Refactored consumers, added profanity filters and rewrote endpoints
This commit is contained in:
Viswamedha Nalabotu
parent
f5968f3bb5
commit
6aa98b2839
22 changed files with 898 additions and 953 deletions
|
|
@ -19,7 +19,7 @@ class AgentConfigAdmin(admin.ModelAdmin):
|
|||
search_fields = ('name', 'system_prompt')
|
||||
readonly_fields = ('uuid', 'created_at', 'updated_at')
|
||||
fieldsets = (
|
||||
(None, {'fields': ('name', 'agent_type', 'organization', 'uuid', 'system_prompt', 'llm_config', 'tool_permissions')}),
|
||||
(None, {'fields': ('name', 'agent_type', 'organization', 'uuid', 'system_prompt', 'llm_config')}),
|
||||
(_('Agent Logic'), {'fields': ()}),
|
||||
(_('Metadata'), {'fields': ('created_at', 'updated_at')}),
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,912 +0,0 @@
|
|||
import json
|
||||
import logging
|
||||
import re
|
||||
from uuid import uuid4
|
||||
|
||||
import httpx
|
||||
from channels.db import database_sync_to_async
|
||||
from channels.generic.websocket import AsyncWebsocketConsumer
|
||||
from django.conf import settings
|
||||
from django.db.models import Q
|
||||
from django.utils import timezone
|
||||
|
||||
from apps.onboarding.mcp import MCPRouter
|
||||
from apps.onboarding.models import AgentConfig, OnboardingFlow, OnboardingSession
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class OnboardingConsumer(AsyncWebsocketConsumer):
|
||||
async def connect(self):
|
||||
self.user = self.scope["user"]
|
||||
|
||||
self.context_uuid = self.scope["url_route"]["kwargs"].get("session_uuid")
|
||||
|
||||
if not self.user.is_authenticated:
|
||||
logger.warning("WebSocket connect denied: unauthenticated user")
|
||||
await self.close()
|
||||
return
|
||||
|
||||
self.router = MCPRouter()
|
||||
logger.info("WebSocket connected: user_id=%s context_uuid=%s", self.user.id, self.context_uuid)
|
||||
await self.accept()
|
||||
|
||||
async def disconnect(self, close_code):
|
||||
logger.info("WebSocket disconnected: user_id=%s context_uuid=%s close_code=%s", getattr(self.user, 'id', None), self.context_uuid, close_code)
|
||||
|
||||
def _build_system_prompt(self, config):
|
||||
base_prompt = config.system_prompt or "You are a helpful onboarding assistant."
|
||||
permissions = config.tool_permissions or []
|
||||
if permissions:
|
||||
return f"{base_prompt}\n\nAllowed tools: {', '.join(str(p) for p in permissions)}"
|
||||
return base_prompt
|
||||
|
||||
async def receive(self, text_data):
|
||||
try:
|
||||
data = json.loads(text_data)
|
||||
action = data.get("action")
|
||||
logger.info("WebSocket received action=%s user_id=%s context_uuid=%s", action, self.user.id, self.context_uuid)
|
||||
|
||||
if action == "start_full_onboarding":
|
||||
role_uuid = data.get("role_uuid")
|
||||
if not role_uuid:
|
||||
await self.send_log("error", "Missing role_uuid for full onboarding generation")
|
||||
return
|
||||
if not await self.can_manage_role(role_uuid, self.user.id):
|
||||
await self.send_log("error", "Forbidden")
|
||||
return
|
||||
await self.run_full_onboarding_generation(role_uuid)
|
||||
elif action == "progress_monitor":
|
||||
role_uuid = data.get("role_uuid") or self.context_uuid
|
||||
target_user_uuid = data.get("user_uuid")
|
||||
flow_uuid = data.get("flow_uuid")
|
||||
if not role_uuid:
|
||||
await self.send_log("error", "Missing role_uuid for progress monitoring")
|
||||
return
|
||||
if not await self.can_access_role(role_uuid, self.user.id):
|
||||
await self.send_log("error", "Forbidden")
|
||||
return
|
||||
target_user_id = self.user.id
|
||||
if target_user_uuid and str(target_user_uuid) != str(self.user.uuid):
|
||||
target_user_id = await self.resolve_target_user_id(role_uuid, self.user.id, target_user_uuid)
|
||||
if not target_user_id:
|
||||
await self.send_log("error", "Forbidden")
|
||||
return
|
||||
|
||||
await self.run_progress_monitor(role_uuid, target_user_id=target_user_id, flow_uuid=flow_uuid)
|
||||
else:
|
||||
|
||||
user_message = data.get("query") or data.get("message")
|
||||
requested_max_tokens = data.get("max_tokens")
|
||||
if not user_message:
|
||||
await self.send_log("error", "Missing query/message payload")
|
||||
return
|
||||
config = await self.get_config_for_user(self.context_uuid, self.user.id)
|
||||
if config is None:
|
||||
await self.send_log("error", "Forbidden")
|
||||
return
|
||||
ai_response = await self.orchestrate_ai(
|
||||
user_message,
|
||||
config,
|
||||
max_tokens=requested_max_tokens,
|
||||
)
|
||||
|
||||
await self.send(json.dumps({
|
||||
"type": "completed",
|
||||
"timestamp": timezone.now().isoformat(),
|
||||
"message": "Inference complete.",
|
||||
"content": {
|
||||
"response": ai_response,
|
||||
}
|
||||
}))
|
||||
except Exception as e:
|
||||
logger.exception("WebSocket receive error: user_id=%s context_uuid=%s", getattr(self.user, 'id', None), self.context_uuid)
|
||||
await self.send_log("error", f"Consumer encountered an error: {str(e)}")
|
||||
|
||||
async def run_full_onboarding_generation(self, role_uuid):
|
||||
"""
|
||||
The Master Script that builds the JSON structure sequentially.
|
||||
Pipeline: Curriculum Agent -> Knowledge Agent -> Assessment Agent
|
||||
"""
|
||||
|
||||
logger.info("Starting full onboarding generation: role_uuid=%s user_id=%s", role_uuid, self.user.id)
|
||||
await self.send_log("status", "Phase 1: Generating Curriculum...", "curriculum")
|
||||
ca_config = await self.get_config_by_type(role_uuid, 'curriculum')
|
||||
if not ca_config:
|
||||
await self.send_log("error", "Missing curriculum AgentConfig for this role")
|
||||
return
|
||||
|
||||
ca_prompt = (
|
||||
"Based on available documentation, create an onboarding curriculum for this role. "
|
||||
"Output ONLY a valid JSON array of 3-5 strings representing module titles. "
|
||||
"Example: [\"Introduction\", \"Safety\", \"Operations\"]"
|
||||
)
|
||||
ca_response = await self.orchestrate_ai(
|
||||
ca_prompt,
|
||||
ca_config,
|
||||
min_internal_turns=1,
|
||||
max_tokens=384,
|
||||
)
|
||||
topics = self._extract_json_list(ca_response)
|
||||
if not topics:
|
||||
logger.warning("Curriculum generation produced no topics: role_uuid=%s", role_uuid)
|
||||
await self.send_log("error", "Curriculum generation returned no topics")
|
||||
return
|
||||
|
||||
full_structure = []
|
||||
module_briefs = []
|
||||
|
||||
|
||||
for index, topic in enumerate(topics):
|
||||
|
||||
await self.send_log("status", f"Phase 2: Researching {topic}...", "knowledge")
|
||||
ka_config = await self.get_config_by_type(role_uuid, 'knowledge')
|
||||
if not ka_config:
|
||||
await self.send_log("error", "Missing knowledge AgentConfig for this role")
|
||||
return
|
||||
|
||||
knowledge_hits = await self.fetch_knowledge_context(role_uuid, topic)
|
||||
context_markdown = self.format_knowledge_context(knowledge_hits)
|
||||
|
||||
page_content = await self.orchestrate_ai(
|
||||
(
|
||||
f"Write a practical onboarding training guide for the topic '{topic}'. "
|
||||
"Think step-by-step internally before writing the final answer. "
|
||||
"Use the MCP search context below as your primary source, and call additional tools if needed. "
|
||||
"If no indexed documents are available, provide a concise best-practice overview and clearly say no indexed documents were found. "
|
||||
"Use Markdown formatting and do NOT include a table of contents in this section. "
|
||||
"Generate substantial depth: target 900-1400 words. "
|
||||
"Include these sections in order: Overview, Core Concepts, Role-Specific Workflow, Practical Examples, Common Pitfalls, and Action Checklist. "
|
||||
"In Practical Examples, provide at least 2 concrete examples relevant to this role/topic. "
|
||||
"In Action Checklist, provide at least 8 actionable checklist items.\n\n"
|
||||
f"Role UUID: {role_uuid}\n"
|
||||
f"Topic: {topic}\n"
|
||||
f"MCP search context:\n{context_markdown}"
|
||||
),
|
||||
ka_config,
|
||||
min_internal_turns=2,
|
||||
max_tokens=2400,
|
||||
)
|
||||
|
||||
|
||||
full_structure.append({
|
||||
"title": topic,
|
||||
"body": page_content,
|
||||
"order": index,
|
||||
"fields": [],
|
||||
"meta": {
|
||||
"topic_index": index,
|
||||
"table_of_contents": [str(item) for item in topics],
|
||||
},
|
||||
})
|
||||
|
||||
module_briefs.append({
|
||||
"topic": str(topic),
|
||||
"summary_excerpt": str(page_content)[:1200],
|
||||
})
|
||||
|
||||
await self.send_log("status", "Phase 3: Creating final assessment quiz...", "assessment")
|
||||
aa_config = await self.get_config_by_type(role_uuid, 'assessment')
|
||||
if not aa_config:
|
||||
await self.send_log("error", "Missing assessment AgentConfig for this role")
|
||||
return
|
||||
|
||||
question_count = 8
|
||||
try:
|
||||
random_result = await self.router.handle_tool_call("random_int", {"min": 6, "max": 10})
|
||||
if isinstance(random_result, dict) and isinstance(random_result.get("value"), int):
|
||||
question_count = int(random_result["value"])
|
||||
except Exception:
|
||||
question_count = 8
|
||||
|
||||
quiz_prompt = (
|
||||
"Create a final onboarding quiz that assesses all generated modules. "
|
||||
f"Output ONLY a valid JSON array of exactly {question_count} question objects. "
|
||||
"Use a mix of question types: at least 2 short-answer questions and at least 2 multiple-choice questions. "
|
||||
"For multiple-choice objects: field_type='select', options (4 unique strings), and validation.correct_option. "
|
||||
"For short-answer objects: field_type='textarea' (or 'text') and validation.accepted_answers (array of valid answers/keywords). "
|
||||
"Each object MUST include key, label, field_type, required=true, and validation.explanation. "
|
||||
"Cover all topics with balanced difficulty and avoid ambiguous wording.\n\n"
|
||||
f"Modules JSON:\n{json.dumps(module_briefs, ensure_ascii=False)}"
|
||||
)
|
||||
quiz_response = await self.orchestrate_ai(
|
||||
quiz_prompt,
|
||||
aa_config,
|
||||
min_internal_turns=1,
|
||||
max_tokens=1600,
|
||||
)
|
||||
quiz_fields = self._sanitize_quiz_fields(self._extract_json_list(quiz_response))
|
||||
|
||||
if not quiz_fields:
|
||||
await self.send_log("status", "Assessment output invalid, retrying quiz generation...", "assessment")
|
||||
retry_response = await self.orchestrate_ai(
|
||||
f"{quiz_prompt}\n\nReturn ONLY raw JSON. Do not use markdown fences. Do not include explanations outside JSON.",
|
||||
aa_config,
|
||||
min_internal_turns=1,
|
||||
max_tokens=1600,
|
||||
)
|
||||
quiz_fields = self._sanitize_quiz_fields(self._extract_json_list(retry_response))
|
||||
|
||||
if not quiz_fields:
|
||||
await self.send_log("status", "Assessment output still invalid. Using fallback final quiz.", "assessment")
|
||||
quiz_fields = self._build_fallback_quiz_fields([str(topic) for topic in topics], count=question_count)
|
||||
|
||||
full_structure.append({
|
||||
"title": "Final Assessment Quiz",
|
||||
"body": (
|
||||
"### Final Quiz\n"
|
||||
"Answer all questions below. You need **80%** to complete onboarding. "
|
||||
"You can update answers and submit when ready."
|
||||
),
|
||||
"order": len(full_structure),
|
||||
"fields": quiz_fields,
|
||||
"meta": {
|
||||
"page_type": "final_quiz",
|
||||
"pass_mark": 80,
|
||||
},
|
||||
})
|
||||
|
||||
|
||||
await self.save_full_flow(role_uuid, full_structure)
|
||||
logger.info("Full onboarding generation completed: role_uuid=%s pages=%s", role_uuid, len(full_structure))
|
||||
|
||||
|
||||
await self.send(json.dumps({
|
||||
"type": "completed",
|
||||
"timestamp": timezone.now().isoformat(),
|
||||
"message": "Onboarding pipeline complete and structure saved."
|
||||
}))
|
||||
|
||||
async def run_progress_monitor(self, role_uuid, target_user_id=None, flow_uuid=None):
|
||||
logger.info(
|
||||
"Starting progress monitor: role_uuid=%s requester_id=%s target_user_id=%s flow_uuid=%s",
|
||||
role_uuid,
|
||||
self.user.id,
|
||||
target_user_id or self.user.id,
|
||||
flow_uuid,
|
||||
)
|
||||
await self.send_log("status", "Progress Monitor is analyzing your onboarding progress...", "monitor")
|
||||
|
||||
monitor_config = await self.get_config_by_type(role_uuid, 'monitor')
|
||||
if not monitor_config:
|
||||
await self.send_log("error", "Missing Progress Monitor AgentConfig for this role")
|
||||
return
|
||||
|
||||
progress_context = await self.get_role_progress_context(
|
||||
role_uuid,
|
||||
target_user_id or self.user.id,
|
||||
flow_uuid=flow_uuid,
|
||||
)
|
||||
|
||||
monitor_prompt = (
|
||||
"You are a progress monitoring agent for onboarding. "
|
||||
"Analyze the role onboarding data below and provide concise feedback with:\n"
|
||||
"1) current status\n2) strengths\n3) gaps\n4) next actions\n"
|
||||
"Use prior learner question/answer evidence and any saved marking details when available. "
|
||||
"If evidence is insufficient, explicitly state what is missing.\n"
|
||||
"Keep it short and practical.\n\n"
|
||||
f"Progress context JSON:\n{json.dumps(progress_context)}"
|
||||
)
|
||||
|
||||
try:
|
||||
feedback = await self.orchestrate_ai(
|
||||
monitor_prompt,
|
||||
monitor_config,
|
||||
min_internal_turns=1,
|
||||
max_tokens=640,
|
||||
raise_on_error=True,
|
||||
)
|
||||
except Exception as exc:
|
||||
await self.send_log("error", f"Inference failed: {str(exc)}")
|
||||
return
|
||||
|
||||
if str(feedback).startswith("Error:"):
|
||||
await self.send_log("error", str(feedback))
|
||||
return
|
||||
|
||||
await self.send(json.dumps({
|
||||
"type": "completed",
|
||||
"timestamp": timezone.now().isoformat(),
|
||||
"message": "Progress analysis complete.",
|
||||
"content": {
|
||||
"role_uuid": role_uuid,
|
||||
"feedback": feedback,
|
||||
"status": progress_context.get("latest_status", "unknown"),
|
||||
"user_id": target_user_id or self.user.id,
|
||||
"flow_uuid": flow_uuid,
|
||||
"is_completed": progress_context.get("is_completed", False),
|
||||
}
|
||||
}))
|
||||
logger.info("Progress monitor completed: role_uuid=%s target_user_id=%s", role_uuid, target_user_id or self.user.id)
|
||||
|
||||
async def orchestrate_ai(
|
||||
self,
|
||||
user_message,
|
||||
config,
|
||||
min_internal_turns=2,
|
||||
max_turns=6,
|
||||
max_tokens=None,
|
||||
raise_on_error=False,
|
||||
):
|
||||
"""
|
||||
Handles the multi-turn ReAct loop (Reasoning + Tool Use).
|
||||
"""
|
||||
messages = [
|
||||
{"role": "system", "content": self._build_system_prompt(config)},
|
||||
{"role": "user", "content": user_message}
|
||||
]
|
||||
|
||||
llm_config = config.llm_config if isinstance(config.llm_config, dict) else {}
|
||||
|
||||
resolved_max_tokens = max_tokens
|
||||
if resolved_max_tokens is None:
|
||||
resolved_max_tokens = llm_config.get("max_tokens", 1024)
|
||||
|
||||
try:
|
||||
resolved_max_tokens = max(64, int(resolved_max_tokens))
|
||||
except Exception:
|
||||
resolved_max_tokens = 1024
|
||||
|
||||
last_content = ""
|
||||
min_internal_turns = max(1, int(min_internal_turns or 1))
|
||||
max_turns = max(min_internal_turns, int(max_turns or 1))
|
||||
|
||||
async with httpx.AsyncClient(timeout=60.0) as client:
|
||||
for turn in range(max_turns):
|
||||
await self.send_log("thought", f"Agent is thinking (Turn {turn+1})...")
|
||||
|
||||
try:
|
||||
response = await client.post(
|
||||
settings.INFERENCE_CHAT_COMPLETIONS_ENDPOINT,
|
||||
json={
|
||||
"model": llm_config.get("model_id", "meta-llama-3.1-8b"),
|
||||
"messages": messages,
|
||||
"tools": self.router.get_tool_definitions(),
|
||||
"tool_choice": "auto",
|
||||
"max_tokens": resolved_max_tokens,
|
||||
}
|
||||
)
|
||||
response.raise_for_status()
|
||||
res_json = response.json()
|
||||
|
||||
ai_message = res_json["choices"][0]["message"]
|
||||
messages.append(ai_message)
|
||||
|
||||
if ai_message.get("tool_calls"):
|
||||
for tool_call in ai_message["tool_calls"]:
|
||||
fn_name = tool_call["function"]["name"]
|
||||
fn_args = json.loads(tool_call["function"]["arguments"])
|
||||
|
||||
await self.send(json.dumps({
|
||||
"type": "tool_start",
|
||||
"message": f"Accessing knowledge base: {fn_name}...",
|
||||
"content": fn_args
|
||||
}))
|
||||
|
||||
|
||||
result = await self.router.handle_tool_call(fn_name, fn_args)
|
||||
|
||||
messages.append({
|
||||
"role": "tool",
|
||||
"tool_call_id": tool_call["id"],
|
||||
"name": fn_name,
|
||||
"content": json.dumps(result)
|
||||
})
|
||||
continue
|
||||
|
||||
else:
|
||||
last_content = str(ai_message.get("content") or "").strip()
|
||||
|
||||
if (turn + 1) < min_internal_turns:
|
||||
messages.append({
|
||||
"role": "user",
|
||||
"content": (
|
||||
"Run one more internal reasoning pass before finalizing. "
|
||||
"If additional evidence is needed, call tools. "
|
||||
"Then return only the improved final answer."
|
||||
),
|
||||
})
|
||||
continue
|
||||
|
||||
return last_content
|
||||
|
||||
except Exception as e:
|
||||
await self.send_log("error", f"Inference failed: {str(e)}")
|
||||
logger.exception("Inference failed: user_id=%s context_uuid=%s", self.user.id, self.context_uuid)
|
||||
if raise_on_error:
|
||||
raise
|
||||
return f"Error: {str(e)}"
|
||||
|
||||
return last_content
|
||||
|
||||
|
||||
|
||||
async def fetch_knowledge_context(self, role_uuid, topic):
|
||||
query = f"onboarding training content for {topic}"
|
||||
await self.send(json.dumps({
|
||||
"type": "tool_start",
|
||||
"message": "Accessing knowledge base: search_knowledge...",
|
||||
"content": {"query": query, "role_uuid": role_uuid}
|
||||
}))
|
||||
|
||||
try:
|
||||
result = await self.router.handle_tool_call(
|
||||
"search_knowledge",
|
||||
{
|
||||
"query": query,
|
||||
"role_uuid": role_uuid,
|
||||
},
|
||||
)
|
||||
|
||||
await self.send(json.dumps({
|
||||
"type": "tool_result",
|
||||
"message": f"Retrieved {len(result) if isinstance(result, list) else 0} knowledge chunk(s)",
|
||||
"content": result,
|
||||
"timestamp": timezone.now().isoformat(),
|
||||
}))
|
||||
|
||||
return result if isinstance(result, list) else []
|
||||
except Exception as exc:
|
||||
await self.send_log("error", f"Knowledge retrieval failed for topic '{topic}': {str(exc)}")
|
||||
return []
|
||||
|
||||
def format_knowledge_context(self, knowledge_hits):
|
||||
if not knowledge_hits:
|
||||
return "No indexed MCP documents found for this role/topic."
|
||||
|
||||
lines = []
|
||||
for idx, item in enumerate(knowledge_hits[:5]):
|
||||
source = item.get("source", "Unknown Source") if isinstance(item, dict) else "Unknown Source"
|
||||
relevance = item.get("relevance") if isinstance(item, dict) else None
|
||||
content = item.get("content", "") if isinstance(item, dict) else ""
|
||||
safe_content = str(content).strip()[:1600]
|
||||
lines.append(
|
||||
f"[{idx + 1}] Source: {source} | Relevance: {relevance}\n{safe_content}"
|
||||
)
|
||||
|
||||
return "\n\n".join(lines)
|
||||
|
||||
def _coerce_list_payload(self, payload):
|
||||
if isinstance(payload, list):
|
||||
return payload
|
||||
if isinstance(payload, dict):
|
||||
for key in ('questions', 'items', 'fields', 'quiz'):
|
||||
value = payload.get(key)
|
||||
if isinstance(value, list):
|
||||
return value
|
||||
return []
|
||||
|
||||
def _extract_json_list(self, text):
|
||||
"""Extracts a JSON list from model output, tolerating wrappers and markdown fences."""
|
||||
if not text:
|
||||
return []
|
||||
|
||||
candidate_texts = [str(text).strip()]
|
||||
|
||||
for block in re.findall(r'```(?:json)?\s*([\s\S]*?)```', str(text), re.IGNORECASE):
|
||||
candidate_texts.append(block.strip())
|
||||
|
||||
decoder = json.JSONDecoder()
|
||||
|
||||
for candidate in candidate_texts:
|
||||
if not candidate:
|
||||
continue
|
||||
|
||||
try:
|
||||
parsed = json.loads(candidate)
|
||||
coerced = self._coerce_list_payload(parsed)
|
||||
if coerced:
|
||||
return coerced
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
for idx, char in enumerate(candidate):
|
||||
if char not in '[{':
|
||||
continue
|
||||
try:
|
||||
parsed, _ = decoder.raw_decode(candidate[idx:])
|
||||
except Exception:
|
||||
continue
|
||||
|
||||
coerced = self._coerce_list_payload(parsed)
|
||||
if coerced:
|
||||
return coerced
|
||||
|
||||
return []
|
||||
|
||||
def _sanitize_quiz_fields(self, raw_fields):
|
||||
sanitized = []
|
||||
seen_keys = set()
|
||||
|
||||
for index, field in enumerate(raw_fields or []):
|
||||
if not isinstance(field, dict):
|
||||
continue
|
||||
|
||||
key = str(field.get('key') or f'final_quiz_q_{index + 1}').strip().lower().replace(' ', '_')
|
||||
if not key:
|
||||
key = f'final_quiz_q_{index + 1}'
|
||||
|
||||
if key in seen_keys:
|
||||
key = f'{key}_{index + 1}'
|
||||
seen_keys.add(key)
|
||||
|
||||
label = str(field.get('label') or '').strip()
|
||||
if not label:
|
||||
continue
|
||||
|
||||
field_type = str(field.get('field_type') or 'select').strip().lower()
|
||||
if field_type not in ('select', 'text', 'textarea'):
|
||||
field_type = 'select'
|
||||
|
||||
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)
|
||||
|
||||
validation = field.get('validation') if isinstance(field.get('validation'), dict) else {}
|
||||
if field_type == 'select':
|
||||
if len(options) < 2:
|
||||
continue
|
||||
|
||||
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 ''),
|
||||
},
|
||||
})
|
||||
continue
|
||||
|
||||
accepted_answers_raw = validation.get('accepted_answers')
|
||||
if isinstance(accepted_answers_raw, list):
|
||||
accepted_answers = [str(item).strip() for item in accepted_answers_raw if str(item).strip()]
|
||||
else:
|
||||
accepted_single = str(validation.get('correct_answer') or '').strip()
|
||||
accepted_answers = [accepted_single] if accepted_single else []
|
||||
|
||||
if not accepted_answers:
|
||||
continue
|
||||
|
||||
sanitized.append({
|
||||
'key': key,
|
||||
'label': label,
|
||||
'field_type': 'textarea' if field_type == 'textarea' else 'text',
|
||||
'options': [],
|
||||
'required': True,
|
||||
'validation': {
|
||||
'accepted_answers': accepted_answers,
|
||||
'explanation': str(validation.get('explanation') or ''),
|
||||
},
|
||||
})
|
||||
|
||||
return sanitized
|
||||
|
||||
def _build_fallback_quiz_fields(self, topics, count=8):
|
||||
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(count):
|
||||
topic = safe_topics[index % len(safe_topics)]
|
||||
key = f'final_quiz_q_{index + 1}'
|
||||
|
||||
if index % 3 == 0:
|
||||
fallback_fields.append({
|
||||
'key': key,
|
||||
'label': f"In one or two sentences, what is the safest approach when handling {topic}?",
|
||||
'field_type': 'textarea',
|
||||
'options': [],
|
||||
'required': True,
|
||||
'validation': {
|
||||
'accepted_answers': [
|
||||
'best practices',
|
||||
'documentation',
|
||||
'quality',
|
||||
'compliance',
|
||||
],
|
||||
'explanation': 'Good answers reference documented best practices, quality checks, and compliance.',
|
||||
},
|
||||
})
|
||||
continue
|
||||
|
||||
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,
|
||||
'accepted_answers': [
|
||||
str(item).strip()
|
||||
for item in (validation.get('accepted_answers') if isinstance(validation.get('accepted_answers'), list) else [])
|
||||
if str(item).strip()
|
||||
],
|
||||
'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):
|
||||
if log_type == "error":
|
||||
logger.error("Consumer log event: type=%s message=%s content=%s", log_type, message, content)
|
||||
elif log_type == "status":
|
||||
logger.info("Consumer log event: type=%s message=%s content=%s", log_type, message, content)
|
||||
else:
|
||||
logger.debug("Consumer log event: type=%s message=%s content=%s", log_type, message, content)
|
||||
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(Q(flow__uuid=flow_uuid) | Q(flow__isnull=True, 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,
|
||||
"final_quiz_result": {},
|
||||
"final_quiz_qa": [],
|
||||
}
|
||||
|
||||
state = latest_session.state or {}
|
||||
responses = state.get("responses", {})
|
||||
completed_modules = state.get("completed_modules", [])
|
||||
progress = state.get("progress_percentage", state.get("progress", 0))
|
||||
final_quiz_result = state.get("final_quiz_result", {})
|
||||
|
||||
flow_for_context = latest_session.flow or scoped_flow or active_flow
|
||||
structure = flow_for_context.structure if flow_for_context and isinstance(flow_for_context.structure, list) else []
|
||||
|
||||
quiz_page = None
|
||||
for page in structure:
|
||||
if not isinstance(page, dict):
|
||||
continue
|
||||
meta = page.get("meta") if isinstance(page.get("meta"), dict) else {}
|
||||
if str(meta.get("page_type") or "").strip() == "final_quiz":
|
||||
quiz_page = page
|
||||
break
|
||||
|
||||
if quiz_page is None and structure:
|
||||
quiz_page = structure[-1] if isinstance(structure[-1], dict) else None
|
||||
|
||||
quiz_fields = quiz_page.get("fields") if isinstance(quiz_page, dict) and isinstance(quiz_page.get("fields"), list) else []
|
||||
quiz_page_uuid = str(quiz_page.get("uuid") or "") if isinstance(quiz_page, dict) else ""
|
||||
|
||||
quiz_responses = {}
|
||||
if isinstance(responses, dict) and quiz_page_uuid:
|
||||
candidate = responses.get(quiz_page_uuid, {})
|
||||
if isinstance(candidate, dict):
|
||||
quiz_responses = candidate
|
||||
|
||||
grading_details = (
|
||||
final_quiz_result.get("grading_details", [])
|
||||
if isinstance(final_quiz_result, dict)
|
||||
else []
|
||||
)
|
||||
grading_by_key = {}
|
||||
if isinstance(grading_details, list):
|
||||
for detail in grading_details:
|
||||
if not isinstance(detail, dict):
|
||||
continue
|
||||
key = str(detail.get("key") or "").strip()
|
||||
if not key:
|
||||
continue
|
||||
grading_by_key[key] = {
|
||||
"correct": bool(detail.get("correct")),
|
||||
"reason": str(detail.get("reason") or ""),
|
||||
}
|
||||
|
||||
final_quiz_qa = []
|
||||
for field in quiz_fields:
|
||||
if not isinstance(field, dict):
|
||||
continue
|
||||
key = str(field.get("key") or "").strip()
|
||||
if not key:
|
||||
continue
|
||||
|
||||
detail = grading_by_key.get(key, {})
|
||||
final_quiz_qa.append(
|
||||
{
|
||||
"key": key,
|
||||
"label": str(field.get("label") or key),
|
||||
"field_type": str(field.get("field_type") or ""),
|
||||
"answer": quiz_responses.get(key),
|
||||
"marked_correct": detail.get("correct"),
|
||||
"marking_reason": detail.get("reason", ""),
|
||||
}
|
||||
)
|
||||
|
||||
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(latest_session.flow.uuid) if latest_session.flow_id else 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',
|
||||
"final_quiz_result": final_quiz_result if isinstance(final_quiz_result, dict) else {},
|
||||
"final_quiz_qa": final_quiz_qa,
|
||||
}
|
||||
|
||||
@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
|
||||
4
apps/onboarding/consumers/__init__.py
Normal file
4
apps/onboarding/consumers/__init__.py
Normal file
|
|
@ -0,0 +1,4 @@
|
|||
from .base import *
|
||||
from .chat import *
|
||||
from .generate import *
|
||||
from .progress import *
|
||||
214
apps/onboarding/consumers/base.py
Normal file
214
apps/onboarding/consumers/base.py
Normal file
|
|
@ -0,0 +1,214 @@
|
|||
import json
|
||||
import logging
|
||||
from enum import Enum
|
||||
|
||||
import httpx
|
||||
from channels.db import database_sync_to_async
|
||||
from channels.generic.websocket import AsyncWebsocketConsumer
|
||||
from django.conf import settings
|
||||
from django.db.models import Q
|
||||
from django.utils import timezone
|
||||
|
||||
from apps.accounts.models import User, Role
|
||||
from apps.accounts.permissions import get_organization_from_object, can_manage_organization
|
||||
from apps.onboarding.consumers.prompts import OnboardingPrompts
|
||||
from apps.onboarding.mcp import mcp_router, MCPRouter
|
||||
from apps.onboarding.models import AgentConfig
|
||||
from apps.onboarding.utils.content_moderator import ContentModerator
|
||||
|
||||
__all__ = ["BaseOnboardingConsumer", "LogType"]
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class LogType(Enum):
|
||||
STATUS = "status" # General progress updates
|
||||
ERROR = "error" # Failures
|
||||
INFO = "info" # Debug/Verbose logs
|
||||
THOUGHT = "thought" # AI internal reasoning turns
|
||||
TOOL_START = "tool_start" # When an MCP tool is called
|
||||
TOOL_RESULT = "tool_result" # When data comes back from a tool
|
||||
COMPLETED = "completed" # The final completion signal
|
||||
|
||||
class BaseOnboardingConsumer(AsyncWebsocketConsumer):
|
||||
"""
|
||||
Base consumer for all onboarding-related WebSocket consumers.
|
||||
"""
|
||||
|
||||
user: User
|
||||
router: MCPRouter
|
||||
logger: logging.Logger = logger
|
||||
moderator: ContentModerator = ContentModerator()
|
||||
|
||||
### Connection Management ###
|
||||
async def connect(self):
|
||||
self.user = self.scope["user"]
|
||||
if not self.user.is_authenticated:
|
||||
self.logger.warning("WebSocket connection rejected: unauthenticated user attempted to connect")
|
||||
return await self.close()
|
||||
self.parse_extra()
|
||||
self.router = mcp_router
|
||||
self.logger.info(f"WebSocket connected: user={self.user.full_name}")
|
||||
return await self.accept()
|
||||
|
||||
async def disconnect(self, close_code: int):
|
||||
self.logger.info(f"WebSocket disconnected: user={self.user.full_name} close_code={close_code}")
|
||||
|
||||
### Event Handling ###
|
||||
async def receive(self, text_data: str):
|
||||
"""
|
||||
Main entry point for incoming messages.
|
||||
"""
|
||||
try:
|
||||
data = self.from_json(text_data)
|
||||
for field in ("query", "message"):
|
||||
value = data.get(field)
|
||||
if value and not self.moderator.is_clean(str(value)):
|
||||
return await self.send_error("Message blocked: content did not pass moderation.")
|
||||
action_name = data.get("action", "message")
|
||||
method = getattr(self, f"action_{action_name}", None)
|
||||
if method:
|
||||
self.logger.info(f"Dispatching action: {action_name}")
|
||||
await method(data)
|
||||
else:
|
||||
await self.send_error(f"Action '{action_name}' not supported on this endpoint.")
|
||||
except Exception as e:
|
||||
await self.send_error(f"An unexpected error occurred when processing the event.")
|
||||
self.logger.exception(f"WebSocket receive critical failure: {str(e)}")
|
||||
|
||||
### MCP Handling ###
|
||||
async def orchestrate(self, message: str, config: AgentConfig, minimum_turns: int = 2, maximum_turns: int = 5,
|
||||
max_tokens: int | None = None, raise_on_error: bool = False, request_timeout: int = 60.0) -> str:
|
||||
"""
|
||||
Orchestrates a multi-turn conversation with the agent, including tool calls and reasoning steps.
|
||||
"""
|
||||
llm_config = config.llm_config if isinstance(config.llm_config, dict) else {}
|
||||
resolved_max_tokens = max_tokens or llm_config.get("max_tokens", 1024)
|
||||
messages = [
|
||||
{"role": "system", "content": config.system_prompt or OnboardingPrompts.default_system_prompt()},
|
||||
{"role": "user", "content": message}
|
||||
]
|
||||
last_content = ""
|
||||
async with httpx.AsyncClient(timeout = request_timeout) as client:
|
||||
for turn in range(1, maximum_turns + 1):
|
||||
await self.send_log(LogType.THOUGHT, f"Agent reasoning (Turn {turn})...")
|
||||
try:
|
||||
response = await client.post(
|
||||
settings.INFERENCE_CHAT_COMPLETIONS_ENDPOINT,
|
||||
json={
|
||||
"model": llm_config.get("model_id", "meta-llama-3.1-8b"),
|
||||
"messages": messages,
|
||||
"tools": self.router.get_tool_definitions(),
|
||||
"tool_choice": "auto",
|
||||
"max_tokens": resolved_max_tokens,
|
||||
}
|
||||
)
|
||||
response.raise_for_status()
|
||||
ai_message = response.json()["choices"][0]["message"]
|
||||
messages.append(ai_message)
|
||||
if ai_message.get("tool_calls"):
|
||||
for tool_call in ai_message["tool_calls"]:
|
||||
fn_name = tool_call["function"]["name"]
|
||||
fn_args = json.loads(tool_call["function"]["arguments"])
|
||||
await self.send_log(LogType.TOOL_START, f"Calling tool: {fn_name}", content=fn_args)
|
||||
result = await self.router.handle_tool_call(fn_name, fn_args)
|
||||
await self.send_log(LogType.TOOL_RESULT, f"Tool {fn_name} returned data", content=result)
|
||||
messages.append({
|
||||
"role": "tool",
|
||||
"tool_call_id": tool_call["id"],
|
||||
"name": fn_name,
|
||||
"content": self.to_json(result)
|
||||
})
|
||||
continue
|
||||
else:
|
||||
last_content = self.moderator.censor(str(ai_message.get("content") or "").strip())
|
||||
if turn < minimum_turns:
|
||||
messages.append({
|
||||
"role": "user",
|
||||
"content": OnboardingPrompts.force_reasoning_prompt()
|
||||
})
|
||||
continue
|
||||
return last_content
|
||||
except Exception as e:
|
||||
await self.send_error(f"AI Orchestration failed: {str(e)}")
|
||||
if raise_on_error:
|
||||
raise e
|
||||
return f"Error: {str(e)}"
|
||||
return last_content
|
||||
|
||||
### Regular Helpers ###
|
||||
async def send_log(self, log_type: LogType, message: str, content: str | dict | None = None):
|
||||
if log_type == LogType.ERROR:
|
||||
self.logger.error(f"[{log_type.value}]: message={str(message)[:100]} content={str(content)[:60]}")
|
||||
else:
|
||||
self.logger.info(f"[{log_type.value}]: message={str(message)[:100]} content={str(content)[:60]}")
|
||||
await self.send(self.to_json({ "type": log_type.value, "message": message, "content": content, "timestamp": self.get_timestamp()}))
|
||||
|
||||
async def send_error(self, message: str):
|
||||
await self.send_log(LogType.ERROR, message)
|
||||
|
||||
def parse_extra(self):
|
||||
"""
|
||||
Override for custom parsing
|
||||
"""
|
||||
pass
|
||||
|
||||
def to_json(self, data: dict | list) -> str:
|
||||
return json.dumps(data, default=str)
|
||||
|
||||
def from_json(self, data: str) -> dict | list:
|
||||
return json.loads(data)
|
||||
|
||||
def get_timestamp(self):
|
||||
return timezone.now().isoformat()
|
||||
|
||||
def parse_max_tokens(self, max_tokens: int | None) -> int:
|
||||
if max_tokens is None:
|
||||
return None
|
||||
if isinstance(max_tokens, int) and max_tokens > 0:
|
||||
return max_tokens
|
||||
return None
|
||||
|
||||
### Database Helpers ###
|
||||
@database_sync_to_async
|
||||
def get_config(self, config_uuid):
|
||||
return AgentConfig.objects.get(uuid = config_uuid)
|
||||
|
||||
@database_sync_to_async
|
||||
def get_role(self, role_uuid):
|
||||
return Role.objects.get(uuid = role_uuid)
|
||||
|
||||
@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_config_for_user(self, config_uuid):
|
||||
return AgentConfig.objects.filter(uuid = config_uuid).filter(
|
||||
Q(organization__owner__id=self.user.id) | Q(organization__members__id=self.user.id)
|
||||
).first()
|
||||
|
||||
@database_sync_to_async
|
||||
def can_manage_role(self, role_uuid):
|
||||
role = Role.objects.filter(uuid=role_uuid).first()
|
||||
if role is None:
|
||||
return False
|
||||
return can_manage_organization(self.user, get_organization_from_object(role))
|
||||
|
||||
@database_sync_to_async
|
||||
def can_access_role(self, role_uuid):
|
||||
role = Role.objects.filter(uuid=role_uuid).first()
|
||||
if role is None:
|
||||
return False
|
||||
return role.members.filter(id=self.user.id).exists() or can_manage_organization(self.user, get_organization_from_object(role))
|
||||
25
apps/onboarding/consumers/chat.py
Normal file
25
apps/onboarding/consumers/chat.py
Normal file
|
|
@ -0,0 +1,25 @@
|
|||
from apps.onboarding.consumers.base import BaseOnboardingConsumer, LogType
|
||||
|
||||
__all__ = ["OnboardingChatConsumer"]
|
||||
|
||||
class OnboardingChatConsumer(BaseOnboardingConsumer):
|
||||
"""
|
||||
Route: /ws/onboarding/chat/<config_uuid>/
|
||||
"""
|
||||
|
||||
config_uuid: str
|
||||
|
||||
def parse_extra(self):
|
||||
self.config_uuid = self.scope["url_route"]["kwargs"].get("config_uuid")
|
||||
|
||||
async def action_message(self, data: dict):
|
||||
user_query = data.get("query")
|
||||
if not user_query:
|
||||
return await self.send_error("Missing 'query' field in payload.")
|
||||
max_tokens = self.parse_max_tokens(data.get("max_tokens"))
|
||||
config = await self.get_config_for_user(self.config_uuid)
|
||||
if config is None:
|
||||
await self.send_error("Forbidden or Invalid Config UUID")
|
||||
return
|
||||
response = await self.orchestrate(user_query, config, max_tokens=max_tokens)
|
||||
await self.send_log(LogType.COMPLETED, "Inferenced complete.", {"response": response})
|
||||
402
apps/onboarding/consumers/generate.py
Normal file
402
apps/onboarding/consumers/generate.py
Normal file
|
|
@ -0,0 +1,402 @@
|
|||
import json
|
||||
import re
|
||||
from uuid import uuid4
|
||||
|
||||
from channels.db import database_sync_to_async
|
||||
|
||||
from apps.accounts.models import Role
|
||||
from apps.onboarding.consumers.base import BaseOnboardingConsumer, LogType
|
||||
from apps.onboarding.consumers.prompts import OnboardingPrompts
|
||||
|
||||
from apps.onboarding.models import OnboardingFlow
|
||||
|
||||
__all__ = ["OnboardingGenerateConsumer"]
|
||||
|
||||
class OnboardingGenerateConsumer(BaseOnboardingConsumer):
|
||||
"""
|
||||
Route: /ws/onboarding/generate/<role_uuid>/
|
||||
"""
|
||||
|
||||
role_uuid: str
|
||||
|
||||
def parse_extra(self):
|
||||
self.role_uuid = self.scope["url_route"]["kwargs"].get("role_uuid")
|
||||
|
||||
async def action_start_full_onboarding(self, data: dict) -> None:
|
||||
user_message = data.get("message")
|
||||
if user_message and not self.moderator.is_clean(user_message):
|
||||
return await self.send_error("Your message did not pass moderation. Please revise your input.")
|
||||
if not await self.can_access_role(self.role_uuid):
|
||||
return await self.send_error("You are not a member of this role or do not have permission to access it")
|
||||
await self.run_pipeline(await self.get_role(self.role_uuid))
|
||||
|
||||
async def run_pipeline(self, role: Role) -> None:
|
||||
"""
|
||||
Full orchestration pipeline for generating curriculum, knowledge content, and assessment quiz for a given role
|
||||
"""
|
||||
self.logger.info(f"Starting onboarding generation pipeline for role={role.name} (uuid={role.uuid}) by user={self.user.full_name}")
|
||||
await self.send_log(LogType.STATUS, f"Phase 1: Generating Curriculum...", "curriculum")
|
||||
ca_config = await self.get_config_by_type(role.uuid, 'curriculum')
|
||||
if not ca_config:
|
||||
return await self.send_error("Missing curriculum AgentConfig for this role")
|
||||
ca_response = await self.orchestrate(OnboardingPrompts.curriculum_generation_prompt(), ca_config, minimum_turns=1, max_tokens=384)
|
||||
topics = self._extract_json_list(ca_response)
|
||||
if not topics:
|
||||
return await self.send_log(LogType.ERROR, "Curriculum generation returned no topics", f"Curriculum generation produced no topics for role={role.name} (uuid={role.uuid})")
|
||||
full_structure = []
|
||||
module_briefs = []
|
||||
for index, topic in enumerate(topics):
|
||||
await self.send_log(LogType.STATUS, f"Phase 2: Researching {topic}...", "knowledge")
|
||||
ka_config = await self.get_config_by_type(role.uuid, 'knowledge')
|
||||
if not ka_config:
|
||||
return await self.send_error("Missing knowledge AgentConfig for this role")
|
||||
knowledge_hits = await self.fetch_knowledge_context(role.uuid, topic)
|
||||
context_markdown = self.format_knowledge_context(knowledge_hits)
|
||||
ka_response = await self.orchestrate(
|
||||
OnboardingPrompts.knowledge_generation_prompt(topic, context_markdown),
|
||||
ka_config, minimum_turns=2, max_tokens=2400
|
||||
)
|
||||
full_structure.append({
|
||||
"title": topic,
|
||||
"body": ka_response,
|
||||
"order": index,
|
||||
"fields": [],
|
||||
"meta": {
|
||||
"topic_index": index,
|
||||
"table_of_contents": [str(item) for item in topics],
|
||||
},
|
||||
})
|
||||
module_briefs.append({
|
||||
"topic": str(topic),
|
||||
"summary_excerpt": str(ka_response)[:1200],
|
||||
})
|
||||
|
||||
await self.send_log(LogType.STATUS, "Phase 3: Creating final assessment quiz...", "assessment")
|
||||
aa_config = await self.get_config_by_type(role.uuid, 'assessment')
|
||||
if not aa_config:
|
||||
return await self.send_error("Missing assessment AgentConfig for this role")
|
||||
question_count = 8
|
||||
try:
|
||||
random_result = await self.router.handle_tool_call("random_int", {"min": 6, "max": 10})
|
||||
if isinstance(random_result, dict) and isinstance(random_result.get("value"), int):
|
||||
question_count = int(random_result["value"])
|
||||
except Exception:
|
||||
question_count = 8
|
||||
quiz_response = await self.orchestrate(
|
||||
OnboardingPrompts.quiz_generation_prompt(question_count, module_briefs),
|
||||
aa_config,
|
||||
minimum_turns=1,
|
||||
max_tokens=1600,
|
||||
)
|
||||
quiz_fields = self._sanitize_quiz_fields(self._extract_json_list(quiz_response))
|
||||
|
||||
if not quiz_fields:
|
||||
await self.send_log(LogType.STATUS, "Assessment output invalid, retrying quiz generation...", "assessment")
|
||||
retry_response = await self.orchestrate(
|
||||
OnboardingPrompts.quiz_generation_retry_prompt(question_count, module_briefs),
|
||||
aa_config,
|
||||
minimum_turns=1,
|
||||
max_tokens=1600,
|
||||
)
|
||||
quiz_fields = self._sanitize_quiz_fields(self._extract_json_list(retry_response))
|
||||
|
||||
if not quiz_fields:
|
||||
await self.send_log(LogType.STATUS, "Assessment output still invalid. Using fallback final quiz.", "assessment")
|
||||
quiz_fields = self._build_fallback_quiz_fields([str(topic) for topic in topics], count=question_count)
|
||||
|
||||
full_structure.append({
|
||||
"title": "Final Assessment Quiz",
|
||||
"body": (
|
||||
"### Final Quiz\n"
|
||||
"Answer all questions below. You need **80%** to complete onboarding. "
|
||||
"You can update answers and submit when ready."
|
||||
),
|
||||
"order": len(full_structure),
|
||||
"fields": quiz_fields,
|
||||
"meta": {
|
||||
"page_type": "final_quiz",
|
||||
"pass_mark": 80,
|
||||
},
|
||||
})
|
||||
await self.save_full_flow(role.uuid, full_structure)
|
||||
self.logger.info("Full onboarding generation completed: role_uuid=%s pages=%s", role.uuid, len(full_structure))
|
||||
await self.send_log(LogType.COMPLETED, "Onboarding pipeline complete and structure saved.")
|
||||
|
||||
async def fetch_knowledge_context(self, role_uuid, topic):
|
||||
query = f"onboarding training content for {topic}"
|
||||
await self.send_log(LogType.TOOL_START, "Accessing knowledge base: search_knowledge...", {"query": query, "role_uuid": str(role_uuid)})
|
||||
|
||||
try:
|
||||
result = await self.router.handle_tool_call(
|
||||
"search_knowledge",
|
||||
{
|
||||
"query": query,
|
||||
"role_uuid": str(role_uuid),
|
||||
},
|
||||
)
|
||||
|
||||
await self.send_log(LogType.TOOL_RESULT, f"Retrieved {len(result) if isinstance(result, list) else 0} knowledge chunk(s)", result)
|
||||
|
||||
return result if isinstance(result, list) else []
|
||||
except Exception as exc:
|
||||
await self.send_log(LogType.ERROR, f"Knowledge retrieval failed for topic '{topic}': {str(exc)}")
|
||||
return []
|
||||
|
||||
def _coerce_list_payload(self, payload):
|
||||
if isinstance(payload, list):
|
||||
return payload
|
||||
if isinstance(payload, dict):
|
||||
for key in ('questions', 'items', 'fields', 'quiz'):
|
||||
value = payload.get(key)
|
||||
if isinstance(value, list):
|
||||
return value
|
||||
return []
|
||||
|
||||
def _extract_json_list(self, text: str) -> list:
|
||||
"""
|
||||
Extracts a JSON list from model output, tolerating wrappers and markdown fences
|
||||
"""
|
||||
if not text:
|
||||
return []
|
||||
candidate_texts = [str(text).strip()]
|
||||
|
||||
for block in re.findall(r'```(?:json)?\s*([\s\S]*?)```', str(text), re.IGNORECASE):
|
||||
candidate_texts.append(block.strip())
|
||||
|
||||
decoder = json.JSONDecoder()
|
||||
|
||||
for candidate in candidate_texts:
|
||||
if not candidate:
|
||||
continue
|
||||
|
||||
try:
|
||||
parsed = json.loads(candidate)
|
||||
coerced = self._coerce_list_payload(parsed)
|
||||
if coerced:
|
||||
return coerced
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
for idx, char in enumerate(candidate):
|
||||
if char not in '[{':
|
||||
continue
|
||||
try:
|
||||
parsed, _ = decoder.raw_decode(candidate[idx:])
|
||||
except Exception:
|
||||
continue
|
||||
|
||||
coerced = self._coerce_list_payload(parsed)
|
||||
if coerced:
|
||||
return coerced
|
||||
|
||||
return []
|
||||
|
||||
def format_knowledge_context(self, knowledge_hits):
|
||||
|
||||
if not knowledge_hits:
|
||||
return "No indexed MCP documents found for this role/topic."
|
||||
|
||||
lines = []
|
||||
for idx, item in enumerate(knowledge_hits[:5]):
|
||||
source = item.get("source", "Unknown Source") if isinstance(item, dict) else "Unknown Source"
|
||||
relevance = item.get("relevance") if isinstance(item, dict) else None
|
||||
content = item.get("content", "") if isinstance(item, dict) else ""
|
||||
safe_content = str(content).strip()[:1600]
|
||||
lines.append(
|
||||
f"[{idx + 1}] Source: {source} | Relevance: {relevance}\n{safe_content}"
|
||||
)
|
||||
|
||||
return "\n\n".join(lines)
|
||||
|
||||
def _sanitize_quiz_fields(self, raw_fields):
|
||||
sanitized = []
|
||||
seen_keys = set()
|
||||
|
||||
for index, field in enumerate(raw_fields or []):
|
||||
if not isinstance(field, dict):
|
||||
continue
|
||||
|
||||
key = str(field.get('key') or f'final_quiz_q_{index + 1}').strip().lower().replace(' ', '_')
|
||||
if not key:
|
||||
key = f'final_quiz_q_{index + 1}'
|
||||
|
||||
if key in seen_keys:
|
||||
key = f'{key}_{index + 1}'
|
||||
seen_keys.add(key)
|
||||
|
||||
label = str(field.get('label') or '').strip()
|
||||
if not label:
|
||||
continue
|
||||
|
||||
field_type = str(field.get('field_type') or 'select').strip().lower()
|
||||
if field_type not in ('select', 'text', 'textarea'):
|
||||
field_type = 'select'
|
||||
|
||||
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)
|
||||
|
||||
validation = field.get('validation') if isinstance(field.get('validation'), dict) else {}
|
||||
if field_type == 'select':
|
||||
if len(options) < 2:
|
||||
continue
|
||||
|
||||
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 ''),
|
||||
},
|
||||
})
|
||||
continue
|
||||
|
||||
accepted_answers_raw = validation.get('accepted_answers')
|
||||
if isinstance(accepted_answers_raw, list):
|
||||
accepted_answers = [str(item).strip() for item in accepted_answers_raw if str(item).strip()]
|
||||
else:
|
||||
accepted_single = str(validation.get('correct_answer') or '').strip()
|
||||
accepted_answers = [accepted_single] if accepted_single else []
|
||||
|
||||
if not accepted_answers:
|
||||
continue
|
||||
|
||||
sanitized.append({
|
||||
'key': key,
|
||||
'label': label,
|
||||
'field_type': 'textarea' if field_type == 'textarea' else 'text',
|
||||
'options': [],
|
||||
'required': True,
|
||||
'validation': {
|
||||
'accepted_answers': accepted_answers,
|
||||
'explanation': str(validation.get('explanation') or ''),
|
||||
},
|
||||
})
|
||||
|
||||
return sanitized
|
||||
|
||||
def _build_fallback_quiz_fields(self, topics, count=8):
|
||||
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(count):
|
||||
topic = safe_topics[index % len(safe_topics)]
|
||||
key = f'final_quiz_q_{index + 1}'
|
||||
|
||||
if index % 3 == 0:
|
||||
fallback_fields.append({
|
||||
'key': key,
|
||||
'label': f"In one or two sentences, what is the safest approach when handling {topic}?",
|
||||
'field_type': 'textarea',
|
||||
'options': [],
|
||||
'required': True,
|
||||
'validation': {
|
||||
'accepted_answers': [
|
||||
'best practices',
|
||||
'documentation',
|
||||
'quality',
|
||||
'compliance',
|
||||
],
|
||||
'explanation': 'Good answers reference documented best practices, quality checks, and compliance.',
|
||||
},
|
||||
})
|
||||
continue
|
||||
|
||||
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,
|
||||
'accepted_answers': [
|
||||
str(item).strip()
|
||||
for item in (validation.get('accepted_answers') if isinstance(validation.get('accepted_answers'), list) else [])
|
||||
if str(item).strip()
|
||||
],
|
||||
'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
|
||||
146
apps/onboarding/consumers/progress.py
Normal file
146
apps/onboarding/consumers/progress.py
Normal file
|
|
@ -0,0 +1,146 @@
|
|||
from django.db.models import Q
|
||||
from channels.db import database_sync_to_async
|
||||
|
||||
from apps.accounts.models import Role
|
||||
from apps.onboarding.consumers.base import BaseOnboardingConsumer, LogType
|
||||
from apps.onboarding.consumers.prompts import OnboardingPrompts
|
||||
from apps.onboarding.models import OnboardingFlow, OnboardingSession
|
||||
|
||||
__all__ = ["OnboardingProgressConsumer"]
|
||||
|
||||
class OnboardingProgressConsumer(BaseOnboardingConsumer):
|
||||
"""
|
||||
Route: /ws/onboarding/progress/<role_uuid>/<flow_uuid>/<user_uuid>/
|
||||
"""
|
||||
|
||||
role_uuid: str
|
||||
flow_uuid: str
|
||||
target_user_uuid: str
|
||||
|
||||
def parse_extra(self):
|
||||
self.role_uuid = self.scope["url_route"]["kwargs"].get("role_uuid")
|
||||
self.flow_uuid = self.scope["url_route"]["kwargs"].get("flow_uuid")
|
||||
self.target_user_uuid = self.scope["url_route"]["kwargs"].get("user_uuid")
|
||||
|
||||
async def action_progress_monitor(self, data: dict):
|
||||
"""
|
||||
Analyzes the learner's progress and provides strengths, gaps, and next actions.
|
||||
"""
|
||||
if not await self.can_access_role(self.role_uuid):
|
||||
return await self.send_error("Forbidden: You do not have access to this role.")
|
||||
|
||||
target_user_id = self.user.id
|
||||
if self.target_user_uuid and str(self.target_user_uuid) != str(self.user.uuid):
|
||||
target_user_id = await self.resolve_target_user_id(self.role_uuid, self.user.id, self.target_user_uuid)
|
||||
if not target_user_id:
|
||||
return await self.send_error("Forbidden: You cannot monitor this user.")
|
||||
|
||||
await self.send_log(LogType.STATUS, "Progress Monitor is analyzing your onboarding progress...", "monitor")
|
||||
|
||||
monitor_config = await self.get_config_by_type(self.role_uuid, 'monitor')
|
||||
if not monitor_config:
|
||||
return await self.send_error("Missing Progress Monitor AgentConfig for this role")
|
||||
|
||||
progress_context = await self.get_role_progress_context(
|
||||
self.role_uuid,
|
||||
target_user_id,
|
||||
flow_uuid=self.flow_uuid,
|
||||
)
|
||||
|
||||
feedback = await self.orchestrate(
|
||||
OnboardingPrompts.progress_monitoring_prompt(progress_context),
|
||||
monitor_config,
|
||||
minimum_turns=1,
|
||||
max_tokens=640,
|
||||
raise_on_error=True
|
||||
)
|
||||
|
||||
await self.send_log(LogType.COMPLETED, "Progress analysis complete.", {
|
||||
"role_uuid": self.role_uuid,
|
||||
"feedback": feedback,
|
||||
"status": progress_context.get("latest_status", "unknown"),
|
||||
"user_id": target_user_id,
|
||||
"flow_uuid": self.flow_uuid,
|
||||
"is_completed": progress_context.get("is_completed", False),
|
||||
})
|
||||
|
||||
### Database Helpers ###
|
||||
@database_sync_to_async
|
||||
def get_role_progress_context(self, role_uuid, user_id, flow_uuid=None):
|
||||
|
||||
role = Role.objects.get(uuid=role_uuid)
|
||||
active_flow = OnboardingFlow.objects.filter(role=role, is_active=True).order_by('-updated_at').first()
|
||||
scoped_flow = OnboardingFlow.objects.filter(role=role, uuid=flow_uuid).first() if flow_uuid else None
|
||||
|
||||
sessions = OnboardingSession.objects.filter(user_id=user_id, role=role).order_by('-updated_at')
|
||||
if flow_uuid:
|
||||
sessions = sessions.filter(Q(flow__uuid=flow_uuid) | Q(flow__isnull=True, 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",
|
||||
"flow_uuid": str((scoped_flow or active_flow).uuid) if (scoped_flow or active_flow) else None,
|
||||
"is_completed": False,
|
||||
"progress": 0,
|
||||
}
|
||||
|
||||
state = latest_session.state or {}
|
||||
flow_for_context = latest_session.flow or scoped_flow or active_flow
|
||||
structure = flow_for_context.structure if flow_for_context and isinstance(flow_for_context.structure, list) else []
|
||||
|
||||
# Find the Final Quiz page for grading details
|
||||
quiz_page = next((p for p in structure if isinstance(p, dict) and p.get("meta", {}).get("page_type") == "final_quiz"), None)
|
||||
if quiz_page is None and structure:
|
||||
quiz_page = structure[-1]
|
||||
|
||||
quiz_page_uuid = str(quiz_page.get("uuid") or "") if quiz_page else ""
|
||||
responses = state.get("responses", {})
|
||||
quiz_responses = responses.get(quiz_page_uuid, {}) if isinstance(responses, dict) else {}
|
||||
|
||||
final_quiz_result = state.get("final_quiz_result", {})
|
||||
grading_details = final_quiz_result.get("grading_details", []) if isinstance(final_quiz_result, dict) else []
|
||||
|
||||
# Build QA map for the AI
|
||||
final_quiz_qa = []
|
||||
if quiz_page:
|
||||
for field in quiz_page.get("fields", []):
|
||||
key = field.get("key")
|
||||
detail = next((d for d in grading_details if d.get("key") == key), {})
|
||||
final_quiz_qa.append({
|
||||
"label": field.get("label"),
|
||||
"answer": quiz_responses.get(key),
|
||||
"marked_correct": detail.get("correct"),
|
||||
"reason": detail.get("reason", "")
|
||||
})
|
||||
|
||||
return {
|
||||
"role_name": role.name,
|
||||
"latest_status": latest_session.status,
|
||||
"progress": state.get("progress_percentage", 0),
|
||||
"completed_modules": state.get("completed_modules", []),
|
||||
"is_completed": latest_session.status == 'completed',
|
||||
"final_quiz_qa": final_quiz_qa,
|
||||
}
|
||||
|
||||
@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 not all([role, requester, target]):
|
||||
return None
|
||||
|
||||
# Check if requester is Owner or Manager
|
||||
is_privileged = (role.organization.owner.id == requester_id or
|
||||
(bool(requester.is_manager) and role.organization.members.filter(id=requester_id).exists()))
|
||||
|
||||
if is_privileged and role.members.filter(id=target.id).exists():
|
||||
return target.id
|
||||
return None
|
||||
71
apps/onboarding/consumers/prompts.py
Normal file
71
apps/onboarding/consumers/prompts.py
Normal file
|
|
@ -0,0 +1,71 @@
|
|||
import json
|
||||
|
||||
__all__ = ["OnboardingPrompts"]
|
||||
|
||||
class OnboardingPrompts:
|
||||
|
||||
@staticmethod
|
||||
def default_system_prompt():
|
||||
return (
|
||||
"You are a helpful onboarding assistant that helps new employees get onboarded to their new company."
|
||||
"You may use relevant tools to assist you to provide the best support."
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def force_reasoning_prompt():
|
||||
return "Double check your reasoning and provide the final improved answer."
|
||||
|
||||
@staticmethod
|
||||
def curriculum_generation_prompt():
|
||||
return (
|
||||
"Based on available documentation, create an onboarding curriculum for this role. "
|
||||
"Output ONLY a valid JSON array of 3-5 strings representing module titles. "
|
||||
"Example: [\"Introduction\", \"Safety\", \"Operations\"]"
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def knowledge_generation_prompt(topic, context_markdown):
|
||||
return (
|
||||
f"Write a practical onboarding training guide for the topic '{topic}'. "
|
||||
"Think step-by-step internally before writing the final answer. "
|
||||
"Use the MCP search context below as your primary source, and call additional tools if needed. "
|
||||
"If no indexed documents are available, provide a concise best-practice overview and clearly say no indexed documents were found. "
|
||||
"Use Markdown formatting and do NOT include a table of contents in this section. "
|
||||
"Generate substantial depth: target 900-1400 words. "
|
||||
"Include these sections in order: Overview, Core Concepts, Role-Specific Workflow, Practical Examples, Common Pitfalls, and Action Checklist. "
|
||||
"In Practical Examples, provide at least 2 concrete examples relevant to this role/topic. "
|
||||
"In Action Checklist, provide at least 8 actionable checklist items.\n\n"
|
||||
f"Topic: {topic}\n"
|
||||
f"MCP search context:\n{context_markdown}"
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def quiz_generation_prompt(question_count, module_briefs):
|
||||
return (
|
||||
"Create a final onboarding quiz that assesses all generated modules. "
|
||||
f"Output ONLY a valid JSON array of exactly {question_count} question objects. "
|
||||
"Use a mix of question types: at least 2 short-answer questions and at least 2 multiple-choice questions. "
|
||||
"For multiple-choice objects: field_type='select', options (4 unique strings), and validation.correct_option. "
|
||||
"For short-answer objects: field_type='textarea' (or 'text') and validation.accepted_answers (array of valid answers/keywords). "
|
||||
"Each object MUST include key, label, field_type, required=true, and validation.explanation. "
|
||||
"Cover all topics with balanced difficulty and avoid ambiguous wording.\n\n"
|
||||
f"Modules JSON:\n{json.dumps(module_briefs, ensure_ascii=False)}"
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def quiz_generation_retry_prompt(question_count, module_briefs):
|
||||
return OnboardingPrompts.quiz_generation_prompt(question_count, module_briefs) + (
|
||||
"Return ONLY raw JSON. Do not use markdown fences. Do not include explanations outside JSON."
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def progress_monitoring_prompt(progress_context):
|
||||
return (
|
||||
"You are a progress monitoring agent for onboarding. "
|
||||
"Analyze the role onboarding data below and provide concise feedback with:\n"
|
||||
"1) current status\n2) strengths\n3) gaps\n4) next actions\n"
|
||||
"Use prior learner question/answer evidence and any saved marking details when available. "
|
||||
"If evidence is insufficient, explicitly state what is missing.\n"
|
||||
"Keep it short and practical.\n\n"
|
||||
f"Progress context JSON:\n{json.dumps(progress_context)}"
|
||||
)
|
||||
|
|
@ -203,4 +203,6 @@ class MCPRouter:
|
|||
return {'value': value, 'min': min_value, 'max': max_value}
|
||||
|
||||
tools = _collect_tools(locals())
|
||||
_tool_name_to_method = {tool['name']: tool['method'] for tool in tools}
|
||||
_tool_name_to_method = {tool['name']: tool['method'] for tool in tools}
|
||||
|
||||
mcp_router = MCPRouter()
|
||||
|
|
@ -25,7 +25,6 @@ class Migration(migrations.Migration):
|
|||
('agent_type', models.CharField(choices=[('curriculum', 'Curriculum Agent (CA)'), ('knowledge', 'Knowledge Agent (KA)'), ('assessment', 'Assessment Agent (AA)'), ('monitor', 'Progress Monitor Agent (PMA)')], max_length=40, verbose_name='Agent Type')),
|
||||
('llm_config', models.JSONField(blank=True, default=dict, null=True, verbose_name='LLM Configuration')),
|
||||
('system_prompt', models.TextField(blank=True, default='', verbose_name='System Prompt')),
|
||||
('tool_permissions', models.JSONField(blank=True, default=list, null=True, verbose_name='Tool Permissions')),
|
||||
('organization', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='agent_configs', to='accounts.organization', verbose_name='Organization')),
|
||||
('role', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='agent_configs', to='accounts.role', verbose_name='Role')),
|
||||
],
|
||||
|
|
|
|||
|
|
@ -19,14 +19,13 @@ class AgentConfig(IdentifierMixin, TimeStampMixin, Model):
|
|||
llm_config = JSONField(default=dict, blank=True, null=True, verbose_name=_("LLM Configuration"))
|
||||
|
||||
system_prompt = TextField(verbose_name=_("System Prompt"), blank=True, default='')
|
||||
tool_permissions = JSONField(default=list, blank=True, null=True, verbose_name=_("Tool Permissions"))
|
||||
|
||||
class Meta:
|
||||
verbose_name = _('Agent Config')
|
||||
verbose_name_plural = _('Agent Configs')
|
||||
|
||||
def __str__(self):
|
||||
return f"{self.name} ({self.get_agent_type_display()})"
|
||||
return f"{self.name} ({self.organization.name})"
|
||||
|
||||
|
||||
class OnboardingFlow(IdentifierMixin, TimeStampMixin, Model):
|
||||
|
|
|
|||
|
|
@ -1,7 +1,9 @@
|
|||
from django.urls import path
|
||||
|
||||
from .consumers import OnboardingConsumer
|
||||
from apps.onboarding.consumers import OnboardingChatConsumer, OnboardingGenerateConsumer, OnboardingProgressConsumer
|
||||
|
||||
websocket_urlpatterns = [
|
||||
path("ws/onboarding/<uuid:session_uuid>/", OnboardingConsumer.as_asgi()),
|
||||
path("ws/onboarding/chat/<uuid:config_uuid>/", OnboardingChatConsumer.as_asgi()),
|
||||
path("ws/onboarding/generate/<uuid:role_uuid>/", OnboardingGenerateConsumer.as_asgi()),
|
||||
path("ws/onboarding/progress/<uuid:role_uuid>/<uuid:flow_uuid>/<uuid:user_uuid>/", OnboardingProgressConsumer.as_asgi()),
|
||||
]
|
||||
|
|
@ -11,7 +11,7 @@ class AgentConfigSerializer(ModelSerializer):
|
|||
model = AgentConfig
|
||||
fields = [
|
||||
'id', 'uuid', 'organization', 'role', 'name', 'agent_type',
|
||||
'system_prompt', 'llm_config', 'tool_permissions',
|
||||
'system_prompt', 'llm_config',
|
||||
'created_at', 'updated_at'
|
||||
]
|
||||
read_only_fields = ['id', 'uuid', 'created_at', 'updated_at']
|
||||
|
|
|
|||
|
|
@ -72,7 +72,6 @@ class OnboardingApiTests(TestCase):
|
|||
'agent_type': 'monitor',
|
||||
'system_prompt': 'Monitor progress',
|
||||
'llm_config': {'model': 'local'},
|
||||
'tool_permissions': ['read'],
|
||||
}, format='json')
|
||||
self.assertEqual(response.status_code, HTTP_201_CREATED)
|
||||
|
||||
|
|
@ -90,7 +89,7 @@ class OnboardingApiTests(TestCase):
|
|||
'agent_type': 'knowledge',
|
||||
'system_prompt': 'Updated',
|
||||
'llm_config': {'model': 'local'},
|
||||
'tool_permissions': ['read'],
|
||||
|
||||
},
|
||||
format='json',
|
||||
)
|
||||
|
|
|
|||
|
|
@ -26,8 +26,7 @@ class OnboardingModelTests(TestCase):
|
|||
name='Operator Knowledge Agent',
|
||||
agent_type='knowledge',
|
||||
llm_config={'model_id': 'x'},
|
||||
system_prompt='Assist user',
|
||||
tool_permissions=['search'],
|
||||
system_prompt='Assist user'
|
||||
)
|
||||
|
||||
self.assertEqual(config.organization, self.org)
|
||||
|
|
@ -35,7 +34,6 @@ class OnboardingModelTests(TestCase):
|
|||
self.assertEqual(config.agent_type, 'knowledge')
|
||||
self.assertEqual(config.llm_config, {'model_id': 'x'})
|
||||
self.assertEqual(config.system_prompt, 'Assist user')
|
||||
self.assertEqual(config.tool_permissions, ['search'])
|
||||
self.assertIsNotNone(config.id)
|
||||
self.assertIsNotNone(config.uuid)
|
||||
self.assertIsNotNone(config.created_at)
|
||||
|
|
|
|||
14
apps/onboarding/utils/content_moderator.py
Normal file
14
apps/onboarding/utils/content_moderator.py
Normal file
|
|
@ -0,0 +1,14 @@
|
|||
from better_profanity import profanity
|
||||
|
||||
profanity.load_censor_words()
|
||||
|
||||
class ContentModerator:
|
||||
def is_clean(self, text: str) -> bool:
|
||||
if not isinstance(text, str):
|
||||
return True
|
||||
return not profanity.contains_profanity(text.strip())
|
||||
|
||||
def censor(self, text: str) -> str:
|
||||
if not isinstance(text, str):
|
||||
return text
|
||||
return profanity.censor(text)
|
||||
|
|
@ -255,7 +255,6 @@ class AgentConfigViewSet(ModelViewSet):
|
|||
agent_type=agent_type,
|
||||
system_prompt=str(request.data.get('system_prompt') or ''),
|
||||
llm_config=request.data.get('llm_config') or {},
|
||||
tool_permissions=request.data.get('tool_permissions') or [],
|
||||
)
|
||||
|
||||
serializer = self.get_serializer(config)
|
||||
|
|
@ -269,14 +268,13 @@ class AgentConfigViewSet(ModelViewSet):
|
|||
'agent_type': request.data.get('agent_type'),
|
||||
'system_prompt': request.data.get('system_prompt'),
|
||||
'llm_config': request.data.get('llm_config'),
|
||||
'tool_permissions': request.data.get('tool_permissions'),
|
||||
}
|
||||
|
||||
for field, value in updatable_fields.items():
|
||||
if value is not None:
|
||||
setattr(config, field, value)
|
||||
|
||||
config.save(update_fields=['name', 'agent_type', 'system_prompt', 'llm_config', 'tool_permissions', 'updated_at'])
|
||||
config.save(update_fields=['name', 'agent_type', 'system_prompt', 'llm_config', 'updated_at'])
|
||||
serializer = self.get_serializer(config)
|
||||
return Response(serializer.data, status=HTTP_200_OK)
|
||||
|
||||
|
|
@ -291,10 +289,8 @@ class AgentConfigViewSet(ModelViewSet):
|
|||
config.system_prompt = request.data.get('system_prompt')
|
||||
if 'llm_config' in request.data:
|
||||
config.llm_config = request.data.get('llm_config')
|
||||
if 'tool_permissions' in request.data:
|
||||
config.tool_permissions = request.data.get('tool_permissions')
|
||||
|
||||
config.save(update_fields=['name', 'agent_type', 'system_prompt', 'llm_config', 'tool_permissions', 'updated_at'])
|
||||
config.save(update_fields=['name', 'agent_type', 'system_prompt', 'llm_config', 'updated_at'])
|
||||
serializer = self.get_serializer(config)
|
||||
return Response(serializer.data, status=HTTP_200_OK)
|
||||
|
||||
|
|
@ -528,9 +524,6 @@ class OnboardingSessionViewSet(ModelViewSet):
|
|||
if not config:
|
||||
return "You are a helpful onboarding assistant."
|
||||
base_prompt = config.system_prompt or "You are a helpful onboarding assistant."
|
||||
permissions = config.tool_permissions or []
|
||||
if permissions:
|
||||
return f"{base_prompt}\n\nAllowed tools: {', '.join(str(p) for p in permissions)}"
|
||||
return base_prompt
|
||||
|
||||
def _get_knowledge_agent_config(self, session):
|
||||
|
|
|
|||
|
|
@ -1,3 +1,4 @@
|
|||
better-profanity==0.7.0
|
||||
celery[redis]==5.6.2
|
||||
channels==4.3.2
|
||||
channels-redis==4.3.0
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@ export const useAgentStore = defineStore('agent', () => {
|
|||
}
|
||||
|
||||
const wsProtocol = window.location.protocol === 'https:' ? 'wss' : 'ws'
|
||||
const wsUrl = `${wsProtocol}://${window.location.host}/ws/onboarding/${id}/`
|
||||
const wsUrl = `${wsProtocol}://${window.location.host}/ws/onboarding/chat/${id}/`
|
||||
socket.value = new WebSocket(wsUrl)
|
||||
|
||||
socket.value.onopen = () => {
|
||||
|
|
@ -102,8 +102,8 @@ export const useAgentStore = defineStore('agent', () => {
|
|||
executionStatus.value = 'running'
|
||||
socket.value.send(
|
||||
JSON.stringify({
|
||||
action: 'message',
|
||||
query: data.query,
|
||||
role_uuid: data.role_uuid,
|
||||
max_tokens: data.max_tokens,
|
||||
}),
|
||||
)
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@ export const useOnboardingAgentStore = defineStore('onboarding-agent', () => {
|
|||
}
|
||||
|
||||
const wsProtocol = window.location.protocol === 'https:' ? 'wss' : 'ws'
|
||||
const wsUrl = `${wsProtocol}://${window.location.host}/ws/onboarding/${id}/`
|
||||
const wsUrl = `${wsProtocol}://${window.location.host}/ws/onboarding/generate/${id}/`
|
||||
socket.value = new WebSocket(wsUrl)
|
||||
|
||||
socket.value.onopen = () => {
|
||||
|
|
|
|||
|
|
@ -27,7 +27,6 @@ const agentUuid = route.params.agentUuid as string
|
|||
|
||||
const agent = ref<AgentConfig>({
|
||||
name: 'Loading...',
|
||||
description: '',
|
||||
status: 'idle',
|
||||
uuid: agentUuid,
|
||||
agent_type: 'knowledge',
|
||||
|
|
@ -193,10 +192,6 @@ onUnmounted(() => {
|
|||
</Tag>
|
||||
</div>
|
||||
|
||||
<Typography.Paragraph type="secondary">
|
||||
{{ agent.description || 'No description available' }}
|
||||
</Typography.Paragraph>
|
||||
|
||||
<Typography.Title :level="4" class="section-title">Configuration</Typography.Title>
|
||||
<div class="execution-controls">
|
||||
<Space direction="vertical" style="width: 100%" :size="12">
|
||||
|
|
|
|||
|
|
@ -79,9 +79,9 @@ const buildProgressReport = (session: ProgressSessionApi | null) => {
|
|||
].join('\n')
|
||||
}
|
||||
|
||||
const websocketUrl = (id: string) => {
|
||||
const websocketUrl = (id: string, userUuid?: string, flowUuid?: string) => {
|
||||
const protocol = window.location.protocol === 'https:' ? 'wss' : 'ws'
|
||||
return `${protocol}://${window.location.host}/ws/onboarding/${id}/`
|
||||
return `${protocol}://${window.location.host}/ws/onboarding/progress/${id}/${flowUuid}/${userUuid}/`
|
||||
}
|
||||
|
||||
const closeMonitorSocket = () => {
|
||||
|
|
@ -107,7 +107,7 @@ const runProgressMonitor = async () => {
|
|||
|
||||
try {
|
||||
closeMonitorSocket()
|
||||
const ws = new WebSocket(websocketUrl(roleId.value))
|
||||
const ws = new WebSocket(websocketUrl(roleId.value, selectedUserUuid.value || undefined, selectedFlowUuid.value || undefined))
|
||||
monitorSocket.value = ws
|
||||
|
||||
await new Promise<void>((resolve, reject) => {
|
||||
|
|
@ -120,14 +120,7 @@ const runProgressMonitor = async () => {
|
|||
}, 30000)
|
||||
|
||||
ws.onopen = () => {
|
||||
ws.send(
|
||||
JSON.stringify({
|
||||
action: 'progress_monitor',
|
||||
role_uuid: roleId.value,
|
||||
user_uuid: selectedUserUuid.value || undefined,
|
||||
flow_uuid: selectedFlowUuid.value || undefined,
|
||||
}),
|
||||
)
|
||||
ws.send(JSON.stringify({action: 'progress_monitor'}),)
|
||||
}
|
||||
|
||||
ws.onmessage = (event) => {
|
||||
|
|
|
|||
Loading…
Reference in a new issue