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be5a5d4 6020272 be5a5d4 6020272 be5a5d4 6020272 be5a5d4 6020272 be5a5d4 6020272 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 | from __future__ import annotations
from typing import Any
from .trajectory_store import trajectory_store
class ExampleExtractor:
"""Extract few-shot examples from successful trajectories."""
def extract_classification_examples(
self,
task_id: str,
persona: str,
limit: int = 2,
) -> list[dict[str, Any]]:
"""Extract classification action examples from high-scoring runs."""
trajectories = trajectory_store.get_similar_trajectories(task_id, persona, limit)
examples = []
for traj in trajectories:
for step in traj.get("trajectory", []):
action = step.get("action", {})
if action.get("action_type") == "classify":
examples.append(
{
"email_id": action.get("email_id"),
"label": action.get("label"),
"reason": step.get("reason", ""),
}
)
if len(examples) >= limit:
return examples
return examples
def extract_reply_examples(
self,
task_id: str,
persona: str,
limit: int = 2,
) -> list[dict[str, Any]]:
"""Extract reply action examples from high-scoring runs."""
trajectories = trajectory_store.get_similar_trajectories(task_id, persona, limit)
examples = []
for traj in trajectories:
for step in traj.get("trajectory", []):
action = step.get("action", {})
if action.get("action_type") == "reply":
examples.append(
{
"email_id": action.get("email_id"),
"content": action.get("content", "")[:200],
"sender_role": step.get("sender_role", "unknown"),
}
)
if len(examples) >= limit:
return examples
return examples
def extract_escalation_examples(
self,
task_id: str,
persona: str,
limit: int = 2,
) -> list[dict[str, Any]]:
"""Extract escalation action examples from high-scoring runs."""
trajectories = trajectory_store.get_similar_trajectories(task_id, persona, limit)
examples = []
for traj in trajectories:
for step in traj.get("trajectory", []):
action = step.get("action", {})
if action.get("action_type") == "escalate":
examples.append(
{
"email_id": action.get("email_id"),
"escalate_to": action.get("escalate_to"),
"reason": step.get("reason", ""),
}
)
if len(examples) >= limit:
return examples
return examples
def extract_prioritization_examples(
self,
task_id: str,
persona: str,
limit: int = 2,
) -> list[dict[str, Any]]:
"""Extract prioritization examples from high-scoring runs."""
trajectories = trajectory_store.get_similar_trajectories(task_id, persona, limit)
examples = []
for traj in trajectories:
for step in traj.get("trajectory", []):
action = step.get("action", {})
if action.get("action_type") == "prioritize":
examples.append(
{
"priority_order": action.get("priority_order", []),
"reason": step.get("reason", ""),
}
)
if len(examples) >= limit:
return examples
return examples
def extract_all_examples(
self,
task_id: str,
persona: str,
) -> dict[str, list[dict[str, Any]]]:
"""Extract all types of examples for a task/persona combination."""
return {
"classify": self.extract_classification_examples(task_id, persona),
"reply": self.extract_reply_examples(task_id, persona),
"escalate": self.extract_escalation_examples(task_id, persona),
"prioritize": self.extract_prioritization_examples(task_id, persona),
}
example_extractor = ExampleExtractor()
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