Locator Agent

from locator_agent.vector_db import LocatorVectorDB
from locator_agent.generator import LocatorProposalGenerator
from locator_agent.scorer import ProposalScorer

db = LocatorVectorDB(collection="playwright-locators")
generator = LocatorProposalGenerator(model="gpt-4")
scorer = ProposalScorer()

failing_locator = "button:has-text('Submit') >> nth=0"
page_context = {"url": "/checkout", "dom_snapshot": "..."}

similar = db.search(failing_locator, k=5)
proposals = generator.propose(
    failing=failing_locator,
    similar=similar,
    context=page_context,
)
ranked = scorer.rank(proposals, history=db.reliability_stats)

for i, proposal in enumerate(ranked, 1):
    print(f"{i}. [{proposal.score:.0%}] {proposal.locator}")

class LocatorRecord(BaseModel):
    locator: str
    page_url: str
    element_type: str
    aria_role: str | None
    text_content: str | None
    reliability_score: float
    passing_runs: int
    total_runs: int
    embedding: list[float]

    @property
    def confidence(self) -> float:
        if self.total_runs == 0:
            return 0.0
        return self.passing_runs / self.total_runs

    def to_metadata(self) -> dict:
        return {
            "locator": self.locator,
            "url": self.page_url,
            "type": self.element_type,
            "confidence": self.confidence,
        }