Add Python 3.14 support and drop Python 3.9

.devcontainer/devcontainer.json

@@ -1,6 +1,6 @@
 {
     "name": "Azure Search OpenAI Demo",
-    "image": "mcr.microsoft.com/devcontainers/python:3.11-bookworm",
+    "image": "mcr.microsoft.com/devcontainers/python:3.13-bookworm",
     "features": {
         "ghcr.io/devcontainers/features/node:1": {
             // This should match the version of Node.js in Github Actions workflows

.github/workflows/evaluate.yaml

@@ -128,7 +128,7 @@ jobs:
         uses: astral-sh/setup-uv@v6
         with:
           enable-cache: true
-          version: "0.4.20"
+          version: "0.9.5"
           cache-dependency-glob: "requirements**.txt"
           python-version: "3.11"
 

.github/workflows/python-test.yaml

@@ -25,7 +25,7 @@ jobs:
       fail-fast: false
       matrix:
         os: ["ubuntu-latest", "windows-latest"]
-        python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
+        python_version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
         node_version: ["20.14", "22"]
     steps:
         - uses: actions/checkout@v5
@@ -36,7 +36,7 @@ jobs:
           uses: astral-sh/setup-uv@v6
           with:
             enable-cache: true
-            version: "0.4.20"
+            version: "0.9.5"
             cache-dependency-glob: "requirements**.txt"
             python-version: ${{ matrix.python_version }}
             activate-environment: true

AGENTS.md

@@ -95,7 +95,7 @@ When sending pull requests, make sure to follow the PULL_REQUEST_TEMPLATE.md for
 To upgrade a particular package in the backend, use the following command, replacing `<package-name>` with the name of the package you want to upgrade:
 
 ```shell
-cd app/backend && uv pip compile requirements.in -o requirements.txt --python-version 3.9 --upgrade-package package-name
+cd app/backend && uv pip compile requirements.in -o requirements.txt --python-version 3.10 --upgrade-package package-name
 ```
 
 ## Checking Python type hints

README.md

@@ -131,7 +131,7 @@ A related option is VS Code Dev Containers, which will open the project in your
 1. Install the required tools:
 
     - [Azure Developer CLI](https://aka.ms/azure-dev/install)
-    - [Python 3.9, 3.10, or 3.11](https://www.python.org/downloads/)
+    - [Python 3.10, 3.11, 3.12, 3.13, or 3.14](https://www.python.org/downloads/)
       - **Important**: Python and the pip package manager must be in the path in Windows for the setup scripts to work.
       - **Important**: Ensure you can run `python --version` from console. On Ubuntu, you might need to run `sudo apt install python-is-python3` to link `python` to `python3`.
     - [Node.js 20+](https://nodejs.org/download/)

app/backend/Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.11-bullseye
+FROM python:3.13-bookworm
 
 WORKDIR /app
 

app/backend/app.py

@@ -5,9 +5,9 @@
 import mimetypes
 import os
 import time
-from collections.abc import AsyncGenerator, Awaitable
+from collections.abc import AsyncGenerator, Awaitable, Callable
 from pathlib import Path
-from typing import Any, Callable, Union, cast
+from typing import Any, cast
 
 from azure.cognitiveservices.speech import (
     ResultReason,
@@ -478,7 +478,7 @@ async def setup_clients():
     # Use the current user identity for keyless authentication to Azure services.
     # This assumes you use 'azd auth login' locally, and managed identity when deployed on Azure.
     # The managed identity is setup in the infra/ folder.
-    azure_credential: Union[AzureDeveloperCliCredential, ManagedIdentityCredential]
+    azure_credential: AzureDeveloperCliCredential | ManagedIdentityCredential
     azure_ai_token_provider: Callable[[], Awaitable[str]]
     if RUNNING_ON_AZURE:
         current_app.logger.info("Setting up Azure credential using ManagedIdentityCredential")

app/backend/approaches/approach.py

@@ -2,7 +2,7 @@
 from abc import ABC
 from collections.abc import AsyncGenerator, Awaitable
 from dataclasses import dataclass, field
-from typing import Any, Optional, TypedDict, Union, cast
+from typing import Any, TypedDict, cast
 
 from azure.search.documents.agent.aio import KnowledgeAgentRetrievalClient
 from azure.search.documents.agent.models import (
@@ -39,18 +39,18 @@
 
 @dataclass
 class Document:
-    id: Optional[str] = None
-    content: Optional[str] = None
-    category: Optional[str] = None
-    sourcepage: Optional[str] = None
-    sourcefile: Optional[str] = None
-    oids: Optional[list[str]] = None
-    groups: Optional[list[str]] = None
-    captions: Optional[list[QueryCaptionResult]] = None
-    score: Optional[float] = None
-    reranker_score: Optional[float] = None
-    search_agent_query: Optional[str] = None
-    images: Optional[list[dict[str, Any]]] = None
+    id: str | None = None
+    content: str | None = None
+    category: str | None = None
+    sourcepage: str | None = None
+    sourcefile: str | None = None
+    oids: list[str] | None = None
+    groups: list[str] | None = None
+    captions: list[QueryCaptionResult] | None = None
+    score: float | None = None
+    reranker_score: float | None = None
+    search_agent_query: str | None = None
+    images: list[dict[str, Any]] | None = None
 
     def serialize_for_results(self) -> dict[str, Any]:
         result_dict = {
@@ -84,8 +84,8 @@ def serialize_for_results(self) -> dict[str, Any]:
 @dataclass
 class ThoughtStep:
     title: str
-    description: Optional[Any]
-    props: Optional[dict[str, Any]] = None
+    description: Any | None
+    props: dict[str, Any] | None = None
 
     def update_token_usage(self, usage: CompletionUsage) -> None:
         if self.props:
@@ -94,23 +94,23 @@ def update_token_usage(self, usage: CompletionUsage) -> None:
 
 @dataclass
 class DataPoints:
-    text: Optional[list[str]] = None
-    images: Optional[list] = None
-    citations: Optional[list[str]] = None
+    text: list[str] | None = None
+    images: list | None = None
+    citations: list[str] | None = None
 
 
 @dataclass
 class ExtraInfo:
     data_points: DataPoints
     thoughts: list[ThoughtStep] = field(default_factory=list)
-    followup_questions: Optional[list[Any]] = None
+    followup_questions: list[Any] | None = None
 
 
 @dataclass
 class TokenUsageProps:
     prompt_tokens: int
     completion_tokens: int
-    reasoning_tokens: Optional[int]
+    reasoning_tokens: int | None
     total_tokens: int
 
     @classmethod
@@ -153,19 +153,19 @@ def __init__(
         search_client: SearchClient,
         openai_client: AsyncOpenAI,
         auth_helper: AuthenticationHelper,
-        query_language: Optional[str],
-        query_speller: Optional[str],
-        embedding_deployment: Optional[str],  # Not needed for non-Azure OpenAI or for retrieval_mode="text"
+        query_language: str | None,
+        query_speller: str | None,
+        embedding_deployment: str | None,  # Not needed for non-Azure OpenAI or for retrieval_mode="text"
         embedding_model: str,
         embedding_dimensions: int,
         embedding_field: str,
         openai_host: str,
         prompt_manager: PromptManager,
-        reasoning_effort: Optional[str] = None,
+        reasoning_effort: str | None = None,
         multimodal_enabled: bool = False,
-        image_embeddings_client: Optional[ImageEmbeddings] = None,
-        global_blob_manager: Optional[BlobManager] = None,
-        user_blob_manager: Optional[AdlsBlobManager] = None,
+        image_embeddings_client: ImageEmbeddings | None = None,
+        global_blob_manager: BlobManager | None = None,
+        user_blob_manager: AdlsBlobManager | None = None,
     ):
         self.search_client = search_client
         self.openai_client = openai_client
@@ -185,7 +185,7 @@ def __init__(
         self.global_blob_manager = global_blob_manager
         self.user_blob_manager = user_blob_manager
 
-    def build_filter(self, overrides: dict[str, Any], auth_claims: dict[str, Any]) -> Optional[str]:
+    def build_filter(self, overrides: dict[str, Any], auth_claims: dict[str, Any]) -> str | None:
         include_category = overrides.get("include_category")
         exclude_category = overrides.get("exclude_category")
         security_filter = self.auth_helper.build_security_filters(overrides, auth_claims)
@@ -201,16 +201,16 @@ def build_filter(self, overrides: dict[str, Any], auth_claims: dict[str, Any]) -
     async def search(
         self,
         top: int,
-        query_text: Optional[str],
-        filter: Optional[str],
+        query_text: str | None,
+        filter: str | None,
         vectors: list[VectorQuery],
         use_text_search: bool,
         use_vector_search: bool,
         use_semantic_ranker: bool,
         use_semantic_captions: bool,
-        minimum_search_score: Optional[float] = None,
-        minimum_reranker_score: Optional[float] = None,
-        use_query_rewriting: Optional[bool] = None,
+        minimum_search_score: float | None = None,
+        minimum_reranker_score: float | None = None,
+        use_query_rewriting: bool | None = None,
     ) -> list[Document]:
         search_text = query_text if use_text_search else ""
         search_vectors = vectors if use_vector_search else []
@@ -271,10 +271,10 @@ async def run_agentic_retrieval(
         messages: list[ChatCompletionMessageParam],
         agent_client: KnowledgeAgentRetrievalClient,
         search_index_name: str,
-        top: Optional[int] = None,
-        filter_add_on: Optional[str] = None,
-        minimum_reranker_score: Optional[float] = None,
-        results_merge_strategy: Optional[str] = None,
+        top: int | None = None,
+        filter_add_on: str | None = None,
+        minimum_reranker_score: float | None = None,
+        results_merge_strategy: str | None = None,
     ) -> tuple[KnowledgeAgentRetrievalResponse, list[Document]]:
         # STEP 1: Invoke agentic retrieval
         response = await agent_client.retrieve(
@@ -358,7 +358,7 @@ async def get_sources_content(
         use_semantic_captions: bool,
         include_text_sources: bool,
         download_image_sources: bool,
-        user_oid: Optional[str] = None,
+        user_oid: str | None = None,
     ) -> DataPoints:
         """Extract text/image sources & citations from documents.
 
@@ -408,15 +408,15 @@ def clean_source(s: str) -> str:
                     citations.append(self.get_image_citation(doc.sourcepage or "", img["url"]))
         return DataPoints(text=text_sources, images=image_sources, citations=citations)
 
-    def get_citation(self, sourcepage: Optional[str]):
+    def get_citation(self, sourcepage: str | None):
         return sourcepage or ""
 
-    def get_image_citation(self, sourcepage: Optional[str], image_url: str):
+    def get_image_citation(self, sourcepage: str | None, image_url: str):
         sourcepage_citation = self.get_citation(sourcepage)
         image_filename = image_url.split("/")[-1]
         return f"{sourcepage_citation}({image_filename})"
 
-    async def download_blob_as_base64(self, blob_url: str, user_oid: Optional[str] = None) -> Optional[str]:
+    async def download_blob_as_base64(self, blob_url: str, user_oid: str | None = None) -> str | None:
         """
         Downloads a blob from either Azure Blob Storage or Azure Data Lake Storage and returns it as a base64 encoded string.
 
@@ -484,7 +484,7 @@ async def compute_multimodal_embedding(self, q: str):
         multimodal_query_vector = await self.image_embeddings_client.create_embedding_for_text(q)
         return VectorizedQuery(vector=multimodal_query_vector, k_nearest_neighbors=50, fields="images/embedding")
 
-    def get_system_prompt_variables(self, override_prompt: Optional[str]) -> dict[str, str]:
+    def get_system_prompt_variables(self, override_prompt: str | None) -> dict[str, str]:
         # Allows client to replace the entire prompt, or to inject into the existing prompt using >>>
         if override_prompt is None:
             return {}
@@ -511,17 +511,17 @@ def get_lowest_reasoning_effort(self, model: str) -> ChatCompletionReasoningEffo
 
     def create_chat_completion(
         self,
-        chatgpt_deployment: Optional[str],
+        chatgpt_deployment: str | None,
         chatgpt_model: str,
         messages: list[ChatCompletionMessageParam],
         overrides: dict[str, Any],
         response_token_limit: int,
         should_stream: bool = False,
-        tools: Optional[list[ChatCompletionToolParam]] = None,
-        temperature: Optional[float] = None,
-        n: Optional[int] = None,
-        reasoning_effort: Optional[ChatCompletionReasoningEffort] = None,
-    ) -> Union[Awaitable[ChatCompletion], Awaitable[AsyncStream[ChatCompletionChunk]]]:
+        tools: list[ChatCompletionToolParam] | None = None,
+        temperature: float | None = None,
+        n: int | None = None,
+        reasoning_effort: ChatCompletionReasoningEffort | None = None,
+    ) -> Awaitable[ChatCompletion] | Awaitable[AsyncStream[ChatCompletionChunk]]:
         if chatgpt_model in self.GPT_REASONING_MODELS:
             params: dict[str, Any] = {
                 # max_tokens is not supported
@@ -562,9 +562,9 @@ def format_thought_step_for_chatcompletion(
         messages: list[ChatCompletionMessageParam],
         overrides: dict[str, Any],
         model: str,
-        deployment: Optional[str],
-        usage: Optional[CompletionUsage] = None,
-        reasoning_effort: Optional[ChatCompletionReasoningEffort] = None,
+        deployment: str | None,
+        usage: CompletionUsage | None = None,
+        reasoning_effort: ChatCompletionReasoningEffort | None = None,
     ) -> ThoughtStep:
         properties: dict[str, Any] = {"model": model}
         if deployment:

app/backend/approaches/chatreadretrieveread.py

@@ -1,7 +1,7 @@
 import json
 import re
 from collections.abc import AsyncGenerator, Awaitable
-from typing import Any, Optional, Union, cast
+from typing import Any, cast
 
 from azure.search.documents.agent.aio import KnowledgeAgentRetrievalClient
 from azure.search.documents.aio import SearchClient
@@ -39,14 +39,14 @@ def __init__(
         *,
         search_client: SearchClient,
         search_index_name: str,
-        agent_model: Optional[str],
-        agent_deployment: Optional[str],
+        agent_model: str | None,
+        agent_deployment: str | None,
         agent_client: KnowledgeAgentRetrievalClient,
         auth_helper: AuthenticationHelper,
         openai_client: AsyncOpenAI,
         chatgpt_model: str,
-        chatgpt_deployment: Optional[str],  # Not needed for non-Azure OpenAI
-        embedding_deployment: Optional[str],  # Not needed for non-Azure OpenAI or for retrieval_mode="text"
+        chatgpt_deployment: str | None,  # Not needed for non-Azure OpenAI
+        embedding_deployment: str | None,  # Not needed for non-Azure OpenAI or for retrieval_mode="text"
         embedding_model: str,
         embedding_dimensions: int,
         embedding_field: str,
@@ -55,11 +55,11 @@ def __init__(
         query_language: str,
         query_speller: str,
         prompt_manager: PromptManager,
-        reasoning_effort: Optional[str] = None,
+        reasoning_effort: str | None = None,
         multimodal_enabled: bool = False,
-        image_embeddings_client: Optional[ImageEmbeddings] = None,
-        global_blob_manager: Optional[BlobManager] = None,
-        user_blob_manager: Optional[AdlsBlobManager] = None,
+        image_embeddings_client: ImageEmbeddings | None = None,
+        global_blob_manager: BlobManager | None = None,
+        user_blob_manager: AdlsBlobManager | None = None,
     ):
         self.search_client = search_client
         self.search_index_name = search_index_name
@@ -107,7 +107,7 @@ def get_search_query(self, chat_completion: ChatCompletion, user_query: str):
                 return query_text
         return user_query
 
-    def extract_followup_questions(self, content: Optional[str]):
+    def extract_followup_questions(self, content: str | None):
         if content is None:
             return content, []
         return content.split("<<")[0], re.findall(r"<<([^>>]+)>>", content)
@@ -218,7 +218,7 @@ async def run_until_final_call(
         overrides: dict[str, Any],
         auth_claims: dict[str, Any],
         should_stream: bool = False,
-    ) -> tuple[ExtraInfo, Union[Awaitable[ChatCompletion], Awaitable[AsyncStream[ChatCompletionChunk]]]]:
+    ) -> tuple[ExtraInfo, Awaitable[ChatCompletion] | Awaitable[AsyncStream[ChatCompletionChunk]]]:
         use_agentic_retrieval = True if overrides.get("use_agentic_retrieval") else False
         original_user_query = messages[-1]["content"]
 
@@ -246,7 +246,7 @@ async def run_until_final_call(
         )
 
         chat_coroutine = cast(
-            Union[Awaitable[ChatCompletion], Awaitable[AsyncStream[ChatCompletionChunk]]],
+            Awaitable[ChatCompletion] | Awaitable[AsyncStream[ChatCompletionChunk]],
             self.create_chat_completion(
                 self.chatgpt_deployment,
                 self.chatgpt_model,