760 lines
No EOL
30 KiB
Python
760 lines
No EOL
30 KiB
Python
import json
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import logging
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import re
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from uuid import uuid4
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import httpx
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from channels.db import database_sync_to_async
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from channels.generic.websocket import AsyncWebsocketConsumer
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from django.conf import settings
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from django.db.models import Q
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from django.utils import timezone
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from apps.onboarding.mcp import MCPRouter
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from apps.onboarding.models import AgentConfig, OnboardingFlow, OnboardingSession
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logger = logging.getLogger(__name__)
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class OnboardingConsumer(AsyncWebsocketConsumer):
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async def connect(self):
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self.user = self.scope["user"]
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self.context_uuid = self.scope["url_route"]["kwargs"].get("session_uuid")
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if not self.user.is_authenticated:
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await self.close()
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return
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self.router = MCPRouter()
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await self.accept()
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async def disconnect(self, close_code):
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pass
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def _build_system_prompt(self, config):
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base_prompt = config.system_prompt or "You are a helpful onboarding assistant."
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permissions = config.tool_permissions or []
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if permissions:
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return f"{base_prompt}\n\nAllowed tools: {', '.join(str(p) for p in permissions)}"
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return base_prompt
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async def receive(self, text_data):
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try:
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data = json.loads(text_data)
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action = data.get("action")
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if action == "start_full_onboarding":
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role_uuid = data.get("role_uuid")
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if not role_uuid:
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await self.send_log("error", "Missing role_uuid for full onboarding generation")
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return
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if not await self.can_manage_role(role_uuid, self.user.id):
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await self.send_log("error", "Forbidden")
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return
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await self.run_full_onboarding_generation(role_uuid)
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elif action == "progress_monitor":
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role_uuid = data.get("role_uuid") or self.context_uuid
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target_user_uuid = data.get("user_uuid")
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flow_uuid = data.get("flow_uuid")
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if not role_uuid:
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await self.send_log("error", "Missing role_uuid for progress monitoring")
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return
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if not await self.can_access_role(role_uuid, self.user.id):
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await self.send_log("error", "Forbidden")
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return
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target_user_id = self.user.id
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if target_user_uuid and str(target_user_uuid) != str(self.user.uuid):
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target_user_id = await self.resolve_target_user_id(role_uuid, self.user.id, target_user_uuid)
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if not target_user_id:
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await self.send_log("error", "Forbidden")
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return
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await self.run_progress_monitor(role_uuid, target_user_id=target_user_id, flow_uuid=flow_uuid)
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else:
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user_message = data.get("query") or data.get("message")
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requested_max_tokens = data.get("max_tokens")
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if not user_message:
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await self.send_log("error", "Missing query/message payload")
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return
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config = await self.get_config_for_user(self.context_uuid, self.user.id)
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if config is None:
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await self.send_log("error", "Forbidden")
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return
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ai_response = await self.orchestrate_ai(
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user_message,
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config,
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max_tokens=requested_max_tokens,
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)
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await self.send(json.dumps({
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"type": "completed",
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"timestamp": timezone.now().isoformat(),
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"message": "Inference complete.",
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"content": {
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"response": ai_response,
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}
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}))
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except Exception as e:
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logger.error(f"WebSocket Receive Error: {str(e)}")
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await self.send_log("error", f"Consumer encountered an error: {str(e)}")
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async def run_full_onboarding_generation(self, role_uuid):
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"""
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The Master Script that builds the JSON structure sequentially.
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Pipeline: Curriculum Agent -> Knowledge Agent -> Assessment Agent
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"""
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await self.send_log("status", "Phase 1: Generating Curriculum...", "curriculum")
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ca_config = await self.get_config_by_type(role_uuid, 'curriculum')
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if not ca_config:
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await self.send_log("error", "Missing curriculum AgentConfig for this role")
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return
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ca_prompt = (
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"Based on available documentation, create an onboarding curriculum for this role. "
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"Output ONLY a valid JSON array of 3-5 strings representing module titles. "
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"Example: [\"Introduction\", \"Safety\", \"Operations\"]"
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)
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ca_response = await self.orchestrate_ai(
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ca_prompt,
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ca_config,
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min_internal_turns=1,
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max_tokens=384,
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)
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topics = self._extract_json_list(ca_response)
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if not topics:
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await self.send_log("error", "Curriculum generation returned no topics")
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return
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full_structure = []
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module_briefs = []
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for index, topic in enumerate(topics):
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await self.send_log("status", f"Phase 2: Researching {topic}...", "knowledge")
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ka_config = await self.get_config_by_type(role_uuid, 'knowledge')
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if not ka_config:
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await self.send_log("error", "Missing knowledge AgentConfig for this role")
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return
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knowledge_hits = await self.fetch_knowledge_context(role_uuid, topic)
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context_markdown = self.format_knowledge_context(knowledge_hits)
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page_content = await self.orchestrate_ai(
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(
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f"Write a practical onboarding training guide for the topic '{topic}'. "
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"Think step-by-step internally before writing the final answer. "
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"Use the MCP search context below as your primary source, and call additional tools if needed. "
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"If no indexed documents are available, provide a concise best-practice overview and clearly say no indexed documents were found. "
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"Use Markdown formatting and do NOT include a table of contents in this section. "
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"Generate substantial depth: target 900-1400 words. "
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"Include these sections in order: Overview, Core Concepts, Role-Specific Workflow, Practical Examples, Common Pitfalls, and Action Checklist. "
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"In Practical Examples, provide at least 2 concrete examples relevant to this role/topic. "
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"In Action Checklist, provide at least 8 actionable checklist items.\n\n"
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f"Role UUID: {role_uuid}\n"
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f"Topic: {topic}\n"
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f"MCP search context:\n{context_markdown}"
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),
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ka_config,
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min_internal_turns=2,
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max_tokens=2400,
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)
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full_structure.append({
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"title": topic,
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"body": page_content,
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"order": index,
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"fields": [],
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"meta": {
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"topic_index": index,
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"table_of_contents": [str(item) for item in topics],
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},
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})
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module_briefs.append({
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"topic": str(topic),
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"summary_excerpt": str(page_content)[:1200],
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})
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await self.send_log("status", "Phase 3: Creating final assessment quiz...", "assessment")
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aa_config = await self.get_config_by_type(role_uuid, 'assessment')
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if not aa_config:
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await self.send_log("error", "Missing assessment AgentConfig for this role")
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return
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quiz_prompt = (
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"Create a final onboarding quiz that assesses all generated modules. "
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"Output ONLY a valid JSON array of 8 multiple-choice question objects. "
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"Each object MUST include: 'key' (snake_case), 'label', 'field_type' ('select'), "
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"'options' (array of 4 unique strings), 'required' (true), and 'validation' with "
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"'correct_option' (exactly matching one option) and 'explanation' (short rationale). "
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"Cover all topics with balanced difficulty and avoid ambiguous choices.\n\n"
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f"Modules JSON:\n{json.dumps(module_briefs, ensure_ascii=False)}"
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)
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quiz_response = await self.orchestrate_ai(
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quiz_prompt,
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aa_config,
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min_internal_turns=1,
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max_tokens=1600,
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)
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quiz_fields = self._sanitize_quiz_fields(self._extract_json_list(quiz_response))
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if not quiz_fields:
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await self.send_log("status", "Assessment output invalid, retrying quiz generation...", "assessment")
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retry_response = await self.orchestrate_ai(
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f"{quiz_prompt}\n\nReturn ONLY raw JSON. Do not use markdown fences. Do not include explanations outside JSON.",
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aa_config,
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min_internal_turns=1,
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max_tokens=1600,
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)
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quiz_fields = self._sanitize_quiz_fields(self._extract_json_list(retry_response))
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if not quiz_fields:
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await self.send_log("status", "Assessment output still invalid. Using fallback final quiz.", "assessment")
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quiz_fields = self._build_fallback_quiz_fields([str(topic) for topic in topics])
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full_structure.append({
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"title": "Final Assessment Quiz",
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"body": (
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"### Final Quiz\n"
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"Answer all questions below. You need **80%** to complete onboarding. "
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"You can update answers and submit when ready."
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),
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"order": len(full_structure),
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"fields": quiz_fields,
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"meta": {
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"page_type": "final_quiz",
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"pass_mark": 80,
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},
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})
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await self.save_full_flow(role_uuid, full_structure)
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await self.send(json.dumps({
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"type": "completed",
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"timestamp": timezone.now().isoformat(),
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"message": "Onboarding pipeline complete and structure saved."
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}))
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async def run_progress_monitor(self, role_uuid, target_user_id=None, flow_uuid=None):
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await self.send_log("status", "Progress Monitor is analyzing your onboarding progress...", "monitor")
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monitor_config = await self.get_config_by_type(role_uuid, 'monitor')
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if not monitor_config:
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await self.send_log("error", "Missing Progress Monitor AgentConfig for this role")
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return
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progress_context = await self.get_role_progress_context(
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role_uuid,
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target_user_id or self.user.id,
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flow_uuid=flow_uuid,
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)
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monitor_prompt = (
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"You are a progress monitoring agent for onboarding. "
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"Analyze the role onboarding data below and provide concise feedback with:\n"
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"1) current status\n2) strengths\n3) gaps\n4) next actions\n"
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"Keep it short and practical.\n\n"
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f"Progress context JSON:\n{json.dumps(progress_context)}"
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)
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try:
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feedback = await self.orchestrate_ai(
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monitor_prompt,
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monitor_config,
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min_internal_turns=1,
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max_tokens=640,
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raise_on_error=True,
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)
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except Exception as exc:
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await self.send_log("error", f"Inference failed: {str(exc)}")
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return
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if str(feedback).startswith("Error:"):
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await self.send_log("error", str(feedback))
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return
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await self.send(json.dumps({
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"type": "completed",
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"timestamp": timezone.now().isoformat(),
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"message": "Progress analysis complete.",
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"content": {
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"role_uuid": role_uuid,
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"feedback": feedback,
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"status": progress_context.get("latest_status", "unknown"),
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"user_id": target_user_id or self.user.id,
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"flow_uuid": flow_uuid,
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"is_completed": progress_context.get("is_completed", False),
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}
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}))
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async def orchestrate_ai(
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self,
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user_message,
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config,
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min_internal_turns=2,
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max_turns=6,
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max_tokens=None,
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raise_on_error=False,
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):
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"""
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Handles the multi-turn ReAct loop (Reasoning + Tool Use).
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"""
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messages = [
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{"role": "system", "content": self._build_system_prompt(config)},
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{"role": "user", "content": user_message}
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]
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llm_config = config.llm_config if isinstance(config.llm_config, dict) else {}
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resolved_max_tokens = max_tokens
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if resolved_max_tokens is None:
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resolved_max_tokens = llm_config.get("max_tokens", 1024)
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try:
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resolved_max_tokens = max(64, int(resolved_max_tokens))
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except Exception:
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resolved_max_tokens = 1024
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last_content = ""
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min_internal_turns = max(1, int(min_internal_turns or 1))
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max_turns = max(min_internal_turns, int(max_turns or 1))
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async with httpx.AsyncClient(timeout=60.0) as client:
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for turn in range(max_turns):
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await self.send_log("thought", f"Agent is thinking (Turn {turn+1})...")
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try:
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response = await client.post(
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settings.INFERENCE_CHAT_COMPLETIONS_ENDPOINT,
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json={
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"model": llm_config.get("model_id", "meta-llama-3.1-8b"),
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"messages": messages,
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"tools": self.router.get_tool_definitions(),
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"tool_choice": "auto",
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"max_tokens": resolved_max_tokens,
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}
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)
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response.raise_for_status()
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res_json = response.json()
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ai_message = res_json["choices"][0]["message"]
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messages.append(ai_message)
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if ai_message.get("tool_calls"):
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for tool_call in ai_message["tool_calls"]:
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fn_name = tool_call["function"]["name"]
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fn_args = json.loads(tool_call["function"]["arguments"])
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await self.send(json.dumps({
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"type": "tool_start",
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"message": f"Accessing knowledge base: {fn_name}...",
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"content": fn_args
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}))
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result = await self.router.handle_tool_call(fn_name, fn_args)
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messages.append({
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"role": "tool",
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"tool_call_id": tool_call["id"],
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"name": fn_name,
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"content": json.dumps(result)
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})
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continue
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else:
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last_content = str(ai_message.get("content") or "").strip()
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if (turn + 1) < min_internal_turns:
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messages.append({
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"role": "user",
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"content": (
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"Run one more internal reasoning pass before finalizing. "
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"If additional evidence is needed, call tools. "
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"Then return only the improved final answer."
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),
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})
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continue
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return last_content
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except Exception as e:
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await self.send_log("error", f"Inference failed: {str(e)}")
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if raise_on_error:
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raise
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return f"Error: {str(e)}"
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return last_content
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async def fetch_knowledge_context(self, role_uuid, topic):
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query = f"onboarding training content for {topic}"
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await self.send(json.dumps({
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"type": "tool_start",
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"message": "Accessing knowledge base: search_knowledge...",
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"content": {"query": query, "role_uuid": role_uuid}
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}))
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try:
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result = await self.router.handle_tool_call(
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"search_knowledge",
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{
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"query": query,
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"role_uuid": role_uuid,
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},
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)
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await self.send(json.dumps({
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"type": "tool_result",
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"message": f"Retrieved {len(result) if isinstance(result, list) else 0} knowledge chunk(s)",
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"content": result,
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"timestamp": timezone.now().isoformat(),
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}))
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return result if isinstance(result, list) else []
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except Exception as exc:
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await self.send_log("error", f"Knowledge retrieval failed for topic '{topic}': {str(exc)}")
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return []
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def format_knowledge_context(self, knowledge_hits):
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if not knowledge_hits:
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return "No indexed MCP documents found for this role/topic."
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lines = []
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for idx, item in enumerate(knowledge_hits[:5]):
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source = item.get("source", "Unknown Source") if isinstance(item, dict) else "Unknown Source"
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relevance = item.get("relevance") if isinstance(item, dict) else None
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content = item.get("content", "") if isinstance(item, dict) else ""
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safe_content = str(content).strip()[:1600]
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lines.append(
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f"[{idx + 1}] Source: {source} | Relevance: {relevance}\n{safe_content}"
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)
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return "\n\n".join(lines)
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def _coerce_list_payload(self, payload):
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if isinstance(payload, list):
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return payload
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if isinstance(payload, dict):
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for key in ('questions', 'items', 'fields', 'quiz'):
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value = payload.get(key)
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if isinstance(value, list):
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return value
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return []
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def _extract_json_list(self, text):
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"""Extracts a JSON list from model output, tolerating wrappers and markdown fences."""
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if not text:
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return []
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candidate_texts = [str(text).strip()]
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for block in re.findall(r'```(?:json)?\s*([\s\S]*?)```', str(text), re.IGNORECASE):
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candidate_texts.append(block.strip())
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decoder = json.JSONDecoder()
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for candidate in candidate_texts:
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if not candidate:
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continue
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try:
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parsed = json.loads(candidate)
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coerced = self._coerce_list_payload(parsed)
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if coerced:
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return coerced
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except Exception:
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pass
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for idx, char in enumerate(candidate):
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if char not in '[{':
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continue
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try:
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parsed, _ = decoder.raw_decode(candidate[idx:])
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except Exception:
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continue
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coerced = self._coerce_list_payload(parsed)
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if coerced:
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return coerced
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return []
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def _sanitize_quiz_fields(self, raw_fields):
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sanitized = []
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seen_keys = set()
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for index, field in enumerate(raw_fields or []):
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if not isinstance(field, dict):
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continue
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key = str(field.get('key') or f'final_quiz_q_{index + 1}').strip().lower().replace(' ', '_')
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if not key:
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key = f'final_quiz_q_{index + 1}'
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if key in seen_keys:
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key = f'{key}_{index + 1}'
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seen_keys.add(key)
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label = str(field.get('label') or '').strip()
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if not label:
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continue
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|
|
raw_options = field.get('options') if isinstance(field.get('options'), list) else []
|
|
options = []
|
|
for option in raw_options:
|
|
option_text = str(option).strip()
|
|
if option_text and option_text not in options:
|
|
options.append(option_text)
|
|
|
|
if len(options) < 2:
|
|
continue
|
|
|
|
validation = field.get('validation') if isinstance(field.get('validation'), dict) else {}
|
|
correct_option = str(validation.get('correct_option') or '').strip()
|
|
if correct_option not in options:
|
|
correct_option = options[0]
|
|
|
|
sanitized.append({
|
|
'key': key,
|
|
'label': label,
|
|
'field_type': 'select',
|
|
'options': options[:5],
|
|
'required': True,
|
|
'validation': {
|
|
'correct_option': correct_option,
|
|
'explanation': str(validation.get('explanation') or ''),
|
|
},
|
|
})
|
|
|
|
return sanitized
|
|
|
|
def _build_fallback_quiz_fields(self, topics):
|
|
safe_topics = [str(topic).strip() for topic in (topics or []) if str(topic).strip()]
|
|
if not safe_topics:
|
|
safe_topics = ['onboarding fundamentals']
|
|
|
|
fallback_fields = []
|
|
for index in range(8):
|
|
topic = safe_topics[index % len(safe_topics)]
|
|
key = f'final_quiz_q_{index + 1}'
|
|
correct = f"Use documented best practices for {topic}."
|
|
options = [
|
|
correct,
|
|
f"Skip review steps for {topic} to move faster.",
|
|
f"Rely only on assumptions instead of evidence for {topic}.",
|
|
f"Ignore quality and compliance checks in {topic} tasks.",
|
|
]
|
|
fallback_fields.append({
|
|
'key': key,
|
|
'label': f"Which approach is most appropriate when working on {topic}?",
|
|
'field_type': 'select',
|
|
'options': options,
|
|
'required': True,
|
|
'validation': {
|
|
'correct_option': correct,
|
|
'explanation': f"{correct} balances reliability, quality, and role expectations.",
|
|
},
|
|
})
|
|
|
|
return fallback_fields
|
|
|
|
def _normalize_structure(self, structure):
|
|
normalized_pages = []
|
|
for index, page in enumerate(structure or []):
|
|
fields = []
|
|
for field_index, field in enumerate(page.get('fields', []) if isinstance(page, dict) else []):
|
|
if not isinstance(field, dict):
|
|
continue
|
|
key = str(field.get('key') or f'field_{field_index + 1}')
|
|
raw_options = field.get('options') if isinstance(field.get('options'), list) else []
|
|
options = [str(option) for option in raw_options if str(option).strip()]
|
|
|
|
validation = field.get('validation') if isinstance(field.get('validation'), dict) else {}
|
|
correct_option = validation.get('correct_option')
|
|
if correct_option is not None:
|
|
correct_option = str(correct_option)
|
|
|
|
normalized_validation = {
|
|
'correct_option': correct_option if correct_option in options else None,
|
|
'explanation': str(validation.get('explanation') or ''),
|
|
}
|
|
|
|
fields.append({
|
|
'uuid': str(uuid4()),
|
|
'key': key,
|
|
'label': str(field.get('label') or key.replace('_', ' ').title()),
|
|
'field_type': str(field.get('field_type') or 'text'),
|
|
'required': bool(field.get('required', False)),
|
|
'options': options,
|
|
'default_value': field.get('default_value', ''),
|
|
'validation': normalized_validation,
|
|
})
|
|
|
|
page_title = page.get('title') if isinstance(page, dict) else None
|
|
page_body = page.get('body') if isinstance(page, dict) else ''
|
|
page_order = page.get('order') if isinstance(page, dict) else index
|
|
normalized_pages.append({
|
|
'uuid': str(uuid4()),
|
|
'title': str(page_title or f'Module {index + 1}'),
|
|
'body': str(page_body or ''),
|
|
'order': int(page_order if isinstance(page_order, int) else index),
|
|
'fields': fields,
|
|
'meta': page.get('meta') if isinstance(page.get('meta'), dict) else {},
|
|
})
|
|
return normalized_pages
|
|
|
|
@database_sync_to_async
|
|
def save_full_flow(self, role_uuid, structure):
|
|
"""Saves the final nested structure to the OnboardingFlow model."""
|
|
from apps.accounts.models import Role
|
|
role = Role.objects.get(uuid=role_uuid)
|
|
normalized_structure = self._normalize_structure(structure)
|
|
flow, _ = OnboardingFlow.objects.update_or_create(
|
|
role=role,
|
|
defaults={
|
|
'title': f"AI Onboarding: {role.name}",
|
|
'structure': normalized_structure,
|
|
'is_active': True
|
|
}
|
|
)
|
|
return flow
|
|
|
|
async def send_log(self, log_type, message, content=None):
|
|
await self.send(json.dumps({
|
|
"type": log_type,
|
|
"message": message,
|
|
"content": content,
|
|
"timestamp": timezone.now().isoformat()
|
|
}))
|
|
|
|
@database_sync_to_async
|
|
def get_config(self, config_uuid):
|
|
return AgentConfig.objects.get(uuid=config_uuid)
|
|
|
|
@database_sync_to_async
|
|
def get_config_for_user(self, config_uuid, user_id):
|
|
return AgentConfig.objects.filter(
|
|
uuid=config_uuid,
|
|
).filter(
|
|
Q(organization__owner__id=user_id) | Q(organization__members__id=user_id)
|
|
).first()
|
|
|
|
@database_sync_to_async
|
|
def can_access_role(self, role_uuid, user_id):
|
|
from apps.accounts.models import Role
|
|
|
|
role = Role.objects.filter(uuid=role_uuid).first()
|
|
if role is None:
|
|
return False
|
|
|
|
if role.organization.owner.id == user_id:
|
|
return True
|
|
|
|
return role.organization.members.filter(id=user_id).exists()
|
|
|
|
@database_sync_to_async
|
|
def can_manage_role(self, role_uuid, user_id):
|
|
from apps.accounts.models import Role, User
|
|
|
|
role = Role.objects.filter(uuid=role_uuid).first()
|
|
user = User.objects.filter(id=user_id).first()
|
|
if role is None or user is None:
|
|
return False
|
|
|
|
if role.organization.owner.id == user_id:
|
|
return True
|
|
|
|
return bool(user.is_manager) and role.organization.members.filter(id=user_id).exists()
|
|
|
|
@database_sync_to_async
|
|
def get_config_by_type(self, role_uuid, agent_type):
|
|
role_specific = AgentConfig.objects.filter(
|
|
role__uuid=role_uuid,
|
|
agent_type=agent_type,
|
|
).order_by('-updated_at').first()
|
|
|
|
if role_specific:
|
|
return role_specific
|
|
|
|
return AgentConfig.objects.filter(
|
|
organization__roles__uuid=role_uuid,
|
|
role__isnull=True,
|
|
agent_type=agent_type,
|
|
).order_by('-updated_at').first()
|
|
|
|
@database_sync_to_async
|
|
def get_role_progress_context(self, role_uuid, user_id, flow_uuid=None):
|
|
from apps.accounts.models import Role
|
|
|
|
role = Role.objects.get(uuid=role_uuid)
|
|
active_flow = OnboardingFlow.objects.filter(role=role, is_active=True).order_by('-updated_at').first()
|
|
scoped_flow = None
|
|
if flow_uuid:
|
|
scoped_flow = OnboardingFlow.objects.filter(role=role, uuid=flow_uuid).first()
|
|
|
|
sessions = OnboardingSession.objects.filter(user_id=user_id, role=role).order_by('-updated_at')
|
|
if flow_uuid:
|
|
sessions = sessions.filter(state__flow_uuid=str(flow_uuid))
|
|
|
|
latest_session = sessions.first()
|
|
|
|
if not latest_session:
|
|
return {
|
|
"role_uuid": str(role.uuid),
|
|
"role_name": role.name,
|
|
"latest_status": "not_started",
|
|
"session_count": 0,
|
|
"flow_exists": bool(scoped_flow or active_flow),
|
|
"flow_uuid": str((scoped_flow or active_flow).uuid) if (scoped_flow or active_flow) else None,
|
|
"progress": 0,
|
|
"responses_count": 0,
|
|
"completed_modules": [],
|
|
"is_completed": False,
|
|
}
|
|
|
|
state = latest_session.state or {}
|
|
responses = state.get("responses", {})
|
|
completed_modules = state.get("completed_modules", [])
|
|
progress = state.get("progress_percentage", state.get("progress", 0))
|
|
|
|
return {
|
|
"role_uuid": str(role.uuid),
|
|
"role_name": role.name,
|
|
"latest_status": latest_session.status,
|
|
"session_count": sessions.count(),
|
|
"flow_exists": bool(scoped_flow or active_flow),
|
|
"flow_uuid": str((scoped_flow or active_flow).uuid) if (scoped_flow or active_flow) else None,
|
|
"progress": progress,
|
|
"responses_count": len(responses) if isinstance(responses, dict) else 0,
|
|
"completed_modules": completed_modules if isinstance(completed_modules, list) else [],
|
|
"updated_at": latest_session.updated_at.isoformat() if latest_session.updated_at else None,
|
|
"is_completed": latest_session.status == 'completed',
|
|
}
|
|
|
|
@database_sync_to_async
|
|
def resolve_target_user_id(self, role_uuid, requester_id, target_user_uuid):
|
|
from apps.accounts.models import Role, User
|
|
|
|
role = Role.objects.filter(uuid=role_uuid).first()
|
|
requester = User.objects.filter(id=requester_id).first()
|
|
target = User.objects.filter(uuid=target_user_uuid).first()
|
|
if role is None or requester is None or target is None:
|
|
return None
|
|
|
|
is_owner = role.organization.owner.id == requester_id
|
|
is_manager_member = bool(requester.is_manager) and role.organization.members.filter(id=requester_id).exists()
|
|
if not (is_owner or is_manager_member):
|
|
return None
|
|
|
|
if not role.members.filter(id=target.id).exists():
|
|
return None
|
|
|
|
return target.id |