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
40 lines
1.3 KiB
Python
40 lines
1.3 KiB
Python
"""The contract every search backend implements."""
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from __future__ import annotations
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from abc import ABC, abstractmethod
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from apps.search.types import SearchRequest, SearchResult
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class SearchBackend(ABC):
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"""
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Strategy interface for keyword search.
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Two implementations ship with the project:
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* :class:`apps.search.backends.elastic.ElasticsearchBackend` - the default,
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used in every environment that runs the ELK stack.
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* :class:`apps.search.backends.postgres.PostgresBackend` - a full-text
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fallback that keeps the API (and the test suite) working without any
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external search service.
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Keeping the surface this small is deliberate: the views, the serializers and
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the frontend are completely unaware of which engine answered the query.
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"""
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#: Human readable identifier, surfaced in the API response and health check.
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name: str = "base"
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@abstractmethod
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def search(self, request: SearchRequest) -> SearchResult:
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"""Execute a search and return ranked hits plus facet counts."""
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@abstractmethod
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def suggest(self, prefix: str, limit: int = 8) -> list[str]:
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"""Return type-ahead completions for a partial name or skill."""
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@abstractmethod
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def ping(self) -> bool:
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"""Raise or return ``False`` when the backend is not usable."""
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