mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
Python: [Feature Branch] Merge from main to Azure AI branch (#2111)
* 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 Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * 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 ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * .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 Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Enable feature build pipelines --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> 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 ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * .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.* Co-authored-by: rogerbarreto <19890735+rogerbarreto@users.noreply.github.com> * Remove unrelated changes to package-lock.json and yarn.lock Co-authored-by: markwallace-microsoft <127216156+markwallace-microsoft@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: rogerbarreto <19890735+rogerbarreto@users.noreply.github.com> Co-authored-by: markwallace-microsoft <127216156+markwallace-microsoft@users.noreply.github.com> --------- Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com> Co-authored-by: rogerbarreto <19890735+rogerbarreto@users.noreply.github.com> Co-authored-by: markwallace-microsoft <127216156+markwallace-microsoft@users.noreply.github.com> * .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 ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * 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 ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * 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 ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Chris <66376200+crickman@users.noreply.github.com> * 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 Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Add AdditionalProperties tests to AgentRunOptions Co-authored-by: kzu <169707+kzu@users.noreply.github.com> --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: kzu <169707+kzu@users.noreply.github.com> * 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) --------- Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: Reuben Bond <203839+ReubenBond@users.noreply.github.com> Co-authored-by: Peter Ibekwe <109177538+peibekwe@users.noreply.github.com> Co-authored-by: Jeff Handley <jeffhandley@users.noreply.github.com> Co-authored-by: Daniel Roth <daroth@microsoft.com> Co-authored-by: Victor Dibia <chuvidi2003@gmail.com> Co-authored-by: Mark Wallace <127216156+markwallace-microsoft@users.noreply.github.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: Shawn Henry <sphenry@gmail.com> Co-authored-by: Javier Calvarro Nelson <jacalvar@microsoft.com> Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com> Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com> Co-authored-by: Korolev Dmitry <deagle.gross@gmail.com> Co-authored-by: westey <164392973+westey-m@users.noreply.github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Reuben Bond <reuben.bond@gmail.com> Co-authored-by: Tao Chen <taochen@microsoft.com> Co-authored-by: wuweng <wuweng@microsoft.com> Co-authored-by: Chris <66376200+crickman@users.noreply.github.com> Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com> Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com> Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com> Co-authored-by: Jacob Alber <jaalber@microsoft.com> Co-authored-by: Giles Odigwe <79032838+giles17@users.noreply.github.com> Co-authored-by: Daniel Cazzulino <daniel@cazzulino.com> Co-authored-by: kzu <169707+kzu@users.noreply.github.com>
This commit is contained in:
Dmytro Struk
committed by
GitHub
Unverified
GitHub
parent
85fcd230bf
commit
361c47f30f
@@ -23,6 +23,7 @@ This folder contains examples demonstrating different ways to create and use age
|
||||
| [`openai_responses_client_image_analysis.py`](openai_responses_client_image_analysis.py) | Demonstrates how to use vision capabilities with agents to analyze images. |
|
||||
| [`openai_responses_client_image_generation.py`](openai_responses_client_image_generation.py) | Demonstrates how to use image generation capabilities with OpenAI agents to create images based on text descriptions. Requires PIL (Pillow) for image display. |
|
||||
| [`openai_responses_client_reasoning.py`](openai_responses_client_reasoning.py) | Demonstrates how to use reasoning capabilities with OpenAI agents, showing how the agent can provide detailed reasoning for its responses. |
|
||||
| [`openai_responses_client_streaming_image_generation.py`](openai_responses_client_streaming_image_generation.py) | Demonstrates streaming image generation with partial images for real-time image creation feedback and improved user experience. |
|
||||
| [`openai_responses_client_with_code_interpreter.py`](openai_responses_client_with_code_interpreter.py) | Shows how to use the HostedCodeInterpreterTool with OpenAI agents to write and execute Python code. Includes helper methods for accessing code interpreter data from response chunks. |
|
||||
| [`openai_responses_client_with_explicit_settings.py`](openai_responses_client_with_explicit_settings.py) | Shows how to initialize an agent with a specific responses client, configuring settings explicitly including API key and model ID. |
|
||||
| [`openai_responses_client_with_file_search.py`](openai_responses_client_with_file_search.py) | Demonstrates how to use file search capabilities with OpenAI agents, allowing the agent to search through uploaded files to answer questions. |
|
||||
|
||||
+96
@@ -0,0 +1,96 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
import asyncio
|
||||
import base64
|
||||
|
||||
import anyio
|
||||
from agent_framework import DataContent
|
||||
from agent_framework.openai import OpenAIResponsesClient
|
||||
|
||||
"""OpenAI Responses Client Streaming Image Generation Example
|
||||
|
||||
Demonstrates streaming partial image generation using OpenAI's image generation tool.
|
||||
Shows progressive image rendering with partial images for improved user experience.
|
||||
|
||||
Note: The number of partial images received depends on generation speed:
|
||||
- High quality/complex images: More partials (generation takes longer)
|
||||
- Low quality/simple images: Fewer partials (generation completes quickly)
|
||||
- You may receive fewer partial images than requested if generation is fast
|
||||
|
||||
Important: The final partial image IS the complete, full-quality image. Each partial
|
||||
represents a progressive refinement, with the last one being the finished result.
|
||||
"""
|
||||
|
||||
|
||||
async def save_image_from_data_uri(data_uri: str, filename: str) -> None:
|
||||
"""Save an image from a data URI to a file."""
|
||||
try:
|
||||
if data_uri.startswith("data:image/"):
|
||||
# Extract base64 data
|
||||
base64_data = data_uri.split(",", 1)[1]
|
||||
image_bytes = base64.b64decode(base64_data)
|
||||
|
||||
# Save to file
|
||||
await anyio.Path(filename).write_bytes(image_bytes)
|
||||
print(f" Saved: {filename} ({len(image_bytes) / 1024:.1f} KB)")
|
||||
except Exception as e:
|
||||
print(f" Error saving {filename}: {e}")
|
||||
|
||||
|
||||
async def main():
|
||||
"""Demonstrate streaming image generation with partial images."""
|
||||
print("=== OpenAI Streaming Image Generation Example ===\n")
|
||||
|
||||
# Create agent with streaming image generation enabled
|
||||
agent = OpenAIResponsesClient().create_agent(
|
||||
instructions="You are a helpful agent that can generate images.",
|
||||
tools=[
|
||||
{
|
||||
"type": "image_generation",
|
||||
"size": "1024x1024",
|
||||
"quality": "high",
|
||||
"partial_images": 3,
|
||||
}
|
||||
],
|
||||
)
|
||||
|
||||
query = "Draw a beautiful sunset over a calm ocean with sailboats"
|
||||
print(f" User: {query}")
|
||||
print()
|
||||
|
||||
# Track partial images
|
||||
image_count = 0
|
||||
|
||||
# Create output directory
|
||||
output_dir = anyio.Path("generated_images")
|
||||
await output_dir.mkdir(exist_ok=True)
|
||||
|
||||
print(" Streaming response:")
|
||||
async for update in agent.run_stream(query):
|
||||
for content in update.contents:
|
||||
# Handle partial images
|
||||
# The final partial image IS the complete, full-quality image. Each partial
|
||||
# represents a progressive refinement, with the last one being the finished result.
|
||||
if isinstance(content, DataContent) and content.additional_properties.get("is_partial_image"):
|
||||
print(f" Image {image_count} received")
|
||||
|
||||
# Extract file extension from media_type (e.g., "image/png" -> "png")
|
||||
extension = "png" # Default fallback
|
||||
if content.media_type and "/" in content.media_type:
|
||||
extension = content.media_type.split("/")[-1]
|
||||
|
||||
# Save images with correct extension
|
||||
filename = output_dir / f"image{image_count}.{extension}"
|
||||
await save_image_from_data_uri(content.uri, str(filename))
|
||||
|
||||
image_count += 1
|
||||
|
||||
# Summary
|
||||
print("\n Summary:")
|
||||
print(f" Images received: {image_count}")
|
||||
print(" Output directory: generated_images")
|
||||
print("\n Streaming image generation completed!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -0,0 +1,19 @@
|
||||
# Auto-generated Dockerfiles from DevUI deployment
|
||||
*/Dockerfile
|
||||
|
||||
# Python cache
|
||||
__pycache__/
|
||||
*.pyc
|
||||
*.pyo
|
||||
*.pyd
|
||||
|
||||
# Environment files (may contain secrets)
|
||||
.env
|
||||
*.env
|
||||
|
||||
# IDE files
|
||||
.vscode/
|
||||
.idea/
|
||||
*.swp
|
||||
*.swo
|
||||
*~
|
||||
@@ -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()
|
||||
)
|
||||
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
"""Sample weather agent for Agent Framework Debug UI."""
|
||||
|
||||
import logging
|
||||
import os
|
||||
from collections.abc import Awaitable, Callable
|
||||
from typing import Annotated
|
||||
@@ -14,8 +15,20 @@ from agent_framework import (
|
||||
Role,
|
||||
chat_middleware,
|
||||
function_middleware,
|
||||
ai_function
|
||||
)
|
||||
from agent_framework.azure import AzureOpenAIChatClient
|
||||
from agent_framework_devui import register_cleanup
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def cleanup_resources():
|
||||
"""Cleanup function that runs when DevUI shuts down."""
|
||||
logger.info("=" * 60)
|
||||
logger.info(" Cleaning up resources...")
|
||||
logger.info(" (In production, this would close credentials, sessions, etc.)")
|
||||
logger.info("=" * 60)
|
||||
|
||||
|
||||
@chat_middleware
|
||||
@@ -93,6 +106,14 @@ def get_forecast(
|
||||
|
||||
return f"Weather forecast for {location}:\n" + "\n".join(forecast)
|
||||
|
||||
@ai_function(approval_mode="always_require")
|
||||
def send_email(
|
||||
recipient: Annotated[str, "The email address of the recipient."],
|
||||
subject: Annotated[str, "The subject of the email."],
|
||||
body: Annotated[str, "The body content of the email."],
|
||||
) -> str:
|
||||
"""Simulate sending an email."""
|
||||
return f"Email sent to {recipient} with subject '{subject}'."
|
||||
|
||||
# Agent instance following Agent Framework conventions
|
||||
agent = ChatAgent(
|
||||
@@ -106,10 +127,13 @@ agent = ChatAgent(
|
||||
chat_client=AzureOpenAIChatClient(
|
||||
api_key=os.environ.get("AZURE_OPENAI_API_KEY", ""),
|
||||
),
|
||||
tools=[get_weather, get_forecast],
|
||||
tools=[get_weather, get_forecast, send_email],
|
||||
middleware=[security_filter_middleware, atlantis_location_filter_middleware],
|
||||
)
|
||||
|
||||
# Register cleanup hook - demonstrates resource cleanup on shutdown
|
||||
register_cleanup(agent, cleanup_resources)
|
||||
|
||||
|
||||
def main():
|
||||
"""Launch the Azure weather agent in DevUI."""
|
||||
|
||||
@@ -191,11 +191,11 @@ dependencies
|
||||
Besides the Application Insights native UI, you can also use Grafana to visualize the telemetry data in Application Insights. There are two tailored dashboards for you to get started quickly:
|
||||
|
||||
#### Agent Overview dashboard
|
||||
Grafana Dashboard Gallery link: <https://aka.ms/amg/dash/af-agent>
|
||||
Open dashboard in Azure portal: <https://aka.ms/amg/dash/af-agent>
|
||||
|
||||
|
||||
#### Workflow Overview dashboard
|
||||
Grafana Dashboard Gallery link: <https://aka.ms/amg/dash/af-workflow>
|
||||
Open dashboard in Azure portal: <https://aka.ms/amg/dash/af-workflow>
|
||||
|
||||
|
||||
## Aspire Dashboard
|
||||
|
||||
@@ -78,6 +78,7 @@ Once comfortable with these, explore the rest of the samples below.
|
||||
|---|---|---|
|
||||
| Human-In-The-Loop (Guessing Game) | [human-in-the-loop/guessing_game_with_human_input.py](./human-in-the-loop/guessing_game_with_human_input.py) | Interactive request/response prompts with a human |
|
||||
| Azure Agents Tool Feedback Loop | [agents/azure_chat_agents_tool_calls_with_feedback.py](./agents/azure_chat_agents_tool_calls_with_feedback.py) | Two-agent workflow that streams tool calls and pauses for human guidance between passes |
|
||||
| Agents with Approval Requests in Workflows | [human-in-the-loop/agents_with_approval_requests.py](./human-in-the-loop/agents_with_approval_requests.py) | Agents that create approval requests during workflow execution and wait for human approval to proceed |
|
||||
|
||||
### observability
|
||||
|
||||
@@ -96,6 +97,7 @@ Once comfortable with these, explore the rest of the samples below.
|
||||
| Group Chat with Simple Function Selector | [orchestration/group_chat_simple_selector.py](./orchestration/group_chat_simple_selector.py) | Group chat with a simple function selector for next speaker |
|
||||
| Handoff (Simple) | [orchestration/handoff_simple.py](./orchestration/handoff_simple.py) | Single-tier routing: triage agent routes to specialists, control returns to user after each specialist response |
|
||||
| Handoff (Specialist-to-Specialist) | [orchestration/handoff_specialist_to_specialist.py](./orchestration/handoff_specialist_to_specialist.py) | Multi-tier routing: specialists can hand off to other specialists using `.add_handoff()` fluent API |
|
||||
| Handoff (Return-to-Previous) | [orchestration/handoff_return_to_previous.py](./orchestration/handoff_return_to_previous.py) | Return-to-previous routing: after user input, routes back to the previous specialist instead of coordinator using `.enable_return_to_previous()` |
|
||||
| Magentic Workflow (Multi-Agent) | [orchestration/magentic.py](./orchestration/magentic.py) | Orchestrate multiple agents with Magentic manager and streaming |
|
||||
| Magentic + Human Plan Review | [orchestration/magentic_human_plan_update.py](./orchestration/magentic_human_plan_update.py) | Human reviews/updates the plan before execution |
|
||||
| Magentic + Checkpoint Resume | [orchestration/magentic_checkpoint.py](./orchestration/magentic_checkpoint.py) | Resume Magentic orchestration from saved checkpoints |
|
||||
|
||||
+340
@@ -0,0 +1,340 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
from dataclasses import dataclass
|
||||
from typing import Annotated, Never
|
||||
|
||||
from agent_framework import (
|
||||
AgentExecutorResponse,
|
||||
ChatMessage,
|
||||
Executor,
|
||||
FunctionApprovalRequestContent,
|
||||
FunctionApprovalResponseContent,
|
||||
WorkflowBuilder,
|
||||
WorkflowContext,
|
||||
ai_function,
|
||||
executor,
|
||||
handler,
|
||||
)
|
||||
from agent_framework.openai import OpenAIChatClient
|
||||
|
||||
"""
|
||||
Sample: Agents in a workflow with AI functions requiring approval
|
||||
|
||||
This sample creates a workflow that automatically replies to incoming emails.
|
||||
If historical email data is needed, it uses an AI function to read the data,
|
||||
which requires human approval before execution.
|
||||
|
||||
This sample works as follows:
|
||||
1. An incoming email is received by the workflow.
|
||||
2. The EmailPreprocessor executor preprocesses the email, adding special notes if the sender is important.
|
||||
3. The preprocessed email is sent to the Email Writer agent, which generates a response.
|
||||
4. If the agent needs to read historical email data, it calls the read_historical_email_data AI function,
|
||||
which triggers an approval request.
|
||||
5. The sample automatically approves the request for demonstration purposes.
|
||||
6. Once approved, the AI function executes and returns the historical email data to the agent.
|
||||
7. The agent uses the historical data to compose a comprehensive email response.
|
||||
8. The response is sent to the conclude_workflow_executor, which yields the final response.
|
||||
|
||||
Purpose:
|
||||
Show how to integrate AI functions with approval requests into a workflow.
|
||||
|
||||
Demonstrate:
|
||||
- Creating AI functions that require approval before execution.
|
||||
- Building a workflow that includes an agent and executors.
|
||||
- Handling approval requests during workflow execution.
|
||||
|
||||
Prerequisites:
|
||||
- Azure AI Agent Service configured, along with the required environment variables.
|
||||
- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
|
||||
- Basic familiarity with WorkflowBuilder, edges, events, RequestInfoEvent, and streaming runs.
|
||||
"""
|
||||
|
||||
|
||||
@ai_function
|
||||
def get_current_date() -> str:
|
||||
"""Get the current date in YYYY-MM-DD format."""
|
||||
# For demonstration purposes, we return a fixed date.
|
||||
return "2025-11-07"
|
||||
|
||||
|
||||
@ai_function
|
||||
def get_team_members_email_addresses() -> list[dict[str, str]]:
|
||||
"""Get the email addresses of team members."""
|
||||
# In a real implementation, this might query a database or directory service.
|
||||
return [
|
||||
{
|
||||
"name": "Alice",
|
||||
"email": "alice@contoso.com",
|
||||
"position": "Software Engineer",
|
||||
"manager": "John Doe",
|
||||
},
|
||||
{
|
||||
"name": "Bob",
|
||||
"email": "bob@contoso.com",
|
||||
"position": "Product Manager",
|
||||
"manager": "John Doe",
|
||||
},
|
||||
{
|
||||
"name": "Charlie",
|
||||
"email": "charlie@contoso.com",
|
||||
"position": "Senior Software Engineer",
|
||||
"manager": "John Doe",
|
||||
},
|
||||
{
|
||||
"name": "Mike",
|
||||
"email": "mike@contoso.com",
|
||||
"position": "Principal Software Engineer Manager",
|
||||
"manager": "VP of Engineering",
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
@ai_function
|
||||
def get_my_information() -> dict[str, str]:
|
||||
"""Get my personal information."""
|
||||
return {
|
||||
"name": "John Doe",
|
||||
"email": "john@contoso.com",
|
||||
"position": "Software Engineer Manager",
|
||||
"manager": "Mike",
|
||||
}
|
||||
|
||||
|
||||
@ai_function(approval_mode="always_require")
|
||||
async def read_historical_email_data(
|
||||
email_address: Annotated[str, "The email address to read historical data from"],
|
||||
start_date: Annotated[str, "The start date in YYYY-MM-DD format"],
|
||||
end_date: Annotated[str, "The end date in YYYY-MM-DD format"],
|
||||
) -> list[dict[str, str]]:
|
||||
"""Read historical email data for a given email address and date range."""
|
||||
historical_data = {
|
||||
"alice@contoso.com": [
|
||||
{
|
||||
"from": "alice@contoso.com",
|
||||
"to": "john@contoso.com",
|
||||
"date": "2025-11-05",
|
||||
"subject": "Bug Bash Results",
|
||||
"body": "We just completed the bug bash and found a few issues that need immediate attention.",
|
||||
},
|
||||
{
|
||||
"from": "alice@contoso.com",
|
||||
"to": "john@contoso.com",
|
||||
"date": "2025-11-03",
|
||||
"subject": "Code Freeze",
|
||||
"body": "We are entering code freeze starting tomorrow.",
|
||||
},
|
||||
],
|
||||
"bob@contoso.com": [
|
||||
{
|
||||
"from": "bob@contoso.com",
|
||||
"to": "john@contoso.com",
|
||||
"date": "2025-11-04",
|
||||
"subject": "Team Outing",
|
||||
"body": "Don't forget about the team outing this Friday!",
|
||||
},
|
||||
{
|
||||
"from": "bob@contoso.com",
|
||||
"to": "john@contoso.com",
|
||||
"date": "2025-11-02",
|
||||
"subject": "Requirements Update",
|
||||
"body": "The requirements for the new feature have been updated. Please review them.",
|
||||
},
|
||||
],
|
||||
"charlie@contoso.com": [
|
||||
{
|
||||
"from": "charlie@contoso.com",
|
||||
"to": "john@contoso.com",
|
||||
"date": "2025-11-05",
|
||||
"subject": "Project Update",
|
||||
"body": "The bug bash went well. A few critical bugs but should be fixed by the end of the week.",
|
||||
},
|
||||
{
|
||||
"from": "charlie@contoso.com",
|
||||
"to": "john@contoso.com",
|
||||
"date": "2025-11-06",
|
||||
"subject": "Code Review",
|
||||
"body": "Please review my latest code changes.",
|
||||
},
|
||||
],
|
||||
}
|
||||
|
||||
emails = historical_data.get(email_address, [])
|
||||
return [email for email in emails if start_date <= email["date"] <= end_date]
|
||||
|
||||
|
||||
@ai_function(approval_mode="always_require")
|
||||
async def send_email(
|
||||
to: Annotated[str, "The recipient email address"],
|
||||
subject: Annotated[str, "The email subject"],
|
||||
body: Annotated[str, "The email body"],
|
||||
) -> str:
|
||||
"""Send an email."""
|
||||
await asyncio.sleep(1) # Simulate sending email
|
||||
return "Email successfully sent."
|
||||
|
||||
|
||||
@dataclass
|
||||
class Email:
|
||||
sender: str
|
||||
subject: str
|
||||
body: str
|
||||
|
||||
|
||||
class EmailPreprocessor(Executor):
|
||||
def __init__(self, special_email_addresses: set[str]) -> None:
|
||||
super().__init__(id="email_preprocessor")
|
||||
self.special_email_addresses = special_email_addresses
|
||||
|
||||
@handler
|
||||
async def preprocess(self, email: Email, ctx: WorkflowContext[str]) -> None:
|
||||
"""Preprocess the incoming email."""
|
||||
message = str(email)
|
||||
if email.sender in self.special_email_addresses:
|
||||
note = (
|
||||
"Pay special attention to this sender. This email is very important. "
|
||||
"Gather relevant information from all previous emails within my team before responding."
|
||||
)
|
||||
message = f"{note}\n\n{message}"
|
||||
|
||||
await ctx.send_message(message)
|
||||
|
||||
|
||||
@executor(id="conclude_workflow_executor")
|
||||
async def conclude_workflow(
|
||||
email_response: AgentExecutorResponse,
|
||||
ctx: WorkflowContext[Never, str],
|
||||
) -> None:
|
||||
"""Conclude the workflow by yielding the final email response."""
|
||||
await ctx.yield_output(email_response.agent_run_response.text)
|
||||
|
||||
|
||||
async def main() -> None:
|
||||
# Create the agent and executors
|
||||
chat_client = OpenAIChatClient()
|
||||
email_writer = chat_client.create_agent(
|
||||
name="Email Writer",
|
||||
instructions=("You are an excellent email assistant. You respond to incoming emails."),
|
||||
# tools with `approval_mode="always_require"` will trigger approval requests
|
||||
tools=[
|
||||
read_historical_email_data,
|
||||
send_email,
|
||||
get_current_date,
|
||||
get_team_members_email_addresses,
|
||||
get_my_information,
|
||||
],
|
||||
)
|
||||
email_preprocessor = EmailPreprocessor(special_email_addresses={"mike@contoso.com"})
|
||||
|
||||
# Build the workflow
|
||||
workflow = (
|
||||
WorkflowBuilder()
|
||||
.set_start_executor(email_preprocessor)
|
||||
.add_edge(email_preprocessor, email_writer)
|
||||
.add_edge(email_writer, conclude_workflow)
|
||||
.build()
|
||||
)
|
||||
|
||||
# Simulate an incoming email
|
||||
incoming_email = Email(
|
||||
sender="mike@contoso.com",
|
||||
subject="Important: Project Update",
|
||||
body="Please provide your team's status update on the project since last week.",
|
||||
)
|
||||
|
||||
responses: dict[str, FunctionApprovalResponseContent] = {}
|
||||
output: list[ChatMessage] | None = None
|
||||
while True:
|
||||
if responses:
|
||||
events = await workflow.send_responses(responses)
|
||||
responses.clear()
|
||||
else:
|
||||
events = await workflow.run(incoming_email)
|
||||
|
||||
request_info_events = events.get_request_info_events()
|
||||
for request_info_event in request_info_events:
|
||||
# We should only expect FunctionApprovalRequestContent in this sample
|
||||
if not isinstance(request_info_event.data, FunctionApprovalRequestContent):
|
||||
raise ValueError(f"Unexpected request info content type: {type(request_info_event.data)}")
|
||||
|
||||
# Pretty print the function call details
|
||||
arguments = json.dumps(request_info_event.data.function_call.parse_arguments(), indent=2)
|
||||
print(
|
||||
f"Received approval request for function: {request_info_event.data.function_call.name} "
|
||||
f"with args:\n{arguments}"
|
||||
)
|
||||
|
||||
# For demo purposes, we automatically approve the request
|
||||
# The expected response type of the request is `FunctionApprovalResponseContent`,
|
||||
# which can be created via `create_response` method on the request content
|
||||
print("Performing automatic approval for demo purposes...")
|
||||
responses[request_info_event.request_id] = request_info_event.data.create_response(approved=True)
|
||||
|
||||
# Once we get an output event, we can conclude the workflow
|
||||
# Outputs can only be produced by the conclude_workflow_executor in this sample
|
||||
if outputs := events.get_outputs():
|
||||
# We expect only one output from the conclude_workflow_executor
|
||||
output = outputs[0]
|
||||
break
|
||||
|
||||
if not output:
|
||||
raise RuntimeError("Workflow did not produce any output event.")
|
||||
|
||||
print("Final email response conversation:")
|
||||
print(output)
|
||||
|
||||
"""
|
||||
Sample Output:
|
||||
Received approval request for function: read_historical_email_data with args:
|
||||
{
|
||||
"email_address": "alice@contoso.com",
|
||||
"start_date": "2025-10-31",
|
||||
"end_date": "2025-11-07"
|
||||
}
|
||||
Performing automatic approval for demo purposes...
|
||||
Received approval request for function: read_historical_email_data with args:
|
||||
{
|
||||
"email_address": "bob@contoso.com",
|
||||
"start_date": "2025-10-31",
|
||||
"end_date": "2025-11-07"
|
||||
}
|
||||
Performing automatic approval for demo purposes...
|
||||
Received approval request for function: read_historical_email_data with args:
|
||||
{
|
||||
"email_address": "charlie@contoso.com",
|
||||
"start_date": "2025-10-31",
|
||||
"end_date": "2025-11-07"
|
||||
}
|
||||
Performing automatic approval for demo purposes...
|
||||
Received approval request for function: send_email with args:
|
||||
{
|
||||
"to": "mike@contoso.com",
|
||||
"subject": "Team's Status Update on the Project",
|
||||
"body": "
|
||||
Hi Mike,
|
||||
|
||||
Here's the status update from our team:
|
||||
- **Bug Bash and Code Freeze:**
|
||||
- We recently completed a bug bash, during which several issues were identified. Alice and Charlie are working on fixing these critical bugs, and we anticipate resolving them by the end of this week.
|
||||
- We have entered a code freeze as of November 4, 2025.
|
||||
|
||||
- **Requirements Update:**
|
||||
- Bob has updated the requirements for a new feature, and all team members are reviewing these changes to ensure alignment.
|
||||
|
||||
- **Ongoing Reviews:**
|
||||
- Charlie has submitted his latest code changes for review to ensure they meet our quality standards.
|
||||
|
||||
Please let me know if you need more detailed information or have any questions.
|
||||
|
||||
Best regards,
|
||||
John"
|
||||
}
|
||||
Performing automatic approval for demo purposes...
|
||||
Final email response conversation:
|
||||
I've sent the status update to Mike with the relevant information from the team. Let me know if there's anything else you need
|
||||
""" # noqa: E501
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -0,0 +1,294 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
import asyncio
|
||||
from collections.abc import AsyncIterable
|
||||
from typing import cast
|
||||
|
||||
from agent_framework import (
|
||||
ChatAgent,
|
||||
HandoffBuilder,
|
||||
HandoffUserInputRequest,
|
||||
RequestInfoEvent,
|
||||
WorkflowEvent,
|
||||
WorkflowOutputEvent,
|
||||
)
|
||||
from agent_framework.azure import AzureOpenAIChatClient
|
||||
from azure.identity import AzureCliCredential
|
||||
|
||||
"""Sample: Handoff workflow with return-to-previous routing enabled.
|
||||
|
||||
This interactive sample demonstrates the return-to-previous feature where user inputs
|
||||
route directly back to the specialist currently handling their request, rather than
|
||||
always going through the coordinator for re-evaluation.
|
||||
|
||||
Routing Pattern (with return-to-previous enabled):
|
||||
User -> Coordinator -> Technical Support -> User -> Technical Support -> ...
|
||||
|
||||
Routing Pattern (default, without return-to-previous):
|
||||
User -> Coordinator -> Technical Support -> User -> Coordinator -> Technical Support -> ...
|
||||
|
||||
This is useful when a specialist needs multiple turns with the user to gather
|
||||
information or resolve an issue, avoiding unnecessary coordinator involvement.
|
||||
|
||||
Specialist-to-Specialist Handoff:
|
||||
When a user's request changes to a topic outside the current specialist's domain,
|
||||
the specialist can hand off DIRECTLY to another specialist without going back through
|
||||
the coordinator:
|
||||
|
||||
User -> Coordinator -> Technical Support -> User -> Technical Support (billing question)
|
||||
-> Billing -> User -> Billing ...
|
||||
|
||||
Example Interaction:
|
||||
1. User reports a technical issue
|
||||
2. Coordinator routes to technical support specialist
|
||||
3. Technical support asks clarifying questions
|
||||
4. User provides details (routes directly back to technical support)
|
||||
5. Technical support continues troubleshooting with full context
|
||||
6. Issue resolved, user asks about billing
|
||||
7. Technical support hands off DIRECTLY to billing specialist
|
||||
8. Billing specialist helps with payment
|
||||
9. User continues with billing (routes directly to billing)
|
||||
|
||||
Prerequisites:
|
||||
- `az login` (Azure CLI authentication)
|
||||
- Environment variables configured for AzureOpenAIChatClient (AZURE_OPENAI_ENDPOINT, etc.)
|
||||
|
||||
Usage:
|
||||
Run the script and interact with the support workflow by typing your requests.
|
||||
Type 'exit' or 'quit' to end the conversation.
|
||||
|
||||
Key Concepts:
|
||||
- Return-to-previous: Direct routing to current agent handling the conversation
|
||||
- Current agent tracking: Framework remembers which agent is actively helping the user
|
||||
- Context preservation: Specialist maintains full conversation context
|
||||
- Domain switching: Specialists can hand back to coordinator when topic changes
|
||||
"""
|
||||
|
||||
|
||||
def create_agents(chat_client: AzureOpenAIChatClient) -> tuple[ChatAgent, ChatAgent, ChatAgent, ChatAgent]:
|
||||
"""Create and configure the coordinator and specialist agents.
|
||||
|
||||
Returns:
|
||||
Tuple of (coordinator, technical_support, account_specialist, billing_agent)
|
||||
"""
|
||||
coordinator = chat_client.create_agent(
|
||||
instructions=(
|
||||
"You are a customer support coordinator. Analyze the user's request and route to "
|
||||
"the appropriate specialist:\n"
|
||||
"- technical_support for technical issues, troubleshooting, repairs, hardware/software problems\n"
|
||||
"- account_specialist for account changes, profile updates, settings, login issues\n"
|
||||
"- billing_agent for payments, invoices, refunds, charges, billing questions\n"
|
||||
"\n"
|
||||
"When you receive a request, immediately call the matching handoff tool without explaining. "
|
||||
"Read the most recent user message to determine the correct specialist."
|
||||
),
|
||||
name="coordinator",
|
||||
)
|
||||
|
||||
technical_support = chat_client.create_agent(
|
||||
instructions=(
|
||||
"You provide technical support. Help users troubleshoot technical issues, "
|
||||
"arrange repairs, and answer technical questions. "
|
||||
"Gather information through conversation. "
|
||||
"If the user asks about billing, payments, invoices, or refunds, hand off to billing_agent. "
|
||||
"If the user asks about account settings or profile changes, hand off to account_specialist."
|
||||
),
|
||||
name="technical_support",
|
||||
)
|
||||
|
||||
account_specialist = chat_client.create_agent(
|
||||
instructions=(
|
||||
"You handle account management. Help with profile updates, account settings, "
|
||||
"and preferences. Gather information through conversation. "
|
||||
"If the user asks about technical issues or troubleshooting, hand off to technical_support. "
|
||||
"If the user asks about billing, payments, invoices, or refunds, hand off to billing_agent."
|
||||
),
|
||||
name="account_specialist",
|
||||
)
|
||||
|
||||
billing_agent = chat_client.create_agent(
|
||||
instructions=(
|
||||
"You handle billing only. Process payments, explain invoices, handle refunds. "
|
||||
"If the user asks about technical issues or troubleshooting, hand off to technical_support. "
|
||||
"If the user asks about account settings or profile changes, hand off to account_specialist."
|
||||
),
|
||||
name="billing_agent",
|
||||
)
|
||||
|
||||
return coordinator, technical_support, account_specialist, billing_agent
|
||||
|
||||
|
||||
def handle_events(events: list[WorkflowEvent]) -> list[RequestInfoEvent]:
|
||||
"""Process events and return pending input requests."""
|
||||
pending_requests: list[RequestInfoEvent] = []
|
||||
for event in events:
|
||||
if isinstance(event, RequestInfoEvent):
|
||||
pending_requests.append(event)
|
||||
request_data = cast(HandoffUserInputRequest, event.data)
|
||||
print(f"\n{'=' * 60}")
|
||||
print(f"AWAITING INPUT FROM: {request_data.awaiting_agent_id.upper()}")
|
||||
print(f"{'=' * 60}")
|
||||
for msg in request_data.conversation[-3:]:
|
||||
author = msg.author_name or msg.role.value
|
||||
prefix = ">>> " if author == request_data.awaiting_agent_id else " "
|
||||
print(f"{prefix}[{author}]: {msg.text}")
|
||||
elif isinstance(event, WorkflowOutputEvent):
|
||||
print(f"\n{'=' * 60}")
|
||||
print("[WORKFLOW COMPLETE]")
|
||||
print(f"{'=' * 60}")
|
||||
return pending_requests
|
||||
|
||||
|
||||
async def _drain(stream: AsyncIterable[WorkflowEvent]) -> list[WorkflowEvent]:
|
||||
"""Drain an async iterable into a list."""
|
||||
events: list[WorkflowEvent] = []
|
||||
async for event in stream:
|
||||
events.append(event)
|
||||
return events
|
||||
|
||||
|
||||
async def main() -> None:
|
||||
"""Demonstrate return-to-previous routing in a handoff workflow."""
|
||||
chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
|
||||
coordinator, technical, account, billing = create_agents(chat_client)
|
||||
|
||||
print("Handoff Workflow with Return-to-Previous Routing")
|
||||
print("=" * 60)
|
||||
print("\nThis interactive demo shows how user inputs route directly")
|
||||
print("to the specialist handling your request, avoiding unnecessary")
|
||||
print("coordinator re-evaluation on each turn.")
|
||||
print("\nSpecialists can hand off directly to other specialists when")
|
||||
print("your request changes topics (e.g., from technical to billing).")
|
||||
print("\nType 'exit' or 'quit' to end the conversation.\n")
|
||||
|
||||
# Configure handoffs with return-to-previous enabled
|
||||
# Specialists can hand off directly to other specialists when topic changes
|
||||
workflow = (
|
||||
HandoffBuilder(
|
||||
name="return_to_previous_demo",
|
||||
participants=[coordinator, technical, account, billing],
|
||||
)
|
||||
.set_coordinator(coordinator)
|
||||
.add_handoff(coordinator, [technical, account, billing]) # Coordinator routes to all specialists
|
||||
.add_handoff(technical, [billing, account]) # Technical can route to billing or account
|
||||
.add_handoff(account, [technical, billing]) # Account can route to technical or billing
|
||||
.add_handoff(billing, [technical, account]) # Billing can route to technical or account
|
||||
.enable_return_to_previous(True) # Enable the `return to previous handoff` feature
|
||||
.with_termination_condition(lambda conv: sum(1 for msg in conv if msg.role.value == "user") >= 10)
|
||||
.build()
|
||||
)
|
||||
|
||||
# Get initial user request
|
||||
initial_request = input("You: ").strip() # noqa: ASYNC250
|
||||
if not initial_request or initial_request.lower() in ["exit", "quit"]:
|
||||
print("Goodbye!")
|
||||
return
|
||||
|
||||
# Start workflow with initial message
|
||||
events = await _drain(workflow.run_stream(initial_request))
|
||||
pending_requests = handle_events(events)
|
||||
|
||||
# Interactive loop: keep prompting for user input
|
||||
while pending_requests:
|
||||
user_input = input("\nYou: ").strip() # noqa: ASYNC250
|
||||
|
||||
if not user_input or user_input.lower() in ["exit", "quit"]:
|
||||
print("\nEnding conversation. Goodbye!")
|
||||
break
|
||||
|
||||
responses = {req.request_id: user_input for req in pending_requests}
|
||||
events = await _drain(workflow.send_responses_streaming(responses))
|
||||
pending_requests = handle_events(events)
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("Conversation ended.")
|
||||
|
||||
"""
|
||||
Sample Output:
|
||||
|
||||
Handoff Workflow with Return-to-Previous Routing
|
||||
============================================================
|
||||
|
||||
This interactive demo shows how user inputs route directly
|
||||
to the specialist handling your request, avoiding unnecessary
|
||||
coordinator re-evaluation on each turn.
|
||||
|
||||
Specialists can hand off directly to other specialists when
|
||||
your request changes topics (e.g., from technical to billing).
|
||||
|
||||
Type 'exit' or 'quit' to end the conversation.
|
||||
|
||||
You: I need help with my bill, I was charged twice by mistake.
|
||||
|
||||
============================================================
|
||||
AWAITING INPUT FROM: BILLING_AGENT
|
||||
============================================================
|
||||
[user]: I need help with my bill, I was charged twice by mistake.
|
||||
[coordinator]: You will be connected to a billing agent who can assist you with the double charge on your bill.
|
||||
>>> [billing_agent]: I'm here to help with billing concerns! I'm sorry you were charged twice. Could you
|
||||
please provide the invoice number or your account email so I can look into this and begin processing a refund?
|
||||
|
||||
You: Invoice 1234
|
||||
|
||||
============================================================
|
||||
AWAITING INPUT FROM: BILLING_AGENT
|
||||
============================================================
|
||||
>>> [billing_agent]: I'm here to help with billing concerns! I'm sorry you were charged twice.
|
||||
Could you please provide the invoice number or your account email so I can look into this and begin
|
||||
processing a refund?
|
||||
[user]: Invoice 1234
|
||||
>>> [billing_agent]: Thank you for providing the invoice number (1234). I will review the details and work
|
||||
on processing a refund for the duplicate charge.
|
||||
|
||||
Can you confirm which payment method you used for this bill (e.g., credit card, PayPal)?
|
||||
This helps ensure your refund is processed to the correct account.
|
||||
|
||||
You: I used my credit card, which is on autopay.
|
||||
|
||||
============================================================
|
||||
AWAITING INPUT FROM: BILLING_AGENT
|
||||
============================================================
|
||||
>>> [billing_agent]: Thank you for providing the invoice number (1234). I will review the details and work on
|
||||
processing a refund for the duplicate charge.
|
||||
|
||||
Can you confirm which payment method you used for this bill (e.g., credit card, PayPal)? This helps ensure
|
||||
your refund is processed to the correct account.
|
||||
[user]: I used my credit card, which is on autopay.
|
||||
>>> [billing_agent]: Thank you for confirming your payment method. I will look into invoice 1234 and
|
||||
process a refund for the duplicate charge to your credit card.
|
||||
|
||||
You will receive a notification once the refund is completed. If you have any further questions about your billing
|
||||
or need an update, please let me know!
|
||||
|
||||
You: Actually I also can't turn on my modem. It reset and now won't turn on.
|
||||
|
||||
============================================================
|
||||
AWAITING INPUT FROM: TECHNICAL_SUPPORT
|
||||
============================================================
|
||||
[user]: Actually I also can't turn on my modem. It reset and now won't turn on.
|
||||
[billing_agent]: I'm connecting you with technical support for assistance with your modem not turning on after
|
||||
the reset. They'll be able to help troubleshoot and resolve this issue.
|
||||
|
||||
At the same time, technical support will also handle your refund request for the duplicate charge on invoice 1234
|
||||
to your credit card on autopay.
|
||||
|
||||
You will receive updates from the appropriate teams shortly.
|
||||
>>> [technical_support]: Thanks for letting me know about your modem issue! To help you further, could you tell me:
|
||||
|
||||
1. Is there any light showing on the modem at all, or is it completely off?
|
||||
2. Have you tried unplugging the modem from power and plugging it back in?
|
||||
3. Do you hear or feel anything (like a slight hum or vibration) when the modem is plugged in?
|
||||
|
||||
Let me know, and I'll guide you through troubleshooting or arrange a repair if needed.
|
||||
|
||||
You: exit
|
||||
|
||||
Ending conversation. Goodbye!
|
||||
|
||||
============================================================
|
||||
Conversation ended.
|
||||
"""
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
Reference in New Issue
Block a user