Files
planet/backend/app/services/location/resolvers/registry.py
linkong e1984c7a35 release: bump version to 0.49.0
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-05-08 17:42:27 +08:00

324 lines
12 KiB
Python

"""Resolver that matches a query against a local JSON registry.
Registry schema (a single JSON file):
{
"locations": [
{
"canonical_name": "...",
"aliases": ["...", "..."],
"operator": "...",
"site": "...",
"city": "...",
"country": "...",
"region": "...",
"latitude": 0.0,
"longitude": 0.0,
"precision": "precise" | "site" | "city",
"confidence": 0.0,
"verification_status": "verified",
"source_note": "...",
"verified_at": "YYYY-MM-DD"
}
],
"city_fallbacks": [ {city, country, latitude, longitude, ...} ]
}
"""
from __future__ import annotations
import json
from functools import lru_cache
from pathlib import Path
from typing import Any, Callable, Iterable
from ..models import (
RENDERABLE_PRECISIONS,
LocationCandidate,
LocationQuery,
ResolverOutput,
)
from ..text import (
city_key,
normalize_country_text,
normalize_text,
parse_float,
)
# Field-priority weights when scoring "this query field text contains this
# alias text". Tuned to match the legacy compute-center ordering — name beats
# site beats operator beats city — which generalizes well to other domains.
_DEFAULT_FIELD_PRIORITY = {
"name": 8,
"site": 6,
"operator": 5,
"city": 3,
}
def default_score_alias_match(
alias_field: str, record_field: str, alias_text: str
) -> int:
score = max(0, len(alias_text))
score += _DEFAULT_FIELD_PRIORITY.get(alias_field, 1)
if alias_field == record_field:
score += 4
if alias_field == "name" and record_field in {"name", "name_short", "alias"}:
score += 6
if alias_field == "site" and record_field in {"site", "organization"}:
score += 4
if alias_field == "operator" and record_field in {"operator", "organization"}:
score += 4
return score
@lru_cache(maxsize=32)
def _load_registry_file(path: str) -> dict[str, Any]:
with Path(path).open("r", encoding="utf-8") as handle:
return json.load(handle)
@lru_cache(maxsize=32)
def _build_alias_index(
path: str,
) -> tuple[tuple[dict[str, Any], tuple[tuple[str, str], ...]], ...]:
index: list[tuple[dict[str, Any], tuple[tuple[str, str], ...]]] = []
for entry in _load_registry_file(path).get("locations", []):
aliases: list[tuple[str, str]] = []
seen: set[str] = set()
for alias in [entry.get("canonical_name"), *(entry.get("aliases") or [])]:
normalized = normalize_text(alias)
if normalized and normalized not in seen:
aliases.append(("name", normalized))
seen.add(normalized)
for field_name in ("operator", "site", "city"):
value = entry.get(field_name)
normalized = normalize_text(value)
if normalized and normalized not in seen:
aliases.append((field_name, normalized))
seen.add(normalized)
index.append((entry, tuple(aliases)))
return tuple(index)
def _query_corpus(query: LocationQuery) -> dict[str, str]:
"""Map a query into normalized strings keyed by source field."""
fields: dict[str, str] = {
"name": query.name or "",
"city": query.city or "",
"country": query.country or "",
}
for alias in query.aliases:
if alias and alias != query.name:
fields["name_short"] = alias
break
extra = query.extra or {}
for key in ("site", "operator", "organization"):
value = extra.get(key)
if value:
fields[key] = str(value)
return {key: normalize_text(value) for key, value in fields.items() if value}
def _country_compatible(entry: dict[str, Any], query: LocationQuery) -> bool:
record_country = normalize_country_text(query.country)
entry_country = normalize_country_text(entry.get("country"))
if not record_country or not entry_country:
return True
return normalize_text(record_country) == normalize_text(entry_country)
def _normalized_alias_matches(alias_normalized: str, record_text: str) -> bool:
alias_tokens = alias_normalized.split()
record_tokens = record_text.split()
if not alias_tokens or not record_tokens:
return False
if len(alias_tokens) == 1:
return alias_tokens[0] in record_tokens
window_size = len(alias_tokens)
return any(
record_tokens[index : index + window_size] == alias_tokens
for index in range(0, len(record_tokens) - window_size + 1)
)
def _entry_to_candidate(
entry: dict[str, Any],
*,
matched_alias: str,
matched_fields: Iterable[str],
source: str,
score_explainer: str,
confidence_floor: float,
) -> LocationCandidate:
canonical_name = entry.get("canonical_name") or matched_alias
# Registry entries are treated as candidates unless explicitly verified.
# This prevents migrated hard-coded hints from appearing as factual
# location evidence.
is_verified = entry.get("verification_status") == "verified"
precision = entry.get("precision") or "city"
if precision not in RENDERABLE_PRECISIONS:
precision = "city"
fields_summary = ", ".join(sorted(set(matched_fields))) or "name"
confidence_value = parse_float(entry.get("confidence"))
confidence = (
float(confidence_value)
if confidence_value is not None
else confidence_floor
)
return LocationCandidate(
latitude=float(parse_float(entry.get("latitude")) or 0.0),
longitude=float(parse_float(entry.get("longitude")) or 0.0),
display_name=canonical_name,
precision=precision,
confidence=confidence,
query=f"local_registry::{matched_alias or canonical_name}",
source=source,
source_note=entry.get("source_note")
or f"{score_explainer}: matched {fields_summary}",
matched_fields=tuple(sorted(set(matched_fields))) or ("name",),
needs_confirmation=bool(entry.get("needs_confirmation")) or not is_verified,
city=entry.get("city"),
region=entry.get("region"),
country=entry.get("country"),
matched_location_name=canonical_name,
location_verified_at=entry.get("verified_at") if is_verified else None,
suggested_registry_entry=None,
)
class RegistryResolver:
"""Match a query against a JSON registry (plus its city_fallbacks table)."""
def __init__(
self,
*,
registry_path: Path | str,
name: str = "local_registry",
city_fallback_source: str = "local_registry_city",
city_fallback_confidence_default: float = 0.65,
confidence_default: float = 0.85,
score_alias_match: Callable[[str, str, str], int] = default_score_alias_match,
) -> None:
self.name = name
self._registry_path = str(Path(registry_path))
self._city_fallback_source = city_fallback_source
self._city_fallback_confidence_default = city_fallback_confidence_default
self._confidence_default = confidence_default
self._score = score_alias_match
def reload(self) -> None:
"""Drop the cached registry — useful when the JSON file is edited."""
_load_registry_file.cache_clear()
_build_alias_index.cache_clear()
def resolve(self, query: LocationQuery) -> ResolverOutput:
candidates: list[LocationCandidate] = []
candidates.extend(self._registry_candidates(query))
city_candidate = self._city_fallback_candidate(query)
if city_candidate is not None:
candidates.append(city_candidate)
return ResolverOutput(candidates=tuple(candidates))
# ── internals ──────────────────────────────────────────────
def _registry_candidates(
self, query: LocationQuery
) -> list[LocationCandidate]:
corpus = _query_corpus(query)
if not corpus:
return []
# When the query carries a name (a record-specific identifier), require
# at least one alias match against a name-class field — otherwise a
# generic shared field like operator="RIPE NCC" would promote every
# registry entry that lists that operator, regardless of whether the
# name matches.
query_has_name = bool(corpus.get("name") or corpus.get("name_short"))
results: list[LocationCandidate] = []
for entry, aliases in _build_alias_index(self._registry_path):
best_alias = ""
best_score = 0
matched_fields: list[str] = []
matched_via_name_alias = False
for alias_field, alias_normalized in aliases:
for record_field, record_text in corpus.items():
if not _normalized_alias_matches(alias_normalized, record_text):
continue
score = self._score(
alias_field, record_field, alias_normalized
)
if score > best_score or (
score == best_score
and len(alias_normalized) > len(best_alias)
):
best_score = score
best_alias = alias_normalized
if record_field not in matched_fields:
matched_fields.append(record_field)
if alias_field == "name" and record_field in {"name", "name_short"}:
matched_via_name_alias = True
if not matched_fields or best_score <= 0:
continue
if query_has_name and not matched_via_name_alias:
continue
if not _country_compatible(entry, query):
continue
results.append(
_entry_to_candidate(
entry,
matched_alias=best_alias,
matched_fields=matched_fields,
source=self.name,
score_explainer="Registry alias match",
confidence_floor=self._confidence_default,
)
)
return results
def _city_fallback_candidate(
self, query: LocationQuery
) -> LocationCandidate | None:
country = normalize_country_text(query.country)
city = city_key(query.city)
if not country or not city:
return None
for fallback in _load_registry_file(self._registry_path).get(
"city_fallbacks", []
):
fallback_country = normalize_country_text(fallback.get("country"))
fallback_city = city_key(fallback.get("city"))
if fallback_country != country or fallback_city != city:
continue
confidence_value = parse_float(fallback.get("confidence"))
confidence = (
float(confidence_value)
if confidence_value is not None
else self._city_fallback_confidence_default
)
return LocationCandidate(
latitude=float(parse_float(fallback.get("latitude")) or 0.0),
longitude=float(parse_float(fallback.get("longitude")) or 0.0),
display_name=fallback.get("city") or "",
precision="city",
confidence=confidence,
query=(
f"city_fallback::{fallback.get('city')}, "
f"{fallback.get('country')}"
),
source=self._city_fallback_source,
source_note=fallback.get("source_note")
or f"City fallback for {fallback.get('city')}, {fallback.get('country')}",
matched_fields=("city", "country"),
needs_confirmation=False,
city=fallback.get("city"),
region=fallback.get("region"),
country=fallback.get("country"),
matched_location_name=fallback.get("city"),
location_verified_at=fallback.get("verified_at"),
suggested_registry_entry=None,
)
return None