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Add contextuality validation and low-context workflow status

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ي
2026-03-06 21:42:49 +05:30
parent 5d49351c2d
commit 7096f03623
3 changed files with 201 additions and 6 deletions
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157
backend/services/today_workflow_service.py
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@@ -11,6 +11,8 @@ from services.llm_providers.main_text_generation import llm_text_gen
from loguru import logger
PILLAR_IDS = ["plan", "generate", "publish", "analyze", "engage", "remarket"]
MIN_TASK_EVIDENCE_LINKS = 1
PLAN_CONTEXT_THRESHOLD = 0.65
def _today_date_str() -> str:
@@ -139,6 +141,116 @@ def _sanitize_task(task: Dict[str, Any]) -> Optional[Dict[str, Any]]:
return sanitized
def _derive_onboarding_evidence_links(onboarding_data: Dict[str, Any], limit: int = 2) -> List[str]:
if not isinstance(onboarding_data, dict):
return []
links: List[str] = []
for key, value in onboarding_data.items():
if key == "workflow_config":
continue
if value in (None, "", [], {}):
continue
links.append(f"onboarding:{key}")
if len(links) >= limit:
break
return links
def _valid_evidence_links(evidence_links: Any, grounding: Dict[str, Any]) -> List[str]:
if not isinstance(evidence_links, list):
return []
onboarding_data = grounding.get("onboarding_data", {}) if isinstance(grounding, dict) else {}
if not isinstance(onboarding_data, dict):
onboarding_data = {}
valid_onboarding_keys = {str(k) for k in onboarding_data.keys()}
recent_alerts = grounding.get("recent_agent_alerts", []) if isinstance(grounding, dict) else []
valid_alert_ids = {
str(a.get("alert_id"))
for a in recent_alerts
if isinstance(a, dict) and a.get("alert_id") is not None
}
valid_links: List[str] = []
for raw in evidence_links:
link = str(raw or "").strip()
if not link:
continue
if link.startswith("onboarding:"):
key = link.split(":", 1)[1].strip()
if key and key in valid_onboarding_keys:
valid_links.append(link)
elif link.startswith("alert:"):
alert_id = link.split(":", 1)[1].strip()
if alert_id and alert_id in valid_alert_ids:
valid_links.append(link)
return valid_links
def validate_plan_contextuality(plan: Dict[str, Any], grounding: Dict[str, Any]) -> Dict[str, Any]:
tasks = plan.get("tasks") if isinstance(plan, dict) else None
if not isinstance(tasks, list) or not tasks:
return {
"score": 0.0,
"threshold": PLAN_CONTEXT_THRESHOLD,
"is_contextual": False,
"task_scores": [],
"tasks_below_min_evidence": 0,
"min_evidence_links": MIN_TASK_EVIDENCE_LINKS,
}
task_scores = []
below_min_evidence = 0
for idx, task in enumerate(tasks):
metadata = task.get("metadata") if isinstance(task, dict) else {}
metadata = metadata if isinstance(metadata, dict) else {}
evidence_links = _valid_evidence_links(metadata.get("evidence_links"), grounding)
has_min_evidence = len(evidence_links) >= MIN_TASK_EVIDENCE_LINKS
if not has_min_evidence:
below_min_evidence += 1
reasoning_text = str(metadata.get("reasoning") or task.get("description") or "").lower()
onboarding_hits = sum(1 for l in evidence_links if l.startswith("onboarding:"))
alert_hits = sum(1 for l in evidence_links if l.startswith("alert:"))
score = 0.0
if has_min_evidence:
score += 0.6
if onboarding_hits > 0:
score += 0.2
if alert_hits > 0:
score += 0.2
elif "alert" in reasoning_text:
score += 0.1
task_scores.append(
{
"task_index": idx,
"pillarId": task.get("pillarId"),
"title": task.get("title"),
"score": min(score, 1.0),
"evidence_links": evidence_links,
"has_min_evidence": has_min_evidence,
}
)
plan_score = sum(t["score"] for t in task_scores) / len(task_scores)
is_contextual = plan_score >= PLAN_CONTEXT_THRESHOLD and below_min_evidence == 0
return {
"score": round(plan_score, 3),
"threshold": PLAN_CONTEXT_THRESHOLD,
"is_contextual": is_contextual,
"task_scores": task_scores,
"tasks_below_min_evidence": below_min_evidence,
"min_evidence_links": MIN_TASK_EVIDENCE_LINKS,
}
def _build_single_task_for_missing_pillar(
user_id: str,
date: str,
@@ -253,6 +365,7 @@ def build_grounding_context(db: Session, user_id: str, date: str) -> Dict[str, A
return {
"recent_agent_alerts": [
{
"alert_id": a.id,
"title": a.title,
"message": a.message,
"created_at": a.created_at.isoformat(),
@@ -272,9 +385,15 @@ from services.task_memory_service import TaskMemoryService
# Initialize orchestration service (singleton)
orchestration_service = AgentOrchestrationService()
async def generate_agent_enhanced_plan(db: Session, user_id: str, date: str) -> Dict[str, Any]:
async def generate_agent_enhanced_plan(
db: Session,
user_id: str,
date: str,
grounding: Optional[Dict[str, Any]] = None,
strict_contextuality: bool = False,
) -> Dict[str, Any]:
activity = AgentActivityService(db, user_id)
grounding = build_grounding_context(db, user_id, date)
grounding = grounding or build_grounding_context(db, user_id, date)
memory_service = TaskMemoryService(user_id, db)
# 1. Get Orchestrator
@@ -351,7 +470,7 @@ async def generate_agent_enhanced_plan(db: Session, user_id: str, date: str) ->
# 4. Final Selection
# If we have agent tasks, use them. Otherwise fall back to LLM generation.
if agent_tasks:
if agent_tasks and not strict_contextuality:
logger.info(f"Generated {len(agent_tasks)} tasks via Agent Committee")
# Convert TaskProposal objects to dicts for frontend
@@ -369,7 +488,8 @@ async def generate_agent_enhanced_plan(db: Session, user_id: str, date: str) ->
"metadata": {
"source_agent": prop.source_agent,
"reasoning": prop.reasoning,
"context_data": prop.context_data
"context_data": prop.context_data,
"evidence_links": _derive_onboarding_evidence_links(grounding.get("onboarding_data", {}), limit=2),
}
})
@@ -425,6 +545,15 @@ async def generate_agent_enhanced_plan(db: Session, user_id: str, date: str) ->
f"Grounding context (Alerts):\n{json.dumps(grounding.get('recent_agent_alerts', []), indent=2)}\n"
)
if strict_contextuality:
prompt += (
"\nStrict contextuality mode (must follow):\n"
f"- Every task.metadata must include evidence_links with at least {MIN_TASK_EVIDENCE_LINKS} entries.\n"
"- evidence_links entries must use either 'onboarding:<field_name>' or 'alert:<alert_id>' format.\n"
"- Include metadata.reasoning that explains how the evidence applies to the task.\n"
"- Reject generic tasks without explicit ties to onboarding data or active alerts.\n"
)
run = activity.start_run(agent_type="TodayWorkflowGenerator", prompt=prompt[:4000])
activity.log_event(
event_type="plan",
@@ -492,7 +621,25 @@ async def get_or_create_daily_workflow_plan(db: Session, user_id: str, date: Opt
if existing:
return existing, False
plan_data = await generate_agent_enhanced_plan(db, user_id, date_str)
grounding = build_grounding_context(db, user_id, date_str)
plan_data = await generate_agent_enhanced_plan(db, user_id, date_str, grounding=grounding)
validation = validate_plan_contextuality(plan_data, grounding)
if not validation.get("is_contextual"):
logger.info("Plan contextuality below threshold for user {}. Running strict regeneration.", user_id)
regenerated_plan = await generate_agent_enhanced_plan(
db,
user_id,
date_str,
grounding=grounding,
strict_contextuality=True,
)
regenerated_validation = validate_plan_contextuality(regenerated_plan, grounding)
plan_data = regenerated_plan
validation = regenerated_validation
plan_data["quality_status"] = "contextual" if validation.get("is_contextual") else "low_context"
plan_data["contextuality_validation"] = validation
tasks = plan_data.get("tasks", [])
def _create_plan():