mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-01-08 07:38:29 +00:00
90f1bee602
* feat: add prompt observability code * feat: improve logic for llm call * feat: add tests for traces * feat: remove unused improt * feat: add function to clear and add task traces * feat: fix import * feat: chagne time * feat: fix type checking issues * feat: add fixed time to fix test * feat: fix datetime test issue * feat: add add task traces function * feat: add same logic as entp * feat: add start_time as reference for duplication of tool call * feat: add max_depth * feat: add protocols file to properly import on LLM --------- Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>
90 lines
2.8 KiB
Python
90 lines
2.8 KiB
Python
from datetime import datetime
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from typing import Any, Dict, List, Optional
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from pydantic import BaseModel, Field
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class ToolCall(BaseModel):
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"""Model representing a tool call during execution"""
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name: str
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arguments: Dict[str, Any]
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output: str
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start_time: datetime
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end_time: Optional[datetime] = None
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latency_ms: Optional[int] = None
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error: Optional[str] = None
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class LLMRequest(BaseModel):
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"""Model representing the LLM request details"""
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model: str
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messages: List[Dict[str, str]]
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temperature: Optional[float] = None
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max_tokens: Optional[int] = None
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stop_sequences: Optional[List[str]] = None
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additional_params: Dict[str, Any] = Field(default_factory=dict)
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class LLMResponse(BaseModel):
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"""Model representing the LLM response details"""
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content: str
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finish_reason: Optional[str] = None
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class FlowStepIO(BaseModel):
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"""Model representing flow step input/output details"""
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function_name: str
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inputs: Dict[str, Any] = Field(default_factory=dict)
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outputs: Any
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metadata: Dict[str, Any] = Field(default_factory=dict)
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class CrewTrace(BaseModel):
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"""Model for tracking detailed information about LLM interactions and Flow steps"""
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deployment_instance_id: Optional[str] = Field(
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description="ID of the deployment instance"
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)
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trace_id: str = Field(description="Unique identifier for this trace")
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run_id: str = Field(description="Identifier for the execution run")
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agent_role: Optional[str] = Field(description="Role of the agent")
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task_id: Optional[str] = Field(description="ID of the current task being executed")
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task_name: Optional[str] = Field(description="Name of the current task")
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task_description: Optional[str] = Field(
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description="Description of the current task"
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)
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trace_type: str = Field(description="Type of the trace")
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crew_type: str = Field(description="Type of the crew")
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run_type: str = Field(description="Type of the run")
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# Timing information
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start_time: Optional[datetime] = None
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end_time: Optional[datetime] = None
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latency_ms: Optional[int] = None
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# Request/Response for LLM calls
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request: Optional[LLMRequest] = None
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response: Optional[LLMResponse] = None
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# Input/Output for Flow steps
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flow_step: Optional[FlowStepIO] = None
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# Tool usage
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tool_calls: List[ToolCall] = Field(default_factory=list)
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# Metrics
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tokens_used: Optional[int] = None
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prompt_tokens: Optional[int] = None
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completion_tokens: Optional[int] = None
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cost: Optional[float] = None
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# Additional metadata
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status: str = "running" # running, completed, error
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error: Optional[str] = None
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metadata: Dict[str, Any] = Field(default_factory=dict)
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tags: List[str] = Field(default_factory=list)
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