From b6c3e4e51eeab703b40284459c6e9fff2151216c Mon Sep 17 00:00:00 2001 From: YurenHao0426 Date: Wed, 18 Mar 2026 18:25:09 -0500 Subject: Initial release: VARS - personalized LLM with RAG and user vector learning --- src/personalization/models/embedding/base.py | 37 ++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) create mode 100644 src/personalization/models/embedding/base.py (limited to 'src/personalization/models/embedding/base.py') 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) + + -- cgit v1.2.3