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Added usage metrics

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This commit is contained in:
Adithya Suresh
2025-09-08 14:44:25 +10:00
parent 446d99d194
commit d1f72c1c0b
2 changed files with 72 additions and 20 deletions
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55
src/pipecat/processors/frameworks/strands_agents.py
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@@ -15,6 +15,7 @@ from pipecat.frames.frames import (
LLMFullResponseStartFrame,
LLMTextFrame,
)
from pipecat.metrics.metrics import LLMTokenUsage
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContextFrame
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
@@ -84,29 +85,79 @@ class StrandsAgentsProcessor(FrameProcessor):
text: The user input text to process through the agent or graph.
"""
logger.debug(f"Invoking Strands agent with: {text}")
await self.push_frame(LLMFullResponseStartFrame())
try:
await self.push_frame(LLMFullResponseStartFrame())
await self.start_processing_metrics()
await self.start_ttfb_metrics()
ttfb_tracking = True
if self.graph:
# Graph does not stream; await full result then emit assistant text
graph_result = await self.graph.invoke_async(text)
if ttfb_tracking:
await self.stop_ttfb_metrics()
ttfb_tracking = False
try:
node_result = graph_result.results[self.graph_exit_node]
logger.debug(f"Node result: {node_result}")
for agent_result in node_result.get_agent_results():
# Push to TTS service
message = getattr(agent_result, "message", None)
if isinstance(message, dict) and "content" in message:
for block in message["content"]:
if isinstance(block, dict) and "text" in block:
await self.push_frame(LLMTextFrame(str(block["text"])))
# Update usage metrics
await self._report_usage_metrics(
agent_result.metrics.accumulated_usage.get('inputTokens', 0),
agent_result.metrics.accumulated_usage.get('outputTokens', 0),
agent_result.metrics.accumulated_usage.get('totalTokens', 0)
)
except Exception as parse_err:
logger.warning(f"Failed to extract messages from GraphResult: {parse_err}")
else:
# Agent supports streaming events via async iterator
async for event in self.agent.stream_async(text):
# Push to TTS service
if isinstance(event, dict) and "data" in event:
await self.push_frame(LLMTextFrame(str(event["data"])))
if ttfb_tracking:
await self.stop_ttfb_metrics()
ttfb_tracking = False
# Update usage metrics
if isinstance(event, dict) and "event" in event and "metadata" in event['event']:
if 'usage' in event['event']['metadata']:
usage = event['event']['metadata']['usage']
await self._report_usage_metrics(usage.get('inputTokens', 0), usage.get('outputTokens', 0), usage.get('totalTokens', 0))
except GeneratorExit:
logger.warning(f"{self} generator was closed prematurely")
except Exception as e:
logger.exception(f"{self} an unknown error occurred: {e}")
finally:
await self.push_frame(LLMFullResponseEndFrame())
if ttfb_tracking:
await self.stop_ttfb_metrics()
ttfb_tracking = False
await self.stop_processing_metrics()
await self.push_frame(LLMFullResponseEndFrame())
def can_generate_metrics(self) -> bool:
"""Check if this service can generate performance metrics.
Returns:
True as this service supports metrics generation.
"""
return True
async def _report_usage_metrics(
self,
prompt_tokens: int,
completion_tokens: int,
total_tokens: int
):
tokens = LLMTokenUsage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=total_tokens
)
await self.start_llm_usage_metrics(tokens)