# filename: service.py """ Lightweight Embeddings Service Module This module provides a service for generating and comparing embeddings from text and images using state-of-the-art transformer models. It supports both CPU and GPU inference. Key Features: - Text and image embedding generation - Cross-modal similarity ranking - Batch processing support - Asynchronous API support Supported Text Model IDs: - "multilingual-e5-small" - "paraphrase-multilingual-MiniLM-L12-v2" - "bge-m3" Supported Image Model ID (default): - "google/siglip-base-patch16-256-multilingual" """ from __future__ import annotations import logging from enum import Enum from typing import List, Union, Literal, Dict, Optional, NamedTuple from dataclasses import dataclass from pathlib import Path from io import BytesIO import requests import numpy as np import torch from PIL import Image from sentence_transformers import SentenceTransformer from transformers import AutoProcessor, AutoModel # Configure logging logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) # Default Model IDs TEXT_MODEL_ID = "Xenova/multilingual-e5-small" IMAGE_MODEL_ID = "google/siglip-base-patch16-256-multilingual" class TextModelType(str, Enum): """ Enumeration of supported text models. Please ensure the ONNX files and Hugging Face model IDs are consistent with your local or remote environment. """ MULTILINGUAL_E5_SMALL = "multilingual-e5-small" PARAPHRASE_MULTILINGUAL_MINILM_L12_V2 = "paraphrase-multilingual-MiniLM-L12-v2" BGE_M3 = "bge-m3" class ModelInfo(NamedTuple): """ Simple container for mapping a given text model type to its Hugging Face model repository and the local ONNX file path. """ model_id: str onnx_file: str @dataclass class ModelConfig: """ Configuration settings for model providers, backends, and defaults. """ provider: str = "CPUExecutionProvider" backend: str = "onnx" logit_scale: float = 4.60517 text_model_type: TextModelType = TextModelType.MULTILINGUAL_E5_SMALL image_model_id: str = IMAGE_MODEL_ID @property def text_model_info(self) -> ModelInfo: """ Retrieves the ModelInfo for the currently selected text_model_type. """ model_configs = { TextModelType.MULTILINGUAL_E5_SMALL: ModelInfo( "Xenova/multilingual-e5-small", "onnx/model_quantized.onnx", ), TextModelType.PARAPHRASE_MULTILINGUAL_MINILM_L12_V2: ModelInfo( "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", "onnx/model_quint8_avx2.onnx", ), TextModelType.BGE_M3: ModelInfo( "BAAI/bge-m3", "model.onnx", ), } return model_configs[self.text_model_type] class EmbeddingsService: """ Service for generating and comparing text/image embeddings. This service supports multiple text models and a single image model. It provides methods for: - Generating text embeddings - Generating image embeddings - Ranking candidates by similarity """ def __init__(self, config: Optional[ModelConfig] = None) -> None: """ Initialize the EmbeddingsService. Args: config: Optional ModelConfig object to override default settings. """ # Determine whether GPU (CUDA) is available self.device = "cuda" if torch.cuda.is_available() else "cpu" # Use the provided config or fall back to defaults self.config = config or ModelConfig() # Dictionary to hold multiple text models self.text_models: Dict[TextModelType, SentenceTransformer] = {} # Load all models (text + image) into memory self._load_models() def _load_models(self) -> None: """ Load text and image models into memory. This pre-loads all text models defined in the TextModelType enum and a single image model, enabling quick switching at runtime. """ try: # Load all text models for model_type in TextModelType: model_info = ModelConfig(text_model_type=model_type).text_model_info logger.info(f"Loading text model: {model_info.model_id}") self.text_models[model_type] = SentenceTransformer( model_info.model_id, device=self.device, backend=self.config.backend, model_kwargs={ "provider": self.config.provider, "file_name": model_info.onnx_file, }, ) logger.info(f"Loading image model: {self.config.image_model_id}") self.image_model = AutoModel.from_pretrained(self.config.image_model_id).to( self.device ) self.image_processor = AutoProcessor.from_pretrained( self.config.image_model_id ) logger.info(f"All models loaded successfully on {self.device}.") except Exception as e: logger.error( "Model loading failed. Please ensure you have valid model IDs and local files.\n" f"Error details: {str(e)}" ) raise RuntimeError(f"Failed to load models: {str(e)}") from e @staticmethod def _validate_text_input(input_text: Union[str, List[str]]) -> List[str]: """ Validate and standardize the input for text embeddings. Args: input_text: Either a single string or a list of strings. Returns: A list of strings to process. Raises: ValueError: If input_text is empty or not string-based. """ if isinstance(input_text, str): return [input_text] if not isinstance(input_text, list) or not all( isinstance(x, str) for x in input_text ): raise ValueError( "Text input must be a single string or a list of strings. " "Found a different data type instead." ) if not input_text: raise ValueError("Text input list cannot be empty.") return input_text @staticmethod def _validate_modality(modality: str) -> None: """ Validate the input modality. Args: modality: Must be either 'text' or 'image'. Raises: ValueError: If modality is neither 'text' nor 'image'. """ if modality not in ["text", "image"]: raise ValueError( "Invalid modality. Please specify 'text' or 'image' for embeddings." ) def _process_image(self, image_path: Union[str, Path]) -> torch.Tensor: """ Load and preprocess an image from either a local path or a URL. Args: image_path: Path to the local image file or a URL. Returns: Torch Tensor suitable for model input. Raises: ValueError: If the image file or URL cannot be loaded. """ try: if str(image_path).startswith("http"): response = requests.get(image_path, timeout=10) response.raise_for_status() image_content = BytesIO(response.content) else: image_content = image_path image = Image.open(image_content).convert("RGB") processed = self.image_processor(images=image, return_tensors="pt").to( self.device ) return processed except Exception as e: raise ValueError( f"Failed to process image at '{image_path}'. Check the path/URL and file format.\n" f"Details: {str(e)}" ) from e def _generate_text_embeddings(self, texts: List[str]) -> np.ndarray: """ Helper method to generate text embeddings for a list of texts using the currently configured text model. Args: texts: A list of text strings. Returns: Numpy array of shape (num_texts, embedding_dim). Raises: RuntimeError: If the text model fails to generate embeddings. """ try: logger.info( f"Generating embeddings for {len(texts)} text items using model: " f"{self.config.text_model_type}" ) # Select the preloaded text model based on the current config model = self.text_models[self.config.text_model_type] embeddings = model.encode(texts) return embeddings except Exception as e: error_msg = ( f"Error generating text embeddings with model: {self.config.text_model_type}. " f"Details: {str(e)}" ) logger.error(error_msg) raise RuntimeError(error_msg) from e def _generate_image_embeddings( self, input_data: Union[str, List[str]], batch_size: Optional[int] ) -> np.ndarray: """ Helper method to generate image embeddings. Args: input_data: Either a single image path/URL or a list of them. batch_size: Batch size for processing images in chunks. If None, process all at once. Returns: Numpy array of shape (num_images, embedding_dim). Raises: RuntimeError: If the image model fails to generate embeddings. """ try: if isinstance(input_data, str): # Single image scenario processed = self._process_image(input_data) with torch.no_grad(): embedding = self.image_model.get_image_features(**processed) return embedding.cpu().numpy() # Multiple images scenario logger.info(f"Generating embeddings for {len(input_data)} images.") if batch_size is None: # Process all images at once processed_batches = [ self._process_image(img_path) for img_path in input_data ] with torch.no_grad(): # Concatenate all images along the batch dimension batch_keys = processed_batches[0].keys() concatenated = { k: torch.cat([pb[k] for pb in processed_batches], dim=0) for k in batch_keys } embedding = self.image_model.get_image_features(**concatenated) return embedding.cpu().numpy() # Process images in smaller batches embeddings_list = [] for i, img_path in enumerate(input_data): if i % batch_size == 0: logger.debug( f"Processing image batch {i // batch_size + 1} with size up to {batch_size}." ) processed = self._process_image(img_path) with torch.no_grad(): embedding = self.image_model.get_image_features(**processed) embeddings_list.append(embedding.cpu().numpy()) return np.vstack(embeddings_list) except Exception as e: error_msg = ( f"Error generating image embeddings with model: {self.config.image_model_id}. " f"Details: {str(e)}" ) logger.error(error_msg) raise RuntimeError(error_msg) from e async def generate_embeddings( self, input_data: Union[str, List[str]], modality: Literal["text", "image"] = "text", batch_size: Optional[int] = None, ) -> np.ndarray: """ Asynchronously generate embeddings for text or image inputs. Args: input_data: A string or list of strings (text/image paths/URLs). modality: "text" for text data or "image" for image data. batch_size: Optional batch size for processing images in chunks. Returns: Numpy array of embeddings. Raises: ValueError: If the modality is invalid. """ self._validate_modality(modality) if modality == "text": texts = self._validate_text_input(input_data) return self._generate_text_embeddings(texts) else: return self._generate_image_embeddings(input_data, batch_size) async def rank( self, queries: Union[str, List[str]], candidates: List[str], modality: Literal["text", "image"] = "text", batch_size: Optional[int] = None, ) -> Dict[str, List[List[float]]]: """ Rank a set of candidate texts against one or more queries using cosine similarity and a softmax to produce probability-like scores. Args: queries: Query text(s) or image path(s)/URL(s). candidates: Candidate texts to be ranked. (Note: This implementation always treats candidates as text.) modality: "text" for text queries or "image" for image queries. batch_size: Batch size if images are processed in chunks. Returns: Dictionary containing: - "probabilities": 2D list of softmax-normalized scores. - "cosine_similarities": 2D list of raw cosine similarity values. Raises: RuntimeError: If the query or candidate embeddings fail to generate. """ logger.info( f"Ranking {len(candidates)} candidates against " f"{len(queries) if isinstance(queries, list) else 1} query item(s)." ) # Generate embeddings for queries query_embeds = await self.generate_embeddings( queries, modality=modality, batch_size=batch_size ) # Generate embeddings for candidates (always text) candidate_embeds = await self.generate_embeddings( candidates, modality="text", batch_size=batch_size ) # Compute cosine similarity and scaled probabilities cosine_sims = self.cosine_similarity(query_embeds, candidate_embeds) logit_scale = np.exp(self.config.logit_scale) probabilities = self.softmax(logit_scale * cosine_sims) return { "probabilities": probabilities.tolist(), "cosine_similarities": cosine_sims.tolist(), } def estimate_tokens(self, input_data: Union[str, List[str]]) -> int: """ Roughly estimate the total number of tokens in the given text(s). Args: input_data: A string or list of strings representing text input. Returns: Estimated token count (int). Raises: ValueError: If the input is not valid text data. """ texts = self._validate_text_input(input_data) # Very rough approximation: assume ~4 characters per token total_chars = sum(len(t) for t in texts) return max(1, round(total_chars / 4)) @staticmethod def softmax(scores: np.ndarray) -> np.ndarray: """ Apply softmax along the last dimension of the scores array. Args: scores: Numpy array of shape (..., num_candidates). Returns: Numpy array of softmax-normalized values, same shape as scores. """ exp_scores = np.exp(scores - np.max(scores, axis=-1, keepdims=True)) return exp_scores / np.sum(exp_scores, axis=-1, keepdims=True) @staticmethod def cosine_similarity( query_embeds: np.ndarray, candidate_embeds: np.ndarray ) -> np.ndarray: """ Compute the cosine similarity between two sets of vectors. Args: query_embeds: Numpy array of shape (num_queries, embed_dim). candidate_embeds: Numpy array of shape (num_candidates, embed_dim). Returns: 2D Numpy array of shape (num_queries, num_candidates) containing cosine similarity scores. """ # Normalize embeddings query_norm = query_embeds / np.linalg.norm(query_embeds, axis=1, keepdims=True) candidate_norm = candidate_embeds / np.linalg.norm( candidate_embeds, axis=1, keepdims=True ) return np.dot(query_norm, candidate_norm.T)