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Python: DevUI - Internal Refactor, Conversations API support, and per… (#1235)
* Python: DevUI - Internal Refactor, Conversations API support, and performance improvements Comprehensive refactor of DevUI package including samples relocation, frontend reorganization, OpenAI Conversations API support, and critical performance and code quality improvements. Key Changes: Architecture & Organization - Moved DevUI samples to python/samples/getting_started/devui/ - Consolidated with other framework samples for better discoverability - Added .env.example files and comprehensive README - Restructured frontend components into feature-based folders (agent, workflow, gallery, layout) - Created new OpenAI-compliant message renderers (devui should render oai responses types primarily) New Features - Added _conversations.py (467 lines) - Full conversation storage abstraction, replaces the /threads endpoint to better match oai conversations api - Implements OpenAI Conversations API for thread management, Supports in-memory and extensible storage backends API Simplification - Use 'model' field as entity_id (agent/workflow name) instead of extra_body - Use standard OpenAI 'conversation' field for conversation context. Performance & Quality Improvements - Improved context management in MessageMapper with bounded memory (~500KB max) - Implemented hybrid LRU + cleanup approach to prevent unbounded memory growth - General QOL improvement - Eliminated ~150 lines of dead/duplicate code, Consolidated helper functions into _utils.py, Extracted magic numbers to module-level constants, Optimized conversation item lookups with index-based approach Testing - Added test_conversations.py (13 tests) - Added test_performance_fixes.py (9 tests) - Updated existing tests for code consolidation - 53 tests passing Impact: 76 files changed: +4,106 insertions, -2,373 deletions All linting and formatting checks passing. No breaking changes - backward compatible. Migration: Samples moved to python/samples/getting_started/devui/ * readme lint fixes * initial support for function approval and minor ui fixes
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# Copyright (c) Microsoft. All rights reserved.
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"""Spam detection workflow sample for DevUI testing."""
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from .workflow import workflow
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__all__ = ["workflow"]
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# Copyright (c) Microsoft. All rights reserved.
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"""Spam Detection Workflow Sample for DevUI.
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The following sample demonstrates a comprehensive 5-step workflow with multiple executors
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that process, analyze, detect spam, and handle email messages. This workflow illustrates
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complex branching logic and realistic processing delays to demonstrate the workflow framework.
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Workflow Steps:
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1. Email Preprocessor - Cleans and prepares the email
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2. Content Analyzer - Analyzes email content and structure
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3. Spam Detector - Determines if the message is spam
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4a. Spam Handler - Processes spam messages (quarantine, log, remove)
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4b. Message Responder - Handles legitimate messages (validate, respond)
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5. Final Processor - Completes the workflow with logging and cleanup
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"""
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import asyncio
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import logging
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from dataclasses import dataclass
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from agent_framework import (
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Case,
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Default,
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Executor,
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WorkflowBuilder,
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WorkflowContext,
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handler,
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)
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from pydantic import BaseModel, Field
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from typing_extensions import Never
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@dataclass
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class EmailContent:
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"""A data class to hold the processed email content."""
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original_message: str
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cleaned_message: str
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word_count: int
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has_suspicious_patterns: bool = False
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@dataclass
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class ContentAnalysis:
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"""A data class to hold content analysis results."""
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email_content: EmailContent
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sentiment_score: float
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contains_links: bool
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has_attachments: bool
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risk_indicators: list[str]
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@dataclass
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class SpamDetectorResponse:
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"""A data class to hold the spam detection results."""
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analysis: ContentAnalysis
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is_spam: bool = False
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confidence_score: float = 0.0
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spam_reasons: list[str] | None = None
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def __post_init__(self):
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"""Initialize spam_reasons list if None."""
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if self.spam_reasons is None:
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self.spam_reasons = []
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@dataclass
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class ProcessingResult:
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"""A data class to hold the final processing result."""
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original_message: str
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action_taken: str
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processing_time: float
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status: str
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is_spam: bool
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confidence_score: float
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spam_reasons: list[str]
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class EmailRequest(BaseModel):
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"""Request model for email processing."""
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email: str = Field(
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description="The email message to be processed.",
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default="Hi there, are you interested in our new urgent offer today? Click here!",
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)
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class EmailPreprocessor(Executor):
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"""Step 1: An executor that preprocesses and cleans email content."""
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@handler
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async def handle_email(self, email: EmailRequest, ctx: WorkflowContext[EmailContent]) -> None:
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"""Clean and preprocess the email message."""
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await asyncio.sleep(1.5) # Simulate preprocessing time
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# Simulate email cleaning
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cleaned = email.email.strip().lower()
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word_count = len(email.email.split())
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# Check for suspicious patterns
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suspicious_patterns = ["urgent", "limited time", "act now", "free money"]
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has_suspicious = any(pattern in cleaned for pattern in suspicious_patterns)
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result = EmailContent(
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original_message=email.email,
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cleaned_message=cleaned,
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word_count=word_count,
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has_suspicious_patterns=has_suspicious,
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)
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await ctx.send_message(result)
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class ContentAnalyzer(Executor):
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"""Step 2: An executor that analyzes email content and structure."""
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@handler
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async def handle_email_content(self, email_content: EmailContent, ctx: WorkflowContext[ContentAnalysis]) -> None:
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"""Analyze the email content for various indicators."""
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await asyncio.sleep(2.0) # Simulate analysis time
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# Simulate content analysis
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sentiment_score = 0.5 if email_content.has_suspicious_patterns else 0.8
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contains_links = "http" in email_content.cleaned_message or "www" in email_content.cleaned_message
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has_attachments = "attachment" in email_content.cleaned_message
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# Build risk indicators
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risk_indicators: list[str] = []
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if email_content.has_suspicious_patterns:
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risk_indicators.append("suspicious_language")
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if contains_links:
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risk_indicators.append("contains_links")
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if has_attachments:
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risk_indicators.append("has_attachments")
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if email_content.word_count < 10:
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risk_indicators.append("too_short")
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analysis = ContentAnalysis(
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email_content=email_content,
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sentiment_score=sentiment_score,
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contains_links=contains_links,
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has_attachments=has_attachments,
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risk_indicators=risk_indicators,
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)
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await ctx.send_message(analysis)
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class SpamDetector(Executor):
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"""Step 3: An executor that determines if a message is spam based on analysis."""
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def __init__(self, spam_keywords: list[str], id: str):
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"""Initialize the executor with spam keywords."""
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super().__init__(id=id)
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self._spam_keywords = spam_keywords
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@handler
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async def handle_analysis(self, analysis: ContentAnalysis, ctx: WorkflowContext[SpamDetectorResponse]) -> None:
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"""Determine if the message is spam based on content analysis."""
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await asyncio.sleep(1.8) # Simulate detection time
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# Check for spam keywords
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email_text = analysis.email_content.cleaned_message
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keyword_matches = [kw for kw in self._spam_keywords if kw in email_text]
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# Calculate spam probability
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spam_score = 0.0
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spam_reasons: list[str] = []
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if keyword_matches:
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spam_score += 0.4
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spam_reasons.append(f"spam_keywords: {keyword_matches}")
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if analysis.email_content.has_suspicious_patterns:
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spam_score += 0.3
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spam_reasons.append("suspicious_patterns")
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if len(analysis.risk_indicators) >= 3:
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spam_score += 0.2
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spam_reasons.append("high_risk_indicators")
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if analysis.sentiment_score < 0.4:
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spam_score += 0.1
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spam_reasons.append("negative_sentiment")
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is_spam = spam_score >= 0.5
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result = SpamDetectorResponse(
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analysis=analysis, is_spam=is_spam, confidence_score=spam_score, spam_reasons=spam_reasons
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)
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await ctx.send_message(result)
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class SpamHandler(Executor):
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"""Step 4a: An executor that handles spam messages with quarantine and logging."""
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@handler
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async def handle_spam_detection(
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self,
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spam_result: SpamDetectorResponse,
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ctx: WorkflowContext[ProcessingResult],
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) -> None:
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"""Handle spam messages by quarantining and logging."""
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if not spam_result.is_spam:
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raise RuntimeError("Message is not spam, cannot process with spam handler.")
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await asyncio.sleep(2.2) # Simulate spam handling time
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result = ProcessingResult(
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original_message=spam_result.analysis.email_content.original_message,
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action_taken="quarantined_and_logged",
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processing_time=2.2,
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status="spam_handled",
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is_spam=spam_result.is_spam,
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confidence_score=spam_result.confidence_score,
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spam_reasons=spam_result.spam_reasons or [],
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)
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await ctx.send_message(result)
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class MessageResponder(Executor):
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"""Step 4b: An executor that responds to legitimate messages."""
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@handler
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async def handle_spam_detection(
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self,
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spam_result: SpamDetectorResponse,
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ctx: WorkflowContext[ProcessingResult],
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) -> None:
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"""Respond to legitimate messages."""
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if spam_result.is_spam:
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raise RuntimeError("Message is spam, cannot respond with message responder.")
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await asyncio.sleep(2.5) # Simulate response time
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result = ProcessingResult(
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original_message=spam_result.analysis.email_content.original_message,
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action_taken="responded_and_filed",
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processing_time=2.5,
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status="message_processed",
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is_spam=spam_result.is_spam,
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confidence_score=spam_result.confidence_score,
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spam_reasons=spam_result.spam_reasons or [],
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)
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await ctx.send_message(result)
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class FinalProcessor(Executor):
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"""Step 5: An executor that completes the workflow with final logging and cleanup."""
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@handler
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async def handle_processing_result(
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self,
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result: ProcessingResult,
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ctx: WorkflowContext[Never, str],
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) -> None:
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"""Complete the workflow with final processing and logging."""
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await asyncio.sleep(1.5) # Simulate final processing time
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total_time = result.processing_time + 1.5
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# Include classification details in completion message
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classification = "SPAM" if result.is_spam else "LEGITIMATE"
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reasons = ", ".join(result.spam_reasons) if result.spam_reasons else "none"
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completion_message = (
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f"Email classified as {classification} (confidence: {result.confidence_score:.2f}). "
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f"Reasons: {reasons}. "
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f"Action: {result.action_taken}, "
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f"Status: {result.status}, "
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f"Total time: {total_time:.1f}s"
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)
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await ctx.yield_output(completion_message)
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# Create the workflow instance that DevUI can discover
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spam_keywords = ["spam", "advertisement", "offer", "click here", "winner", "congratulations", "urgent"]
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# Create all the executors for the 5-step workflow
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email_preprocessor = EmailPreprocessor(id="email_preprocessor")
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content_analyzer = ContentAnalyzer(id="content_analyzer")
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spam_detector = SpamDetector(spam_keywords, id="spam_detector")
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spam_handler = SpamHandler(id="spam_handler")
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message_responder = MessageResponder(id="message_responder")
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final_processor = FinalProcessor(id="final_processor")
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# Build the comprehensive 5-step workflow with branching logic
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workflow = (
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WorkflowBuilder(
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name="Email Spam Detector",
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description="5-step email classification workflow with spam/legitimate routing",
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)
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.set_start_executor(email_preprocessor)
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.add_edge(email_preprocessor, content_analyzer)
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.add_edge(content_analyzer, spam_detector)
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.add_switch_case_edge_group(
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spam_detector,
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[
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Case(condition=lambda x: x.is_spam, target=spam_handler),
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Default(target=message_responder),
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],
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)
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.add_edge(spam_handler, final_processor)
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.add_edge(message_responder, final_processor)
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.build()
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)
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# Note: Workflow metadata is determined by executors and graph structure
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def main():
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"""Launch the spam detection workflow in DevUI."""
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from agent_framework.devui import serve
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# Setup logging
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logging.basicConfig(level=logging.INFO, format="%(message)s")
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logger = logging.getLogger(__name__)
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logger.info("Starting Spam Detection Workflow")
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logger.info("Available at: http://localhost:8090")
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logger.info("Entity ID: workflow_spam_detection")
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# Launch server with the workflow
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serve(entities=[workflow], port=8090, auto_open=True)
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if __name__ == "__main__":
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main()
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