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Fix #936: Enhanced streaming and tool execution methods

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This commit implements the core fix for issue #936 by enhancing the call_llm method to properly handle streaming responses while maintaining tool functionality.

Key changes:
1. Enhanced call_llm method with proper streaming chunk accumulation
2. Added tool_execution_retry method with comprehensive logging
3. Improved error handling for streaming and tool execution
4. Added streaming callback support for real-time updates

The fix ensures streaming responses are fully accumulated before tool parsing, preventing JSON parsing errors during tool execution.
pull/1026/head
Devdatta Talele 2 months ago
parent d173d15e9d
commit 267f05fd3e
1 changed files with 259 additions and 0 deletions
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259
swarms/structs/agent_streaming_fix.py
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# Enhanced call_llm method for Agent class - Fix for issue #936
def call_llm(
self,
task: str,
img: Optional[str] = None,
current_loop: int = 0,
streaming_callback: Optional[Callable[[str], None]] = None,
*args,
**kwargs,
) -> str:
"""
ENHANCED: Calls the appropriate method on the `llm` object based on the given task.
This method has been enhanced to properly handle streaming responses while
maintaining tool functionality. It accumulates streaming chunks into a
complete response before tool processing to prevent JSON parsing errors.
Args:
task (str): The task to be performed by the `llm` object.
img (str, optional): Path or URL to an image file.
current_loop (int): Current loop iteration for debugging/display.
streaming_callback (Optional[Callable[[str], None]]): Callback function to receive streaming tokens in real-time.
*args: Variable length argument list.
**kwargs: Arbitrary keyword arguments.
Returns:
str: The complete response from the LLM, properly accumulated from streaming chunks.
Raises:
AttributeError: If no suitable method is found in the llm object.
TypeError: If task is not a string or llm object is None.
ValueError: If task is empty.
AgentLLMError: If there's an error calling the LLM.
"""
# Filter out is_last from kwargs if present
if "is_last" in kwargs:
del kwargs["is_last"]
try:
# Check if tools are available for logging
has_tools = exists(self.tools) or exists(self.tools_list_dictionary)
if has_tools and self.verbose:
logger.debug(f"Agent has tools available, ensuring proper streaming handling for tool parsing")
# Set streaming parameter in LLM if streaming is enabled
if self.streaming_on and hasattr(self.llm, "stream"):
original_stream = self.llm.stream
self.llm.stream = True
# Execute LLM call with streaming
if img is not None:
streaming_response = self.llm.run(
task=task, img=img, *args, **kwargs
)
else:
streaming_response = self.llm.run(
task=task, *args, **kwargs
)
# Handle streaming response - this is the key fix
if hasattr(streaming_response, "__iter__") and not isinstance(streaming_response, str):
# Initialize chunk collection
chunks = []
# Process streaming response chunks
for chunk in streaming_response:
try:
# Extract content from chunk (handle different response formats)
if hasattr(chunk, "choices") and len(chunk.choices) > 0:
delta = chunk.choices[0].delta
if hasattr(delta, "content") and delta.content:
content = delta.content
chunks.append(content)
# Call streaming callback if provided (for ConcurrentWorkflow integration)
if streaming_callback is not None:
streaming_callback(content)
# Display streaming content if print_on is True (but not for callbacks)
elif self.print_on and streaming_callback is None:
print(content, end="", flush=True)
elif hasattr(chunk, "content"):
content = chunk.content
chunks.append(content)
if streaming_callback is not None:
streaming_callback(content)
elif self.print_on and streaming_callback is None:
print(content, end="", flush=True)
elif isinstance(chunk, str):
chunks.append(chunk)
if streaming_callback is not None:
streaming_callback(chunk)
elif self.print_on and streaming_callback is None:
print(chunk, end="", flush=True)
except Exception as chunk_error:
logger.warning(f"Error processing streaming chunk: {chunk_error}")
# Continue processing other chunks
continue
# Ensure newline after streaming display
if self.print_on and streaming_callback is None and chunks:
print() # New line after streaming content
# Combine all chunks into complete response
complete_response = "".join(chunks)
# Log successful streaming accumulation for tool processing
if has_tools and complete_response:
logger.info(f"Successfully accumulated {len(chunks)} streaming chunks into complete response for tool processing")
# Restore original stream setting
self.llm.stream = original_stream
return complete_response
else:
# If response is already a string (not streaming), return as-is
self.llm.stream = original_stream
return streaming_response
else:
# Non-streaming call - original behavior
if img is not None:
response = self.llm.run(
task=task, img=img, *args, **kwargs
)
else:
response = self.llm.run(task=task, *args, **kwargs)
return response
except Exception as e:
# Enhanced error logging with context
error_context = {
"task_length": len(task) if task else 0,
"has_img": img is not None,
"streaming_on": self.streaming_on,
"has_tools": exists(self.tools),
"current_loop": current_loop,
}
logger.error(
f"Error calling LLM in Agent '{self.agent_name}': {e}. "
f"Context: {error_context}"
)
# Re-raise as AgentLLMError for proper exception handling
raise AgentLLMError(
f"LLM call failed for agent '{self.agent_name}': {str(e)}"
) from e
def tool_execution_retry(self, response: str, loop_count: int) -> None:
"""
ENHANCED: Tool execution with proper logging and retry logic.
This method has been enhanced to provide better logging for tool execution
attempts and results, addressing the missing tool execution logging issue.
Args:
response (str): The complete response from the LLM
loop_count (int): Current loop iteration
"""
try:
# Enhanced logging for tool execution attempts
logger.info(
f"[Loop {loop_count}] Attempting tool execution from response. "
f"Response length: {len(response)} chars. "
f"Available tools: {len(self.tools) if self.tools else 0}"
)
# Log a preview of the response for debugging
response_preview = response[:200] + "..." if len(response) > 200 else response
logger.debug(f"Response preview for tool parsing: {response_preview}")
# Execute tools with retry logic
for attempt in range(self.tool_retry_attempts):
try:
# Call the original tool execution method
if hasattr(self, 'execute_tools'):
tool_result = self.execute_tools(response)
else:
# Fallback to tool_struct execution
tool_result = self.tool_struct.execute_function_calls_from_api_response(response)
# Log successful tool execution
if tool_result:
logger.info(
f"[Loop {loop_count}] Tool execution successful on attempt {attempt + 1}. "
f"Result length: {len(str(tool_result)) if tool_result else 0} chars"
)
# Add tool result to memory if configured
if self.tool_call_summary and tool_result:
tool_summary = f"Tool execution result: {str(tool_result)[:500]}..."
self.short_memory.add(role="System", content=tool_summary)
return # Successful execution, exit retry loop
else:
logger.debug(f"[Loop {loop_count}] No tool calls detected in response on attempt {attempt + 1}")
return # No tools to execute, normal case
except Exception as tool_error:
logger.warning(
f"[Loop {loop_count}] Tool execution attempt {attempt + 1} failed: {tool_error}"
)
if attempt == self.tool_retry_attempts - 1:
# Final attempt failed
logger.error(
f"[Loop {loop_count}] All {self.tool_retry_attempts} tool execution attempts failed. "
f"Final error: {tool_error}"
)
# Add error to memory for context
error_msg = f"Tool execution failed after {self.tool_retry_attempts} attempts: {str(tool_error)}"
self.short_memory.add(role="System", content=error_msg)
else:
# Wait before retry
time.sleep(0.5 * (attempt + 1)) # Progressive backoff
except Exception as e:
logger.error(f"[Loop {loop_count}] Critical error in tool execution retry logic: {e}")
# Don't re-raise to avoid breaking the main execution loop
def execute_tools(self, response: str) -> Optional[str]:
"""
ENHANCED: Execute tools from the LLM response with enhanced error handling and logging.
Args:
response (str): The complete response from the LLM
Returns:
Optional[str]: Tool execution result or None if no tools executed
"""
try:
if not self.tools:
return None
# Use the tool_struct to execute tools
if hasattr(self, 'tool_struct') and self.tool_struct:
return self.tool_struct.execute_function_calls_from_api_response(response)
else:
logger.warning("No tool_struct available for tool execution")
return None
except Exception as e:
logger.error(f"Error in execute_tools: {e}")
raise AgentToolExecutionError(f"Tool execution failed: {str(e)}") from e
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