Add model wrapper modules (embedding, reranker, llm, preference_extractor)

Add Python wrappers for:
- Qwen3/Nemotron embedding models
- BGE/Qwen3 rerankers
- vLLM/Llama/Qwen LLM backends
- GPT-4o/LLM-based preference extractors

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

1 files changed, 37 insertions, 0 deletions

diff --git a/src/personalization/models/embedding/base.py b/src/personalization/models/embedding/base.py
new file mode 100644
index 0000000..9f9d4d1
--- /dev/null
+++ b/src/personalization/models/embedding/base.py
@@ -0,0 +1,37 @@
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import Iterable, List, Sequence
+
+import torch
+
+
+class EmbeddingModel(ABC):
+    @abstractmethod
+    def encode(
+        self,
+        texts: Sequence[str],
+        batch_size: int = 8,
+        max_length: int = 512,
+        normalize: bool = True,
+        return_tensor: bool = False,
+    ) -> List[List[float]] | torch.Tensor:
+        """Encode a batch of texts into dense embeddings."""
+        raise NotImplementedError
+
+
+def _mean_pool(last_hidden_state: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:
+    # last_hidden_state: [batch, seq_len, hidden]
+    # attention_mask: [batch, seq_len]
+    mask = attention_mask.unsqueeze(-1).type_as(last_hidden_state)  # [b, s, 1]
+    summed = (last_hidden_state * mask).sum(dim=1)
+    counts = mask.sum(dim=1).clamp_min(1e-6)
+    return summed / counts
+
+
+def _maybe_normalize(x: torch.Tensor, normalize: bool) -> torch.Tensor:
+    if not normalize:
+        return x
+    return torch.nn.functional.normalize(x, p=2, dim=-1)
+
+