feat: complete multimodal ticket intake
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hailin
250
backend/apps/tickets/similarity.py
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250
backend/apps/tickets/similarity.py
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"""相似工单候选生成。
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相似结果只用于非阻断提示，不合并工单，也不改变正式工单的创建和状态流转。
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"""
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import re
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from collections.abc import Iterable
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from difflib import SequenceMatcher
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from django.contrib.postgres.search import SearchQuery, SearchRank, SearchVector, TrigramSimilarity
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from django.db import connection, transaction
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from django.db.models import F, Q
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from django.db.models.functions import Greatest
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from apps.tickets.models import Ticket, TicketSimilaritySuggestion
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ALGORITHM_VERSION = "fts_trigram_v2_context"
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MIN_CANDIDATE_SCORE = 0.12
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def _normalize_text(value: str) -> str:
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return re.sub(r"\s+", " ", value.casefold()).strip()
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def _terms(value: str) -> set[str]:
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normalized = _normalize_text(value)
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words = set(re.findall(r"[a-z0-9][a-z0-9_.:-]{1,}|[\u4e00-\u9fff]{2,}", normalized))
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chinese = "".join(re.findall(r"[\u4e00-\u9fff]", normalized))
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words.update(chinese[index : index + 2] for index in range(max(len(chinese) - 1, 0)))
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return {term for term in words if term}
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def _keyword_similarity(left: str, right: str) -> float:
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left_terms = _terms(left)
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right_terms = _terms(right)
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if not left_terms or not right_terms:
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return 0.0
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return len(left_terms & right_terms) / len(left_terms | right_terms)
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def _excerpt(text: str, query: str, limit: int = 180) -> str:
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normalized = text.strip()
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if len(normalized) <= limit:
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return normalized
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terms = sorted(_terms(query), key=len, reverse=True)
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position = next(
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(normalized.casefold().find(term) for term in terms if term in normalized.casefold()),
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0,
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)
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start = max(position - limit // 3, 0)
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end = min(start + limit, len(normalized))
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prefix = "…" if start else ""
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suffix = "…" if end < len(normalized) else ""
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return f"{prefix}{normalized[start:end]}{suffix}"
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def _serialize_candidate(
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ticket: Ticket,
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*,
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score: float,
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title_similarity: float,
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keyword_score: float,
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query: str,
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) -> dict:
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return {
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"ticket": ticket,
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"score": max(0.0, min(float(score), 1.0)),
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"title_similarity": max(0.0, min(float(title_similarity), 1.0)),
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"keyword_score": max(0.0, min(float(keyword_score), 1.0)),
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"matched_excerpt": _excerpt(ticket.description, query),
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}
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def _postgres_candidates(
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queryset,
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*,
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title: str,
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query_text: str,
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limit: int,
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) -> list[dict]:
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search_query = SearchQuery(query_text, config="simple", search_type="plain")
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search_vector = (
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SearchVector("title", config="simple", weight="A")
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+ SearchVector("description", config="simple", weight="B")
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+ SearchVector("problem_performance", config="simple", weight="B")
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+ SearchVector("error_information", config="simple", weight="B")
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+ SearchVector("product_name", config="simple", weight="C")
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+ SearchVector("module_name", config="simple", weight="C")
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+ SearchVector("related_system", config="simple", weight="C")
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+ SearchVector("attempted_actions", config="simple", weight="C")
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+ SearchVector("customer_request", config="simple", weight="C")
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+ SearchVector("current_result", config="simple", weight="C")
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)
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rows = (
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queryset.annotate(
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keyword_score=SearchRank(search_vector, search_query),
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title_similarity=TrigramSimilarity("title", title),
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)
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.annotate(score=Greatest(F("title_similarity"), F("keyword_score")))
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.filter(Q(title_similarity__gte=MIN_CANDIDATE_SCORE) | Q(keyword_score__gte=0.05))
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.order_by("-score", "-updated_at")[:limit]
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)
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return [
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_serialize_candidate(
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ticket,
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score=ticket.score,
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title_similarity=ticket.title_similarity,
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keyword_score=ticket.keyword_score,
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query=query_text,
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)
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for ticket in rows
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]
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def _portable_candidates(
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queryset: Iterable[Ticket],
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*,
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title: str,
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query_text: str,
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limit: int,
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) -> list[dict]:
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normalized_title = _normalize_text(title)
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candidates = []
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for ticket in queryset:
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title_similarity = SequenceMatcher(
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None,
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normalized_title,
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_normalize_text(ticket.title),
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).ratio()
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keyword_score = _keyword_similarity(
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query_text,
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" ".join(
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[
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ticket.title,
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ticket.description,
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ticket.problem_performance,
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ticket.error_information,
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ticket.product_name,
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ticket.module_name,
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ticket.related_system,
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ticket.attempted_actions,
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ticket.customer_request,
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ticket.current_result,
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*ticket.tags,
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]
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),
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)
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score = max(title_similarity, keyword_score)
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if score < MIN_CANDIDATE_SCORE:
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continue
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candidates.append(
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_serialize_candidate(
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ticket,
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score=score,
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title_similarity=title_similarity,
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keyword_score=keyword_score,
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query=query_text,
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)
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)
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return sorted(
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candidates,
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key=lambda item: (item["score"], item["ticket"].updated_at),
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reverse=True,
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)[:limit]
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def find_similar_tickets(ticket: Ticket, *, limit: int = 5) -> list[dict]:
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"""返回可解释的相似候选，不修改或合并任何工单。"""
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title = ticket.title.strip() or ticket.description[:200]
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query_text = " ".join(
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value
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for value in [
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ticket.title,
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ticket.description,
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ticket.problem_performance,
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ticket.error_information,
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ticket.product_name,
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ticket.module_name,
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ticket.related_system,
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ticket.attempted_actions,
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ticket.customer_request,
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ticket.current_result,
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*ticket.tags,
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]
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if value
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).strip()
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if not query_text:
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return []
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queryset = Ticket.objects.filter(archived_at__isnull=True).exclude(id=ticket.id).select_related(
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"category"
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)
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if connection.vendor == "postgresql":
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return _postgres_candidates(
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queryset,
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title=title,
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query_text=query_text,
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limit=limit,
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)
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return _portable_candidates(
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queryset,
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title=title,
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query_text=query_text,
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limit=limit,
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)
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@transaction.atomic
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def refresh_ticket_similarity_suggestions(
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ticket: Ticket,
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*,
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limit: int = 5,
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) -> list[TicketSimilaritySuggestion]:
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"""原子替换指定工单的相似候选快照。"""
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ticket = Ticket.objects.select_for_update().get(id=ticket.id)
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candidates = find_similar_tickets(ticket, limit=limit)
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TicketSimilaritySuggestion.objects.filter(
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source_ticket=ticket,
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algorithm_version=ALGORITHM_VERSION,
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).delete()
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TicketSimilaritySuggestion.objects.bulk_create(
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[
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TicketSimilaritySuggestion(
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source_ticket=ticket,
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candidate_ticket=item["ticket"],
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score=item["score"],
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title_similarity=item["title_similarity"],
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keyword_score=item["keyword_score"],
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matched_excerpt=item["matched_excerpt"],
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algorithm_version=ALGORITHM_VERSION,
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)
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for item in candidates
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]
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)
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return list(
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TicketSimilaritySuggestion.objects.filter(
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source_ticket=ticket,
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algorithm_version=ALGORITHM_VERSION,
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).select_related("candidate_ticket__category")
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)
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def schedule_ticket_similarity_refresh(ticket: Ticket) -> None:
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transaction.on_commit(
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lambda ticket_id=ticket.id: refresh_ticket_similarity_suggestions(
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Ticket.objects.get(id=ticket_id)
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),
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robust=True,
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)
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