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
This commit is contained in:
Yamen Ahmad
@@ -1,6 +1,6 @@
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"""Course plan generation pipeline.
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Two public entry points:
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Three public entry points:
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* :py:meth:`generate_plan` — given a short brief (course title, CEFR level,
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duration, skill coverage, grammar focus, resources), produce a full
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@@ -14,11 +14,22 @@ Two public entry points:
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+ practice, writing prompt, vocabulary list) and persist each as an
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:py:class:`encoach.course.plan.material` row.
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* :py:meth:`generate_deliverables_from_outline` — NEW: Parse a course
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outline (like GE1 PDF) and extract structured deliverables (learning
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outcomes) that the AI uses to generate targeted materials.
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* :py:meth:`generate_rich_media` — NEW: Generate images, audio, or video
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to accompany teaching materials.
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We deliberately ask the LLM to return strict JSON and then normalise it
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server-side — the frontend gets a stable shape no matter how loose the
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model's output is. Any parse failure is swallowed and reported back
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through the standard error channel so the caller can retry without the
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server crashing.
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Resource-aware generation: Before generating materials, the pipeline
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fetches registered resources (textbooks, videos) and includes them in the
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prompt so the AI can reference real content instead of hallucinating.
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"""
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import json
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@@ -188,6 +199,12 @@ class CoursePlanPipeline:
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# Decide once per instance whether to route through the LangGraph
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# AgentRuntime or fall back to the direct chat_json path.
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self._use_agent = self._resolve_agent_flag(env)
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# Import agent tools for resource-aware generation
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try:
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from odoo.addons.encoach_ai.services.agent_tools import invoke as agent_invoke
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self._agent_invoke = agent_invoke
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except ImportError:
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self._agent_invoke = None
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@staticmethod
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def _resolve_agent_flag(env):
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@@ -494,3 +511,291 @@ class CoursePlanPipeline:
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elif isinstance(value, dict):
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lines.append(f"{key}: " + json.dumps(value, ensure_ascii=False))
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return "\n".join(lines)
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# ------------------------------------------------------------------
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# NEW: Deliverable detection from course outlines (GE1-style)
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# ------------------------------------------------------------------
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def generate_deliverables_from_outline(self, plan_id, course_outline_text):
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"""Parse a course outline (PDF/text) and extract structured deliverables.
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Creates :py:class:`encoach.course.plan.deliverable` rows that
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the AI uses when generating week materials.
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"""
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plan = self.env['encoach.course.plan'].sudo().browse(int(plan_id))
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if not plan.exists():
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raise ValueError('Plan not found')
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# Use agent tool to detect deliverables
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if self._agent_invoke:
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result = self._agent_invoke(
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self.env, "deliverables.detect",
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{
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"course_outline_text": course_outline_text,
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"cefr_level": plan.cefr_level,
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"total_weeks": plan.total_weeks,
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}
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)
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if result.get("ok"):
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# Create deliverable records
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Deliverable = self.env['encoach.course.plan.deliverable'].sudo()
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created = []
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for d in result.get("deliverables", []):
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try:
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rec = Deliverable.create({
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'plan_id': plan.id,
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'week_number': int(d.get('week_number', 1)),
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'code': d.get('code', ''),
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'skill': d.get('skill', 'integrated'),
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'description': d.get('description', ''),
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'cefr_level': d.get('cefr_level', plan.cefr_level),
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'resource_dependencies_json': json.dumps(d.get('resource_hints', []), ensure_ascii=False),
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'ai_generated': True,
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'generation_prompt': f"Extracted from course outline: {course_outline_text[:200]}...",
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})
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created.append(rec)
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except Exception as exc:
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_logger.warning("Failed to create deliverable: %s", exc)
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# Save resource dependencies too
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ResourceDep = self.env['encoach.course.plan.resource.dep'].sudo()
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for r in result.get("resources_needed", []):
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try:
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ResourceDep.create({
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'plan_id': plan.id,
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'name': r.get('title', 'Unknown Resource'),
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'resource_type': r.get('type', 'other'),
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'citation': r.get('citation', ''),
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'ai_usage_notes': r.get('purpose', ''),
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'is_required': True,
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})
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except Exception as exc:
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_logger.warning("Failed to save resource: %s", exc)
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return {
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'deliverables_created': len(created),
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'resources_needed': len(result.get('resources_needed', [])),
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}
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else:
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raise RuntimeError(result.get('error', 'Deliverable detection failed'))
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else:
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raise RuntimeError('Agent tools not available for deliverable detection')
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# ------------------------------------------------------------------
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# NEW: Resource-aware week material generation
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# ------------------------------------------------------------------
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def generate_week_materials_with_resources(self, plan_id, week_number):
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"""Generate materials using registered resource dependencies."""
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plan = self.env['encoach.course.plan'].sudo().browse(int(plan_id))
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if not plan.exists():
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raise ValueError('Plan not found')
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week = plan.week_ids.filtered(lambda w: w.week_number == int(week_number))
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if not week:
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raise ValueError(f'Week {week_number} not found')
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week = week[0]
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# Fetch available resources
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resources = []
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if self._agent_invoke:
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res_result = self._agent_invoke(
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self.env, "resources.fetch",
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{"plan_id": plan.id, "is_available": True}
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)
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if res_result.get("ok"):
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resources = res_result.get("resources", [])
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# Fetch deliverables for this week
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deliverables = []
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if self._agent_invoke:
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del_result = self._agent_invoke(
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self.env, "deliverables.fetch",
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{"plan_id": plan.id, "week_number": int(week_number)}
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)
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if del_result.get("ok"):
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deliverables = del_result.get("deliverables", [])
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# Build enhanced prompt with resources and deliverables
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resource_context = ""
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if resources:
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resource_context = "\n\nAvailable Resources:\n" + json.dumps(resources, indent=2, ensure_ascii=False)
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deliverable_context = ""
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if deliverables:
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deliverable_context = "\n\nTarget Deliverables (must be addressed in materials):\n" + json.dumps(deliverables, indent=2, ensure_ascii=False)
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# Call base generation with enhanced context
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outcomes = plan._loads(plan.outcomes_json, {})
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items = week._loads(week.items_json, [])
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system_msg = (
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"You are an expert English language teacher creating ready-to-use classroom materials. "
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"Your output MUST be valid JSON matching the schema. USE the available resources "
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"provided below. Address ALL deliverables listed."
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)
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user_msg = (
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f"Course: {plan.name}\n"
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f"CEFR: {(plan.cefr_level or '').upper()}\n"
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f"Week {week.week_number} — {week.date_label or ''}\n"
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f"Unit: {week.unit or ''}\n"
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f"Focus: {week.focus or ''}\n\n"
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f"Week items:\n{json.dumps(items, indent=2, ensure_ascii=False)}\n\n"
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f"Full outcome catalogue:\n{json.dumps(outcomes, indent=2, ensure_ascii=False)}"
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f"{resource_context}"
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f"{deliverable_context}\n\n"
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+ _WEEK_JSON_HINT
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)
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content = self._invoke_agent_or_chat(
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agent_key="course_week_materials",
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system_msg=system_msg,
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user_msg=user_msg,
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variables={
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"course": plan.name,
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"cefr_level": (plan.cefr_level or "").lower(),
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"week_number": week.week_number,
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},
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temperature=0.6,
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max_tokens=6000,
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action="course_plan.generate_week_with_resources",
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)
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if content is None or 'error' in content:
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raise RuntimeError(
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(content or {}).get('error', 'AI generation failed.')
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)
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# Wipe previous materials
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existing = self.env['encoach.course.plan.material'].sudo().search([
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('plan_id', '=', plan.id), ('week_id', '=', week.id),
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])
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if existing:
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existing.unlink()
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# Create new materials with resource tracking
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Material = self.env['encoach.course.plan.material'].sudo()
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created = []
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for m in content.get('materials') or []:
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try:
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rec = Material.create({
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'plan_id': plan.id,
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'week_id': week.id,
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'skill': (m.get('skill') or 'integrated').strip().lower(),
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'material_type': (m.get('material_type') or 'other').strip(),
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'title': (m.get('title') or '').strip() or 'Untitled',
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'summary': (m.get('summary') or '').strip(),
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'body_json': json.dumps(m.get('body') or {}, ensure_ascii=False),
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'body_text': self._flatten_body(m.get('body') or {}),
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'required_resources_json': json.dumps(m.get('resources_used', []), ensure_ascii=False),
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'ai_generation_notes': m.get('generation_notes', ''),
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})
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created.append(rec)
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except Exception as exc:
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_logger.warning("Skipping bad material row: %s", exc)
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return created
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# ------------------------------------------------------------------
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# NEW: Rich media generation for materials
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# ------------------------------------------------------------------
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def suggest_media_for_material(self, material_id):
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"""Suggest what media (images, audio) would enhance a material."""
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material = self.env['encoach.course.plan.material'].sudo().browse(int(material_id))
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if not material.exists():
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raise ValueError('Material not found')
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if not self._agent_invoke:
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raise RuntimeError('Agent tools not available')
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# Get visual suggestions
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result = self._agent_invoke(
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self.env, "media.suggest_visuals",
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{
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"content_description": material.body_text or material.summary or '',
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"material_type": material.material_type,
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"target_audience": f"{material.plan_id.cefr_level} level learners",
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}
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)
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if result.get("ok"):
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return {
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'suggestions': result.get('visuals', []),
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'material_id': material_id,
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}
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return {'error': result.get('error', 'Could not generate suggestions')}
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def generate_media_for_material(self, material_id, media_type='image'):
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"""Generate actual media (image/audio) for a material."""
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material = self.env['encoach.course.plan.material'].sudo().browse(int(material_id))
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if not material.exists():
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raise ValueError('Material not found')
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if not self._agent_invoke:
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raise RuntimeError('Agent tools not available')
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if media_type == 'image':
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# Get suggestions first
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suggestions = self.suggest_media_for_material(material_id)
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if 'error' in suggestions:
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return suggestions
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# Use first suggestion's prompt
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visuals = suggestions.get('suggestions', [])
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if not visuals:
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return {'error': 'No visual suggestions available'}
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prompt = visuals[0].get('prompt_for_ai', visuals[0].get('description', ''))
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result = self._agent_invoke(
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self.env, "media.generate_image",
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{
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"prompt": prompt,
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"material_id": material_id,
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"style": visuals[0].get('complexity', 'educational'),
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}
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)
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if result.get("ok"):
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# Update material with media info
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material.write({
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'media_type': 'image',
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'media_generation_prompt': result.get('prompt_used', prompt),
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'media_asset_url': result.get('note', ''), # Would be actual URL after generation
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})
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return {
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'media_type': 'image',
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'prompt': prompt,
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'suggestion': visuals[0],
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'material_id': material_id,
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}
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return {'error': result.get('error', 'Image generation failed')}
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elif media_type == 'audio':
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# For listening scripts, generate audio
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body = material._loads(material.body_json, {})
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text = body.get('script', body.get('text', ''))
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if not text:
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return {'error': 'No text available for audio generation'}
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result = self._agent_invoke(
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self.env, "media.generate_audio",
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{
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"text": text[:3000], # Limit length
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"material_id": material_id,
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"purpose": "listening_exercise" if material.material_type == 'listening_script' else "pronunciation_guide",
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}
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)
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if result.get("ok"):
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material.write({
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'media_type': 'audio',
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'media_generation_prompt': f"Audio for: {text[:200]}...",
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})
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return {
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'media_type': 'audio',
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'service': result.get('service'),
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'material_id': material_id,
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}
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return {'error': result.get('error', 'Audio generation failed')}
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return {'error': f'Unsupported media type: {media_type}'}
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