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| """CPU-only, unnormalized embeddings from multilingual-e5-small.""" | |
| from __future__ import annotations | |
| import math | |
| import os | |
| import threading | |
| from dataclasses import dataclass | |
| from pathlib import Path | |
| from typing import Literal | |
| MODEL_ID = "intfloat/multilingual-e5-small" | |
| MODEL_DIR = Path(os.getenv("E5_MODEL_DIR", "/opt/models/multilingual-e5-small")) | |
| EXPECTED_DIMENSIONS = 384 | |
| MAX_INPUT_TOKENS = 512 | |
| class TooManyTokens(ValueError): | |
| def __init__(self, count: int, limit: int, input_index: int | None = None) -> None: | |
| self.count = count | |
| self.limit = limit | |
| self.input_index = input_index | |
| item = f"Input item {input_index}" if input_index is not None else "Input" | |
| super().__init__(f"{item} contains {count} tokens; maximum is {limit} (including prefix and special tokens)") | |
| class EmbeddingResult: | |
| tokens_count: int | |
| embedding: list[float] | |
| class EmbeddingEngine: | |
| """Run official E5 attention-mask-aware mean pooling without L2 normalization.""" | |
| def __init__(self, tokenizer: object, model: object, torch_module: object) -> None: | |
| self.tokenizer = tokenizer | |
| self.model = model | |
| self.torch = torch_module | |
| self._lock = threading.Lock() # Prevent multiple CPU-heavy inferences in parallel. | |
| model_limit = int(getattr(model.config, "max_position_embeddings", MAX_INPUT_TOKENS)) | |
| self.max_tokens = min(MAX_INPUT_TOKENS, model_limit) | |
| self.dimensions = int(model.config.hidden_size) | |
| if self.dimensions != EXPECTED_DIMENSIONS: | |
| raise RuntimeError(f"Unexpected embedding dimensions: {self.dimensions}") | |
| def load(cls) -> EmbeddingEngine: | |
| import torch | |
| from transformers import AutoModel, AutoTokenizer | |
| threads = max(1, min(2, int(os.getenv("CPU_THREADS", "2")))) | |
| torch.set_num_threads(threads) | |
| tokenizer = AutoTokenizer.from_pretrained(str(MODEL_DIR), local_files_only=True) | |
| model = AutoModel.from_pretrained(str(MODEL_DIR), local_files_only=True) | |
| model.to("cpu") | |
| model.eval() # Turn off dropout for repeatable output. | |
| return cls(tokenizer, model, torch) | |
| def encode(self, text: str, kind: Literal["query", "passage"]) -> EmbeddingResult: | |
| # The prefix is required by E5. The caller's original text is never modified. | |
| input_text = f"{kind}: {text}" | |
| with self._lock: | |
| encoded = self.tokenizer( | |
| input_text, | |
| return_tensors="pt", | |
| add_special_tokens=True, | |
| truncation=False, | |
| ) | |
| tokens_count = int(encoded["attention_mask"].sum().item()) | |
| if tokens_count > self.max_tokens: | |
| raise TooManyTokens(tokens_count, self.max_tokens) | |
| with self.torch.inference_mode(): | |
| output = self.model(**encoded) | |
| token_embeddings = output.last_hidden_state | |
| attention_mask = encoded["attention_mask"].unsqueeze(-1) | |
| pooled = (token_embeddings * attention_mask).sum(dim=1) / attention_mask.sum(dim=1).clamp(min=1) | |
| vector = pooled.squeeze(0).cpu().tolist() | |
| if len(vector) != self.dimensions or any(not math.isfinite(value) for value in vector): | |
| raise RuntimeError("Model produced an invalid embedding") | |
| return EmbeddingResult(tokens_count=tokens_count, embedding=vector) | |