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

scripts/kibana_setup.sh

@@ -0,0 +1,42 @@
+#!/usr/bin/env bash
+# =============================================================================
+# Registers the `app-logs-*` data view in Kibana so the logs are searchable
+# straight after `make up`, without clicking through the UI.
+#
+#   ./scripts/kibana_setup.sh [kibana-url]
+# =============================================================================
+set -Eeuo pipefail
+
+KIBANA_URL="${1:-http://localhost:${KIBANA_PORT:-5601}}"
+DATA_VIEW_TITLE="app-logs-*"
+
+echo "Waiting for Kibana at ${KIBANA_URL} ..."
+for _ in $(seq 1 60); do
+  if curl -fsS "${KIBANA_URL}/api/status" 2>/dev/null | grep -q '"level":"available"'; then
+    echo "Kibana is available."
+    break
+  fi
+  sleep 5
+done
+
+echo "Creating the '${DATA_VIEW_TITLE}' data view ..."
+response=$(
+  curl -sS -o /dev/null -w '%{http_code}' -X POST "${KIBANA_URL}/api/data_views/data_view" \
+    -H 'kbn-xsrf: true' \
+    -H 'Content-Type: application/json' \
+    -d "{\"data_view\":{\"title\":\"${DATA_VIEW_TITLE}\",\"name\":\"Application logs\",\"timeFieldName\":\"@timestamp\"}}"
+) || true
+
+case "${response}" in
+  200|201) echo "Data view created." ;;
+  400|409) echo "Data view already exists - nothing to do." ;;
+  *) echo "Unexpected response from Kibana: HTTP ${response}" >&2; exit 1 ;;
+esac
+
+echo
+echo "Open Kibana → Analytics → Discover and pick 'Application logs'."
+echo "Useful queries:"
+echo "  event: \"search_executed\" and took_ms > 200"
+echo "  event: \"search_executed\" and result_count: 0"
+echo "  level: \"ERROR\""
+echo "  request_id: \"<the X-Request-ID header from the browser>\""