feat: LinkedIn profile search engine (Django + React + Elasticsearch + ELK)

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

backend/apps/search/backends/__init__.py

@@ -0,0 +1,36 @@
+"""Backend factory - resolves ``SEARCH["BACKEND"]`` to a concrete strategy."""
+
+from __future__ import annotations
+
+from functools import cache
+
+from django.conf import settings
+from django.core.exceptions import ImproperlyConfigured
+
+from apps.search.backends.base import SearchBackend
+
+__all__ = ["SearchBackend", "get_search_backend"]
+
+
+@cache
+def get_search_backend(name: str | None = None) -> SearchBackend:
+    """
+    Return the configured backend.
+
+    Cached because both implementations hold a connection pool that should be
+    created once per process, not once per request.
+    """
+    backend = (name or settings.SEARCH["BACKEND"]).lower()
+
+    if backend == "elasticsearch":
+        from apps.search.backends.elastic import ElasticsearchBackend
+
+        return ElasticsearchBackend()
+    if backend == "postgres":
+        from apps.search.backends.postgres import PostgresBackend
+
+        return PostgresBackend()
+
+    raise ImproperlyConfigured(
+        f"Unknown SEARCH['BACKEND'] {backend!r}; expected 'elasticsearch' or 'postgres'."
+    )