File size: 2,485 Bytes
17640d9 aafbcb7 17640d9 aafbcb7 17640d9 aafbcb7 17640d9 aafbcb7 17640d9 aafbcb7 17640d9 aafbcb7 17640d9 aafbcb7 17640d9 | 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 | import random
from datetime import datetime
RESURFACE_AFTER = {
"act": 1,
"learn": 3,
"reference": 7,
}
INTENT_WEIGHT = {
"act": 100,
"learn": 70,
"reference": 40,
"ephemeral": 30,
}
# Mood adjusts intent weights. Missing keys = use default above.
MOOD_OVERRIDES = {
"focused": {"act": 150, "learn": 60, "reference": 30, "ephemeral": -1}, # task mode, no distractions
"curious": {"act": 40, "learn": 150, "reference": 80, "ephemeral": 30}, # deep learning mode
"restless": {"act": 30, "learn": 60, "reference": 80, "ephemeral": 150}, # variety/browse, suppress tasks
"tired": {"act": -1, "learn": 30, "reference": 80, "ephemeral": 120}, # no tasks, light content only
"inspired": {"act": 100, "learn": 120, "reference": 40, "ephemeral": 60}, # creative energy, learn + act
}
def _days_since(dt_str):
if not dt_str:
return 999
try:
dt = datetime.strptime(dt_str, "%Y-%m-%d %H:%M:%S")
return (datetime.now() - dt).days
except Exception:
return 999
def score(capture, mood=None):
intent = capture.get("intent", "reference")
weights = MOOD_OVERRIDES.get(mood, INTENT_WEIGHT) if mood else INTENT_WEIGHT
weight = weights.get(intent, INTENT_WEIGHT.get(intent, 40))
if weight < 0:
return -1
days_since_surfaced = _days_since(capture.get("last_surfaced_at"))
days_since_created = _days_since(capture.get("created_at"))
threshold = RESURFACE_AFTER.get(intent, 3)
if days_since_surfaced < threshold:
return -1
novelty = 80 if capture.get("last_surfaced_at") is None else 0
age_bonus = min(days_since_created * 2, 60)
jitter = random.randint(0, 15)
return weight + novelty + age_bonus + jitter
def pick(captures, n=3, mode=None, mood=None):
"""
Score and pick top-n captures to surface.
mode: None | 'learn' | 'act' | 'reference' — hard intent filter
mood: None | 'focused' | 'learning' | 'browsing' | 'bored' — adjusts weights
"""
pool = captures
if mode in ("learn", "act", "reference"):
pool = [c for c in captures if c.get("intent") == mode]
if len(pool) < n:
pool = captures
scored = [(score(c, mood=mood), c) for c in pool]
scored.sort(key=lambda x: x[0], reverse=True)
eligible = [(s, c) for s, c in scored if s >= 0]
if eligible:
return [c for _, c in eligible[:n]]
return [c for _, c in scored[:n]]
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