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/models/ai_log.py

@@ -0,0 +1,35 @@
+from odoo import fields, models
+
+
+class EncoachAILog(models.Model):
+    _name = "encoach.ai.log"
+    _description = "AI Service Call Log"
+    _order = "create_date desc"
+
+    service = fields.Selection(
+        [
+            ("openai", "OpenAI"),
+            ("whisper", "Whisper"),
+            ("polly", "AWS Polly"),
+            ("elevenlabs", "ElevenLabs"),
+            ("gptzero", "GPTZero"),
+            ("elai", "ELAI"),
+            ("coach", "AI Coach"),
+        ],
+        required=True,
+        index=True,
+    )
+    action = fields.Char(index=True)
+    model_used = fields.Char()
+    prompt_tokens = fields.Integer(default=0)
+    completion_tokens = fields.Integer(default=0)
+    total_tokens = fields.Integer(default=0)
+    latency_ms = fields.Integer()
+    status = fields.Selection(
+        [("success", "Success"), ("error", "Error"), ("timeout", "Timeout")],
+        default="success",
+    )
+    error_message = fields.Text()
+    user_id = fields.Many2one("res.users", default=lambda self: self.env.uid)
+    input_preview = fields.Text()
+    output_preview = fields.Text()