Added quiz and content streamlining with tweaks
This commit is contained in:
Viswamedha Nalabotu
parent
623b46c691
commit
348ee6734a
1 changed files with 336 additions and 48 deletions
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@ -1,12 +1,14 @@
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import json
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import httpx
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import re
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import logging
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import re
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from uuid import uuid4
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from channels.generic.websocket import AsyncWebsocketConsumer
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import httpx
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from channels.db import database_sync_to_async
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from django.utils import timezone
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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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@ -46,21 +48,35 @@ class OnboardingConsumer(AsyncWebsocketConsumer):
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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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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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await self.run_progress_monitor(role_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(self.context_uuid)
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ai_response = await self.orchestrate_ai(user_message, config)
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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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@ -91,16 +107,19 @@ class OnboardingConsumer(AsyncWebsocketConsumer):
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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(ca_prompt, ca_config)
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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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toc_lines = [f"{idx + 1}. {title}" for idx, title in enumerate(topics)]
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toc_markdown = "## Table of Contents\n\n" + "\n".join(toc_lines)
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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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@ -117,38 +136,90 @@ class OnboardingConsumer(AsyncWebsocketConsumer):
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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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"Use the MCP search context provided below as the primary source. "
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"If the context is empty, 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.\n\n"
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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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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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if index == 0:
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page_content = f"{toc_markdown}\n\n---\n\n{page_content}"
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await self.send_log("status", f"Phase 3: Creating quiz for {topic}...", "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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aa_prompt = (
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f"Based on this content: '{page_content[:1000]}', create 2 multiple choice questions. "
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"Output ONLY a JSON array of objects with keys: 'key', 'label', 'field_type' (use 'select'), "
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"'options' (array of strings), and 'required' (true)."
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)
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quiz_response = await self.orchestrate_ai(aa_prompt, aa_config)
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quiz_fields = self._extract_json_list(quiz_response)
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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": quiz_fields
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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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@ -179,7 +250,12 @@ class OnboardingConsumer(AsyncWebsocketConsumer):
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f"Progress context JSON:\n{json.dumps(progress_context)}"
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)
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feedback = await self.orchestrate_ai(monitor_prompt, monitor_config)
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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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)
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await self.send(json.dumps({
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"type": "completed",
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@ -192,7 +268,14 @@ class OnboardingConsumer(AsyncWebsocketConsumer):
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}
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}))
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async def orchestrate_ai(self, user_message, config):
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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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):
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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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@ -201,18 +284,34 @@ class OnboardingConsumer(AsyncWebsocketConsumer):
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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(5):
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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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f"{settings.INFERENCE_URL}/v1/chat/completions",
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json={
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"model": config.llm_config.get("model_id", "meta-llama-3.1-8b"),
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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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"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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@ -244,12 +343,27 @@ class OnboardingConsumer(AsyncWebsocketConsumer):
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continue
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else:
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return ai_message["content"]
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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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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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@ -297,18 +411,133 @@ class OnboardingConsumer(AsyncWebsocketConsumer):
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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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"""Regex helper to pull JSON out of LLM conversational filler."""
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try:
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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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match = re.search(r'\[.*\]', text, re.DOTALL)
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if match:
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return json.loads(match.group())
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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 []
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options = []
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for option in raw_options:
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option_text = str(option).strip()
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if option_text and option_text not in options:
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options.append(option_text)
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if len(options) < 2:
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continue
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validation = field.get('validation') if isinstance(field.get('validation'), dict) else {}
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correct_option = str(validation.get('correct_option') or '').strip()
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if correct_option not in options:
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correct_option = options[0]
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sanitized.append({
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'key': key,
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'label': label,
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'field_type': 'select',
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'options': options[:5],
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'required': True,
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'validation': {
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'correct_option': correct_option,
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'explanation': str(validation.get('explanation') or ''),
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},
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})
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return sanitized
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def _build_fallback_quiz_fields(self, topics):
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safe_topics = [str(topic).strip() for topic in (topics or []) if str(topic).strip()]
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if not safe_topics:
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safe_topics = ['onboarding fundamentals']
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fallback_fields = []
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for index in range(8):
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topic = safe_topics[index % len(safe_topics)]
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key = f'final_quiz_q_{index + 1}'
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correct = f"Use documented best practices for {topic}."
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options = [
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correct,
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f"Skip review steps for {topic} to move faster.",
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f"Rely only on assumptions instead of evidence for {topic}.",
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f"Ignore quality and compliance checks in {topic} tasks.",
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]
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fallback_fields.append({
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'key': key,
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'label': f"Which approach is most appropriate when working on {topic}?",
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'field_type': 'select',
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'options': options,
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'required': True,
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'validation': {
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'correct_option': correct,
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'explanation': f"{correct} balances reliability, quality, and role expectations.",
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},
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})
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return fallback_fields
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def _normalize_structure(self, structure):
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normalized_pages = []
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for index, page in enumerate(structure or []):
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@ -317,14 +546,28 @@ class OnboardingConsumer(AsyncWebsocketConsumer):
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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'field_{field_index + 1}')
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raw_options = field.get('options') if isinstance(field.get('options'), list) else []
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options = [str(option) for option in raw_options if str(option).strip()]
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validation = field.get('validation') if isinstance(field.get('validation'), dict) else {}
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correct_option = validation.get('correct_option')
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if correct_option is not None:
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correct_option = str(correct_option)
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normalized_validation = {
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'correct_option': correct_option if correct_option in options else None,
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'explanation': str(validation.get('explanation') or ''),
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}
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fields.append({
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'uuid': str(uuid4()),
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'key': key,
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'label': str(field.get('label') or key.replace('_', ' ').title()),
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'field_type': str(field.get('field_type') or 'text'),
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'required': bool(field.get('required', False)),
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'options': field.get('options') if isinstance(field.get('options'), list) else [],
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'options': options,
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'default_value': field.get('default_value', ''),
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'validation': normalized_validation,
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})
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page_title = page.get('title') if isinstance(page, dict) else None
|
||||
|
|
@ -336,6 +579,7 @@ class OnboardingConsumer(AsyncWebsocketConsumer):
|
|||
'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
|
||||
|
||||
|
|
@ -367,10 +611,54 @@ class OnboardingConsumer(AsyncWebsocketConsumer):
|
|||
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()
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue