feat: Generation Page AI workflows + AI/Vector modules + exam session fixes

Generation Page (complete rebuild):
- Full production-parity exam generation wizard with 4 IELTS modules
- Reading: AI passage gen, 5 exercise types (MCQ, Fill, Write, T/F, Match)
- Listening: 4 section types, AI context gen, TTS audio gen (ElevenLabs)
- Writing: Task 1/2, AI instruction gen, word limits, marks
- Speaking: 3 parts, AI script gen, avatar video gen (7 avatars)
- Per-module config: timer, CEFR difficulty, access, approval, rubrics
- Exam submission workflow (draft/published)

Exam Structures:
- New encoach.exam.structure model + CRUD controller
- ExamStructuresPage wired to real API

AI Module (encoach_ai):
- OpenAI service, ElevenLabs TTS, AWS Polly, ELAI avatars
- AI settings model with Odoo config parameters
- 7 generation endpoints (passage, exercises, instructions, scripts, context)

Vector Module (encoach_vector):
- pgvector integration for RAG-based content search
- Embedding service with sentence-transformers

Exam Session Fixes:
- Fixed ExamSession.tsx field mapping (question_type→type, exam_title→title)
- Fixed submit payload to include attempt_id and answers
- Fixed normalizeType to handle null/undefined

Tested: 12/12 API tests passed, browser-verified with real OpenAI calls
Made-with: Cursor

custom_addons/encoach_ai/services/elai_service.py

@@ -0,0 +1,108 @@
+"""ELAI avatar video generation service."""
+
+import logging
+import time
+
+_logger = logging.getLogger(__name__)
+
+try:
+    import requests as _requests
+except ImportError:
+    _requests = None
+
+ELAI_BASE = "https://apis.elai.io/api/v1"
+
+
+class ElaiService:
+    """Generate avatar videos for listening exercises and instructional content."""
+
+    def __init__(self, env):
+        self.env = env
+        self._get_param = env["ir.config_parameter"].sudo().get_param
+
+    def _get_token(self):
+        token = self._get_param("encoach_ai.elai_token", "")
+        if not token:
+            import os
+            token = os.environ.get("ELAI_TOKEN", "")
+        if not token:
+            raise RuntimeError("ELAI token not configured — set in AI Settings")
+        return token
+
+    def _headers(self):
+        return {
+            "Authorization": f"Bearer {self._get_token()}",
+            "Content-Type": "application/json",
+        }
+
+    def _log(self, action, latency, status="success", error=None):
+        try:
+            self.env["encoach.ai.log"].sudo().create({
+                "service": "elai",
+                "action": action,
+                "latency_ms": latency,
+                "status": status,
+                "error_message": error,
+            })
+        except Exception:
+            pass
+
+    def list_avatars(self):
+        """List available ELAI avatars."""
+        if not _requests:
+            raise RuntimeError("requests package not installed")
+        resp = _requests.get(f"{ELAI_BASE}/avatars", headers=self._headers(), timeout=15)
+        resp.raise_for_status()
+        return resp.json()
+
+    def create_video(self, script, *, avatar_id=None, title="EnCoach Video", language="en"):
+        """Create an avatar video from a script.
+
+        Returns:
+            dict with 'video_id', 'status'
+        """
+        if not _requests:
+            raise RuntimeError("requests package not installed")
+        payload = {
+            "name": title,
+            "slides": [
+                {
+                    "speech": script,
+                    "avatar": avatar_id or "default",
+                    "language": language,
+                }
+            ],
+        }
+        t0 = time.time()
+        try:
+            resp = _requests.post(
+                f"{ELAI_BASE}/videos",
+                json=payload,
+                headers=self._headers(),
+                timeout=30,
+            )
+            resp.raise_for_status()
+            data = resp.json()
+            self._log("create_video", int((time.time() - t0) * 1000))
+            return {"video_id": data.get("_id", data.get("id")), "status": data.get("status", "pending")}
+        except Exception as exc:
+            self._log("create_video", int((time.time() - t0) * 1000), "error", str(exc))
+            raise
+
+    def get_video_status(self, video_id):
+        """Check video generation status."""
+        if not _requests:
+            raise RuntimeError("requests package not installed")
+        resp = _requests.get(
+            f"{ELAI_BASE}/videos/{video_id}",
+            headers=self._headers(),
+            timeout=15,
+        )
+        resp.raise_for_status()
+        data = resp.json()
+        return {
+            "video_id": video_id,
+            "status": data.get("status", "unknown"),
+            "url": data.get("url", ""),
+            "duration": data.get("duration"),
+        }