Yamen Ahmad
3b62075d7e
feat(platform): course-plan student visibility, multi-voice TTS, OCR sources, branches
...
Backend (encoach_ai_course):
- workbook_attempt model + scoring + REST endpoints for student attempts
- dialogue_parser splits scripts by speaker, classifies gender, strips labels
- media_service: multi-voice TTS via Polly/ElevenLabs, ffmpeg concatenation,
manual media upload endpoint (audio/image/video) with size validation
- source_indexer: OCR fallback (pytesseract + pdf2image) for scanned PDFs,
page-streaming to stay under memory limit
- exercise_extractor + rag_context for RAG-grounded interactive workbooks
- course_plan_pipeline: v2 generator that grounds week material on indexed
sources and persists grounded_on_json metadata
- security: access rules for new models
Backend (encoach_lms_api):
- branches model + controller (entity-scoped LMS branches)
- classroom_ext + course_ext (assignment + section workflow)
- classrooms controller: students/teachers/assign-course endpoints
Frontend:
- StudentDashboard: surface assigned AI course plans alongside enrollments;
enrolled-courses stat now counts plans+enrollments
- InteractiveWorkbook + PlanReader components
- AdminCoursePlanDetail: media drawer with upload buttons (audio/image/video),
hidden file inputs, upload mutation
- AdminBranches page + sidebar entry
- coursePlan/lms/classrooms services + types updated for new endpoints
- i18n: studentDash.myCoursePlans/noCoursePlans (en + ar)
Infra & docs:
- odoo.conf: bump memory limits to 4G/5G for OCR + sentence-transformers
- .gitignore: ignore *.tsbuildinfo
- docs/ASSIGNMENT_WORKFLOW.{md,pdf}
- smoke_*.py end-to-end tests for assignment workflow, entity isolation,
course-plan RAG pipeline
Made-with: Cursor
2026-04-28 00:17:51 +04:00
Yamen Ahmad
fa6f4976c3
chore(ops): add scripts/backup-db.sh — Odoo-format DB backup
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Produces an Odoo-compatible <db>.zip restorable via /web/database/manager
on the live VPS (or curl -F backup_file=@... /web/database/restore).
Why this script instead of POST /web/database/backup:
the Odoo HTTP backup endpoint shells out to pg_dump from $PATH and on
this host that returns "Command pg_dump not found" because pg_dump
lives in the conda env that Odoo wasn't launched from. This script
calls pg_dump directly with PGBIN, copies the filestore, builds a
correct manifest.json (odoo_dump, db_name, version, pg_version,
modules), and zips the three artefacts in the exact layout Odoo's
restore code expects.
Layout matches Odoo's own backup output:
<db>.zip
|- dump.sql (pg_dump --no-owner --format=p)
|- manifest.json (odoo_dump=1, version, pg_version, installed modules)
|- filestore/ (data_dir/filestore/<db>/ contents)
Defaults are encoach_v2 / yamenahmad / /tmp/odoo-data, all overridable
via env vars (DB, DB_USER, DATA_DIR, PGBIN, OUT_DIR). Output filename
is encoach_v2_YYYYMMDD_HHMMSS.zip in ./backups/.
Verified: latest run produced encoach_v2_20260425_031949.zip — 30 MB
compressed, 815 entries, 633 tables x 633 COPY data blocks, 575
filestore files, 94 modules in manifest.
Made-with: Cursor
2026-04-25 03:20:41 +04:00
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
2026-04-25 03:14:22 +04:00
