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from pydantic import BaseModel, Field
from typing import Any, List, Dict, Optional
class MeetingsRequest(BaseModel):
working_group: str
class MeetingsResponse(BaseModel):
meetings: Dict[str, str]
# --------------------------------------
class DataRequest(BaseModel):
working_group: str
meeting: str
class DataResponse(BaseModel):
data: List[Dict[Any, Any]]
# --------------------------------------
class DocInfo(BaseModel):
document: str
url: str
class RequirementsRequest(BaseModel):
documents: List[DocInfo]
class DocRequirements(BaseModel):
document: str
context: str
requirements: List[str]
class RequirementsResponse(BaseModel):
requirements: List[DocRequirements]
# --------------------------------------
class RequirementInfo(BaseModel):
req_id: int = Field(..., description="The ID of this requirement")
context: str = Field(..., description="Context for the requirement.")
requirement: str = Field(..., description="The requirement itself.")
document: str = Field(...,
description="The document the requirement is extracted from.")
class ReqSearchLLMResponse(BaseModel):
selected: List[int]
class ReqSearchRequest(BaseModel):
query: str
requirements: List[RequirementInfo]
class ReqSearchResponse(BaseModel):
requirements: List[RequirementInfo]
# --------------------------------------
class DownloadRequest(BaseModel):
documents: List[str] = Field(
description="List of document IDs to download")
class ReqGroupingCategory(BaseModel):
"""Represents the category of requirements grouped together"""
id: int = Field(..., description="ID of the grouping category")
title: str = Field(..., description="Title given to the grouping category")
requirements: List[RequirementInfo] = Field(
..., description="List of grouped requirements")
class SolutionModel(BaseModel):
Context: str = Field(...,
description="Full context provided for this category.")
Requirements: List[str] = Field(...,
description="List of each requirement as string.")
Problem_Description: str = Field(..., alias="Problem Description",
description="Description of the problem being solved.")
Solution_Description: str = Field(..., alias="Solution Description",
description="Detailed description of the solution.")
References: list[dict] = Field(
..., description="References to documents used for the solution.")
Category_Id: int = Field(
..., description="ID of the requirements category the solution is based on")
class Config:
validate_by_name = True # Enables alias handling on input/output
# ============================================================= Categorize requirements endpoint
class ReqGroupingRequest(BaseModel):
"""Request schema of a requirement grouping call."""
requirements: list[RequirementInfo]
max_n_categories: Optional[int] = Field(
default=None, description="Max number of categories to construct. Defaults to None")
class ReqGroupingResponse(BaseModel):
"""Response of a requirement grouping call."""
categories: List[ReqGroupingCategory]
# INFO: keep in sync with prompt
class _ReqGroupingCategory(BaseModel):
title: str = Field(..., description="Title given to the grouping category")
items: list[int] = Field(
..., description="List of the IDs of the requirements belonging to the category.")
class _ReqGroupingOutput(BaseModel):
categories: list[_ReqGroupingCategory] = Field(
..., description="List of grouping categories")
# =================================================================== search solution response endpoint
class _SolutionSearchOutput(BaseModel):
solution: SolutionModel
class _SearchedSolutionModel(BaseModel):
""""Internal model used for solutions searched using gemini"""
requirement_ids: List[int] = Field(...,
description="List of each requirement ID addressed by the solution")
problem_description: str = Field(...,
description="Description of the problem being solved.")
solution_description: str = Field(...,
description="Detailed description of the solution.")
class SolutionSearchResponse(BaseModel):
"""Response model for solution search"""
solutions: list[SolutionModel]
class SolutionSearchV2Request(BaseModel):
"""Response of a requirement grouping call."""
categories: List[ReqGroupingCategory]
user_constraints: Optional[str] = Field(
default=None, description="Additional user constraints to respect when generating the solutions.")
# =========================================================== Criticize solution endpoint
class CriticizeSolutionsRequest(BaseModel):
solutions: list[SolutionModel]
class _SolutionCriticism(BaseModel):
technical_challenges: List[str] = Field(
..., description="Technical challenges encountered by the solution")
weaknesses: List[str] = Field(...,
description="Identified weaknesses of the solution")
limitations: List[str] = Field(...,
description="Identified limitations of the solution")
class _SolutionCriticismOutput(BaseModel):
criticisms: List[_SolutionCriticism]
class SolutionCriticism(BaseModel):
solution: SolutionModel
criticism: _SolutionCriticism
class CritiqueResponse(BaseModel):
critiques: List[SolutionCriticism]
# ==========================================================================
class _RefinedSolutionModel(BaseModel):
"""Internal model used for solution refining"""
problem_description: str = Field(...,
description="New description of the problem being solved.")
solution_description: str = Field(...,
description="New detailed description of the solution.")
# ================================================================= search solutions using insight finder endpoints
# Helpers to extract constraints for Insights Finder
class InsightFinderConstraintItem(BaseModel):
title: str
description: str
class InsightFinderConstraintsList(BaseModel):
constraints: list[InsightFinderConstraintItem]
# =================================================
class Technology(BaseModel):
"""Represents a single technology entry with its details."""
title: str
purpose: str
key_components: str
advantages: str
limitations: str
id: int
class TechnologyData(BaseModel):
"""Represents the top-level object containing a list of technologies."""
technologies: List[Technology]
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