Create Relay Bot MVP

app/proactive/classifier.py

@@ -0,0 +1,36 @@
+from app.llm.prompts import CLASSIFIER_PROMPT
+from app.llm.schemas import Classification
+
+
+def _confidence(value: object) -> float:
+    named_levels = {"high": 0.9, "medium": 0.6, "low": 0.3}
+    if isinstance(value, str) and value.lower() in named_levels:
+        return named_levels[value.lower()]
+    try:
+        return min(1.0, max(0.0, float(value)))
+    except (TypeError, ValueError):
+        return 0.0
+
+
+def _flag(value: object) -> bool:
+    if isinstance(value, str):
+        return value.strip().lower() in {"true", "yes", "1"}
+    return bool(value)
+
+
+class MessageClassifier:
+    def __init__(self, llm):
+        self.llm = llm
+
+    async def classify(self, text: str) -> Classification:
+        data = await self.llm.json(
+            [{"role": "user", "content": f"{CLASSIFIER_PROMPT}\nMessage:\n{text}"}]
+        )
+        return Classification(
+            is_question=_flag(data.get("is_question")),
+            project_related=_flag(data.get("project_related")),
+            asks_new_decision=_flag(data.get("asks_new_decision")),
+            rhetorical=_flag(data.get("rhetorical")),
+            blocker=_flag(data.get("blocker")),
+            confidence=_confidence(data.get("confidence", 0)),
+        )

app/proactive/policy.py

@@ -0,0 +1,38 @@
+from dataclasses import dataclass
+from hashlib import sha256
+
+from app.llm.schemas import Classification
+
+
+@dataclass(frozen=True)
+class ProactiveDecision:
+    respond: bool
+    reason: str
+    fingerprint: str
+
+
+def decide(
+    mode: str,
+    classification: Classification,
+    *,
+    is_mention: bool,
+    duplicate: bool,
+    cooldown: bool,
+    threshold: float,
+) -> ProactiveDecision:
+    fingerprint = sha256(repr(classification).encode()).hexdigest()
+    if mode == "off":
+        return ProactiveDecision(False, "mode_off", fingerprint)
+    if mode == "mentions" and not is_mention:
+        return ProactiveDecision(False, "not_mentioned", fingerprint)
+    if duplicate or cooldown:
+        return ProactiveDecision(False, "anti_spam", fingerprint)
+    if not classification.is_question or classification.rhetorical:
+        return ProactiveDecision(False, "not_actionable_question", fingerprint)
+    if not classification.project_related:
+        return ProactiveDecision(False, "not_project_related", fingerprint)
+    if classification.asks_new_decision:
+        return ProactiveDecision(False, "requires_human_decision", fingerprint)
+    if classification.confidence < threshold:
+        return ProactiveDecision(False, "low_confidence", fingerprint)
+    return ProactiveDecision(True, "confirmed_fact_candidate", fingerprint)