File size: 5,494 Bytes
7302343 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 | """Feedback ingest — updates user memory, thread memory, cold-start counter.
Spec: docs/05-Memory.md §3, §4, §9-10.
Called on every ModAction feedback event. Pure business logic with an
injected session — no direct I/O beyond the session calls.
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import TYPE_CHECKING
import structlog
from store.postgres import (
append_audit,
increment_cold_start_count,
record_feedback,
upsert_thread_memory,
upsert_user_memory,
)
if TYPE_CHECKING:
from sqlalchemy.ext.asyncio import AsyncSession
from store.types import FeedbackInput
logger = structlog.get_logger(__name__)
async def process_feedback(
session: AsyncSession,
*,
feedback: FeedbackInput,
target_author_id: str = "",
post_id: str = "",
) -> IngestResult:
"""Process a single feedback event — the I-3.4 entry point.
Updates:
1. Durable feedback row (Postgres).
2. User memory counters + risk tier for the target author.
3. Thread memory mod_actions_taken (if post_id available).
4. Subreddit cold_start_count.
5. Audit log entry.
All writes happen within the caller's session (single transaction).
"""
log = logger.bind(
correlation_id=feedback.correlation_id,
subreddit_id=feedback.subreddit_id,
mod_action=feedback.mod_action,
)
# 1. Durable feedback record.
await record_feedback(session, feedback=feedback)
# 2. User memory update.
user_memory = None
if target_author_id:
violations_delta, approvals_delta = _compute_deltas(feedback.mod_action)
user_memory = await upsert_user_memory(
session,
subreddit_id=feedback.subreddit_id,
user_id=target_author_id,
prior_violations_delta=violations_delta,
prior_approvals_delta=approvals_delta,
)
# Recompute risk tier based on updated counters.
new_tier = compute_risk_tier(
prior_violations=user_memory.prior_violations,
prior_approvals=user_memory.prior_approvals,
created_at=user_memory.last_seen_at,
)
if new_tier != user_memory.risk_tier:
user_memory = await upsert_user_memory(
session,
subreddit_id=feedback.subreddit_id,
user_id=target_author_id,
risk_tier=new_tier,
)
log.info(
"ingest.risk_tier_changed",
user_id=target_author_id,
old_tier=user_memory.risk_tier,
new_tier=new_tier,
)
# 3. Thread memory update.
if post_id:
await upsert_thread_memory(
session,
subreddit_id=feedback.subreddit_id,
post_id=post_id,
mod_action_entry={
"action": feedback.mod_action,
"moderator": feedback.moderator_name or feedback.moderator_id,
"correlation_id": feedback.correlation_id,
"target_id": feedback.target_id,
},
)
# 4. Increment cold-start counter.
new_count = await increment_cold_start_count(
session, subreddit_id=feedback.subreddit_id
)
# 5. Audit log.
await append_audit(
session,
subreddit_id=feedback.subreddit_id,
event_type="feedback.processed",
actor=feedback.moderator_name or feedback.moderator_id or "unknown",
correlation_id=feedback.correlation_id,
detail={
"mod_action": feedback.mod_action,
"target_id": feedback.target_id,
"source": feedback.source,
"aligned": feedback.aligned,
},
)
log.info(
"ingest.completed",
cold_start_count=new_count,
user_updated=bool(target_author_id),
thread_updated=bool(post_id),
)
return IngestResult(
cold_start_count=new_count,
risk_tier_changed=user_memory is not None,
)
# === Risk tier computation ================================================
def compute_risk_tier(
*,
prior_violations: int,
prior_approvals: int,
created_at: object = None,
) -> str:
"""Derive risk tier from user memory counters.
Simplified MVP rules per docs/05-Memory.md §3.4:
- "watched" if prior_violations >= 3
- "trusted" if prior_approvals >= 5 and prior_violations == 0
- "new" if no history (both zero)
- "neutral" otherwise
Full trust_score computation with time decay is post-MVP (nightly batch).
"""
if prior_violations == 0 and prior_approvals == 0:
return "new"
if prior_violations >= 3:
return "watched"
if prior_approvals >= 5 and prior_violations == 0:
return "trusted"
return "neutral"
# === Helpers ==============================================================
def _compute_deltas(mod_action: str) -> tuple[int, int]:
"""Return (violations_delta, approvals_delta) for a given mod action."""
if mod_action == "REMOVE":
return (1, 0)
if mod_action == "APPROVE":
return (0, 1)
# ESCALATE and LOCK don't directly change counters.
return (0, 0)
# === Result type ==========================================================
@dataclass(frozen=True)
class IngestResult:
"""Returned to the caller so the endpoint can include metadata."""
cold_start_count: int
risk_tier_changed: bool
|