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
@@ -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")