3a5c3bcde3
When a subsequent revision of a proof is uploaded, the analysis now takes place in context of the previous version's results. The system identifies: - Resolved issues: fixed in the new revision - Outstanding issues: still present from previous version - New issues: introduced in the new revision Key changes: - Add resolvedIssues, outstandingIssues, newIssues fields to SubReview - Add PreviousReviewContext model for passing previous review data - Update all specialist agents to accept previous_review context - Extend GeminiService with include_revision_fields parameter - Add get_latest_version_review() repository method - Update LeadAgent to synthesize cross-version context in summary - Fetch previous analysis in WebSocket handler for revisions First version analysis continues to work exactly as before with revision fields set to null. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
111 lines
2.7 KiB
Python
Executable file
111 lines
2.7 KiB
Python
Executable file
from enum import Enum
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from typing import Optional
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from pydantic import BaseModel
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class RagStatus(str, Enum):
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"""RAG status for agent reviews."""
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RED = "Red"
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AMBER = "Amber"
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GREEN = "Green"
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ERROR = "Error"
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class OverallStatus(str, Enum):
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"""Overall status for the proof review."""
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PASSED = "Passed"
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FAILED = "Failed"
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ANALYSIS_ERROR = "Analysis Error"
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REQUIRES_MANUAL_LEGAL_REVIEW = "Requires Manual Legal Review"
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class SubReview(BaseModel):
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"""Individual agent review result."""
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ragStatus: RagStatus
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feedback: str
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issues: list[str]
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isFinancialPromotion: Optional[bool] = None
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financialPromotionReason: Optional[str] = None
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# Revision-aware fields (populated when analyzing version N > 1)
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resolvedIssues: Optional[list[str]] = None
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outstandingIssues: Optional[list[str]] = None
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newIssues: Optional[list[str]] = None
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class Config:
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use_enum_values = True
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class PreviousReviewContext(BaseModel):
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"""Context from a previous version's review for revision-aware analysis."""
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version: int
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ragStatus: RagStatus
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feedback: str
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issues: list[str]
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class Config:
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use_enum_values = True
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class AgentReview(BaseModel):
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"""Complete review from all agents."""
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legalAgentReview: SubReview
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brandAgentReview: SubReview
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channelBestPracticesAgentReview: SubReview
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channelTechSpecsAgentReview: SubReview
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leadAgentSummary: str
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overallStatus: OverallStatus
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financialPromotionReason: Optional[str] = None
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class Config:
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use_enum_values = True
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# WebSocket message types
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class AnalyzeRequest(BaseModel):
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"""Request to analyze a proof via WebSocket."""
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type: str # Should be "analyze"
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file_data: str # Base64 encoded file
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file_type: str # MIME type
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is_wip: bool = False
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class AgentStartedMessage(BaseModel):
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"""Message sent when an agent starts processing."""
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type: str = "agent_started"
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agent_name: str
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class AgentCompletedMessage(BaseModel):
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"""Message sent when an agent completes."""
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type: str = "agent_completed"
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agent_name: str
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review: SubReview
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class Config:
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use_enum_values = True
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class SummaryMessage(BaseModel):
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"""Message sent when summary is ready."""
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type: str = "summary"
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lead_agent_summary: str
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overall_status: OverallStatus
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financial_promotion_reason: Optional[str] = None
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class Config:
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use_enum_values = True
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class CompleteMessage(BaseModel):
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"""Message sent when analysis is complete."""
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type: str = "complete"
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result: AgentReview
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class Config:
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use_enum_values = True
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class ErrorMessage(BaseModel):
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"""Message sent when an error occurs."""
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type: str = "error"
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message: str
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