Add RAG rewrite, 60-session experiment scripts, and analysis tools

- RAG rewrite adapter and vector preference pipeline in personalized_llm
- 60-session experiment queue scripts (reflection, rag, rag_vector, rag_rewrite)
- Vector-preference correlation analysis and visualization scripts
- Local reward model batch processing improvements
- Updated CLAUDE.md with full experiment documentation and notes

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

1 files changed, 53 insertions, 0 deletions

diff --git a/src/personalization/models/preference_extractor/rule_extractor.py b/src/personalization/models/preference_extractor/rule_extractor.py
index 0f743d9..42f43ed 100644
--- a/src/personalization/models/preference_extractor/rule_extractor.py
+++ b/src/personalization/models/preference_extractor/rule_extractor.py
@@ -119,6 +119,59 @@ class QwenRuleExtractor(PreferenceExtractor):
             return text[start : end + 1]
         return None
 
+    @torch.inference_mode()
+    def batch_extract_preferences(self, queries: List[str], batch_size: int = 64) -> List[Dict[str, Any]]:
+        """
+        Batch extract preferences from multiple queries using left-padded batching.
+        """
+        if not queries:
+            return []
+
+        # Save and set padding side for decoder-only batched generation
+        orig_padding_side = self.tokenizer.padding_side
+        self.tokenizer.padding_side = "left"
+
+        all_results = []
+        prompts = [self.build_preference_prompt(q) for q in queries]
+
+        for start in range(0, len(prompts), batch_size):
+            batch_prompts = prompts[start:start + batch_size]
+            inputs = self.tokenizer(
+                batch_prompts, return_tensors="pt", padding=True, truncation=True
+            ).to(self.model.device)
+
+            outputs = self.model.generate(
+                **inputs,
+                do_sample=False,
+                max_new_tokens=512,
+                pad_token_id=self.tokenizer.pad_token_id,
+                eos_token_id=self.tokenizer.eos_token_id,
+            )
+
+            for i in range(len(batch_prompts)):
+                input_len = (inputs["attention_mask"][i] == 1).sum().item()
+                gen_ids = outputs[i][input_len:]
+                text = self.tokenizer.decode(gen_ids, skip_special_tokens=True)
+
+                try:
+                    data = json.loads(text)
+                    validated = PreferenceList.model_validate(data)
+                    all_results.append(validated.model_dump())
+                except Exception:
+                    extracted_json = self._extract_json_substring(text)
+                    if extracted_json:
+                        try:
+                            data = json.loads(extracted_json)
+                            validated = PreferenceList.model_validate(data)
+                            all_results.append(validated.model_dump())
+                            continue
+                        except Exception:
+                            pass
+                    all_results.append({"preferences": []})
+
+        self.tokenizer.padding_side = orig_padding_side
+        return all_results
+
     def extract_turn(self, turns: List[ChatTurn]) -> PreferenceList:
         """
         Extract preferences from the LAST user turn in the history.