Python: Add load_dotenv() to samples for .env file support (#4043)

* Initial plan * Add load_dotenv() to 303 Python samples for environment variable loading Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com> * Update SAMPLE_GUIDELINES.md to document load_dotenv() requirement Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com> * Update samples README.md to document .env file usage Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com> * Run ruff format on all changed sample files Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com> * Clarify load_dotenv() usage in README - local dev vs production Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com> * Remove deprecated getting_started folder as requested Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com> * Document env_file_path parameter for per-client configuration Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com> * Merge main branch to resolve conflicts Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com> * Fix run_evaluation.py file that was empty in merge commit Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com> * Remove dotnet changes from merge - out of scope for this PR Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com> * Remove package and test changes from merge - only sample changes needed Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com> * Remove test_func_utils.py - only sample changes needed Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com> * Revert sample files not in original changeset - keep only load_dotenv additions Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com> * Move load_dotenv() outside snippet tag in 06_host_your_agent.py Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com> * Fix comment placement - move load_dotenv before code comments Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com> * Fix load_dotenv() placement across all samples - after docstring, before code comments Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com> * Merge latest main branch with load_dotenv changes Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com> * Remove non-sample changes from merge - keep only load_dotenv additions Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com> * Revert non-load_dotenv sample changes from merge Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com> * Fix run_evaluation.py - use main's improved version (file already had load_dotenv) Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com> * Manual update * Manual update 2 * Fix Role usage and load_dotenv placement per PR review feedback Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Fix Role usage - use string literals not enum attributes Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Fix SAMPLE_GUIDELINES.md example - load_dotenv before docstring per guidance Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Move load_dotenv() before docstrings in all samples per SAMPLE_GUIDELINES ordering Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Address PR review: rename files, fix placement, add session usage, remove note Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Update Redis README to reference renamed file redis_history_provider.py Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: TaoChenOSU <12570346+TaoChenOSU@users.noreply.github.com> Co-authored-by: Tao Chen <taochen@microsoft.com> Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
2026-06-16 21:04:09 +08:00 · 2026-02-19 10:55:13 +00:00
parent 3ea9c5fa5d
commit b05fc9e849
317 changed files with 1654 additions and 479 deletions
@@ -6,6 +6,10 @@ import os
 import httpx
 from a2a.client import A2ACardResolver
 from agent_framework.a2a import A2AAgent
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()

 """
 Agent2Agent (A2A) Protocol Integration Sample
@@ -12,12 +12,15 @@ from typing import Any

 from agent_framework.azure import AgentFunctionApp, AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()


 # 1. Instantiate the agent with the chosen deployment and instructions.
 def _create_agent() -> Any:
    """Create the Joker agent."""
-
    return AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
        name="Joker",
        instructions="You are good at telling jokes.",
@@ -16,15 +16,20 @@ from typing import Any
 from agent_framework import tool
 from agent_framework.azure import AgentFunctionApp, AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()

 logger = logging.getLogger(__name__)


-# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/02-agents/tools/function_tool_with_approval.py and samples/02-agents/tools/function_tool_with_approval_and_sessions.py.
+# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production;
+# see samples/02-agents/tools/function_tool_with_approval.py
+# and samples/02-agents/tools/function_tool_with_approval_and_sessions.py.
@tool(approval_mode="never_require")
 def get_weather(location: str) -> dict[str, Any]:
    """Get current weather for a location."""
-
    logger.info(f"🔧 [TOOL CALLED] get_weather(location={location})")
    result = {
        "location": location,
@@ -40,9 +45,7 @@ def get_weather(location: str) -> dict[str, Any]:
 def calculate_tip(bill_amount: float, tip_percentage: float = 15.0) -> dict[str, Any]:
    """Calculate tip amount and total bill."""

-    logger.info(
-        f"🔧 [TOOL CALLED] calculate_tip(bill_amount={bill_amount}, tip_percentage={tip_percentage})"
-    )
+    logger.info(f"🔧 [TOOL CALLED] calculate_tip(bill_amount={bill_amount}, tip_percentage={tip_percentage})")
    tip = bill_amount * (tip_percentage / 100)
    total = bill_amount + tip
    result = {
@@ -29,9 +29,13 @@ from agent_framework.azure import (
    AzureOpenAIChatClient,
 )
 from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
 from redis_stream_response_handler import RedisStreamResponseHandler, StreamChunk
 from tools import get_local_events, get_weather_forecast

+# Load environment variables from .env file
+load_dotenv()
+
 logger = logging.getLogger(__name__)

 # Configuration
@@ -217,9 +221,7 @@ async def stream(req: func.HttpRequest) -> func.HttpResponse:
        # Get optional cursor from query string
        cursor = req.params.get("cursor")

-        logger.info(
-            f"Resuming stream for conversation {conversation_id} from cursor: {cursor or '(beginning)'}"
-        )
+        logger.info(f"Resuming stream for conversation {conversation_id} from cursor: {cursor or '(beginning)'}")

        # Check Accept header to determine response format
        accept_header = req.headers.get("Accept", "")
@@ -25,6 +25,7 @@ class StreamChunk:
        is_done: Whether this is the final chunk in the stream.
        error: Error message if an error occurred, otherwise None.
    """
+
    entry_id: str
    text: str | None = None
    is_done: bool = False
@@ -84,7 +85,7 @@ class RedisStreamResponseHandler:
                "text": text,
                "sequence": str(sequence),
                "timestamp": str(int(time.time() * 1000)),
-            }
+            },
        )
        await self._redis.expire(stream_key, self._stream_ttl)

@@ -107,7 +108,7 @@ class RedisStreamResponseHandler:
                "sequence": str(sequence),
                "timestamp": str(int(time.time() * 1000)),
                "done": "true",
-            }
+            },
        )
        await self._redis.expire(stream_key, self._stream_ttl)

@@ -152,7 +153,7 @@ class RedisStreamResponseHandler:
                            timeout_seconds = self.MAX_EMPTY_READS * self.POLL_INTERVAL_MS / 1000
                            yield StreamChunk(
                                entry_id=start_id,
-                                error=f"Stream not found or timed out after {timeout_seconds} seconds"
+                                error=f"Stream not found or timed out after {timeout_seconds} seconds",
                            )
                            return

@@ -19,6 +19,10 @@ import azure.functions as func
 from agent_framework.azure import AgentFunctionApp, AzureOpenAIChatClient
 from azure.durable_functions import DurableOrchestrationClient, DurableOrchestrationContext
 from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()

 logger = logging.getLogger(__name__)

@@ -29,7 +33,6 @@ WRITER_AGENT_NAME = "WriterAgent"
 # 2. Create the writer agent that will be invoked twice within the orchestration.
 def _create_writer_agent() -> Any:
    """Create the writer agent with the same persona as the C# sample."""
-
    instructions = (
        "You refine short pieces of text. When given an initial sentence you enhance it;\n"
        "when given an improved sentence you polish it further."
@@ -58,10 +61,7 @@ def single_agent_orchestration(context: DurableOrchestrationContext) -> Generato
        session=writer_session,
    )

-    improved_prompt = (
-        "Improve this further while keeping it under 25 words: "
-        f"{initial.text}"
-    )
+    improved_prompt = f"Improve this further while keeping it under 25 words: {initial.text}"

    refined = yield writer.run(
        messages=improved_prompt,
@@ -20,6 +20,10 @@ from agent_framework import AgentResponse
 from agent_framework.azure import AgentFunctionApp, AzureOpenAIChatClient
 from azure.durable_functions import DurableOrchestrationClient, DurableOrchestrationContext
 from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()

 logger = logging.getLogger(__name__)

@@ -56,7 +60,6 @@ app.add_agent(agents[1])
@app.orchestration_trigger(context_name="context")
 def multi_agent_concurrent_orchestration(context: DurableOrchestrationContext) -> Generator[Any, Any, dict[str, str]]:
    """Fan out to two domain-specific agents and aggregate their responses."""
-
    prompt = context.get_input()
    if not prompt or not str(prompt).strip():
        raise ValueError("Prompt is required")
@@ -20,8 +20,12 @@ import azure.functions as func
 from agent_framework.azure import AgentFunctionApp, AzureOpenAIChatClient
 from azure.durable_functions import DurableOrchestrationClient, DurableOrchestrationContext
 from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
 from pydantic import BaseModel, ValidationError

+# Load environment variables from .env file
+load_dotenv()
+
 logger = logging.getLogger(__name__)

 # 1. Define agent names shared across the orchestration.
@@ -20,8 +20,12 @@ import azure.functions as func
 from agent_framework.azure import AgentFunctionApp, AzureOpenAIChatClient
 from azure.durable_functions import DurableOrchestrationClient, DurableOrchestrationContext
 from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
 from pydantic import BaseModel, ValidationError

+# Load environment variables from .env file
+load_dotenv()
+
 logger = logging.getLogger(__name__)

 # 1. Define orchestration constants used throughout the workflow.
@@ -136,9 +140,7 @@ def content_generation_hitl_orchestration(context: DurableOrchestrationContext)
                )
                return {"content": content.content}

-            context.set_custom_status(
-                "Content rejected by human reviewer. Incorporating feedback and regenerating..."
-            )
+            context.set_custom_status("Content rejected by human reviewer. Incorporating feedback and regenerating...")

            # Check if we've exhausted attempts
            if attempt >= payload.max_review_attempts:
@@ -162,15 +164,11 @@ def content_generation_hitl_orchestration(context: DurableOrchestrationContext)
            context.set_custom_status(
                f"Human approval timed out after {payload.approval_timeout_hours} hour(s). Treating as rejection."
            )
-            raise TimeoutError(
-                f"Human approval timed out after {payload.approval_timeout_hours} hour(s)."
-            )
+            raise TimeoutError(f"Human approval timed out after {payload.approval_timeout_hours} hour(s).")

    # If we exit the loop without returning, max attempts were exhausted
    context.set_custom_status("Max review attempts exhausted.")
-    raise RuntimeError(
-        f"Content could not be approved after {payload.max_review_attempts} iteration(s)."
-    )
+    raise RuntimeError(f"Content could not be approved after {payload.max_review_attempts} iteration(s).")


 # 5. HTTP endpoint that starts the human-in-the-loop orchestration.
@@ -25,6 +25,10 @@ Authentication uses AzureCliCredential (Azure Identity).
 """

 from agent_framework.azure import AgentFunctionApp, AzureOpenAIChatClient
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()

 # Create Azure OpenAI Chat Client
 # This uses AzureCliCredential for authentication (requires 'az login')
@@ -62,6 +62,7 @@ RECOMMENDATION_AGENT_NAME = "RecommendationAgent"

 class SentimentResult(BaseModel):
    """Result from sentiment analysis."""
+
    sentiment: str  # positive, negative, neutral
    confidence: float
    explanation: str
@@ -69,18 +70,21 @@ class SentimentResult(BaseModel):

 class KeywordResult(BaseModel):
    """Result from keyword extraction."""
+
    keywords: list[str]
    categories: list[str]


 class SummaryResult(BaseModel):
    """Result from summarization."""
+
    summary: str
    key_points: list[str]


 class RecommendationResult(BaseModel):
    """Result from recommendation engine."""
+
    recommendations: list[str]
    priority: str

@@ -88,6 +92,7 @@ class RecommendationResult(BaseModel):
@dataclass
 class DocumentInput:
    """Input document to be processed."""
+
    document_id: str
    content: str

@@ -95,6 +100,7 @@ class DocumentInput:
@dataclass
 class ProcessorResult:
    """Result from a document processor (executor)."""
+
    processor_name: str
    document_id: str
    content: str
@@ -106,6 +112,7 @@ class ProcessorResult:
@dataclass
 class AggregatedResults:
    """Aggregated results from parallel processors."""
+
    document_id: str
    content: str
    processor_results: list[ProcessorResult]
@@ -114,6 +121,7 @@ class AggregatedResults:
@dataclass
 class AgentAnalysis:
    """Analysis result from an agent."""
+
    agent_name: str
    result: str

@@ -121,6 +129,7 @@ class AgentAnalysis:
@dataclass
 class FinalReport:
    """Final combined report."""
+
    document_id: str
    analyses: list[AgentAnalysis]

@@ -131,10 +140,7 @@ class FinalReport:


@executor(id="input_router")
-async def input_router(
-    doc: str,
-    ctx: WorkflowContext[DocumentInput]
-) -> None:
+async def input_router(doc: str, ctx: WorkflowContext[DocumentInput]) -> None:
    """Route input document to parallel processors.

    Accepts a JSON string from the HTTP request and converts to DocumentInput.
@@ -150,10 +156,7 @@ async def input_router(


@executor(id="word_count_processor")
-async def word_count_processor(
-    doc: DocumentInput,
-    ctx: WorkflowContext[ProcessorResult]
-) -> None:
+async def word_count_processor(doc: DocumentInput, ctx: WorkflowContext[ProcessorResult]) -> None:
    """Process document and count words - runs as an activity."""
    logger.info("[word_count_processor] Processing document: %s", doc.document_id)

@@ -174,10 +177,7 @@ async def word_count_processor(


@executor(id="format_analyzer_processor")
-async def format_analyzer_processor(
-    doc: DocumentInput,
-    ctx: WorkflowContext[ProcessorResult]
-) -> None:
+async def format_analyzer_processor(doc: DocumentInput, ctx: WorkflowContext[ProcessorResult]) -> None:
    """Analyze document format - runs as an activity in parallel with word_count."""
    logger.info("[format_analyzer_processor] Processing document: %s", doc.document_id)

@@ -200,10 +200,7 @@ async def format_analyzer_processor(


@executor(id="aggregator")
-async def aggregator(
-    results: list[ProcessorResult],
-    ctx: WorkflowContext[AggregatedResults]
-) -> None:
+async def aggregator(results: list[ProcessorResult], ctx: WorkflowContext[AggregatedResults]) -> None:
    """Aggregate results from parallel processors - receives fan-in input."""
    logger.info("[aggregator] Aggregating %d results", len(results))

@@ -221,10 +218,7 @@ async def aggregator(


@executor(id="prepare_for_agents")
-async def prepare_for_agents(
-    aggregated: AggregatedResults,
-    ctx: WorkflowContext[str]
-) -> None:
+async def prepare_for_agents(aggregated: AggregatedResults, ctx: WorkflowContext[str]) -> None:
    """Prepare content for agent analysis - broadcasts to multiple agents."""
    logger.info("[prepare_for_agents] Preparing content for agents")

@@ -233,10 +227,7 @@ async def prepare_for_agents(


@executor(id="prepare_for_mixed")
-async def prepare_for_mixed(
-    analyses: list[AgentExecutorResponse],
-    ctx: WorkflowContext[str]
-) -> None:
+async def prepare_for_mixed(analyses: list[AgentExecutorResponse], ctx: WorkflowContext[str]) -> None:
    """Prepare results for mixed agent+executor parallel processing.

    Combines agent analysis results into a string that can be consumed by
@@ -262,10 +253,7 @@ async def prepare_for_mixed(


@executor(id="statistics_processor")
-async def statistics_processor(
-    analysis_text: str,
-    ctx: WorkflowContext[ProcessorResult]
-) -> None:
+async def statistics_processor(analysis_text: str, ctx: WorkflowContext[ProcessorResult]) -> None:
    """Calculate statistics from the analysis - runs in parallel with SummaryAgent."""
    logger.info("[statistics_processor] Calculating statistics")

@@ -18,17 +18,19 @@ import os

 from agent_framework.azure import DurableAIAgentClient
 from azure.identity import DefaultAzureCredential
+from dotenv import load_dotenv
 from durabletask.azuremanaged.client import DurableTaskSchedulerClient

+# Load environment variables from .env file
+load_dotenv()
+
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)


 def get_client(
-    taskhub: str | None = None,
-    endpoint: str | None = None,
-    log_handler: logging.Handler | None = None
+    taskhub: str | None = None, endpoint: str | None = None, log_handler: logging.Handler | None = None
 ) -> DurableAIAgentClient:
    """Create a configured DurableAIAgentClient.

@@ -53,7 +55,7 @@ def get_client(
        secure_channel=endpoint_url != "http://localhost:8080",
        taskhub=taskhub_name,
        token_credential=credential,
-        log_handler=log_handler
+        log_handler=log_handler,
    )

    return DurableAIAgentClient(dts_client)
@@ -18,8 +18,12 @@ import os
 from agent_framework import Agent
 from agent_framework.azure import AzureOpenAIChatClient, DurableAIAgentWorker
 from azure.identity import AzureCliCredential, DefaultAzureCredential
+from dotenv import load_dotenv
 from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker

+# Load environment variables from .env file
+load_dotenv()
+
 # Configure logging
 logging.basicConfig(level=logging.WARNING)
 logger = logging.getLogger(__name__)
@@ -38,9 +42,7 @@ def create_joker_agent() -> Agent:


 def get_worker(
-    taskhub: str | None = None,
-    endpoint: str | None = None,
-    log_handler: logging.Handler | None = None
+    taskhub: str | None = None, endpoint: str | None = None, log_handler: logging.Handler | None = None
 ) -> DurableTaskSchedulerWorker:
    """Create a configured DurableTaskSchedulerWorker.

@@ -65,7 +67,7 @@ def get_worker(
        secure_channel=endpoint_url != "http://localhost:8080",
        taskhub=taskhub_name,
        token_credential=credential,
-        log_handler=log_handler
+        log_handler=log_handler,
    )


@@ -19,17 +19,19 @@ import os

 from agent_framework.azure import DurableAIAgentClient
 from azure.identity import DefaultAzureCredential
+from dotenv import load_dotenv
 from durabletask.azuremanaged.client import DurableTaskSchedulerClient

+# Load environment variables from .env file
+load_dotenv()
+
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)


 def get_client(
-    taskhub: str | None = None,
-    endpoint: str | None = None,
-    log_handler: logging.Handler | None = None
+    taskhub: str | None = None, endpoint: str | None = None, log_handler: logging.Handler | None = None
 ) -> DurableAIAgentClient:
    """Create a configured DurableAIAgentClient.

@@ -54,7 +56,7 @@ def get_client(
        secure_channel=endpoint_url != "http://localhost:8080",
        taskhub=taskhub_name,
        token_credential=credential,
-        log_handler=log_handler
+        log_handler=log_handler,
    )

    return DurableAIAgentClient(dts_client)
@@ -20,8 +20,12 @@ from typing import Any
 from agent_framework import tool
 from agent_framework.azure import AzureOpenAIChatClient, DurableAIAgentWorker
 from azure.identity import AzureCliCredential, DefaultAzureCredential
+from dotenv import load_dotenv
 from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker

+# Load environment variables from .env file
+load_dotenv()
+
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -22,9 +22,13 @@ from datetime import timedelta
 import redis.asyncio as aioredis
 from agent_framework.azure import DurableAIAgentClient
 from azure.identity import DefaultAzureCredential
+from dotenv import load_dotenv
 from durabletask.azuremanaged.client import DurableTaskSchedulerClient
 from redis_stream_response_handler import RedisStreamResponseHandler

+# Load environment variables from .env file
+load_dotenv()
+
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -54,9 +58,7 @@ async def get_stream_handler() -> RedisStreamResponseHandler:


 def get_client(
-    taskhub: str | None = None,
-    endpoint: str | None = None,
-    log_handler: logging.Handler | None = None
+    taskhub: str | None = None, endpoint: str | None = None, log_handler: logging.Handler | None = None
 ) -> DurableAIAgentClient:
    """Create a configured DurableAIAgentClient.

@@ -81,7 +83,7 @@ def get_client(
        secure_channel=endpoint_url != "http://localhost:8080",
        taskhub=taskhub_name,
        token_credential=credential,
-        log_handler=log_handler
+        log_handler=log_handler,
    )

    return DurableAIAgentClient(dts_client)
@@ -106,7 +108,9 @@ async def stream_from_redis(thread_id: str, cursor: str | None = None) -> None:
            chunk_count = 0
            async for chunk in stream_handler.read_stream(thread_id, cursor):
                chunk_count += 1
-                logger.debug(f"Received chunk #{chunk_count}: error={chunk.error}, is_done={chunk.is_done}, text_len={len(chunk.text) if chunk.text else 0}")
+                logger.debug(
+                    f"Received chunk #{chunk_count}: error={chunk.error}, is_done={chunk.is_done}, text_len={len(chunk.text) if chunk.text else 0}"
+                )

                if chunk.error:
                    logger.error(f"Stream error: {chunk.error}")
@@ -175,9 +179,6 @@ def run_client(agent_client: DurableAIAgentClient) -> None:


 if __name__ == "__main__":
-    from dotenv import load_dotenv
-    load_dotenv()
-
    # Create the client
    client = get_client()

@@ -25,6 +25,7 @@ class StreamChunk:
        is_done: Whether this is the final chunk in the stream.
        error: Error message if an error occurred, otherwise None.
    """
+
    entry_id: str
    text: str | None = None
    is_done: bool = False
@@ -60,7 +61,9 @@ class RedisStreamResponseHandler:
        """Enter async context manager."""
        return self

-    async def __aexit__(self, exc_type: type[BaseException] | None, exc_val: BaseException | None, exc_tb: object) -> None:
+    async def __aexit__(
+        self, exc_type: type[BaseException] | None, exc_val: BaseException | None, exc_tb: object
+    ) -> None:
        """Exit async context manager and close Redis connection."""
        await self._redis.aclose()

@@ -84,7 +87,7 @@ class RedisStreamResponseHandler:
                "text": text,
                "sequence": str(sequence),
                "timestamp": str(int(time.time() * 1000)),
-            }
+            },
        )
        await self._redis.expire(stream_key, self._stream_ttl)

@@ -107,7 +110,7 @@ class RedisStreamResponseHandler:
                "sequence": str(sequence),
                "timestamp": str(int(time.time() * 1000)),
                "done": "true",
-            }
+            },
        )
        await self._redis.expire(stream_key, self._stream_ttl)

@@ -152,7 +155,7 @@ class RedisStreamResponseHandler:
                            timeout_seconds = self.MAX_EMPTY_READS * self.POLL_INTERVAL_MS / 1000
                            yield StreamChunk(
                                entry_id=start_id,
-                                error=f"Stream not found or timed out after {timeout_seconds} seconds"
+                                error=f"Stream not found or timed out after {timeout_seconds} seconds",
                            )
                            return

@@ -4,6 +4,7 @@

 In a real application, these would call actual weather and events APIs.
 """
+
 from typing import Annotated

 from agent_framework import tool
@@ -26,10 +26,14 @@ from agent_framework.azure import (
    DurableAIAgentWorker,
 )
 from azure.identity import AzureCliCredential, DefaultAzureCredential
+from dotenv import load_dotenv
 from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker
 from redis_stream_response_handler import RedisStreamResponseHandler
 from tools import get_local_events, get_weather_forecast

+# Load environment variables from .env file
+load_dotenv()
+
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -169,9 +173,7 @@ to make the itinerary easy to scan and visually appealing.""",


 def get_worker(
-    taskhub: str | None = None,
-    endpoint: str | None = None,
-    log_handler: logging.Handler | None = None
+    taskhub: str | None = None, endpoint: str | None = None, log_handler: logging.Handler | None = None
 ) -> DurableTaskSchedulerWorker:
    """Create a configured DurableTaskSchedulerWorker.

@@ -196,7 +198,7 @@ def get_worker(
        secure_channel=endpoint_url != "http://localhost:8080",
        taskhub=taskhub_name,
        token_credential=credential,
-        log_handler=log_handler
+        log_handler=log_handler,
    )


@@ -27,9 +27,7 @@ logger = logging.getLogger(__name__)


 def get_client(
-    taskhub: str | None = None,
-    endpoint: str | None = None,
-    log_handler: logging.Handler | None = None
+    taskhub: str | None = None, endpoint: str | None = None, log_handler: logging.Handler | None = None
 ) -> DurableTaskSchedulerClient:
    """Create a configured DurableTaskSchedulerClient.

@@ -54,7 +52,7 @@ def get_client(
        secure_channel=endpoint_url != "http://localhost:8080",
        taskhub=taskhub_name,
        token_credential=credential,
-        log_handler=log_handler
+        log_handler=log_handler,
    )


@@ -67,7 +65,7 @@ def run_client(client: DurableTaskSchedulerClient) -> None:
    logger.debug("Starting single agent chaining orchestration...")

    # Start the orchestration
-    instance_id = client.schedule_new_orchestration(    # type: ignore
+    instance_id = client.schedule_new_orchestration(  # type: ignore
        orchestrator="single_agent_chaining_orchestration",
        input="",
    )
@@ -76,10 +74,7 @@ def run_client(client: DurableTaskSchedulerClient) -> None:
    logger.debug("Waiting for orchestration to complete...")

    # Retrieve the final state
-    metadata = client.wait_for_orchestration_completion(
-        instance_id=instance_id,
-        timeout=300
-    )
+    metadata = client.wait_for_orchestration_completion(instance_id=instance_id, timeout=300)

    if metadata and metadata.runtime_status.name == "COMPLETED":
        result = metadata.serialized_output
@@ -20,9 +20,13 @@ from collections.abc import Generator
 from agent_framework import Agent, AgentResponse
 from agent_framework.azure import AzureOpenAIChatClient, DurableAIAgentOrchestrationContext, DurableAIAgentWorker
 from azure.identity import AzureCliCredential, DefaultAzureCredential
+from dotenv import load_dotenv
 from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker
 from durabletask.task import OrchestrationContext, Task

+# Load environment variables from .env file
+load_dotenv()
+
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -102,10 +106,7 @@ def single_agent_chaining_orchestration(
    logger.info(f"[Orchestration] Initial response: {initial_response.text}")

    # Second run: Refine the initial response on the same thread
-    improved_prompt = (
-        f"Improve this further while keeping it under 25 words: "
-        f"{initial_response.text}"
-    )
+    improved_prompt = f"Improve this further while keeping it under 25 words: {initial_response.text}"

    logger.info("[Orchestration] Second agent run: Refining the sentence: %s", improved_prompt)
    refined_response = yield writer.run(
@@ -120,9 +121,7 @@ def single_agent_chaining_orchestration(


 def get_worker(
-    taskhub: str | None = None,
-    endpoint: str | None = None,
-    log_handler: logging.Handler | None = None
+    taskhub: str | None = None, endpoint: str | None = None, log_handler: logging.Handler | None = None
 ) -> DurableTaskSchedulerWorker:
    """Create a configured DurableTaskSchedulerWorker.

@@ -147,7 +146,7 @@ def get_worker(
        secure_channel=endpoint_url != "http://localhost:8080",
        taskhub=taskhub_name,
        token_credential=credential,
-        log_handler=log_handler
+        log_handler=log_handler,
    )


@@ -172,7 +171,7 @@ def setup_worker(worker: DurableTaskSchedulerWorker) -> DurableAIAgentWorker:

    # Register the orchestration function
    logger.debug("Registering orchestration function...")
-    worker.add_orchestrator(single_agent_chaining_orchestration)    # type: ignore
+    worker.add_orchestrator(single_agent_chaining_orchestration)  # type: ignore
    logger.debug(f"✓ Registered orchestration: {single_agent_chaining_orchestration.__name__}")

    return agent_worker
@@ -27,9 +27,7 @@ logger = logging.getLogger(__name__)


 def get_client(
-    taskhub: str | None = None,
-    endpoint: str | None = None,
-    log_handler: logging.Handler | None = None
+    taskhub: str | None = None, endpoint: str | None = None, log_handler: logging.Handler | None = None
 ) -> DurableTaskSchedulerClient:
    """Create a configured DurableTaskSchedulerClient.

@@ -54,7 +52,7 @@ def get_client(
        secure_channel=endpoint_url != "http://localhost:8080",
        taskhub=taskhub_name,
        token_credential=credential,
-        log_handler=log_handler
+        log_handler=log_handler,
    )


@@ -66,7 +64,7 @@ def run_client(client: DurableTaskSchedulerClient, prompt: str = "What is temper
        prompt: The prompt to send to both agents
    """
    # Start the orchestration with the prompt as input
-    instance_id = client.schedule_new_orchestration(    # type: ignore
+    instance_id = client.schedule_new_orchestration(  # type: ignore
        orchestrator="multi_agent_concurrent_orchestration",
        input=prompt,
    )
@@ -21,9 +21,13 @@ from typing import Any
 from agent_framework import Agent, AgentResponse
 from agent_framework.azure import AzureOpenAIChatClient, DurableAIAgentOrchestrationContext, DurableAIAgentWorker
 from azure.identity import AzureCliCredential, DefaultAzureCredential
+from dotenv import load_dotenv
 from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker
 from durabletask.task import OrchestrationContext, Task, when_all

+# Load environment variables from .env file
+load_dotenv()
+
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -57,7 +61,9 @@ def create_chemist_agent() -> "Agent":
    )


-def multi_agent_concurrent_orchestration(context: OrchestrationContext, prompt: str) -> Generator[Task[Any], Any, dict[str, str]]:
+def multi_agent_concurrent_orchestration(
+    context: OrchestrationContext, prompt: str
+) -> Generator[Task[Any], Any, dict[str, str]]:
    """Orchestration that runs both agents in parallel and aggregates results.

    Uses DurableAIAgentOrchestrationContext to wrap the orchestration context and
@@ -84,7 +90,9 @@ def multi_agent_concurrent_orchestration(context: OrchestrationContext, prompt:
    physicist_session = physicist.create_session()
    chemist_session = chemist.create_session()

-    logger.debug(f"[Orchestration] Created sessions - Physicist: {physicist_session.session_id}, Chemist: {chemist_session.session_id}")
+    logger.debug(
+        f"[Orchestration] Created sessions - Physicist: {physicist_session.session_id}, Chemist: {chemist_session.session_id}"
+    )

    # Create tasks from agent.run() calls - these return DurableAgentTask instances
    physicist_task = physicist.run(messages=str(prompt), session=physicist_session)
@@ -112,9 +120,7 @@ def multi_agent_concurrent_orchestration(context: OrchestrationContext, prompt:


 def get_worker(
-    taskhub: str | None = None,
-    endpoint: str | None = None,
-    log_handler: logging.Handler | None = None
+    taskhub: str | None = None, endpoint: str | None = None, log_handler: logging.Handler | None = None
 ) -> DurableTaskSchedulerWorker:
    """Create a configured DurableTaskSchedulerWorker.

@@ -139,7 +145,7 @@ def get_worker(
        secure_channel=endpoint_url != "http://localhost:8080",
        taskhub=taskhub_name,
        token_credential=credential,
-        log_handler=log_handler
+        log_handler=log_handler,
    )


@@ -167,7 +173,7 @@ def setup_worker(worker: DurableTaskSchedulerWorker) -> DurableAIAgentWorker:

    # Register the orchestration function
    logger.debug("Registering orchestration function...")
-    worker.add_orchestrator(multi_agent_concurrent_orchestration)   # type: ignore
+    worker.add_orchestrator(multi_agent_concurrent_orchestration)  # type: ignore
    logger.debug(f"✓ Registered orchestration: {multi_agent_concurrent_orchestration.__name__}")

    return agent_worker
@@ -25,9 +25,7 @@ logger = logging.getLogger(__name__)


 def get_client(
-    taskhub: str | None = None,
-    endpoint: str | None = None,
-    log_handler: logging.Handler | None = None
+    taskhub: str | None = None, endpoint: str | None = None, log_handler: logging.Handler | None = None
 ) -> DurableTaskSchedulerClient:
    """Create a configured DurableTaskSchedulerClient.

@@ -52,14 +50,14 @@ def get_client(
        secure_channel=endpoint_url != "http://localhost:8080",
        taskhub=taskhub_name,
        token_credential=credential,
-        log_handler=log_handler
+        log_handler=log_handler,
    )


 def run_client(
    client: DurableTaskSchedulerClient,
    email_id: str = "email-001",
-    email_content: str = "Hello! I wanted to reach out about our upcoming project meeting."
+    email_content: str = "Hello! I wanted to reach out about our upcoming project meeting.",
 ) -> None:
    """Run client to start and monitor the spam detection orchestration.

@@ -76,7 +74,7 @@ def run_client(
    logger.debug("Starting spam detection orchestration...")

    # Start the orchestration with the email payload
-    instance_id = client.schedule_new_orchestration(    # type: ignore
+    instance_id = client.schedule_new_orchestration(  # type: ignore
        orchestrator="spam_detection_orchestration",
        input=payload,
    )
@@ -85,10 +83,7 @@ def run_client(
    logger.debug("Waiting for orchestration to complete...")

    # Retrieve the final state
-    metadata = client.wait_for_orchestration_completion(
-        instance_id=instance_id,
-        timeout=300
-    )
+    metadata = client.wait_for_orchestration_completion(instance_id=instance_id, timeout=300)

    if metadata and metadata.runtime_status.name == "COMPLETED":
        result = metadata.serialized_output
@@ -124,7 +119,7 @@ async def main() -> None:
        run_client(
            client,
            email_id="email-001",
-            email_content="Hello! I wanted to reach out about our upcoming project meeting scheduled for next week."
+            email_content="Hello! I wanted to reach out about our upcoming project meeting scheduled for next week.",
        )

        # Test with a spam email
@@ -133,7 +128,7 @@ async def main() -> None:
        run_client(
            client,
            email_id="email-002",
-            email_content="URGENT! You've won $1,000,000! Click here now to claim your prize! Limited time offer! Don't miss out!"
+            email_content="URGENT! You've won $1,000,000! Click here now to claim your prize! Limited time offer! Don't miss out!",
        )

    except Exception as e:
@@ -25,10 +25,7 @@ from client import get_client, run_client
 from dotenv import load_dotenv
 from worker import get_worker, setup_worker

-logging.basicConfig(
-    level=logging.INFO,
-    force=True
-)
+logging.basicConfig(level=logging.INFO, force=True)
 logger = logging.getLogger()


@@ -57,7 +54,7 @@ def main():
            run_client(
                client,
                email_id="email-001",
-                email_content="Hello! I wanted to reach out about our upcoming project meeting scheduled for next week."
+                email_content="Hello! I wanted to reach out about our upcoming project meeting scheduled for next week.",
            )

            # Test 2: Spam email
@@ -66,7 +63,7 @@ def main():
            run_client(
                client,
                email_id="email-002",
-                email_content="URGENT! You've won $1,000,000! Click here now to claim your prize! Limited time offer! Don't miss out!"
+                email_content="URGENT! You've won $1,000,000! Click here now to claim your prize! Limited time offer! Don't miss out!",
            )

        except Exception as e:
@@ -21,10 +21,14 @@ from typing import Any, cast
 from agent_framework import Agent, AgentResponse
 from agent_framework.azure import AzureOpenAIChatClient, DurableAIAgentOrchestrationContext, DurableAIAgentWorker
 from azure.identity import AzureCliCredential, DefaultAzureCredential
+from dotenv import load_dotenv
 from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker
 from durabletask.task import ActivityContext, OrchestrationContext, Task
 from pydantic import BaseModel, ValidationError

+# Load environment variables from .env file
+load_dotenv()
+
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -36,17 +40,20 @@ EMAIL_AGENT_NAME = "EmailAssistantAgent"

 class SpamDetectionResult(BaseModel):
    """Result from spam detection agent."""
+
    is_spam: bool
    reason: str


 class EmailResponse(BaseModel):
    """Result from email assistant agent."""
+
    response: str


 class EmailPayload(BaseModel):
    """Input payload for the orchestration."""
+
    email_id: str
    email_content: str

@@ -195,9 +202,7 @@ def spam_detection_orchestration(context: OrchestrationContext, payload_raw: Any


 def get_worker(
-    taskhub: str | None = None,
-    endpoint: str | None = None,
-    log_handler: logging.Handler | None = None
+    taskhub: str | None = None, endpoint: str | None = None, log_handler: logging.Handler | None = None
 ) -> DurableTaskSchedulerWorker:
    """Create a configured DurableTaskSchedulerWorker.

@@ -222,7 +227,7 @@ def get_worker(
        secure_channel=endpoint_url != "http://localhost:8080",
        taskhub=taskhub_name,
        token_credential=credential,
-        log_handler=log_handler
+        log_handler=log_handler,
    )


@@ -257,7 +262,7 @@ def setup_worker(worker: DurableTaskSchedulerWorker) -> DurableAIAgentWorker:

    # Register the orchestration function
    logger.debug("Registering orchestration function...")
-    worker.add_orchestrator(spam_detection_orchestration)   # type: ignore[arg-type]
+    worker.add_orchestrator(spam_detection_orchestration)  # type: ignore[arg-type]
    logger.debug(f"✓ Registered orchestration: {spam_detection_orchestration.__name__}")

    return agent_worker
@@ -31,9 +31,7 @@ HUMAN_APPROVAL_EVENT = "HumanApproval"


 def get_client(
-    taskhub: str | None = None,
-    endpoint: str | None = None,
-    log_handler: logging.Handler | None = None
+    taskhub: str | None = None, endpoint: str | None = None, log_handler: logging.Handler | None = None
 ) -> DurableTaskSchedulerClient:
    """Create a configured DurableTaskSchedulerClient.

@@ -58,7 +56,7 @@ def get_client(
        secure_channel=endpoint_url != "http://localhost:8080",
        taskhub=taskhub_name,
        token_credential=credential,
-        log_handler=log_handler
+        log_handler=log_handler,
    )


@@ -90,11 +88,7 @@ def _log_completion_result(
        logger.error("Orchestration did not complete within the timeout period")


-def _wait_and_log_completion(
-    client: DurableTaskSchedulerClient,
-    instance_id: str,
-    timeout: int = 60
-) -> None:
+def _wait_and_log_completion(client: DurableTaskSchedulerClient, instance_id: str, timeout: int = 60) -> None:
    """Wait for orchestration completion and log the result.

    Args:
@@ -103,20 +97,12 @@ def _wait_and_log_completion(
        timeout: Maximum time to wait for completion in seconds
    """
    logger.debug("Waiting for orchestration to complete...")
-    metadata = client.wait_for_orchestration_completion(
-        instance_id=instance_id,
-        timeout=timeout
-    )
+    metadata = client.wait_for_orchestration_completion(instance_id=instance_id, timeout=timeout)

    _log_completion_result(metadata)


-def send_approval(
-    client: DurableTaskSchedulerClient,
-    instance_id: str,
-    approved: bool,
-    feedback: str = ""
-) -> None:
+def send_approval(client: DurableTaskSchedulerClient, instance_id: str, approved: bool, feedback: str = "") -> None:
    """Send approval or rejection event to the orchestration.

    Args:
@@ -125,30 +111,19 @@ def send_approval(
        approved: Whether to approve or reject
        feedback: Optional feedback message (used when rejected)
    """
-    approval_data = {
-        "approved": approved,
-        "feedback": feedback
-    }
+    approval_data = {"approved": approved, "feedback": feedback}

    logger.debug(f"Sending {'APPROVAL' if approved else 'REJECTION'} to instance {instance_id}")
    if feedback:
        logger.debug(f"Feedback: {feedback}")

    # Raise the external event
-    client.raise_orchestration_event(
-        instance_id=instance_id,
-        event_name=HUMAN_APPROVAL_EVENT,
-        data=approval_data
-    )
+    client.raise_orchestration_event(instance_id=instance_id, event_name=HUMAN_APPROVAL_EVENT, data=approval_data)

    logger.debug("Event sent successfully")


-def wait_for_notification(
-    client: DurableTaskSchedulerClient,
-    instance_id: str,
-    timeout_seconds: int = 10
-) -> bool:
+def wait_for_notification(client: DurableTaskSchedulerClient, instance_id: str, timeout_seconds: int = 10) -> bool:
    """Wait for the orchestration to reach a notification point.

    Polls the orchestration status until it appears to be waiting for approval.
@@ -226,14 +201,14 @@ def run_interactive_client(client: DurableTaskSchedulerClient) -> None:
    payload = {
        "topic": topic,
        "max_review_attempts": max_review_attempts,
-        "approval_timeout_seconds": approval_timeout_seconds
+        "approval_timeout_seconds": approval_timeout_seconds,
    }

    logger.debug(f"Configuration: Topic={topic}, Max attempts={max_review_attempts}, Timeout={timeout_hours}h")

    # Start the orchestration
    logger.debug("Starting content generation orchestration...")
-    instance_id = client.schedule_new_orchestration(    # type: ignore
+    instance_id = client.schedule_new_orchestration(  # type: ignore
        orchestrator="content_generation_hitl_orchestration",
        input=payload,
    )
@@ -25,10 +25,7 @@ from client import get_client, run_interactive_client
 from dotenv import load_dotenv
 from worker import get_worker, setup_worker

-logging.basicConfig(
-    level=logging.INFO,
-    force=True
-)
+logging.basicConfig(level=logging.INFO, force=True)
 logger = logging.getLogger()


@@ -22,10 +22,14 @@ from typing import Any, cast
 from agent_framework import Agent, AgentResponse
 from agent_framework.azure import AzureOpenAIChatClient, DurableAIAgentOrchestrationContext, DurableAIAgentWorker
 from azure.identity import AzureCliCredential, DefaultAzureCredential
+from dotenv import load_dotenv
 from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker
 from durabletask.task import ActivityContext, OrchestrationContext, Task, when_any  # type: ignore
 from pydantic import BaseModel, ValidationError

+# Load environment variables from .env file
+load_dotenv()
+
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -37,6 +41,7 @@ HUMAN_APPROVAL_EVENT = "HumanApproval"

 class ContentGenerationInput(BaseModel):
    """Input for content generation orchestration."""
+
    topic: str
    max_review_attempts: int = 3
    approval_timeout_seconds: float = 300  # 5 minutes for demo (72 hours in production)
@@ -44,12 +49,14 @@ class ContentGenerationInput(BaseModel):

 class GeneratedContent(BaseModel):
    """Structured output from writer agent."""
+
    title: str
    content: str


 class HumanApproval(BaseModel):
    """Human approval decision."""
+
    approved: bool
    feedback: str = ""

@@ -103,8 +110,7 @@ def publish_content(context: ActivityContext, content: dict[str, str]) -> str:


 def content_generation_hitl_orchestration(
-    context: OrchestrationContext,
-    payload_raw: Any
+    context: OrchestrationContext, payload_raw: Any
 ) -> Generator[Task[Any], Any, dict[str, str]]:
    """Human-in-the-loop orchestration for content generation with approval workflow.

@@ -160,7 +166,7 @@ def content_generation_hitl_orchestration(
    initial_response: AgentResponse = yield writer.run(
        messages=f"Write a short article about '{payload.topic}'.",
        session=writer_session,
-            options={"response_format": GeneratedContent},
+        options={"response_format": GeneratedContent},
    )
    content = cast(GeneratedContent, initial_response.value)

@@ -175,13 +181,12 @@ def content_generation_hitl_orchestration(
        attempt += 1
        logger.debug(f"[Orchestration] Review iteration #{attempt}/{payload.max_review_attempts}")

-        context.set_custom_status(f"Requesting human feedback (Attempt {attempt}, timeout {payload.approval_timeout_seconds}s)")
+        context.set_custom_status(
+            f"Requesting human feedback (Attempt {attempt}, timeout {payload.approval_timeout_seconds}s)"
+        )

        # Notify user for approval
-        yield context.call_activity(
-            "notify_user_for_approval",
-            input=content.model_dump()
-        )
+        yield context.call_activity("notify_user_for_approval", input=content.model_dump())

        logger.debug("[Orchestration] Waiting for human approval or timeout...")

@@ -217,16 +222,13 @@ def content_generation_hitl_orchestration(
                else:
                    approval = HumanApproval(approved=False, feedback=approval_data)
            else:
-                approval = HumanApproval(approved=False, feedback=str(approval_data))   # type: ignore
+                approval = HumanApproval(approved=False, feedback=str(approval_data))  # type: ignore

            if approval.approved:
                # Content approved - publish and return
                logger.debug("[Orchestration] Content approved! Publishing...")
                context.set_custom_status("Content approved by human reviewer. Publishing...")
-                publish_task: Task[Any] = context.call_activity(
-                    "publish_content",
-                    input=content.model_dump()
-                )
+                publish_task: Task[Any] = context.call_activity("publish_content", input=content.model_dump())
                yield publish_task

                logger.debug("[Orchestration] Content published successfully")
@@ -256,7 +258,7 @@ def content_generation_hitl_orchestration(
            rewrite_response: AgentResponse = yield writer.run(
                messages=rewrite_prompt,
                session=writer_session,
-                    options={"response_format": GeneratedContent},
+                options={"response_format": GeneratedContent},
            )
            rewritten_content = cast(GeneratedContent, rewrite_response.value)

@@ -270,21 +272,15 @@ def content_generation_hitl_orchestration(
            # Timeout occurred
            logger.error(f"[Orchestration] Approval timeout after {payload.approval_timeout_seconds}s")

-            raise TimeoutError(
-                f"Human approval timed out after {payload.approval_timeout_seconds} second(s)."
-            )
+            raise TimeoutError(f"Human approval timed out after {payload.approval_timeout_seconds} second(s).")

    # If we exit the loop without returning, max attempts were exhausted
    context.set_custom_status("Max review attempts exhausted.")
-    raise RuntimeError(
-        f"Content could not be approved after {payload.max_review_attempts} iteration(s)."
-    )
+    raise RuntimeError(f"Content could not be approved after {payload.max_review_attempts} iteration(s).")


 def get_worker(
-    taskhub: str | None = None,
-    endpoint: str | None = None,
-    log_handler: logging.Handler | None = None
+    taskhub: str | None = None, endpoint: str | None = None, log_handler: logging.Handler | None = None
 ) -> DurableTaskSchedulerWorker:
    """Create a configured DurableTaskSchedulerWorker.

@@ -309,7 +305,7 @@ def get_worker(
        secure_channel=endpoint_url != "http://localhost:8080",
        taskhub=taskhub_name,
        token_credential=credential,
-        log_handler=log_handler
+        log_handler=log_handler,
    )