Yamen Ahmad ed8e75d88c feat(course-plan): GE1-style AI course planning with deliverables, resources, media, assignments
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
2026-04-25 14:57:04 +04:00

EnCoach — Adaptive AI Learning Platform

EnCoach is a smart learning environment for individual and collaborative learning, fully integrated with AI and equipped with intelligent, professional-grade exercises, assessments, and e-exams.

This repository hosts the full stack:

  • Backend — Odoo 19 + ~30 encoach_* modules (Python 3.12, PostgreSQL 16)
  • Frontend — React 18 + Vite + TypeScript single-page app
  • AI layer — OpenAI + pgvector RAG, quality-gate validation, IELTS validator
  • Ops — Docker Compose, JWT auth with refresh tokens, Prometheus-compatible metrics, dynamic OpenAPI 3.0 spec

Canonical trees: backend/ (all server code) and frontend/ (all client code). The legacy new_project/ directory is deprecated — see new_project/DEPRECATED.md.


1. Quickstart

docker compose up -d
# Odoo:     http://localhost:8069
# Frontend: cd frontend && npm install && npm run dev  (http://localhost:8080)

Create a new Odoo database on first visit, then install the encoach_api module to pull in every encoach_* dependency.

Source

Prerequisites: Python 3.12, PostgreSQL 16, Node 20, npm 10.

# Backend
./setup.sh          # creates venv, installs requirements.txt
./run.sh            # starts Odoo on :8069

# Frontend
cd frontend
npm install
npm run dev         # starts Vite on :8080, proxies /api → :8069

See MANUAL-RUN.md for a step-by-step walkthrough and CONNECT-POSTGRES.md for database wiring.


2. Repository layout

odoo19/
├── backend/
│   └── custom_addons/
│       ├── encoach_api/              REST base + JWT + OpenAPI + metrics
│       ├── encoach_core/             Users, entities, roles, permissions
│       ├── encoach_taxonomy/         Subject → Domain → Topic
│       ├── encoach_ai/               OpenAI wrapper, cefr_mapper, validator
│       ├── encoach_ai_course/        AI course/exam generation pipelines
│       ├── encoach_ai_grading/       AI grading (writing/speaking/math/IT)
│       ├── encoach_ai_media/         TTS (Polly), STT (Whisper), ELAI
│       ├── encoach_vector/           pgvector store + RAG embeddings
│       ├── encoach_exam_template/    Canonical exam + student attempt models
│       ├── encoach_scoring/          Score computation, CEFR mapping
│       ├── encoach_quality_gate/     Automated content-quality checks
│       ├── encoach_ielts_validation/ IELTS-specific validators
│       ├── encoach_adaptive/         Adaptive engine, style matcher
│       ├── encoach_lms_api/          OpenEduCat bridge + LMS endpoints
│       ├── encoach_branding/         White-label config per entity
│       └── … (other encoach_* modules)
├── frontend/
│   ├── src/
│   │   ├── pages/                    Route pages (React.lazy code-split)
│   │   ├── components/               Shared UI (shadcn/ui + custom)
│   │   ├── services/                 Thin API wrappers over fetch
│   │   ├── hooks/queries/            React Query hooks + keys
│   │   ├── lib/api-client.ts         Fetch + auto JWT refresh
│   │   └── types/                    Shared DTO types
│   └── vite.config.ts                manualChunks: react, query, charts, radix…
├── docs/
│   ├── PROJECT_SUMMARY.md            Release notes & architecture history
│   ├── adr/                          Architecture Decision Records
│   └── ENCOACH_*.md                  SRS, workflows, user stories
├── docker-compose.yml
├── Dockerfile
├── requirements.txt                  Python deps (pgvector, textstat, etc.)
└── README.md                         You are here

3. Architecture at a glance

┌────────────────────┐     HTTPS/JWT      ┌────────────────────┐
│  React SPA (Vite)  │  ───────────────►  │   Odoo 19 + FastAPI-style   │
│  - React Query     │  ◄───────────────  │   controllers (encoach_*)   │
│  - next-themes     │   X-Request-Id     └──────┬─────────────┘
│  - shadcn/ui       │                           │
└────────────────────┘                           ▼
                                          ┌──────────────┐
                                          │  PostgreSQL  │
                                          │  + pgvector  │
                                          └──────┬───────┘
                                                 │
                                                 ▼
                                          ┌──────────────┐
                                          │   OpenAI,    │
                                          │   Whisper,   │
                                          │   Polly…     │
                                          └──────────────┘

Every request carries an X-Request-Id and emits structured JSON logs. Prometheus-compatible counters are exposed at /api/metrics, and the live OpenAPI 3.0 spec is at /api/openapi.json.


4. Key conventions

  • Canonical response envelope — list endpoints return { items: T[], total, page, size, data: T[] } (see encoach_api.controllers.base.paginated_envelope).
  • CEFR mapping — only encoach_ai.services.cefr_mapper is canonical. Do not reintroduce local band_to_cefr copies.
  • JWT tokens — short-lived access tokens (1h) + revocable refresh tokens (7d). Only access tokens are accepted as Bearer credentials; refresh tokens must go through /api/auth/refresh. See docs/adr/0002-jwt-refresh-token-flow.md.
  • RAG metadata — vector embeddings carry course_id, subject_id, entity_id, taxonomy, content_hash. Chunking kicks in above 2 000 chars.
  • Frontend paginationPaginatedResponse<T> exposes both items and data. Read from items in new code.
  • Frontend theming — tokens live in frontend/src/index.css (:root and .dark). Always use hsl(var(--token)) instead of raw hex.

5. Health, observability, docs

Endpoint Purpose
GET /api/health Liveness (always 200 when server is up)
GET /api/health/ready Readiness (DB + required config)
GET /api/openapi.json Dynamic OpenAPI 3.0 spec generated from @http.route
GET /api/metrics Prometheus-format counters per route

6. Deployment

Staging and production both use Docker Compose. The staging server rebuilds automatically from main; never force-push. See INSTALL-ODOO-SUMMARY.md for bootstrap notes.

Service Staging URL
Odoo backend http://5.189.151.117:8069
React frontend http://5.189.151.117:3000

The .env file is never committed. On staging it lives at /opt/encoach/backend-v2/.env.


7. Further reading

Document Description
docs/PROJECT_SUMMARY.md Release notes + architecture history
docs/adr/ Architecture Decision Records (why we built it this way)
docs/ENCOACH_UNIFIED_SRS.md Unified frontend + backend SRS
docs/ENCOACH_ODOO19_BACKEND_SRS.md Backend SRS v3.0
docs/ENCOACH_WORKFLOWS_BACKEND_SRS.md Backend workflows
docs/ENCOACH_WORKFLOWS_FRONTEND_SRS.md Frontend workflows

8. Contributing

  1. Branch from main — never push direct. Branch protection enforces it.
  2. Run npx tsc --noEmit -p tsconfig.app.json (frontend) and the module smoke tests before opening a PR.
  3. Every architectural decision should be captured as an ADR under docs/adr/. Copy 0000-template.md to start one.
  4. Open the PR against main and request review from devops (Talal).
Description
No description provided
Readme 341 MiB
Languages
Python 34.8%
C 31.4%
JavaScript 27.9%
TypeScript 1.6%
SCSS 1.4%
Other 2.8%