Builds the §24 product on top of the LangGraph runtime from §22:
Phase A (Sources / RAG)
- encoach.course.plan.source model (file | url | text)
- SourceIndexer extracts PDF (pypdf), DOCX (python-docx), HTML, plain
text and embeds chunks via the existing pgvector pipeline scoped to
plan_id, so resources.search only returns the plan's own corpus
- Endpoints: list/create/upload/reindex/delete + plan-scoped retrieval
Phase B (Deliverables)
- services.deliverables.compute_deliverables walks the plan, derives
{planned, generated, ready} per week from material + media state
- GET /api/ai/course-plan/<id>/deliverables drives the new wizard
preview step and the live progress strip on the detail page
Phase C (Multi-modal media)
- encoach.course.plan.media model + MediaService:
audio: AWS Polly (default) or ElevenLabs
image: OpenAI DALL-E 3, capped per plan via system parameter
video: local ffmpeg subprocess (image + audio -> MP4 1280x720)
- Three new agent tools (media.synthesize_audio / generate_image /
compose_video), wired into course_week_materials and a new
course_media_director agent
- Endpoints per material + week-level batch generator
Phase D (Assignments)
- encoach.course.plan.assignment supports mode='batch' (op.batch) or
mode='students' (res.users), with due_date + message + state
- REST endpoints to list / create / delete assignments
Phase E (Student view)
- /api/student/course-plans + /api/student/course-plans/<id>
enforce visibility via assignment.expand_user_ids()
- New /student/course-plans list + read-only drilldown rendering
audio/image/video tiles from /web/content/<attachment_id>
Cross-cutting
- encoach.ai.tool.category: + media (so the new tools register)
- encoach.embedding gains a plan_id filter for plan-scoped RAG
- Wizard adds Sources + Multimedia steps; AdminCoursePlanDetail
rewritten with DeliverablesStrip + SourcesCard + AssignmentsCard +
per-material MediaDrawer
- ~280 new EN + AR i18n keys (full RTL coverage)
- smoke_course_plan.py exercises every phase via odoo-bin shell;
last run: PASS A/B/D/E + DALL-E 3 image (753 KB), Polly audio
fails cleanly when AWS creds aren't configured (expected)
Documentation: §24 added to docs/PROJECT_SUMMARY.md with phase-by-phase
artefact list, endpoints, smoke test, ops notes, and gotchas.
Made-with: Cursor
Based on UTAS GE1 Course Outline structure (Reading/Writing 10hrs + Listening/Speaking 8hrs)
New Models:
- encoach.course.plan.deliverable: Explicit learning outcome tracking by week/skill
- encoach.course.plan.resource.dep: Resource dependencies (textbooks, videos, etc.)
- encoach.course.plan.assignment: Assign plans to classes/students with progress tracking
- encoach.course.plan.assignment.deliverable: Per-student deliverable completion status
Extended Models:
- course.plan.material: Added media fields (media_type, media_asset_url, media_asset_id,
media_generation_prompt, media_metadata_json) for rich content
- New material types: video_lesson, audio_recording, image_visual, interactive, assessment
AI Agent Tools (agent_tools.py):
- deliverables.detect: Parse course outlines (like GE1 PDF) and extract structured outcomes
- deliverables.fetch: Get deliverables for AI to reference when generating
- resources.fetch: Check available resources before generating content
- resources.save: Persist resource dependencies
- media.suggest_visuals: AI suggests images/diagrams for materials
- media.generate_image: Generate educational images (DALL-E integration ready)
- media.generate_audio: Generate TTS audio (ElevenLabs/Polly integration ready)
- assignment.*: Create assignments and track progress
Pipeline Enhancements (course_plan_pipeline.py):
- generate_deliverables_from_outline(): Parse PDF/text outlines into structured deliverables
- generate_week_materials_with_resources(): Resource-aware content generation
- suggest_media_for_material(): AI visual aid suggestions
- generate_media_for_material(): Actual image/audio generation
New AI Agents (agents_defaults.xml):
- deliverable_detector: Parses GE1-style outlines, extracts deliverables week-by-week
- media_generator: Creates images/audio for teaching materials
- Updated course_planner & course_week_materials with resource tools
REST APIs (course_plan.py):
POST /api/ai/course-plan/<id>/deliverables/detect - Parse outline
GET /api/ai/course-plan/<id>/deliverables - List deliverables
PUT /api/ai/course-plan/deliverables/<id> - Update status
GET /api/ai/course-plan/<id>/resources - List resources
POST /api/ai/course-plan/<id>/resources - Add resource
POST /api/ai/course-plan/materials/<id>/media/suggest - Get visual suggestions
POST /api/ai/course-plan/materials/<id>/media/generate - Generate image/audio
POST /api/ai/course-plan/<id>/assignments - Assign to class/student
GET /api/ai/course-plan/<id>/assignments - List assignments
GET /api/ai/course-plan/assignments/<id> - Get with progress
PUT /api/ai/course-plan/assignments/<id>/deliverables/<del_id> - Update status
Security: Added ir.model.access.csv entries for all new models
Made-with: Cursor
