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/base.py

@@ -0,0 +1,39 @@
+"""The contract every search backend implements."""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+
+from apps.search.types import SearchRequest, SearchResult
+
+
+class SearchBackend(ABC):
+    """
+    Strategy interface for keyword search.
+
+    Two implementations ship with the project:
+
+    * :class:`apps.search.backends.elastic.ElasticsearchBackend` - the default,
+      used in every environment that runs the ELK stack.
+    * :class:`apps.search.backends.postgres.PostgresBackend` - a full-text
+      fallback that keeps the API (and the test suite) working without any
+      external search service.
+
+    Keeping the surface this small is deliberate: the views, the serializers and
+    the frontend are completely unaware of which engine answered the query.
+    """
+
+    #: Human readable identifier, surfaced in the API response and health check.
+    name: str = "base"
+
+    @abstractmethod
+    def search(self, request: SearchRequest) -> SearchResult:
+        """Execute a search and return ranked hits plus facet counts."""
+
+    @abstractmethod
+    def suggest(self, prefix: str, limit: int = 8) -> list[str]:
+        """Return type-ahead completions for a partial name or skill."""
+
+    @abstractmethod
+    def ping(self) -> bool:
+        """Raise or return ``False`` when the backend is not usable."""