WIN-JHFT4D3SIVT\caoxiaozhu
36c93a764f
Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-openagent) Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
43 lines
1.7 KiB
Python
43 lines
1.7 KiB
Python
from __future__ import annotations
|
|
|
|
from uuid import uuid4
|
|
|
|
from app.agents.schemas.learning import LearningSignal, PatternCandidate
|
|
|
|
|
|
class LearningPatternMiner:
|
|
def mine(self, signals: list[LearningSignal]) -> list[PatternCandidate]:
|
|
patterns: list[PatternCandidate] = []
|
|
|
|
for signal in signals:
|
|
if signal.signal_type not in {"workflow", "decomposition", "preference"}:
|
|
continue
|
|
|
|
description = self._build_description(signal)
|
|
patterns.append(
|
|
PatternCandidate(
|
|
pattern_id=f"pattern-{uuid4().hex[:10]}",
|
|
pattern_type=signal.signal_type,
|
|
description=description,
|
|
confidence=signal.confidence,
|
|
evidence_refs=signal.evidence_refs[:4],
|
|
)
|
|
)
|
|
|
|
return patterns
|
|
|
|
@staticmethod
|
|
def _build_description(signal: LearningSignal) -> str:
|
|
payload = signal.payload or {}
|
|
if signal.signal_type == "workflow":
|
|
task_type = payload.get("task_type") or "general"
|
|
execution_mode = payload.get("execution_mode") or "direct"
|
|
return f"Completed {task_type} requests worked under {execution_mode} execution."
|
|
if signal.signal_type == "decomposition":
|
|
task_count = payload.get("task_count") or 0
|
|
return f"Requests with {task_count} concrete task refs benefit from structured decomposition."
|
|
if signal.signal_type == "preference":
|
|
preference = payload.get("preference") or "structured response"
|
|
return f"User preference repeatedly points to {preference}."
|
|
return signal.explanation or signal.signal_type
|