mobiniqow
a464e550c9
Full-stack implementation of the Cyberyan full-stack trial task. Backend (Django 5 / DRF) - Normalised PostgreSQL model (Profile, Company, School, Skill, Experience, Education) as the source of truth, with GIN + trigram indexes. - Self-healing CSV reader: the supplied export carries an injected watermark column, missing columns, rows split by broken quoting and a duplicated header. A signature-scored Needleman-Wunsch alignment reconstructs 284 clean records out of 336 physical rows; only 10 rows parse correctly without it. - Pluggable search layer (Strategy): Elasticsearch by default, PostgreSQL full-text search as a fallback selected by a single environment variable. - Keyword search with per-field boosts, fuzziness and phrase boosting, six faceted filters (OR within a filter, AND across filters), an experience range, four sort orders, highlighting and facet aggregations. - JWT auth with rotation + blacklist, three roles, PII masking, scoped rate limits, a single error envelope, OpenAPI schema and health probes. Frontend (React 18 / TypeScript / Vite) - Debounced search with type-ahead, faceted filter panel, result cards, profile drawer, URL-synced search state, automatic token refresh. - Highlight snippets rendered without dangerouslySetInnerHTML. Infrastructure - Hardened multi-stage images (non-root, read-only rootfs, healthchecks). - docker-compose for the full stack plus dev and production overlays. - ELK: structured JSON logs shipped asynchronously to Logstash, routed per event type, with a composable index template and a Kibana setup script. Quality - 61 backend tests (85% coverage), ruff/black/bandit/mypy, ESLint + tsc, GitHub Actions running lint, tests and both image builds. - Persian documentation: README plus architecture, search, API, data, security and operations guides. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01GnFo6fNaHCTAkJbfcdkqtG
73 lines
2.3 KiB
Python
73 lines
2.3 KiB
Python
"""Profile read endpoints (the search endpoints live in ``apps.search``)."""
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from __future__ import annotations
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from django.db.models import QuerySet
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from drf_spectacular.utils import extend_schema
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from rest_framework import generics
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from rest_framework.permissions import IsAuthenticated
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from apps.profiles.models import Profile, Skill
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from apps.profiles.serializers import (
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ProfileDetailSerializer,
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ProfileListSerializer,
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SkillSerializer,
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)
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@extend_schema(tags=["profiles"])
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class ProfileListView(generics.ListAPIView):
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"""
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Plain, unranked listing.
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Useful for browsing and for verifying the dataset; ranked keyword search is
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served by ``/api/v1/search/``.
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"""
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serializer_class = ProfileListSerializer
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permission_classes = [IsAuthenticated]
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def get_queryset(self) -> QuerySet[Profile]:
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return (
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Profile.objects.select_related("current_company")
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.prefetch_related("profile_skills__skill")
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.order_by("full_name", "id")
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)
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@extend_schema(tags=["profiles"])
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class ProfileDetailView(generics.RetrieveAPIView):
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"""Full profile including work history, education and (masked) contacts."""
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serializer_class = ProfileDetailSerializer
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permission_classes = [IsAuthenticated]
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def get_queryset(self) -> QuerySet[Profile]:
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return Profile.objects.select_related("current_company").prefetch_related(
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"profile_skills__skill",
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"experiences__company",
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"educations__school",
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)
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@extend_schema(tags=["profiles"])
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class SkillListView(generics.ListAPIView):
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"""
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Skill dictionary ordered by popularity.
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The frontend uses it to power the skill filter's type-ahead without hitting
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Elasticsearch for every keystroke.
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"""
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serializer_class = SkillSerializer
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permission_classes = [IsAuthenticated]
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pagination_class = None
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def get_queryset(self) -> QuerySet[Skill]:
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queryset = Skill.objects.filter(profile_count__gt=0)
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term = self.request.query_params.get("q", "").strip().lower()
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if term:
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queryset = queryset.filter(name__icontains=term[:64])
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limit = min(int(self.request.query_params.get("limit", 50) or 50), 200)
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return queryset.order_by("-profile_count", "name")[:limit]
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