0ed7f88cab258766b2cd275603cebec170586176
7 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Yamen Ahmad
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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 |
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Yamen Ahmad
|
e2aa8031ff |
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 |
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Yamen Ahmad
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e1f059069f |
feat(i18n,rtl): full Arabic localization + RTL sweep across all layouts
Frontend - i18n: install tailwindcss-rtl, Cairo font, RTL-aware direction in index.css. - Language toggle: localize aria-label / menu label, persist choice, update document dir synchronously. - Sidebar: add `side` prop so the drawer pins to the right in RTL; wire up AdminLmsLayout, RoleLayout (student/teacher) and AppSidebar to pass side = i18n.dir() === 'rtl' ? 'right' : 'left'. - AdminLmsLayout: convert every nav item from hard-coded title to titleKey, translate group labels (incl. the collapsible Training), breadcrumbs, user menu (Profile / Settings / Logout), help button and toggle aria labels; replace physical mr-/right- utilities with logical me-/end-. - AI components (AiTipBanner, AiInsightsPanel, AiAlertBanner, AiSearchBar, AiAssistantDrawer): apply dir="auto" at the container level, localize titles, loading / error / empty states. - Dashboards (admin / student / teacher): wrap numeric values in <bdi>, localize dates via ar-EG, fix flex direction for KPI and assignment cards. - UI primitives (breadcrumb, calendar, carousel, dropdown-menu, menubar, context-menu, pagination, sidebar): flip chevrons in RTL via a scoped CSS rule, swap pl-/pr-/ml-/mr- for ps-/pe-/ms-/me-. - Add logical-direction helpers and bidirectional isolation classes. Locales - Expand en.ts and ar.ts with full `nav`, `sidebarGroup`, `breadcrumb`, `userMenu`, `chrome`, `ai`, and dashboard key sets; keep key parity. API client - `api-client.ts` reads the active language from localStorage/i18n and sends `Accept-Language` on every request so the backend can localize AI output. Backend (encoach_ai) - openai_service: add _LANGUAGE_NAMES, normalize_language, language-aware system prompt injection for every OpenAI call. - coach_service + controllers (coach_controller, ai_controller): thread the requested language from headers / user locale down to OpenAIService. - ai_feedback: fix latent registry error by pointing course_id at op.course instead of the non-existent encoach.course. Other - .gitignore: ignore runtime odoo logs and local caches. Made-with: Cursor |
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Yamen Ahmad
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dcf5ea6941 |
feat(backend): Phase 2/3 hardening release
Roadmap P0 — platform safety & ops
- Merge duplicate encoach.student.attempt/answer models into encoach_scoring
and drop the stale encoach_exam_template copies.
- Remove duplicate /api/exam/* routes; canonicalize on one controller tree.
- Gate raw-SQL seeds in seed_demo_data.py behind an explicit env flag.
- Add /api/health and /api/health/ready (DB + LLM reachability) endpoints.
- Fix docker-compose + ship odoo-docker.conf for container-local runs.
- Enforce OpenAI request_timeout=30s and @jwt_required on all AI/coach routes.
- Promote canonical cefr_mapper to encoach_ai.services.cefr_mapper.
- JWT cache TTL=30s + invalidation hook on user mutation.
Roadmap P1 — exam correctness & data provenance
- Wire QualityChecker + IeltsValidator into exam submit with a
pending_review gate (encoach_ai.services.question_validator).
- Populate RAG metadata (course_id, subject_id, entity_id, taxonomy) on
encoach_vector embeddings and add a chunking pipeline (>2000 chars).
- Add provenance fields on encoach.question (model, prompt_hash, log_id)
and validate LLM output with schema before DB insert.
- Unify response envelope to {items,total,page,size}.
- Approval reject rollback with savepoint atomicity.
- Ticket notifications on status/assignee change.
Roadmap P2 — performance & observability
- Reports: replace Python loops with SQL read_group aggregations.
- X-Request-ID middleware + structured JSON logs.
- In-process/Prometheus counters and openapi.py controller exporting a
spec by scanning @http.route decorators.
- Paymob real checkout + HMAC-SHA512 webhook verification, backed by a
new encoach.paymob.order model and ir.config_parameter credentials.
- JWT refresh tokens + revocation table.
- Composite DB indexes on hot report/ticket/attempt paths.
Roadmap P3 — human-in-the-loop & compliance
- Human-in-the-loop exam review workflow (pending_review → publish) with
new review controller and status transitions.
- encoach.ai.prompt model + versioning + admin editor endpoints (one
active version per key, render-preview dry run).
- Student feedback loop → encoach.ai.feedback (upsert per user/subject,
admin triage + resolve endpoints).
- GDPR export (/api/gdpr/export) and right-to-erasure (/api/gdpr/delete)
with anonymization, tombstone record, and admin-self-erasure guard.
- HttpCase smoke tests for /api/health and /api/health/ready.
Made-with: Cursor
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Yamen Ahmad
|
50f58dc995 |
feat: complete exam lifecycle — AI generation, submission, student session, and results
- Backend: AI generation fallbacks when OpenAI not configured, full exam submission saving all params (difficulty, rubric, entity, grading system, approval workflow) and creating linked question records per section - Backend: new exam session controller with get_session, autosave, submit, status, and results endpoints; student attempt/answer/score models - Backend: new controllers for entities, approval workflows, exam schedules - Frontend: exam session split-layout with passage panel, question types (MCQ, T/F/NG, gap-fill, writing, speaking), timer, and review dialog - Frontend: results page with percentage score, per-answer breakdown table - Frontend: generation page dynamic dropdowns, full payload submission - Frontend: updated types for ExamSessionSection, ExamQuestion options Made-with: Cursor |
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Yamen Ahmad
|
82ec3debcc |
feat: QA fixes, new APIs (assignments, rubrics, custom exams), Generation page enhancements
- Fix ELAI video generation (correct render endpoint, script splitting for 60s limit) - Fix speaking script generation error handling and empty response display - Add custom exam list API (GET /api/exam/custom/list) - Add assignments REST API (list, create, get) - Add rubrics REST API (list, create) - Enhance Generation page: dynamic exam structures, auto-module selection, preview dialog, audio player - Improve submit feedback with exam ID and status in toast notifications - Fix ExamsListPage to show both custom exams and exam sessions - Connect RubricsPage to backend API with fallback data - Add Dockerfile, docker-compose.yml, requirements.txt for deployment - Fix placement, grading, scoring, and auth controllers - Add ErrorBoundary component for frontend resilience - Add QA report and credentials documentation Made-with: Cursor |
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Yamen Ahmad
|
b02ee8b6b7 |
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 |