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feat: enhance tencent trace integration with LLM core metrics (#27126)

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Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
Co-authored-by: crazywoola <100913391+crazywoola@users.noreply.github.com>
This commit is contained in:
XlKsyt
2025-10-29 15:53:30 +08:00
committed by GitHub
parent 82890fe38e
commit 1e9142c213
13 changed files with 609 additions and 57 deletions
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52
api/core/app/apps/advanced_chat/generate_task_pipeline.py
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@@ -1,3 +1,4 @@
import json
import logging
import re
import time
@@ -60,6 +61,7 @@ from core.app.task_pipeline.based_generate_task_pipeline import BasedGenerateTas
from core.app.task_pipeline.message_cycle_manager import MessageCycleManager
from core.base.tts import AppGeneratorTTSPublisher, AudioTrunk
from core.model_runtime.entities.llm_entities import LLMUsage
from core.model_runtime.utils.encoders import jsonable_encoder
from core.ops.ops_trace_manager import TraceQueueManager
from core.workflow.enums import WorkflowExecutionStatus
from core.workflow.nodes import NodeType
@@ -391,6 +393,14 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
if should_direct_answer:
return
current_time = time.perf_counter()
if self._task_state.first_token_time is None and delta_text.strip():
self._task_state.first_token_time = current_time
self._task_state.is_streaming_response = True
if delta_text.strip():
self._task_state.last_token_time = current_time
# Only publish tts message at text chunk streaming
if tts_publisher and queue_message:
tts_publisher.publish(queue_message)
@@ -772,7 +782,33 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
message.answer = answer_text
message.updated_at = naive_utc_now()
message.provider_response_latency = time.perf_counter() - self._base_task_pipeline.start_at
message.message_metadata = self._task_state.metadata.model_dump_json()
# Set usage first before dumping metadata
if graph_runtime_state and graph_runtime_state.llm_usage:
usage = graph_runtime_state.llm_usage
message.message_tokens = usage.prompt_tokens
message.message_unit_price = usage.prompt_unit_price
message.message_price_unit = usage.prompt_price_unit
message.answer_tokens = usage.completion_tokens
message.answer_unit_price = usage.completion_unit_price
message.answer_price_unit = usage.completion_price_unit
message.total_price = usage.total_price
message.currency = usage.currency
self._task_state.metadata.usage = usage
else:
usage = LLMUsage.empty_usage()
self._task_state.metadata.usage = usage
# Add streaming metrics to usage if available
if self._task_state.is_streaming_response and self._task_state.first_token_time:
start_time = self._base_task_pipeline.start_at
first_token_time = self._task_state.first_token_time
last_token_time = self._task_state.last_token_time or first_token_time
usage.time_to_first_token = round(first_token_time - start_time, 3)
usage.time_to_generate = round(last_token_time - first_token_time, 3)
metadata = self._task_state.metadata.model_dump()
message.message_metadata = json.dumps(jsonable_encoder(metadata))
message_files = [
MessageFile(
message_id=message.id,
@@ -790,20 +826,6 @@ class AdvancedChatAppGenerateTaskPipeline(GraphRuntimeStateSupport):
]
session.add_all(message_files)
if graph_runtime_state and graph_runtime_state.llm_usage:
usage = graph_runtime_state.llm_usage
message.message_tokens = usage.prompt_tokens
message.message_unit_price = usage.prompt_unit_price
message.message_price_unit = usage.prompt_price_unit
message.answer_tokens = usage.completion_tokens
message.answer_unit_price = usage.completion_unit_price
message.answer_price_unit = usage.completion_price_unit
message.total_price = usage.total_price
message.currency = usage.currency
self._task_state.metadata.usage = usage
else:
self._task_state.metadata.usage = LLMUsage.empty_usage()
def _seed_graph_runtime_state_from_queue_manager(self) -> None:
"""Bootstrap the cached runtime state from the queue manager when present."""
candidate = self._base_task_pipeline.queue_manager.graph_runtime_state