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
54 lines
1.5 KiB
TypeScript
54 lines
1.5 KiB
TypeScript
import { api } from "@/lib/api-client";
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import type {
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AIAgent,
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AIAgentSummary,
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AIAgentTestRequest,
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AIAgentTestResponse,
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AIAgentUpdateInput,
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AIToolSummary,
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} from "@/types/aiAgent";
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/**
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* REST helpers for the AI Agent configurator.
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*
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* Backend: `backend/custom_addons/encoach_ai/controllers/agents_controller.py`.
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* Mount point: `/api/ai/agents`.
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*/
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export const aiAgentService = {
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async list(params?: { search?: string }): Promise<AIAgentSummary[]> {
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const res = await api.get<{ items?: AIAgentSummary[]; data?: AIAgentSummary[] }>(
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"/ai/agents",
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params,
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);
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return res.items ?? res.data ?? [];
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},
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async get(id: number): Promise<AIAgent> {
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return api.get<AIAgent>(`/ai/agents/${id}`);
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},
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async update(id: number, input: AIAgentUpdateInput): Promise<AIAgent> {
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return api.patch<AIAgent>(`/ai/agents/${id}`, input);
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},
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async test(id: number, body: AIAgentTestRequest): Promise<AIAgentTestResponse> {
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return api.post<AIAgentTestResponse>(`/ai/agents/${id}/test`, body);
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},
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async listTools(): Promise<AIToolSummary[]> {
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const res = await api.get<{ items?: AIToolSummary[]; data?: AIToolSummary[] }>(
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"/ai/agents/tools",
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);
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return res.items ?? res.data ?? [];
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},
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async updateTool(
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id: number,
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input: Partial<Pick<AIToolSummary, "active" | "name" | "description">> & {
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schema?: Record<string, unknown>;
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},
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): Promise<AIToolSummary> {
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return api.patch<AIToolSummary>(`/ai/agents/tools/${id}`, input);
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},
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};
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