From b6c3e4e51eeab703b40284459c6e9fff2151216c Mon Sep 17 00:00:00 2001
From: YurenHao0426 <Blackhao0426@gmail.com>
Date: Wed, 18 Mar 2026 18:25:09 -0500
Subject: Initial release: VARS - personalized LLM with RAG and user vector
 learning

---
 src/personalization/feedback/handlers.py | 87 ++++++++++++++++++++++++++++++++
 1 file changed, 87 insertions(+)
 create mode 100644 src/personalization/feedback/handlers.py

(limited to 'src/personalization/feedback/handlers.py')

diff --git a/src/personalization/feedback/handlers.py b/src/personalization/feedback/handlers.py
new file mode 100644
index 0000000..f0468b6
--- /dev/null
+++ b/src/personalization/feedback/handlers.py
@@ -0,0 +1,87 @@
+from typing import Tuple, List, Optional
+import numpy as np
+
+from personalization.retrieval.preference_store.schemas import MemoryCard
+from personalization.feedback.schemas import TurnSample
+from personalization.feedback.reward_model import estimate_reward
+from personalization.feedback.gating import estimate_retrieval_gating
+from personalization.feedback.llm_reward import (
+    LLMRewardClient, LLMRewardConfig, RewardResult
+)
+
+
+def eval_step(
+    q_t: str,
+    answer_t: str,
+    q_t1: str,
+    memories_t: List[MemoryCard],
+    query_embedding_t: Optional[np.ndarray] = None,
+    query_embedding_t1: Optional[np.ndarray] = None,
+) -> Tuple[float, float]:
+    """
+    Keyword-based evaluation (legacy).
+    Given (q_t, a_t, q_{t+1}, memories), returns (reward_hat, gating_hat).
+    """
+    mem_embs = None
+    if memories_t and memories_t[0].embedding_e:
+        try:
+            mem_embs = np.array([m.embedding_e for m in memories_t])
+        except:
+            pass
+
+    sample = TurnSample(
+        user_id="",
+        session_id="",
+        turn_id=0,
+        query_t=q_t,
+        answer_t=answer_t,
+        query_t1=q_t1,
+        memories=memories_t,
+        query_embedding_t=query_embedding_t,
+        query_embedding_t1=query_embedding_t1,
+        memory_embeddings=mem_embs,
+    )
+
+    r_hat = estimate_reward(sample)
+    g_hat = estimate_retrieval_gating(sample, r_hat)
+
+    return r_hat, g_hat
+
+
+async def eval_step_llm(
+    q_t: str,
+    answer_t: str,
+    q_t1: str,
+    memories_t: List[MemoryCard],
+    client: LLMRewardClient,
+    query_embedding_t: Optional[np.ndarray] = None,
+    query_embedding_t1: Optional[np.ndarray] = None,
+) -> Tuple[float, float]:
+    """
+    LLM-as-judge evaluation (async).
+    Returns (reward, gating) where gating=0.0 if update should be skipped.
+
+    The gating signal is derived from the judge's confidence and label:
+      - If confidence < tau_c or label == topic_shift: gating = 0.0
+      - Otherwise: gating = confidence (continuous, in [tau_c, 1.0])
+
+    This replaces the old heuristic gating with the judge's own confidence.
+    """
+    sample = TurnSample(
+        user_id="",
+        session_id="",
+        turn_id=0,
+        query_t=q_t,
+        answer_t=answer_t,
+        query_t1=q_t1,
+        memories=memories_t,
+        query_embedding_t=query_embedding_t,
+        query_embedding_t1=query_embedding_t1,
+    )
+
+    result: RewardResult = await client.judge(sample)
+
+    if result.should_update:
+        return result.reward, result.confidence
+    else:
+        return 0.0, 0.0
-- 
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