"""Task A: document relevance. Decides whether a collected document is
actually about the target company and matches the user's stated focus,
before it's used as evidence for anything else."""

from __future__ import annotations

from pydantic import BaseModel, Field

from app.analysis.llm.base import LLMProvider
from app.prompts.base import build_user_prompt

SYSTEM_PROMPT = (
    "You are a competitive intelligence analyst. Given a single document and a company "
    "profile, judge only whether the document is genuinely about that company and whether "
    "it relates to the user's stated monitoring focus. Do not summarize the document's "
    "content here - a later task does that. Be conservative: if the document could be about "
    "a different company with a similar name, say so."
)


class RelevanceAssessment(BaseModel):
    is_relevant: bool = Field(description="Is this document genuinely about the target company?")
    matches_focus: bool = Field(description="Does it relate to the user's stated monitoring focus?")
    topic_categories: list[str] = Field(default_factory=list)
    source_reliability: float = Field(ge=0.0, le=1.0)
    reasoning: str


async def assess_relevance(
    llm: LLMProvider,
    *,
    company_name: str,
    company_aliases: list[str],
    monitoring_focus: str | None,
    document_title: str | None,
    document_text: str,
    document_url: str,
) -> RelevanceAssessment:
    evidence = {
        "company_name": company_name,
        "company_aliases": company_aliases,
        "monitoring_focus": monitoring_focus,
        "document_title": document_title,
        "document_url": document_url,
        "document_text": document_text[:4000],
    }
    user_prompt = build_user_prompt(
        "Assess whether this document is about the target company and matches its monitoring focus.",
        evidence,
    )
    return await llm.generate_structured(SYSTEM_PROMPT, user_prompt, RelevanceAssessment)