feat: post-turn state-update pass per present entity

chat/services/state_update.py

@@ -0,0 +1,144 @@
+"""Post-turn state-update pass.
+
+Per Requirements §3.4, after every utterance we run a classifier on each
+present entity (silent witnesses included) to extract directed-edge
+deltas — what changed in *source*'s view of *target*. The classifier
+returns three signals:
+
+- ``affinity_delta`` — signed change in how warmly source feels (typical
+  range -3..+3; the edge handler clamps the running total to 0..100).
+- ``trust_delta`` — signed change in source's trust of target (same
+  shape).
+- ``knowledge_facts`` — concrete things source learned about target
+  during this exchange. Stored verbatim and appended to ``edge.knowledge``.
+
+The wrapper deliberately uses :func:`chat.llm.classify.classify` with a
+``default=StateUpdate()`` so a flapping classifier never blocks the turn
+flow — at worst the edge sits unchanged and the next turn tries again
+(§3.3 "graceful degradation" rule).
+"""
+
+from __future__ import annotations
+
+from pydantic import BaseModel, Field
+
+from chat.llm.classify import classify
+from chat.llm.client import LLMClient
+
+
+class StateUpdate(BaseModel):
+    """One directed-edge update from a single classifier call.
+
+    Defaults are deliberately a no-op (zero deltas, empty facts) so a
+    failing classifier produces a benign event rather than a disruption.
+    """
+
+    affinity_delta: int = 0
+    trust_delta: int = 0
+    knowledge_facts: list[str] = Field(default_factory=list)
+
+
+_SYSTEM_PROMPT = (
+    "You are reading a recent slice of dialogue from a roleplay scene. "
+    "You assess how SOURCE's view of TARGET shifted based on what was "
+    "said — including silent witnessing (SOURCE may not have spoken).\n\n"
+    "Output a JSON object with exactly three fields:\n"
+    "- affinity_delta: signed integer in [-3, 3]. How much warmer "
+    "(positive) or cooler (negative) SOURCE now feels toward TARGET.\n"
+    "- trust_delta: signed integer in [-3, 3]. How much more (positive) "
+    "or less (negative) SOURCE now trusts TARGET.\n"
+    "- knowledge_facts: list of short strings. New, concrete facts "
+    "SOURCE learned about TARGET in this exchange. Use TARGET's actual "
+    "stated content; do not infer or interpret. Empty list is fine.\n\n"
+    "Be conservative. Most turns produce small deltas (-1, 0, +1). "
+    "Reserve +/-2 or +/-3 for moments that materially shift the "
+    "relationship. Knowledge_facts should be specific things stated in "
+    "dialogue (e.g. \"works at the bakery\"), not interpretations "
+    "(\"seems lonely\")."
+)
+
+
+def _format_dialogue(recent_dialogue: list[dict]) -> str:
+    """Render the recent-dialogue slice as plain ``Speaker: text`` lines."""
+    if not recent_dialogue:
+        return "(no dialogue yet)"
+    lines = []
+    for turn in recent_dialogue:
+        speaker = turn.get("speaker", "?")
+        text = turn.get("text", "")
+        lines.append(f"{speaker}: {text}")
+    return "\n".join(lines)
+
+
+def _build_user_prompt(
+    *,
+    source_name: str,
+    source_persona: str,
+    target_name: str,
+    prior_affinity: int,
+    prior_trust: int,
+    prior_summary: str,
+    recent_dialogue: list[dict],
+) -> str:
+    return (
+        f"SOURCE: {source_name}\n"
+        f"SOURCE_PERSONA: {source_persona or '(none)'}\n"
+        f"TARGET: {target_name}\n"
+        f"PRIOR_AFFINITY (0-100): {prior_affinity}\n"
+        f"PRIOR_TRUST (0-100): {prior_trust}\n"
+        f"PRIOR_SUMMARY: {prior_summary or '(none)'}\n\n"
+        f"RECENT_DIALOGUE:\n{_format_dialogue(recent_dialogue)}\n\n"
+        "How did SOURCE's view of TARGET shift? Respond with JSON only."
+    )
+
+
+async def compute_state_update(
+    client: LLMClient,
+    *,
+    model: str,
+    source_id: str,
+    target_id: str,
+    source_name: str,
+    source_persona: str,
+    target_name: str,
+    prior_affinity: int,
+    prior_trust: int,
+    prior_summary: str,
+    recent_dialogue: list[dict],
+    timeout_s: float = 10.0,
+) -> StateUpdate:
+    """Run a classifier pass and return the directed-edge update.
+
+    On classifier failure (after retry) returns the schema default — a
+    no-op ``StateUpdate`` — so the turn flow can keep moving. The
+    ``source_id`` / ``target_id`` arguments are accepted for symmetry
+    with the caller (T20's POST flow uses them when emitting the
+    ``edge_update`` event); they're not currently embedded in the
+    prompt because the classifier reasons about names, not opaque ids.
+    """
+    # ``source_id``/``target_id`` are kept on the signature even though
+    # the prompt only quotes the names: callers in turns.py thread the
+    # ids straight from this function's args into the appended event.
+    del source_id, target_id  # silence unused-arg lint cleanly
+
+    user_prompt = _build_user_prompt(
+        source_name=source_name,
+        source_persona=source_persona,
+        target_name=target_name,
+        prior_affinity=prior_affinity,
+        prior_trust=prior_trust,
+        prior_summary=prior_summary,
+        recent_dialogue=recent_dialogue,
+    )
+
+    return await classify(
+        client,
+        model=model,
+        system=_SYSTEM_PROMPT,
+        user=user_prompt,
+        schema=StateUpdate,
+        default=StateUpdate(),
+        timeout_s=timeout_s,
+    )
+
+