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1b3a21e6f8
- Add TokenMetricLabels BaseModel to enforce consistent label structure - All dify.token.* metrics now use identical 6-label structure: * tenant_id, app_id, operation_type, model_provider, model_name, node_type - Pydantic validation ensures runtime enforcement (extra='forbid', frozen=True) - Enables filtering by operation_type to avoid double-counting: * workflow: aggregated workflow-level tokens * node_execution: individual node-level tokens * message: direct message tokens * rule_generate/code_generate: prompt generation tokens Previously, inconsistent label cardinality made aggregation impossible: - WORKFLOW: 3 labels - NODE_EXECUTION: 6 labels - MESSAGE: 5 labels - PROMPT_GENERATION: 5 labels Now all use the same 6-label structure for consistent querying.
122 lines
4.0 KiB
Python
122 lines
4.0 KiB
Python
from enum import StrEnum
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from typing import cast
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from opentelemetry.util.types import AttributeValue
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from pydantic import BaseModel, ConfigDict
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class EnterpriseTelemetrySpan(StrEnum):
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WORKFLOW_RUN = "dify.workflow.run"
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NODE_EXECUTION = "dify.node.execution"
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DRAFT_NODE_EXECUTION = "dify.node.execution.draft"
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class EnterpriseTelemetryEvent(StrEnum):
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"""Event names for enterprise telemetry logs."""
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APP_CREATED = "dify.app.created"
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APP_UPDATED = "dify.app.updated"
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APP_DELETED = "dify.app.deleted"
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FEEDBACK_CREATED = "dify.feedback.created"
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WORKFLOW_RUN = "dify.workflow.run"
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MESSAGE_RUN = "dify.message.run"
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TOOL_EXECUTION = "dify.tool.execution"
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MODERATION_CHECK = "dify.moderation.check"
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SUGGESTED_QUESTION_GENERATION = "dify.suggested_question.generation"
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DATASET_RETRIEVAL = "dify.dataset.retrieval"
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GENERATE_NAME_EXECUTION = "dify.generate_name.execution"
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PROMPT_GENERATION_EXECUTION = "dify.prompt_generation.execution"
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REHYDRATION_FAILED = "dify.telemetry.rehydration_failed"
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class EnterpriseTelemetryCounter(StrEnum):
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TOKENS = "tokens"
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INPUT_TOKENS = "input_tokens"
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OUTPUT_TOKENS = "output_tokens"
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REQUESTS = "requests"
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ERRORS = "errors"
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FEEDBACK = "feedback"
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DATASET_RETRIEVALS = "dataset_retrievals"
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APP_CREATED = "app_created"
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APP_UPDATED = "app_updated"
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APP_DELETED = "app_deleted"
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class EnterpriseTelemetryHistogram(StrEnum):
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WORKFLOW_DURATION = "workflow_duration"
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NODE_DURATION = "node_duration"
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MESSAGE_DURATION = "message_duration"
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MESSAGE_TTFT = "message_ttft"
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TOOL_DURATION = "tool_duration"
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PROMPT_GENERATION_DURATION = "prompt_generation_duration"
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class TokenMetricLabels(BaseModel):
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"""Unified label structure for all dify.token.* metrics.
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All token counters (dify.tokens.input, dify.tokens.output, dify.tokens.total) MUST
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use this exact label set to ensure consistent filtering and aggregation across
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different operation types.
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Attributes:
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tenant_id: Tenant identifier.
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app_id: Application identifier.
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operation_type: Source of token usage (workflow | node_execution | message |
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rule_generate | code_generate | structured_output | instruction_modify).
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model_provider: LLM provider name. Empty string if not applicable (e.g., workflow-level).
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model_name: LLM model name. Empty string if not applicable (e.g., workflow-level).
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node_type: Workflow node type. Empty string unless operation_type=node_execution.
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Usage:
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labels = TokenMetricLabels(
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tenant_id="tenant-123",
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app_id="app-456",
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operation_type=OperationType.WORKFLOW,
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model_provider="",
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model_name="",
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node_type="",
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)
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exporter.increment_counter(
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EnterpriseTelemetryCounter.INPUT_TOKENS,
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100,
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labels.to_dict()
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)
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Design rationale:
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Without this unified structure, tokens get double-counted when querying totals
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because workflow.total_tokens is already the sum of all node tokens. The
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operation_type label allows filtering to separate workflow-level aggregates from
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node-level detail, while keeping the same label cardinality for consistent queries.
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"""
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tenant_id: str
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app_id: str
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operation_type: str
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model_provider: str
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model_name: str
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node_type: str
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model_config = ConfigDict(extra="forbid", frozen=True)
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def to_dict(self) -> dict[str, AttributeValue]:
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return cast(
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dict[str, AttributeValue],
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{
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"tenant_id": self.tenant_id,
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"app_id": self.app_id,
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"operation_type": self.operation_type,
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"model_provider": self.model_provider,
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"model_name": self.model_name,
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"node_type": self.node_type,
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},
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)
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__all__ = [
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"EnterpriseTelemetryCounter",
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"EnterpriseTelemetryEvent",
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"EnterpriseTelemetryHistogram",
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"EnterpriseTelemetrySpan",
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"TokenMetricLabels",
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]
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