1dd1168fee441cef7605003450a19e9d8b095d27
3 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Yamen Ahmad
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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 |
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Yamen Ahmad
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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 |
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Yamen Ahmad
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6a62a43d61 |
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 |