colpali v1.3 by AndrewOgn

NOTICE

@@ -12,5 +12,11 @@ This distribution includes the following Jina AI models, each with its respectiv
 
 These models are developed by Jina (https://jina.ai/) and are subject to Jina AI's licensing terms.
 
+This distribution includes the following Google models, each with its respective license:
+- vidore/colpali-v1.3
+    - License: gemma
+
+Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms
+
 Additional Notes:
 This project also includes third-party libraries with their respective licenses. Please refer to the documentation of each library for details regarding its usage and licensing terms.

fastembed/image/image_embedding.py

@@ -79,8 +79,7 @@ def embed(
         **kwargs: Any,
     ) -> Iterable[NumpyArray]:
         """
-        Encode a list of documents into list of embeddings.
-        We use mean pooling with attention so that the model can handle variable-length inputs.
+        Encode a list of images into list of embeddings.
 
         Args:
             images: Iterator of image paths or single image path to embed

fastembed/image/transform/operators.py

@@ -139,7 +139,7 @@ def _get_convert_to_rgb(transforms: list[Transform], config: dict[str, Any]) ->
     @classmethod
     def _get_resize(cls, transforms: list[Transform], config: dict[str, Any]) -> None:
         mode = config.get("image_processor_type", "CLIPImageProcessor")
-        if mode == "CLIPImageProcessor":
+        if mode in ("CLIPImageProcessor", "SiglipImageProcessor"):
             if config.get("do_resize", False):
                 size = config["size"]
                 if "shortest_edge" in size:
@@ -202,7 +202,7 @@ def _get_resize(cls, transforms: list[Transform], config: dict[str, Any]) -> Non
     @staticmethod
     def _get_center_crop(transforms: list[Transform], config: dict[str, Any]) -> None:
         mode = config.get("image_processor_type", "CLIPImageProcessor")
-        if mode == "CLIPImageProcessor":
+        if mode in ("CLIPImageProcessor", "SiglipImageProcessor"):
             if config.get("do_center_crop", False):
                 crop_size_raw = config["crop_size"]
                 crop_size: tuple[int, int]

fastembed/late_interaction_multimodal/__init__.py

@@ -0,0 +1,5 @@
+from fastembed.late_interaction_multimodal.late_interaction_multimodal_embedding import (
+    LateInteractionMultimodalEmbedding,
+)
+
+__all__ = ["LateInteractionMultimodalEmbedding"]

fastembed/late_interaction_multimodal/colpali.py

@@ -0,0 +1,301 @@
+from typing import Any, Iterable, Optional, Sequence, Type, Union
+
+import numpy as np
+from tokenizers import Encoding
+
+from fastembed.common import OnnxProvider, ImageInput
+from fastembed.common.onnx_model import OnnxOutputContext
+from fastembed.common.utils import define_cache_dir
+from fastembed.late_interaction_multimodal.late_interaction_multimodal_embedding_base import (
+    LateInteractionMultimodalEmbeddingBase,
+)
+from fastembed.late_interaction_multimodal.onnx_multimodal_model import (
+    OnnxMultimodalModel,
+    TextEmbeddingWorker,
+    ImageEmbeddingWorker,
+)
+
+supported_colpali_models = [
+    {
+        "model": "Qdrant/colpali-v1.3-fp16",
+        "dim": 128,
+        "description": "Text embeddings, Multimodal (text&image), English, 50 tokens query length truncation, 2024.",
+        "license": "mit",
+        "size_in_GB": 6.5,
+        "sources": {
+            "hf": "Qdrant/colpali-v1.3-fp16",
+        },
+        "additional_files": [
+            "model.onnx_data",
+        ],
+        "model_file": "model.onnx",
+    },
+]
+
+
+class ColPali(LateInteractionMultimodalEmbeddingBase, OnnxMultimodalModel[np.ndarray]):
+    QUERY_PREFIX = "Query: "
+    BOS_TOKEN = "<s>"
+    PAD_TOKEN = "<pad>"
+    QUERY_MARKER_TOKEN_ID = [2, 5098]
+    IMAGE_PLACEHOLDER_SIZE = (3, 448, 448)
+    EMPTY_TEXT_PLACEHOLDER = np.array(
+        [257152] * 1024 + [2, 50721, 573, 2416, 235265, 108]
+    )  # This is a tokenization of '<image>' * 1024 + '<bos>Describe the image.\n' line which is used as placeholder
+    # while processing an image
+    EVEN_ATTENTION_MASK = np.array([1] * 1030)
+
+    def __init__(
+        self,
+        model_name: str,
+        cache_dir: Optional[str] = None,
+        threads: Optional[int] = None,
+        providers: Optional[Sequence[OnnxProvider]] = None,
+        cuda: bool = False,
+        device_ids: Optional[list[int]] = None,
+        lazy_load: bool = False,
+        device_id: Optional[int] = None,
+        specific_model_path: Optional[str] = None,
+        **kwargs,
+    ):
+        """
+        Args:
+            model_name (str): The name of the model to use.
+            cache_dir (str, optional): The path to the cache directory.
+                                       Can be set using the `FASTEMBED_CACHE_PATH` env variable.
+                                       Defaults to `fastembed_cache` in the system's temp directory.
+            threads (int, optional): The number of threads single onnxruntime session can use. Defaults to None.
+            providers (Optional[Sequence[OnnxProvider]], optional): The list of onnxruntime providers to use.
+                Mutually exclusive with the `cuda` and `device_ids` arguments. Defaults to None.
+            cuda (bool, optional): Whether to use cuda for inference. Mutually exclusive with `providers`
+                Defaults to False.
+            device_ids (Optional[list[int]], optional): The list of device ids to use for data parallel processing in
+                workers. Should be used with `cuda=True`, mutually exclusive with `providers`. Defaults to None.
+            lazy_load (bool, optional): Whether to load the model during class initialization or on demand.
+                Should be set to True when using multiple-gpu and parallel encoding. Defaults to False.
+            device_id (Optional[int], optional): The device id to use for loading the model in the worker process.
+
+        Raises:
+            ValueError: If the model_name is not in the format <org>/<model> e.g. BAAI/bge-base-en.
+        """
+
+        super().__init__(model_name, cache_dir, threads, **kwargs)
+        self.providers = providers
+        self.lazy_load = lazy_load
+
+        # List of device ids, that can be used for data parallel processing in workers
+        self.device_ids = device_ids
+        self.cuda = cuda
+
+        # This device_id will be used if we need to load model in current process
+        if device_id is not None:
+            self.device_id = device_id
+        elif self.device_ids is not None:
+            self.device_id = self.device_ids[0]
+        else:
+            self.device_id = None
+
+        self.model_description = self._get_model_description(model_name)
+        self.cache_dir = define_cache_dir(cache_dir)
+
+        self._model_dir = self.download_model(
+            self.model_description,
+            self.cache_dir,
+            local_files_only=self._local_files_only,
+            specific_model_path=specific_model_path,
+        )
+        self.mask_token_id = None
+        self.pad_token_id = None
+
+        if not self.lazy_load:
+            self.load_onnx_model()
+
+    @classmethod
+    def list_supported_models(cls) -> list[dict[str, Any]]:
+        """Lists the supported models.
+
+        Returns:
+            list[dict[str, Any]]: A list of dictionaries containing the model information.
+        """
+        return supported_colpali_models
+
+    def load_onnx_model(self) -> None:
+        self._load_onnx_model(
+            model_dir=self._model_dir,
+            model_file=self.model_description["model_file"],
+            threads=self.threads,
+            providers=self.providers,
+            cuda=self.cuda,
+            device_id=self.device_id,
+        )
+
+    def _post_process_onnx_image_output(
+        self,
+        output: OnnxOutputContext,
+    ) -> Iterable[np.ndarray]:
+        """
+        Post-process the ONNX model output to convert it into a usable format.
+
+        Args:
+            output (OnnxOutputContext): The raw output from the ONNX model.
+
+        Returns:
+            Iterable[np.ndarray]: Post-processed output as NumPy arrays.
+        """
+        return output.model_output.reshape(
+            output.model_output.shape[0], -1, self.model_description["dim"]
+        ).astype(np.float32)
+
+    def _post_process_onnx_text_output(
+        self,
+        output: OnnxOutputContext,
+    ) -> Iterable[np.ndarray]:
+        """
+        Post-process the ONNX model output to convert it into a usable format.
+
+        Args:
+            output (OnnxOutputContext): The raw output from the ONNX model.
+
+        Returns:
+            Iterable[np.ndarray]: Post-processed output as NumPy arrays.
+        """
+        return output.model_output.astype(np.float32)
+
+    def tokenize(self, documents: list[str], **_) -> list[Encoding]:
+        texts_query: list[str] = []
+        for query in documents:
+            query = self.BOS_TOKEN + self.QUERY_PREFIX + query + self.PAD_TOKEN * 10
+            query += "\n"
+
+            texts_query.append(query)
+        encoded = self.tokenizer.encode_batch(texts_query)
+        return encoded
+
+    def _preprocess_onnx_text_input(
+        self, onnx_input: dict[str, np.ndarray], **kwargs
+    ) -> dict[str, np.ndarray]:
+        onnx_input["input_ids"] = np.array(
+            [
+                self.QUERY_MARKER_TOKEN_ID + input_ids[2:].tolist()
+                for input_ids in onnx_input["input_ids"]
+            ]
+        )
+        empty_image_placeholder = np.zeros(self.IMAGE_PLACEHOLDER_SIZE, dtype=np.float32)
+        onnx_input["pixel_values"] = np.array(
+            [empty_image_placeholder for _ in onnx_input["input_ids"]]
+        )
+        return onnx_input
+
+    def _preprocess_onnx_image_input(
+        self, onnx_input: dict[str, np.ndarray], **kwargs
+    ) -> dict[str, np.ndarray]:
+        """
+        Add placeholders for text input when processing image data for ONNX.
+        Args:
+            onnx_input (Dict[str, np.ndarray]): Preprocessed image inputs.
+            **kwargs: Additional arguments.
+        Returns:
+            Dict[str, np.ndarray]: ONNX input with text placeholders.
+        """
+
+        onnx_input["input_ids"] = np.array(
+            [self.EMPTY_TEXT_PLACEHOLDER for _ in onnx_input["input_ids"]]
+        )
+        onnx_input["attention_mask"] = np.array(
+            [self.EVEN_ATTENTION_MASK for _ in onnx_input["input_ids"]]
+        )
+        return onnx_input
+
+    def embed_text(
+        self,
+        documents: Union[str, Iterable[str]],
+        batch_size: int = 256,
+        parallel: Optional[int] = None,
+        **kwargs,
+    ) -> Iterable[np.ndarray]:
+        """
+        Encode a list of documents into list of embeddings.
+
+        Args:
+            documents: Iterator of documents or single document to embed
+            batch_size: Batch size for encoding -- higher values will use more memory, but be faster
+            parallel:
+                If > 1, data-parallel encoding will be used, recommended for offline encoding of large datasets.
+                If 0, use all available cores.
+                If None, don't use data-parallel processing, use default onnxruntime threading instead.
+
+        Returns:
+            List of embeddings, one per document
+        """
+        yield from self._embed_documents(
+            model_name=self.model_name,
+            cache_dir=str(self.cache_dir),
+            documents=documents,
+            batch_size=batch_size,
+            parallel=parallel,
+            providers=self.providers,
+            cuda=self.cuda,
+            device_ids=self.device_ids,
+            **kwargs,
+        )
+
+    def embed_image(
+        self,
+        images: ImageInput,
+        batch_size: int = 16,
+        parallel: Optional[int] = None,
+        **kwargs,
+    ) -> Iterable[np.ndarray]:
+        """
+        Encode a list of images into list of embeddings.
+
+        Args:
+            images: Iterator of image paths or single image path to embed
+            batch_size: Batch size for encoding -- higher values will use more memory, but be faster
+            parallel:
+                If > 1, data-parallel encoding will be used, recommended for offline encoding of large datasets.
+                If 0, use all available cores.
+                If None, don't use data-parallel processing, use default onnxruntime threading instead.
+
+        Returns:
+            List of embeddings, one per document
+        """
+        yield from self._embed_images(
+            model_name=self.model_name,
+            cache_dir=str(self.cache_dir),
+            images=images,
+            batch_size=batch_size,
+            parallel=parallel,
+            providers=self.providers,
+            cuda=self.cuda,
+            device_ids=self.device_ids,
+            **kwargs,
+        )
+
+    @classmethod
+    def _get_text_worker_class(cls) -> Type[TextEmbeddingWorker]:
+        return ColPaliTextEmbeddingWorker
+
+    @classmethod
+    def _get_image_worker_class(cls) -> Type[ImageEmbeddingWorker]:
+        return ColPaliImageEmbeddingWorker
+
+
+class ColPaliTextEmbeddingWorker(TextEmbeddingWorker):
+    def init_embedding(self, model_name: str, cache_dir: str, **kwargs) -> ColPali:
+        return ColPali(
+            model_name=model_name,
+            cache_dir=cache_dir,
+            threads=1,
+            **kwargs,
+        )
+
+
+class ColPaliImageEmbeddingWorker(ImageEmbeddingWorker):
+    def init_embedding(self, model_name: str, cache_dir: str, **kwargs) -> ColPali:
+        return ColPali(
+            model_name=model_name,
+            cache_dir=cache_dir,
+            threads=1,
+            **kwargs,
+        )