spaces, spaces)
+
+    # Remove leading spaces and indent to the minimum level
+    processed_lines = []
+    for line in lines[start_index:]:
+        processed_lines.append(line[min_spaces:])
+
+    return "\n".join(processed_lines)
@@ -0,0 +1,30 @@
+---
+name: Basic Prompt
+description: A basic prompt that uses the GPT-3 chat API to answer questions
+authors:
+  - author_1
+  - author_2
+model:
+  api: chat
+  configuration:
+    azure_deployment: gpt-4o-mini
+  parameters:
+    temperature: 1
+    frequency_penalty: 0.5
+    presence_penalty: 0.5
+---
+system:
+You are an AI assistant in a hotel. You help guests with their requests and provide information about the hotel and its services.
+
+# context
+{{#rules}}
+{{rule}}
+{{/rules}}
+
+{{#chat_history}}
+{{role}}:
+{{content}}
+{{/chat_history}}
+
+user:
+{{input}}
@@ -0,0 +1,75 @@
+# ------------------------------------
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ------------------------------------
+
+"""
+DESCRIPTION:
+    Given an AIProjectClient, this sample demonstrates how to get an authenticated 
+    async ChatCompletionsClient from the azure.ai.inference package, and then work with a prompt string.
+    For more information on the azure.ai.inference package see https://pypi.org/project/azure-ai-inference/.
+
+USAGE:
+    python sample_chat_completions_with_azure_ai_inference_client_and_prompt_string.py
+
+    Before running the sample:
+
+    pip install azure-ai-projects azure-identity
+
+    Set these environment variables with your own values:
+    * PROJECT_CONNECTION_STRING - The Azure AI Project connection string, as found in your AI Foundry project.
+    * MODEL_DEPLOYMENT_NAME - The model deployment name, as found in your AI Foundry project.
+"""
+
+import os
+from azure.ai.projects import AIProjectClient
+from azure.ai.projects.prompts import PromptTemplate
+from azure.ai.inference.models import UserMessage
+from azure.identity import DefaultAzureCredential
+
+project_connection_string = os.environ["PROJECT_CONNECTION_STRING"]
+model_deployment_name = os.environ["MODEL_DEPLOYMENT_NAME"]
+
+with AIProjectClient.from_connection_string(
+    credential=DefaultAzureCredential(),
+    conn_str=project_connection_string,
+) as project_client:
+
+    with project_client.inference.get_chat_completions_client() as client:
+
+        prompt_template_str = """
+            system:
+            You are an AI assistant in a hotel. You help guests with their requests and provide information about the hotel and its services.
+
+            # context
+            {{#rules}}
+            {{rule}}
+            {{/rules}}
+
+            {{#chat_history}}
+            {{role}}:
+            {{content}}
+            {{/chat_history}}
+
+            user:
+            {{input}}
+        """
+        prompt_template = PromptTemplate.from_string(api="chat", prompt_template=prompt_template_str)
+
+        input = "When I arrived, can I still have breakfast?"
+        rules = [
+            {"rule": "The check-in time is 3pm"},
+            {"rule": "The check-out time is 11am"},
+            {"rule": "Breakfast is served from 7am to 10am"},
+        ]
+        chat_history = [
+            {"role": "user", "content": "I'll arrive at 2pm. What's the check-in and check-out time?"},
+            {"role": "system", "content": "The check-in time is 3 PM, and the check-out time is 11 AM."},
+        ]
+        messages = prompt_template.create_messages(input=input, rules=rules, chat_history=chat_history)
+
+        response = client.complete(
+            model=model_deployment_name, messages=messages
+        )
+
+        print(response.choices[0].message.content)
@@ -0,0 +1,59 @@
+# ------------------------------------
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+# ------------------------------------
+
+"""
+DESCRIPTION:
+    Given an AIProjectClient, this sample demonstrates how to get an authenticated 
+    async ChatCompletionsClient from the azure.ai.inference package, and then work with Prompty.
+    For more information on the azure.ai.inference package see https://pypi.org/project/azure-ai-inference/.
+
+USAGE:
+    python sample_chat_completions_with_azure_ai_inference_client_and_prompty.py
+
+    Before running the sample:
+
+    pip install azure-ai-projects azure-identity
+
+    Set these environment variables with your own values:
+    * PROJECT_CONNECTION_STRING - The Azure AI Project connection string, as found in your AI Foundry project.
+    * MODEL_DEPLOYMENT_NAME - The model deployment name, as found in your AI Foundry project.
+"""
+
+import os
+from azure.ai.projects import AIProjectClient
+from azure.ai.projects.prompts import PromptTemplate
+from azure.ai.inference.models import UserMessage
+from azure.identity import DefaultAzureCredential
+
+project_connection_string = os.environ["PROJECT_CONNECTION_STRING"]
+model_deployment_name = os.environ["MODEL_DEPLOYMENT_NAME"]
+
+with AIProjectClient.from_connection_string(
+    credential=DefaultAzureCredential(),
+    conn_str=project_connection_string,
+) as project_client:
+
+    with project_client.inference.get_chat_completions_client() as client:
+
+        path = "./sample1.prompty"
+        prompt_template = PromptTemplate.from_prompty(file_path=path)
+
+        input = "When I arrived, can I still have breakfast?"
+        rules = [
+            {"rule": "The check-in time is 3pm"},
+            {"rule": "The check-out time is 11am"},
+            {"rule": "Breakfast is served from 7am to 10am"},
+        ]
+        chat_history = [
+            {"role": "user", "content": "I'll arrive at 2pm. What's the check-in and check-out time?"},
+            {"role": "system", "content": "The check-in time is 3 PM, and the check-out time is 11 AM."},
+        ]
+        messages = prompt_template.create_messages(input=input, rules=rules, chat_history=chat_history)
+
+        response = client.complete(
+            model=model_deployment_name, messages=messages
+        )
+
+        print(response.choices[0].message.content)
@@ -100,4 +100,7 @@
         "typing-extensions>=4.12.2",
     ],
     python_requires=">=3.8",
+    extras_require={
+        "prompts": ["prompty", "pyyaml"],
+    },
 )
diff --git a/shared_requirements.txt b/shared_requirements.txt
@@ -71,4 +71,5 @@ dnspython
 promptflow-core
 promptflow-devkit
 nltk
-azure-monitor-opentelemetry
+azure-monitor-opentelemetry
+prompty