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Python: [Feature Branch] Merge from main to Azure AI branch (#2111)

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* Do not build DevUI assets during .NET project build (#2010)

* .NET: Add unit tests for declarative executor SetMultipleVariables (#2016)

* Add unit tests for create conversation executor

* Update indentation and comment typo.

* Added unit tests for declarative executor SetMultipleVariablesExecutor

* Updated comments and syntactic sugar

* Python: DevUI: Use metadata.entity_id instead of model field (#1984)

* DevUI: Use metadata.entity_id for agent/workflow name instead of model field

* OpenAI Responses: add explicit request validation

* Review feedback

* .NET: DevUI - Do not automatically add/map OpenAI services/endpoints (#2014)

* Don't add OpenAIResponses as part of Dev UI

You should be able to add and remove Dev UI without impacting your other production endpoints.

* Remove `AddDevUI()` and do not map OpenAI endpoints from `MapDevUI()`

* Fix comment wording

* Revise documentation

---------

Co-authored-by: Daniel Roth <daroth@microsoft.com>

* Python: DevUI: Add OpenAI Responses API proxy support  + HIL for Workflows (#1737)

* DevUI: Add OpenAI Responses API proxy support with enhanced UI features

This commit adds support for proxying requests to OpenAI's Responses API,
allowing DevUI to route conversations to OpenAI models when configured to enable testing.

Backend changes:
- Add OpenAI proxy executor with conversation routing logic
- Enhance event mapper to support OpenAI Responses API format
- Extend server endpoints to handle OpenAI proxy mode
- Update models with OpenAI-specific response types
- Remove emojis from logging and CLI output for cleaner text

Frontend changes:
- Add settings modal with OpenAI proxy configuration UI
- Enhance agent and workflow views with improved state management
- Add new UI components (separator, switch) for settings
- Update debug panel with better event filtering
- Improve message renderers for OpenAI content types
- Update types and API client for OpenAI integration

* update ui, settings modal and workflow input form, add register cleanup hooks.

* add workflow HIL support, user mode, other fixes

* feat(devui): add human-in-the-loop (HIL) support with dynamic response schemas

Implement  HIL workflow support allowing workflows to pause for user input
with dynamically generated JSON schemas based on response handler type hints.

Key Features:
- Automatic response schema extraction from @response_handler decorators
- Dynamic form generation in UI based on Pydantic/dataclass response types
- Checkpoint-based conversation storage for HIL requests/responses
- Resume workflow execution after user provides HIL response

Backend Changes:
- Add extract_response_type_from_executor() to introspect response handlers
- Enrich RequestInfoEvent with response_schema via _enrich_request_info_event_with_response_schema()
- Map RequestInfoEvent to response.input.requested OpenAI event format
- Store HIL responses in conversation history and restore checkpoints

Frontend Changes:
- Add HILInputModal component with SchemaFormRenderer for dynamic forms
- Support Pydantic BaseModel and dataclass response types
- Render enum fields as dropdowns, strings as text/textarea, numbers, booleans, arrays, objects
- Display original request context alongside response form

Testing:
- Add  tests for checkpoint storage (test_checkpoints.py)
- Add schema generation tests for all input types (test_schema_generation.py)
- Validate end-to-end HIL flow with spam workflow sample

This enables workflows to seamlessly pause execution and request structured user input
with type-safe, validated forms generated automatically from response type annotations.

* improve HIL support, improve workflow execution view

* ui updates

* ui updates

* improve HIL for workflows, add auth and view modes

* update workflow

* security improvements , ui fixes

* fix mypy error

* update loading spinner in ui

---------

Co-authored-by: Mark Wallace <127216156+markwallace-microsoft@users.noreply.github.com>

* .NET: Remove launchSettings.json from .gitignore in dotnet/samples (#2006)

* Remove launchSettings.json from .gitignore in dotnet/samples

* Update dotnet/samples/GettingStarted/DevUI/DevUI_Step01_BasicUsage/Properties/launchSettings.json

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* Update dotnet/samples/AGUIClientServer/AGUIServer/Properties/launchSettings.json

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

---------

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* DevUI: Serialize workflow input as string to maintain conformance with OpenAI Responses format (#2021)

Co-authored-by: Victor Dibia <chuvidi2003@gmail.com>

* Add Microsoft Agent Framework logo to assets (#2007)

* Updated package versions (#2027)

* DevUI: Prevent line breaks within words in the agent view (#2024)

Co-authored-by: Victor Dibia <chuvidi2003@gmail.com>

* .NET [AG-UI]: Adds support for shared state. (#1996)

* Product changes

* Tests

* Dojo project

* Cleanups

* Python: Fix underlying tool choice bug and all for return to previous Handoff subagent (#2037)

* Fix tool_choice override bug and add enable_return_to_previous support

* Add unit test for handoff checkpointing

* Handle tools when we have them

* added missing chatAgent params (#2044)

* .NET: fix ChatCompletions Tools serialization (#2043)

* fix serialization in chat completions on tools

* nit

* .NET: assign AgentCard's URL to mapped-endpoint if not defined explicitly (#2047)

* fix serialization in chat completions on tools

* nit

* write e2e test for agent card resolve + adjust behavior

* nit

* Version 1.0.0-preview.251110.1 (#2048)

* .NET: Remove moved OpenAPI sample and point to SK one. (#1997)

* Remove moved OpenAPI sample and point to SK one.

* Update dotnet/samples/GettingStarted/Agents/README.md

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---------

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* Bump AWSSDK.Extensions.Bedrock.MEAI from 4.0.4.2 to 4.0.4.6 (#2031)

---
updated-dependencies:
- dependency-name: AWSSDK.Extensions.Bedrock.MEAI
  dependency-version: 4.0.4.6
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

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* .NET: Separate all memory and rag samples into their own folders (#2000)

* Separate all memory and rag samples into their own folders

* Fix broken link.

* Python: .Net: Dotnet devui compatibility fixes (#2026)

* DevUI: Add OpenAI Responses API proxy support with enhanced UI features

This commit adds support for proxying requests to OpenAI's Responses API,
allowing DevUI to route conversations to OpenAI models when configured to enable testing.

Backend changes:
- Add OpenAI proxy executor with conversation routing logic
- Enhance event mapper to support OpenAI Responses API format
- Extend server endpoints to handle OpenAI proxy mode
- Update models with OpenAI-specific response types
- Remove emojis from logging and CLI output for cleaner text

Frontend changes:
- Add settings modal with OpenAI proxy configuration UI
- Enhance agent and workflow views with improved state management
- Add new UI components (separator, switch) for settings
- Update debug panel with better event filtering
- Improve message renderers for OpenAI content types
- Update types and API client for OpenAI integration

* update ui, settings modal and workflow input form, add register cleanup hooks.

* add workflow HIL support, user mode, other fixes

* feat(devui): add human-in-the-loop (HIL) support with dynamic response schemas

Implement  HIL workflow support allowing workflows to pause for user input
with dynamically generated JSON schemas based on response handler type hints.

Key Features:
- Automatic response schema extraction from @response_handler decorators
- Dynamic form generation in UI based on Pydantic/dataclass response types
- Checkpoint-based conversation storage for HIL requests/responses
- Resume workflow execution after user provides HIL response

Backend Changes:
- Add extract_response_type_from_executor() to introspect response handlers
- Enrich RequestInfoEvent with response_schema via _enrich_request_info_event_with_response_schema()
- Map RequestInfoEvent to response.input.requested OpenAI event format
- Store HIL responses in conversation history and restore checkpoints

Frontend Changes:
- Add HILInputModal component with SchemaFormRenderer for dynamic forms
- Support Pydantic BaseModel and dataclass response types
- Render enum fields as dropdowns, strings as text/textarea, numbers, booleans, arrays, objects
- Display original request context alongside response form

Testing:
- Add  tests for checkpoint storage (test_checkpoints.py)
- Add schema generation tests for all input types (test_schema_generation.py)
- Validate end-to-end HIL flow with spam workflow sample

This enables workflows to seamlessly pause execution and request structured user input
with type-safe, validated forms generated automatically from response type annotations.

* improve HIL support, improve workflow execution view

* ui updates

* ui updates

* improve HIL for workflows, add auth and view modes

* update workflow

* security improvements , ui fixes

* fix mypy error

* update loading spinner in ui

* DevUI: Serialize workflow input as string to maintain conformance with OpenAI Responses format

* Phase 1: Add /meta endpoint and fix workflow event naming for .NET DevUI compatibility

* additional fixes for .NET DevUI workflow visualization item ID tracking

**Problem:**
.NET DevUI was generating different item IDs for ExecutorInvokedEvent and
ExecutorCompletedEvent, causing only the first executor to highlight in the
workflow graph. Long executor names and error messages also broke UI layout.

**Changes:**
- Add ExecutorActionItemResource to match Python DevUI implementation
- Track item IDs per executor using dictionary in AgentRunResponseUpdateExtensions
- Reuse same item ID across invoked/completed/failed events for proper pairing
- Add truncateText() utility to workflow-utils.ts
- Truncate executor names to 35 chars in execution timeline
- Truncate error messages to 150 chars in workflow graph nodes

** Details:**
- ExecutorActionItemResource registered with JSON source generation context
- Dictionary cleaned up after executor completion/failure to prevent memory leaks
- Frontend item tracking by unique item.id supports multiple executor runs
- All changes follow existing codebase patterns and conventions

Tested with review-workflow showing correct executor highlighting and state
transitions for sequential and concurrent executors.

* format fixes, remove cors tests

* remove unecessary attributes

---------

Co-authored-by: Mark Wallace <127216156+markwallace-microsoft@users.noreply.github.com>
Co-authored-by: Reuben Bond <reuben.bond@gmail.com>

* DevUI: support having both an agent and a workflow with the same id in discovery (#2023)

* Python: Fix Model ID attribute not showing up in `invoke_agent` span (#2061)

* Best effort to surface the model id to invoke agent span

* Fix tests

* Fix tests

* Version 1.0.0-preview.251107.2 (#2065)

* Version 1.0.0-preview.251110.2 (#2067)

* Update README.md to change Grafana links to Azure portal links for dashboard access (#1983)

* .NET - Enable build & test on branch `feature-foundry-agents` (#2068)

* Tests good, mkay

* Update .github/workflows/dotnet-build-and-test.yml

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* Enable feature build pipelines

---------

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Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com>

* Python: Add concrete AGUIChatClient (#2072)

* Add concrete AGUIChatClient

* Update logging docstrings and conventions

* PR feedback

* Updates to support client-side tool calls

* .NET: Move catalog samples to the HostedAgents folder (#2090)

* move catalog samples to the HostedAgents folder

* move the catalog samples' projects to the HostedAgents folder

* Bump OpenTelemetry.Instrumentation.Runtime from 1.12.0 to 1.13.0 (#1856)

---
updated-dependencies:
- dependency-name: OpenTelemetry.Instrumentation.Runtime
  dependency-version: 1.13.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

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* .NET: Bump Microsoft.SemanticKernel.Agents.Abstractions from 1.66.0 to 1.67.0 (#1962)

* Bump Microsoft.SemanticKernel.Agents.Abstractions from 1.66.0 to 1.67.0

---
updated-dependencies:
- dependency-name: Microsoft.SemanticKernel.Agents.Abstractions
  dependency-version: 1.67.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

* .NET: Bump all Microsoft.SemanticKernel packages from 1.66.* to 1.67.* (#1969)

* Initial plan

* Update all Microsoft.SemanticKernel packages to 1.67.*

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* Remove unrelated changes to package-lock.json and yarn.lock

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---------

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* .NET: fix: WorkflowAsAgent Sample (#1787)

* fix: WorkflowAsAgent Sample

* Also makes ChatForwardingExecutor public

* feat: Expand ChatForwardingExecutor handled types

Make ChatForwardingExecutor match the input types of ChatProtocolExecutor.

* fix: Update for the new AgentRunResponseUpdate merge logic

AIAgent always sends out List<ChatMessage> now.

* Updated (#2076)

* Bump vite in /python/samples/demos/chatkit-integration/frontend (#1918)

Bumps [vite](https://github.com/vitejs/vite/tree/HEAD/packages/vite) from 7.1.9 to 7.1.12.
- [Release notes](https://github.com/vitejs/vite/releases)
- [Changelog](https://github.com/vitejs/vite/blob/v7.1.12/packages/vite/CHANGELOG.md)
- [Commits](https://github.com/vitejs/vite/commits/v7.1.12/packages/vite)

---
updated-dependencies:
- dependency-name: vite
  dependency-version: 7.1.12
  dependency-type: direct:development
...

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* Bump Roslynator.Analyzers from 4.14.0 to 4.14.1 (#1857)

---
updated-dependencies:
- dependency-name: Roslynator.Analyzers
  dependency-version: 4.14.1
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

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* Bump MishaKav/pytest-coverage-comment from 1.1.57 to 1.1.59 (#2034)

Bumps [MishaKav/pytest-coverage-comment](https://github.com/mishakav/pytest-coverage-comment) from 1.1.57 to 1.1.59.
- [Release notes](https://github.com/mishakav/pytest-coverage-comment/releases)
- [Changelog](https://github.com/MishaKav/pytest-coverage-comment/blob/main/CHANGELOG.md)
- [Commits](https://github.com/mishakav/pytest-coverage-comment/compare/v1.1.57...v1.1.59)

---
updated-dependencies:
- dependency-name: MishaKav/pytest-coverage-comment
  dependency-version: 1.1.59
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

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* Python: Handle agent user input request in AgentExecutor (#2022)

* Handle agent user input request in AgentExecutor

* fix test

* Address comments

* Fix tests

* Fix tests

* Address comments

* Address comments

* Python: OpenAI Responses Image Generation Stream Support, Sample and Unit Tests (#1853)

* support for image gen streaming

* small fixes

* fixes

* added comment

* Python: Fix MCP Tool Parameter Descriptions Not Propagated to LLMs (#1978)

* mcp tool description fix

* small fix

* .NET: Allow extending agent run options via additional properties (#1872)

* Allow extending agent run options via additional properties

This mirrors the M.E.AI model in ChatOptions.AdditionalProperties which is very useful when building functionality pipelines.

Fixes https://github.com/microsoft/agent-framework/issues/1815

* Expand XML documentation

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* Add AdditionalProperties tests to AgentRunOptions

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---------

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* Python: Use the last entry in the task history to avoid empty responses (#2101)

* Use the last entry in the task history to avoid empty responses

* History only contains Messages

* Updated package versions (#2104)

---------

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This commit is contained in:
Dmytro Struk
2025-11-11 23:12:09 -08:00
committed by GitHub
Unverified
parent 85fcd230bf
commit 361c47f30f
231 changed files with 19659 additions and 4143 deletions
Download Patch File Download Diff File Expand all files Collapse all files
python/samples/getting_started/devui/spam_workflow/workflow.py
+206 -84
View File
@@ -2,22 +2,22 @@
"""Spam Detection Workflow Sample for DevUI.
The following sample demonstrates a comprehensive 5-step workflow with multiple executors
that process, analyze, detect spam, and handle email messages. This workflow illustrates
complex branching logic and realistic processing delays to demonstrate the workflow framework.
The following sample demonstrates a comprehensive 4-step workflow with multiple executors
that process, detect spam, and handle email messages. This workflow illustrates
complex branching logic with human-in-the-loop approval and realistic processing delays.
Workflow Steps:
1. Email Preprocessor - Cleans and prepares the email
2. Content Analyzer - Analyzes email content and structure
3. Spam Detector - Determines if the message is spam
4a. Spam Handler - Processes spam messages (quarantine, log, remove)
4b. Message Responder - Handles legitimate messages (validate, respond)
5. Final Processor - Completes the workflow with logging and cleanup
2. Spam Detector - Analyzes content and determines if the message is spam (with human approval)
3a. Spam Handler - Processes spam messages (quarantine, log, remove)
3b. Message Responder - Handles legitimate messages (validate, respond)
4. Final Processor - Completes the workflow with logging and cleanup
"""
import asyncio
import logging
from dataclasses import dataclass
from typing import Literal, Annotated
from agent_framework import (
Case,
@@ -26,10 +26,18 @@ from agent_framework import (
WorkflowBuilder,
WorkflowContext,
handler,
response_handler,
)
from pydantic import BaseModel, Field
from typing_extensions import Never
# Define response model with clear user guidance
class SpamDecision(BaseModel):
"""User's decision on whether the email is spam."""
decision: Literal["spam", "not spam"] = Field(
description="Enter 'spam' to mark as spam, or 'not spam' to mark as legitimate"
)
@dataclass
class EmailContent:
@@ -41,25 +49,17 @@ class EmailContent:
has_suspicious_patterns: bool = False
@dataclass
class ContentAnalysis:
"""A data class to hold content analysis results."""
email_content: EmailContent
sentiment_score: float
contains_links: bool
has_attachments: bool
risk_indicators: list[str]
@dataclass
class SpamDetectorResponse:
"""A data class to hold the spam detection results."""
analysis: ContentAnalysis
email_content: EmailContent
is_spam: bool = False
confidence_score: float = 0.0
spam_reasons: list[str] | None = None
human_reviewed: bool = False
human_decision: str | None = None
ai_original_classification: bool = False
def __post_init__(self):
"""Initialize spam_reasons list if None."""
@@ -67,6 +67,16 @@ class SpamDetectorResponse:
self.spam_reasons = []
@dataclass
class SpamApprovalRequest:
"""Human-in-the-loop approval request for spam classification."""
email_message: str = ""
detected_as_spam: bool = False
confidence: float = 0.0
reasons: str = ""
@dataclass
class ProcessingResult:
"""A data class to hold the final processing result."""
@@ -78,6 +88,9 @@ class ProcessingResult:
is_spam: bool
confidence_score: float
spam_reasons: list[str]
was_human_reviewed: bool = False
human_override: str | None = None
ai_original_decision: bool = False
class EmailRequest(BaseModel):
@@ -115,18 +128,27 @@ class EmailPreprocessor(Executor):
await ctx.send_message(result)
class ContentAnalyzer(Executor):
"""Step 2: An executor that analyzes email content and structure."""
class SpamDetector(Executor):
"""Step 2: An executor that analyzes content and determines if a message is spam."""
def __init__(self, spam_keywords: list[str], id: str):
"""Initialize the executor with spam keywords."""
super().__init__(id=id)
self._spam_keywords = spam_keywords
@handler
async def handle_email_content(self, email_content: EmailContent, ctx: WorkflowContext[ContentAnalysis]) -> None:
"""Analyze the email content for various indicators."""
await asyncio.sleep(2.0) # Simulate analysis time
async def handle_email_content(self, email_content: EmailContent, ctx: WorkflowContext[SpamApprovalRequest]) -> None:
"""Analyze email content and determine if the message is spam, then request human approval."""
await asyncio.sleep(2.0) # Simulate analysis and detection time
# Simulate content analysis
email_text = email_content.cleaned_message
# Analyze content for risk indicators
contains_links = "http" in email_text or "www" in email_text
has_attachments = "attachment" in email_text
sentiment_score = 0.5 if email_content.has_suspicious_patterns else 0.8
contains_links = "http" in email_content.cleaned_message or "www" in email_content.cleaned_message
has_attachments = "attachment" in email_content.cleaned_message
# Build risk indicators
risk_indicators: list[str] = []
@@ -139,32 +161,7 @@ class ContentAnalyzer(Executor):
if email_content.word_count < 10:
risk_indicators.append("too_short")
analysis = ContentAnalysis(
email_content=email_content,
sentiment_score=sentiment_score,
contains_links=contains_links,
has_attachments=has_attachments,
risk_indicators=risk_indicators,
)
await ctx.send_message(analysis)
class SpamDetector(Executor):
"""Step 3: An executor that determines if a message is spam based on analysis."""
def __init__(self, spam_keywords: list[str], id: str):
"""Initialize the executor with spam keywords."""
super().__init__(id=id)
self._spam_keywords = spam_keywords
@handler
async def handle_analysis(self, analysis: ContentAnalysis, ctx: WorkflowContext[SpamDetectorResponse]) -> None:
"""Determine if the message is spam based on content analysis."""
await asyncio.sleep(1.8) # Simulate detection time
# Check for spam keywords
email_text = analysis.email_content.cleaned_message
keyword_matches = [kw for kw in self._spam_keywords if kw in email_text]
# Calculate spam probability
@@ -175,29 +172,100 @@ class SpamDetector(Executor):
spam_score += 0.4
spam_reasons.append(f"spam_keywords: {keyword_matches}")
if analysis.email_content.has_suspicious_patterns:
if email_content.has_suspicious_patterns:
spam_score += 0.3
spam_reasons.append("suspicious_patterns")
if len(analysis.risk_indicators) >= 3:
if len(risk_indicators) >= 3:
spam_score += 0.2
spam_reasons.append("high_risk_indicators")
if analysis.sentiment_score < 0.4:
if sentiment_score < 0.4:
spam_score += 0.1
spam_reasons.append("negative_sentiment")
is_spam = spam_score >= 0.5
result = SpamDetectorResponse(
analysis=analysis, is_spam=is_spam, confidence_score=spam_score, spam_reasons=spam_reasons
# Store detection result in executor state for later use
# Store minimal data needed (not complex objects that don't serialize well)
await ctx.set_executor_state({
"original_message": email_content.original_message,
"cleaned_message": email_content.cleaned_message,
"word_count": email_content.word_count,
"has_suspicious_patterns": email_content.has_suspicious_patterns,
"is_spam": is_spam,
"ai_original_classification": is_spam, # Store original AI decision
"confidence_score": spam_score,
"spam_reasons": spam_reasons
})
# Request human approval before proceeding using new API
approval_request = SpamApprovalRequest(
email_message=email_text[:200], # First 200 chars
detected_as_spam=is_spam,
confidence=spam_score,
reasons=", ".join(spam_reasons) if spam_reasons else "no specific reasons"
)
await ctx.request_info(
request_data=approval_request,
response_type=SpamDecision,
)
@response_handler
async def handle_human_response(
self,
original_request: SpamApprovalRequest,
response: SpamDecision,
ctx: WorkflowContext[SpamDetectorResponse]
) -> None:
"""Process human approval response and continue workflow."""
print(f"[SpamDetector] handle_human_response called with response: {response}")
# Get stored detection result
state = await ctx.get_executor_state() or {}
print(f"[SpamDetector] Retrieved state: {state}")
ai_original = state.get("ai_original_classification", False)
confidence_score = state.get("confidence_score", 0.0)
spam_reasons = state.get("spam_reasons", [])
# Parse human decision from the response model
human_decision = response.decision.strip().lower()
# Determine final classification based on human input
if human_decision in ["not spam"]:
is_spam = False
elif human_decision in ["spam"]:
is_spam = True
else:
# Default to AI decision if unclear
is_spam = ai_original
# Reconstruct EmailContent from stored primitives
email_content = EmailContent(
original_message=state.get("original_message", ""),
cleaned_message=state.get("cleaned_message", ""),
word_count=state.get("word_count", 0),
has_suspicious_patterns=state.get("has_suspicious_patterns", False)
)
result = SpamDetectorResponse(
email_content=email_content,
is_spam=is_spam,
confidence_score=confidence_score,
spam_reasons=spam_reasons,
human_reviewed=True,
human_decision=response.decision,
ai_original_classification=ai_original
)
print(f"[SpamDetector] Sending SpamDetectorResponse: is_spam={is_spam}, confidence={confidence_score}, human_reviewed=True")
await ctx.send_message(result)
print(f"[SpamDetector] Message sent successfully")
class SpamHandler(Executor):
"""Step 4a: An executor that handles spam messages with quarantine and logging."""
"""Step 3a: An executor that handles spam messages with quarantine and logging."""
@handler
async def handle_spam_detection(
@@ -212,20 +280,23 @@ class SpamHandler(Executor):
await asyncio.sleep(2.2) # Simulate spam handling time
result = ProcessingResult(
original_message=spam_result.analysis.email_content.original_message,
original_message=spam_result.email_content.original_message,
action_taken="quarantined_and_logged",
processing_time=2.2,
status="spam_handled",
is_spam=spam_result.is_spam,
confidence_score=spam_result.confidence_score,
spam_reasons=spam_result.spam_reasons or [],
was_human_reviewed=spam_result.human_reviewed,
human_override=spam_result.human_decision,
ai_original_decision=spam_result.ai_original_classification,
)
await ctx.send_message(result)
class MessageResponder(Executor):
"""Step 4b: An executor that responds to legitimate messages."""
class LegitimateMessageHandler(Executor):
"""Step 3b: An executor that handles legitimate (non-spam) messages."""
@handler
async def handle_spam_detection(
@@ -240,20 +311,23 @@ class MessageResponder(Executor):
await asyncio.sleep(2.5) # Simulate response time
result = ProcessingResult(
original_message=spam_result.analysis.email_content.original_message,
action_taken="responded_and_filed",
original_message=spam_result.email_content.original_message,
action_taken="delivered_to_inbox",
processing_time=2.5,
status="message_processed",
is_spam=spam_result.is_spam,
confidence_score=spam_result.confidence_score,
spam_reasons=spam_result.spam_reasons or [],
was_human_reviewed=spam_result.human_reviewed,
human_override=spam_result.human_decision,
ai_original_decision=spam_result.ai_original_classification,
)
await ctx.send_message(result)
class FinalProcessor(Executor):
"""Step 5: An executor that completes the workflow with final logging and cleanup."""
"""Step 4: An executor that completes the workflow with final logging and cleanup."""
@handler
async def handle_processing_result(
@@ -266,50 +340,98 @@ class FinalProcessor(Executor):
total_time = result.processing_time + 1.5
# Include classification details in completion message
# Build classification status with human review info
classification = "SPAM" if result.is_spam else "LEGITIMATE"
reasons = ", ".join(result.spam_reasons) if result.spam_reasons else "none"
completion_message = (
f"Email classified as {classification} (confidence: {result.confidence_score:.2f}). "
f"Reasons: {reasons}. "
f"Action: {result.action_taken}, "
f"Status: {result.status}, "
f"Total time: {total_time:.1f}s"
)
# Add human review context
review_status = ""
if result.was_human_reviewed:
if result.ai_original_decision != result.is_spam:
review_status = " (human-overridden)"
else:
review_status = " (human-verified)"
# Build appropriate message based on classification
if result.is_spam:
# For spam messages
spam_indicators = ", ".join(result.spam_reasons) if result.spam_reasons else "none detected"
if result.was_human_reviewed:
ai_status = "SPAM" if result.ai_original_decision else "LEGITIMATE"
human_decision = result.human_override if result.human_override else "unknown"
completion_message = (
f"Email classified as {classification}{review_status}.\n"
f"AI detected: {ai_status} (confidence: {result.confidence_score:.2f})\n"
f"Human reviewer: {human_decision}\n"
f"Spam indicators: {spam_indicators}\n"
f"Action: Message quarantined for review\n"
f"Processing time: {total_time:.1f}s"
)
else:
completion_message = (
f"Email classified as {classification} (confidence: {result.confidence_score:.2f}).\n"
f"Spam indicators: {spam_indicators}\n"
f"Action: Message quarantined for review\n"
f"Processing time: {total_time:.1f}s"
)
else:
# For legitimate messages
if result.was_human_reviewed:
ai_status = "SPAM" if result.ai_original_decision else "LEGITIMATE"
human_decision = result.human_override if result.human_override else "unknown"
completion_message = (
f"Email classified as {classification}{review_status}.\n"
f"AI detected: {ai_status} (confidence: {result.confidence_score:.2f})\n"
f"Human reviewer: {human_decision}\n"
f"Action: Delivered to inbox\n"
f"Processing time: {total_time:.1f}s"
)
else:
completion_message = (
f"Email classified as {classification} (confidence: {result.confidence_score:.2f}).\n"
f"Action: Delivered to inbox\n"
f"Processing time: {total_time:.1f}s"
)
await ctx.yield_output(completion_message)
# DevUI will provide checkpoint storage automatically via the new workflow API
# No need to create checkpoint storage here anymore!
# Create the workflow instance that DevUI can discover
spam_keywords = ["spam", "advertisement", "offer", "click here", "winner", "congratulations", "urgent"]
# Create all the executors for the 5-step workflow
# Create all the executors for the 4-step workflow
email_preprocessor = EmailPreprocessor(id="email_preprocessor")
content_analyzer = ContentAnalyzer(id="content_analyzer")
spam_detector = SpamDetector(spam_keywords, id="spam_detector")
spam_handler = SpamHandler(id="spam_handler")
message_responder = MessageResponder(id="message_responder")
legitimate_message_handler = LegitimateMessageHandler(id="legitimate_message_handler")
final_processor = FinalProcessor(id="final_processor")
# Build the comprehensive 5-step workflow with branching logic
# Build the comprehensive 4-step workflow with branching logic and HIL support
# Note: No .with_checkpointing() call - DevUI will pass checkpoint_storage at runtime
workflow = (
WorkflowBuilder(
name="Email Spam Detector",
description="5-step email classification workflow with spam/legitimate routing",
description="4-step email classification workflow with human-in-the-loop spam approval",
)
.set_start_executor(email_preprocessor)
.add_edge(email_preprocessor, content_analyzer)
.add_edge(content_analyzer, spam_detector)
.add_edge(email_preprocessor, spam_detector)
# HIL handled within spam_detector via @response_handler
# Continue with branching logic after human approval
# Only route SpamDetectorResponse messages (not SpamApprovalRequest)
.add_switch_case_edge_group(
spam_detector,
[
Case(condition=lambda x: x.is_spam, target=spam_handler),
Default(target=message_responder),
Case(condition=lambda x: isinstance(x, SpamDetectorResponse) and x.is_spam, target=spam_handler),
Default(target=legitimate_message_handler), # Default handles non-spam and non-SpamDetectorResponse messages
],
)
.add_edge(spam_handler, final_processor)
.add_edge(message_responder, final_processor)
.add_edge(legitimate_message_handler, final_processor)
.build()
)