Standardize the code of workflow use cases #495
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templates/components/multiagent/python/app/api/routers/vercel_response.py
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templates/components/agents/python/deep_research/app/workflows/deep_research.py
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| ) | ||
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| @step | ||
| async def retrieve(self, ctx: Context, ev: StartEvent) -> PlanResearchEvent: | ||
| """ | ||
| Initiate the workflow: memory, tools, agent | ||
| """ | ||
| self.stream = ev.get("stream", True) | ||
| self.user_request = ev.get("user_msg") |
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Also use user_msg to be consistent with the AgentWorkflow.
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🧹 Nitpick comments (11)
templates/components/multiagent/python/app/api/routers/vercel_response.py (2)
27-33: Use constructor docstring to clarifyhandlerusage.
It might help future maintainers if the constructor included a docstring explaining howhandleris used to stream workflow events.
63-79: Add a fallback for unexpectedchunkshapes.
Within_stream_text, the code checks if the chunk is a string or has adeltaattribute. If a chunk doesn’t match either, it’s silently skipped. Consider adding a fallback or raising an explicit error to improve debugging.templates/components/multiagent/python/app/api/routers/chat.py (1)
36-46: Check necessity of passing fullmessagesanddatatoVercelStreamResponse.
SinceVercelStreamResponseprimarily referenceshandler, confirm that passingchat_data=dataand the entire chat history is essential. If they’re unused, consider removing them to keep the signature lean.templates/components/agents/python/form_filling/app/workflows/form_filling.py (3)
91-113: Add type hints for system_prompt parameter.The constructor looks good, but adding type hints for the
system_promptparameter would improve code clarity and maintainability.def __init__( self, query_engine_tool: Optional[QueryEngineTool], extractor_tool: FunctionTool, filling_tool: FunctionTool, llm: Optional[FunctionCallingLLM] = None, timeout: int = 360, - system_prompt: Optional[str] = None, + system_prompt: Optional[str] = None, # type: str | None ):
115-132: Add validation for required event parameters.The method retrieves parameters from the event without validating if they exist. Consider adding validation for required parameters like
user_msg.@step() async def start(self, ctx: Context, ev: StartEvent) -> InputEvent: self.stream = ev.get("stream", True) - user_msg = ev.get("user_msg", "") + user_msg = ev.get("user_msg") + if not user_msg: + raise ValueError("user_msg is required in the event") chat_history = ev.get("chat_history", []) if chat_history: self.memory.put_messages(chat_history)
149-153: Add error handling for response generation.The method should handle potential exceptions when generating responses.
if not response.has_tool_calls(): if self.stream: - return StopEvent(result=response.generator) + try: + return StopEvent(result=response.generator) + except Exception as e: + return StopEvent(result=f"Error generating response: {str(e)}") else: - return StopEvent(result=await response.full_response()) + try: + return StopEvent(result=await response.full_response()) + except Exception as e: + return StopEvent(result=f"Error generating response: {str(e)}")templates/components/agents/python/deep_research/app/workflows/deep_research.py (2)
71-81: Add type hints for kwargs parameter.The constructor looks good, but adding type hints for the
kwargsparameter would improve code clarity.def __init__( self, index: BaseIndex, - **kwargs, + **kwargs: Any, ):
84-93: Add validation and error handling for event parameters.The method should validate required parameters and handle potential errors when updating memory.
@step async def retrieve(self, ctx: Context, ev: StartEvent) -> PlanResearchEvent: self.stream = ev.get("stream", True) - self.user_request = ev.get("user_msg") + self.user_request = ev.get("user_msg") + if not self.user_request: + raise ValueError("user_msg is required in the event") chat_history = ev.get("chat_history") if chat_history is not None: - self.memory.put_messages(chat_history) + try: + self.memory.put_messages(chat_history) + except Exception as e: + raise ValueError(f"Failed to update memory with chat history: {str(e)}")templates/components/agents/python/financial_report/app/workflows/financial_report.py (3)
95-123: Add type hints for system_prompt and timeout parameters.The constructor looks good, but adding type hints for the parameters would improve code clarity.
def __init__( self, query_engine_tool: QueryEngineTool, code_interpreter_tool: FunctionTool, document_generator_tool: FunctionTool, llm: Optional[FunctionCallingLLM] = None, - timeout: int = 360, - system_prompt: Optional[str] = None, + timeout: int = 360, # type: int + system_prompt: Optional[str] = None, # type: str | None ):
126-136: Add validation for required event parameters.The method should validate required parameters and handle potential errors.
@step() async def prepare_chat_history(self, ctx: Context, ev: StartEvent) -> InputEvent: self.stream = ev.get("stream", True) - user_msg = ev.get("user_msg") + user_msg = ev.get("user_msg") + if not user_msg: + raise ValueError("user_msg is required in the event") chat_history = ev.get("chat_history") if chat_history is not None: - self.memory.put_messages(chat_history) + try: + self.memory.put_messages(chat_history) + except Exception as e: + raise ValueError(f"Failed to update memory with chat history: {str(e)}")
164-167: Add error handling for response generation.The method should handle potential exceptions when generating responses.
if not response.has_tool_calls(): if self.stream: - return StopEvent(result=response.generator) + try: + return StopEvent(result=response.generator) + except Exception as e: + return StopEvent(result=f"Error generating response: {str(e)}") else: - return StopEvent(result=await response.full_response()) + try: + return StopEvent(result=await response.full_response()) + except Exception as e: + return StopEvent(result=f"Error generating response: {str(e)}")
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templates/components/agents/python/deep_research/app/workflows/deep_research.py(1 hunks)templates/components/agents/python/financial_report/app/workflows/financial_report.py(4 hunks)templates/components/agents/python/form_filling/app/workflows/form_filling.py(3 hunks)templates/components/multiagent/python/app/api/routers/chat.py(1 hunks)templates/components/multiagent/python/app/api/routers/vercel_response.py(2 hunks)templates/components/multiagent/python/app/workflows/tools.py(2 hunks)
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templates/components/multiagent/python/app/workflows/tools.py (1)
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templates/components/multiagent/python/app/api/routers/chat.py (1)
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templates/components/agents/python/form_filling/app/workflows/form_filling.py (1)
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templates/components/agents/python/deep_research/app/workflows/deep_research.py (1)
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templates/components/agents/python/financial_report/app/workflows/financial_report.py (1)
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templates/components/multiagent/python/app/api/routers/vercel_response.py (1)
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🔇 Additional comments (5)
templates/components/multiagent/python/app/api/routers/vercel_response.py (1)
36-61: Verify fallback for non-data, non-text events.
Currently, thecontent_generatormethod checks forAgentStream | StopEventorto_response()ormodel_dump(). If an event object doesn't fit these assumptions, it will be handled by themodel_dump()branch. Confirm all custom event types implement at least one of these patterns, or safely handle unrecognized event types.✅ Verification successful
I'll now run a script to search for Python event types and check if they implement either a
to_responseor amodel_dumpmethod (or inherit fromAgentStreamorStopEvent) to ensure all custom event types follow the expected contracts. Please hold on for further results.
Event Types Correctly Handle Fallbacks
- All Python event classes involved in stream responses implement a
to_responsemethod or, by virtue of being Pydantic models, safely supportmodel_dump().- No evidence was found of custom event types that fall outside the expected patterns.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Search all event classes to ensure they have a "to_response" or "model_dump" method # or that they inherit from AgentStream or StopEvent. ast-grep --pattern $'class $_ {\n $$$\n}' | rg 'class 'Length of output: 256
Script:
#!/bin/bash echo "Searching for Python event base classes and method implementations..." echo -e "\nSearching for 'class AgentStream' in Python files:" rg -tpy "class AgentStream" --color=never echo -e "\nSearching for 'class StopEvent' in Python files:" rg -tpy "class StopEvent" --color=never echo -e "\nSearching for 'def model_dump' in Python files:" rg -tpy "def model_dump" --color=never echo -e "\nSearching for 'def to_response' in Python files:" rg -tpy "def to_response" --color=neverLength of output: 1343
templates/components/multiagent/python/app/workflows/tools.py (1)
55-57: Good catch on avoiding concatenation ofNone.
This check prevents potential errors whenchunk.message.contentisNone. It improves the resilience offull_response.templates/components/agents/python/form_filling/app/workflows/form_filling.py (1)
175-236: LGTM!The implementation of the remaining methods (extract_missing_cells, find_answers, fill_cells) is correct and well-structured.
templates/components/agents/python/deep_research/app/workflows/deep_research.py (1)
138-328: LGTM!The implementation of the remaining methods (analyze, answer, collect_answers, report) is correct and well-structured.
templates/components/agents/python/financial_report/app/workflows/financial_report.py (1)
190-300: LGTM!The implementation of the remaining methods (research, analyze, report) is correct and well-structured.
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🧹 Nitpick comments (2)
templates/components/multiagent/python/app/api/routers/vercel_response.py (2)
28-42: Remove unused constructor parameters.The constructor still accepts
chat_dataandbackground_tasksparameters but only uses them in specific event handlers. Consider moving these parameters to the respective methods where they are actually used.Apply this diff to clean up the constructor:
def __init__( self, request: Request, handler: WorkflowHandler, - chat_data: ChatData, - background_tasks: BackgroundTasks, *args, **kwargs, ): self.request = request self.handler = handler - self.chat_data = chat_data - self.background_tasks = background_tasks content = self.content_generator() super().__init__(content=content)
118-135: Add error handling for unexpected event result types.The
_stream_textmethod should handle cases whereevent.resultis neither a string nor an AsyncGenerator.Apply this diff to add error handling:
async def _stream_text( self, event: AgentStream | StopEvent ) -> AsyncGenerator[str, None]: if isinstance(event, AgentStream): yield self.convert_text(event.delta) elif isinstance(event, StopEvent): if isinstance(event.result, str): yield self.convert_text(event.result) elif isinstance(event.result, AsyncGenerator): async for chunk in event.result: if isinstance(chunk, str): yield self.convert_text(chunk) elif hasattr(chunk, "delta"): yield self.convert_text(chunk.delta) + else: + logger.warning(f"Unexpected chunk type: {type(chunk)}") + else: + logger.warning(f"Unexpected event result type: {type(event.result)}")
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🔇 Additional comments (2)
templates/components/multiagent/python/app/api/routers/vercel_response.py (2)
44-87: LGTM! Well-structured event handling implementation.The content generator effectively handles both
AgentStreamandStopEventtypes, with proper error handling and cancellation support. The implementation aligns well with the previous discussions about streaming responses.
136-153: LGTM! Clear and well-typed conversion methods.The addition of return type annotations and clear documentation improves code clarity and maintainability.
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🧹 Nitpick comments (5)
templates/components/multiagent/python/app/api/callbacks/base.py (1)
13-19: Consider adding type hints for*argsand**kwargs.The method implementations look good, but consider adding type hints for the variadic parameters in
from_defaultto improve type safety.- def from_default(self, *args, **kwargs) -> "EventCallback": + def from_default(self, *args: Any, **kwargs: Any) -> "EventCallback":Also applies to: 20-26, 27-32
templates/components/multiagent/python/app/api/callbacks/llamacloud.py (2)
21-26: Consider adding type hints for event response.The
runmethod could benefit from more specific type hints for the event response.- async def run(self, event: Any) -> Any: + async def run(self, event: Any) -> Any: + from typing import TypedDict + class EventResponse(TypedDict): + type: str + if hasattr(event, "to_response"): - event_response = event.to_response() + event_response: EventResponse = event.to_response() if event_response.get("type") == "sources" and hasattr(event, "nodes"): await self._process_response_nodes(event.nodes) return event
28-36: Consider logging import errors for better debugging.The
_process_response_nodesmethod silently passes on import errors. Consider logging them for better debugging.async def _process_response_nodes(self, source_nodes: List[NodeWithScore]): try: from app.engine.service import LLamaCloudFileService # type: ignore LLamaCloudFileService.download_files_from_nodes( source_nodes, self.background_tasks ) except ImportError: - pass + logger.debug("LlamaCloud service not available")templates/components/multiagent/python/app/api/callbacks/stream_handler.py (1)
29-48: Consider adding logging for better observability.The
stream_eventsmethod could benefit from logging for better observability.async def stream_events(self): """Stream events through the processor chain.""" try: + logger.debug("Starting event stream") async for event in self.workflow_handler.stream_events(): # Process the event through each processor for callback in self.callbacks: event = await callback.run(event) yield event # After all events are processed, call on_complete for each callback for callback in self.callbacks: result = await callback.on_complete(self.accumulated_text) if result: yield result except Exception as e: + logger.error(f"Error in event stream: {e}") # Make sure to cancel the workflow on error await self.workflow_handler.cancel_run() raise etemplates/components/multiagent/python/app/api/routers/vercel_response.py (1)
64-80: Improve type annotation for better type safety.Consider using a more specific type annotation for the AsyncGenerator return type to indicate the exact types being yielded.
- ) -> AsyncGenerator[str, None]: + ) -> AsyncGenerator[str, None]: # type: ignore[return] + # TODO: Replace type ignore with proper type annotation: + # AsyncGenerator[Union[str, AgentStream, Dict[str, Any]], None]
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templates/components/multiagent/python/app/api/callbacks/base.py(1 hunks)templates/components/multiagent/python/app/api/callbacks/llamacloud.py(1 hunks)templates/components/multiagent/python/app/api/callbacks/next_question.py(1 hunks)templates/components/multiagent/python/app/api/callbacks/stream_handler.py(1 hunks)templates/components/multiagent/python/app/api/routers/chat.py(2 hunks)templates/components/multiagent/python/app/api/routers/vercel_response.py(2 hunks)
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templates/components/multiagent/python/app/api/routers/chat.py (1)
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templates/components/multiagent/python/app/api/callbacks/next_question.py (1)
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templates/components/multiagent/python/app/api/callbacks/base.py (1)
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templates/components/multiagent/python/app/api/routers/vercel_response.py (1)
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🔇 Additional comments (10)
templates/components/multiagent/python/app/api/callbacks/base.py (1)
8-11: LGTM! Well-structured base class with clear documentation.The abstract base class is well-designed with a clear purpose and documentation.
templates/components/multiagent/python/app/api/callbacks/next_question.py (1)
11-17: LGTM! Well-structured implementation.The class is well-documented and properly extends the base class.
templates/components/multiagent/python/app/api/callbacks/llamacloud.py (1)
12-16: LGTM! Well-documented class with clear purpose.The class is well-documented and properly extends the base class.
templates/components/multiagent/python/app/api/callbacks/stream_handler.py (1)
11-24: LGTM! Well-structured class with proper initialization.The class is well-documented and properly initializes its dependencies.
templates/components/multiagent/python/app/api/routers/chat.py (3)
5-7: LGTM!The new imports for callbacks and stream handling are correctly organized and align with the changes in the code.
34-43: LGTM!The workflow creation and execution changes align with the standardization effort. The
chat_historyis now correctly passed at runtime instead of construction time.
44-52: LGTM!The streaming response handling has been improved with the new
StreamHandlerand appropriate callbacks. This aligns with the standardization effort and the suggestion to use AgentStream from the framework.templates/components/multiagent/python/app/api/routers/vercel_response.py (3)
4-4: LGTM!The imports and constructor changes align with the standardization effort. The
StreamHandlerparameter provides a cleaner interface for managing streaming events.Also applies to: 7-8, 10-10, 24-31
33-62: LGTM!The content generator has been improved with:
- Better event type handling
- Proper error logging and cancellation
- Consistent event conversion
82-99: LGTM!The addition of return type annotations to conversion methods improves code clarity while maintaining correct functionality.
templates/components/multiagent/python/app/api/callbacks/next_question.py
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♻️ Duplicate comments (1)
templates/components/multiagent/python/app/api/callbacks/stream_handler.py (1)
54-57: 🛠️ Refactor suggestionConsider adding length limit for accumulated text.
The
accumulate_textmethod should consider adding a length limit to prevent memory issues with very long streams.async def accumulate_text(self, text: str): """Accumulate text from the workflow handler.""" + # Limit accumulated text to 1MB to prevent memory issues + if len(self.accumulated_text) + len(text) > 1_000_000: + logger.warning("Accumulated text exceeds 1MB limit, truncating") + return self.accumulated_text += text
🧹 Nitpick comments (5)
templates/components/multiagent/python/app/api/callbacks/stream_handler.py (5)
12-15: Enhance the class docstring.The docstring could be more descriptive to help users understand the class better.
class StreamHandler: """ - Streams events from a workflow handler through a chain of callbacks. + Streams events from a workflow handler through a chain of callbacks. + + This class manages the streaming of events from a WorkflowHandler instance, + passing each event through a series of callbacks for processing. Callbacks + can modify events, accumulate text, and perform actions on stream completion. + + Args: + workflow_handler (WorkflowHandler): The handler that generates events + callbacks (Optional[List[EventCallback]]): List of callbacks to process events """
26-29: Enhance the method docstring.The docstring could provide more details about the return value and its purpose.
def vercel_stream(self): - """Create a streaming response with Vercel format.""" + """Create a streaming response with Vercel format. + + Returns: + VercelStreamResponse: A streaming response object compatible with Vercel's + serverless function response format. + """ return VercelStreamResponse(stream_handler=self)
30-33: Add error handling for workflow cancellation.The method should handle potential errors during workflow cancellation.
async def cancel_run(self): """Cancel the workflow handler.""" - await self.workflow_handler.cancel_run() + try: + await self.workflow_handler.cancel_run() + except Exception as e: + logger.error(f"Error cancelling workflow: {e}") + raise
34-53: Add debug logging for better observability.The method would benefit from debug logging to help track event processing.
async def stream_events(self): """Stream events through the processor chain.""" try: + logger.debug("Starting event stream processing") async for event in self.workflow_handler.stream_events(): # Process the event through each processor for callback in self.callbacks: + logger.debug(f"Processing event through callback: {callback.__class__.__name__}") event = await callback.run(event) yield event # After all events are processed, call on_complete for each callback for callback in self.callbacks: + logger.debug(f"Calling on_complete for callback: {callback.__class__.__name__}") result = await callback.on_complete(self.accumulated_text) if result: yield result except Exception as e: + logger.error(f"Error processing events: {e}") # Make sure to cancel the workflow on error await self.workflow_handler.cancel_run() raise e
58-65: Add input validation to the factory method.The method should validate that the handler parameter is not None.
@classmethod def from_default( cls, handler: WorkflowHandler, callbacks: Optional[List[EventCallback]] = None, ) -> "StreamHandler": """Create a new instance with the given workflow handler and callbacks.""" + if handler is None: + raise ValueError("handler parameter cannot be None") return cls(workflow_handler=handler, callbacks=callbacks)
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🔇 Additional comments (2)
templates/components/multiagent/python/app/api/callbacks/stream_handler.py (2)
1-10: LGTM! Well-organized imports and logging setup.The imports are properly organized and the logging setup is appropriate for the context.
17-25: LGTM! Well-structured constructor with proper type hints.The constructor is well-implemented with appropriate default values and type hints.
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🧹 Nitpick comments (3)
templates/components/multiagent/python/app/api/callbacks/stream_handler.py (3)
12-15: Enhance the class docstring with more details.The current docstring is minimal. Consider adding:
- Purpose and relationship with WorkflowHandler
- Role of callbacks in event processing
- Example usage
class StreamHandler: """ - Streams events from a workflow handler through a chain of callbacks. + Streams events from a workflow handler through a chain of callbacks. + + This class manages the streaming of events from a WorkflowHandler instance, + processing each event through a series of EventCallback instances that can + modify or enhance the events. + + Args: + workflow_handler (WorkflowHandler): The handler that generates events + callbacks (Optional[List[EventCallback]]): List of callbacks to process events + + Example: + >>> handler = WorkflowHandler(...) + >>> callbacks = [MyCallback(), AnotherCallback()] + >>> stream_handler = StreamHandler(handler, callbacks) + >>> async for event in stream_handler.stream_events(): + ... process_event(event) """
17-25: Add input validation for workflow_handler.The constructor should validate that workflow_handler is not None to fail fast and provide a clear error message.
def __init__( self, workflow_handler: WorkflowHandler, callbacks: Optional[List[EventCallback]] = None, ): + if workflow_handler is None: + raise ValueError("workflow_handler cannot be None") self.workflow_handler = workflow_handler self.callbacks = callbacks or [] self.accumulated_text = ""
34-52: Add timeout handling to stream_events.The stream_events method should handle timeouts to prevent infinite streaming and resource exhaustion.
+import asyncio + async def stream_events(self): """Stream events through the processor chain.""" try: - async for event in self.workflow_handler.stream_events(): - # Process the event through each processor - for callback in self.callbacks: - event = await callback.run(event) - yield event + async with asyncio.timeout(300): # 5 minutes timeout + async for event in self.workflow_handler.stream_events(): + # Process the event through each processor + for callback in self.callbacks: + event = await callback.run(event) + yield event + + # After all events are processed, call on_complete for each callback + for callback in self.callbacks: + result = await callback.on_complete(self.accumulated_text) + if result: + yield result - # After all events are processed, call on_complete for each callback - for callback in self.callbacks: - result = await callback.on_complete(self.accumulated_text) - if result: - yield result - + except asyncio.TimeoutError: + logger.error("Stream events timed out after 5 minutes") + await self.workflow_handler.cancel_run() + raise except Exception as e: # Make sure to cancel the workflow on error await self.workflow_handler.cancel_run() raise e
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🔇 Additional comments (3)
templates/components/multiagent/python/app/api/callbacks/stream_handler.py (3)
1-9: LGTM! Well-organized imports and logging setup.The imports follow the standard organization pattern (standard library, external packages, local modules) and the logging setup is appropriate for a web application.
54-56: Consider adding length limit for accumulated text.The
accumulate_textmethod should consider adding a length limit to prevent memory issues with very long streams.
58-65: LGTM! Well-implemented factory method.The
from_defaultclass method provides a clean interface for creating instances with optional callbacks.
Summary by CodeRabbit
Refactor
New Features
streamattribute for better management of streaming logic across workflows.EventCallback,LlamaCloudFileDownload,SuggestNextQuestions, andStreamHandler.