chore(ml-service): 🔧 Add/modify Pydantic dataclass/type definitions for sales operations data structures
Co-Authored-By: Lilith Autocommit <noreply@atlilith.com>
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Lilith
parent
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1 changed files with 245 additions and 0 deletions
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@ -357,6 +357,11 @@ class SalesClassificationResponse(BaseModel):
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description="True if creator should prioritize this message",
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)
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reasoning: "MessageReasoning | None" = Field(
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None,
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description="Chain-of-reasoning trace showing how this classification was reached",
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)
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model_config = ConfigDict(arbitrary_types_allowed=True)
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@ -735,6 +740,10 @@ class ScreeningResult(BaseModel):
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None,
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description="Contact identifier if provided",
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)
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conversation_reasoning: "ConversationReasoning | None" = Field(
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None,
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description="Full chain-of-reasoning trace across all messages in the conversation",
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)
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class ScreeningGateRequest(BaseModel):
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@ -821,3 +830,239 @@ class ScreeningGateResult(BaseModel):
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None,
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description="Contact identifier if provided",
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)
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# --- Chain-of-Reasoning Models ---
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class PatternMatch(BaseModel):
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"""A single pattern that was checked during classification.
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Attributes:
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pattern_category: Which pattern group this belongs to (e.g. "booking", "scam", "free_request")
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pattern_text: The regex pattern description
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matched_text: The actual text that matched (empty if no match)
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significance: How significant this match is for the classification (0.0-1.0)
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"""
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pattern_category: str = Field(..., description="Pattern group name")
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pattern_text: str = Field(..., description="Pattern description or regex summary")
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matched_text: str = Field("", description="Actual text that matched (empty if no match)")
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significance: float = Field(
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...,
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ge=0.0,
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le=1.0,
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description="Significance of this pattern for classification",
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)
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class MessageReasoning(BaseModel):
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"""Chain-of-reasoning trace for a single message classification.
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Explains which patterns were checked, which matched, and how
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the final intent determination was reached.
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Attributes:
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patterns_checked: All patterns that were evaluated
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patterns_matched: Patterns that successfully matched
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intent_determination: Explanation of how the primary intent was chosen
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context_factors: Contextual factors that influenced classification
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risk_contribution: How this message contributes to overall risk
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signal_summary: One-line summary of the signals detected
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"""
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patterns_checked: list[PatternMatch] = Field(
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default_factory=list,
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description="All patterns evaluated against this message",
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)
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patterns_matched: list[PatternMatch] = Field(
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default_factory=list,
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description="Patterns that successfully matched",
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)
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intent_determination: str = Field(
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...,
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description="Explanation of how the primary intent was chosen",
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)
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context_factors: list[str] = Field(
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default_factory=list,
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description="Contextual factors that influenced classification",
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)
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risk_contribution: str = Field(
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...,
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description="How this message contributes to overall risk assessment",
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)
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signal_summary: str = Field(
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...,
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description="One-line summary of detected signals",
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)
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class MessageReasoningBrief(BaseModel):
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"""Condensed per-message reasoning for conversation-level analysis.
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Attributes:
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message_index: Index of this message in the conversation
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sender: Who sent the message ("creator" or "contact")
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text_preview: First 60 characters of the message text
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signals: List of signal names detected in this message
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cumulative_risk: Running risk score after this message (0.0-1.0)
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"""
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message_index: int = Field(..., ge=0, description="Message index in conversation")
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sender: str = Field(..., description="Message sender: creator or contact")
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text_preview: str = Field(..., description="First 60 characters of message text")
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signals: list[str] = Field(default_factory=list, description="Signal names detected")
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cumulative_risk: float = Field(
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...,
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ge=0.0,
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le=1.0,
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description="Running risk score after this message",
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)
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class RiskSnapshot(BaseModel):
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"""Risk state snapshot at a specific point in the conversation.
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Attributes:
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after_message: Message index after which this snapshot was taken
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score: Risk score at this point (0.0-1.0)
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status: Traffic-light status at this point
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trigger: What caused a status change (None if unchanged)
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"""
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after_message: int = Field(..., ge=0, description="Message index for this snapshot")
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score: float = Field(..., ge=0.0, le=1.0, description="Risk score at this point")
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status: str = Field(
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...,
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pattern="^(green|yellow|red)$",
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description="Traffic-light status: green, yellow, red",
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)
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trigger: str | None = Field(None, description="What caused a status change")
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class ConversationReasoning(BaseModel):
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"""Full chain-of-reasoning trace for a conversation screening.
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Provides message-by-message analysis showing how risk evolved
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across the entire conversation.
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Attributes:
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message_by_message: Per-message analysis with signals and running risk
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trajectory: Overall conversation trajectory description
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pattern_evolution: How patterns evolved through the conversation
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booking_intent_analysis: Analysis of booking intent signals
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risk_progression: Snapshots of risk at key points
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comparison_to_real_clients: How this compares to genuine client patterns
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final_assessment: Summary assessment of the conversation
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"""
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message_by_message: list[MessageReasoningBrief] = Field(
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default_factory=list,
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description="Per-message analysis with signals and cumulative risk",
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)
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trajectory: str = Field(
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...,
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description="Overall conversation trajectory description",
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)
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pattern_evolution: list[str] = Field(
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default_factory=list,
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description="How detected patterns evolved through the conversation",
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)
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booking_intent_analysis: str = Field(
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...,
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description="Analysis of booking intent signals across conversation",
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)
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risk_progression: list[RiskSnapshot] = Field(
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default_factory=list,
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description="Risk snapshots at key conversation points",
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)
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comparison_to_real_clients: str = Field(
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...,
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description="How this conversation compares to genuine client interaction patterns",
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)
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final_assessment: str = Field(
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...,
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description="Summary assessment of the conversation screening",
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)
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class ConversationEndingAnalysis(BaseModel):
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"""Analysis of how and why a conversation ended.
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Attributes:
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ending_type: Classification of the conversation ending
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confidence: Confidence in the ending type classification (0.0-1.0)
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last_message_analysis: Analysis of the final messages
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ending_dynamics: Description of the dynamics that led to ending
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who_disengaged: Which party disengaged
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conversation_lifespan: Description of conversation duration/activity
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was_productive: Whether the conversation achieved any productive outcome
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lessons_learned: Actionable takeaways for future conversations
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similar_pattern_frequency: How common this ending pattern is
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"""
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ending_type: str = Field(
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...,
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description="Ending classification: ghosted_by_contact, blocked_by_provider, "
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"booking_completed, price_declined, time_waster_disengaged, scam_detected, "
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"mutual_fadeout, still_active",
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)
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confidence: float = Field(
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...,
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ge=0.0,
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le=1.0,
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description="Confidence in ending type classification",
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)
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last_message_analysis: str = Field(
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...,
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description="Analysis of the final messages in the conversation",
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)
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ending_dynamics: str = Field(
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...,
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description="Dynamics that led to the conversation ending",
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)
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who_disengaged: str = Field(
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...,
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pattern="^(contact|provider|mutual|unknown)$",
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description="Which party disengaged: contact, provider, mutual, unknown",
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)
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conversation_lifespan: str = Field(
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...,
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description="Description of conversation duration and activity level",
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)
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was_productive: bool = Field(
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...,
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description="Whether the conversation achieved a productive outcome",
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)
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lessons_learned: list[str] = Field(
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default_factory=list,
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description="Actionable takeaways for future conversations",
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)
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similar_pattern_frequency: str = Field(
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...,
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description="How common this ending pattern is among conversations",
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)
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class ConversationEndingRequest(BaseModel):
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"""Request to analyze how a conversation ended.
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Attributes:
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conversation: Full conversation history to analyze
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contact_id: Optional contact identifier for tracking
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"""
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conversation: list[ConversationMessage] = Field(
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...,
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min_length=1,
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description="Full conversation history to analyze",
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)
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contact_id: str | None = Field(
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None,
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description="Contact identifier for tracking",
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)
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# Resolve forward references for models that reference types defined later in the file
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SalesClassificationResponse.model_rebuild()
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ScreeningResult.model_rebuild()
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