devops/encoach_backend_new_v2
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Author SHA1 Message Date
Yamen Ahmad
1dd1168fee 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
Yamen Ahmad
882179870c feat(ai): LangGraph as core runtime + AI Agents/Tools console + full-demo seed 
Core AI runtime
- New encoach.ai.agent + encoach.ai.tool models with M2M tool binding,
  graph topology (simple|plan_review_revise|rag|react), model + fallback,
  temperature, max_tokens, response_format, max_revisions, quality checks
  and system prompt fields.
- services/agent_runtime.py compiles a langgraph.StateGraph per agent
  and caches the build per (key, write_date). Emits a structured trace
  (output, tool_calls, retrieval_hits, revisions, quality_issues,
  ms, model_used, fallback_used) and auto-falls-back on rate-limit/5xx.
- services/agent_tools.py registers 11 tool handlers wrapping existing
  services: resources.search, rubric.fetch, outcomes.fetch,
  student.profile, quality.cefr_check, quality.ai_detect,
  quality.content_gate, course_plan.save (mutates),
  course_plan.save_materials (mutates), scoring.grade_writing,
  scoring.grade_speaking.
- 7 default agents seeded via data/agents_defaults.xml: course_planner,
  course_week_materials, exam_generator, exercise_generator, lms_tutor,
  writing_grader, speaking_grader.
- Feature flag encoach_ai.use_langgraph_runtime (default True).
- encoach_ai_course pipeline now routes through AgentRuntime when on,
  legacy SDK path kept as fallback.

Admin UI
- /admin/ai/prompts is now a tabbed Agents | Tools | Prompts console.
- AIAgentsPanel: card grid + config dialog (model/temp/graph/tools/
  system prompt) + built-in Test Runner showing live trace.
- AIToolsPanel: registry table with category badges, mutates flag,
  schema viewer, edit dialog.
- New /api/ai/agents* and /api/ai/tools* controller (list/get/update/
  test, list-tools, toggle-tool).
- Sidebar label nav.aiPrompts -> nav.aiAgents (AI Agents and Tools).
- EN + AR (RTL) translations for ~80 new keys.

Smart Wizard pages
- /admin/quick-setup hub + CourseWizard, CoursePlanWizard,
  RubricWizard, ExamStructureWizard step-by-step flows.
- /admin/course-plans list + detail pages.
- /teacher/quick-setup mirror.

Full demo seed + 8-role E2E
- seed_full_demo.py adds the 5 missing user_types (approver, corporate,
  mastercorporate, agent, developer), activates a 2-stage exam-approval
  workflow with one pending request, creates a GE1-aligned 12-week B1
  course plan with 6 detailed Week-1 materials (reading 400w, writing,
  listening 4-min script, speaking, grammar present simple vs continuous,
  vocabulary), and inserts sample ai.log + ai.feedback rows.
- reset_demo_passwords.py forces every demo login back to canonical
  passwords (admin123/teacher123/student123/approver123/corporate123/
  master123/agent123/dev123).
- e2e_full_scenario.py: 46/46 PASS read-only API smoke across all
  8 roles, including a live LangGraph round-trip on writing_grader.
- e2e_approval_chain.py: 6/6 PASS mutation E2E - approver approves
  stage 1, admin approves stage 2, linked encoach.exam.custom flips
  to status=published, verified via psql.

Docs
- docs/PROJECT_SUMMARY.md updated to 2026-04-25: new Latest events
  bullets, refreshed credentials table, full sections 22 (LangGraph
  runtime) and 23 (full demo seed + 8-role E2E).
- docs/ENCOACH_FULL_DEMO_QA_REPORT.md added with credentials,
  per-endpoint PASS/FAIL, mutation chain proof, LangGraph live output.
- backend/GE1 Course Outline_ Fall AY25-26.pdf vendored as the
  reference outline the GE1 plan/materials are aligned to.

Dependencies
- requirements.txt: langgraph>=0.2.0, langchain-core>=0.3.0.
- encoach_ai/__manifest__.py: external_dependencies updated.

Made-with: Cursor
 2026-04-25 03:14:22 +04:00
Yamen Ahmad
907a5c0e92 feat(v3): restructure project + add complete frontend 
- Restructure: move backend from new_project/ to backend/
- Add full React/TypeScript frontend (37 pages, 17 services, 16 type defs, 11 query hooks)
- Add docs/ with SRS specs, user stories, and workflow documentation
- Update .gitignore for new directory layout

Workflows implemented:
  WF1 User Signup, WF2 Placement Test, WF3 Exam Configuration,
  WF4 General English Exam, WF5 Course Generation,
  WF6 Entity Student Onboarding, AI Course Generation,
  Adaptive Learning Engine UI, White-Label Branding, Score Release

Made-with: Cursor
 2026-04-10 17:26:42 +04:00