145 lines
4.4 KiB
Python
145 lines
4.4 KiB
Python
from typing import TYPE_CHECKING
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from pydantic import BaseModel, Field, field_validator
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if TYPE_CHECKING:
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from haiku.rag.store.models import SearchResult
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class ResearchPlan(BaseModel):
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"""A structured research plan with sub-questions to explore."""
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sub_questions: list[str] = Field(
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...,
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description="Specific questions to research, phrased as complete questions",
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)
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@field_validator("sub_questions")
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@classmethod
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def validate_sub_questions(cls, v: list[str]) -> list[str]:
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if len(v) < 1:
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raise ValueError("Must have at least 1 sub-question")
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if len(v) > 12:
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raise ValueError("Cannot have more than 12 sub-questions")
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return v
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class Citation(BaseModel):
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"""Resolved citation with full metadata for display/visual grounding."""
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document_id: str
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chunk_id: str
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document_uri: str
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document_title: str | None = None
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page_numbers: list[int] = Field(default_factory=list)
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headings: list[str] | None = None
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content: str
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class RawSearchAnswer(BaseModel):
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"""Answer to a search query with chunk references."""
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query: str = Field(..., description="The question that was answered")
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answer: str = Field(..., description="The answer to the question")
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cited_chunks: list[str] = Field(
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default_factory=list,
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description="IDs of chunks used to form the answer",
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)
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confidence: float = Field(
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default=1.0,
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description="Confidence score for this answer (0-1)",
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ge=0.0,
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le=1.0,
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)
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class SearchAnswer(RawSearchAnswer):
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"""Answer to a search query with resolved citations."""
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citations: list[Citation] = Field(
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default_factory=list,
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description="Resolved citations with full metadata",
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)
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@classmethod
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def from_raw(
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cls,
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raw: RawSearchAnswer,
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search_results: "list[SearchResult]",
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) -> "SearchAnswer":
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"""Create SearchAnswer from RawSearchAnswer with resolved citations."""
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citations = resolve_citations(raw.cited_chunks, search_results)
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return cls(
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query=raw.query,
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answer=raw.answer,
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cited_chunks=raw.cited_chunks,
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confidence=raw.confidence,
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citations=citations,
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)
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def resolve_citations(
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cited_chunk_ids: list[str],
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search_results: "list[SearchResult]",
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) -> list[Citation]:
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"""Resolve chunk IDs to full Citation objects with metadata."""
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by_id = {r.chunk_id: r for r in search_results if r.chunk_id}
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citations = []
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for chunk_id in cited_chunk_ids:
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r = by_id.get(chunk_id)
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if not r:
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continue
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citations.append(
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Citation(
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document_id=r.document_id or "",
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chunk_id=chunk_id,
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document_uri=r.document_uri or "",
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document_title=r.document_title,
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page_numbers=r.page_numbers,
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headings=r.headings,
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content=r.content,
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)
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)
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return citations
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class EvaluationResult(BaseModel):
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"""Result of research sufficiency evaluation."""
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is_sufficient: bool = Field(
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description="Whether the research is sufficient to answer the original question"
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)
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confidence_score: float = Field(
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ge=0.0,
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le=1.0,
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description="Confidence level in the completeness of research (0-1)",
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)
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reasoning: str = Field(
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description="Explanation of why the research is or isn't complete"
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)
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new_questions: list[str] = Field(
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default_factory=list,
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max_length=3,
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description="New sub-questions to add to the research (max 3)",
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)
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class ResearchReport(BaseModel):
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"""Final research report structure."""
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title: str = Field(description="Concise title for the research")
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executive_summary: str = Field(description="Brief overview of key findings")
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main_findings: list[str] = Field(
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description="Primary research findings with supporting evidence"
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)
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conclusions: list[str] = Field(description="Evidence-based conclusions")
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limitations: list[str] = Field(
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description="Limitations of the current research", default=[]
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)
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recommendations: list[str] = Field(
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description="Actionable recommendations based on findings", default=[]
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)
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sources_summary: str = Field(
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description="Summary of sources used and their reliability"
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)
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