disable tmate

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,6 +1,6 @@
-name: Bug
-description: File a bug report
-title: "[Bug]: "
+name: Bug/New Model Request
+description: File a bug report/Request a new Model
+title: "[Bug/Model Request]: "
 body:
   - type: markdown
     attributes:
@@ -10,22 +10,11 @@ body:
     id: what-happened
     attributes:
       label: What happened?
-      description: Describe the error you encountered.
-      placeholder: <Description>
+      description: Also tell us, what did you expect to happen?
+      placeholder: Tell us what you see!
+      value: "A bug happened!"
     validations:
       required: true
-  - type: textarea
-    id: expected
-    attributes:
-      label: What is the expected behaviour?
-      description: Describe the way you expected the code to behave.
-      placeholder: <Description>
-  - type: textarea
-    id: code-snippet
-    attributes:
-      label: A minimal reproducible example
-      description: It would really help us to fix the problem if you could provide a code snippet that reproduces the issue.
-      placeholder: <Code snippet>
   - type: textarea
     id: python-version
     attributes:
@@ -34,12 +23,21 @@ body:
       placeholder: Python3.10
     validations:
       required: true
-  - type: textarea
+  - type: dropdown
     id: version
     attributes:
-      label: FastEmbed version
+      label: Version
       description: What version of FastEmbed are you running? python -c "import fastembed; print(fastembed.__version__)". If you're not on the latest, please upgrade and see if the problem persists.
-      placeholder: v0.5.1
+      options:
+        - 0.2.7 (Latest)
+        - 0.2.6
+        - 0.2.5
+        - 0.2.4 
+        - 0.2.3 
+        - 0.2.2
+        - 0.2.1
+        - 0.1.x 
+      default: 0
     validations:
       required: true
   - type: dropdown

.github/ISSUE_TEMPLATE/config.yaml

@@ -1,4 +1,4 @@
-blank_issues_enabled: true
+blank_issues_enabled: false
 contact_links:
   - name: GitHub Community Support
     url: https://github.com/qdrant/fastembed/discussions

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -1,22 +0,0 @@
-name: Feature
-description: New functionality request
-title: "[Feature]: "
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for taking the time to fill out this report!
-  - type: textarea
-    id: feature-description
-    attributes:
-      label: What feature would you like to request?
-      description: Please provide the description of the feature you would like to request.
-      placeholder: <Description>
-    validations:
-      required: true
-  - type: textarea
-    id: additional-info
-    attributes:
-      label: Is there any additional information you would like to provide?
-      description: Please provide any additional information that you think might be useful.
-      placeholder: <Info>

.github/ISSUE_TEMPLATE/model-request.yml

@@ -1,22 +0,0 @@
-name: Model
-description: Request a new model
-title: "[Model]: "
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for taking the time to fill out this report!
-  - type: textarea
-    id: model-name
-    attributes:
-      label: Which model would you like to support?
-      description: Please provide the name of the model you would like to see supported.
-      placeholder: Link to the model (e.g. on HuggingFace)
-    validations:
-      required: true
-  - type: textarea
-    id: motivation
-    attributes:
-      label: What are the main advantages of this model?
-      description: Please describe the main advantages of this model comparing to the existing ones and provide links to benchmarks if there are any.
-      placeholder: <Description>

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,19 +0,0 @@
-### All Submissions:
-
-* [ ] Have you followed the guidelines in our Contributing document?
-* [ ] Have you checked to ensure there aren't other open [Pull Requests](../../../pulls) for the same update/change?
-
-<!-- You can erase any parts of this template not applicable to your Pull Request. -->
-
-### New Feature Submissions:
-
-* [ ] Does your submission pass the existing tests?
-* [ ] Have you added tests for your feature?
-* [ ] Have you installed `pre-commit` with `pip3 install pre-commit` and set up hooks with `pre-commit install`?
-
-### New models submission:
-
-* [ ] Have you added an explanation of why it's important to include this model?
-* [ ] Have you added tests for the new model? Were canonical values for tests computed via the original model?
-* [ ] Have you added the code snippet for how canonical values were computed?
-* [ ] Have you successfully ran tests with your changes locally?

.github/workflows/ci.yml

@@ -21,5 +21,5 @@ jobs:
           path: .cache
           restore-keys: |
             mkdocs-material-
-      - run: pip install mkdocs-material mkdocstrings==0.27.0 pillow cairosvg mknotebooks
+      - run: pip install mkdocs-material mkdocstrings pillow cairosvg mknotebooks
       - run: mkdocs gh-deploy --force

.github/workflows/python-tests.yml

@@ -1,10 +1,10 @@
 name: Tests
+run-name: Tests (gpu)
 
 on:
-  pull_request:
+  push:
     branches: [ master, main, gpu ]
-  workflow_dispatch:
-  
+  pull_request:
 
 env:
   CARGO_TERM_COLOR: always
@@ -15,15 +15,13 @@ jobs:
     strategy:
       matrix:
         python-version:
+          - '3.8.x'
           - '3.9.x'
           - '3.10.x'
           - '3.11.x'
           - '3.12.x'
-          - '3.13.x'
         os:
           - ubuntu-latest
-          - macos-latest
-          - windows-latest
 
     runs-on: ${{ matrix.os }}
 
@@ -35,12 +33,14 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
+#      - name: Setup tmate session
+#        uses: mxschmitt/action-tmate@v3
       - name: Install dependencies
         run: |
           python -m pip install poetry
           poetry config virtualenvs.create false
-          poetry install --no-interaction --no-ansi --without dev,docs
+          poetry install --no-interaction --no-ansi --without docs
 
       - name: Run pytest
         run: |
-          poetry run pytest 
+          poetry run pytest

.github/workflows/type-checkers.yml

@@ -1,40 +0,0 @@
-name: type-checkers
-
-on: [push]
-
-jobs:
-  build:
-    runs-on: ${{ matrix.os }}
-    strategy:
-      fail-fast: true
-      matrix:
-        python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
-        os: [ubuntu-latest]
-
-    name: Python ${{ matrix.python-version }} test
-
-    steps:
-      - uses: actions/checkout@v1
-
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v2
-        with:
-          python-version: ${{ matrix.python-version }}
-
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip poetry
-          poetry install --no-interaction --no-ansi --without dev,docs,test
-
-          poetry run pip install "numpy<2.0.0"  # https://github.com/python/mypy/issues/17396
-
-      - name: mypy
-        run: |
-            poetry run mypy fastembed \
-              --disallow-incomplete-defs \
-              --disallow-untyped-defs \
-              --disable-error-code=import-untyped
-
-      - name: pyright
-        run: |
-          poetry run pyright tests/type_stub.py

NOTICE

@@ -5,18 +5,8 @@ This product includes software developed by Qdrant
 This distribution includes the following Jina AI models, each with its respective license:
 - jinaai/jina-colbert-v2
     - License: cc-by-nc-4.0
-- jinaai/jina-reranker-v2-base-multilingual
-    - License: cc-by-nc-4.0
-- jinaai/jina-embeddings-v3
-    - License: cc-by-nc-4.0
 
 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.

README.md

@@ -28,10 +28,10 @@ pip install fastembed-gpu
 
 ```python
 from fastembed import TextEmbedding
-
+from typing import List
 
 # Example list of documents
-documents: list[str] = [
+documents: List[str] = [
     "This is built to be faster and lighter than other embedding libraries e.g. Transformers, Sentence-Transformers, etc.",
     "fastembed is supported by and maintained by Qdrant.",
 ]
@@ -63,23 +63,6 @@ embeddings = list(model.embed(documents))
 
 ```
 
-Dense text embedding can also be extended with models which are not in the list of supported models.
-
-```python
-from fastembed import TextEmbedding
-from fastembed.common.model_description import PoolingType, ModelSource
-
-TextEmbedding.add_custom_model(
-    model="intfloat/multilingual-e5-small",
-    pooling=PoolingType.MEAN,
-    normalization=True,
-    sources=ModelSource(hf="intfloat/multilingual-e5-small"),  # can be used with an `url` to load files from a private storage
-    dim=384,
-    model_file="onnx/model.onnx",  # can be used to load an already supported model with another optimization or quantization, e.g. onnx/model_O4.onnx
-)
-model = TextEmbedding(model_name="intfloat/multilingual-e5-small")
-embeddings = list(model.embed(documents))
-```
 
 
 ### 🔱 Sparse text embeddings
@@ -154,58 +137,6 @@ embeddings = list(model.embed(images))
 # ]
 ```
 
-### Late interaction multimodal models (ColPali)
-
-```python
-from fastembed import LateInteractionMultimodalEmbedding
-
-doc_images = [
-    "./path/to/qdrant_pdf_doc_1_screenshot.jpg",
-    "./path/to/colpali_pdf_doc_2_screenshot.jpg",
-]
-
-query = "What is Qdrant?"
-
-model = LateInteractionMultimodalEmbedding(model_name="Qdrant/colpali-v1.3-fp16")
-doc_images_embeddings = list(model.embed_image(doc_images))
-# shape (2, 1030, 128)
-# [array([[-0.03353882, -0.02090454, ..., -0.15576172, -0.07678223]], dtype=float32)]
-query_embedding = model.embed_text(query)
-# shape (1, 20, 128)
-# [array([[-0.00218201,  0.14758301, ...,  -0.02207947,  0.16833496]], dtype=float32)]
-```
-
-### 🔄 Rerankers
-```python
-from fastembed.rerank.cross_encoder import TextCrossEncoder
-
-query = "Who is maintaining Qdrant?"
-documents: list[str] = [
-    "This is built to be faster and lighter than other embedding libraries e.g. Transformers, Sentence-Transformers, etc.",
-    "fastembed is supported by and maintained by Qdrant.",
-]
-encoder = TextCrossEncoder(model_name="Xenova/ms-marco-MiniLM-L-6-v2")
-scores = list(encoder.rerank(query, documents))
-
-# [-11.48061752319336, 5.472434997558594]
-```
-
-Text cross encoders can also be extended with models which are not in the list of supported models.
-
-```python
-from fastembed.rerank.cross_encoder import TextCrossEncoder 
-from fastembed.common.model_description import ModelSource
-
-TextCrossEncoder.add_custom_model(
-    model="Xenova/ms-marco-MiniLM-L-4-v2",
-    model_file="onnx/model.onnx",
-    sources=ModelSource(hf="Xenova/ms-marco-MiniLM-L-4-v2"),
-)
-model = TextCrossEncoder(model_name="Xenova/ms-marco-MiniLM-L-4-v2")
-scores = list(model.rerank_pairs(
-    [("What is AI?", "Artificial intelligence is ..."), ("What is ML?", "Machine learning is ..."),]
-))
-```
 
 ## ⚡️ FastEmbed on a GPU
 

docs/Getting Started.ipynb

@@ -65,13 +65,15 @@
     }
    ],
    "source": [
+    "from typing import List\n",
+    "\n",
     "import numpy as np\n",
     "\n",
     "from fastembed import TextEmbedding\n",
     "\n",
     "\n",
     "# Example list of documents\n",
-    "documents: list[str] = [\n",
+    "documents: List[str] = [\n",
     "    \"This is built to be faster and lighter than other embedding libraries e.g. Transformers, Sentence-Transformers, etc.\",\n",
     "    \"fastembed is supported by and maintained by Qdrant.\",\n",
     "]\n",

docs/examples/ColBERT_with_FastEmbed.ipynb

@@ -54,14 +54,7 @@
     },
     {
      "data": {
-      "text/plain": [
-       "[{'model': 'colbert-ir/colbertv2.0',\n",
-       "  'dim': 128,\n",
-       "  'description': 'Late interaction model',\n",
-       "  'size_in_GB': 0.44,\n",
-       "  'sources': {'hf': 'colbert-ir/colbertv2.0'},\n",
-       "  'model_file': 'model.onnx'}]"
-      ]
+      "text/plain": "[{'model': 'colbert-ir/colbertv2.0',\n  'dim': 128,\n  'description': 'Late interaction model',\n  'size_in_GB': 0.44,\n  'sources': {'hf': 'colbert-ir/colbertv2.0'},\n  'model_file': 'model.onnx'}]"
      },
      "execution_count": 1,
      "metadata": {},
@@ -219,9 +212,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "((26, 128), (32, 128))"
-      ]
+      "text/plain": "((26, 128), (32, 128))"
      },
      "execution_count": 18,
      "metadata": {},
@@ -280,9 +271,7 @@
     "import numpy as np\n",
     "\n",
     "\n",
-    "def compute_relevance_scores(\n",
-    "    query_embedding: np.array, document_embeddings: np.array, k: int\n",
-    ") -> list[int]:\n",
+    "def compute_relevance_scores(query_embedding: np.array, document_embeddings: np.array, k: int):\n",
     "    \"\"\"\n",
     "    Compute relevance scores for top-k documents given a query.\n",
     "\n",

docs/examples/FastEmbed_GPU.ipynb

@@ -388,6 +388,8 @@
     }
    ],
    "source": [
+    "from typing import List\n",
+    "\n",
     "import numpy as np\n",
     "\n",
     "from fastembed import TextEmbedding\n",
@@ -405,7 +407,9 @@
     "id": "iPtoHf7GeV-i"
    },
    "outputs": [],
-   "source": "documents: list[str] = list(np.repeat(\"Demonstrating GPU acceleration in fastembed\", 500))"
+   "source": [
+    "documents: List[str] = list(np.repeat(\"Demonstrating GPU acceleration in fastembed\", 500))"
+   ]
   },
   {
    "cell_type": "code",

docs/examples/FastEmbed_Multi_GPU.ipynb

@@ -1,88 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Fastembed Multi-GPU Tutorial\n",
-    "This tutorial demonstrates how to leverage multi-GPU support in Fastembed. Fastembed supports embedding text and images utilizing modern GPUs for acceleration. Let's explore how to use Fastembed with multiple GPUs step by step."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Prerequisites\n",
-    "To get started, ensure you have the following installed:\n",
-    "- Python 3.9 or later\n",
-    "- Fastembed (`pip install fastembed-gpu`)\n",
-    "- Refer to [this](https://github.com/qdrant/fastembed/blob/main/docs/examples/FastEmbed_GPU.ipynb) tutorial if you have issues with GPU dependencies\n",
-    "- Access to a multi-GPU server"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Multi-GPU using cuda argument with TextEmbedding Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from fastembed import TextEmbedding\n",
-    "\n",
-    "# define the documents to embed\n",
-    "docs = [\"hello world\", \"flag embedding\"] * 100\n",
-    "\n",
-    "# define gpu ids\n",
-    "device_ids = [0, 1]\n",
-    "\n",
-    "if __name__ == \"__main__\":\n",
-    "    # initialize a TextEmbedding model using CUDA\n",
-    "    text_model = TextEmbedding(\n",
-    "        model_name=\"sentence-transformers/all-MiniLM-L6-v2\",\n",
-    "        cuda=True,\n",
-    "        device_ids=device_ids,\n",
-    "        lazy_load=True,\n",
-    "    )\n",
-    "\n",
-    "    # generate embeddings\n",
-    "    text_embeddings = list(text_model.embed(docs, batch_size=2, parallel=len(device_ids)))\n",
-    "    print(text_embeddings)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this snippet:\n",
-    "- `cuda=True` enables GPU acceleration.\n",
-    "- `device_ids=[0, 1]` specifies GPUs to use. Replace `[0, 1]` with available GPU IDs.\n",
-    "- `lazy_load=True`\n",
-    "\n",
-    "**NOTE**: When using multi-GPU settings, it is important to configure `parallel` and `lazy_load` properly to avoid inefficiencies:\n",
-    "\n",
-    "`parallel`: This parameter enables multi-GPU support by spawning child processes for each GPU specified in device_ids. To ensure proper utilization, the value of `parallel` must match the number of GPUs in device_ids. If using a single GPU, this parameter is not necessary.\n",
-    "\n",
-    "`lazy_load`: Enabling `lazy_load` prevents redundant memory usage. Without `lazy_load`, the model is initially loaded into the memory of the first GPU by the main process. When child processes are spawned for each GPU, the model is reloaded on the first GPU, causing redundant memory consumption and inefficiencies."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.10.15"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/examples/FastEmbed_vs_HF_Comparison.ipynb

@@ -36,7 +36,7 @@
    "outputs": [],
    "source": [
     "import time\n",
-    "from typing import Callable\n",
+    "from typing import Callable, List, Tuple\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
     "import torch.nn.functional as F\n",
@@ -64,7 +64,6 @@
    ],
    "source": [
     "import fastembed\n",
-    "\n",
     "fastembed.__version__"
    ]
   },
@@ -99,7 +98,7 @@
     }
    ],
    "source": [
-    "documents: list[str] = [\n",
+    "documents: List[str] = [\n",
     "    \"Chandrayaan-3 is India's third lunar mission\",\n",
     "    \"It aimed to land a rover on the Moon's surface - joining the US, China and Russia\",\n",
     "    \"The mission is a follow-up to Chandrayaan-2, which had partial success\",\n",
@@ -152,11 +151,11 @@
     "    HuggingFace Transformer implementation of FlagEmbedding\n",
     "    \"\"\"\n",
     "\n",
-    "    def __init__(self, model_id: str) -> None:\n",
+    "    def __init__(self, model_id: str):\n",
     "        self.model = AutoModel.from_pretrained(model_id)\n",
     "        self.tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
     "\n",
-    "    def embed(self, texts: list[str]):\n",
+    "    def embed(self, texts: List[str]):\n",
     "        encoded_input = self.tokenizer(\n",
     "            texts, max_length=512, padding=True, truncation=True, return_tensors=\"pt\"\n",
     "        )\n",
@@ -255,7 +254,7 @@
     "\n",
     "def calculate_time_stats(\n",
     "    embed_func: Callable, documents: list, k: int\n",
-    ") -> tuple[float, float, float]:\n",
+    ") -> Tuple[float, float, float]:\n",
     "    times = []\n",
     "    for _ in range(k):\n",
     "        # Timing the embed_func call\n",
@@ -310,7 +309,7 @@
    ],
    "source": [
     "def plot_character_per_second_comparison(\n",
-    "    hf_stats: tuple[float, float, float], fst_stats: tuple[float, float, float], documents: list\n",
+    "    hf_stats: Tuple[float, float, float], fst_stats: Tuple[float, float, float], documents: list\n",
     "):\n",
     "    # Calculating total characters in documents\n",
     "    total_characters = sum(len(doc) for doc in documents)\n",

docs/examples/Hindi_Tamil_RAG_with_Navarasa7B.ipynb

@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-30T00:45:24.814968Z",
@@ -58,6 +58,8 @@
    },
    "outputs": [],
    "source": [
+    "from typing import List\n",
+    "\n",
     "import numpy as np\n",
     "from datasets import load_dataset\n",
     "from peft import AutoPeftModelForCausalLM\n",
@@ -70,11 +72,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
-    "hf_token = \"<YOUR_HF_TOKEN_HERE>\"  # Get your token from https://huggingface.co/settings/token, needed for Gemma weights"
+    "hf_token = <YOUR_HF_TOKEN_HERE>  # Get your token from https://huggingface.co/settings/token, needed for Gemma weights"
    ]
   },
   {
@@ -244,7 +246,7 @@
    },
    "outputs": [],
    "source": [
-    "context_embeddings: list[np.ndarray] = list(\n",
+    "context_embeddings: List[np.ndarray] = list(\n",
     "    embedding_model.embed(contexts)\n",
     ")  # Note the list() call - this is a generator"
    ]

docs/examples/Hybrid_Search.ipynb

@@ -50,6 +50,7 @@
    "outputs": [],
    "source": [
     "import json\n",
+    "from typing import List, Tuple\n",
     "\n",
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -488,11 +489,11 @@
     }
    ],
    "source": [
-    "def make_sparse_embedding(texts: list[str]) -> list[SparseEmbedding]:\n",
+    "def make_sparse_embedding(texts: List[str]):\n",
     "    return list(sparse_model.embed(texts, batch_size=32))\n",
     "\n",
     "\n",
-    "sparse_embedding: list[SparseEmbedding] = make_sparse_embedding(\n",
+    "sparse_embedding: List[SparseEmbedding] = make_sparse_embedding(\n",
     "    [\"Fastembed is a great library for text embeddings!\"]\n",
     ")\n",
     "sparse_embedding"
@@ -615,7 +616,7 @@
     }
    ],
    "source": [
-    "def get_tokens_and_weights(sparse_embedding, model_name) -> dict[str, float]:\n",
+    "def get_tokens_and_weights(sparse_embedding, model_name):\n",
     "    # Find the tokenizer for the model\n",
     "    tokenizer_source = None\n",
     "    for model_info in SparseTextEmbedding.list_supported_models():\n",
@@ -626,7 +627,7 @@
     "            raise ValueError(f\"Model {model_name} not found in the supported models.\")\n",
     "\n",
     "    tokenizer = AutoTokenizer.from_pretrained(tokenizer_source)\n",
-    "    token_weight_dict: dict[str, float] = {}\n",
+    "    token_weight_dict = {}\n",
     "    for i in range(len(sparse_embedding.indices)):\n",
     "        token = tokenizer.decode([sparse_embedding.indices[i]])\n",
     "        weight = sparse_embedding.values[i]\n",
@@ -661,7 +662,7 @@
    },
    "outputs": [],
    "source": [
-    "def make_dense_embedding(texts: list[str]):\n",
+    "def make_dense_embedding(texts: List[str]):\n",
     "    return list(dense_model.embed(texts))\n",
     "\n",
     "\n",
@@ -871,7 +872,7 @@
    },
    "outputs": [],
    "source": [
-    "def make_points(df: pd.DataFrame) -> list[PointStruct]:\n",
+    "def make_points(df: pd.DataFrame) -> List[PointStruct]:\n",
     "    sparse_vectors = df[\"sparse_embedding\"].tolist()\n",
     "    product_texts = df[\"combined_text\"].tolist()\n",
     "    dense_vectors = df[\"dense_embedding\"].tolist()\n",
@@ -898,7 +899,7 @@
     "    return points\n",
     "\n",
     "\n",
-    "points: list[PointStruct] = make_points(df)"
+    "points: List[PointStruct] = make_points(df)"
    ]
   },
   {
@@ -941,8 +942,8 @@
    "source": [
     "def search(query_text: str):\n",
     "    # # Compute sparse and dense vectors\n",
-    "    query_sparse_vectors: list[SparseEmbedding] = make_sparse_embedding([query_text])\n",
-    "    query_dense_vector: list[np.ndarray] = make_dense_embedding([query_text])\n",
+    "    query_sparse_vectors: List[SparseEmbedding] = make_sparse_embedding([query_text])\n",
+    "    query_dense_vector: List[np.ndarray] = make_dense_embedding([query_text])\n",
     "\n",
     "    search_results = client.search_batch(\n",
     "        collection_name=collection_name,\n",
@@ -1074,7 +1075,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def rank_list(search_result: list[ScoredPoint]):\n",
+    "def rank_list(search_result: List[ScoredPoint]):\n",
     "    return [(point.id, rank + 1) for rank, point in enumerate(search_result)]\n",
     "\n",
     "\n",
@@ -1148,7 +1149,7 @@
    ],
    "source": [
     "def find_point_by_id(\n",
-    "    client: QdrantClient, collection_name: str, rrf_rank_list: list[tuple[int, float]]\n",
+    "    client: QdrantClient, collection_name: str, rrf_rank_list: List[Tuple[int, float]]\n",
     "):\n",
     "    return client.retrieve(\n",
     "        collection_name=collection_name, ids=[item[0] for item in rrf_rank_list]\n",

docs/examples/SPLADE_with_FastEmbed.ipynb

@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-30T00:49:20.516644Z",
@@ -56,7 +56,8 @@
    },
    "outputs": [],
    "source": [
-    "from fastembed import SparseTextEmbedding, SparseEmbedding"
+    "from fastembed import SparseTextEmbedding, SparseEmbedding\n",
+    "from typing import List"
    ]
   },
   {
@@ -133,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-03-30T00:49:28.624109Z",
@@ -142,7 +143,7 @@
    },
    "outputs": [],
    "source": [
-    "documents: list[str] = [\n",
+    "documents: List[str] = [\n",
     "    \"Chandrayaan-3 is India's third lunar mission\",\n",
     "    \"It aimed to land a rover on the Moon's surface - joining the US, China and Russia\",\n",
     "    \"The mission is a follow-up to Chandrayaan-2, which had partial success\",\n",
@@ -156,7 +157,7 @@
     "    \"Chandrayaan-3 was launched from the Satish Dhawan Space Centre in Sriharikota\",\n",
     "    \"Chandrayaan-3 was launched earlier in the year 2023\",\n",
     "]\n",
-    "sparse_embeddings_list: list[SparseEmbedding] = list(\n",
+    "sparse_embeddings_list: List[SparseEmbedding] = list(\n",
     "    model.embed(documents, batch_size=6)\n",
     ")  # batch_size is optional, notice the generator"
    ]
@@ -234,9 +235,7 @@
    "source": [
     "# Let's print the first 5 features and their weights for better understanding.\n",
     "for i in range(5):\n",
-    "    print(\n",
-    "        f\"Token at index {sparse_embeddings_list[0].indices[i]} has weight {sparse_embeddings_list[0].values[i]}\"\n",
-    "    )"
+    "    print(f\"Token at index {sparse_embeddings_list[0].indices[i]} has weight {sparse_embeddings_list[0].values[i]}\")"
    ]
   },
   {
@@ -262,9 +261,7 @@
     "import json\n",
     "from transformers import AutoTokenizer\n",
     "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\n",
-    "    SparseTextEmbedding.list_supported_models()[0][\"sources\"][\"hf\"]\n",
-    ")"
+    "tokenizer = AutoTokenizer.from_pretrained(SparseTextEmbedding.list_supported_models()[0][\"sources\"][\"hf\"])"
    ]
   },
   {
@@ -329,9 +326,7 @@
     "        token_weight_dict[token] = weight\n",
     "\n",
     "    # Sort the dictionary by weights\n",
-    "    token_weight_dict = dict(\n",
-    "        sorted(token_weight_dict.items(), key=lambda item: item[1], reverse=True)\n",
-    "    )\n",
+    "    token_weight_dict = dict(sorted(token_weight_dict.items(), key=lambda item: item[1], reverse=True))\n",
     "    return token_weight_dict\n",
     "\n",
     "\n",

docs/examples/Supported_Models.ipynb

@@ -2,36 +2,38 @@
  "cells": [
   {
    "cell_type": "code",
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-13T09:01:03.324551Z",
-     "start_time": "2024-11-13T09:01:03.234711Z"
+     "end_time": "2024-05-31T18:13:23.806907Z",
+     "start_time": "2024-05-31T18:13:23.797078Z"
     }
    },
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2"
-   ],
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
-   "execution_count": 10
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-13T09:01:04.505772Z",
-     "start_time": "2024-11-13T09:01:04.493296Z"
+     "end_time": "2024-05-31T18:14:31.147674Z",
+     "start_time": "2024-05-31T18:14:31.134015Z"
     }
    },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/hossam/.pyenv/versions/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "\n",
@@ -40,11 +42,8 @@
     "    TextEmbedding,\n",
     "    LateInteractionTextEmbedding,\n",
     "    ImageEmbedding,\n",
-    ")\n",
-    "from fastembed.rerank.cross_encoder import TextCrossEncoder"
-   ],
-   "outputs": [],
-   "execution_count": 11
+    ")"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -55,79 +54,16 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-13T09:01:05.812271Z",
-     "start_time": "2024-11-13T09:01:05.795846Z"
+     "end_time": "2024-05-31T18:13:25.863008Z",
+     "start_time": "2024-05-31T18:13:25.837795Z"
     }
    },
-   "source": [
-    "supported_models = (\n",
-    "    pd.DataFrame(TextEmbedding.list_supported_models())\n",
-    "    .sort_values(\"size_in_GB\")\n",
-    "    .drop(columns=[\"sources\", \"model_file\", \"additional_files\"])\n",
-    "    .reset_index(drop=True)\n",
-    ")\n",
-    "supported_models"
-   ],
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "                                                model   dim  \\\n",
-       "0                              BAAI/bge-small-en-v1.5   384   \n",
-       "1                              BAAI/bge-small-zh-v1.5   512   \n",
-       "2                 snowflake/snowflake-arctic-embed-xs   384   \n",
-       "3              sentence-transformers/all-MiniLM-L6-v2   384   \n",
-       "4                  jinaai/jina-embeddings-v2-small-en   512   \n",
-       "5                                   BAAI/bge-small-en   384   \n",
-       "6                  snowflake/snowflake-arctic-embed-s   384   \n",
-       "7                    nomic-ai/nomic-embed-text-v1.5-Q   768   \n",
-       "8                               BAAI/bge-base-en-v1.5   768   \n",
-       "9   sentence-transformers/paraphrase-multilingual-...   384   \n",
-       "10                          Qdrant/clip-ViT-B-32-text   512   \n",
-       "11                  jinaai/jina-embeddings-v2-base-de   768   \n",
-       "12                                   BAAI/bge-base-en   768   \n",
-       "13                 snowflake/snowflake-arctic-embed-m   768   \n",
-       "14                     nomic-ai/nomic-embed-text-v1.5   768   \n",
-       "15                  jinaai/jina-embeddings-v2-base-en   768   \n",
-       "16                       nomic-ai/nomic-embed-text-v1   768   \n",
-       "17            snowflake/snowflake-arctic-embed-m-long   768   \n",
-       "18                 mixedbread-ai/mxbai-embed-large-v1  1024   \n",
-       "19                jinaai/jina-embeddings-v2-base-code   768   \n",
-       "20  sentence-transformers/paraphrase-multilingual-...   768   \n",
-       "21                 snowflake/snowflake-arctic-embed-l  1024   \n",
-       "22                                 thenlper/gte-large  1024   \n",
-       "23                             BAAI/bge-large-en-v1.5  1024   \n",
-       "24                     intfloat/multilingual-e5-large  1024   \n",
-       "\n",
-       "                                          description     license  size_in_GB  \n",
-       "0   Text embeddings, Unimodal (text), English, 512...         mit       0.067  \n",
-       "1   Text embeddings, Unimodal (text), Chinese, 512...         mit       0.090  \n",
-       "2   Text embeddings, Unimodal (text), English, 512...  apache-2.0       0.090  \n",
-       "3   Text embeddings, Unimodal (text), English, 256...  apache-2.0       0.090  \n",
-       "4   Text embeddings, Unimodal (text), English, 819...  apache-2.0       0.120  \n",
-       "5   Text embeddings, Unimodal (text), English, 512...         mit       0.130  \n",
-       "6   Text embeddings, Unimodal (text), English, 512...  apache-2.0       0.130  \n",
-       "7   Text embeddings, Multimodal (text, image), Eng...  apache-2.0       0.130  \n",
-       "8   Text embeddings, Unimodal (text), English, 512...         mit       0.210  \n",
-       "9   Text embeddings, Unimodal (text), Multilingual...  apache-2.0       0.220  \n",
-       "10  Text embeddings, Multimodal (text&image), Engl...         mit       0.250  \n",
-       "11  Text embeddings, Unimodal (text), Multilingual...  apache-2.0       0.320  \n",
-       "12  Text embeddings, Unimodal (text), English, 512...         mit       0.420  \n",
-       "13  Text embeddings, Unimodal (text), English, 512...  apache-2.0       0.430  \n",
-       "14  Text embeddings, Multimodal (text, image), Eng...  apache-2.0       0.520  \n",
-       "15  Text embeddings, Unimodal (text), English, 819...  apache-2.0       0.520  \n",
-       "16  Text embeddings, Multimodal (text, image), Eng...  apache-2.0       0.520  \n",
-       "17  Text embeddings, Unimodal (text), English, 204...  apache-2.0       0.540  \n",
-       "18  Text embeddings, Unimodal (text), English, 512...  apache-2.0       0.640  \n",
-       "19  Text embeddings, Unimodal (text), Multilingual...  apache-2.0       0.640  \n",
-       "20  Text embeddings, Unimodal (text), Multilingual...  apache-2.0       1.000  \n",
-       "21  Text embeddings, Unimodal (text), English, 512...  apache-2.0       1.020  \n",
-       "22  Text embeddings, Unimodal (text), English, 512...         mit       1.200  \n",
-       "23  Text embeddings, Unimodal (text), English, 512...         mit       1.200  \n",
-       "24  Text embeddings, Unimodal (text), Multilingual...         mit       2.240  "
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -358,14 +294,77 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "                                                model   dim  \\\n",
+       "0                              BAAI/bge-small-en-v1.5   384   \n",
+       "1                              BAAI/bge-small-zh-v1.5   512   \n",
+       "2                 snowflake/snowflake-arctic-embed-xs   384   \n",
+       "3              sentence-transformers/all-MiniLM-L6-v2   384   \n",
+       "4                  jinaai/jina-embeddings-v2-small-en   512   \n",
+       "5                                   BAAI/bge-small-en   384   \n",
+       "6                  snowflake/snowflake-arctic-embed-s   384   \n",
+       "7                    nomic-ai/nomic-embed-text-v1.5-Q   768   \n",
+       "8                               BAAI/bge-base-en-v1.5   768   \n",
+       "9   sentence-transformers/paraphrase-multilingual-...   384   \n",
+       "10                          Qdrant/clip-ViT-B-32-text   512   \n",
+       "11                  jinaai/jina-embeddings-v2-base-de   768   \n",
+       "12                                   BAAI/bge-base-en   768   \n",
+       "13                 snowflake/snowflake-arctic-embed-m   768   \n",
+       "14                     nomic-ai/nomic-embed-text-v1.5   768   \n",
+       "15                  jinaai/jina-embeddings-v2-base-en   768   \n",
+       "16                       nomic-ai/nomic-embed-text-v1   768   \n",
+       "17            snowflake/snowflake-arctic-embed-m-long   768   \n",
+       "18                 mixedbread-ai/mxbai-embed-large-v1  1024   \n",
+       "19                jinaai/jina-embeddings-v2-base-code   768   \n",
+       "20  sentence-transformers/paraphrase-multilingual-...   768   \n",
+       "21                 snowflake/snowflake-arctic-embed-l  1024   \n",
+       "22                                 thenlper/gte-large  1024   \n",
+       "23                             BAAI/bge-large-en-v1.5  1024   \n",
+       "24                     intfloat/multilingual-e5-large  1024   \n",
+       "\n",
+       "                                          description     license  size_in_GB  \n",
+       "0   Text embeddings, Unimodal (text), English, 512...         mit       0.067  \n",
+       "1   Text embeddings, Unimodal (text), Chinese, 512...         mit       0.090  \n",
+       "2   Text embeddings, Unimodal (text), English, 512...  apache-2.0       0.090  \n",
+       "3   Text embeddings, Unimodal (text), English, 256...  apache-2.0       0.090  \n",
+       "4   Text embeddings, Unimodal (text), English, 819...  apache-2.0       0.120  \n",
+       "5   Text embeddings, Unimodal (text), English, 512...         mit       0.130  \n",
+       "6   Text embeddings, Unimodal (text), English, 512...  apache-2.0       0.130  \n",
+       "7   Text embeddings, Multimodal (text, image), Eng...  apache-2.0       0.130  \n",
+       "8   Text embeddings, Unimodal (text), English, 512...         mit       0.210  \n",
+       "9   Text embeddings, Unimodal (text), Multilingual...  apache-2.0       0.220  \n",
+       "10  Text embeddings, Multimodal (text&image), Engl...         mit       0.250  \n",
+       "11  Text embeddings, Unimodal (text), Multilingual...  apache-2.0       0.320  \n",
+       "12  Text embeddings, Unimodal (text), English, 512...         mit       0.420  \n",
+       "13  Text embeddings, Unimodal (text), English, 512...  apache-2.0       0.430  \n",
+       "14  Text embeddings, Multimodal (text, image), Eng...  apache-2.0       0.520  \n",
+       "15  Text embeddings, Unimodal (text), English, 819...  apache-2.0       0.520  \n",
+       "16  Text embeddings, Multimodal (text, image), Eng...  apache-2.0       0.520  \n",
+       "17  Text embeddings, Unimodal (text), English, 204...  apache-2.0       0.540  \n",
+       "18  Text embeddings, Unimodal (text), English, 512...  apache-2.0       0.640  \n",
+       "19  Text embeddings, Unimodal (text), Multilingual...  apache-2.0       0.640  \n",
+       "20  Text embeddings, Unimodal (text), Multilingual...  apache-2.0       1.000  \n",
+       "21  Text embeddings, Unimodal (text), English, 512...  apache-2.0       1.020  \n",
+       "22  Text embeddings, Unimodal (text), English, 512...         mit       1.200  \n",
+       "23  Text embeddings, Unimodal (text), English, 512...         mit       1.200  \n",
+       "24  Text embeddings, Unimodal (text), Multilingual...         mit       2.240  "
       ]
      },
-     "execution_count": 12,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 12
+   "source": [
+    "supported_models = (\n",
+    "    pd.DataFrame(TextEmbedding.list_supported_models())\n",
+    "    .sort_values(\"size_in_GB\")\n",
+    "    .drop(columns=[\"sources\", \"model_file\", \"additional_files\"])\n",
+    "    .reset_index(drop=True)\n",
+    ")\n",
+    "supported_models"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -376,42 +375,16 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-11-13T09:01:07.038954Z",
-     "start_time": "2024-11-13T09:01:07.019656Z"
+     "end_time": "2024-05-31T18:13:27.124747Z",
+     "start_time": "2024-05-31T18:13:27.096212Z"
     }
    },
-   "source": [
-    "(\n",
-    "    pd.DataFrame(SparseTextEmbedding.list_supported_models())\n",
-    "    .sort_values(\"size_in_GB\")\n",
-    "    .drop(columns=[\"sources\", \"model_file\", \"additional_files\"])\n",
-    "    .reset_index(drop=True)\n",
-    ")"
-   ],
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "                                     model  vocab_size  \\\n",
-       "0                              Qdrant/bm25         NaN   \n",
-       "1  Qdrant/bm42-all-minilm-l6-v2-attentions     30522.0   \n",
-       "2               prithivida/Splade_PP_en_v1     30522.0   \n",
-       "3                prithvida/Splade_PP_en_v1     30522.0   \n",
-       "\n",
-       "                                         description     license  size_in_GB  \\\n",
-       "0  BM25 as sparse embeddings meant to be used wit...  apache-2.0       0.010   \n",
-       "1  Light sparse embedding model, which assigns an...  apache-2.0       0.090   \n",
-       "2  Independent Implementation of SPLADE++ Model f...  apache-2.0       0.532   \n",
-       "3  Independent Implementation of SPLADE++ Model f...  apache-2.0       0.532   \n",
-       "\n",
-       "  requires_idf  \n",
-       "0         True  \n",
-       "1         True  \n",
-       "2          NaN  \n",
-       "3          NaN  "
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -479,14 +452,40 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "                                     model  vocab_size  \\\n",
+       "0                              Qdrant/bm25         NaN   \n",
+       "1  Qdrant/bm42-all-minilm-l6-v2-attentions     30522.0   \n",
+       "2               prithivida/Splade_PP_en_v1     30522.0   \n",
+       "3                prithvida/Splade_PP_en_v1     30522.0   \n",
+       "\n",
+       "                                         description     license  size_in_GB  \\\n",
+       "0  BM25 as sparse embeddings meant to be used wit...  apache-2.0       0.010   \n",
+       "1  Light sparse embedding model, which assigns an...  apache-2.0       0.090   \n",
+       "2  Independent Implementation of SPLADE++ Model f...  apache-2.0       0.532   \n",
+       "3  Independent Implementation of SPLADE++ Model f...  apache-2.0       0.532   \n",
+       "\n",
+       "  requires_idf  \n",
+       "0         True  \n",
+       "1         True  \n",
+       "2          NaN  \n",
+       "3          NaN  "
       ]
      },
-     "execution_count": 13,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 13
+   "source": [
+    "(\n",
+    "    pd.DataFrame(SparseTextEmbedding.list_supported_models())\n",
+    "    .sort_values(\"size_in_GB\")\n",
+    "    .drop(columns=[\"sources\", \"model_file\", \"additional_files\"])\n",
+    "    .reset_index(drop=True)\n",
+    ")"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -499,40 +498,17 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 5,
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-11-13T09:01:08.074442Z",
-     "start_time": "2024-11-13T09:01:08.056138Z"
-    }
+     "end_time": "2024-05-31T18:14:34.370252Z",
+     "start_time": "2024-05-31T18:14:34.354270Z"
+    },
+    "collapsed": false
    },
-   "source": [
-    "(\n",
-    "    pd.DataFrame(LateInteractionTextEmbedding.list_supported_models())\n",
-    "    .sort_values(\"size_in_GB\")\n",
-    "    .drop(columns=[\"sources\", \"model_file\"])\n",
-    "    .reset_index(drop=True)\n",
-    ")"
-   ],
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "                                   model  dim  \\\n",
-       "0  answerdotai/answerai-colbert-small-v1   96   \n",
-       "1                 colbert-ir/colbertv2.0  128   \n",
-       "2                 jinaai/jina-colbert-v2  128   \n",
-       "\n",
-       "                                         description       license  \\\n",
-       "0  Text embeddings, Unimodal (text), Multilingual...    apache-2.0   \n",
-       "1                             Late interaction model           mit   \n",
-       "2  New model that expands capabilities of colbert...  cc-by-nc-4.0   \n",
-       "\n",
-       "   size_in_GB        additional_files  \n",
-       "0        0.13                     NaN  \n",
-       "1        0.44                     NaN  \n",
-       "2        2.24  [onnx/model.onnx_data]  "
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -582,7 +558,7 @@
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>jinaai/jina-colbert-v2</td>\n",
-       "      <td>128</td>\n",
+       "      <td>1024</td>\n",
        "      <td>New model that expands capabilities of colbert...</td>\n",
        "      <td>cc-by-nc-4.0</td>\n",
        "      <td>2.24</td>\n",
@@ -591,14 +567,37 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "                                   model   dim  \\\n",
+       "0  answerdotai/answerai-colbert-small-v1    96   \n",
+       "1                 colbert-ir/colbertv2.0   128   \n",
+       "2                 jinaai/jina-colbert-v2  1024   \n",
+       "\n",
+       "                                         description       license  \\\n",
+       "0  Text embeddings, Unimodal (text), Multilingual...    apache-2.0   \n",
+       "1                             Late interaction model           mit   \n",
+       "2  New model that expands capabilities of colbert...  cc-by-nc-4.0   \n",
+       "\n",
+       "   size_in_GB        additional_files  \n",
+       "0        0.13                     NaN  \n",
+       "1        0.44                     NaN  \n",
+       "2        2.24  [onnx/model.onnx_data]  "
       ]
      },
-     "execution_count": 14,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 14
+   "source": [
+    "(\n",
+    "    pd.DataFrame(LateInteractionTextEmbedding.list_supported_models())\n",
+    "    .sort_values(\"size_in_GB\")\n",
+    "    .drop(columns=[\"sources\", \"model_file\"])\n",
+    "    .reset_index(drop=True)\n",
+    ")"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -611,37 +610,17 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 6,
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-11-13T09:01:09.171647Z",
-     "start_time": "2024-11-13T09:01:09.150940Z"
-    }
+     "end_time": "2024-05-31T18:14:42.501881Z",
+     "start_time": "2024-05-31T18:14:42.484726Z"
+    },
+    "collapsed": false
    },
-   "source": [
-    "(\n",
-    "    pd.DataFrame(ImageEmbedding.list_supported_models())\n",
-    "    .sort_values(\"size_in_GB\")\n",
-    "    .drop(columns=[\"sources\", \"model_file\"])\n",
-    "    .reset_index(drop=True)\n",
-    ")"
-   ],
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "                         model   dim  \\\n",
-       "0         Qdrant/resnet50-onnx  2048   \n",
-       "1  Qdrant/clip-ViT-B-32-vision   512   \n",
-       "2       Qdrant/Unicom-ViT-B-32   512   \n",
-       "3       Qdrant/Unicom-ViT-B-16   768   \n",
-       "\n",
-       "                                         description     license  size_in_GB  \n",
-       "0      Image embeddings, Unimodal (image), 2016 year  apache-2.0        0.10  \n",
-       "1  Image embeddings, Multimodal (text&image), 202...         mit        0.34  \n",
-       "2  Image embeddings, Multimodal (text&image), 202...  apache-2.0        0.48  \n",
-       "3  Image embeddings (more detailed than Unicom-Vi...  apache-2.0        0.82  "
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -704,149 +683,39 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "                         model   dim  \\\n",
+       "0         Qdrant/resnet50-onnx  2048   \n",
+       "1  Qdrant/clip-ViT-B-32-vision   512   \n",
+       "2       Qdrant/Unicom-ViT-B-32   512   \n",
+       "3       Qdrant/Unicom-ViT-B-16   768   \n",
+       "\n",
+       "                                         description     license  size_in_GB  \n",
+       "0      Image embeddings, Unimodal (image), 2016 year  apache-2.0        0.10  \n",
+       "1  Image embeddings, Multimodal (text&image), 202...         mit        0.34  \n",
+       "2  Image embeddings, Multimodal (text&image), 202...  apache-2.0        0.48  \n",
+       "3  Image embeddings (more detailed than Unicom-Vi...  apache-2.0        0.82  "
       ]
      },
-     "execution_count": 15,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 15
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Supported Rerank Cross Encoder Models"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-11-13T09:01:10.313943Z",
-     "start_time": "2024-11-13T09:01:10.298428Z"
-    }
-   },
    "source": [
     "(\n",
-    "    pd.DataFrame(TextCrossEncoder.list_supported_models())\n",
+    "    pd.DataFrame(ImageEmbedding.list_supported_models())\n",
     "    .sort_values(\"size_in_GB\")\n",
     "    .drop(columns=[\"sources\", \"model_file\"])\n",
     "    .reset_index(drop=True)\n",
     ")"
-   ],
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "                                       model  size_in_GB  \\\n",
-       "0              Xenova/ms-marco-MiniLM-L-6-v2        0.08   \n",
-       "1             Xenova/ms-marco-MiniLM-L-12-v2        0.12   \n",
-       "2            jinaai/jina-reranker-v1-tiny-en        0.13   \n",
-       "3           jinaai/jina-reranker-v1-turbo-en        0.15   \n",
-       "4                     BAAI/bge-reranker-base        1.04   \n",
-       "5  jinaai/jina-reranker-v2-base-multilingual        1.11   \n",
-       "\n",
-       "                                         description       license  \n",
-       "0  MiniLM-L-6-v2 model optimized for re-ranking t...    apache-2.0  \n",
-       "1  MiniLM-L-12-v2 model optimized for re-ranking ...    apache-2.0  \n",
-       "2  Designed for blazing-fast re-ranking with 8K c...    apache-2.0  \n",
-       "3  Designed for blazing-fast re-ranking with 8K c...    apache-2.0  \n",
-       "4  BGE reranker base model for cross-encoder re-r...           mit  \n",
-       "5  A multi-lingual reranker model for cross-encod...  cc-by-nc-4.0  "
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>model</th>\n",
-       "      <th>size_in_GB</th>\n",
-       "      <th>description</th>\n",
-       "      <th>license</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Xenova/ms-marco-MiniLM-L-6-v2</td>\n",
-       "      <td>0.08</td>\n",
-       "      <td>MiniLM-L-6-v2 model optimized for re-ranking t...</td>\n",
-       "      <td>apache-2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Xenova/ms-marco-MiniLM-L-12-v2</td>\n",
-       "      <td>0.12</td>\n",
-       "      <td>MiniLM-L-12-v2 model optimized for re-ranking ...</td>\n",
-       "      <td>apache-2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>jinaai/jina-reranker-v1-tiny-en</td>\n",
-       "      <td>0.13</td>\n",
-       "      <td>Designed for blazing-fast re-ranking with 8K c...</td>\n",
-       "      <td>apache-2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>jinaai/jina-reranker-v1-turbo-en</td>\n",
-       "      <td>0.15</td>\n",
-       "      <td>Designed for blazing-fast re-ranking with 8K c...</td>\n",
-       "      <td>apache-2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>BAAI/bge-reranker-base</td>\n",
-       "      <td>1.04</td>\n",
-       "      <td>BGE reranker base model for cross-encoder re-r...</td>\n",
-       "      <td>mit</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>jinaai/jina-reranker-v2-base-multilingual</td>\n",
-       "      <td>1.11</td>\n",
-       "      <td>A multi-lingual reranker model for cross-encod...</td>\n",
-       "      <td>cc-by-nc-4.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 16
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": ""
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.8.18 ('base')",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -860,14 +729,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.10.15"
   },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "c4a27af61e455bc18dcf16f5867a2ff0402fa12b01dd0f6ce3a79ae73ad15e91"
-   }
-  }
+  "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 2

docs/index.md

@@ -26,14 +26,14 @@ pip install fastembed
 ```python
 from fastembed import TextEmbedding
 
-documents: list[str] = [
+documents: List[str] = [
     "passage: Hello, World!",
     "query: Hello, World!",
     "passage: This is an example passage.",
     "fastembed is supported by and maintained by Qdrant."
 ]
 embedding_model = TextEmbedding()
-embeddings: list[np.ndarray] = embedding_model.embed(documents)
+embeddings: List[np.ndarray] = embedding_model.embed(documents)
 ```
 
 ## Usage with Qdrant

docs/qdrant/Retrieval_with_FastEmbed.ipynb

@@ -41,6 +41,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from typing import List\n",
     "import numpy as np\n",
     "from fastembed import TextEmbedding"
    ]
@@ -70,7 +71,7 @@
    ],
    "source": [
     "# Example list of documents\n",
-    "documents: list[str] = [\n",
+    "documents: List[str] = [\n",
     "    \"Maharana Pratap was a Rajput warrior king from Mewar\",\n",
     "    \"He fought against the Mughal Empire led by Akbar\",\n",
     "    \"The Battle of Haldighati in 1576 was his most famous battle\",\n",
@@ -86,7 +87,7 @@
     "embedding_model = TextEmbedding(model_name=\"BAAI/bge-small-en\")\n",
     "\n",
     "# We'll use the passage_embed method to get the embeddings for the documents\n",
-    "embeddings: list[np.ndarray] = list(\n",
+    "embeddings: List[np.ndarray] = list(\n",
     "    embedding_model.passage_embed(documents)\n",
     ")  # notice that we are casting the generator to a list\n",
     "\n",

docs/qdrant/Usage_With_Qdrant.ipynb

@@ -46,6 +46,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from typing import List\n",
     "from qdrant_client import QdrantClient"
    ]
   },
@@ -66,7 +67,7 @@
    "outputs": [],
    "source": [
     "# Example list of documents\n",
-    "documents: list[str] = [\n",
+    "documents: List[str] = [\n",
     "    \"Maharana Pratap was a Rajput warrior king from Mewar\",\n",
     "    \"He fought against the Mughal Empire led by Akbar\",\n",
     "    \"The Battle of Haldighati in 1576 was his most famous battle\",\n",
@@ -198,9 +199,7 @@
     }
    ],
    "source": [
-    "search_result = client.query(\n",
-    "    collection_name=\"demo_collection\", query_text=\"This is a query document\"\n",
-    ")\n",
+    "search_result = client.query(collection_name=\"demo_collection\", query_text=\"This is a query document\")\n",
     "print(search_result)"
    ]
   },

experiments/01_ONNX_Port.ipynb

@@ -19,7 +19,7 @@
    "outputs": [],
    "source": [
     "from pathlib import Path\n",
-    "from typing import Any\n",
+    "from typing import List, Tuple, Any\n",
     "\n",
     "import numpy as np\n",
     "import time\n",
@@ -91,11 +91,9 @@
     "    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]\n",
     "\n",
     "\n",
-    "def hf_embed(model_id: str, inputs: list[str]):\n",
+    "def hf_embed(model_id: str, inputs: List[str]):\n",
     "    # Tokenize the input texts\n",
-    "    batch_dict = hf_tokenizer(\n",
-    "        inputs, max_length=512, padding=True, truncation=True, return_tensors=\"pt\"\n",
-    "    )\n",
+    "    batch_dict = hf_tokenizer(inputs, max_length=512, padding=True, truncation=True, return_tensors=\"pt\")\n",
     "\n",
     "    outputs = hf_model(**batch_dict)\n",
     "    embeddings = average_pool(outputs.last_hidden_state, batch_dict[\"attention_mask\"])\n",
@@ -135,9 +133,7 @@
     "optimization_config = AutoOptimizationConfig.O4()\n",
     "optimizer = ORTOptimizer.from_pretrained(model)\n",
     "\n",
-    "optimizer.optimize(\n",
-    "    save_dir=save_dir, optimization_config=optimization_config, use_external_data_format=True\n",
-    ")\n",
+    "optimizer.optimize(save_dir=save_dir, optimization_config=optimization_config, use_external_data_format=True)\n",
     "model = ORTModelForFeatureExtraction.from_pretrained(save_dir)\n",
     "\n",
     "tokenizer.save_pretrained(save_dir)\n",
@@ -175,9 +171,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def measure_pipeline_time(\n",
-    "    pipeline, input_texts: list[str], num_runs=10, **kwargs: Any\n",
-    ") -> tuple[float, float]:\n",
+    "def measure_pipeline_time(pipeline, input_texts: List[str], num_runs=10, **kwargs: Any) -> Tuple[float, float]:\n",
     "    \"\"\"Measures the time it takes to run the pipeline on the input texts.\"\"\"\n",
     "    times = []\n",
     "    total_chars = sum(len(text) for text in input_texts)\n",

fastembed/__init__.py

@@ -2,7 +2,6 @@ import importlib.metadata
 
 from fastembed.image import ImageEmbedding
 from fastembed.late_interaction import LateInteractionTextEmbedding
-from fastembed.late_interaction_multimodal import LateInteractionMultimodalEmbedding
 from fastembed.sparse import SparseEmbedding, SparseTextEmbedding
 from fastembed.text import TextEmbedding
 
@@ -18,5 +17,4 @@ __all__ = [
     "SparseEmbedding",
     "ImageEmbedding",
     "LateInteractionTextEmbedding",
-    "LateInteractionMultimodalEmbedding",
 ]

fastembed/common/__init__.py

@@ -1,3 +1,3 @@
-from fastembed.common.types import ImageInput, OnnxProvider, PathInput
+from fastembed.common.types import ImageInput, OnnxProvider, PathInput, PilInput
 
-__all__ = ["OnnxProvider", "ImageInput", "PathInput"]
+__all__ = ["OnnxProvider", "ImageInput", "PathInput", "PilInput"]

fastembed/common/model_description.py

@@ -1,52 +0,0 @@
-from dataclasses import dataclass, field
-from enum import Enum
-from typing import Optional, Any
-
-
-@dataclass(frozen=True)
-class ModelSource:
-    hf: Optional[str] = None
-    url: Optional[str] = None
-    _deprecated_tar_struct: bool = False
-
-    @property
-    def deprecated_tar_struct(self) -> bool:
-        return self._deprecated_tar_struct
-
-    def __post_init__(self) -> None:
-        if self.hf is None and self.url is None:
-            raise ValueError(
-                f"At least one source should be set, current sources: hf={self.hf}, url={self.url}"
-            )
-
-
-@dataclass(frozen=True)
-class BaseModelDescription:
-    model: str
-    sources: ModelSource
-    model_file: str
-    description: str
-    license: str
-    size_in_GB: float
-    additional_files: list[str] = field(default_factory=list)
-
-
-@dataclass(frozen=True)
-class DenseModelDescription(BaseModelDescription):
-    dim: Optional[int] = None
-    tasks: Optional[dict[str, Any]] = field(default_factory=dict)
-
-    def __post_init__(self) -> None:
-        assert self.dim is not None, "dim is required for dense model description"
-
-
-@dataclass(frozen=True)
-class SparseModelDescription(BaseModelDescription):
-    requires_idf: Optional[bool] = None
-    vocab_size: Optional[int] = None
-
-
-class PoolingType(str, Enum):
-    CLS = "CLS"
-    MEAN = "MEAN"
-    DISABLED = "DISABLED"

fastembed/common/model_management.py

@@ -1,69 +1,29 @@
 import os
 import time
-import json
 import shutil
 import tarfile
 from pathlib import Path
-from typing import Any, Optional, Union, TypeVar, Generic
+from typing import Any, Dict, List, Optional
 
 import requests
-from huggingface_hub import snapshot_download, model_info, list_repo_tree
-from huggingface_hub.hf_api import RepoFile
-from huggingface_hub.utils import (
-    RepositoryNotFoundError,
-    disable_progress_bars,
-    enable_progress_bars,
-)
+from huggingface_hub import snapshot_download
+from huggingface_hub.utils import RepositoryNotFoundError
 from loguru import logger
 from tqdm import tqdm
-from fastembed.common.model_description import BaseModelDescription
-
-T = TypeVar("T", bound=BaseModelDescription)
 
 
-class ModelManagement(Generic[T]):
-    METADATA_FILE = "files_metadata.json"
-
+class ModelManagement:
     @classmethod
-    def list_supported_models(cls) -> list[dict[str, Any]]:
+    def list_supported_models(cls) -> List[Dict[str, Any]]:
         """Lists the supported models.
 
         Returns:
-            list[T]: A list of dictionaries containing the model information.
+            List[Dict[str, Any]]: A list of dictionaries containing the model information.
         """
         raise NotImplementedError()
 
     @classmethod
-    def add_custom_model(
-        cls,
-        *args: Any,
-        **kwargs: Any,
-    ) -> None:
-        """Add a custom model to the existing embedding classes based on the passed model descriptions
-
-        Model description dict should contain the fields same as in one of the model descriptions presented
-         in fastembed.common.model_description
-
-         E.g. for BaseModelDescription:
-              model: str
-              sources: ModelSource
-              model_file: str
-              description: str
-              license: str
-              size_in_GB: float
-              additional_files: list[str]
-
-        Returns:
-            None
-        """
-        raise NotImplementedError()
-
-    @classmethod
-    def _list_supported_models(cls) -> list[T]:
-        raise NotImplementedError()
-
-    @classmethod
-    def _get_model_description(cls, model_name: str) -> T:
+    def _get_model_description(cls, model_name: str) -> Dict[str, Any]:
         """
         Gets the model description from the model_name.
 
@@ -74,10 +34,10 @@ class ModelManagement(Generic[T]):
             ValueError: If the model_name is not supported.
 
         Returns:
-            T: The model description.
+            Dict[str, Any]: The model description.
         """
-        for model in cls._list_supported_models():
-            if model_name.lower() == model.model.lower():
+        for model in cls.list_supported_models():
+            if model_name.lower() == model["model"].lower():
                 return model
 
         raise ValueError(f"Model {model_name} is not supported in {cls.__name__}.")
@@ -133,75 +93,22 @@ class ModelManagement(Generic[T]):
     def download_files_from_huggingface(
         cls,
         hf_source_repo: str,
-        cache_dir: str,
-        extra_patterns: list[str],
+        cache_dir: Optional[str] = None,
+        extra_patterns: Optional[List[str]] = None,
         local_files_only: bool = False,
-        **kwargs: Any,
+        **kwargs,
     ) -> str:
         """
         Downloads a model from HuggingFace Hub.
         Args:
             hf_source_repo (str): Name of the model on HuggingFace Hub, e.g. "qdrant/all-MiniLM-L6-v2-onnx".
             cache_dir (Optional[str]): The path to the cache directory.
-            extra_patterns (list[str]): extra patterns to allow in the snapshot download, typically
+            extra_patterns (Optional[List[str]]): extra patterns to allow in the snapshot download, typically
                 includes the required model files.
             local_files_only (bool, optional): Whether to only use local files. Defaults to False.
         Returns:
             Path: The path to the model directory.
         """
-
-        def _verify_files_from_metadata(
-            model_dir: Path, stored_metadata: dict[str, Any], repo_files: list[RepoFile]
-        ) -> bool:
-            try:
-                for rel_path, meta in stored_metadata.items():
-                    file_path = model_dir / rel_path
-
-                    if not file_path.exists():
-                        return False
-
-                    if repo_files:  # online verification
-                        file_info = next((f for f in repo_files if f.path == file_path.name), None)
-                        if (
-                            not file_info
-                            or file_info.size != meta["size"]
-                            or file_info.blob_id != meta["blob_id"]
-                        ):
-                            return False
-
-                    else:  # offline verification
-                        if file_path.stat().st_size != meta["size"]:
-                            return False
-                return True
-            except (OSError, KeyError) as e:
-                logger.error(f"Error verifying files: {str(e)}")
-                return False
-
-        def _collect_file_metadata(
-            model_dir: Path, repo_files: list[RepoFile]
-        ) -> dict[str, dict[str, Union[int, str]]]:
-            meta: dict[str, dict[str, Union[int, str]]] = {}
-            file_info_map = {f.path: f for f in repo_files}
-            for file_path in model_dir.rglob("*"):
-                if file_path.is_file() and file_path.name != cls.METADATA_FILE:
-                    repo_file = file_info_map.get(file_path.name)
-                    if repo_file:
-                        meta[str(file_path.relative_to(model_dir))] = {
-                            "size": repo_file.size,
-                            "blob_id": repo_file.blob_id,
-                        }
-            return meta
-
-        def _save_file_metadata(
-            model_dir: Path, meta: dict[str, dict[str, Union[int, str]]]
-        ) -> None:
-            try:
-                if not model_dir.exists():
-                    model_dir.mkdir(parents=True, exist_ok=True)
-                (model_dir / cls.METADATA_FILE).write_text(json.dumps(meta))
-            except (OSError, ValueError) as e:
-                logger.warning(f"Error saving metadata: {str(e)}")
-
         allow_patterns = [
             "config.json",
             "tokenizer.json",
@@ -209,59 +116,10 @@ class ModelManagement(Generic[T]):
             "special_tokens_map.json",
             "preprocessor_config.json",
         ]
+        if extra_patterns is not None:
+            allow_patterns.extend(extra_patterns)
 
-        allow_patterns.extend(extra_patterns)
-
-        snapshot_dir = Path(cache_dir) / f"models--{hf_source_repo.replace('/', '--')}"
-        metadata_file = snapshot_dir / cls.METADATA_FILE
-
-        if local_files_only:
-            disable_progress_bars()
-            if metadata_file.exists():
-                metadata = json.loads(metadata_file.read_text())
-                verified = _verify_files_from_metadata(snapshot_dir, metadata, repo_files=[])
-                if not verified:
-                    logger.warning(
-                        "Local file sizes do not match the metadata."
-                    )  # do not raise, still make an attempt to load the model
-            else:
-                logger.warning(
-                    "Metadata file not found. Proceeding without checking local files."
-                )  # if users have downloaded models from hf manually, or they're updating from previous versions of
-                # fastembed
-            result = snapshot_download(
-                repo_id=hf_source_repo,
-                allow_patterns=allow_patterns,
-                cache_dir=cache_dir,
-                local_files_only=local_files_only,
-                **kwargs,
-            )
-            return result
-
-        repo_revision = model_info(hf_source_repo).sha
-        repo_tree = list(list_repo_tree(hf_source_repo, revision=repo_revision, repo_type="model"))
-
-        allowed_extensions = {".json", ".onnx", ".txt"}
-        repo_files = (
-            [
-                f
-                for f in repo_tree
-                if isinstance(f, RepoFile) and Path(f.path).suffix in allowed_extensions
-            ]
-            if repo_tree
-            else []
-        )
-
-        verified_metadata = False
-
-        if snapshot_dir.exists() and metadata_file.exists():
-            metadata = json.loads(metadata_file.read_text())
-            verified_metadata = _verify_files_from_metadata(snapshot_dir, metadata, repo_files)
-
-        if verified_metadata:
-            disable_progress_bars()
-
-        result = snapshot_download(
+        return snapshot_download(
             repo_id=hf_source_repo,
             allow_patterns=allow_patterns,
             cache_dir=cache_dir,
@@ -269,26 +127,8 @@ class ModelManagement(Generic[T]):
             **kwargs,
         )
 
-        if (
-            not verified_metadata
-        ):  # metadata is not up-to-date, update it and check whether the files have been
-            # downloaded correctly
-            metadata = _collect_file_metadata(snapshot_dir, repo_files)
-
-            download_successful = _verify_files_from_metadata(
-                snapshot_dir, metadata, repo_files=[]
-            )  # offline verification
-            if not download_successful:
-                raise ValueError(
-                    "Files have been corrupted during downloading process. "
-                    "Please check your internet connection and try again."
-                )
-            _save_file_metadata(snapshot_dir, metadata)
-
-        return result
-
     @classmethod
-    def decompress_to_cache(cls, targz_path: str, cache_dir: str) -> str:
+    def decompress_to_cache(cls, targz_path: str, cache_dir: str):
         """
         Decompresses a .tar.gz file to a cache directory.
 
@@ -326,14 +166,9 @@ class ModelManagement(Generic[T]):
 
     @classmethod
     def retrieve_model_gcs(
-        cls,
-        model_name: str,
-        source_url: str,
-        cache_dir: str,
-        deprecated_tar_struct: bool = False,
-        local_files_only: bool = False,
+        cls, model_name: str, source_url: str, cache_dir: str, local_files_only: bool = False
     ) -> Path:
-        fast_model_name = f"{'fast-' if deprecated_tar_struct else ''}{model_name.split('/')[-1]}"
+        fast_model_name = f"fast-{model_name.split('/')[-1]}"
         cache_tmp_dir = Path(cache_dir) / "tmp"
         model_tmp_dir = cache_tmp_dir / fast_model_name
         model_dir = Path(cache_dir) / fast_model_name
@@ -375,12 +210,14 @@ class ModelManagement(Generic[T]):
         return model_dir
 
     @classmethod
-    def download_model(cls, model: T, cache_dir: str, retries: int = 3, **kwargs: Any) -> Path:
+    def download_model(
+        cls, model: Dict[str, Any], cache_dir: Path, retries: int = 3, **kwargs
+    ) -> Path:
         """
         Downloads a model from HuggingFace Hub or Google Cloud Storage.
 
         Args:
-            model (T): The model description.
+            model (Dict[str, Any]): The model description.
                 Example:
                 ```
                 {
@@ -401,26 +238,23 @@ class ModelManagement(Generic[T]):
             Path: The path to the downloaded model directory.
         """
         local_files_only = kwargs.get("local_files_only", False)
-        specific_model_path: Optional[str] = kwargs.pop("specific_model_path", None)
-        if specific_model_path:
-            return Path(specific_model_path)
         retries = 1 if local_files_only else retries
-        hf_source = model.sources.hf
-        url_source = model.sources.url
+        hf_source = model.get("sources", {}).get("hf")
+        url_source = model.get("sources", {}).get("url")
 
         sleep = 3.0
         while retries > 0:
             retries -= 1
 
             if hf_source:
-                extra_patterns = [model.model_file]
-                extra_patterns.extend(model.additional_files)
+                extra_patterns = [model["model_file"]]
+                extra_patterns.extend(model.get("additional_files", []))
 
                 try:
                     return Path(
                         cls.download_files_from_huggingface(
                             hf_source,
-                            cache_dir=cache_dir,
+                            cache_dir=str(cache_dir),
                             extra_patterns=extra_patterns,
                             **kwargs,
                         )
@@ -431,15 +265,12 @@ class ModelManagement(Generic[T]):
                             f"Could not download model from HuggingFace: {e} "
                             "Falling back to other sources."
                         )
-                finally:
-                    enable_progress_bars()
             if url_source or local_files_only:
                 try:
                     return cls.retrieve_model_gcs(
-                        model.model,
-                        str(url_source),
+                        model["model"],
+                        url_source,
                         str(cache_dir),
-                        deprecated_tar_struct=model.sources.deprecated_tar_struct,
                         local_files_only=local_files_only,
                     )
                 except Exception:
@@ -455,4 +286,4 @@ class ModelManagement(Generic[T]):
             time.sleep(sleep)
             sleep *= 3
 
-        raise ValueError(f"Could not load model {model.model} from any source.")
+        raise ValueError(f"Could not load model {model['model']} from any source.")

fastembed/common/onnx_model.py

@@ -1,15 +1,22 @@
 import warnings
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Any, Generic, Iterable, Optional, Sequence, Type, TypeVar
+from typing import (
+    Any,
+    Dict,
+    Generic,
+    Iterable,
+    Optional,
+    Sequence,
+    Tuple,
+    Type,
+    TypeVar,
+)
 
 import numpy as np
 import onnxruntime as ort
 
-from numpy.typing import NDArray
-from tokenizers import Tokenizer
-
-from fastembed.common.types import OnnxProvider, NumpyArray
+from fastembed.common.types import OnnxProvider
 from fastembed.parallel_processor import Worker
 
 # Holds type of the embedding result
@@ -18,26 +25,26 @@ T = TypeVar("T")
 
 @dataclass
 class OnnxOutputContext:
-    model_output: NumpyArray
-    attention_mask: Optional[NDArray[np.int64]] = None
-    input_ids: Optional[NDArray[np.int64]] = None
+    model_output: np.ndarray
+    attention_mask: Optional[np.ndarray] = None
+    input_ids: Optional[np.ndarray] = None
 
 
 class OnnxModel(Generic[T]):
     @classmethod
-    def _get_worker_class(cls) -> Type["EmbeddingWorker[T]"]:
+    def _get_worker_class(cls) -> Type["EmbeddingWorker"]:
         raise NotImplementedError("Subclasses must implement this method")
 
     def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[T]:
         raise NotImplementedError("Subclasses must implement this method")
 
     def __init__(self) -> None:
-        self.model: Optional[ort.InferenceSession] = None
-        self.tokenizer: Optional[Tokenizer] = None
+        self.model = None
+        self.tokenizer = None
 
     def _preprocess_onnx_input(
-        self, onnx_input: dict[str, NumpyArray], **kwargs: Any
-    ) -> dict[str, NumpyArray]:
+        self, onnx_input: Dict[str, np.ndarray], **kwargs
+    ) -> Dict[str, np.ndarray]:
         """
         Preprocess the onnx input.
         """
@@ -55,13 +62,6 @@ class OnnxModel(Generic[T]):
         model_path = model_dir / model_file
         # List of Execution Providers: https://onnxruntime.ai/docs/execution-providers
 
-        if cuda and providers is not None:
-            warnings.warn(
-                f"`cuda` and `providers` are mutually exclusive parameters, cuda: {cuda}, providers: {providers}",
-                category=UserWarning,
-                stacklevel=6,
-            )
-
         if providers is not None:
             onnx_providers = list(providers)
         elif cuda:
@@ -73,7 +73,7 @@ class OnnxModel(Generic[T]):
             onnx_providers = ["CPUExecutionProvider"]
 
         available_providers = ort.get_available_providers()
-        requested_provider_names: list[str] = []
+        requested_provider_names = []
         for provider in onnx_providers:
             # check providers available
             provider_name = provider if isinstance(provider, str) else provider[0]
@@ -94,7 +94,6 @@ class OnnxModel(Generic[T]):
             str(model_path), providers=onnx_providers, sess_options=so
         )
         if "CUDAExecutionProvider" in requested_provider_names:
-            assert self.model is not None
             current_providers = self.model.get_providers()
             if "CUDAExecutionProvider" not in current_providers:
                 warnings.warn(
@@ -107,30 +106,30 @@ class OnnxModel(Generic[T]):
     def load_onnx_model(self) -> None:
         raise NotImplementedError("Subclasses must implement this method")
 
-    def onnx_embed(self, *args: Any, **kwargs: Any) -> OnnxOutputContext:
+    def onnx_embed(self, *args, **kwargs) -> OnnxOutputContext:
         raise NotImplementedError("Subclasses must implement this method")
 
 
-class EmbeddingWorker(Worker, Generic[T]):
+class EmbeddingWorker(Worker):
     def init_embedding(
         self,
         model_name: str,
         cache_dir: str,
-        **kwargs: Any,
-    ) -> OnnxModel[T]:
+        **kwargs,
+    ) -> OnnxModel:
         raise NotImplementedError()
 
     def __init__(
         self,
         model_name: str,
         cache_dir: str,
-        **kwargs: Any,
+        **kwargs,
     ):
         self.model = self.init_embedding(model_name, cache_dir, **kwargs)
 
     @classmethod
-    def start(cls, model_name: str, cache_dir: str, **kwargs: Any) -> "EmbeddingWorker[T]":
+    def start(cls, model_name: str, cache_dir: str, **kwargs: Any) -> "EmbeddingWorker":
         return cls(model_name=model_name, cache_dir=cache_dir, **kwargs)
 
-    def process(self, items: Iterable[tuple[int, Any]]) -> Iterable[tuple[int, Any]]:
+    def process(self, items: Iterable[Tuple[int, Any]]) -> Iterable[Tuple[int, Any]]:
         raise NotImplementedError("Subclasses must implement this method")

fastembed/common/preprocessor_utils.py

@@ -1,13 +1,12 @@
 import json
-from typing import Any
 from pathlib import Path
-
+from typing import Tuple
 from tokenizers import AddedToken, Tokenizer
 
 from fastembed.image.transform.operators import Compose
 
 
-def load_special_tokens(model_dir: Path) -> dict[str, Any]:
+def load_special_tokens(model_dir: Path) -> dict:
     tokens_map_path = model_dir / "special_tokens_map.json"
     if not tokens_map_path.exists():
         raise ValueError(f"Could not find special_tokens_map.json in {model_dir}")
@@ -18,7 +17,7 @@ def load_special_tokens(model_dir: Path) -> dict[str, Any]:
     return tokens_map
 
 
-def load_tokenizer(model_dir: Path) -> tuple[Tokenizer, dict[str, int]]:
+def load_tokenizer(model_dir: Path) -> Tuple[Tokenizer, dict]:
     config_path = model_dir / "config.json"
     if not config_path.exists():
         raise ValueError(f"Could not find config.json in {model_dir}")
@@ -60,7 +59,7 @@ def load_tokenizer(model_dir: Path) -> tuple[Tokenizer, dict[str, int]]:
         elif isinstance(token, dict):
             tokenizer.add_special_tokens([AddedToken(**token)])
 
-    special_token_to_id: dict[str, int] = {}
+    special_token_to_id = {}
 
     for token in tokens_map.values():
         if isinstance(token, str):

fastembed/common/types.py

@@ -1,9 +1,7 @@
-from pathlib import Path
+import os
 import sys
 from PIL import Image
-from typing import Any, Union
-import numpy as np
-from numpy.typing import NDArray
+from typing import Any, Dict, Iterable, Tuple, Union
 
 if sys.version_info >= (3, 10):
     from typing import TypeAlias
@@ -11,14 +9,8 @@ else:
     from typing_extensions import TypeAlias
 
 
-PathInput: TypeAlias = Union[str, Path]
-ImageInput: TypeAlias = Union[PathInput, Image.Image]
+PathInput: TypeAlias = Union[str, os.PathLike]
+PilInput: TypeAlias = Union[Image.Image, Iterable[Image.Image]]
+ImageInput: TypeAlias = Union[PathInput, Iterable[PathInput], PilInput]
 
-OnnxProvider: TypeAlias = Union[str, tuple[str, dict[Any, Any]]]
-NumpyArray = Union[
-    NDArray[np.float32],
-    NDArray[np.float16],
-    NDArray[np.int8],
-    NDArray[np.int64],
-    NDArray[np.int32],
-]
+OnnxProvider: TypeAlias = Union[str, Tuple[str, Dict[Any, Any]]]

fastembed/common/utils.py

@@ -1,21 +1,16 @@
 import os
-import sys
-import re
 import tempfile
-import unicodedata
-from pathlib import Path
 from itertools import islice
-from typing import Iterable, Optional, TypeVar
-
+from pathlib import Path
+from typing import Generator, Iterable, Optional, Union
+import unicodedata
+import sys
 import numpy as np
-from numpy.typing import NDArray
-
-from fastembed.common.types import NumpyArray
-
-T = TypeVar("T")
+import re
+from typing import Set
 
 
-def normalize(input_array: NumpyArray, p: int = 2, dim: int = 1, eps: float = 1e-12) -> NumpyArray:
+def normalize(input_array, p=2, dim=1, eps=1e-12) -> np.ndarray:
     # Calculate the Lp norm along the specified dimension
     norm = np.linalg.norm(input_array, ord=p, axis=dim, keepdims=True)
     norm = np.maximum(norm, eps)  # Avoid division by zero
@@ -23,16 +18,7 @@ def normalize(input_array: NumpyArray, p: int = 2, dim: int = 1, eps: float = 1e
     return normalized_array
 
 
-def mean_pooling(input_array: NumpyArray, attention_mask: NDArray[np.int64]) -> NumpyArray:
-    input_mask_expanded = np.expand_dims(attention_mask, axis=-1).astype(np.int64)
-    input_mask_expanded = np.tile(input_mask_expanded, (1, 1, input_array.shape[-1]))
-    sum_embeddings = np.sum(input_array * input_mask_expanded, axis=1)
-    sum_mask = np.sum(input_mask_expanded, axis=1)
-    pooled_embeddings = sum_embeddings / np.maximum(sum_mask, 1e-9)
-    return pooled_embeddings
-
-
-def iter_batch(iterable: Iterable[T], size: int) -> Iterable[list[T]]:
+def iter_batch(iterable: Union[Iterable, Generator], size: int) -> Iterable:
     """
     >>> list(iter_batch([1,2,3,4,5], 3))
     [[1, 2, 3], [4, 5]]
@@ -59,7 +45,7 @@ def define_cache_dir(cache_dir: Optional[str] = None) -> Path:
     return cache_path
 
 
-def get_all_punctuation() -> set[str]:
+def get_all_punctuation() -> Set[str]:
     return set(
         chr(i) for i in range(sys.maxunicode) if unicodedata.category(chr(i)).startswith("P")
     )

fastembed/embedding.py

@@ -1,4 +1,4 @@
-from typing import Optional, Any
+from typing import Optional
 
 from loguru import logger
 
@@ -19,6 +19,6 @@ class JinaEmbedding(TextEmbedding):
         model_name: str = "jinaai/jina-embeddings-v2-base-en",
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
-        **kwargs: Any,
+        **kwargs,
     ):
         super().__init__(model_name, cache_dir, threads, **kwargs)

fastembed/image/image_embedding.py

@@ -1,23 +1,22 @@
-from typing import Any, Iterable, Optional, Sequence, Type, Union
-from dataclasses import asdict
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Type
+
+import numpy as np
 
-from fastembed.common.types import NumpyArray
 from fastembed.common import ImageInput, OnnxProvider
 from fastembed.image.image_embedding_base import ImageEmbeddingBase
 from fastembed.image.onnx_embedding import OnnxImageEmbedding
-from fastembed.common.model_description import DenseModelDescription
 
 
 class ImageEmbedding(ImageEmbeddingBase):
-    EMBEDDINGS_REGISTRY: list[Type[ImageEmbeddingBase]] = [OnnxImageEmbedding]
+    EMBEDDINGS_REGISTRY: List[Type[ImageEmbeddingBase]] = [OnnxImageEmbedding]
 
     @classmethod
-    def list_supported_models(cls) -> list[dict[str, Any]]:
+    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.
+            List[Dict[str, Any]]: A list of dictionaries containing the model information.
 
             Example:
                 ```
@@ -36,13 +35,9 @@ class ImageEmbedding(ImageEmbeddingBase):
                 ]
                 ```
         """
-        return [asdict(model) for model in cls._list_supported_models()]
-
-    @classmethod
-    def _list_supported_models(cls) -> list[DenseModelDescription]:
-        result: list[DenseModelDescription] = []
+        result = []
         for embedding in cls.EMBEDDINGS_REGISTRY:
-            result.extend(embedding._list_supported_models())
+            result.extend(embedding.list_supported_models())
         return result
 
     def __init__(
@@ -52,14 +47,14 @@ class ImageEmbedding(ImageEmbeddingBase):
         threads: Optional[int] = None,
         providers: Optional[Sequence[OnnxProvider]] = None,
         cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        device_ids: Optional[List[int]] = None,
         lazy_load: bool = False,
-        **kwargs: Any,
+        **kwargs,
     ):
         super().__init__(model_name, cache_dir, threads, **kwargs)
         for EMBEDDING_MODEL_TYPE in self.EMBEDDINGS_REGISTRY:
-            supported_models = EMBEDDING_MODEL_TYPE._list_supported_models()
-            if any(model_name.lower() == model.model.lower() for model in supported_models):
+            supported_models = EMBEDDING_MODEL_TYPE.list_supported_models()
+            if any(model_name.lower() == model["model"].lower() for model in supported_models):
                 self.model = EMBEDDING_MODEL_TYPE(
                     model_name,
                     cache_dir,
@@ -79,13 +74,14 @@ class ImageEmbedding(ImageEmbeddingBase):
 
     def embed(
         self,
-        images: Union[ImageInput, Iterable[ImageInput]],
+        images: ImageInput,
         batch_size: int = 16,
         parallel: Optional[int] = None,
-        **kwargs: Any,
-    ) -> Iterable[NumpyArray]:
+        **kwargs,
+    ) -> Iterable[np.ndarray]:
         """
-        Encode a list of images into list of embeddings.
+        Encode a list of documents into list of embeddings.
+        We use mean pooling with attention so that the model can handle variable-length inputs.
 
         Args:
             images: Iterator of image paths or single image path to embed

fastembed/image/image_embedding_base.py

@@ -1,18 +1,18 @@
-from typing import Iterable, Optional, Any, Union
+from typing import Iterable, Optional
+
+import numpy as np
 
-from fastembed.common.model_description import DenseModelDescription
-from fastembed.common.types import NumpyArray
 from fastembed.common.model_management import ModelManagement
 from fastembed.common.types import ImageInput
 
 
-class ImageEmbeddingBase(ModelManagement[DenseModelDescription]):
+class ImageEmbeddingBase(ModelManagement):
     def __init__(
         self,
         model_name: str,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
-        **kwargs: Any,
+        **kwargs,
     ):
         self.model_name = model_name
         self.cache_dir = cache_dir
@@ -21,11 +21,11 @@ class ImageEmbeddingBase(ModelManagement[DenseModelDescription]):
 
     def embed(
         self,
-        images: Union[ImageInput, Iterable[ImageInput]],
+        images: ImageInput,
         batch_size: int = 16,
         parallel: Optional[int] = None,
-        **kwargs: Any,
-    ) -> Iterable[NumpyArray]:
+        **kwargs,
+    ) -> Iterable[np.ndarray]:
         """
         Embeds a list of images into a list of embeddings.
 
@@ -39,6 +39,6 @@ class ImageEmbeddingBase(ModelManagement[DenseModelDescription]):
             **kwargs: Additional keyword argument to pass to the embed method.
 
         Yields:
-            Iterable[NdArray]: The embeddings.
+            Iterable[np.ndarray]: The embeddings.
         """
         raise NotImplementedError()

fastembed/image/onnx_embedding.py

@@ -1,65 +1,62 @@
-from typing import Any, Iterable, Optional, Sequence, Type, Union
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Type
 
+import numpy as np
 
-from fastembed.common.types import NumpyArray
 from fastembed.common import ImageInput, OnnxProvider
 from fastembed.common.onnx_model import OnnxOutputContext
 from fastembed.common.utils import define_cache_dir, normalize
 from fastembed.image.image_embedding_base import ImageEmbeddingBase
 from fastembed.image.onnx_image_model import ImageEmbeddingWorker, OnnxImageModel
 
-from fastembed.common.model_description import DenseModelDescription, ModelSource
-
-supported_onnx_models: list[DenseModelDescription] = [
-    DenseModelDescription(
-        model="Qdrant/clip-ViT-B-32-vision",
-        dim=512,
-        description="Image embeddings, Multimodal (text&image), 2021 year",
-        license="mit",
-        size_in_GB=0.34,
-        sources=ModelSource(hf="Qdrant/clip-ViT-B-32-vision"),
-        model_file="model.onnx",
-    ),
-    DenseModelDescription(
-        model="Qdrant/resnet50-onnx",
-        dim=2048,
-        description="Image embeddings, Unimodal (image), 2016 year",
-        license="apache-2.0",
-        size_in_GB=0.1,
-        sources=ModelSource(hf="Qdrant/resnet50-onnx"),
-        model_file="model.onnx",
-    ),
-    DenseModelDescription(
-        model="Qdrant/Unicom-ViT-B-16",
-        dim=768,
-        description="Image embeddings (more detailed than Unicom-ViT-B-32), Multimodal (text&image), 2023 year",
-        license="apache-2.0",
-        size_in_GB=0.82,
-        sources=ModelSource(hf="Qdrant/Unicom-ViT-B-16"),
-        model_file="model.onnx",
-    ),
-    DenseModelDescription(
-        model="Qdrant/Unicom-ViT-B-32",
-        dim=512,
-        description="Image embeddings, Multimodal (text&image), 2023 year",
-        license="apache-2.0",
-        size_in_GB=0.48,
-        sources=ModelSource(hf="Qdrant/Unicom-ViT-B-32"),
-        model_file="model.onnx",
-    ),
-    DenseModelDescription(
-        model="jinaai/jina-clip-v1",
-        dim=768,
-        description="Image embeddings, Multimodal (text&image), 2024 year",
-        license="apache-2.0",
-        size_in_GB=0.34,
-        sources=ModelSource(hf="jinaai/jina-clip-v1"),
-        model_file="onnx/vision_model.onnx",
-    ),
+supported_onnx_models = [
+    {
+        "model": "Qdrant/clip-ViT-B-32-vision",
+        "dim": 512,
+        "description": "Image embeddings, Multimodal (text&image), 2021 year",
+        "license": "mit",
+        "size_in_GB": 0.34,
+        "sources": {
+            "hf": "Qdrant/clip-ViT-B-32-vision",
+        },
+        "model_file": "model.onnx",
+    },
+    {
+        "model": "Qdrant/resnet50-onnx",
+        "dim": 2048,
+        "description": "Image embeddings, Unimodal (image), 2016 year",
+        "license": "apache-2.0",
+        "size_in_GB": 0.1,
+        "sources": {
+            "hf": "Qdrant/resnet50-onnx",
+        },
+        "model_file": "model.onnx",
+    },
+    {
+        "model": "Qdrant/Unicom-ViT-B-16",
+        "dim": 768,
+        "description": "Image embeddings (more detailed than Unicom-ViT-B-32), Multimodal (text&image), 2023 year",
+        "license": "apache-2.0",
+        "size_in_GB": 0.82,
+        "sources": {
+            "hf": "Qdrant/Unicom-ViT-B-16",
+        },
+        "model_file": "model.onnx",
+    },
+    {
+        "model": "Qdrant/Unicom-ViT-B-32",
+        "dim": 512,
+        "description": "Image embeddings, Multimodal (text&image), 2023 year",
+        "license": "apache-2.0",
+        "size_in_GB": 0.48,
+        "sources": {
+            "hf": "Qdrant/Unicom-ViT-B-32",
+        },
+        "model_file": "model.onnx",
+    },
 ]
 
 
-class OnnxImageEmbedding(ImageEmbeddingBase, OnnxImageModel[NumpyArray]):
+class OnnxImageEmbedding(ImageEmbeddingBase, OnnxImageModel[np.ndarray]):
     def __init__(
         self,
         model_name: str,
@@ -67,11 +64,10 @@ class OnnxImageEmbedding(ImageEmbeddingBase, OnnxImageModel[NumpyArray]):
         threads: Optional[int] = None,
         providers: Optional[Sequence[OnnxProvider]] = None,
         cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        device_ids: Optional[List[int]] = None,
         lazy_load: bool = False,
         device_id: Optional[int] = None,
-        specific_model_path: Optional[str] = None,
-        **kwargs: Any,
+        **kwargs,
     ):
         """
         Args:
@@ -84,12 +80,11 @@ class OnnxImageEmbedding(ImageEmbeddingBase, OnnxImageModel[NumpyArray]):
                 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
+            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.
-            specific_model_path (Optional[str], optional): The specific path to the onnx model dir if it should be imported from somewhere else
 
         Raises:
             ValueError: If the model_name is not in the format <org>/<model> e.g. BAAI/bge-base-en.
@@ -104,19 +99,17 @@ class OnnxImageEmbedding(ImageEmbeddingBase, OnnxImageModel[NumpyArray]):
         self.cuda = cuda
 
         # This device_id will be used if we need to load model in current process
-        self.device_id: Optional[int] = None
         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 = str(define_cache_dir(cache_dir))
+        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.model_description, self.cache_dir, local_files_only=self._local_files_only
         )
 
         if not self.lazy_load:
@@ -128,7 +121,7 @@ class OnnxImageEmbedding(ImageEmbeddingBase, OnnxImageModel[NumpyArray]):
         """
         self._load_onnx_model(
             model_dir=self._model_dir,
-            model_file=self.model_description.model_file,
+            model_file=self.model_description["model_file"],
             threads=self.threads,
             providers=self.providers,
             cuda=self.cuda,
@@ -136,22 +129,22 @@ class OnnxImageEmbedding(ImageEmbeddingBase, OnnxImageModel[NumpyArray]):
         )
 
     @classmethod
-    def _list_supported_models(cls) -> list[DenseModelDescription]:
+    def list_supported_models(cls) -> List[Dict[str, Any]]:
         """
         Lists the supported models.
 
         Returns:
-            list[DenseModelDescription]: A list of DenseModelDescription objects containing the model information.
+            List[Dict[str, Any]]: A list of dictionaries containing the model information.
         """
         return supported_onnx_models
 
     def embed(
         self,
-        images: Union[ImageInput, Iterable[ImageInput]],
+        images: ImageInput,
         batch_size: int = 16,
         parallel: Optional[int] = None,
-        **kwargs: Any,
-    ) -> Iterable[NumpyArray]:
+        **kwargs,
+    ) -> Iterable[np.ndarray]:
         """
         Encode a list of images into list of embeddings.
         We use mean pooling with attention so that the model can handle variable-length inputs.
@@ -181,24 +174,24 @@ class OnnxImageEmbedding(ImageEmbeddingBase, OnnxImageModel[NumpyArray]):
         )
 
     @classmethod
-    def _get_worker_class(cls) -> Type["ImageEmbeddingWorker[NumpyArray]"]:
+    def _get_worker_class(cls) -> Type["ImageEmbeddingWorker"]:
         return OnnxImageEmbeddingWorker
 
     def _preprocess_onnx_input(
-        self, onnx_input: dict[str, NumpyArray], **kwargs: Any
-    ) -> dict[str, NumpyArray]:
+        self, onnx_input: Dict[str, np.ndarray], **kwargs
+    ) -> Dict[str, np.ndarray]:
         """
         Preprocess the onnx input.
         """
 
         return onnx_input
 
-    def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[NumpyArray]:
-        return normalize(output.model_output)
+    def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[np.ndarray]:
+        return normalize(output.model_output).astype(np.float32)
 
 
-class OnnxImageEmbeddingWorker(ImageEmbeddingWorker[NumpyArray]):
-    def init_embedding(self, model_name: str, cache_dir: str, **kwargs: Any) -> OnnxImageEmbedding:
+class OnnxImageEmbeddingWorker(ImageEmbeddingWorker):
+    def init_embedding(self, model_name: str, cache_dir: str, **kwargs) -> OnnxImageEmbedding:
         return OnnxImageEmbedding(
             model_name=model_name,
             cache_dir=cache_dir,

fastembed/image/onnx_image_model.py

@@ -2,13 +2,11 @@ import contextlib
 import os
 from multiprocessing import get_all_start_methods
 from pathlib import Path
-from typing import Any, Iterable, Optional, Sequence, Type, Union
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Type
 
 import numpy as np
 from PIL import Image
 
-from fastembed.image.transform.operators import Compose
-from fastembed.common.types import NumpyArray
 from fastembed.common import ImageInput, OnnxProvider
 from fastembed.common.onnx_model import EmbeddingWorker, OnnxModel, OnnxOutputContext, T
 from fastembed.common.preprocessor_utils import load_preprocessor
@@ -20,7 +18,7 @@ from fastembed.parallel_processor import ParallelWorkerPool
 
 class OnnxImageModel(OnnxModel[T]):
     @classmethod
-    def _get_worker_class(cls) -> Type["ImageEmbeddingWorker[T]"]:
+    def _get_worker_class(cls) -> Type["ImageEmbeddingWorker"]:
         raise NotImplementedError("Subclasses must implement this method")
 
     def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[T]:
@@ -28,11 +26,11 @@ class OnnxImageModel(OnnxModel[T]):
 
     def __init__(self) -> None:
         super().__init__()
-        self.processor: Optional[Compose] = None
+        self.processor = None
 
     def _preprocess_onnx_input(
-        self, onnx_input: dict[str, NumpyArray], **kwargs: Any
-    ) -> dict[str, NumpyArray]:
+        self, onnx_input: Dict[str, np.ndarray], **kwargs
+    ) -> Dict[str, np.ndarray]:
         """
         Preprocess the onnx input.
         """
@@ -60,21 +58,19 @@ class OnnxImageModel(OnnxModel[T]):
     def load_onnx_model(self) -> None:
         raise NotImplementedError("Subclasses must implement this method")
 
-    def _build_onnx_input(self, encoded: NumpyArray) -> dict[str, NumpyArray]:
-        input_name = self.model.get_inputs()[0].name  # type: ignore[union-attr]
-        return {input_name: encoded}
+    def _build_onnx_input(self, encoded: np.ndarray) -> Dict[str, np.ndarray]:
+        return {node.name: encoded for node in self.model.get_inputs()}
 
-    def onnx_embed(self, images: list[ImageInput], **kwargs: Any) -> OnnxOutputContext:
+    def onnx_embed(self, images: List[ImageInput], **kwargs) -> OnnxOutputContext:
         with contextlib.ExitStack():
             image_files = [
                 Image.open(image) if not isinstance(image, Image.Image) else image
                 for image in images
             ]
-            assert self.processor is not None, "Processor is not initialized"
-            encoded = np.array(self.processor(image_files))
+            encoded = self.processor(image_files)
         onnx_input = self._build_onnx_input(encoded)
         onnx_input = self._preprocess_onnx_input(onnx_input)
-        model_output = self.model.run(None, onnx_input)  # type: ignore[union-attr]
+        model_output = self.model.run(None, onnx_input)
         embeddings = model_output[0].reshape(len(images), -1)
         return OnnxOutputContext(model_output=embeddings)
 
@@ -82,13 +78,13 @@ class OnnxImageModel(OnnxModel[T]):
         self,
         model_name: str,
         cache_dir: str,
-        images: Union[ImageInput, Iterable[ImageInput]],
+        images: ImageInput,
         batch_size: int = 256,
         parallel: Optional[int] = None,
         providers: Optional[Sequence[OnnxProvider]] = None,
         cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
-        **kwargs: Any,
+        device_ids: Optional[List[int]] = None,
+        **kwargs,
     ) -> Iterable[T]:
         is_small = False
 
@@ -125,11 +121,11 @@ class OnnxImageModel(OnnxModel[T]):
                 start_method=start_method,
             )
             for batch in pool.ordered_map(iter_batch(images, batch_size), **params):
-                yield from self._post_process_onnx_output(batch)  # type: ignore
+                yield from self._post_process_onnx_output(batch)
 
 
-class ImageEmbeddingWorker(EmbeddingWorker[T]):
-    def process(self, items: Iterable[tuple[int, Any]]) -> Iterable[tuple[int, Any]]:
+class ImageEmbeddingWorker(EmbeddingWorker):
+    def process(self, items: Iterable[Tuple[int, Any]]) -> Iterable[Tuple[int, Any]]:
         for idx, batch in items:
             embeddings = self.model.onnx_embed(batch)
             yield idx, embeddings

fastembed/image/transform/functional.py

@@ -1,10 +1,8 @@
-from typing import Union
+from typing import Sized, Tuple, Union
 
 import numpy as np
 from PIL import Image
 
-from fastembed.common.types import NumpyArray
-
 
 def convert_to_rgb(image: Image.Image) -> Image.Image:
     if image.mode == "RGB":
@@ -15,9 +13,9 @@ def convert_to_rgb(image: Image.Image) -> Image.Image:
 
 
 def center_crop(
-    image: Union[Image.Image, NumpyArray],
-    size: tuple[int, int],
-) -> NumpyArray:
+    image: Union[Image.Image, np.ndarray],
+    size: Tuple[int, int],
+) -> np.ndarray:
     if isinstance(image, np.ndarray):
         _, orig_height, orig_width = image.shape
     else:
@@ -42,7 +40,7 @@ def center_crop(
     new_height = max(crop_height, orig_height)
     new_width = max(crop_width, orig_width)
     new_shape = image.shape[:-2] + (new_height, new_width)
-    new_image = np.zeros_like(image, shape=new_shape, dtype=np.float32)
+    new_image = np.zeros_like(image, shape=new_shape)
 
     top_pad = (new_height - orig_height) // 2
     bottom_pad = top_pad + orig_height
@@ -63,42 +61,45 @@ def center_crop(
 
 
 def normalize(
-    image: NumpyArray,
-    mean: Union[float, list[float]],
-    std: Union[float, list[float]],
-) -> NumpyArray:
+    image: np.ndarray,
+    mean=Union[float, np.ndarray],
+    std=Union[float, np.ndarray],
+) -> np.ndarray:
+    if not isinstance(image, np.ndarray):
+        raise ValueError("image must be a numpy array")
+
     num_channels = image.shape[1] if len(image.shape) == 4 else image.shape[0]
 
     if not np.issubdtype(image.dtype, np.floating):
         image = image.astype(np.float32)
 
-    mean = mean if isinstance(mean, list) else [mean] * num_channels
+    if isinstance(mean, Sized):
+        if len(mean) != num_channels:
+            raise ValueError(
+                f"mean must have {num_channels} elements if it is an iterable, got {len(mean)}"
+            )
+    else:
+        mean = [mean] * num_channels
+    mean = np.array(mean, dtype=image.dtype)
 
-    if len(mean) != num_channels:
-        raise ValueError(
-            f"mean must have the same number of channels as the image, image has {num_channels} channels, got "
-            f"{len(mean)}"
-        )
+    if isinstance(std, Sized):
+        if len(std) != num_channels:
+            raise ValueError(
+                f"std must have {num_channels} elements if it is an iterable, got {len(std)}"
+            )
+    else:
+        std = [std] * num_channels
+    std = np.array(std, dtype=image.dtype)
 
-    mean_arr = np.array(mean, dtype=np.float32)
-
-    std = std if isinstance(std, list) else [std] * num_channels
-    if len(std) != num_channels:
-        raise ValueError(
-            f"std must have the same number of channels as the image, image has {num_channels} channels, got {len(std)}"
-        )
-
-    std_arr = np.array(std, dtype=np.float32)
-
-    image = ((image.T - mean_arr) / std_arr).T
+    image = ((image.T - mean) / std).T
     return image
 
 
 def resize(
-    image: Image.Image,
-    size: Union[int, tuple[int, int]],
-    resample: Union[int, Image.Resampling] = Image.Resampling.BILINEAR,
-) -> Image.Image:
+    image: Image,
+    size: Union[int, Tuple[int, int]],
+    resample: Image.Resampling = Image.Resampling.BILINEAR,
+) -> Image:
     if isinstance(size, tuple):
         return image.resize(size, resample)
 
@@ -113,37 +114,11 @@ def resize(
     return image.resize(new_size, resample)
 
 
-def rescale(image: NumpyArray, scale: float, dtype: type = np.float32) -> NumpyArray:
+def rescale(image: np.ndarray, scale: float, dtype=np.float32) -> np.ndarray:
     return (image * scale).astype(dtype)
 
 
-def pil2ndarray(image: Union[Image.Image, NumpyArray]) -> NumpyArray:
+def pil2ndarray(image: Union[Image.Image, np.ndarray]):
     if isinstance(image, Image.Image):
         return np.asarray(image).transpose((2, 0, 1))
     return image
-
-
-def pad2square(
-    image: Image.Image,
-    size: int,
-    fill_color: Union[str, int, tuple[int, ...]] = 0,
-) -> Image.Image:
-    height, width = image.height, image.width
-
-    left, right = 0, width
-    top, bottom = 0, height
-
-    crop_required = False
-    if width > size:
-        left = (width - size) // 2
-        right = left + size
-        crop_required = True
-
-    if height > size:
-        top = (height - size) // 2
-        bottom = top + size
-        crop_required = True
-
-    new_image = Image.new(mode="RGB", size=(size, size), color=fill_color)
-    new_image.paste(image.crop((left, top, right, bottom)) if crop_required else image)
-    return new_image

fastembed/image/transform/operators.py

@@ -1,8 +1,8 @@
-from typing import Any, Union, Optional
+from typing import Any, Dict, List, Tuple, Union
 
+import numpy as np
 from PIL import Image
 
-from fastembed.common.types import NumpyArray
 from fastembed.image.transform.functional import (
     center_crop,
     convert_to_rgb,
@@ -10,111 +10,93 @@ from fastembed.image.transform.functional import (
     pil2ndarray,
     rescale,
     resize,
-    pad2square,
 )
 
 
 class Transform:
-    def __call__(self, images: list[Any]) -> Union[list[Image.Image], list[NumpyArray]]:
+    def __call__(self, images: List) -> Union[List[Image.Image], List[np.ndarray]]:
         raise NotImplementedError("Subclasses must implement this method")
 
 
 class ConvertToRGB(Transform):
-    def __call__(self, images: list[Image.Image]) -> list[Image.Image]:
+    def __call__(self, images: List[Image.Image]) -> List[Image.Image]:
         return [convert_to_rgb(image=image) for image in images]
 
 
 class CenterCrop(Transform):
-    def __init__(self, size: tuple[int, int]):
+    def __init__(self, size: Tuple[int, int]):
         self.size = size
 
-    def __call__(self, images: list[Image.Image]) -> list[NumpyArray]:
+    def __call__(self, images: List[Image.Image]) -> List[np.ndarray]:
         return [center_crop(image=image, size=self.size) for image in images]
 
 
 class Normalize(Transform):
-    def __init__(self, mean: Union[float, list[float]], std: Union[float, list[float]]):
+    def __init__(self, mean: Union[float, List[float]], std: Union[float, List[float]]):
         self.mean = mean
         self.std = std
 
-    def __call__(self, images: list[NumpyArray]) -> list[NumpyArray]:
+    def __call__(self, images: List[np.ndarray]) -> List[np.ndarray]:
         return [normalize(image, mean=self.mean, std=self.std) for image in images]
 
 
 class Resize(Transform):
     def __init__(
         self,
-        size: Union[int, tuple[int, int]],
+        size: Union[int, Tuple[int, int]],
         resample: Image.Resampling = Image.Resampling.BICUBIC,
     ):
         self.size = size
         self.resample = resample
 
-    def __call__(self, images: list[Image.Image]) -> list[Image.Image]:
-        return [resize(image, size=self.size, resample=self.resample) for image in images]
+    def __call__(self, images: List[Image.Image]) -> List[Image.Image]:
+        return [
+            resize(image, size=self.size, resample=self.resample) for image in images
+        ]
 
 
 class Rescale(Transform):
     def __init__(self, scale: float = 1 / 255):
         self.scale = scale
 
-    def __call__(self, images: list[NumpyArray]) -> list[NumpyArray]:
+    def __call__(self, images: List[np.ndarray]) -> List[np.ndarray]:
         return [rescale(image, scale=self.scale) for image in images]
 
 
 class PILtoNDarray(Transform):
-    def __call__(self, images: list[Union[Image.Image, NumpyArray]]) -> list[NumpyArray]:
+    def __call__(
+        self, images: List[Union[Image.Image, np.ndarray]]
+    ) -> List[np.ndarray]:
         return [pil2ndarray(image) for image in images]
 
 
-class PadtoSquare(Transform):
-    def __init__(
-        self,
-        size: int,
-        fill_color: Union[str, int, tuple[int, ...]],
-    ):
-        self.size = size
-        self.fill_color = fill_color
-
-    def __call__(self, images: list[Image.Image]) -> list[Image.Image]:
-        return [
-            pad2square(image=image, size=self.size, fill_color=self.fill_color) for image in images
-        ]
-
-
 class Compose:
-    def __init__(self, transforms: list[Transform]):
+    def __init__(self, transforms: List[Transform]):
         self.transforms = transforms
 
     def __call__(
-        self, images: Union[list[Image.Image], list[NumpyArray]]
-    ) -> Union[list[NumpyArray], list[Image.Image]]:
+        self, images: Union[List[Image.Image], List[np.ndarray]]
+    ) -> Union[List[np.ndarray], List[Image.Image]]:
         for transform in self.transforms:
             images = transform(images)
         return images
 
     @classmethod
-    def from_config(cls, config: dict[str, Any]) -> "Compose":
+    def from_config(cls, config: Dict[str, Any]) -> "Compose":
         """Creates processor from a config dict.
         Args:
-            config (dict[str, Any]): Configuration dictionary.
+            config (Dict[str, Any]): Configuration dictionary.
 
                 Valid keys:
                     - do_resize
-                    - resize_mode
                     - size
-                    - fill_color
                     - do_center_crop
                     - crop_size
                     - do_rescale
                     - rescale_factor
                     - do_normalize
                     - image_mean
-                    - mean
                     - image_std
-                    - std
-                    - resample
-                    - interpolation
                 Valid size keys (nested):
                     - {"height", "width"}
                     - {"shortest_edge"}
@@ -122,10 +104,9 @@ class Compose:
         Returns:
             Compose: Image processor.
         """
-        transforms: list[Transform] = []
+        transforms = []
         cls._get_convert_to_rgb(transforms, config)
         cls._get_resize(transforms, config)
-        cls._get_pad2square(transforms, config)
         cls._get_center_crop(transforms, config)
         cls._get_pil2ndarray(transforms, config)
         cls._get_rescale(transforms, config)
@@ -133,13 +114,13 @@ class Compose:
         return cls(transforms=transforms)
 
     @staticmethod
-    def _get_convert_to_rgb(transforms: list[Transform], config: dict[str, Any]) -> None:
+    def _get_convert_to_rgb(transforms: List[Transform], config: Dict[str, Any]):
         transforms.append(ConvertToRGB())
 
-    @classmethod
-    def _get_resize(cls, transforms: list[Transform], config: dict[str, Any]) -> None:
+    @staticmethod
+    def _get_resize(transforms: List[Transform], config: Dict[str, Any]):
         mode = config.get("image_processor_type", "CLIPImageProcessor")
-        if mode in ("CLIPImageProcessor", "SiglipImageProcessor"):
+        if mode == "CLIPImageProcessor":
             if config.get("do_resize", False):
                 size = config["size"]
                 if "shortest_edge" in size:
@@ -180,90 +161,38 @@ class Compose:
                         resample=config.get("resample", Image.Resampling.BICUBIC),
                     )
                 )
-        elif mode == "JinaCLIPImageProcessor":
-            interpolation = config.get("interpolation")
-            if isinstance(interpolation, str):
-                resample = cls._interpolation_resolver(interpolation)
-            else:
-                resample = interpolation or Image.Resampling.BICUBIC
-
-            if "size" in config:
-                resize_mode = config.get("resize_mode", "shortest")
-                if resize_mode == "shortest":
-                    transforms.append(
-                        Resize(
-                            size=config["size"],
-                            resample=resample,
-                        )
-                    )
-        else:
-            raise ValueError(f"Preprocessor {mode} is not supported")
 
     @staticmethod
-    def _get_center_crop(transforms: list[Transform], config: dict[str, Any]) -> None:
+    def _get_center_crop(transforms: List[Transform], config: Dict[str, Any]):
         mode = config.get("image_processor_type", "CLIPImageProcessor")
-        if mode in ("CLIPImageProcessor", "SiglipImageProcessor"):
+        if mode == "CLIPImageProcessor":
             if config.get("do_center_crop", False):
-                crop_size_raw = config["crop_size"]
-                crop_size: tuple[int, int]
-                if isinstance(crop_size_raw, int):
-                    crop_size = (crop_size_raw, crop_size_raw)
-                elif isinstance(crop_size_raw, dict):
-                    crop_size = (crop_size_raw["height"], crop_size_raw["width"])
+                crop_size = config["crop_size"]
+                if isinstance(crop_size, int):
+                    crop_size = (crop_size, crop_size)
+                elif isinstance(crop_size, dict):
+                    crop_size = (crop_size["height"], crop_size["width"])
                 else:
-                    raise ValueError(f"Invalid crop size: {crop_size_raw}")
+                    raise ValueError(f"Invalid crop size: {crop_size}")
                 transforms.append(CenterCrop(size=crop_size))
         elif mode == "ConvNextFeatureExtractor":
             pass
-        elif mode == "JinaCLIPImageProcessor":
-            pass
         else:
             raise ValueError(f"Preprocessor {mode} is not supported")
 
     @staticmethod
-    def _get_pil2ndarray(transforms: list[Transform], config: dict[str, Any]) -> None:
+    def _get_pil2ndarray(transforms: List[Transform], config: Dict[str, Any]):
         transforms.append(PILtoNDarray())
 
     @staticmethod
-    def _get_rescale(transforms: list[Transform], config: dict[str, Any]) -> None:
+    def _get_rescale(transforms: List[Transform], config: Dict[str, Any]):
         if config.get("do_rescale", True):
             rescale_factor = config.get("rescale_factor", 1 / 255)
             transforms.append(Rescale(scale=rescale_factor))
 
     @staticmethod
-    def _get_normalize(transforms: list[Transform], config: dict[str, Any]) -> None:
+    def _get_normalize(transforms: List[Transform], config: Dict[str, Any]):
         if config.get("do_normalize", False):
-            transforms.append(Normalize(mean=config["image_mean"], std=config["image_std"]))
-        elif "mean" in config and "std" in config:
-            transforms.append(Normalize(mean=config["mean"], std=config["std"]))
-
-    @staticmethod
-    def _get_pad2square(transforms: list[Transform], config: dict[str, Any]) -> None:
-        mode = config.get("image_processor_type", "CLIPImageProcessor")
-        if mode == "CLIPImageProcessor":
-            pass
-        elif mode == "ConvNextFeatureExtractor":
-            pass
-        elif mode == "JinaCLIPImageProcessor":
             transforms.append(
-                PadtoSquare(
-                    size=config["size"],
-                    fill_color=config.get("fill_color", 0),
-                )
+                Normalize(mean=config["image_mean"], std=config["image_std"])
             )
-
-    @staticmethod
-    def _interpolation_resolver(resample: Optional[str] = None) -> Image.Resampling:
-        interpolation_map = {
-            "nearest": Image.Resampling.NEAREST,
-            "lanczos": Image.Resampling.LANCZOS,
-            "bilinear": Image.Resampling.BILINEAR,
-            "bicubic": Image.Resampling.BICUBIC,
-            "box": Image.Resampling.BOX,
-            "hamming": Image.Resampling.HAMMING,
-        }
-
-        if resample and (method := interpolation_map.get(resample.lower())):
-            return method
-
-        raise ValueError(f"Unknown interpolation method: {resample}")

fastembed/late_interaction/colbert.py

@@ -1,10 +1,9 @@
 import string
-from typing import Any, Iterable, Optional, Sequence, Type, Union
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Type, Union
 
 import numpy as np
 from tokenizers import Encoding
 
-from fastembed.common.types import NumpyArray
 from fastembed.common import OnnxProvider
 from fastembed.common.onnx_model import OnnxOutputContext
 from fastembed.common.utils import define_cache_dir
@@ -12,41 +11,44 @@ from fastembed.late_interaction.late_interaction_embedding_base import (
     LateInteractionTextEmbeddingBase,
 )
 from fastembed.text.onnx_text_model import OnnxTextModel, TextEmbeddingWorker
-from fastembed.common.model_description import DenseModelDescription, ModelSource
 
-supported_colbert_models: list[DenseModelDescription] = [
-    DenseModelDescription(
-        model="colbert-ir/colbertv2.0",
-        dim=128,
-        description="Late interaction model",
-        license="mit",
-        size_in_GB=0.44,
-        sources=ModelSource(hf="colbert-ir/colbertv2.0"),
-        model_file="model.onnx",
-    ),
-    DenseModelDescription(
-        model="answerdotai/answerai-colbert-small-v1",
-        dim=96,
-        description="Text embeddings, Unimodal (text), Multilingual (~100 languages), 512 input tokens truncation, 2024 year",
-        license="apache-2.0",
-        size_in_GB=0.13,
-        sources=ModelSource(hf="answerdotai/answerai-colbert-small-v1"),
-        model_file="vespa_colbert.onnx",
-    ),
+supported_colbert_models = [
+    {
+        "model": "colbert-ir/colbertv2.0",
+        "dim": 128,
+        "description": "Late interaction model",
+        "license": "mit",
+        "size_in_GB": 0.44,
+        "sources": {
+            "hf": "colbert-ir/colbertv2.0",
+        },
+        "model_file": "model.onnx",
+    },
+    {
+        "model": "answerdotai/answerai-colbert-small-v1",
+        "dim": 96,
+        "description": "Text embeddings, Unimodal (text), Multilingual (~100 languages), 512 input tokens truncation, 2024 year",
+        "license": "apache-2.0",
+        "size_in_GB": 0.13,
+        "sources": {
+            "hf": "answerdotai/answerai-colbert-small-v1",
+        },
+        "model_file": "vespa_colbert.onnx",
+    },
 ]
 
 
-class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[NumpyArray]):
+class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[np.ndarray]):
     QUERY_MARKER_TOKEN_ID = 1
     DOCUMENT_MARKER_TOKEN_ID = 2
-    MIN_QUERY_LENGTH = 31  # it's 32, we add one additional special token in the beginning
+    MIN_QUERY_LENGTH = 32
     MASK_TOKEN = "[MASK]"
 
     def _post_process_onnx_output(
         self, output: OnnxOutputContext, is_doc: bool = True
-    ) -> Iterable[NumpyArray]:
+    ) -> Iterable[np.ndarray]:
         if not is_doc:
-            return output.model_output
+            return output.model_output.astype(np.float32)
 
         if output.input_ids is None or output.attention_mask is None:
             raise ValueError(
@@ -54,37 +56,36 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[NumpyArray]):
             )
 
         for i, token_sequence in enumerate(output.input_ids):
-            for j, token_id in enumerate(token_sequence):  # type: ignore
+            for j, token_id in enumerate(token_sequence):
                 if token_id in self.skip_list or token_id == self.pad_token_id:
                     output.attention_mask[i, j] = 0
 
-        output.model_output *= np.expand_dims(output.attention_mask, 2)
+        output.model_output *= np.expand_dims(output.attention_mask, 2).astype(np.float32)
         norm = np.linalg.norm(output.model_output, ord=2, axis=2, keepdims=True)
         norm_clamped = np.maximum(norm, 1e-12)
         output.model_output /= norm_clamped
-        return output.model_output
+        return output.model_output.astype(np.float32)
 
     def _preprocess_onnx_input(
-        self, onnx_input: dict[str, NumpyArray], is_doc: bool = True, **kwargs: Any
-    ) -> dict[str, NumpyArray]:
-        marker_token = self.DOCUMENT_MARKER_TOKEN_ID if is_doc else self.QUERY_MARKER_TOKEN_ID
-        onnx_input["input_ids"] = np.insert(
-            onnx_input["input_ids"].astype(np.int64), 1, marker_token, axis=1
-        )
-        onnx_input["attention_mask"] = np.insert(
-            onnx_input["attention_mask"].astype(np.int64), 1, 1, axis=1
-        )
+        self, onnx_input: Dict[str, np.ndarray], is_doc: bool = True
+    ) -> Dict[str, np.ndarray]:
+        if is_doc:
+            onnx_input["input_ids"][:, 1] = self.DOCUMENT_MARKER_TOKEN_ID
+        else:
+            onnx_input["input_ids"][:, 1] = self.QUERY_MARKER_TOKEN_ID
         return onnx_input
 
-    def tokenize(self, documents: list[str], is_doc: bool = True, **kwargs: Any) -> list[Encoding]:
+    def tokenize(self, documents: List[str], is_doc: bool = True) -> List[Encoding]:
         return (
             self._tokenize_documents(documents=documents)
             if is_doc
             else self._tokenize_query(query=next(iter(documents)))
         )
 
-    def _tokenize_query(self, query: str) -> list[Encoding]:
-        assert self.tokenizer is not None
+    def _tokenize_query(self, query: str) -> List[Encoding]:
+        # "@ " is added to a query to be replaced with a special query token
+        # make sure that "@ " is considered as a single token
+        query = f"@ {query}"
         encoded = self.tokenizer.encode_batch([query])
         # colbert authors recommend to pad queries with [MASK] tokens for query augmentation to improve performance
         if len(encoded[0].ids) < self.MIN_QUERY_LENGTH:
@@ -103,16 +104,19 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[NumpyArray]):
                 self.tokenizer.enable_padding(**prev_padding)
         return encoded
 
-    def _tokenize_documents(self, documents: list[str]) -> list[Encoding]:
-        encoded = self.tokenizer.encode_batch(documents)  # type: ignore[union-attr]
+    def _tokenize_documents(self, documents: List[str]) -> List[Encoding]:
+        # "@ " is added to a document to be replaced with a special document token
+        # make sure that "@ " is considered as a single token
+        documents = ["@ " + doc for doc in documents]
+        encoded = self.tokenizer.encode_batch(documents)
         return encoded
 
     @classmethod
-    def _list_supported_models(cls) -> list[DenseModelDescription]:
+    def list_supported_models(cls) -> List[Dict[str, Any]]:
         """Lists the supported models.
 
         Returns:
-            list[DenseModelDescription]: A list of DenseModelDescription objects containing the model information.
+            List[Dict[str, Any]]: A list of dictionaries containing the model information.
         """
         return supported_colbert_models
 
@@ -123,11 +127,10 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[NumpyArray]):
         threads: Optional[int] = None,
         providers: Optional[Sequence[OnnxProvider]] = None,
         cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        device_ids: Optional[List[int]] = None,
         lazy_load: bool = False,
         device_id: Optional[int] = None,
-        specific_model_path: Optional[str] = None,
-        **kwargs: Any,
+        **kwargs,
     ):
         """
         Args:
@@ -140,12 +143,11 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[NumpyArray]):
                 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
+            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.
-            specific_model_path (Optional[str], optional): The specific path to the onnx model dir if it should be imported from somewhere else
 
         Raises:
             ValueError: If the model_name is not in the format <org>/<model> e.g. BAAI/bge-base-en.
@@ -160,24 +162,22 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[NumpyArray]):
         self.cuda = cuda
 
         # This device_id will be used if we need to load model in current process
-        self.device_id: Optional[int] = None
         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 = str(define_cache_dir(cache_dir))
+        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.model_description, self.cache_dir, local_files_only=self._local_files_only
         )
-        self.mask_token_id: Optional[int] = None
-        self.pad_token_id: Optional[int] = None
-        self.skip_list: set[int] = set()
+        self.mask_token_id = None
+        self.pad_token_id = None
+        self.skip_list = set()
 
         if not self.lazy_load:
             self.load_onnx_model()
@@ -185,30 +185,26 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[NumpyArray]):
     def load_onnx_model(self) -> None:
         self._load_onnx_model(
             model_dir=self._model_dir,
-            model_file=self.model_description.model_file,
+            model_file=self.model_description["model_file"],
             threads=self.threads,
             providers=self.providers,
             cuda=self.cuda,
             device_id=self.device_id,
         )
-        assert self.tokenizer is not None
         self.mask_token_id = self.special_token_to_id[self.MASK_TOKEN]
         self.pad_token_id = self.tokenizer.padding["pad_id"]
         self.skip_list = {
             self.tokenizer.encode(symbol, add_special_tokens=False).ids[0]
             for symbol in string.punctuation
         }
-        current_max_length = self.tokenizer.truncation["max_length"]
-        # ensure not to overflow after adding document-marker
-        self.tokenizer.enable_truncation(max_length=current_max_length - 1)
 
     def embed(
         self,
         documents: Union[str, Iterable[str]],
         batch_size: int = 256,
         parallel: Optional[int] = None,
-        **kwargs: Any,
-    ) -> Iterable[NumpyArray]:
+        **kwargs,
+    ) -> Iterable[np.ndarray]:
         """
         Encode a list of documents into list of embeddings.
         We use mean pooling with attention so that the model can handle variable-length inputs.
@@ -236,7 +232,7 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[NumpyArray]):
             **kwargs,
         )
 
-    def query_embed(self, query: Union[str, Iterable[str]], **kwargs: Any) -> Iterable[NumpyArray]:
+    def query_embed(self, query: Union[str, List[str]], **kwargs) -> Iterable[np.ndarray]:
         if isinstance(query, str):
             query = [query]
 
@@ -249,12 +245,12 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[NumpyArray]):
             )
 
     @classmethod
-    def _get_worker_class(cls) -> Type[TextEmbeddingWorker[NumpyArray]]:
+    def _get_worker_class(cls) -> Type[TextEmbeddingWorker]:
         return ColbertEmbeddingWorker
 
 
-class ColbertEmbeddingWorker(TextEmbeddingWorker[NumpyArray]):
-    def init_embedding(self, model_name: str, cache_dir: str, **kwargs: Any) -> Colbert:
+class ColbertEmbeddingWorker(TextEmbeddingWorker):
+    def init_embedding(self, model_name: str, cache_dir: str, **kwargs) -> Colbert:
         return Colbert(
             model_name=model_name,
             cache_dir=cache_dir,

fastembed/late_interaction/jina_colbert.py

@@ -1,55 +1,59 @@
-from typing import Any, Type
+from typing import Any, Dict, List, Type
 
-from fastembed.common.types import NumpyArray
-from fastembed.late_interaction.colbert import Colbert, ColbertEmbeddingWorker
-from fastembed.common.model_description import DenseModelDescription, ModelSource
+import numpy as np
 
-supported_jina_colbert_models: list[DenseModelDescription] = [
-    DenseModelDescription(
-        model="jinaai/jina-colbert-v2",
-        dim=128,
-        description="New model that expands capabilities of colbert-v1 with multilingual and context length of 8192, 2024 year",
-        license="cc-by-nc-4.0",
-        size_in_GB=2.24,
-        sources=ModelSource(hf="jinaai/jina-colbert-v2"),
-        model_file="onnx/model.onnx",
-        additional_files=["onnx/model.onnx_data"],
-    )
+from fastembed.late_interaction.colbert import Colbert
+from fastembed.text.onnx_text_model import TextEmbeddingWorker
+
+supported_jina_colbert_models = [
+    {
+        "model": "jinaai/jina-colbert-v2",
+        "dim": 128,
+        "description": "New model that expands capabilities of colbert-v1 with multilingual and context length of 8192, 2024 year",
+        "license": "cc-by-nc-4.0",
+        "size_in_GB": 2.24,
+        "sources": {
+            "hf": "jinaai/jina-colbert-v2",
+        },
+        "model_file": "onnx/model.onnx",
+        "additional_files": ["onnx/model.onnx_data"],
+    },
 ]
 
 
 class JinaColbert(Colbert):
     QUERY_MARKER_TOKEN_ID = 250002
     DOCUMENT_MARKER_TOKEN_ID = 250003
-    MIN_QUERY_LENGTH = 31  # it's 32, we add one additional special token in the beginning
+    MIN_QUERY_LENGTH = 32
     MASK_TOKEN = "<mask>"
 
     @classmethod
-    def _get_worker_class(cls) -> Type[ColbertEmbeddingWorker]:
+    def _get_worker_class(cls) -> Type[TextEmbeddingWorker]:
         return JinaColbertEmbeddingWorker
 
     @classmethod
-    def _list_supported_models(cls) -> list[DenseModelDescription]:
+    def list_supported_models(cls) -> List[Dict[str, Any]]:
         """Lists the supported models.
 
         Returns:
-            list[DenseModelDescription]: A list of DenseModelDescription objects containing the model information.
+            List[Dict[str, Any]]: A list of dictionaries containing the model information.
         """
         return supported_jina_colbert_models
 
     def _preprocess_onnx_input(
-        self, onnx_input: dict[str, NumpyArray], is_doc: bool = True, **kwargs: Any
-    ) -> dict[str, NumpyArray]:
-        onnx_input = super()._preprocess_onnx_input(onnx_input, is_doc)
-
-        # the attention mask for jina-colbert-v2 is always 1 in queries
-        if not is_doc:
+        self, onnx_input: Dict[str, np.ndarray], is_doc: bool = True
+    ) -> Dict[str, np.ndarray]:
+        if is_doc:
+            onnx_input["input_ids"][:, 1] = self.DOCUMENT_MARKER_TOKEN_ID
+        else:
+            onnx_input["input_ids"][:, 1] = self.QUERY_MARKER_TOKEN_ID
+            # the attention mask for jina-colbert-v2 is always 1 in queries
             onnx_input["attention_mask"][:] = 1
         return onnx_input
 
 
-class JinaColbertEmbeddingWorker(ColbertEmbeddingWorker):
-    def init_embedding(self, model_name: str, cache_dir: str, **kwargs: Any) -> JinaColbert:
+class JinaColbertEmbeddingWorker(TextEmbeddingWorker):
+    def init_embedding(self, model_name: str, cache_dir: str, **kwargs) -> JinaColbert:
         return JinaColbert(
             model_name=model_name,
             cache_dir=cache_dir,

fastembed/late_interaction/late_interaction_embedding_base.py

@@ -1,17 +1,17 @@
-from typing import Iterable, Optional, Union, Any
+from typing import Iterable, Optional, Union
+
+import numpy as np
 
-from fastembed.common.model_description import DenseModelDescription
-from fastembed.common.types import NumpyArray
 from fastembed.common.model_management import ModelManagement
 
 
-class LateInteractionTextEmbeddingBase(ModelManagement[DenseModelDescription]):
+class LateInteractionTextEmbeddingBase(ModelManagement):
     def __init__(
         self,
         model_name: str,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
-        **kwargs: Any,
+        **kwargs,
     ):
         self.model_name = model_name
         self.cache_dir = cache_dir
@@ -23,11 +23,11 @@ class LateInteractionTextEmbeddingBase(ModelManagement[DenseModelDescription]):
         documents: Union[str, Iterable[str]],
         batch_size: int = 256,
         parallel: Optional[int] = None,
-        **kwargs: Any,
-    ) -> Iterable[NumpyArray]:
+        **kwargs,
+    ) -> Iterable[np.ndarray]:
         raise NotImplementedError()
 
-    def passage_embed(self, texts: Iterable[str], **kwargs: Any) -> Iterable[NumpyArray]:
+    def passage_embed(self, texts: Iterable[str], **kwargs) -> Iterable[np.ndarray]:
         """
         Embeds a list of text passages into a list of embeddings.
 
@@ -36,13 +36,15 @@ class LateInteractionTextEmbeddingBase(ModelManagement[DenseModelDescription]):
             **kwargs: Additional keyword argument to pass to the embed method.
 
         Yields:
-            Iterable[NdArray]: The embeddings.
+            Iterable[np.ndarray]: The embeddings.
         """
 
         # This is model-specific, so that different models can have specialized implementations
         yield from self.embed(texts, **kwargs)
 
-    def query_embed(self, query: Union[str, Iterable[str]], **kwargs: Any) -> Iterable[NumpyArray]:
+    def query_embed(
+        self, query: Union[str, Iterable[str]], **kwargs
+    ) -> Iterable[np.ndarray]:
         """
         Embeds queries
 
@@ -50,11 +52,11 @@ class LateInteractionTextEmbeddingBase(ModelManagement[DenseModelDescription]):
             query (Union[str, Iterable[str]]): The query to embed, or an iterable e.g. list of queries.
 
         Returns:
-            Iterable[NdArray]: The embeddings.
+            Iterable[np.ndarray]: The embeddings.
         """
 
         # This is model-specific, so that different models can have specialized implementations
         if isinstance(query, str):
             yield from self.embed([query], **kwargs)
-        else:
+        if isinstance(query, Iterable):
             yield from self.embed(query, **kwargs)

fastembed/late_interaction/late_interaction_text_embedding.py

@@ -1,8 +1,7 @@
-from typing import Any, Iterable, Optional, Sequence, Type, Union
-from dataclasses import asdict
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Type, Union
+
+import numpy as np
 
-from fastembed.common.model_description import DenseModelDescription
-from fastembed.common.types import NumpyArray
 from fastembed.common import OnnxProvider
 from fastembed.late_interaction.colbert import Colbert
 from fastembed.late_interaction.jina_colbert import JinaColbert
@@ -12,15 +11,15 @@ from fastembed.late_interaction.late_interaction_embedding_base import (
 
 
 class LateInteractionTextEmbedding(LateInteractionTextEmbeddingBase):
-    EMBEDDINGS_REGISTRY: list[Type[LateInteractionTextEmbeddingBase]] = [Colbert, JinaColbert]
+    EMBEDDINGS_REGISTRY: List[Type[LateInteractionTextEmbeddingBase]] = [Colbert, JinaColbert]
 
     @classmethod
-    def list_supported_models(cls) -> list[dict[str, Any]]:
+    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.
+            List[Dict[str, Any]]: A list of dictionaries containing the model information.
 
             Example:
                 ```
@@ -39,13 +38,9 @@ class LateInteractionTextEmbedding(LateInteractionTextEmbeddingBase):
                 ]
                 ```
         """
-        return [asdict(model) for model in cls._list_supported_models()]
-
-    @classmethod
-    def _list_supported_models(cls) -> list[DenseModelDescription]:
-        result: list[DenseModelDescription] = []
+        result = []
         for embedding in cls.EMBEDDINGS_REGISTRY:
-            result.extend(embedding._list_supported_models())
+            result.extend(embedding.list_supported_models())
         return result
 
     def __init__(
@@ -55,14 +50,14 @@ class LateInteractionTextEmbedding(LateInteractionTextEmbeddingBase):
         threads: Optional[int] = None,
         providers: Optional[Sequence[OnnxProvider]] = None,
         cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        device_ids: Optional[List[int]] = None,
         lazy_load: bool = False,
-        **kwargs: Any,
+        **kwargs,
     ):
         super().__init__(model_name, cache_dir, threads, **kwargs)
         for EMBEDDING_MODEL_TYPE in self.EMBEDDINGS_REGISTRY:
-            supported_models = EMBEDDING_MODEL_TYPE._list_supported_models()
-            if any(model_name.lower() == model.model.lower() for model in supported_models):
+            supported_models = EMBEDDING_MODEL_TYPE.list_supported_models()
+            if any(model_name.lower() == model["model"].lower() for model in supported_models):
                 self.model = EMBEDDING_MODEL_TYPE(
                     model_name,
                     cache_dir,
@@ -85,8 +80,8 @@ class LateInteractionTextEmbedding(LateInteractionTextEmbeddingBase):
         documents: Union[str, Iterable[str]],
         batch_size: int = 256,
         parallel: Optional[int] = None,
-        **kwargs: Any,
-    ) -> Iterable[NumpyArray]:
+        **kwargs,
+    ) -> Iterable[np.ndarray]:
         """
         Encode a list of documents into list of embeddings.
         We use mean pooling with attention so that the model can handle variable-length inputs.
@@ -104,7 +99,7 @@ class LateInteractionTextEmbedding(LateInteractionTextEmbeddingBase):
         """
         yield from self.model.embed(documents, batch_size, parallel, **kwargs)
 
-    def query_embed(self, query: Union[str, Iterable[str]], **kwargs: Any) -> Iterable[NumpyArray]:
+    def query_embed(self, query: Union[str, Iterable[str]], **kwargs) -> Iterable[np.ndarray]:
         """
         Embeds queries
 
@@ -112,7 +107,7 @@ class LateInteractionTextEmbedding(LateInteractionTextEmbeddingBase):
             query (Union[str, Iterable[str]]): The query to embed, or an iterable e.g. list of queries.
 
         Returns:
-            Iterable[NdArray]: The embeddings.
+            Iterable[np.ndarray]: The embeddings.
         """
 
         # This is model-specific, so that different models can have specialized implementations

fastembed/late_interaction_multimodal/__init__.py

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

fastembed/late_interaction_multimodal/colpali.py

@@ -1,300 +0,0 @@
-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.types import NumpyArray
-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,
-)
-from fastembed.common.model_description import DenseModelDescription, ModelSource
-
-supported_colpali_models: list[DenseModelDescription] = [
-    DenseModelDescription(
-        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=ModelSource(hf="Qdrant/colpali-v1.3-fp16"),
-        additional_files=["model.onnx_data"],
-        model_file="model.onnx",
-    ),
-]
-
-
-class ColPali(LateInteractionMultimodalEmbeddingBase, OnnxMultimodalModel[NumpyArray]):
-    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: Any,
-    ):
-        """
-        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
-        self.device_id: Optional[int] = None
-        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]
-
-        self.model_description = self._get_model_description(model_name)
-        self.cache_dir = str(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[DenseModelDescription]:
-        """Lists the supported models.
-
-        Returns:
-            list[DenseModelDescription]: A list of DenseModelDescription objects 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[NumpyArray]:
-        """
-        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[NumpyArray]: Post-processed output as NumPy arrays.
-        """
-        assert self.model_description.dim is not None, "Model dim is not defined"
-        return output.model_output.reshape(
-            output.model_output.shape[0], -1, self.model_description.dim
-        )
-
-    def _post_process_onnx_text_output(
-        self,
-        output: OnnxOutputContext,
-    ) -> Iterable[NumpyArray]:
-        """
-        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[NumpyArray]: Post-processed output as NumPy arrays.
-        """
-        return output.model_output
-
-    def tokenize(self, documents: list[str], **kwargs: Any) -> 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)  # type: ignore[union-attr]
-        return encoded
-
-    def _preprocess_onnx_text_input(
-        self, onnx_input: dict[str, NumpyArray], **kwargs: Any
-    ) -> dict[str, NumpyArray]:
-        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: NumpyArray = 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: Any
-    ) -> dict[str, NumpyArray]:
-        """
-        Add placeholders for text input when processing image data for ONNX.
-        Args:
-            onnx_input (Dict[str, NumpyArray]): Preprocessed image inputs.
-            **kwargs: Additional arguments.
-        Returns:
-            Dict[str, NumpyArray]: ONNX input with text placeholders.
-        """
-        onnx_input["input_ids"] = np.array(
-            [self.EMPTY_TEXT_PLACEHOLDER for _ in onnx_input["pixel_values"]]
-        )
-        onnx_input["attention_mask"] = np.array(
-            [self.EVEN_ATTENTION_MASK for _ in onnx_input["pixel_values"]]
-        )
-        return onnx_input
-
-    def embed_text(
-        self,
-        documents: Union[str, Iterable[str]],
-        batch_size: int = 256,
-        parallel: Optional[int] = None,
-        **kwargs: Any,
-    ) -> Iterable[NumpyArray]:
-        """
-        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: Union[ImageInput, Iterable[ImageInput]],
-        batch_size: int = 16,
-        parallel: Optional[int] = None,
-        **kwargs: Any,
-    ) -> Iterable[NumpyArray]:
-        """
-        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[NumpyArray]]:
-        return ColPaliTextEmbeddingWorker
-
-    @classmethod
-    def _get_image_worker_class(cls) -> Type[ImageEmbeddingWorker[NumpyArray]]:
-        return ColPaliImageEmbeddingWorker
-
-
-class ColPaliTextEmbeddingWorker(TextEmbeddingWorker[NumpyArray]):
-    def init_embedding(self, model_name: str, cache_dir: str, **kwargs: Any) -> ColPali:
-        return ColPali(
-            model_name=model_name,
-            cache_dir=cache_dir,
-            threads=1,
-            **kwargs,
-        )
-
-
-class ColPaliImageEmbeddingWorker(ImageEmbeddingWorker[NumpyArray]):
-    def init_embedding(self, model_name: str, cache_dir: str, **kwargs: Any) -> ColPali:
-        return ColPali(
-            model_name=model_name,
-            cache_dir=cache_dir,
-            threads=1,
-            **kwargs,
-        )

fastembed/late_interaction_multimodal/late_interaction_multimodal_embedding.py

@@ -1,130 +0,0 @@
-from typing import Any, Iterable, Optional, Sequence, Type, Union
-from dataclasses import asdict
-
-from fastembed.common import OnnxProvider, ImageInput
-from fastembed.common.types import NumpyArray
-from fastembed.late_interaction_multimodal.colpali import ColPali
-
-from fastembed.late_interaction_multimodal.late_interaction_multimodal_embedding_base import (
-    LateInteractionMultimodalEmbeddingBase,
-)
-from fastembed.common.model_description import DenseModelDescription
-
-
-class LateInteractionMultimodalEmbedding(LateInteractionMultimodalEmbeddingBase):
-    EMBEDDINGS_REGISTRY: list[Type[LateInteractionMultimodalEmbeddingBase]] = [ColPali]
-
-    @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.
-
-            Example:
-                ```
-                [
-                    {
-                         "model": "Qdrant/colpali-v1.3-fp16",
-                         "dim": 128,
-                         "description": "Text embeddings, Unimodal (text), Aligned to image latent space, ColBERT-compatible, 512 tokens max, 2024.",
-                         "license": "mit",
-                         "size_in_GB": 6.06,
-                         "sources": {
-                            "hf": "Qdrant/colpali-v1.3-fp16",
-                         },
-                         "additional_files": [
-                            "model.onnx_data",
-                         ],
-                        "model_file": "model.onnx",
-                    },
-                ]
-                ```
-        """
-        return [asdict(model) for model in cls._list_supported_models()]
-
-    @classmethod
-    def _list_supported_models(cls) -> list[DenseModelDescription]:
-        result: list[DenseModelDescription] = []
-        for embedding in cls.EMBEDDINGS_REGISTRY:
-            result.extend(embedding._list_supported_models())
-        return result
-
-    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,
-        **kwargs: Any,
-    ):
-        super().__init__(model_name, cache_dir, threads, **kwargs)
-        for EMBEDDING_MODEL_TYPE in self.EMBEDDINGS_REGISTRY:
-            supported_models = EMBEDDING_MODEL_TYPE._list_supported_models()
-            if any(model_name.lower() == model.model.lower() for model in supported_models):
-                self.model = EMBEDDING_MODEL_TYPE(
-                    model_name,
-                    cache_dir,
-                    threads=threads,
-                    providers=providers,
-                    cuda=cuda,
-                    device_ids=device_ids,
-                    lazy_load=lazy_load,
-                    **kwargs,
-                )
-                return
-
-        raise ValueError(
-            f"Model {model_name} is not supported in LateInteractionMultimodalEmbedding."
-            "Please check the supported models using `LateInteractionMultimodalEmbedding.list_supported_models()`"
-        )
-
-    def embed_text(
-        self,
-        documents: Union[str, Iterable[str]],
-        batch_size: int = 256,
-        parallel: Optional[int] = None,
-        **kwargs: Any,
-    ) -> Iterable[NumpyArray]:
-        """
-        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.model.embed_text(documents, batch_size, parallel, **kwargs)
-
-    def embed_image(
-        self,
-        images: Union[ImageInput, Iterable[ImageInput]],
-        batch_size: int = 16,
-        parallel: Optional[int] = None,
-        **kwargs: Any,
-    ) -> Iterable[NumpyArray]:
-        """
-        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 image
-        """
-        yield from self.model.embed_image(images, batch_size, parallel, **kwargs)

fastembed/late_interaction_multimodal/late_interaction_multimodal_embedding_base.py

@@ -1,67 +0,0 @@
-from typing import Iterable, Optional, Union, Any
-
-
-from fastembed.common import ImageInput
-from fastembed.common.model_description import DenseModelDescription
-from fastembed.common.model_management import ModelManagement
-from fastembed.common.types import NumpyArray
-
-
-class LateInteractionMultimodalEmbeddingBase(ModelManagement[DenseModelDescription]):
-    def __init__(
-        self,
-        model_name: str,
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
-        **kwargs: Any,
-    ):
-        self.model_name = model_name
-        self.cache_dir = cache_dir
-        self.threads = threads
-        self._local_files_only = kwargs.pop("local_files_only", False)
-
-    def embed_text(
-        self,
-        documents: Union[str, Iterable[str]],
-        batch_size: int = 256,
-        parallel: Optional[int] = None,
-        **kwargs: Any,
-    ) -> Iterable[NumpyArray]:
-        """
-        Embeds a list of documents into a list of embeddings.
-
-        Args:
-            documents (Iterable[str]): The list of texts 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.
-            **kwargs: Additional keyword argument to pass to the embed method.
-
-        Yields:
-            Iterable[NumpyArray]: The embeddings.
-        """
-        raise NotImplementedError()
-
-    def embed_image(
-        self,
-        images: Union[ImageInput, Iterable[ImageInput]],
-        batch_size: int = 16,
-        parallel: Optional[int] = None,
-        **kwargs: Any,
-    ) -> Iterable[NumpyArray]:
-        """
-        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 image
-        """
-        raise NotImplementedError()

fastembed/late_interaction_multimodal/onnx_multimodal_model.py

@@ -1,271 +0,0 @@
-import contextlib
-import os
-from multiprocessing import get_all_start_methods
-from pathlib import Path
-from typing import Any, Iterable, Optional, Sequence, Type, Union
-
-import numpy as np
-from PIL import Image
-from tokenizers import Encoding, Tokenizer
-
-from fastembed.common import OnnxProvider, ImageInput
-from fastembed.common.onnx_model import EmbeddingWorker, OnnxModel, OnnxOutputContext, T
-from fastembed.common.preprocessor_utils import load_tokenizer, load_preprocessor
-from fastembed.common.types import NumpyArray
-from fastembed.common.utils import iter_batch
-from fastembed.image.transform.operators import Compose
-from fastembed.parallel_processor import ParallelWorkerPool
-
-
-class OnnxMultimodalModel(OnnxModel[T]):
-    ONNX_OUTPUT_NAMES: Optional[list[str]] = None
-
-    def __init__(self) -> None:
-        super().__init__()
-        self.tokenizer: Optional[Tokenizer] = None
-        self.processor: Optional[Compose] = None
-        self.special_token_to_id: dict[str, int] = {}
-
-    def _preprocess_onnx_text_input(
-        self, onnx_input: dict[str, NumpyArray], **kwargs: Any
-    ) -> dict[str, NumpyArray]:
-        """
-        Preprocess the onnx input.
-        """
-        return onnx_input
-
-    def _preprocess_onnx_image_input(
-        self, onnx_input: dict[str, NumpyArray], **kwargs: Any
-    ) -> dict[str, NumpyArray]:
-        """
-        Preprocess the onnx input.
-        """
-        return onnx_input
-
-    @classmethod
-    def _get_text_worker_class(cls) -> Type["TextEmbeddingWorker[T]"]:
-        raise NotImplementedError("Subclasses must implement this method")
-
-    @classmethod
-    def _get_image_worker_class(cls) -> Type["ImageEmbeddingWorker[T]"]:
-        raise NotImplementedError("Subclasses must implement this method")
-
-    def _post_process_onnx_image_output(self, output: OnnxOutputContext) -> Iterable[T]:
-        raise NotImplementedError("Subclasses must implement this method")
-
-    def _post_process_onnx_text_output(self, output: OnnxOutputContext) -> Iterable[T]:
-        raise NotImplementedError("Subclasses must implement this method")
-
-    def _load_onnx_model(
-        self,
-        model_dir: Path,
-        model_file: str,
-        threads: Optional[int],
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_id: Optional[int] = None,
-    ) -> None:
-        super()._load_onnx_model(
-            model_dir=model_dir,
-            model_file=model_file,
-            threads=threads,
-            providers=providers,
-            cuda=cuda,
-            device_id=device_id,
-        )
-        self.tokenizer, self.special_token_to_id = load_tokenizer(model_dir=model_dir)
-        assert self.tokenizer is not None
-        self.processor = load_preprocessor(model_dir=model_dir)
-
-    def load_onnx_model(self) -> None:
-        raise NotImplementedError("Subclasses must implement this method")
-
-    def tokenize(self, documents: list[str], **kwargs: Any) -> list[Encoding]:
-        return self.tokenizer.encode_batch(documents)  # type: ignore[union-attr]
-
-    def onnx_embed_text(
-        self,
-        documents: list[str],
-        **kwargs: Any,
-    ) -> OnnxOutputContext:
-        encoded = self.tokenize(documents, **kwargs)
-        input_ids = np.array([e.ids for e in encoded])
-        attention_mask = np.array([e.attention_mask for e in encoded])  # type: ignore[union-attr]
-        input_names = {node.name for node in self.model.get_inputs()}  # type: ignore[union-attr]
-        onnx_input: dict[str, NumpyArray] = {
-            "input_ids": np.array(input_ids, dtype=np.int64),
-        }
-        if "attention_mask" in input_names:
-            onnx_input["attention_mask"] = np.array(attention_mask, dtype=np.int64)
-        if "token_type_ids" in input_names:
-            onnx_input["token_type_ids"] = np.array(
-                [np.zeros(len(e), dtype=np.int64) for e in input_ids], dtype=np.int64
-            )
-
-        onnx_input = self._preprocess_onnx_text_input(onnx_input, **kwargs)
-        model_output = self.model.run(self.ONNX_OUTPUT_NAMES, onnx_input)  # type: ignore[union-attr]
-        return OnnxOutputContext(
-            model_output=model_output[0],
-            attention_mask=onnx_input.get("attention_mask", attention_mask),
-            input_ids=onnx_input.get("input_ids", input_ids),
-        )
-
-    def _embed_documents(
-        self,
-        model_name: str,
-        cache_dir: str,
-        documents: Union[str, Iterable[str]],
-        batch_size: int = 256,
-        parallel: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
-        **kwargs: Any,
-    ) -> Iterable[T]:
-        is_small = False
-
-        if isinstance(documents, str):
-            documents = [documents]
-            is_small = True
-
-        if isinstance(documents, list):
-            if len(documents) < batch_size:
-                is_small = True
-
-        if parallel is None or is_small:
-            if not hasattr(self, "model") or self.model is None:
-                self.load_onnx_model()
-            for batch in iter_batch(documents, batch_size):
-                yield from self._post_process_onnx_text_output(self.onnx_embed_text(batch))
-        else:
-            if parallel == 0:
-                parallel = os.cpu_count()
-
-            start_method = "forkserver" if "forkserver" in get_all_start_methods() else "spawn"
-            params = {
-                "model_name": model_name,
-                "cache_dir": cache_dir,
-                "providers": providers,
-                **kwargs,
-            }
-
-            pool = ParallelWorkerPool(
-                num_workers=parallel or 1,
-                worker=self._get_text_worker_class(),
-                cuda=cuda,
-                device_ids=device_ids,
-                start_method=start_method,
-            )
-            for batch in pool.ordered_map(iter_batch(documents, batch_size), **params):
-                yield from self._post_process_onnx_text_output(batch)  # type: ignore
-
-    def onnx_embed_image(self, images: list[ImageInput], **kwargs: Any) -> OnnxOutputContext:
-        with contextlib.ExitStack():
-            image_files = [
-                Image.open(image) if not isinstance(image, Image.Image) else image
-                for image in images
-            ]
-            assert self.processor is not None, "Processor is not initialized"
-            encoded = np.array(self.processor(image_files))
-        onnx_input = {"pixel_values": encoded}
-        onnx_input = self._preprocess_onnx_image_input(onnx_input, **kwargs)
-        model_output = self.model.run(None, onnx_input)  # type: ignore[union-attr]
-        embeddings = model_output[0].reshape(len(images), -1)
-        return OnnxOutputContext(model_output=embeddings)
-
-    def _embed_images(
-        self,
-        model_name: str,
-        cache_dir: str,
-        images: Union[Iterable[ImageInput], ImageInput],
-        batch_size: int = 256,
-        parallel: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
-        **kwargs: Any,
-    ) -> Iterable[T]:
-        is_small = False
-
-        if isinstance(images, (str, Path, Image.Image)):
-            images = [images]
-            is_small = True
-
-        if isinstance(images, list) and len(images) < batch_size:
-            is_small = True
-
-        if parallel is None or is_small:
-            if not hasattr(self, "model") or self.model is None:
-                self.load_onnx_model()
-
-            for batch in iter_batch(images, batch_size):
-                yield from self._post_process_onnx_image_output(self.onnx_embed_image(batch))
-        else:
-            if parallel == 0:
-                parallel = os.cpu_count()
-
-            start_method = "forkserver" if "forkserver" in get_all_start_methods() else "spawn"
-            params = {
-                "model_name": model_name,
-                "cache_dir": cache_dir,
-                "providers": providers,
-                **kwargs,
-            }
-
-            pool = ParallelWorkerPool(
-                num_workers=parallel or 1,
-                worker=self._get_image_worker_class(),
-                cuda=cuda,
-                device_ids=device_ids,
-                start_method=start_method,
-            )
-            for batch in pool.ordered_map(iter_batch(images, batch_size), **params):
-                yield from self._post_process_onnx_image_output(batch)  # type: ignore
-
-
-class TextEmbeddingWorker(EmbeddingWorker[T]):
-    def __init__(
-        self,
-        model_name: str,
-        cache_dir: str,
-        **kwargs: Any,
-    ):
-        self.model: OnnxMultimodalModel
-        super().__init__(model_name, cache_dir, **kwargs)
-
-    def init_embedding(
-        self,
-        model_name: str,
-        cache_dir: str,
-        **kwargs: Any,
-    ) -> OnnxMultimodalModel:
-        raise NotImplementedError()
-
-    def process(self, items: Iterable[tuple[int, Any]]) -> Iterable[tuple[int, Any]]:
-        for idx, batch in items:
-            onnx_output = self.model.onnx_embed_text(batch)
-            yield idx, onnx_output
-
-
-class ImageEmbeddingWorker(EmbeddingWorker[T]):
-    def __init__(
-        self,
-        model_name: str,
-        cache_dir: str,
-        **kwargs: Any,
-    ):
-        self.model: OnnxMultimodalModel
-        super().__init__(model_name, cache_dir, **kwargs)
-
-    def init_embedding(
-        self,
-        model_name: str,
-        cache_dir: str,
-        **kwargs: Any,
-    ) -> OnnxMultimodalModel:
-        raise NotImplementedError()
-
-    def process(self, items: Iterable[tuple[int, Any]]) -> Iterable[tuple[int, Any]]:
-        for idx, batch in items:
-            embeddings = self.model.onnx_embed_image(batch)
-            yield idx, embeddings

fastembed/parallel_processor.py

@@ -1,22 +1,14 @@
 import logging
 import os
 from collections import defaultdict
-from copy import deepcopy
 from enum import Enum
-from dataclasses import dataclass
-from multiprocessing import shared_memory, Manager, Lock
 from multiprocessing import Queue, get_context
 from multiprocessing.context import BaseContext
 from multiprocessing.process import BaseProcess
 from multiprocessing.sharedctypes import Synchronized as BaseValue
 from queue import Empty
-from typing import Any, Iterable, Optional, Type
-
-import numpy as np
-from numpy.typing import NDArray
-
-from fastembed.common.types import NumpyArray
-
+from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
+from copy import deepcopy
 
 # Single item should be processed in less than:
 processing_timeout = 10 * 60  # seconds
@@ -24,13 +16,6 @@ processing_timeout = 10 * 60  # seconds
 max_internal_batch_size = 200
 
 
-@dataclass
-class OnnxOutputContext:
-    model_output: NumpyArray
-    attention_mask: Optional[NDArray[np.int64]] = None
-    input_ids: Optional[NDArray[np.int64]] = None
-
-
 class QueueSignals(str, Enum):
     stop = "stop"
     confirm = "confirm"
@@ -39,62 +24,20 @@ class QueueSignals(str, Enum):
 
 class Worker:
     @classmethod
-    def start(cls, *args: Any, **kwargs: Any) -> "Worker":
+    def start(cls, **kwargs: Any) -> "Worker":
         raise NotImplementedError()
 
-    def process(self, items: Iterable[tuple[int, Any]]) -> Iterable[tuple[int, Any]]:
+    def process(self, items: Iterable[Tuple[int, Any]]) -> Iterable[Tuple[int, Any]]:
         raise NotImplementedError()
 
 
-class SharedMemoryPool:
-    def __init__(self, lock: Lock):
-        self._lock = lock
-        self._pool: dict[str, tuple[shared_memory.SharedMemory, int, np.dtype]] = {}
-        self._free_buffers: list[str] = []
-
-    def allocate(self, size: int, dtype: np.dtype) -> tuple[shared_memory.SharedMemory, str]:
-        best_match = None
-        best_size = float("inf")
-        for buf_name in self._free_buffers:
-            shm, buf_size, buf_dtype = self._pool[buf_name]
-            # get best match for needed size
-            if buf_size >= size and buf_dtype == dtype and buf_size < best_size:
-                best_match = buf_name
-                best_size = buf_size
-            if best_match:
-                self._free_buffers.remove(best_match)
-                return self._pool[best_match][0], best_match
-            shm = shared_memory.SharedMemory(create=True, size=size)
-            self._pool[shm.name] = (shm, size, dtype)
-            return shm, shm.name
-        # if no match found, create new buffer
-        shm = shared_memory.SharedMemory(create=True, size=size)
-        self._pool[shm.name] = (shm, size, dtype)
-        return shm, shm.name
-
-    def release(self, name: str) -> None:
-        if name in self._pool and name not in self._free_buffers:
-            self._free_buffers.append(name)
-
-    def cleanup(self) -> None:
-        for shm, _, _ in self._pool.values():
-            shm.close()
-            shm.unlink()
-        self._pool.clear()
-        self._free_buffers.clear()
-
-    def __del__(self):
-        self.cleanup()
-
-
 def _worker(
     worker_class: Type[Worker],
     input_queue: Queue,
     output_queue: Queue,
     num_active_workers: BaseValue,
     worker_id: int,
-    shared_pool: SharedMemoryPool,
-    kwargs: Optional[dict[str, Any]] = None,
+    kwargs: Optional[Dict[str, Any]] = None,
 ) -> None:
     """
     A worker that pulls data pints off the input queue, and places the execution result on the output queue.
@@ -111,6 +54,7 @@ def _worker(
     try:
         worker = worker_class.start(**kwargs)
 
+        # Keep going until you get an item that's None.
         def input_queue_iterable() -> Iterable[Any]:
             while True:
                 item = input_queue.get()
@@ -119,24 +63,7 @@ def _worker(
                 yield item
 
         for processed_item in worker.process(input_queue_iterable()):
-            idx, output_context = processed_item
-            output_metadata = {}
-            for field in ["model_output", "attention_mask", "input_ids"]:
-                array = getattr(output_context, field, None)
-                if array is not None:
-                    shm, shm_name = shared_pool.allocate(array.nbytes, array.dtype)
-                    shm_array = np.ndarray(array.shape, dtype=array.dtype, buffer=shm.buf)
-                    np.copyto(shm_array, array)
-                    output_metadata[field] = {
-                        "name": shm_name,
-                        "shape": array.shape,
-                        "dtype": array.dtype.str,
-                    }
-                    shm.close()
-            output_queue.put((idx, output_metadata))
-            for field in output_metadata:  # mark release to reuse
-                shared_pool.release(output_metadata[field]["name"])
-
+            output_queue.put(processed_item)
     except Exception as e:  # pylint: disable=broad-except
         logging.exception(e)
         output_queue.put(QueueSignals.error)
@@ -166,7 +93,7 @@ class ParallelWorkerPool:
         num_workers: int,
         worker: Type[Worker],
         start_method: Optional[str] = None,
-        device_ids: Optional[list[int]] = None,
+        device_ids: Optional[List[int]] = None,
         cuda: bool = False,
     ):
         self.worker_class = worker
@@ -174,14 +101,12 @@ class ParallelWorkerPool:
         self.input_queue: Optional[Queue] = None
         self.output_queue: Optional[Queue] = None
         self.ctx: BaseContext = get_context(start_method)
-        self.processes: list[BaseProcess] = []
+        self.processes: List[BaseProcess] = []
         self.queue_size = self.num_workers * max_internal_batch_size
         self.emergency_shutdown = False
         self.device_ids = device_ids
         self.cuda = cuda
         self.num_active_workers: Optional[BaseValue] = None
-        self.manager = Manager()
-        self.shared_pool = SharedMemoryPool(self.manager.Lock())
 
     def start(self, **kwargs: Any) -> None:
         self.input_queue = self.ctx.Queue(self.queue_size)
@@ -207,7 +132,6 @@ class ParallelWorkerPool:
                     self.output_queue,
                     self.num_active_workers,
                     worker_id,
-                    self.shared_pool,
                     worker_kwargs,
                 ),
             )
@@ -215,7 +139,7 @@ class ParallelWorkerPool:
             self.processes.append(process)
 
     def ordered_map(self, stream: Iterable[Any], *args: Any, **kwargs: Any) -> Iterable[Any]:
-        buffer: defaultdict[int, Any] = defaultdict(Any)  # type: ignore
+        buffer = defaultdict(Any)
         next_expected = 0
 
         for idx, item in self.semi_ordered_map(stream, *args, **kwargs):
@@ -226,7 +150,7 @@ class ParallelWorkerPool:
 
     def semi_ordered_map(
         self, stream: Iterable[Any], *args: Any, **kwargs: Any
-    ) -> Iterable[tuple[int, Any]]:
+    ) -> Iterable[Tuple[int, Any]]:
         try:
             self.start(**kwargs)
 
@@ -253,17 +177,7 @@ class ParallelWorkerPool:
                     if out_item == QueueSignals.error:
                         self.join_or_terminate()
                         raise RuntimeError("Thread unexpectedly terminated")
-
-                    idx, output_metadata = out_item
-                    output_arrays = {}
-                    for field, meta in output_metadata.items():
-                        shm = shared_memory.SharedMemory(name=meta["name"])
-                        array = np.ndarray(
-                            meta["shape"], dtype=meta["dtype"], buffer=shm.buf
-                        ).copy()
-                        output_arrays[field] = array
-                        shm.close()
-                    yield (idx, OnnxOutputContext(**output_arrays))
+                    yield out_item
                     read += 1
 
                 self.input_queue.put((idx, item))
@@ -278,18 +192,9 @@ class ParallelWorkerPool:
                 if out_item == QueueSignals.error:
                     self.join_or_terminate()
                     raise RuntimeError("Thread unexpectedly terminated")
-
-                idx, output_metadata = out_item
-                output_arrays = {}
-                for field, meta in output_metadata.items():
-                    shm = shared_memory.SharedMemory(name=meta["name"])
-                    array = np.ndarray(meta["shape"], dtype=meta["dtype"], buffer=shm.buf).copy()
-                    output_arrays[field] = array
-                    shm.close()
-                yield (idx, OnnxOutputContext(**output_arrays))
+                yield out_item
                 read += 1
         finally:
-            self.shared_pool.cleanup()
             assert self.input_queue is not None, "Input queue is None"
             assert self.output_queue is not None, "Output queue is None"
             self.join()
@@ -325,13 +230,11 @@ class ParallelWorkerPool:
             if process.is_alive():
                 process.terminate()
         self.processes.clear()
-        self.shared_pool.cleanup()
 
     def join(self) -> None:
         for process in self.processes:
             process.join()
         self.processes.clear()
-        self.shared_pool.cleanup()
 
     def __del__(self) -> None:
         """
@@ -346,4 +249,3 @@ class ParallelWorkerPool:
         for process in self.processes:
             if process.is_alive():
                 process.terminate()
-        self.shared_pool.cleanup()

fastembed/py.typed

@@ -1 +0,0 @@
-partial

fastembed/rerank/cross_encoder/custom_text_cross_encoder.py

@@ -1,46 +0,0 @@
-from typing import Optional, Sequence, Any
-
-from fastembed.common import OnnxProvider
-from fastembed.common.model_description import BaseModelDescription
-from fastembed.rerank.cross_encoder.onnx_text_cross_encoder import OnnxTextCrossEncoder
-
-
-class CustomTextCrossEncoder(OnnxTextCrossEncoder):
-    SUPPORTED_MODELS: list[BaseModelDescription] = []
-
-    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: Any,
-    ):
-        super().__init__(
-            model_name=model_name,
-            cache_dir=cache_dir,
-            threads=threads,
-            providers=providers,
-            cuda=cuda,
-            device_ids=device_ids,
-            lazy_load=lazy_load,
-            device_id=device_id,
-            specific_model_path=specific_model_path,
-            **kwargs,
-        )
-
-    @classmethod
-    def _list_supported_models(cls) -> list[BaseModelDescription]:
-        return cls.SUPPORTED_MODELS
-
-    @classmethod
-    def add_model(
-        cls,
-        model_description: BaseModelDescription,
-    ) -> None:
-        cls.SUPPORTED_MODELS.append(model_description)

fastembed/rerank/cross_encoder/onnx_text_cross_encoder.py

@@ -1,76 +1,53 @@
-from typing import Any, Iterable, Optional, Sequence, Type
+from typing import List, Iterable, Dict, Any, Sequence, Optional
 
 from loguru import logger
 
 from fastembed.common import OnnxProvider
-from fastembed.common.onnx_model import OnnxOutputContext
-from fastembed.common.utils import define_cache_dir
-from fastembed.rerank.cross_encoder.onnx_text_model import (
-    OnnxCrossEncoderModel,
-    TextRerankerWorker,
-)
+from fastembed.rerank.cross_encoder.onnx_text_model import OnnxCrossEncoderModel
 from fastembed.rerank.cross_encoder.text_cross_encoder_base import TextCrossEncoderBase
-from fastembed.common.model_description import BaseModelDescription, ModelSource
+from fastembed.common.utils import define_cache_dir
 
-supported_onnx_models: list[BaseModelDescription] = [
-    BaseModelDescription(
-        model="Xenova/ms-marco-MiniLM-L-6-v2",
-        description="MiniLM-L-6-v2 model optimized for re-ranking tasks.",
-        license="apache-2.0",
-        size_in_GB=0.08,
-        sources=ModelSource(hf="Xenova/ms-marco-MiniLM-L-6-v2"),
-        model_file="onnx/model.onnx",
-    ),
-    BaseModelDescription(
-        model="Xenova/ms-marco-MiniLM-L-12-v2",
-        description="MiniLM-L-12-v2 model optimized for re-ranking tasks.",
-        license="apache-2.0",
-        size_in_GB=0.12,
-        sources=ModelSource(hf="Xenova/ms-marco-MiniLM-L-12-v2"),
-        model_file="onnx/model.onnx",
-    ),
-    BaseModelDescription(
-        model="BAAI/bge-reranker-base",
-        description="BGE reranker base model for cross-encoder re-ranking.",
-        license="mit",
-        size_in_GB=1.04,
-        sources=ModelSource(hf="BAAI/bge-reranker-base"),
-        model_file="onnx/model.onnx",
-    ),
-    BaseModelDescription(
-        model="jinaai/jina-reranker-v1-tiny-en",
-        description="Designed for blazing-fast re-ranking with 8K context length and fewer parameters than jina-reranker-v1-turbo-en.",
-        license="apache-2.0",
-        size_in_GB=0.13,
-        sources=ModelSource(hf="jinaai/jina-reranker-v1-tiny-en"),
-        model_file="onnx/model.onnx",
-    ),
-    BaseModelDescription(
-        model="jinaai/jina-reranker-v1-turbo-en",
-        description="Designed for blazing-fast re-ranking with 8K context length.",
-        license="apache-2.0",
-        size_in_GB=0.15,
-        sources=ModelSource(hf="jinaai/jina-reranker-v1-turbo-en"),
-        model_file="onnx/model.onnx",
-    ),
-    BaseModelDescription(
-        model="jinaai/jina-reranker-v2-base-multilingual",
-        description="A multi-lingual reranker model for cross-encoder re-ranking with 1K context length and sliding window",
-        license="cc-by-nc-4.0",
-        size_in_GB=1.11,
-        sources=ModelSource(hf="jinaai/jina-reranker-v2-base-multilingual"),
-        model_file="onnx/model.onnx",
-    ),
+supported_onnx_models = [
+    {
+        "model": "Xenova/ms-marco-MiniLM-L-6-v2",
+        "size_in_GB": 0.08,
+        "sources": {
+            "hf": "Xenova/ms-marco-MiniLM-L-6-v2",
+        },
+        "model_file": "onnx/model.onnx",
+        "description": "MiniLM-L-6-v2 model optimized for re-ranking tasks.",
+        "license": "apache-2.0",
+    },
+    {
+        "model": "Xenova/ms-marco-MiniLM-L-12-v2",
+        "size_in_GB": 0.12,
+        "sources": {
+            "hf": "Xenova/ms-marco-MiniLM-L-12-v2",
+        },
+        "model_file": "onnx/model.onnx",
+        "description": "MiniLM-L-12-v2 model optimized for re-ranking tasks.",
+        "license": "apache-2.0",
+    },
+    {
+        "model": "BAAI/bge-reranker-base",
+        "size_in_GB": 1.04,
+        "sources": {
+            "hf": "BAAI/bge-reranker-base",
+        },
+        "model_file": "onnx/model.onnx",
+        "description": "BGE reranker base model for cross-encoder re-ranking.",
+        "license": "mit",
+    },
 ]
 
 
 class OnnxTextCrossEncoder(TextCrossEncoderBase, OnnxCrossEncoderModel):
     @classmethod
-    def _list_supported_models(cls) -> list[BaseModelDescription]:
+    def list_supported_models(cls) -> List[Dict[str, Any]]:
         """Lists the supported models.
 
         Returns:
-            list[BaseModelDescription]: A list of BaseModelDescription objects containing the model information.
+            List[Dict[str, Any]]: A list of dictionaries containing the model information.
         """
         return supported_onnx_models
 
@@ -81,11 +58,10 @@ class OnnxTextCrossEncoder(TextCrossEncoderBase, OnnxCrossEncoderModel):
         threads: Optional[int] = None,
         providers: Optional[Sequence[OnnxProvider]] = None,
         cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        device_ids: Optional[List[int]] = None,
         lazy_load: bool = False,
         device_id: Optional[int] = None,
-        specific_model_path: Optional[str] = None,
-        **kwargs: Any,
+        **kwargs,
     ):
         """
         Args:
@@ -98,12 +74,11 @@ class OnnxTextCrossEncoder(TextCrossEncoderBase, OnnxCrossEncoderModel):
                 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
+            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.
-            specific_model_path (Optional[str], optional): The specific path to the onnx model dir if it should be imported from somewhere else
 
         Raises:
             ValueError: If the model_name is not in the format <org>/<model> e.g. Xenova/ms-marco-MiniLM-L-6-v2.
@@ -123,19 +98,17 @@ class OnnxTextCrossEncoder(TextCrossEncoderBase, OnnxCrossEncoderModel):
             )
 
         # This device_id will be used if we need to load model in current process
-        self.device_id: Optional[int] = None
         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 = str(define_cache_dir(cache_dir))
+        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.model_description, self.cache_dir, local_files_only=self._local_files_only
         )
 
         if not self.lazy_load:
@@ -144,7 +117,7 @@ class OnnxTextCrossEncoder(TextCrossEncoderBase, OnnxCrossEncoderModel):
     def load_onnx_model(self) -> None:
         self._load_onnx_model(
             model_dir=self._model_dir,
-            model_file=self.model_description.model_file,
+            model_file=self.model_description["model_file"],
             threads=self.threads,
             providers=self.providers,
             cuda=self.cuda,
@@ -156,7 +129,7 @@ class OnnxTextCrossEncoder(TextCrossEncoderBase, OnnxCrossEncoderModel):
         query: str,
         documents: Iterable[str],
         batch_size: int = 64,
-        **kwargs: Any,
+        **kwargs,
     ) -> Iterable[float]:
         """Reranks documents based on their relevance to a given query.
 
@@ -172,44 +145,3 @@ class OnnxTextCrossEncoder(TextCrossEncoderBase, OnnxCrossEncoderModel):
         yield from self._rerank_documents(
             query=query, documents=documents, batch_size=batch_size, **kwargs
         )
-
-    def rerank_pairs(
-        self,
-        pairs: Iterable[tuple[str, str]],
-        batch_size: int = 64,
-        parallel: Optional[int] = None,
-        **kwargs: Any,
-    ) -> Iterable[float]:
-        yield from self._rerank_pairs(
-            model_name=self.model_name,
-            cache_dir=str(self.cache_dir),
-            pairs=pairs,
-            batch_size=batch_size,
-            parallel=parallel,
-            providers=self.providers,
-            cuda=self.cuda,
-            device_ids=self.device_ids,
-            **kwargs,
-        )
-
-    @classmethod
-    def _get_worker_class(cls) -> Type[TextRerankerWorker]:
-        return TextCrossEncoderWorker
-
-    def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[float]:
-        return (float(elem) for elem in output.model_output)
-
-
-class TextCrossEncoderWorker(TextRerankerWorker):
-    def init_embedding(
-        self,
-        model_name: str,
-        cache_dir: str,
-        **kwargs: Any,
-    ) -> OnnxTextCrossEncoder:
-        return OnnxTextCrossEncoder(
-            model_name=model_name,
-            cache_dir=cache_dir,
-            threads=1,
-            **kwargs,
-        )

fastembed/rerank/cross_encoder/onnx_text_model.py

@@ -1,29 +1,16 @@
-import os
-from multiprocessing import get_all_start_methods
+from typing import Sequence, Optional, List, Dict, Iterable
 from pathlib import Path
-from typing import Any, Iterable, Optional, Sequence, Type
 
 import numpy as np
 from tokenizers import Encoding
 
-from fastembed.common.onnx_model import (
-    EmbeddingWorker,
-    OnnxModel,
-    OnnxOutputContext,
-    OnnxProvider,
-)
-from fastembed.common.types import NumpyArray
+from fastembed.common.onnx_model import OnnxModel, OnnxProvider
 from fastembed.common.preprocessor_utils import load_tokenizer
 from fastembed.common.utils import iter_batch
-from fastembed.parallel_processor import ParallelWorkerPool
 
 
-class OnnxCrossEncoderModel(OnnxModel[float]):
-    ONNX_OUTPUT_NAMES: Optional[list[str]] = None
-
-    @classmethod
-    def _get_worker_class(cls) -> Type["TextRerankerWorker"]:
-        raise NotImplementedError("Subclasses must implement this method")
+class OnnxCrossEncoderModel(OnnxModel):
+    ONNX_OUTPUT_NAMES: Optional[List[str]] = None
 
     def _load_onnx_model(
         self,
@@ -43,127 +30,41 @@ class OnnxCrossEncoderModel(OnnxModel[float]):
             device_id=device_id,
         )
         self.tokenizer, _ = load_tokenizer(model_dir=model_dir)
-        assert self.tokenizer is not None
 
-    def tokenize(self, pairs: list[tuple[str, str]], **_: Any) -> list[Encoding]:
-        return self.tokenizer.encode_batch(pairs)  # type: ignore[union-attr]
+    def tokenize(self, query: str, documents: List[str], **kwargs) -> List[Encoding]:
+        return self.tokenizer.encode_batch([(query, doc) for doc in documents])
 
-    def _build_onnx_input(self, tokenized_input: list[Encoding]) -> dict[str, NumpyArray]:
-        input_names: set[str] = {node.name for node in self.model.get_inputs()}  # type: ignore[union-attr]
-        inputs: dict[str, NumpyArray] = {
+    def onnx_embed(self, query: str, documents: List[str], **kwargs) -> List[float]:
+        tokenized_input = self.tokenize(query, documents, **kwargs)
+
+        inputs = {
             "input_ids": np.array([enc.ids for enc in tokenized_input], dtype=np.int64),
+            "attention_mask": np.array(
+                [enc.attention_mask for enc in tokenized_input], dtype=np.int64
+            ),
         }
+        input_names = {node.name for node in self.model.get_inputs()}
         if "token_type_ids" in input_names:
             inputs["token_type_ids"] = np.array(
                 [enc.type_ids for enc in tokenized_input], dtype=np.int64
             )
-        if "attention_mask" in input_names:
-            inputs["attention_mask"] = np.array(
-                [enc.attention_mask for enc in tokenized_input], dtype=np.int64
-            )
-        return inputs
 
-    def onnx_embed(self, query: str, documents: list[str], **kwargs: Any) -> OnnxOutputContext:
-        pairs = [(query, doc) for doc in documents]
-        return self.onnx_embed_pairs(pairs, **kwargs)
-
-    def onnx_embed_pairs(self, pairs: list[tuple[str, str]], **kwargs: Any) -> OnnxOutputContext:
-        tokenized_input = self.tokenize(pairs, **kwargs)
-        inputs = self._build_onnx_input(tokenized_input)
         onnx_input = self._preprocess_onnx_input(inputs, **kwargs)
-        outputs = self.model.run(self.ONNX_OUTPUT_NAMES, onnx_input)  # type: ignore[union-attr]
-        relevant_output = outputs[0]
-        scores: NumpyArray = relevant_output[:, 0]
-        return OnnxOutputContext(model_output=scores)
+        outputs = self.model.run(self.ONNX_OUTPUT_NAMES, onnx_input)
+        return outputs[0][:, 0].tolist()
 
     def _rerank_documents(
-        self, query: str, documents: Iterable[str], batch_size: int, **kwargs: Any
+        self, query: str, documents: Iterable[str], batch_size: int, **kwargs
     ) -> Iterable[float]:
         if not hasattr(self, "model") or self.model is None:
             self.load_onnx_model()
         for batch in iter_batch(documents, batch_size):
-            yield from self._post_process_onnx_output(self.onnx_embed(query, batch, **kwargs))
-
-    def _rerank_pairs(
-        self,
-        model_name: str,
-        cache_dir: str,
-        pairs: Iterable[tuple[str, str]],
-        batch_size: int,
-        parallel: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
-        **kwargs: Any,
-    ) -> Iterable[float]:
-        is_small = False
-
-        if isinstance(pairs, tuple):
-            pairs = [pairs]
-            is_small = True
-
-        if isinstance(pairs, list):
-            if len(pairs) < batch_size:
-                is_small = True
-
-        if parallel is None or is_small:
-            if not hasattr(self, "model") or self.model is None:
-                self.load_onnx_model()
-            for batch in iter_batch(pairs, batch_size):
-                yield from self._post_process_onnx_output(self.onnx_embed_pairs(batch, **kwargs))
-        else:
-            if parallel == 0:
-                parallel = os.cpu_count()
-
-            start_method = "forkserver" if "forkserver" in get_all_start_methods() else "spawn"
-            params = {
-                "model_name": model_name,
-                "cache_dir": cache_dir,
-                "providers": providers,
-                **kwargs,
-            }
-
-            pool = ParallelWorkerPool(
-                num_workers=parallel or 1,
-                worker=self._get_worker_class(),
-                cuda=cuda,
-                device_ids=device_ids,
-                start_method=start_method,
-            )
-            for batch in pool.ordered_map(iter_batch(pairs, batch_size), **params):
-                yield from self._post_process_onnx_output(batch)  # type: ignore
-
-    def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[float]:
-        raise NotImplementedError("Subclasses must implement this method")
+            yield from self.onnx_embed(query, batch, **kwargs)
 
     def _preprocess_onnx_input(
-        self, onnx_input: dict[str, NumpyArray], **kwargs: Any
-    ) -> dict[str, NumpyArray]:
+        self, onnx_input: Dict[str, np.ndarray], **kwargs
+    ) -> Dict[str, np.ndarray]:
         """
         Preprocess the onnx input.
         """
         return onnx_input
-
-
-class TextRerankerWorker(EmbeddingWorker[float]):
-    def __init__(
-        self,
-        model_name: str,
-        cache_dir: str,
-        **kwargs: Any,
-    ):
-        self.model: OnnxCrossEncoderModel
-        super().__init__(model_name, cache_dir, **kwargs)
-
-    def init_embedding(
-        self,
-        model_name: str,
-        cache_dir: str,
-        **kwargs: Any,
-    ) -> OnnxCrossEncoderModel:
-        raise NotImplementedError()
-
-    def process(self, items: Iterable[tuple[int, Any]]) -> Iterable[tuple[int, Any]]:
-        for idx, batch in items:
-            onnx_output = self.model.onnx_embed_pairs(batch)
-            yield idx, onnx_output

fastembed/rerank/cross_encoder/text_cross_encoder.py

@@ -1,29 +1,21 @@
-from typing import Any, Iterable, Optional, Sequence, Type
-from dataclasses import asdict
-
-from fastembed.common import OnnxProvider
-from fastembed.rerank.cross_encoder.onnx_text_cross_encoder import OnnxTextCrossEncoder
-from fastembed.rerank.cross_encoder.custom_text_cross_encoder import CustomTextCrossEncoder
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Type
 
 from fastembed.rerank.cross_encoder.text_cross_encoder_base import TextCrossEncoderBase
-from fastembed.common.model_description import (
-    ModelSource,
-    BaseModelDescription,
-)
+from fastembed.rerank.cross_encoder.onnx_text_cross_encoder import OnnxTextCrossEncoder
+from fastembed.common import OnnxProvider
 
 
 class TextCrossEncoder(TextCrossEncoderBase):
-    CROSS_ENCODER_REGISTRY: list[Type[TextCrossEncoderBase]] = [
+    CROSS_ENCODER_REGISTRY: List[Type[TextCrossEncoderBase]] = [
         OnnxTextCrossEncoder,
-        CustomTextCrossEncoder,
     ]
 
     @classmethod
-    def list_supported_models(cls) -> list[dict[str, Any]]:
+    def list_supported_models(cls) -> List[Dict[str, Any]]:
         """Lists the supported models.
 
         Returns:
-            list[BaseModelDescription]: A list of dictionaries containing the model information.
+            List[Dict[str, Any]]: A list of dictionaries containing the model information.
 
             Example:
                 ```
@@ -41,13 +33,9 @@ class TextCrossEncoder(TextCrossEncoderBase):
                 ]
                 ```
         """
-        return [asdict(model) for model in cls._list_supported_models()]
-
-    @classmethod
-    def _list_supported_models(cls) -> list[BaseModelDescription]:
-        result: list[BaseModelDescription] = []
+        result = []
         for encoder in cls.CROSS_ENCODER_REGISTRY:
-            result.extend(encoder._list_supported_models())
+            result.extend(encoder.list_supported_models())
         return result
 
     def __init__(
@@ -57,15 +45,15 @@ class TextCrossEncoder(TextCrossEncoderBase):
         threads: Optional[int] = None,
         providers: Optional[Sequence[OnnxProvider]] = None,
         cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        device_ids: Optional[List[int]] = None,
         lazy_load: bool = False,
-        **kwargs: Any,
+        **kwargs,
     ):
         super().__init__(model_name, cache_dir, threads, **kwargs)
 
         for CROSS_ENCODER_TYPE in self.CROSS_ENCODER_REGISTRY:
-            supported_models = CROSS_ENCODER_TYPE._list_supported_models()
-            if any(model_name.lower() == model.model.lower() for model in supported_models):
+            supported_models = CROSS_ENCODER_TYPE.list_supported_models()
+            if any(model_name.lower() == model["model"].lower() for model in supported_models):
                 self.model = CROSS_ENCODER_TYPE(
                     model_name=model_name,
                     cache_dir=cache_dir,
@@ -84,7 +72,7 @@ class TextCrossEncoder(TextCrossEncoderBase):
         )
 
     def rerank(
-        self, query: str, documents: Iterable[str], batch_size: int = 64, **kwargs: Any
+        self, query: str, documents: Iterable[str], batch_size: int = 64, **kwargs
     ) -> Iterable[float]:
         """Rerank a list of documents based on a query.
 
@@ -97,67 +85,3 @@ class TextCrossEncoder(TextCrossEncoderBase):
             Iterable of scores for each document
         """
         yield from self.model.rerank(query, documents, batch_size=batch_size, **kwargs)
-
-    def rerank_pairs(
-        self,
-        pairs: Iterable[tuple[str, str]],
-        batch_size: int = 64,
-        parallel: Optional[int] = None,
-        **kwargs: Any,
-    ) -> Iterable[float]:
-        """
-        Rerank a list of query-document pairs.
-
-        Args:
-            pairs (Iterable[tuple[str, str]]): An iterable of tuples, where each tuple contains a query and a document
-                to be scored together.
-            batch_size (int, optional): The number of query-document pairs to process in a single batch. Defaults to 64.
-            parallel (Optional[int], optional): The number of parallel processes to use for reranking.
-                If None, parallelization is disabled. Defaults to None.
-            **kwargs (Any): Additional arguments to pass to the underlying reranking model.
-
-        Returns:
-            Iterable[float]: An iterable of scores corresponding to each query-document pair in the input.
-            Higher scores indicate a stronger match between the query and the document.
-
-        Example:
-            >>> encoder = TextCrossEncoder("Xenova/ms-marco-MiniLM-L-6-v2")
-            >>> pairs = [("What is AI?", "Artificial intelligence is ..."), ("What is ML?", "Machine learning is ...")]
-            >>> scores = list(encoder.rerank_pairs(pairs))
-            >>> print(list(map(lambda x: round(x, 2), scores)))
-            [-1.24, -10.6]
-        """
-        yield from self.model.rerank_pairs(
-            pairs, batch_size=batch_size, parallel=parallel, **kwargs
-        )
-
-    @classmethod
-    def add_custom_model(
-        cls,
-        model: str,
-        sources: ModelSource,
-        model_file: str = "onnx/model.onnx",
-        description: str = "",
-        license: str = "",
-        size_in_gb: float = 0.0,
-        additional_files: Optional[list[str]] = None,
-    ) -> None:
-        registered_models = cls._list_supported_models()
-        for registered_model in registered_models:
-            if model == registered_model.model:
-                raise ValueError(
-                    f"Model {model} is already registered in CrossEncoderModel, if you still want to add this model, "
-                    f"please use another model name"
-                )
-
-        CustomTextCrossEncoder.add_model(
-            BaseModelDescription(
-                model=model,
-                sources=sources,
-                model_file=model_file,
-                description=description,
-                license=license,
-                size_in_GB=size_in_gb,
-                additional_files=additional_files or [],
-            )
-        )

fastembed/rerank/cross_encoder/text_cross_encoder_base.py

@@ -1,16 +1,15 @@
-from typing import Any, Iterable, Optional
+from typing import Iterable, Optional
 
-from fastembed.common.model_description import BaseModelDescription
 from fastembed.common.model_management import ModelManagement
 
 
-class TextCrossEncoderBase(ModelManagement[BaseModelDescription]):
+class TextCrossEncoderBase(ModelManagement):
     def __init__(
         self,
         model_name: str,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
-        **kwargs: Any,
+        **kwargs,
     ):
         self.model_name = model_name
         self.cache_dir = cache_dir
@@ -22,9 +21,9 @@ class TextCrossEncoderBase(ModelManagement[BaseModelDescription]):
         query: str,
         documents: Iterable[str],
         batch_size: int = 64,
-        **kwargs: Any,
+        **kwargs,
     ) -> Iterable[float]:
-        """Rerank a list of documents given a query.
+        """Reranks a list of documents given a query.
 
         Args:
             query (str): The query to rerank the documents.
@@ -33,27 +32,6 @@ class TextCrossEncoderBase(ModelManagement[BaseModelDescription]):
             **kwargs: Additional keyword argument to pass to the rerank method.
 
         Yields:
-            Iterable[float]: The scores of the reranked the documents.
-        """
-        raise NotImplementedError("This method should be overridden by subclasses")
-
-    def rerank_pairs(
-        self,
-        pairs: Iterable[tuple[str, str]],
-        batch_size: int = 64,
-        parallel: Optional[int] = None,
-        **kwargs: Any,
-    ) -> Iterable[float]:
-        """Rerank query-document pairs.
-        Args:
-            pairs (Iterable[tuple[str, str]]): Query-document pairs to rerank
-            batch_size (int): The batch size to use for reranking.
-            parallel: 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.
-            **kwargs: Additional keyword argument to pass to the rerank method.
-        Yields:
-            Iterable[float]: Scores for each individual pair
+            Iterable[float]: The scores of reranked the documents.
         """
         raise NotImplementedError("This method should be overridden by subclasses")

fastembed/sparse/bm25.py

@@ -2,7 +2,7 @@ import os
 from collections import defaultdict
 from multiprocessing import get_all_start_methods
 from pathlib import Path
-from typing import Any, Iterable, Optional, Type, Union
+from typing import Any, Dict, Iterable, List, Optional, Tuple, Type, Union
 
 import mmh3
 import numpy as np
@@ -19,7 +19,6 @@ from fastembed.sparse.sparse_embedding_base import (
     SparseTextEmbeddingBase,
 )
 from fastembed.sparse.utils.tokenizer import SimpleTokenizer
-from fastembed.common.model_description import SparseModelDescription, ModelSource
 
 supported_languages = [
     "arabic",
@@ -53,18 +52,19 @@ supported_languages = [
     "turkish",
 ]
 
-supported_bm25_models: list[SparseModelDescription] = [
-    SparseModelDescription(
-        model="Qdrant/bm25",
-        vocab_size=0,
-        description="BM25 as sparse embeddings meant to be used with Qdrant",
-        license="apache-2.0",
-        size_in_GB=0.01,
-        sources=ModelSource(hf="Qdrant/bm25"),
-        additional_files=[f"{lang}.txt" for lang in supported_languages],
-        requires_idf=True,
-        model_file="mock.file",
-    ),
+supported_bm25_models = [
+    {
+        "model": "Qdrant/bm25",
+        "description": "BM25 as sparse embeddings meant to be used with Qdrant",
+        "license": "apache-2.0",
+        "size_in_GB": 0.01,
+        "sources": {
+            "hf": "Qdrant/bm25",
+        },
+        "model_file": "mock.file",  # bm25 does not require a model, so we just use a mock
+        "additional_files": [f"{lang}.txt" for lang in supported_languages],
+        "requires_idf": True,
+    },
 ]
 
 
@@ -92,8 +92,6 @@ class Bm25(SparseTextEmbeddingBase):
         b (float, optional): The b parameter in the BM25 formula. Defines the importance of the document length.
             Defaults to 0.75.
         avg_len (float, optional): The average length of the documents in the corpus. Defaults to 256.0.
-        language (str): Specifies the language for the stemmer.
-        disable_stemmer (bool): Disable the stemmer.
     Raises:
         ValueError: If the model_name is not in the format <org>/<model> e.g. BAAI/bge-base-en.
     """
@@ -107,9 +105,7 @@ class Bm25(SparseTextEmbeddingBase):
         avg_len: float = 256.0,
         language: str = "english",
         token_max_length: int = 40,
-        disable_stemmer: bool = False,
-        specific_model_path: Optional[str] = None,
-        **kwargs: Any,
+        **kwargs,
     ):
         super().__init__(model_name, cache_dir, **kwargs)
 
@@ -123,39 +119,30 @@ class Bm25(SparseTextEmbeddingBase):
         self.avg_len = avg_len
 
         model_description = self._get_model_description(model_name)
-        self.cache_dir = str(define_cache_dir(cache_dir))
+        self.cache_dir = define_cache_dir(cache_dir)
 
         self._model_dir = self.download_model(
-            model_description,
-            self.cache_dir,
-            local_files_only=self._local_files_only,
-            specific_model_path=specific_model_path,
+            model_description, self.cache_dir, local_files_only=self._local_files_only
         )
 
         self.token_max_length = token_max_length
         self.punctuation = set(get_all_punctuation())
-        self.disable_stemmer = disable_stemmer
-
-        if disable_stemmer:
-            self.stopwords: set[str] = set()
-            self.stemmer = None
-        else:
-            self.stopwords = set(self._load_stopwords(self._model_dir, self.language))
-            self.stemmer = SnowballStemmer(language)
+        self.stopwords = set(self._load_stopwords(self._model_dir, self.language))
 
+        self.stemmer = SnowballStemmer(language)
         self.tokenizer = SimpleTokenizer
 
     @classmethod
-    def _list_supported_models(cls) -> list[SparseModelDescription]:
+    def list_supported_models(cls) -> List[Dict[str, Any]]:
         """Lists the supported models.
 
         Returns:
-            list[SparseModelDescription]: A list of SparseModelDescription objects containing the model information.
+            List[Dict[str, Any]]: A list of dictionaries containing the model information.
         """
         return supported_bm25_models
 
     @classmethod
-    def _load_stopwords(cls, model_dir: Path, language: str) -> list[str]:
+    def _load_stopwords(cls, model_dir: Path, language: str) -> List[str]:
         stopwords_path = model_dir / f"{language}.txt"
         if not stopwords_path.exists():
             return []
@@ -195,9 +182,6 @@ class Bm25(SparseTextEmbeddingBase):
                 "k": self.k,
                 "b": self.b,
                 "avg_len": self.avg_len,
-                "language": self.language,
-                "token_max_length": self.token_max_length,
-                "disable_stemmer": self.disable_stemmer,
             }
             pool = ParallelWorkerPool(
                 num_workers=parallel or 1,
@@ -206,14 +190,14 @@ class Bm25(SparseTextEmbeddingBase):
             )
             for batch in pool.ordered_map(iter_batch(documents, batch_size), **params):
                 for record in batch:
-                    yield record  # type: ignore
+                    yield record
 
     def embed(
         self,
         documents: Union[str, Iterable[str]],
         batch_size: int = 256,
         parallel: Optional[int] = None,
-        **kwargs: Any,
+        **kwargs,
     ) -> Iterable[SparseEmbedding]:
         """
         Encode a list of documents into list of embeddings.
@@ -238,21 +222,19 @@ class Bm25(SparseTextEmbeddingBase):
             parallel=parallel,
         )
 
-    def _stem(self, tokens: list[str]) -> list[str]:
-        stemmed_tokens: list[str] = []
+    def _stem(self, tokens: List[str]) -> List[str]:
+        stemmed_tokens = []
         for token in tokens:
-            lower_token = token.lower()
-
             if token in self.punctuation:
                 continue
 
-            if lower_token in self.stopwords:
+            if token.lower() in self.stopwords:
                 continue
 
             if len(token) > self.token_max_length:
                 continue
 
-            stemmed_token = self.stemmer.stem_word(lower_token) if self.stemmer else lower_token
+            stemmed_token = self.stemmer.stem_word(token.lower())
 
             if stemmed_token:
                 stemmed_tokens.append(stemmed_token)
@@ -260,9 +242,9 @@ class Bm25(SparseTextEmbeddingBase):
 
     def raw_embed(
         self,
-        documents: list[str],
-    ) -> list[SparseEmbedding]:
-        embeddings: list[SparseEmbedding] = []
+        documents: List[str],
+    ) -> List[SparseEmbedding]:
+        embeddings = []
         for document in documents:
             document = remove_non_alphanumeric(document)
             tokens = self.tokenizer.tokenize(document)
@@ -271,7 +253,7 @@ class Bm25(SparseTextEmbeddingBase):
             embeddings.append(SparseEmbedding.from_dict(token_id2value))
         return embeddings
 
-    def _term_frequency(self, tokens: list[str]) -> dict[int, float]:
+    def _term_frequency(self, tokens: List[str]) -> Dict[int, float]:
         """Calculate the term frequency part of the BM25 formula.
 
         (
@@ -281,13 +263,13 @@ class Bm25(SparseTextEmbeddingBase):
         )
 
         Args:
-            tokens (list[str]): The list of tokens in the document.
+            tokens (List[str]): The list of tokens in the document.
 
         Returns:
-            dict[int, float]: The token_id to term frequency mapping.
+            Dict[int, float]: The token_id to term frequency mapping.
         """
-        tf_map: dict[int, float] = {}
-        counter: defaultdict[str, int] = defaultdict(int)
+        tf_map = {}
+        counter = defaultdict(int)
         for stemmed_token in tokens:
             counter[stemmed_token] += 1
 
@@ -305,9 +287,7 @@ class Bm25(SparseTextEmbeddingBase):
     def compute_token_id(cls, token: str) -> int:
         return abs(mmh3.hash(token))
 
-    def query_embed(
-        self, query: Union[str, Iterable[str]], **kwargs: Any
-    ) -> Iterable[SparseEmbedding]:
+    def query_embed(self, query: Union[str, Iterable[str]], **kwargs) -> Iterable[SparseEmbedding]:
         """To emulate BM25 behaviour, we don't need to use weights in the query, and
         it's enough to just hash the tokens and assign a weight of 1.0 to them.
         """
@@ -335,7 +315,7 @@ class Bm25Worker(Worker):
         self,
         model_name: str,
         cache_dir: str,
-        **kwargs: Any,
+        **kwargs,
     ):
         self.model = self.init_embedding(model_name, cache_dir, **kwargs)
 
@@ -343,13 +323,11 @@ class Bm25Worker(Worker):
     def start(cls, model_name: str, cache_dir: str, **kwargs: Any) -> "Bm25Worker":
         return cls(model_name=model_name, cache_dir=cache_dir, **kwargs)
 
-    def process(
-        self, items: Iterable[tuple[int, Any]]
-    ) -> Iterable[tuple[int, list[SparseEmbedding]]]:
+    def process(self, items: Iterable[Tuple[int, Any]]) -> Iterable[Tuple[int, Any]]:
         for idx, batch in items:
             onnx_output = self.model.raw_embed(batch)
             yield idx, onnx_output
 
     @staticmethod
-    def init_embedding(model_name: str, cache_dir: str, **kwargs: Any) -> Bm25:
+    def init_embedding(model_name: str, cache_dir: str, **kwargs) -> Bm25:
         return Bm25(model_name=model_name, cache_dir=cache_dir, **kwargs)

fastembed/sparse/bm42.py

@@ -1,7 +1,7 @@
 import math
 import string
 from pathlib import Path
-from typing import Any, Iterable, Optional, Sequence, Type, Union
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Type, Union
 
 import mmh3
 import numpy as np
@@ -15,20 +15,21 @@ from fastembed.sparse.sparse_embedding_base import (
     SparseTextEmbeddingBase,
 )
 from fastembed.text.onnx_text_model import OnnxTextModel, TextEmbeddingWorker
-from fastembed.common.model_description import SparseModelDescription, ModelSource
 
-supported_bm42_models: list[SparseModelDescription] = [
-    SparseModelDescription(
-        model="Qdrant/bm42-all-minilm-l6-v2-attentions",
-        vocab_size=30522,
-        description="Light sparse embedding model, which assigns an importance score to each token in the text",
-        license="apache-2.0",
-        size_in_GB=0.09,
-        sources=ModelSource(hf="Qdrant/all_miniLM_L6_v2_with_attentions"),
-        model_file="model.onnx",
-        additional_files=["stopwords.txt"],
-        requires_idf=True,
-    ),
+supported_bm42_models = [
+    {
+        "model": "Qdrant/bm42-all-minilm-l6-v2-attentions",
+        "vocab_size": 30522,
+        "description": "Light sparse embedding model, which assigns an importance score to each token in the text",
+        "license": "apache-2.0",
+        "size_in_GB": 0.09,
+        "sources": {
+            "hf": "Qdrant/all_miniLM_L6_v2_with_attentions",
+        },
+        "model_file": "model.onnx",
+        "additional_files": ["stopwords.txt"],
+        "requires_idf": True,
+    },
 ]
 
 MODEL_TO_LANGUAGE = {
@@ -62,11 +63,10 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
         providers: Optional[Sequence[OnnxProvider]] = None,
         alpha: float = 0.5,
         cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        device_ids: Optional[List[int]] = None,
         lazy_load: bool = False,
         device_id: Optional[int] = None,
-        specific_model_path: Optional[str] = None,
-        **kwargs: Any,
+        **kwargs,
     ):
         """
         Args:
@@ -81,12 +81,11 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
                 It is recommended to only change this parameter based on training data for a specific dataset.
             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
+            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.
-            specific_model_path (Optional[str], optional): The specific path to the onnx model dir if it should be imported from somewhere else
 
         Raises:
             ValueError: If the model_name is not in the format <org>/<model> e.g. BAAI/bge-base-en.
@@ -101,26 +100,24 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
         self.cuda = cuda
 
         # This device_id will be used if we need to load model in current process
-        self.device_id: Optional[int] = None
         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 = str(define_cache_dir(cache_dir))
+        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.model_description, self.cache_dir, local_files_only=self._local_files_only
         )
 
-        self.invert_vocab: dict[int, str] = {}
+        self.invert_vocab = {}
 
-        self.special_tokens: set[str] = set()
-        self.special_tokens_ids: set[int] = set()
+        self.special_tokens = set()
+        self.special_tokens_ids = set()
         self.punctuation = set(string.punctuation)
         self.stopwords = set(self._load_stopwords(self._model_dir))
         self.stemmer = SnowballStemmer(MODEL_TO_LANGUAGE[model_name])
@@ -132,29 +129,28 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
     def load_onnx_model(self) -> None:
         self._load_onnx_model(
             model_dir=self._model_dir,
-            model_file=self.model_description.model_file,
+            model_file=self.model_description["model_file"],
             threads=self.threads,
             providers=self.providers,
             cuda=self.cuda,
             device_id=self.device_id,
         )
-
-        for token, idx in self.tokenizer.get_vocab().items():  # type: ignore[union-attr]
+        for token, idx in self.tokenizer.get_vocab().items():
             self.invert_vocab[idx] = token
         self.special_tokens = set(self.special_token_to_id.keys())
         self.special_tokens_ids = set(self.special_token_to_id.values())
         self.stopwords = set(self._load_stopwords(self._model_dir))
 
-    def _filter_pair_tokens(self, tokens: list[tuple[str, Any]]) -> list[tuple[str, Any]]:
-        result: list[tuple[str, Any]] = []
+    def _filter_pair_tokens(self, tokens: List[Tuple[str, Any]]) -> List[Tuple[str, Any]]:
+        result = []
         for token, value in tokens:
             if token in self.stopwords or token in self.punctuation:
                 continue
             result.append((token, value))
         return result
 
-    def _stem_pair_tokens(self, tokens: list[tuple[str, Any]]) -> list[tuple[str, Any]]:
-        result: list[tuple[str, Any]] = []
+    def _stem_pair_tokens(self, tokens: List[Tuple[str, Any]]) -> List[Tuple[str, Any]]:
+        result = []
         for token, value in tokens:
             processed_token = self.stemmer.stem_word(token)
             result.append((processed_token, value))
@@ -162,22 +158,22 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
 
     @classmethod
     def _aggregate_weights(
-        cls, tokens: list[tuple[str, list[int]]], weights: list[float]
-    ) -> list[tuple[str, float]]:
-        result: list[tuple[str, float]] = []
+        cls, tokens: List[Tuple[str, List[int]]], weights: List[float]
+    ) -> List[Tuple[str, float]]:
+        result = []
         for token, idxs in tokens:
             sum_weight = sum(weights[idx] for idx in idxs)
             result.append((token, sum_weight))
         return result
 
     def _reconstruct_bpe(
-        self, bpe_tokens: Iterable[tuple[int, str]]
-    ) -> list[tuple[str, list[int]]]:
-        result: list[tuple[str, list[int]]] = []
-        acc: str = ""
-        acc_idx: list[int] = []
+        self, bpe_tokens: Iterable[Tuple[int, str]]
+    ) -> List[Tuple[str, List[int]]]:
+        result = []
+        acc = ""
+        acc_idx = []
 
-        continuing_subword_prefix = self.tokenizer.model.continuing_subword_prefix  # type: ignore[union-attr]
+        continuing_subword_prefix = self.tokenizer.model.continuing_subword_prefix
         continuing_subword_prefix_len = len(continuing_subword_prefix)
 
         for idx, token in bpe_tokens:
@@ -199,13 +195,13 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
 
         return result
 
-    def _rescore_vector(self, vector: dict[str, float]) -> dict[int, float]:
+    def _rescore_vector(self, vector: Dict[str, float]) -> Dict[int, float]:
         """
         Orders all tokens in the vector by their importance and generates a new score based on the importance order.
         So that the scoring doesn't depend on absolute values assigned by the model, but on the relative importance.
         """
 
-        new_vector: dict[int, float] = {}
+        new_vector = {}
 
         for token, value in vector.items():
             token_id = abs(mmh3.hash(token))
@@ -221,7 +217,7 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
         if output.input_ids is None:
             raise ValueError("input_ids must be provided for document post-processing")
 
-        token_ids_batch = output.input_ids.astype(int)
+        token_ids_batch = output.input_ids
 
         # attention_value shape: (batch_size, num_heads, num_tokens, num_tokens)
         pooled_attention = np.mean(output.model_output[:, :, 0], axis=1) * output.attention_mask
@@ -240,7 +236,7 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
 
             weighted = self._aggregate_weights(stemmed, attention_value)
 
-            max_token_weight: dict[str, float] = {}
+            max_token_weight = {}
 
             for token, weight in weighted:
                 max_token_weight[token] = max(max_token_weight.get(token, 0), weight)
@@ -250,16 +246,16 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
             yield SparseEmbedding.from_dict(rescored)
 
     @classmethod
-    def _list_supported_models(cls) -> list[SparseModelDescription]:
+    def list_supported_models(cls) -> List[Dict[str, Any]]:
         """Lists the supported models.
 
         Returns:
-            list[SparseModelDescription]: A list of SparseModelDescription objects containing the model information.
+            List[Dict[str, Any]]: A list of dictionaries containing the model information.
         """
         return supported_bm42_models
 
     @classmethod
-    def _load_stopwords(cls, model_dir: Path) -> list[str]:
+    def _load_stopwords(cls, model_dir: Path) -> List[str]:
         stopwords_path = model_dir / "stopwords.txt"
         if not stopwords_path.exists():
             return []
@@ -272,7 +268,7 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
         documents: Union[str, Iterable[str]],
         batch_size: int = 256,
         parallel: Optional[int] = None,
-        **kwargs: Any,
+        **kwargs,
     ) -> Iterable[SparseEmbedding]:
         """
         Encode a list of documents into list of embeddings.
@@ -302,16 +298,14 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
         )
 
     @classmethod
-    def _query_rehash(cls, tokens: Iterable[str]) -> dict[int, float]:
-        result: dict[int, float] = {}
+    def _query_rehash(cls, tokens: Iterable[str]) -> Dict[int, float]:
+        result = {}
         for token in tokens:
             token_id = abs(mmh3.hash(token))
             result[token_id] = 1.0
         return result
 
-    def query_embed(
-        self, query: Union[str, Iterable[str]], **kwargs: Any
-    ) -> Iterable[SparseEmbedding]:
+    def query_embed(self, query: Union[str, Iterable[str]], **kwargs) -> Iterable[SparseEmbedding]:
         """
         To emulate BM25 behaviour, we don't need to use smart weights in the query, and
         it's enough to just hash the tokens and assign a weight of 1.0 to them.
@@ -324,7 +318,7 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
             self.load_onnx_model()
 
         for text in query:
-            encoded = self.tokenizer.encode(text)  # type: ignore[union-attr]
+            encoded = self.tokenizer.encode(text)
             document_tokens_with_ids = enumerate(encoded.tokens)
             reconstructed = self._reconstruct_bpe(document_tokens_with_ids)
             filtered = self._filter_pair_tokens(reconstructed)
@@ -333,12 +327,12 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
             yield SparseEmbedding.from_dict(self._query_rehash(token for token, _ in stemmed))
 
     @classmethod
-    def _get_worker_class(cls) -> Type[TextEmbeddingWorker[SparseEmbedding]]:
+    def _get_worker_class(cls) -> Type[TextEmbeddingWorker]:
         return Bm42TextEmbeddingWorker
 
 
-class Bm42TextEmbeddingWorker(TextEmbeddingWorker[SparseEmbedding]):
-    def init_embedding(self, model_name: str, cache_dir: str, **kwargs: Any) -> Bm42:
+class Bm42TextEmbeddingWorker(TextEmbeddingWorker):
+    def init_embedding(self, model_name: str, cache_dir: str, **kwargs) -> Bm42:
         return Bm42(
             model_name=model_name,
             cache_dir=cache_dir,

fastembed/sparse/sparse_embedding_base.py

@@ -1,43 +1,40 @@
 from dataclasses import dataclass
-from typing import Iterable, Optional, Union, Any
+from typing import Dict, Iterable, Optional, Union
 
 import numpy as np
-from numpy.typing import NDArray
 
-from fastembed.common.model_description import SparseModelDescription
-from fastembed.common.types import NumpyArray
 from fastembed.common.model_management import ModelManagement
 
 
 @dataclass
 class SparseEmbedding:
-    values: NumpyArray
-    indices: Union[NDArray[np.int64], NDArray[np.int32]]
+    values: np.ndarray
+    indices: np.ndarray
 
-    def as_object(self) -> dict[str, NumpyArray]:
+    def as_object(self) -> Dict[str, np.ndarray]:
         return {
             "values": self.values,
             "indices": self.indices,
         }
 
-    def as_dict(self) -> dict[int, float]:
-        return {int(i): float(v) for i, v in zip(self.indices, self.values)}  # type: ignore
+    def as_dict(self) -> Dict[int, float]:
+        return {i: v for i, v in zip(self.indices, self.values)}
 
     @classmethod
-    def from_dict(cls, data: dict[int, float]) -> "SparseEmbedding":
+    def from_dict(cls, data: Dict[int, float]) -> "SparseEmbedding":
         if len(data) == 0:
             return cls(values=np.array([]), indices=np.array([]))
         indices, values = zip(*data.items())
         return cls(values=np.array(values), indices=np.array(indices))
 
 
-class SparseTextEmbeddingBase(ModelManagement[SparseModelDescription]):
+class SparseTextEmbeddingBase(ModelManagement):
     def __init__(
         self,
         model_name: str,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
-        **kwargs: Any,
+        **kwargs,
     ):
         self.model_name = model_name
         self.cache_dir = cache_dir
@@ -49,11 +46,13 @@ class SparseTextEmbeddingBase(ModelManagement[SparseModelDescription]):
         documents: Union[str, Iterable[str]],
         batch_size: int = 256,
         parallel: Optional[int] = None,
-        **kwargs: Any,
+        **kwargs,
     ) -> Iterable[SparseEmbedding]:
         raise NotImplementedError()
 
-    def passage_embed(self, texts: Iterable[str], **kwargs: Any) -> Iterable[SparseEmbedding]:
+    def passage_embed(
+        self, texts: Iterable[str], **kwargs
+    ) -> Iterable[SparseEmbedding]:
         """
         Embeds a list of text passages into a list of embeddings.
 
@@ -69,7 +68,7 @@ class SparseTextEmbeddingBase(ModelManagement[SparseModelDescription]):
         yield from self.embed(texts, **kwargs)
 
     def query_embed(
-        self, query: Union[str, Iterable[str]], **kwargs: Any
+        self, query: Union[str, Iterable[str]], **kwargs
     ) -> Iterable[SparseEmbedding]:
         """
         Embeds queries
@@ -84,5 +83,5 @@ class SparseTextEmbeddingBase(ModelManagement[SparseModelDescription]):
         # This is model-specific, so that different models can have specialized implementations
         if isinstance(query, str):
             yield from self.embed([query], **kwargs)
-        else:
+        if isinstance(query, Iterable):
             yield from self.embed(query, **kwargs)

fastembed/sparse/sparse_text_embedding.py

@@ -1,5 +1,4 @@
-from typing import Any, Iterable, Optional, Sequence, Type, Union
-from dataclasses import asdict
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Type, Union
 
 from fastembed.common import OnnxProvider
 from fastembed.sparse.bm25 import Bm25
@@ -10,19 +9,18 @@ from fastembed.sparse.sparse_embedding_base import (
 )
 from fastembed.sparse.splade_pp import SpladePP
 import warnings
-from fastembed.common.model_description import SparseModelDescription
 
 
 class SparseTextEmbedding(SparseTextEmbeddingBase):
-    EMBEDDINGS_REGISTRY: list[Type[SparseTextEmbeddingBase]] = [SpladePP, Bm42, Bm25]
+    EMBEDDINGS_REGISTRY: List[Type[SparseTextEmbeddingBase]] = [SpladePP, Bm42, Bm25]
 
     @classmethod
-    def list_supported_models(cls) -> list[dict[str, Any]]:
+    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.
+            List[Dict[str, Any]]: A list of dictionaries containing the model information.
 
             Example:
                 ```
@@ -40,13 +38,9 @@ class SparseTextEmbedding(SparseTextEmbeddingBase):
                 ]
                 ```
         """
-        return [asdict(model) for model in cls._list_supported_models()]
-
-    @classmethod
-    def _list_supported_models(cls) -> list[SparseModelDescription]:
-        result: list[SparseModelDescription] = []
+        result = []
         for embedding in cls.EMBEDDINGS_REGISTRY:
-            result.extend(embedding._list_supported_models())
+            result.extend(embedding.list_supported_models())
         return result
 
     def __init__(
@@ -56,9 +50,9 @@ class SparseTextEmbedding(SparseTextEmbeddingBase):
         threads: Optional[int] = None,
         providers: Optional[Sequence[OnnxProvider]] = None,
         cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        device_ids: Optional[List[int]] = None,
         lazy_load: bool = False,
-        **kwargs: Any,
+        **kwargs,
     ):
         super().__init__(model_name, cache_dir, threads, **kwargs)
         if model_name == "prithvida/Splade_PP_en_v1":
@@ -71,8 +65,8 @@ class SparseTextEmbedding(SparseTextEmbeddingBase):
             model_name = "prithivida/Splade_PP_en_v1"
 
         for EMBEDDING_MODEL_TYPE in self.EMBEDDINGS_REGISTRY:
-            supported_models = EMBEDDING_MODEL_TYPE._list_supported_models()
-            if any(model_name.lower() == model.model.lower() for model in supported_models):
+            supported_models = EMBEDDING_MODEL_TYPE.list_supported_models()
+            if any(model_name.lower() == model["model"].lower() for model in supported_models):
                 self.model = EMBEDDING_MODEL_TYPE(
                     model_name,
                     cache_dir,
@@ -95,7 +89,7 @@ class SparseTextEmbedding(SparseTextEmbeddingBase):
         documents: Union[str, Iterable[str]],
         batch_size: int = 256,
         parallel: Optional[int] = None,
-        **kwargs: Any,
+        **kwargs,
     ) -> Iterable[SparseEmbedding]:
         """
         Encode a list of documents into list of embeddings.
@@ -114,9 +108,7 @@ class SparseTextEmbedding(SparseTextEmbeddingBase):
         """
         yield from self.model.embed(documents, batch_size, parallel, **kwargs)
 
-    def query_embed(
-        self, query: Union[str, Iterable[str]], **kwargs: Any
-    ) -> Iterable[SparseEmbedding]:
+    def query_embed(self, query: Union[str, Iterable[str]], **kwargs) -> Iterable[SparseEmbedding]:
         """
         Embeds queries
 

fastembed/sparse/splade_pp.py

@@ -1,4 +1,4 @@
-from typing import Any, Iterable, Optional, Sequence, Type, Union
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Type, Union
 
 import numpy as np
 from fastembed.common import OnnxProvider
@@ -9,27 +9,30 @@ from fastembed.sparse.sparse_embedding_base import (
     SparseTextEmbeddingBase,
 )
 from fastembed.text.onnx_text_model import OnnxTextModel, TextEmbeddingWorker
-from fastembed.common.model_description import SparseModelDescription, ModelSource
 
-supported_splade_models: list[SparseModelDescription] = [
-    SparseModelDescription(
-        model="prithivida/Splade_PP_en_v1",
-        vocab_size=30522,
-        description="Independent Implementation of SPLADE++ Model for English.",
-        license="apache-2.0",
-        size_in_GB=0.532,
-        sources=ModelSource(hf="Qdrant/SPLADE_PP_en_v1"),
-        model_file="model.onnx",
-    ),
-    SparseModelDescription(
-        model="prithvida/Splade_PP_en_v1",
-        vocab_size=30522,
-        description="Independent Implementation of SPLADE++ Model for English.",
-        license="apache-2.0",
-        size_in_GB=0.532,
-        sources=ModelSource(hf="Qdrant/SPLADE_PP_en_v1"),
-        model_file="model.onnx",
-    ),
+supported_splade_models = [
+    {
+        "model": "prithivida/Splade_PP_en_v1",
+        "vocab_size": 30522,
+        "description": "Independent Implementation of SPLADE++ Model for English.",
+        "license": "apache-2.0",
+        "size_in_GB": 0.532,
+        "sources": {
+            "hf": "Qdrant/SPLADE_PP_en_v1",
+        },
+        "model_file": "model.onnx",
+    },
+    {
+        "model": "prithvida/Splade_PP_en_v1",
+        "vocab_size": 30522,
+        "description": "Independent Implementation of SPLADE++ Model for English.",
+        "license": "apache-2.0",
+        "size_in_GB": 0.532,
+        "sources": {
+            "hf": "Qdrant/SPLADE_PP_en_v1",
+        },
+        "model_file": "model.onnx",
+    },
 ]
 
 
@@ -52,11 +55,11 @@ class SpladePP(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
             yield SparseEmbedding(values=scores, indices=indices)
 
     @classmethod
-    def _list_supported_models(cls) -> list[SparseModelDescription]:
+    def list_supported_models(cls) -> List[Dict[str, Any]]:
         """Lists the supported models.
 
         Returns:
-            list[SparseModelDescription]: A list of SparseModelDescription objects containing the model information.
+            List[Dict[str, Any]]: A list of dictionaries containing the model information.
         """
         return supported_splade_models
 
@@ -67,11 +70,10 @@ class SpladePP(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
         threads: Optional[int] = None,
         providers: Optional[Sequence[OnnxProvider]] = None,
         cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        device_ids: Optional[List[int]] = None,
         lazy_load: bool = False,
         device_id: Optional[int] = None,
-        specific_model_path: Optional[str] = None,
-        **kwargs: Any,
+        **kwargs,
     ):
         """
         Args:
@@ -84,12 +86,11 @@ class SpladePP(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
                 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
+            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.
-            specific_model_path (Optional[str], optional): The specific path to the onnx model dir if it should be imported from somewhere else
 
         Raises:
             ValueError: If the model_name is not in the format <org>/<model> e.g. BAAI/bge-base-en.
@@ -103,20 +104,18 @@ class SpladePP(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
         self.cuda = cuda
 
         # This device_id will be used if we need to load model in current process
-        self.device_id: Optional[int] = None
         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 = str(define_cache_dir(cache_dir))
+        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.model_description, self.cache_dir, local_files_only=self._local_files_only
         )
 
         if not self.lazy_load:
@@ -125,7 +124,7 @@ class SpladePP(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
     def load_onnx_model(self) -> None:
         self._load_onnx_model(
             model_dir=self._model_dir,
-            model_file=self.model_description.model_file,
+            model_file=self.model_description["model_file"],
             threads=self.threads,
             providers=self.providers,
             cuda=self.cuda,
@@ -137,7 +136,7 @@ class SpladePP(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
         documents: Union[str, Iterable[str]],
         batch_size: int = 256,
         parallel: Optional[int] = None,
-        **kwargs: Any,
+        **kwargs,
     ) -> Iterable[SparseEmbedding]:
         """
         Encode a list of documents into list of embeddings.
@@ -167,12 +166,12 @@ class SpladePP(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
         )
 
     @classmethod
-    def _get_worker_class(cls) -> Type[TextEmbeddingWorker[SparseEmbedding]]:
+    def _get_worker_class(cls) -> Type[TextEmbeddingWorker]:
         return SpladePPEmbeddingWorker
 
 
-class SpladePPEmbeddingWorker(TextEmbeddingWorker[SparseEmbedding]):
-    def init_embedding(self, model_name: str, cache_dir: str, **kwargs: Any) -> SpladePP:
+class SpladePPEmbeddingWorker(TextEmbeddingWorker):
+    def init_embedding(self, model_name: str, cache_dir: str, **kwargs) -> SpladePP:
         return SpladePP(
             model_name=model_name,
             cache_dir=cache_dir,

fastembed/sparse/utils/tokenizer.py

@@ -1,11 +1,11 @@
 # This code is a modified copy of the `NLTKWordTokenizer` class from `NLTK` library.
 
 import re
+from typing import List
 
 
 class SimpleTokenizer:
-    @staticmethod
-    def tokenize(text: str) -> list[str]:
+    def tokenize(text: str) -> List[str]:
         text = re.sub(r"[^\w]", " ", text.lower())
         text = re.sub(r"\s+", " ", text)
 
@@ -80,7 +80,7 @@ class WordTokenizer:
     ]
 
     @classmethod
-    def tokenize(cls, text: str) -> list[str]:
+    def tokenize(cls, text: str) -> List[str]:
         """Return a tokenized copy of `text`.
 
         >>> s = '''Good muffins cost $3.88 (roughly 3,36 euros)\nin New York.'''

fastembed/text/clip_embedding.py

@@ -1,41 +1,41 @@
-from typing import Any, Iterable, Type
+from typing import Any, Dict, Iterable, List, Type
+
+import numpy as np
 
-from fastembed.common.types import NumpyArray
 from fastembed.common.onnx_model import OnnxOutputContext
 from fastembed.text.onnx_embedding import OnnxTextEmbedding, OnnxTextEmbeddingWorker
-from fastembed.common.model_description import DenseModelDescription, ModelSource
+from fastembed.text.onnx_text_model import TextEmbeddingWorker
 
-supported_clip_models: list[DenseModelDescription] = [
-    DenseModelDescription(
-        model="Qdrant/clip-ViT-B-32-text",
-        dim=512,
-        description=(
-            "Text embeddings, Multimodal (text&image), English, 77 input tokens truncation, "
-            "Prefixes for queries/documents: not necessary, 2021 year"
-        ),
-        license="mit",
-        size_in_GB=0.25,
-        sources=ModelSource(hf="Qdrant/clip-ViT-B-32-text"),
-        model_file="model.onnx",
-    ),
+supported_clip_models = [
+    {
+        "model": "Qdrant/clip-ViT-B-32-text",
+        "dim": 512,
+        "description": "Text embeddings, Multimodal (text&image), English, 77 input tokens truncation, Prefixes for queries/documents: not necessary, 2021 year",
+        "license": "mit",
+        "size_in_GB": 0.25,
+        "sources": {
+            "hf": "Qdrant/clip-ViT-B-32-text",
+        },
+        "model_file": "model.onnx",
+    },
 ]
 
 
 class CLIPOnnxEmbedding(OnnxTextEmbedding):
     @classmethod
-    def _get_worker_class(cls) -> Type[OnnxTextEmbeddingWorker]:
+    def _get_worker_class(cls) -> Type[TextEmbeddingWorker]:
         return CLIPEmbeddingWorker
 
     @classmethod
-    def _list_supported_models(cls) -> list[DenseModelDescription]:
+    def list_supported_models(cls) -> List[Dict[str, Any]]:
         """Lists the supported models.
 
         Returns:
-            list[DenseModelDescription]: A list of DenseModelDescription objects containing the model information.
+            List[Dict[str, Any]]: A list of dictionaries containing the model information.
         """
         return supported_clip_models
 
-    def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[NumpyArray]:
+    def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[np.ndarray]:
         return output.model_output
 
 
@@ -44,7 +44,7 @@ class CLIPEmbeddingWorker(OnnxTextEmbeddingWorker):
         self,
         model_name: str,
         cache_dir: str,
-        **kwargs: Any,
+        **kwargs,
     ) -> OnnxTextEmbedding:
         return CLIPOnnxEmbedding(
             model_name=model_name,

fastembed/text/custom_text_embedding.py

@@ -1,91 +0,0 @@
-from typing import Optional, Sequence, Any, Iterable
-
-from dataclasses import dataclass
-
-import numpy as np
-from numpy.typing import NDArray
-
-from fastembed.common import OnnxProvider
-from fastembed.common.model_description import (
-    PoolingType,
-    DenseModelDescription,
-)
-from fastembed.common.onnx_model import OnnxOutputContext
-from fastembed.common.types import NumpyArray
-from fastembed.common.utils import normalize, mean_pooling
-from fastembed.text.onnx_embedding import OnnxTextEmbedding
-
-
-@dataclass(frozen=True)
-class PostprocessingConfig:
-    pooling: PoolingType
-    normalization: bool
-
-
-class CustomTextEmbedding(OnnxTextEmbedding):
-    SUPPORTED_MODELS: list[DenseModelDescription] = []
-    POSTPROCESSING_MAPPING: dict[str, PostprocessingConfig] = {}
-
-    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: Any,
-    ):
-        super().__init__(
-            model_name=model_name,
-            cache_dir=cache_dir,
-            threads=threads,
-            providers=providers,
-            cuda=cuda,
-            device_ids=device_ids,
-            lazy_load=lazy_load,
-            device_id=device_id,
-            specific_model_path=specific_model_path,
-            **kwargs,
-        )
-        self._pooling = self.POSTPROCESSING_MAPPING[model_name].pooling
-        self._normalization = self.POSTPROCESSING_MAPPING[model_name].normalization
-
-    @classmethod
-    def _list_supported_models(cls) -> list[DenseModelDescription]:
-        return cls.SUPPORTED_MODELS
-
-    def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[NumpyArray]:
-        return self._normalize(self._pool(output.model_output, output.attention_mask))
-
-    def _pool(
-        self, embeddings: NumpyArray, attention_mask: Optional[NDArray[np.int64]] = None
-    ) -> NumpyArray:
-        if self._pooling == PoolingType.CLS:
-            return embeddings[:, 0]
-
-        if self._pooling == PoolingType.MEAN:
-            if attention_mask is None:
-                raise ValueError("attention_mask must be provided for mean pooling")
-            return mean_pooling(embeddings, attention_mask)
-
-        if self._pooling == PoolingType.DISABLED:
-            return embeddings
-
-    def _normalize(self, embeddings: NumpyArray) -> NumpyArray:
-        return normalize(embeddings) if self._normalization else embeddings
-
-    @classmethod
-    def add_model(
-        cls,
-        model_description: DenseModelDescription,
-        pooling: PoolingType,
-        normalization: bool,
-    ) -> None:
-        cls.SUPPORTED_MODELS.append(model_description)
-        cls.POSTPROCESSING_MAPPING[model_description.model] = PostprocessingConfig(
-            pooling=pooling, normalization=normalization
-        )

fastembed/text/e5_onnx_embedding.py

@@ -0,0 +1,72 @@
+from typing import Any, Dict, List, Type
+
+import numpy as np
+
+from fastembed.text.onnx_embedding import OnnxTextEmbedding, OnnxTextEmbeddingWorker
+from fastembed.text.onnx_text_model import TextEmbeddingWorker
+
+supported_multilingual_e5_models = [
+    {
+        "model": "intfloat/multilingual-e5-large",
+        "dim": 1024,
+        "description": "Text embeddings, Unimodal (text), Multilingual (~100 languages), 512 input tokens truncation, Prefixes for queries/documents: necessary, 2024 year.",
+        "license": "mit",
+        "size_in_GB": 2.24,
+        "sources": {
+            "url": "https://storage.googleapis.com/qdrant-fastembed/fast-multilingual-e5-large.tar.gz",
+            "hf": "qdrant/multilingual-e5-large-onnx",
+        },
+        "model_file": "model.onnx",
+        "additional_files": ["model.onnx_data"],
+    },
+    {
+        "model": "sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
+        "dim": 768,
+        "description": "Text embeddings, Unimodal (text), Multilingual (~50 languages), 384 input tokens truncation, Prefixes for queries/documents: not necessary, 2021 year.",
+        "license": "apache-2.0",
+        "size_in_GB": 1.00,
+        "sources": {
+            "hf": "xenova/paraphrase-multilingual-mpnet-base-v2",
+        },
+        "model_file": "onnx/model.onnx",
+    },
+]
+
+
+class E5OnnxEmbedding(OnnxTextEmbedding):
+    @classmethod
+    def _get_worker_class(cls) -> Type["TextEmbeddingWorker"]:
+        return E5OnnxEmbeddingWorker
+
+    @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_multilingual_e5_models
+
+    def _preprocess_onnx_input(
+        self, onnx_input: Dict[str, np.ndarray], **kwargs
+    ) -> Dict[str, np.ndarray]:
+        """
+        Preprocess the onnx input.
+        """
+        onnx_input.pop("token_type_ids", None)
+        return onnx_input
+
+
+class E5OnnxEmbeddingWorker(OnnxTextEmbeddingWorker):
+    def init_embedding(
+        self,
+        model_name: str,
+        cache_dir: str,
+        **kwargs,
+    ) -> E5OnnxEmbedding:
+        return E5OnnxEmbedding(
+            model_name=model_name,
+            cache_dir=cache_dir,
+            threads=1,
+            **kwargs,
+        )

fastembed/text/multitask_embedding.py

@@ -1,100 +0,0 @@
-from enum import Enum
-from typing import Any, Type, Iterable, Union, Optional
-
-import numpy as np
-
-from fastembed.common.types import NumpyArray
-from fastembed.text.pooled_normalized_embedding import PooledNormalizedEmbedding
-from fastembed.text.onnx_embedding import OnnxTextEmbeddingWorker
-from fastembed.common.model_description import DenseModelDescription, ModelSource
-
-supported_multitask_models: list[DenseModelDescription] = [
-    DenseModelDescription(
-        model="jinaai/jina-embeddings-v3",
-        dim=1024,
-        tasks={
-            "retrieval.query": 0,
-            "retrieval.passage": 1,
-            "separation": 2,
-            "classification": 3,
-            "text-matching": 4,
-        },
-        description=(
-            "Multi-task unimodal (text) embedding model, multi-lingual (~100), "
-            "1024 tokens truncation, and 8192 sequence length. Prefixes for queries/documents: not necessary, 2024 year."
-        ),
-        license="cc-by-nc-4.0",
-        size_in_GB=2.29,
-        sources=ModelSource(hf="jinaai/jina-embeddings-v3"),
-        model_file="onnx/model.onnx",
-        additional_files=["onnx/model.onnx_data"],
-    ),
-]
-
-
-class Task(int, Enum):
-    RETRIEVAL_QUERY = 0
-    RETRIEVAL_PASSAGE = 1
-    SEPARATION = 2
-    CLASSIFICATION = 3
-    TEXT_MATCHING = 4
-
-
-class JinaEmbeddingV3(PooledNormalizedEmbedding):
-    PASSAGE_TASK = Task.RETRIEVAL_PASSAGE
-    QUERY_TASK = Task.RETRIEVAL_QUERY
-
-    def __init__(self, *args: Any, **kwargs: Any):
-        super().__init__(*args, **kwargs)
-        self.current_task_id: Union[Task, int] = self.PASSAGE_TASK
-
-    @classmethod
-    def _get_worker_class(cls) -> Type[OnnxTextEmbeddingWorker]:
-        return JinaEmbeddingV3Worker
-
-    @classmethod
-    def _list_supported_models(cls) -> list[DenseModelDescription]:
-        return supported_multitask_models
-
-    def _preprocess_onnx_input(
-        self, onnx_input: dict[str, NumpyArray], **kwargs: Any
-    ) -> dict[str, NumpyArray]:
-        onnx_input["task_id"] = np.array(self.current_task_id, dtype=np.int64)
-        return onnx_input
-
-    def embed(
-        self,
-        documents: Union[str, Iterable[str]],
-        batch_size: int = 256,
-        parallel: Optional[int] = None,
-        task_id: int = PASSAGE_TASK,
-        **kwargs: Any,
-    ) -> Iterable[NumpyArray]:
-        self.current_task_id = task_id
-        kwargs["task_id"] = task_id
-        yield from super().embed(documents, batch_size, parallel, **kwargs)
-
-    def query_embed(self, query: Union[str, Iterable[str]], **kwargs: Any) -> Iterable[NumpyArray]:
-        self.current_task_id = self.QUERY_TASK
-        yield from super().embed(query, **kwargs)
-
-    def passage_embed(self, texts: Iterable[str], **kwargs: Any) -> Iterable[NumpyArray]:
-        self.current_task_id = self.PASSAGE_TASK
-        yield from super().embed(texts, **kwargs)
-
-
-class JinaEmbeddingV3Worker(OnnxTextEmbeddingWorker):
-    def init_embedding(
-        self,
-        model_name: str,
-        cache_dir: str,
-        **kwargs: Any,
-    ) -> JinaEmbeddingV3:
-        model = JinaEmbeddingV3(
-            model_name=model_name,
-            cache_dir=cache_dir,
-            threads=1,
-            **kwargs,
-        )
-        model.current_task_id = kwargs["task_id"]
-        return model

fastembed/text/onnx_embedding.py

@@ -1,198 +1,182 @@
-from typing import Any, Iterable, Optional, Sequence, Type, Union
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Type, Union
 
-from fastembed.common.types import NumpyArray, OnnxProvider
+import numpy as np
+
+from fastembed.common import OnnxProvider
 from fastembed.common.onnx_model import OnnxOutputContext
 from fastembed.common.utils import define_cache_dir, normalize
 from fastembed.text.onnx_text_model import OnnxTextModel, TextEmbeddingWorker
 from fastembed.text.text_embedding_base import TextEmbeddingBase
-from fastembed.common.model_description import DenseModelDescription, ModelSource
 
-supported_onnx_models: list[DenseModelDescription] = [
-    DenseModelDescription(
-        model="BAAI/bge-base-en",
-        dim=768,
-        description=(
-            "Text embeddings, Unimodal (text), English, 512 input tokens truncation, "
-            "Prefixes for queries/documents: necessary, 2023 year."
-        ),
-        license="mit",
-        size_in_GB=0.42,
-        sources=ModelSource(
-            hf="Qdrant/fast-bge-base-en",
-            url="https://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en.tar.gz",
-            _deprecated_tar_struct=True,
-        ),
-        model_file="model_optimized.onnx",
-    ),
-    DenseModelDescription(
-        model="BAAI/bge-base-en-v1.5",
-        dim=768,
-        description=(
-            "Text embeddings, Unimodal (text), English, 512 input tokens truncation, "
-            "Prefixes for queries/documents: not so necessary, 2023 year."
-        ),
-        license="mit",
-        size_in_GB=0.21,
-        sources=ModelSource(
-            hf="qdrant/bge-base-en-v1.5-onnx-q",
-            url="https://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en-v1.5.tar.gz",
-            _deprecated_tar_struct=True,
-        ),
-        model_file="model_optimized.onnx",
-    ),
-    DenseModelDescription(
-        model="BAAI/bge-large-en-v1.5",
-        dim=1024,
-        description=(
-            "Text embeddings, Unimodal (text), English, 512 input tokens truncation, "
-            "Prefixes for queries/documents: not so necessary, 2023 year."
-        ),
-        license="mit",
-        size_in_GB=1.20,
-        sources=ModelSource(hf="qdrant/bge-large-en-v1.5-onnx"),
-        model_file="model.onnx",
-    ),
-    DenseModelDescription(
-        model="BAAI/bge-small-en",
-        dim=384,
-        description=(
-            "Text embeddings, Unimodal (text), English, 512 input tokens truncation, "
-            "Prefixes for queries/documents: necessary, 2023 year."
-        ),
-        license="mit",
-        size_in_GB=0.13,
-        sources=ModelSource(
-            hf="Qdrant/bge-small-en",
-            url="https://storage.googleapis.com/qdrant-fastembed/BAAI-bge-small-en.tar.gz",
-            _deprecated_tar_struct=True,
-        ),
-        model_file="model_optimized.onnx",
-    ),
-    DenseModelDescription(
-        model="BAAI/bge-small-en-v1.5",
-        dim=384,
-        description=(
-            "Text embeddings, Unimodal (text), English, 512 input tokens truncation, "
-            "Prefixes for queries/documents: not so necessary, 2023 year."
-        ),
-        license="mit",
-        size_in_GB=0.067,
-        sources=ModelSource(hf="qdrant/bge-small-en-v1.5-onnx-q"),
-        model_file="model_optimized.onnx",
-    ),
-    DenseModelDescription(
-        model="BAAI/bge-small-zh-v1.5",
-        dim=512,
-        description=(
-            "Text embeddings, Unimodal (text), Chinese, 512 input tokens truncation, "
-            "Prefixes for queries/documents: not so necessary, 2023 year."
-        ),
-        license="mit",
-        size_in_GB=0.09,
-        sources=ModelSource(
-            hf="Qdrant/bge-small-zh-v1.5",
-            url="https://storage.googleapis.com/qdrant-fastembed/fast-bge-small-zh-v1.5.tar.gz",
-            _deprecated_tar_struct=True,
-        ),
-        model_file="model_optimized.onnx",
-    ),
-    DenseModelDescription(
-        model="mixedbread-ai/mxbai-embed-large-v1",
-        dim=1024,
-        description=(
-            "Text embeddings, Unimodal (text), English, 512 input tokens truncation, "
-            "Prefixes for queries/documents: necessary, 2024 year."
-        ),
-        license="apache-2.0",
-        size_in_GB=0.64,
-        sources=ModelSource(hf="mixedbread-ai/mxbai-embed-large-v1"),
-        model_file="onnx/model.onnx",
-    ),
-    DenseModelDescription(
-        model="snowflake/snowflake-arctic-embed-xs",
-        dim=384,
-        description=(
-            "Text embeddings, Unimodal (text), English, 512 input tokens truncation, "
-            "Prefixes for queries/documents: necessary, 2024 year."
-        ),
-        license="apache-2.0",
-        size_in_GB=0.09,
-        sources=ModelSource(hf="snowflake/snowflake-arctic-embed-xs"),
-        model_file="onnx/model.onnx",
-    ),
-    DenseModelDescription(
-        model="snowflake/snowflake-arctic-embed-s",
-        dim=384,
-        description=(
-            "Text embeddings, Unimodal (text), English, 512 input tokens truncation, "
-            "Prefixes for queries/documents: necessary, 2024 year."
-        ),
-        license="apache-2.0",
-        size_in_GB=0.13,
-        sources=ModelSource(hf="snowflake/snowflake-arctic-embed-s"),
-        model_file="onnx/model.onnx",
-    ),
-    DenseModelDescription(
-        model="snowflake/snowflake-arctic-embed-m",
-        dim=768,
-        description=(
-            "Text embeddings, Unimodal (text), English, 512 input tokens truncation, "
-            "Prefixes for queries/documents: necessary, 2024 year."
-        ),
-        license="apache-2.0",
-        size_in_GB=0.43,
-        sources=ModelSource(hf="Snowflake/snowflake-arctic-embed-m"),
-        model_file="onnx/model.onnx",
-    ),
-    DenseModelDescription(
-        model="snowflake/snowflake-arctic-embed-m-long",
-        dim=768,
-        description=(
-            "Text embeddings, Unimodal (text), English, 2048 input tokens truncation, "
-            "Prefixes for queries/documents: necessary, 2024 year."
-        ),
-        license="apache-2.0",
-        size_in_GB=0.54,
-        sources=ModelSource(hf="snowflake/snowflake-arctic-embed-m-long"),
-        model_file="onnx/model.onnx",
-    ),
-    DenseModelDescription(
-        model="snowflake/snowflake-arctic-embed-l",
-        dim=1024,
-        description=(
-            "Text embeddings, Unimodal (text), English, 512 input tokens truncation, "
-            "Prefixes for queries/documents: necessary, 2024 year."
-        ),
-        license="apache-2.0",
-        size_in_GB=1.02,
-        sources=ModelSource(hf="snowflake/snowflake-arctic-embed-l"),
-        model_file="onnx/model.onnx",
-    ),
-    DenseModelDescription(
-        model="jinaai/jina-clip-v1",
-        dim=768,
-        description=(
-            "Text embeddings, Multimodal (text&image), English, Prefixes for queries/documents: "
-            "not necessary, 2024 year"
-        ),
-        license="apache-2.0",
-        size_in_GB=0.55,
-        sources=ModelSource(hf="jinaai/jina-clip-v1"),
-        model_file="onnx/text_model.onnx",
-    ),
+supported_onnx_models = [
+    {
+        "model": "BAAI/bge-base-en",
+        "dim": 768,
+        "description": "Text embeddings, Unimodal (text), English, 512 input tokens truncation, Prefixes for queries/documents: necessary, 2023 year.",
+        "license": "mit",
+        "size_in_GB": 0.42,
+        "sources": {
+            "url": "https://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en.tar.gz",
+        },
+        "model_file": "model_optimized.onnx",
+    },
+    {
+        "model": "BAAI/bge-base-en-v1.5",
+        "dim": 768,
+        "description": "Text embeddings, Unimodal (text), English, 512 input tokens truncation, Prefixes for queries/documents: not so necessary, 2023 year.",
+        "license": "mit",
+        "size_in_GB": 0.21,
+        "sources": {
+            "url": "https://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en-v1.5.tar.gz",
+            "hf": "qdrant/bge-base-en-v1.5-onnx-q",
+        },
+        "model_file": "model_optimized.onnx",
+    },
+    {
+        "model": "BAAI/bge-large-en-v1.5",
+        "dim": 1024,
+        "description": "Text embeddings, Unimodal (text), English, 512 input tokens truncation, Prefixes for queries/documents: not so necessary, 2023 year.",
+        "license": "mit",
+        "size_in_GB": 1.20,
+        "sources": {
+            "hf": "qdrant/bge-large-en-v1.5-onnx",
+        },
+        "model_file": "model.onnx",
+    },
+    {
+        "model": "BAAI/bge-small-en",
+        "dim": 384,
+        "description": "Text embeddings, Unimodal (text), English, 512 input tokens truncation, Prefixes for queries/documents: necessary, 2023 year.",
+        "license": "mit",
+        "size_in_GB": 0.13,
+        "sources": {
+            "url": "https://storage.googleapis.com/qdrant-fastembed/BAAI-bge-small-en.tar.gz",
+        },
+        "model_file": "model_optimized.onnx",
+    },
+    {
+        "model": "BAAI/bge-small-en-v1.5",
+        "dim": 384,
+        "description": "Text embeddings, Unimodal (text), English, 512 input tokens truncation, Prefixes for queries/documents: not so necessary, 2023 year.",
+        "license": "mit",
+        "size_in_GB": 0.067,
+        "sources": {
+            "hf": "qdrant/bge-small-en-v1.5-onnx-q",
+        },
+        "model_file": "model_optimized.onnx",
+    },
+    {
+        "model": "BAAI/bge-small-zh-v1.5",
+        "dim": 512,
+        "description": "Text embeddings, Unimodal (text), Chinese, 512 input tokens truncation, Prefixes for queries/documents: not so necessary, 2023 year.",
+        "license": "mit",
+        "size_in_GB": 0.09,
+        "sources": {
+            "url": "https://storage.googleapis.com/qdrant-fastembed/fast-bge-small-zh-v1.5.tar.gz",
+        },
+        "model_file": "model_optimized.onnx",
+    },
+    {
+        "model": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+        "dim": 384,
+        "description": "Text embeddings, Unimodal (text), Multilingual (~50 languages), 512 input tokens truncation, Prefixes for queries/documents: not necessary, 2019 year.",
+        "license": "apache-2.0",
+        "size_in_GB": 0.22,
+        "sources": {
+            "hf": "qdrant/paraphrase-multilingual-MiniLM-L12-v2-onnx-Q",
+        },
+        "model_file": "model_optimized.onnx",
+    },
+    {
+        "model": "thenlper/gte-large",
+        "dim": 1024,
+        "description": "Text embeddings, Unimodal (text), English, 512 input tokens truncation, Prefixes for queries/documents: not necessary, 2023 year.",
+        "license": "mit",
+        "size_in_GB": 1.20,
+        "sources": {
+            "hf": "qdrant/gte-large-onnx",
+        },
+        "model_file": "model.onnx",
+    },
+    {
+        "model": "mixedbread-ai/mxbai-embed-large-v1",
+        "dim": 1024,
+        "description": "Text embeddings, Unimodal (text), English, 512 input tokens truncation, Prefixes for queries/documents: necessary, 2024 year.",
+        "license": "apache-2.0",
+        "size_in_GB": 0.64,
+        "sources": {
+            "hf": "mixedbread-ai/mxbai-embed-large-v1",
+        },
+        "model_file": "onnx/model.onnx",
+    },
+    {
+        "model": "snowflake/snowflake-arctic-embed-xs",
+        "dim": 384,
+        "description": "Text embeddings, Unimodal (text), English, 512 input tokens truncation, Prefixes for queries/documents: necessary, 2024 year.",
+        "license": "apache-2.0",
+        "size_in_GB": 0.09,
+        "sources": {
+            "hf": "snowflake/snowflake-arctic-embed-xs",
+        },
+        "model_file": "onnx/model.onnx",
+    },
+    {
+        "model": "snowflake/snowflake-arctic-embed-s",
+        "dim": 384,
+        "description": "Text embeddings, Unimodal (text), English, 512 input tokens truncation, Prefixes for queries/documents: necessary, 2024 year.",
+        "license": "apache-2.0",
+        "size_in_GB": 0.13,
+        "sources": {
+            "hf": "snowflake/snowflake-arctic-embed-s",
+        },
+        "model_file": "onnx/model.onnx",
+    },
+    {
+        "model": "snowflake/snowflake-arctic-embed-m",
+        "dim": 768,
+        "description": "Text embeddings, Unimodal (text), English, 512 input tokens truncation, Prefixes for queries/documents: necessary, 2024 year.",
+        "license": "apache-2.0",
+        "size_in_GB": 0.43,
+        "sources": {
+            "hf": "Snowflake/snowflake-arctic-embed-m",
+        },
+        "model_file": "onnx/model.onnx",
+    },
+    {
+        "model": "snowflake/snowflake-arctic-embed-m-long",
+        "dim": 768,
+        "description": "Text embeddings, Unimodal (text), English, 2048 input tokens truncation, Prefixes for queries/documents: necessary, 2024 year.",
+        "license": "apache-2.0",
+        "size_in_GB": 0.54,
+        "sources": {
+            "hf": "snowflake/snowflake-arctic-embed-m-long",
+        },
+        "model_file": "onnx/model.onnx",
+    },
+    {
+        "model": "snowflake/snowflake-arctic-embed-l",
+        "dim": 1024,
+        "description": "Text embeddings, Unimodal (text), English, 512 input tokens truncation, Prefixes for queries/documents: necessary, 2024 year.",
+        "license": "apache-2.0",
+        "size_in_GB": 1.02,
+        "sources": {
+            "hf": "snowflake/snowflake-arctic-embed-l",
+        },
+        "model_file": "onnx/model.onnx",
+    },
 ]
 
 
-class OnnxTextEmbedding(TextEmbeddingBase, OnnxTextModel[NumpyArray]):
+class OnnxTextEmbedding(TextEmbeddingBase, OnnxTextModel[np.ndarray]):
     """Implementation of the Flag Embedding model."""
 
     @classmethod
-    def _list_supported_models(cls) -> list[DenseModelDescription]:
+    def list_supported_models(cls) -> List[Dict[str, Any]]:
         """
         Lists the supported models.
 
         Returns:
-            list[DenseModelDescription]: A list of DenseModelDescription objects containing the model information.
+            List[Dict[str, Any]]: A list of dictionaries containing the model information.
         """
         return supported_onnx_models
 
@@ -203,11 +187,10 @@ class OnnxTextEmbedding(TextEmbeddingBase, OnnxTextModel[NumpyArray]):
         threads: Optional[int] = None,
         providers: Optional[Sequence[OnnxProvider]] = None,
         cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        device_ids: Optional[List[int]] = None,
         lazy_load: bool = False,
         device_id: Optional[int] = None,
-        specific_model_path: Optional[str] = None,
-        **kwargs: Any,
+        **kwargs,
     ):
         """
         Args:
@@ -220,12 +203,11 @@ class OnnxTextEmbedding(TextEmbeddingBase, OnnxTextModel[NumpyArray]):
                 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
+            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.
-            specific_model_path (Optional[str], optional): The specific path to the onnx model dir if it should be imported from somewhere else
 
         Raises:
             ValueError: If the model_name is not in the format <org>/<model> e.g. BAAI/bge-base-en.
@@ -239,19 +221,17 @@ class OnnxTextEmbedding(TextEmbeddingBase, OnnxTextModel[NumpyArray]):
         self.cuda = cuda
 
         # This device_id will be used if we need to load model in current process
-        self.device_id: Optional[int] = None
         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 = str(define_cache_dir(cache_dir))
+        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.model_description, self.cache_dir, local_files_only=self._local_files_only
         )
 
         if not self.lazy_load:
@@ -262,8 +242,8 @@ class OnnxTextEmbedding(TextEmbeddingBase, OnnxTextModel[NumpyArray]):
         documents: Union[str, Iterable[str]],
         batch_size: int = 256,
         parallel: Optional[int] = None,
-        **kwargs: Any,
-    ) -> Iterable[NumpyArray]:
+        **kwargs,
+    ) -> Iterable[np.ndarray]:
         """
         Encode a list of documents into list of embeddings.
         We use mean pooling with attention so that the model can handle variable-length inputs.
@@ -292,32 +272,25 @@ class OnnxTextEmbedding(TextEmbeddingBase, OnnxTextModel[NumpyArray]):
         )
 
     @classmethod
-    def _get_worker_class(cls) -> Type["TextEmbeddingWorker[NumpyArray]"]:
+    def _get_worker_class(cls) -> Type["TextEmbeddingWorker"]:
         return OnnxTextEmbeddingWorker
 
     def _preprocess_onnx_input(
-        self, onnx_input: dict[str, NumpyArray], **kwargs: Any
-    ) -> dict[str, NumpyArray]:
+        self, onnx_input: Dict[str, np.ndarray], **kwargs
+    ) -> Dict[str, np.ndarray]:
         """
         Preprocess the onnx input.
         """
         return onnx_input
 
-    def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[NumpyArray]:
+    def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[np.ndarray]:
         embeddings = output.model_output
-
-        if embeddings.ndim == 3:  # (batch_size, seq_len, embedding_dim)
-            processed_embeddings = embeddings[:, 0]
-        elif embeddings.ndim == 2:  # (batch_size, embedding_dim)
-            processed_embeddings = embeddings
-        else:
-            raise ValueError(f"Unsupported embedding shape: {embeddings.shape}")
-        return normalize(processed_embeddings)
+        return normalize(embeddings[:, 0]).astype(np.float32)
 
     def load_onnx_model(self) -> None:
         self._load_onnx_model(
             model_dir=self._model_dir,
-            model_file=self.model_description.model_file,
+            model_file=self.model_description["model_file"],
             threads=self.threads,
             providers=self.providers,
             cuda=self.cuda,
@@ -325,12 +298,12 @@ class OnnxTextEmbedding(TextEmbeddingBase, OnnxTextModel[NumpyArray]):
         )
 
 
-class OnnxTextEmbeddingWorker(TextEmbeddingWorker[NumpyArray]):
+class OnnxTextEmbeddingWorker(TextEmbeddingWorker):
     def init_embedding(
         self,
         model_name: str,
         cache_dir: str,
-        **kwargs: Any,
+        **kwargs,
     ) -> OnnxTextEmbedding:
         return OnnxTextEmbedding(
             model_name=model_name,

fastembed/text/onnx_text_model.py

@@ -1,13 +1,12 @@
 import os
 from multiprocessing import get_all_start_methods
 from pathlib import Path
-from typing import Any, Iterable, Optional, Sequence, Type, Union
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Type, Union
 
 import numpy as np
-from numpy.typing import NDArray
-from tokenizers import Encoding, Tokenizer
+from tokenizers import Encoding
 
-from fastembed.common.types import NumpyArray, OnnxProvider
+from fastembed.common import OnnxProvider
 from fastembed.common.onnx_model import EmbeddingWorker, OnnxModel, OnnxOutputContext, T
 from fastembed.common.preprocessor_utils import load_tokenizer
 from fastembed.common.utils import iter_batch
@@ -15,10 +14,10 @@ from fastembed.parallel_processor import ParallelWorkerPool
 
 
 class OnnxTextModel(OnnxModel[T]):
-    ONNX_OUTPUT_NAMES: Optional[list[str]] = None
+    ONNX_OUTPUT_NAMES: Optional[List[str]] = None
 
     @classmethod
-    def _get_worker_class(cls) -> Type["TextEmbeddingWorker[T]"]:
+    def _get_worker_class(cls) -> Type["TextEmbeddingWorker"]:
         raise NotImplementedError("Subclasses must implement this method")
 
     def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[T]:
@@ -26,12 +25,12 @@ class OnnxTextModel(OnnxModel[T]):
 
     def __init__(self) -> None:
         super().__init__()
-        self.tokenizer: Optional[Tokenizer] = None
-        self.special_token_to_id: dict[str, int] = {}
+        self.tokenizer = None
+        self.special_token_to_id = {}
 
     def _preprocess_onnx_input(
-        self, onnx_input: dict[str, NumpyArray], **kwargs: Any
-    ) -> dict[str, Union[NumpyArray, NDArray[np.int64]]]:
+        self, onnx_input: Dict[str, np.ndarray], **kwargs
+    ) -> Dict[str, np.ndarray]:
         """
         Preprocess the onnx input.
         """
@@ -59,19 +58,19 @@ class OnnxTextModel(OnnxModel[T]):
     def load_onnx_model(self) -> None:
         raise NotImplementedError("Subclasses must implement this method")
 
-    def tokenize(self, documents: list[str], **kwargs: Any) -> list[Encoding]:
-        return self.tokenizer.encode_batch(documents)  # type: ignore[union-attr]
+    def tokenize(self, documents: List[str], **kwargs) -> List[Encoding]:
+        return self.tokenizer.encode_batch(documents)
 
     def onnx_embed(
         self,
-        documents: list[str],
-        **kwargs: Any,
+        documents: List[str],
+        **kwargs,
     ) -> OnnxOutputContext:
         encoded = self.tokenize(documents, **kwargs)
         input_ids = np.array([e.ids for e in encoded])
         attention_mask = np.array([e.attention_mask for e in encoded])
-        input_names = {node.name for node in self.model.get_inputs()}  # type: ignore[union-attr]
-        onnx_input: dict[str, NumpyArray] = {
+        input_names = {node.name for node in self.model.get_inputs()}
+        onnx_input = {
             "input_ids": np.array(input_ids, dtype=np.int64),
         }
         if "attention_mask" in input_names:
@@ -80,9 +79,10 @@ class OnnxTextModel(OnnxModel[T]):
             onnx_input["token_type_ids"] = np.array(
                 [np.zeros(len(e), dtype=np.int64) for e in input_ids], dtype=np.int64
             )
+
         onnx_input = self._preprocess_onnx_input(onnx_input, **kwargs)
 
-        model_output = self.model.run(self.ONNX_OUTPUT_NAMES, onnx_input)  # type: ignore[union-attr]
+        model_output = self.model.run(self.ONNX_OUTPUT_NAMES, onnx_input)
         return OnnxOutputContext(
             model_output=model_output[0],
             attention_mask=onnx_input.get("attention_mask", attention_mask),
@@ -98,8 +98,8 @@ class OnnxTextModel(OnnxModel[T]):
         parallel: Optional[int] = None,
         providers: Optional[Sequence[OnnxProvider]] = None,
         cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
-        **kwargs: Any,
+        device_ids: Optional[List[int]] = None,
+        **kwargs,
     ) -> Iterable[T]:
         is_small = False
 
@@ -136,11 +136,11 @@ class OnnxTextModel(OnnxModel[T]):
                 start_method=start_method,
             )
             for batch in pool.ordered_map(iter_batch(documents, batch_size), **params):
-                yield from self._post_process_onnx_output(batch)  # type: ignore
+                yield from self._post_process_onnx_output(batch)
 
 
-class TextEmbeddingWorker(EmbeddingWorker[T]):
-    def process(self, items: Iterable[tuple[int, Any]]) -> Iterable[tuple[int, OnnxOutputContext]]:
+class TextEmbeddingWorker(EmbeddingWorker):
+    def process(self, items: Iterable[Tuple[int, Any]]) -> Iterable[Tuple[int, Any]]:
         for idx, batch in items:
             onnx_output = self.model.onnx_embed(batch)
             yield idx, onnx_output

fastembed/text/pooled_embedding.py

@@ -1,122 +1,80 @@
-from typing import Any, Iterable, Type
+from typing import Any, Dict, Iterable, List, Type
 
 import numpy as np
-from numpy.typing import NDArray
 
-from fastembed.common.types import NumpyArray
 from fastembed.common.onnx_model import OnnxOutputContext
-from fastembed.common.utils import mean_pooling
 from fastembed.text.onnx_embedding import OnnxTextEmbedding, OnnxTextEmbeddingWorker
-from fastembed.common.model_description import DenseModelDescription, ModelSource
+from fastembed.text.onnx_text_model import TextEmbeddingWorker
 
-supported_pooled_models: list[DenseModelDescription] = [
-    DenseModelDescription(
-        model="nomic-ai/nomic-embed-text-v1.5",
-        dim=768,
-        description=(
-            "Text embeddings, Multimodal (text, image), English, 8192 input tokens truncation, "
-            "Prefixes for queries/documents: necessary, 2024 year."
-        ),
-        license="apache-2.0",
-        size_in_GB=0.52,
-        sources=ModelSource(hf="nomic-ai/nomic-embed-text-v1.5"),
-        model_file="onnx/model.onnx",
-    ),
-    DenseModelDescription(
-        model="nomic-ai/nomic-embed-text-v1.5-Q",
-        dim=768,
-        description=(
-            "Text embeddings, Multimodal (text, image), English, 8192 input tokens truncation, "
-            "Prefixes for queries/documents: necessary, 2024 year."
-        ),
-        license="apache-2.0",
-        size_in_GB=0.13,
-        sources=ModelSource(hf="nomic-ai/nomic-embed-text-v1.5"),
-        model_file="onnx/model_quantized.onnx",
-    ),
-    DenseModelDescription(
-        model="nomic-ai/nomic-embed-text-v1",
-        dim=768,
-        description=(
-            "Text embeddings, Multimodal (text, image), English, 8192 input tokens truncation, "
-            "Prefixes for queries/documents: necessary, 2024 year."
-        ),
-        license="apache-2.0",
-        size_in_GB=0.52,
-        sources=ModelSource(hf="nomic-ai/nomic-embed-text-v1"),
-        model_file="onnx/model.onnx",
-    ),
-    DenseModelDescription(
-        model="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
-        dim=384,
-        description=(
-            "Text embeddings, Unimodal (text), Multilingual (~50 languages), 512 input tokens truncation, "
-            "Prefixes for queries/documents: not necessary, 2019 year."
-        ),
-        license="apache-2.0",
-        size_in_GB=0.22,
-        sources=ModelSource(hf="qdrant/paraphrase-multilingual-MiniLM-L12-v2-onnx-Q"),
-        model_file="model_optimized.onnx",
-    ),
-    DenseModelDescription(
-        model="sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
-        dim=768,
-        description=(
-            "Text embeddings, Unimodal (text), Multilingual (~50 languages), 384 input tokens truncation, "
-            "Prefixes for queries/documents: not necessary, 2021 year."
-        ),
-        license="apache-2.0",
-        size_in_GB=1.00,
-        sources=ModelSource(hf="xenova/paraphrase-multilingual-mpnet-base-v2"),
-        model_file="onnx/model.onnx",
-    ),
-    DenseModelDescription(
-        model="intfloat/multilingual-e5-large",
-        dim=1024,
-        description=(
-            "Text embeddings, Unimodal (text), Multilingual (~100 languages), 512 input tokens truncation, "
-            "Prefixes for queries/documents: necessary, 2024 year."
-        ),
-        license="mit",
-        size_in_GB=2.24,
-        sources=ModelSource(
-            hf="qdrant/multilingual-e5-large-onnx",
-            url="https://storage.googleapis.com/qdrant-fastembed/fast-multilingual-e5-large.tar.gz",
-            _deprecated_tar_struct=True,
-        ),
-        model_file="model.onnx",
-        additional_files=["model.onnx_data"],
-    ),
+supported_pooled_models = [
+    {
+        "model": "nomic-ai/nomic-embed-text-v1.5",
+        "dim": 768,
+        "description": "Text embeddings, Multimodal (text, image), English, 8192 input tokens truncation, Prefixes for queries/documents: necessary, 2024 year.",
+        "license": "apache-2.0",
+        "size_in_GB": 0.52,
+        "sources": {
+            "hf": "nomic-ai/nomic-embed-text-v1.5",
+        },
+        "model_file": "onnx/model.onnx",
+    },
+    {
+        "model": "nomic-ai/nomic-embed-text-v1.5-Q",
+        "dim": 768,
+        "description": "Text embeddings, Multimodal (text, image), English, 8192 input tokens truncation, Prefixes for queries/documents: necessary, 2024 year.",
+        "license": "apache-2.0",
+        "size_in_GB": 0.13,
+        "sources": {
+            "hf": "nomic-ai/nomic-embed-text-v1.5",
+        },
+        "model_file": "onnx/model_quantized.onnx",
+    },
+    {
+        "model": "nomic-ai/nomic-embed-text-v1",
+        "dim": 768,
+        "description": "Text embeddings, Multimodal (text, image), English, 8192 input tokens truncation, Prefixes for queries/documents: necessary, 2024 year.",
+        "license": "apache-2.0",
+        "size_in_GB": 0.52,
+        "sources": {
+            "hf": "nomic-ai/nomic-embed-text-v1",
+        },
+        "model_file": "onnx/model.onnx",
+    },
 ]
 
 
 class PooledEmbedding(OnnxTextEmbedding):
     @classmethod
-    def _get_worker_class(cls) -> Type[OnnxTextEmbeddingWorker]:
+    def _get_worker_class(cls) -> Type[TextEmbeddingWorker]:
         return PooledEmbeddingWorker
 
     @classmethod
-    def mean_pooling(
-        cls, model_output: NumpyArray, attention_mask: NDArray[np.int64]
-    ) -> NumpyArray:
-        return mean_pooling(model_output, attention_mask)
+    def mean_pooling(cls, model_output: np.ndarray, attention_mask: np.ndarray) -> np.ndarray:
+        token_embeddings = model_output
+        input_mask_expanded = np.expand_dims(attention_mask, axis=-1)
+        input_mask_expanded = np.tile(input_mask_expanded, (1, 1, token_embeddings.shape[-1]))
+        input_mask_expanded = input_mask_expanded.astype(float)
+        sum_embeddings = np.sum(token_embeddings * input_mask_expanded, axis=1)
+        sum_mask = np.sum(input_mask_expanded, axis=1)
+        pooled_embeddings = sum_embeddings / np.maximum(sum_mask, 1e-9)
+        return pooled_embeddings
 
     @classmethod
-    def _list_supported_models(cls) -> list[DenseModelDescription]:
+    def list_supported_models(cls) -> List[Dict[str, Any]]:
         """Lists the supported models.
 
         Returns:
-            list[DenseModelDescription]: A list of DenseModelDescription objects containing the model information.
+            List[Dict[str, Any]]: A list of dictionaries containing the model information.
         """
         return supported_pooled_models
 
-    def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[NumpyArray]:
+    def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[np.ndarray]:
         if output.attention_mask is None:
             raise ValueError("attention_mask must be provided for document post-processing")
 
         embeddings = output.model_output
         attn_mask = output.attention_mask
-        return self.mean_pooling(embeddings, attn_mask)
+        return self.mean_pooling(embeddings, attn_mask).astype(np.float32)
 
 
 class PooledEmbeddingWorker(OnnxTextEmbeddingWorker):
@@ -124,7 +82,7 @@ class PooledEmbeddingWorker(OnnxTextEmbeddingWorker):
         self,
         model_name: str,
         cache_dir: str,
-        **kwargs: Any,
+        **kwargs,
     ) -> OnnxTextEmbedding:
         return PooledEmbedding(
             model_name=model_name,

fastembed/text/pooled_normalized_embedding.py

@@ -1,150 +1,86 @@
-from typing import Any, Iterable, Type
+from typing import Any, Dict, Iterable, List, Type
 
+import numpy as np
 
-from fastembed.common.types import NumpyArray
 from fastembed.common.onnx_model import OnnxOutputContext
 from fastembed.common.utils import normalize
 from fastembed.text.onnx_embedding import OnnxTextEmbedding, OnnxTextEmbeddingWorker
+from fastembed.text.onnx_text_model import TextEmbeddingWorker
 from fastembed.text.pooled_embedding import PooledEmbedding
-from fastembed.common.model_description import DenseModelDescription, ModelSource
 
-supported_pooled_normalized_models: list[DenseModelDescription] = [
-    DenseModelDescription(
-        model="sentence-transformers/all-MiniLM-L6-v2",
-        dim=384,
-        description=(
-            "Text embeddings, Unimodal (text), English, 256 input tokens truncation, "
-            "Prefixes for queries/documents: not necessary, 2021 year."
-        ),
-        license="apache-2.0",
-        size_in_GB=0.09,
-        sources=ModelSource(
-            url="https://storage.googleapis.com/qdrant-fastembed/sentence-transformers-all-MiniLM-L6-v2.tar.gz",
-            hf="qdrant/all-MiniLM-L6-v2-onnx",
-            _deprecated_tar_struct=True,
-        ),
-        model_file="model.onnx",
-    ),
-    DenseModelDescription(
-        model="jinaai/jina-embeddings-v2-base-en",
-        dim=768,
-        description=(
-            "Text embeddings, Unimodal (text), English, 8192 input tokens truncation, "
-            "Prefixes for queries/documents: not necessary, 2023 year."
-        ),
-        license="apache-2.0",
-        size_in_GB=0.52,
-        sources=ModelSource(hf="xenova/jina-embeddings-v2-base-en"),
-        model_file="onnx/model.onnx",
-    ),
-    DenseModelDescription(
-        model="jinaai/jina-embeddings-v2-small-en",
-        dim=512,
-        description=(
-            "Text embeddings, Unimodal (text), English, 8192 input tokens truncation, "
-            "Prefixes for queries/documents: not necessary, 2023 year."
-        ),
-        license="apache-2.0",
-        size_in_GB=0.12,
-        sources=ModelSource(hf="xenova/jina-embeddings-v2-small-en"),
-        model_file="onnx/model.onnx",
-    ),
-    DenseModelDescription(
-        model="jinaai/jina-embeddings-v2-base-de",
-        dim=768,
-        description=(
-            "Text embeddings, Unimodal (text), Multilingual (German, English), 8192 input tokens truncation, "
-            "Prefixes for queries/documents: not necessary, 2024 year."
-        ),
-        license="apache-2.0",
-        size_in_GB=0.32,
-        sources=ModelSource(hf="jinaai/jina-embeddings-v2-base-de"),
-        model_file="onnx/model_fp16.onnx",
-    ),
-    DenseModelDescription(
-        model="jinaai/jina-embeddings-v2-base-code",
-        dim=768,
-        description=(
-            "Text embeddings, Unimodal (text), Multilingual (English, 30 programming languages), "
-            "8192 input tokens truncation, Prefixes for queries/documents: not necessary, 2024 year."
-        ),
-        license="apache-2.0",
-        size_in_GB=0.64,
-        sources=ModelSource(hf="jinaai/jina-embeddings-v2-base-code"),
-        model_file="onnx/model.onnx",
-    ),
-    DenseModelDescription(
-        model="jinaai/jina-embeddings-v2-base-zh",
-        dim=768,
-        description=(
-            "Text embeddings, Unimodal (text), supports mixed Chinese-English input text, "
-            "8192 input tokens truncation, Prefixes for queries/documents: not necessary, 2024 year."
-        ),
-        license="apache-2.0",
-        size_in_GB=0.64,
-        sources=ModelSource(hf="jinaai/jina-embeddings-v2-base-zh"),
-        model_file="onnx/model.onnx",
-    ),
-    DenseModelDescription(
-        model="jinaai/jina-embeddings-v2-base-es",
-        dim=768,
-        description=(
-            "Text embeddings, Unimodal (text), supports mixed Spanish-English input text, "
-            "8192 input tokens truncation, Prefixes for queries/documents: not necessary, 2024 year."
-        ),
-        license="apache-2.0",
-        size_in_GB=0.64,
-        sources=ModelSource(hf="jinaai/jina-embeddings-v2-base-es"),
-        model_file="onnx/model.onnx",
-    ),
-    DenseModelDescription(
-        model="thenlper/gte-base",
-        dim=768,
-        description=(
-            "General text embeddings, Unimodal (text), supports English only input text, "
-            "512 input tokens truncation, Prefixes for queries/documents: not necessary, 2024 year."
-        ),
-        license="mit",
-        size_in_GB=0.44,
-        sources=ModelSource(hf="thenlper/gte-base"),
-        model_file="onnx/model.onnx",
-    ),
-    DenseModelDescription(
-        model="thenlper/gte-large",
-        dim=1024,
-        description=(
-            "Text embeddings, Unimodal (text), English, 512 input tokens truncation, "
-            "Prefixes for queries/documents: not necessary, 2023 year."
-        ),
-        license="mit",
-        size_in_GB=1.20,
-        sources=ModelSource(hf="qdrant/gte-large-onnx"),
-        model_file="model.onnx",
-    ),
+supported_pooled_normalized_models = [
+    {
+        "model": "sentence-transformers/all-MiniLM-L6-v2",
+        "dim": 384,
+        "description": "Text embeddings, Unimodal (text), English, 256 input tokens truncation, Prefixes for queries/documents: not necessary, 2021 year.",
+        "license": "apache-2.0",
+        "size_in_GB": 0.09,
+        "sources": {
+            "url": "https://storage.googleapis.com/qdrant-fastembed/sentence-transformers-all-MiniLM-L6-v2.tar.gz",
+            "hf": "qdrant/all-MiniLM-L6-v2-onnx",
+        },
+        "model_file": "model.onnx",
+    },
+    {
+        "model": "jinaai/jina-embeddings-v2-base-en",
+        "dim": 768,
+        "description": "Text embeddings, Unimodal (text), English, 8192 input tokens truncation, Prefixes for queries/documents: not necessary, 2023 year.",
+        "license": "apache-2.0",
+        "size_in_GB": 0.52,
+        "sources": {"hf": "xenova/jina-embeddings-v2-base-en"},
+        "model_file": "onnx/model.onnx",
+    },
+    {
+        "model": "jinaai/jina-embeddings-v2-small-en",
+        "dim": 512,
+        "description": "Text embeddings, Unimodal (text), English, 8192 input tokens truncation, Prefixes for queries/documents: not necessary, 2023 year.",
+        "license": "apache-2.0",
+        "size_in_GB": 0.12,
+        "sources": {"hf": "xenova/jina-embeddings-v2-small-en"},
+        "model_file": "onnx/model.onnx",
+    },
+    {
+        "model": "jinaai/jina-embeddings-v2-base-de",
+        "dim": 768,
+        "description": "Text embeddings, Unimodal (text), Multilingual (German, English), 8192 input tokens truncation, Prefixes for queries/documents: not necessary, 2024 year.",
+        "license": "apache-2.0",
+        "size_in_GB": 0.32,
+        "sources": {"hf": "jinaai/jina-embeddings-v2-base-de"},
+        "model_file": "onnx/model_fp16.onnx",
+    },
+    {
+        "model": "jinaai/jina-embeddings-v2-base-code",
+        "dim": 768,
+        "description": "Text embeddings, Unimodal (text), Multilingual (English, 30 programming languages), 8192 input tokens truncation, Prefixes for queries/documents: not necessary, 2024 year.",
+        "license": "apache-2.0",
+        "size_in_GB": 0.64,
+        "sources": {"hf": "jinaai/jina-embeddings-v2-base-code"},
+        "model_file": "onnx/model.onnx",
+    },
 ]
 
 
 class PooledNormalizedEmbedding(PooledEmbedding):
     @classmethod
-    def _get_worker_class(cls) -> Type[OnnxTextEmbeddingWorker]:
+    def _get_worker_class(cls) -> Type[TextEmbeddingWorker]:
         return PooledNormalizedEmbeddingWorker
 
     @classmethod
-    def _list_supported_models(cls) -> list[DenseModelDescription]:
+    def list_supported_models(cls) -> List[Dict[str, Any]]:
         """Lists the supported models.
 
         Returns:
-            list[DenseModelDescription]: A list of DenseModelDescription objects containing the model information.
+            List[Dict[str, Any]]: A list of dictionaries containing the model information.
         """
         return supported_pooled_normalized_models
 
-    def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[NumpyArray]:
+    def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[np.ndarray]:
         if output.attention_mask is None:
             raise ValueError("attention_mask must be provided for document post-processing")
 
         embeddings = output.model_output
         attn_mask = output.attention_mask
-        return normalize(self.mean_pooling(embeddings, attn_mask))
+        return normalize(self.mean_pooling(embeddings, attn_mask)).astype(np.float32)
 
 
 class PooledNormalizedEmbeddingWorker(OnnxTextEmbeddingWorker):
@@ -152,7 +88,7 @@ class PooledNormalizedEmbeddingWorker(OnnxTextEmbeddingWorker):
         self,
         model_name: str,
         cache_dir: str,
-        **kwargs: Any,
+        **kwargs,
     ) -> OnnxTextEmbedding:
         return PooledNormalizedEmbedding(
             model_name=model_name,

fastembed/text/text_embedding.py

@@ -1,81 +1,55 @@
-import warnings
-from typing import Any, Iterable, Optional, Sequence, Type, Union
-from dataclasses import asdict
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Type, Union
 
-from fastembed.common.types import NumpyArray, OnnxProvider
+import numpy as np
+
+from fastembed.common import OnnxProvider
 from fastembed.text.clip_embedding import CLIPOnnxEmbedding
-from fastembed.text.custom_text_embedding import CustomTextEmbedding
+from fastembed.text.e5_onnx_embedding import E5OnnxEmbedding
 from fastembed.text.pooled_normalized_embedding import PooledNormalizedEmbedding
 from fastembed.text.pooled_embedding import PooledEmbedding
-from fastembed.text.multitask_embedding import JinaEmbeddingV3
 from fastembed.text.onnx_embedding import OnnxTextEmbedding
 from fastembed.text.text_embedding_base import TextEmbeddingBase
-from fastembed.common.model_description import DenseModelDescription, ModelSource, PoolingType
 
 
 class TextEmbedding(TextEmbeddingBase):
-    EMBEDDINGS_REGISTRY: list[Type[TextEmbeddingBase]] = [
+    EMBEDDINGS_REGISTRY: List[Type[TextEmbeddingBase]] = [
         OnnxTextEmbedding,
+        E5OnnxEmbedding,
         CLIPOnnxEmbedding,
         PooledNormalizedEmbedding,
         PooledEmbedding,
-        JinaEmbeddingV3,
-        CustomTextEmbedding,
     ]
 
     @classmethod
-    def list_supported_models(cls) -> list[dict[str, Any]]:
-        """Lists the supported models.
+    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.
+            List[Dict[str, Any]]: A list of dictionaries containing the model information.
+
+            Example:
+                ```
+                [
+                    {
+                        "model": "intfloat/multilingual-e5-large",
+                        "dim": 1024,
+                        "description": "Multilingual model, e5-large. Recommend using this model for non-English languages",
+                        "license": "mit",
+                        "size_in_GB": 2.24,
+                        "sources": {
+                            "gcp": "https://storage.googleapis.com/qdrant-fastembed/fast-multilingual-e5-large.tar.gz",
+                            "hf": "qdrant/multilingual-e5-large-onnx",
+                        }
+                    }
+                ]
+                ```
         """
-        return [asdict(model) for model in cls._list_supported_models()]
-
-    @classmethod
-    def _list_supported_models(cls) -> list[DenseModelDescription]:
-        result: list[DenseModelDescription] = []
+        result = []
         for embedding in cls.EMBEDDINGS_REGISTRY:
-            result.extend(embedding._list_supported_models())
+            result.extend(embedding.list_supported_models())
         return result
 
-    @classmethod
-    def add_custom_model(
-        cls,
-        model: str,
-        pooling: PoolingType,
-        normalization: bool,
-        sources: ModelSource,
-        dim: int,
-        model_file: str = "onnx/model.onnx",
-        description: str = "",
-        license: str = "",
-        size_in_gb: float = 0.0,
-        additional_files: Optional[list[str]] = None,
-    ) -> None:
-        registered_models = cls._list_supported_models()
-        for registered_model in registered_models:
-            if model == registered_model.model:
-                raise ValueError(
-                    f"Model {model} is already registered in TextEmbedding, if you still want to add this model, "
-                    f"please use another model name"
-                )
-
-        CustomTextEmbedding.add_model(
-            DenseModelDescription(
-                model=model,
-                sources=sources,
-                dim=dim,
-                model_file=model_file,
-                description=description,
-                license=license,
-                size_in_GB=size_in_gb,
-                additional_files=additional_files or [],
-            ),
-            pooling=pooling,
-            normalization=normalization,
-        )
-
     def __init__(
         self,
         model_name: str = "BAAI/bge-small-en-v1.5",
@@ -83,34 +57,14 @@ class TextEmbedding(TextEmbeddingBase):
         threads: Optional[int] = None,
         providers: Optional[Sequence[OnnxProvider]] = None,
         cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        device_ids: Optional[List[int]] = None,
         lazy_load: bool = False,
-        **kwargs: Any,
+        **kwargs,
     ):
         super().__init__(model_name, cache_dir, threads, **kwargs)
-        if model_name == "nomic-ai/nomic-embed-text-v1.5-Q":
-            warnings.warn(
-                "The model 'nomic-ai/nomic-embed-text-v1.5-Q' has been updated on HuggingFace. Please review "
-                "the latest documentation on HF and release notes to ensure compatibility with your workflow. ",
-                UserWarning,
-                stacklevel=2,
-            )
-        if model_name in {
-            "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
-            "thenlper/gte-large",
-            "intfloat/multilingual-e5-large",
-            "sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
-        }:
-            warnings.warn(
-                f"The model {model_name} now uses mean pooling instead of CLS embedding. "
-                f"In order to preserve the previous behaviour, consider either pinning fastembed version to 0.5.1 or "
-                "using `add_custom_model` functionality.",
-                UserWarning,
-                stacklevel=2,
-            )
         for EMBEDDING_MODEL_TYPE in self.EMBEDDINGS_REGISTRY:
-            supported_models = EMBEDDING_MODEL_TYPE._list_supported_models()
-            if any(model_name.lower() == model.model.lower() for model in supported_models):
+            supported_models = EMBEDDING_MODEL_TYPE.list_supported_models()
+            if any(model_name.lower() == model["model"].lower() for model in supported_models):
                 self.model = EMBEDDING_MODEL_TYPE(
                     model_name=model_name,
                     cache_dir=cache_dir,
@@ -124,7 +78,7 @@ class TextEmbedding(TextEmbeddingBase):
                 return
 
         raise ValueError(
-            f"Model {model_name} is not supported in TextEmbedding. "
+            f"Model {model_name} is not supported in TextEmbedding."
             "Please check the supported models using `TextEmbedding.list_supported_models()`"
         )
 
@@ -133,8 +87,8 @@ class TextEmbedding(TextEmbeddingBase):
         documents: Union[str, Iterable[str]],
         batch_size: int = 256,
         parallel: Optional[int] = None,
-        **kwargs: Any,
-    ) -> Iterable[NumpyArray]:
+        **kwargs,
+    ) -> Iterable[np.ndarray]:
         """
         Encode a list of documents into list of embeddings.
         We use mean pooling with attention so that the model can handle variable-length inputs.
@@ -151,30 +105,3 @@ class TextEmbedding(TextEmbeddingBase):
             List of embeddings, one per document
         """
         yield from self.model.embed(documents, batch_size, parallel, **kwargs)
-
-    def query_embed(self, query: Union[str, Iterable[str]], **kwargs: Any) -> Iterable[NumpyArray]:
-        """
-        Embeds queries
-
-        Args:
-            query (Union[str, Iterable[str]]): The query to embed, or an iterable e.g. list of queries.
-
-        Returns:
-            Iterable[NumpyArray]: The embeddings.
-        """
-        # This is model-specific, so that different models can have specialized implementations
-        yield from self.model.query_embed(query, **kwargs)
-
-    def passage_embed(self, texts: Iterable[str], **kwargs: Any) -> Iterable[NumpyArray]:
-        """
-        Embeds a list of text passages into a list of embeddings.
-
-        Args:
-            texts (Iterable[str]): The list of texts to embed.
-            **kwargs: Additional keyword argument to pass to the embed method.
-
-        Yields:
-            Iterable[SparseEmbedding]: The sparse embeddings.
-        """
-        # This is model-specific, so that different models can have specialized implementations
-        yield from self.model.passage_embed(texts, **kwargs)

fastembed/text/text_embedding_base.py

@@ -1,17 +1,17 @@
-from typing import Iterable, Optional, Union, Any
+from typing import Iterable, Optional, Union
+
+import numpy as np
 
-from fastembed.common.model_description import DenseModelDescription
-from fastembed.common.types import NumpyArray
 from fastembed.common.model_management import ModelManagement
 
 
-class TextEmbeddingBase(ModelManagement[DenseModelDescription]):
+class TextEmbeddingBase(ModelManagement):
     def __init__(
         self,
         model_name: str,
         cache_dir: Optional[str] = None,
         threads: Optional[int] = None,
-        **kwargs: Any,
+        **kwargs,
     ):
         self.model_name = model_name
         self.cache_dir = cache_dir
@@ -23,11 +23,11 @@ class TextEmbeddingBase(ModelManagement[DenseModelDescription]):
         documents: Union[str, Iterable[str]],
         batch_size: int = 256,
         parallel: Optional[int] = None,
-        **kwargs: Any,
-    ) -> Iterable[NumpyArray]:
+        **kwargs,
+    ) -> Iterable[np.ndarray]:
         raise NotImplementedError()
 
-    def passage_embed(self, texts: Iterable[str], **kwargs: Any) -> Iterable[NumpyArray]:
+    def passage_embed(self, texts: Iterable[str], **kwargs) -> Iterable[np.ndarray]:
         """
         Embeds a list of text passages into a list of embeddings.
 
@@ -36,13 +36,15 @@ class TextEmbeddingBase(ModelManagement[DenseModelDescription]):
             **kwargs: Additional keyword argument to pass to the embed method.
 
         Yields:
-            Iterable[NumpyArray]: The embeddings.
+            Iterable[np.ndarray]: The embeddings.
         """
 
         # This is model-specific, so that different models can have specialized implementations
         yield from self.embed(texts, **kwargs)
 
-    def query_embed(self, query: Union[str, Iterable[str]], **kwargs: Any) -> Iterable[NumpyArray]:
+    def query_embed(
+        self, query: Union[str, Iterable[str]], **kwargs
+    ) -> Iterable[np.ndarray]:
         """
         Embeds queries
 
@@ -50,11 +52,11 @@ class TextEmbeddingBase(ModelManagement[DenseModelDescription]):
             query (Union[str, Iterable[str]]): The query to embed, or an iterable e.g. list of queries.
 
         Returns:
-            Iterable[NumpyArray]: The embeddings.
+            Iterable[np.ndarray]: The embeddings.
         """
 
         # This is model-specific, so that different models can have specialized implementations
         if isinstance(query, str):
             yield from self.embed([query], **kwargs)
-        else:
+        if isinstance(query, Iterable):
             yield from self.embed(query, **kwargs)

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
-name = "fastembed"
-version = "0.6.0"
+name = "fastembed-gpu"
+version = "0.4.2"
 description = "Fast, light, accurate library built for retrieval embedding generation"
 authors = ["Qdrant Team <info@qdrant.tech>", "NirantK <nirant.bits@gmail.com>"]
 license = "Apache License"
@@ -11,53 +11,39 @@ repository = "https://github.com/qdrant/fastembed"
 keywords = ["vector", "embedding", "neural", "search", "qdrant", "sentence-transformers"]
 
 [tool.poetry.dependencies]
-python = ">=3.9.0"
-numpy = [
-    { version = ">=1.21", python = ">=3.10,<3.12" },
-    { version = ">=1.26", python = ">=3.12,<3.13" },
-    { version = ">=2.1.0", python = ">=3.13" },
-    { version = ">=1.21,<2.1.0", python = "<3.10" },
-]
-onnxruntime = [
-    { version = ">1.20.0", python = ">=3.13" },
-    { version = ">=1.17.0,<1.20.0", python = "<3.10" },
-    { version = ">=1.17.0,!=1.20.0", python = ">=3.10,<3.13" },
-]
+python = ">=3.8.0,<3.13"
+onnx = "^1.15.0"
+onnxruntime-gpu = ">=1.17.0,<1.20.0"
 tqdm = "^4.66"
 requests = "^2.31"
 tokenizers = ">=0.15,<1.0"
 huggingface-hub = ">=0.20,<1.0"
 loguru = "^0.7.2"
-pillow = ">=10.3.0,<12.0.0"
-mmh3 = ">=4.1.0,<6.0.0"
+numpy = [
+    { version = ">=1.21, <2", python = "<3.12" },
+    { version = ">=1.26, <2", python = ">=3.12" }
+]
+pillow = "^10.3.0"
+mmh3 = "^4.1.0"
 py-rust-stemmers = "^0.1.0"
 
-[tool.poetry.group.test.dependencies]
+[tool.poetry.group.dev.dependencies]
 pytest = "^7.4.2"
 ruff = ">=0.3.1,<1.0"
-
-[tool.poetry.group.dev.dependencies]
 notebook = ">=7.0.2"
-pre-commit = "^3.6.2"
-onnx = ">=1.15.0"
+pre-commit = {version = "^3.6.2", python = ">=3.9,<3.12" }
 
 [tool.poetry.group.docs.dependencies]
 mkdocs-material = "^9.5.10"
 mkdocstrings = "^0.24.0"
-pillow = ">=10.3.0,<12.0.0"
+pillow = "^10.2.0"
 cairosvg = "^2.7.1"
 mknotebooks = "^0.8.0"
 
-[tool.poetry.group.types.dependencies]
-pyright = ">=1.1.293"
-mypy = "^1.0.0"
 
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
 
-[tool.pyright]
-typeCheckingMode = "strict"
-
 [tool.ruff]
 line-length = 99

tests/profiling.py

@@ -9,7 +9,7 @@
 
 # %%
 import time
-from typing import Callable
+from typing import Callable, List, Tuple
 
 import matplotlib.pyplot as plt
 import torch.nn.functional as F
@@ -23,7 +23,7 @@ from fastembed.embedding import DefaultEmbedding
 # data is a list of strings, each string is a document.
 
 # %%
-documents: list[str] = [
+documents: List[str] = [
     "Chandrayaan-3 is India's third lunar mission",
     "It aimed to land a rover on the Moon's surface - joining the US, China and Russia",
     "The mission is a follow-up to Chandrayaan-2, which had partial success",
@@ -56,7 +56,7 @@ class HF:
         self.model = AutoModel.from_pretrained(model_id)
         self.tokenizer = AutoTokenizer.from_pretrained(model_id)
 
-    def embed(self, texts: list[str]):
+    def embed(self, texts: List[str]):
         encoded_input = self.tokenizer(
             texts, max_length=512, padding=True, truncation=True, return_tensors="pt"
         )
@@ -88,7 +88,7 @@ embedding_model = DefaultEmbedding()
 # %%
 def calculate_time_stats(
     embed_func: Callable, documents: list, k: int
-) -> tuple[float, float, float]:
+) -> Tuple[float, float, float]:
     times = []
     for _ in range(k):
         # Timing the embed_func call
@@ -111,8 +111,8 @@ print(f"FastEmbed (Average, Max, Min): {fst_stats}")
 
 # %%
 def plot_character_per_second_comparison(
-    hf_stats: tuple[float, float, float],
-    fst_stats: tuple[float, float, float],
+    hf_stats: Tuple[float, float, float],
+    fst_stats: Tuple[float, float, float],
     documents: list,
 ):
     # Calculating total characters in documents

tests/test_attention_embeddings.py

@@ -8,7 +8,7 @@ from tests.utils import delete_model_cache
 
 
 @pytest.mark.parametrize("model_name", ["Qdrant/bm42-all-minilm-l6-v2-attentions", "Qdrant/bm25"])
-def test_attention_embeddings(model_name: str) -> None:
+def test_attention_embeddings(model_name):
     is_ci = os.getenv("CI")
     model = SparseTextEmbedding(model_name=model_name)
 
@@ -71,7 +71,7 @@ def test_attention_embeddings(model_name: str) -> None:
 
 
 @pytest.mark.parametrize("model_name", ["Qdrant/bm42-all-minilm-l6-v2-attentions", "Qdrant/bm25"])
-def test_parallel_processing(model_name: str) -> None:
+def test_parallel_processing(model_name):
     is_ci = os.getenv("CI")
 
     model = SparseTextEmbedding(model_name=model_name)
@@ -96,7 +96,7 @@ def test_parallel_processing(model_name: str) -> None:
 
 
 @pytest.mark.parametrize("model_name", ["Qdrant/bm25"])
-def test_multilanguage(model_name: str) -> None:
+def test_multilanguage(model_name):
     is_ci = os.getenv("CI")
 
     docs = ["Mangez-vous vraiment des grenouilles?", "Je suis au lit"]
@@ -122,7 +122,7 @@ def test_multilanguage(model_name: str) -> None:
 
 
 @pytest.mark.parametrize("model_name", ["Qdrant/bm25"])
-def test_special_characters(model_name: str) -> None:
+def test_special_characters(model_name):
     is_ci = os.getenv("CI")
 
     docs = [
@@ -145,7 +145,7 @@ def test_special_characters(model_name: str) -> None:
 
 
 @pytest.mark.parametrize("model_name", ["Qdrant/bm42-all-minilm-l6-v2-attentions"])
-def test_lazy_load(model_name: str) -> None:
+def test_lazy_load(model_name):
     model = SparseTextEmbedding(model_name=model_name, lazy_load=True)
     assert not hasattr(model.model, "model")
     docs = ["hello world", "flag embedding"]

tests/test_common.py

@@ -1,30 +0,0 @@
-from fastembed import (
-    TextEmbedding,
-    SparseTextEmbedding,
-    ImageEmbedding,
-    LateInteractionMultimodalEmbedding,
-    LateInteractionTextEmbedding,
-)
-
-
-def test_text_list_supported_models():
-    for model_type in [
-        TextEmbedding,
-        SparseTextEmbedding,
-        ImageEmbedding,
-        LateInteractionMultimodalEmbedding,
-        LateInteractionTextEmbedding,
-    ]:
-        supported_models = model_type.list_supported_models()
-        assert isinstance(supported_models, list)
-        description = supported_models[0]
-        assert isinstance(description, dict)
-
-        assert "model" in description and description["model"]
-        if model_type != SparseTextEmbedding:
-            assert "dim" in description and description["dim"]
-        assert "license" in description and description["license"]
-        assert "size_in_GB" in description and description["size_in_GB"]
-        assert "model_file" in description and description["model_file"]
-        assert "sources" in description and description["sources"]
-        assert "hf" in description["sources"] or "url" in description["sources"]

tests/test_custom_models.py

@@ -1,229 +0,0 @@
-import itertools
-import os
-import numpy as np
-import pytest
-
-from fastembed.common.model_description import (
-    PoolingType,
-    ModelSource,
-    DenseModelDescription,
-    BaseModelDescription,
-)
-from fastembed.common.onnx_model import OnnxOutputContext
-from fastembed.common.utils import normalize, mean_pooling
-from fastembed.text.custom_text_embedding import CustomTextEmbedding, PostprocessingConfig
-from fastembed.rerank.cross_encoder.custom_text_cross_encoder import CustomTextCrossEncoder
-from fastembed.rerank.cross_encoder import TextCrossEncoder
-from fastembed.text.text_embedding import TextEmbedding
-from tests.utils import delete_model_cache
-
-
-@pytest.fixture(autouse=True)
-def restore_custom_models_fixture():
-    CustomTextEmbedding.SUPPORTED_MODELS = []
-    CustomTextCrossEncoder.SUPPORTED_MODELS = []
-    yield
-    CustomTextEmbedding.SUPPORTED_MODELS = []
-    CustomTextCrossEncoder.SUPPORTED_MODELS = []
-
-
-def test_text_custom_model():
-    is_ci = os.getenv("CI")
-    custom_model_name = "intfloat/multilingual-e5-small"
-    canonical_vector = np.array(
-        [3.1317e-02, 3.0939e-02, -3.5117e-02, -6.7274e-02, 8.5084e-02], dtype=np.float32
-    )
-    pooling = PoolingType.MEAN
-    normalization = True
-    dim = 384
-    size_in_gb = 0.47
-    source = ModelSource(hf=custom_model_name)
-
-    TextEmbedding.add_custom_model(
-        custom_model_name,
-        pooling=pooling,
-        normalization=normalization,
-        sources=source,
-        dim=dim,
-        size_in_gb=size_in_gb,
-    )
-
-    assert CustomTextEmbedding.SUPPORTED_MODELS[0] == DenseModelDescription(
-        model=custom_model_name,
-        sources=source,
-        model_file="onnx/model.onnx",
-        description="",
-        license="",
-        size_in_GB=size_in_gb,
-        additional_files=[],
-        dim=dim,
-        tasks={},
-    )
-    assert CustomTextEmbedding.POSTPROCESSING_MAPPING[custom_model_name] == PostprocessingConfig(
-        pooling=pooling, normalization=normalization
-    )
-
-    model = TextEmbedding(custom_model_name)
-    docs = ["hello world", "flag embedding"]
-    embeddings = list(model.embed(docs))
-    embeddings = np.stack(embeddings, axis=0)
-    assert embeddings.shape == (2, dim)
-
-    assert np.allclose(embeddings[0, : canonical_vector.shape[0]], canonical_vector, atol=1e-3)
-    if is_ci:
-        delete_model_cache(model.model._model_dir)
-
-
-def test_cross_encoder_custom_model():
-    is_ci = os.getenv("CI")
-    custom_model_name = "Xenova/ms-marco-MiniLM-L-4-v2"
-    size_in_gb = 0.08
-    source = ModelSource(hf=custom_model_name)
-    canonical_vector = np.array([-5.7170815, -11.112114], dtype=np.float32)
-
-    TextCrossEncoder.add_custom_model(
-        custom_model_name,
-        model_file="onnx/model.onnx",
-        sources=source,
-        size_in_gb=size_in_gb,
-    )
-
-    assert CustomTextCrossEncoder.SUPPORTED_MODELS[0] == BaseModelDescription(
-        model=custom_model_name,
-        sources=source,
-        model_file="onnx/model.onnx",
-        description="",
-        license="",
-        size_in_GB=size_in_gb,
-    )
-
-    model = TextCrossEncoder(custom_model_name)
-    pairs = [
-        ("What is AI?", "Artificial intelligence is ..."),
-        ("What is ML?", "Machine learning is ..."),
-    ]
-    scores = list(model.rerank_pairs(pairs))
-
-    embeddings = np.stack(scores, axis=0)
-    assert embeddings.shape == (2,)
-    assert np.allclose(embeddings, canonical_vector, atol=1e-3)
-    if is_ci:
-        delete_model_cache(model.model._model_dir)
-
-
-def test_mock_add_custom_models():
-    dim = 5
-    size_in_gb = 0.1
-    source = ModelSource(hf="artificial")
-
-    num_tokens = 10
-    dummy_pooled_embedding = np.random.random((1, dim)).astype(np.float32)
-    dummy_token_embedding = np.random.random((1, num_tokens, dim)).astype(np.float32)
-    dummy_attention_mask = np.ones((1, num_tokens)).astype(np.int64)
-
-    dummy_token_output = OnnxOutputContext(
-        model_output=dummy_token_embedding, attention_mask=dummy_attention_mask
-    )
-    dummy_pooled_output = OnnxOutputContext(model_output=dummy_pooled_embedding)
-    input_data = {
-        f"{PoolingType.MEAN.lower()}-normalized": dummy_token_output,
-        f"{PoolingType.MEAN.lower()}": dummy_token_output,
-        f"{PoolingType.CLS.lower()}-normalized": dummy_token_output,
-        f"{PoolingType.CLS.lower()}": dummy_token_output,
-        f"{PoolingType.DISABLED.lower()}-normalized": dummy_pooled_output,
-        f"{PoolingType.DISABLED.lower()}": dummy_pooled_output,
-    }
-
-    expected_output = {
-        f"{PoolingType.MEAN.lower()}-normalized": normalize(
-            mean_pooling(dummy_token_embedding, dummy_attention_mask)
-        ),
-        f"{PoolingType.MEAN.lower()}": mean_pooling(dummy_token_embedding, dummy_attention_mask),
-        f"{PoolingType.CLS.lower()}-normalized": normalize(dummy_token_embedding[:, 0]),
-        f"{PoolingType.CLS.lower()}": dummy_token_embedding[:, 0],
-        f"{PoolingType.DISABLED.lower()}-normalized": normalize(dummy_pooled_embedding),
-        f"{PoolingType.DISABLED.lower()}": dummy_pooled_embedding,
-    }
-
-    for pooling, normalization in itertools.product(
-        (PoolingType.MEAN, PoolingType.CLS, PoolingType.DISABLED), (True, False)
-    ):
-        model_name = f"{pooling.name.lower()}{'-normalized' if normalization else ''}"
-        TextEmbedding.add_custom_model(
-            model_name,
-            pooling=pooling,
-            normalization=normalization,
-            sources=source,
-            dim=dim,
-            size_in_gb=size_in_gb,
-        )
-
-        custom_text_embedding = CustomTextEmbedding(
-            model_name,
-            lazy_load=True,
-            specific_model_path="./",  # disable model downloading and loading
-        )
-
-        post_processed_output = next(
-            iter(custom_text_embedding._post_process_onnx_output(input_data[model_name]))
-        )
-        assert np.allclose(post_processed_output, expected_output[model_name], atol=1e-3)
-
-
-def test_do_not_add_existing_model():
-    existing_base_model = "sentence-transformers/all-MiniLM-L6-v2"
-    custom_model_name = "intfloat/multilingual-e5-small"
-
-    with pytest.raises(ValueError, match=f"Model {existing_base_model} is already registered"):
-        TextEmbedding.add_custom_model(
-            existing_base_model,
-            pooling=PoolingType.MEAN,
-            normalization=True,
-            sources=ModelSource(hf=existing_base_model),
-            dim=384,
-            size_in_gb=0.47,
-        )
-
-    TextEmbedding.add_custom_model(
-        custom_model_name,
-        pooling=PoolingType.MEAN,
-        normalization=False,
-        sources=ModelSource(hf=existing_base_model),
-        dim=384,
-        size_in_gb=0.47,
-    )
-
-    with pytest.raises(ValueError, match=f"Model {custom_model_name} is already registered"):
-        TextEmbedding.add_custom_model(
-            custom_model_name,
-            pooling=PoolingType.MEAN,
-            normalization=True,
-            sources=ModelSource(hf=custom_model_name),
-            dim=384,
-            size_in_gb=0.47,
-        )
-
-
-def test_do_not_add_existing_cross_encoder():
-    existing_base_model = "Xenova/ms-marco-MiniLM-L-6-v2"
-    custom_model_name = "Xenova/ms-marco-MiniLM-L-4-v2"
-
-    with pytest.raises(ValueError, match=f"Model {existing_base_model} is already registered"):
-        TextCrossEncoder.add_custom_model(
-            existing_base_model,
-            sources=ModelSource(hf=existing_base_model),
-            size_in_gb=0.08,
-        )
-
-    TextCrossEncoder.add_custom_model(
-        custom_model_name,
-        sources=ModelSource(hf=existing_base_model),
-        size_in_gb=0.08,
-    )
-
-    with pytest.raises(ValueError, match=f"Model {custom_model_name} is already registered"):
-        TextCrossEncoder.add_custom_model(
-            custom_model_name,
-            sources=ModelSource(hf=custom_model_name),
-            size_in_gb=0.08,
-        )

tests/test_image_onnx_embeddings.py

@@ -8,7 +8,7 @@ from PIL import Image
 
 from fastembed import ImageEmbedding
 from tests.config import TEST_MISC_DIR
-from tests.utils import delete_model_cache, should_test_model
+from tests.utils import delete_model_cache
 
 CANONICAL_VECTOR_VALUES = {
     "Qdrant/clip-ViT-B-32-vision": np.array([-0.0098, 0.0128, -0.0274, 0.002, -0.0059]),
@@ -21,24 +21,19 @@ CANONICAL_VECTOR_VALUES = {
     "Qdrant/Unicom-ViT-B-32": np.array(
         [0.0418, 0.0550, 0.0003, 0.0253, -0.0185, 0.0016, -0.0368, -0.0402, -0.0891, -0.0186]
     ),
-    "jinaai/jina-clip-v1": np.array(
-        [-0.029, 0.0216, 0.0396, 0.0283, -0.0023, 0.0151, 0.011, -0.0235, 0.0251, -0.0343]
-    ),
 }
 
 
-@pytest.mark.parametrize("model_name", ["Qdrant/clip-ViT-B-32-vision"])
-def test_embedding(model_name: str) -> None:
+def test_embedding():
     is_ci = os.getenv("CI")
-    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
 
-    for model_desc in ImageEmbedding._list_supported_models():
-        if not should_test_model(model_desc, model_name, is_ci, is_manual):
+    for model_desc in ImageEmbedding.list_supported_models():
+        if not is_ci and model_desc["size_in_GB"] > 1:
             continue
 
-        dim = model_desc.dim
+        dim = model_desc["dim"]
 
-        model = ImageEmbedding(model_name=model_desc.model)
+        model = ImageEmbedding(model_name=model_desc["model"])
 
         images = [
             TEST_MISC_DIR / "image.jpeg",
@@ -50,20 +45,20 @@ def test_embedding(model_name: str) -> None:
         embeddings = np.stack(embeddings, axis=0)
         assert embeddings.shape == (len(images), dim)
 
-        canonical_vector = CANONICAL_VECTOR_VALUES[model_desc.model]
+        canonical_vector = CANONICAL_VECTOR_VALUES[model_desc["model"]]
 
         assert np.allclose(
             embeddings[0, : canonical_vector.shape[0]], canonical_vector, atol=1e-3
-        ), model_desc.model
+        ), model_desc["model"]
 
-        assert np.allclose(embeddings[1], embeddings[2]), model_desc.model
+        assert np.allclose(embeddings[1], embeddings[2]), model_desc["model"]
 
         if is_ci:
             delete_model_cache(model.model._model_dir)
 
 
 @pytest.mark.parametrize("n_dims,model_name", [(512, "Qdrant/clip-ViT-B-32-vision")])
-def test_batch_embedding(n_dims: int, model_name: str) -> None:
+def test_batch_embedding(n_dims, model_name):
     is_ci = os.getenv("CI")
     model = ImageEmbedding(model_name=model_name)
     n_images = 32
@@ -76,18 +71,14 @@ def test_batch_embedding(n_dims: int, model_name: str) -> None:
 
     embeddings = list(model.embed(images, batch_size=10))
     embeddings = np.stack(embeddings, axis=0)
-    assert np.allclose(embeddings[1], embeddings[2])
-
-    canonical_vector = CANONICAL_VECTOR_VALUES[model_name]
 
     assert embeddings.shape == (len(test_images) * n_images, n_dims)
-    assert np.allclose(embeddings[0, : canonical_vector.shape[0]], canonical_vector, atol=1e-3)
     if is_ci:
         delete_model_cache(model.model._model_dir)
 
 
 @pytest.mark.parametrize("n_dims,model_name", [(512, "Qdrant/clip-ViT-B-32-vision")])
-def test_parallel_processing(n_dims: int, model_name: str) -> None:
+def test_parallel_processing(n_dims, model_name):
     is_ci = os.getenv("CI")
     model = ImageEmbedding(model_name=model_name)
 
@@ -115,7 +106,7 @@ def test_parallel_processing(n_dims: int, model_name: str) -> None:
 
 
 @pytest.mark.parametrize("model_name", ["Qdrant/clip-ViT-B-32-vision"])
-def test_lazy_load(model_name: str) -> None:
+def test_lazy_load(model_name):
     is_ci = os.getenv("CI")
     model = ImageEmbedding(model_name=model_name, lazy_load=True)
     assert not hasattr(model.model, "model")

tests/test_late_interaction_embeddings.py

@@ -6,7 +6,7 @@ import numpy as np
 from fastembed.late_interaction.late_interaction_text_embedding import (
     LateInteractionTextEmbedding,
 )
-from tests.utils import delete_model_cache, should_test_model
+from tests.utils import delete_model_cache
 
 # vectors are abridged and rounded for brevity
 CANONICAL_COLUMN_VALUES = {
@@ -153,37 +153,31 @@ CANONICAL_QUERY_VALUES = {
 docs = ["Hello World"]
 
 
-@pytest.mark.parametrize("model_name", ["answerdotai/answerai-colbert-small-v1"])
-def test_batch_embedding(model_name: str):
+def test_batch_embedding():
     is_ci = os.getenv("CI")
     docs_to_embed = docs * 10
 
-    model = LateInteractionTextEmbedding(model_name=model_name)
-    result = list(model.embed(docs_to_embed, batch_size=6))
-    expected_result = CANONICAL_COLUMN_VALUES[model_name]
+    for model_name, expected_result in CANONICAL_COLUMN_VALUES.items():
+        print("evaluating", model_name)
+        model = LateInteractionTextEmbedding(model_name=model_name)
+        result = list(model.embed(docs_to_embed, batch_size=6))
 
-    for value in result:
-        token_num, abridged_dim = expected_result.shape
-        assert np.allclose(value[:, :abridged_dim], expected_result, atol=2e-3)
+        for value in result:
+            token_num, abridged_dim = expected_result.shape
+            assert np.allclose(value[:, :abridged_dim], expected_result, atol=2e-3)
 
-    if is_ci:
-        delete_model_cache(model.model._model_dir)
+        if is_ci:
+            delete_model_cache(model.model._model_dir)
 
 
-@pytest.mark.parametrize("model_name", ["answerdotai/answerai-colbert-small-v1"])
-def test_single_embedding(model_name: str):
+def test_single_embedding():
     is_ci = os.getenv("CI")
-    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
     docs_to_embed = docs
 
-    for model_desc in LateInteractionTextEmbedding._list_supported_models():
-        if not should_test_model(model_desc, model_name, is_ci, is_manual):
-            continue
-
+    for model_name, expected_result in CANONICAL_COLUMN_VALUES.items():
         print("evaluating", model_name)
         model = LateInteractionTextEmbedding(model_name=model_name)
         result = next(iter(model.embed(docs_to_embed, batch_size=6)))
-        expected_result = CANONICAL_COLUMN_VALUES[model_name]
         token_num, abridged_dim = expected_result.shape
         assert np.allclose(result[:, :abridged_dim], expected_result, atol=2e-3)
 
@@ -191,20 +185,14 @@ def test_single_embedding(model_name: str):
             delete_model_cache(model.model._model_dir)
 
 
-@pytest.mark.parametrize("model_name", ["answerdotai/answerai-colbert-small-v1"])
-def test_single_embedding_query(model_name: str):
+def test_single_embedding_query():
     is_ci = os.getenv("CI")
-    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
     queries_to_embed = docs
 
-    for model_desc in LateInteractionTextEmbedding._list_supported_models():
-        if not should_test_model(model_desc, model_name, is_ci, is_manual):
-            continue
-
+    for model_name, expected_result in CANONICAL_QUERY_VALUES.items():
         print("evaluating", model_name)
         model = LateInteractionTextEmbedding(model_name=model_name)
         result = next(iter(model.query_embed(queries_to_embed)))
-        expected_result = CANONICAL_QUERY_VALUES[model_name]
         token_num, abridged_dim = expected_result.shape
         assert np.allclose(result[:, :abridged_dim], expected_result, atol=2e-3)
 
@@ -212,11 +200,10 @@ def test_single_embedding_query(model_name: str):
             delete_model_cache(model.model._model_dir)
 
 
-@pytest.mark.parametrize("token_dim,model_name", [(96, "answerdotai/answerai-colbert-small-v1")])
-def test_parallel_processing(token_dim: int, model_name: str):
+def test_parallel_processing():
     is_ci = os.getenv("CI")
-    model = LateInteractionTextEmbedding(model_name=model_name)
-
+    model = LateInteractionTextEmbedding(model_name="colbert-ir/colbertv2.0")
+    token_dim = 128
     docs = ["hello world", "flag embedding"] * 100
     embeddings = list(model.embed(docs, batch_size=10, parallel=2))
     embeddings = np.stack(embeddings, axis=0)
@@ -235,8 +222,11 @@ def test_parallel_processing(token_dim: int, model_name: str):
         delete_model_cache(model.model._model_dir)
 
 
-@pytest.mark.parametrize("model_name", ["answerdotai/answerai-colbert-small-v1"])
-def test_lazy_load(model_name: str):
+@pytest.mark.parametrize(
+    "model_name",
+    ["colbert-ir/colbertv2.0"],
+)
+def test_lazy_load(model_name):
     is_ci = os.getenv("CI")
 
     model = LateInteractionTextEmbedding(model_name=model_name, lazy_load=True)

tests/test_late_interaction_multimodal.py

@@ -1,83 +0,0 @@
-import os
-
-import pytest
-from PIL import Image
-import numpy as np
-
-from fastembed import LateInteractionMultimodalEmbedding
-from tests.config import TEST_MISC_DIR
-
-
-# vectors are abridged and rounded for brevity
-CANONICAL_IMAGE_VALUES = {
-    "Qdrant/colpali-v1.3-fp16": np.array(
-        [
-            [-0.0345, -0.022, 0.0567, -0.0518, -0.0782, 0.1714, -0.1738],
-            [-0.1181, -0.099, 0.0268, 0.0774, 0.0228, 0.0563, -0.1021],
-            [-0.117, -0.0683, 0.0371, 0.0921, 0.0107, 0.0659, -0.0666],
-            [-0.1393, -0.0948, 0.037, 0.0951, -0.0126, 0.0678, -0.087],
-            [-0.0957, -0.081, 0.0404, 0.052, 0.0409, 0.0335, -0.064],
-            [-0.0626, -0.0445, 0.056, 0.0592, -0.0229, 0.0409, -0.0301],
-            [-0.1299, -0.0691, 0.1097, 0.0728, 0.0123, 0.0519, 0.0122],
-        ]
-    ),
-}
-
-CANONICAL_QUERY_VALUES = {
-    "Qdrant/colpali-v1.3-fp16": np.array(
-        [
-            [-0.0023, 0.1477, 0.1594, 0.046, -0.0196, 0.0554, 0.1567],
-            [-0.0139, -0.0057, 0.0932, 0.0052, -0.0678, 0.0131, 0.0537],
-            [0.0054, 0.0364, 0.2078, -0.074, 0.0355, 0.061, 0.1593],
-            [-0.0076, -0.0154, 0.2266, 0.0103, 0.0089, -0.024, 0.098],
-            [-0.0274, 0.0098, 0.2106, -0.0634, 0.0616, -0.0021, 0.0708],
-            [0.0074, 0.0025, 0.1631, -0.0802, 0.0418, -0.0219, 0.1022],
-            [-0.0165, -0.0106, 0.1672, -0.0768, 0.0389, -0.0038, 0.1137],
-        ]
-    ),
-}
-
-queries = ["hello world", "flag embedding"]
-images = [
-    TEST_MISC_DIR / "image.jpeg",
-    str(TEST_MISC_DIR / "image.jpeg"),
-    Image.open((TEST_MISC_DIR / "image.jpeg")),
-]
-
-
-def test_batch_embedding():
-    if os.getenv("CI"):
-        pytest.skip("Colpali is too large to test in CI")
-
-    for model_name, expected_result in CANONICAL_IMAGE_VALUES.items():
-        print("evaluating", model_name)
-        model = LateInteractionMultimodalEmbedding(model_name=model_name)
-        result = list(model.embed_image(images, batch_size=2))
-
-        for value in result:
-            token_num, abridged_dim = expected_result.shape
-            assert np.allclose(value[:token_num, :abridged_dim], expected_result, atol=2e-3)
-
-
-def test_single_embedding():
-    if os.getenv("CI"):
-        pytest.skip("Colpali is too large to test in CI")
-
-    for model_name, expected_result in CANONICAL_IMAGE_VALUES.items():
-        print("evaluating", model_name)
-        model = LateInteractionMultimodalEmbedding(model_name=model_name)
-        result = next(iter(model.embed_image(images, batch_size=6)))
-        token_num, abridged_dim = expected_result.shape
-        assert np.allclose(result[:token_num, :abridged_dim], expected_result, atol=2e-3)
-
-
-def test_single_embedding_query():
-    if os.getenv("CI"):
-        pytest.skip("Colpali is too large to test in CI")
-
-    for model_name, expected_result in CANONICAL_QUERY_VALUES.items():
-        print("evaluating", model_name)
-        model = LateInteractionMultimodalEmbedding(model_name=model_name)
-        result = next(iter(model.embed_text(queries)))
-        token_num, abridged_dim = expected_result.shape
-        assert np.allclose(result[:token_num, :abridged_dim], expected_result, atol=2e-3)

tests/test_multi_gpu.py

@@ -1,5 +1,4 @@
 import pytest
-from typing import Optional
 from fastembed import (
     TextEmbedding,
     SparseTextEmbedding,
@@ -14,7 +13,7 @@ CACHE_DIR = "../model_cache"
 
 @pytest.mark.skip(reason="Requires a multi-gpu server")
 @pytest.mark.parametrize("device_id", [None, 0, 1])
-def test_gpu_via_providers(device_id: Optional[int]) -> None:
+def test_gpu_via_providers(device_id):
     docs = ["hello world", "flag embedding"]
 
     device_id = device_id if device_id is not None else 0
@@ -86,7 +85,7 @@ def test_gpu_via_providers(device_id: Optional[int]) -> None:
 
 @pytest.mark.skip(reason="Requires a multi-gpu server")
 @pytest.mark.parametrize("device_ids", [None, [0], [1], [0, 1]])
-def test_gpu_cuda_device_ids(device_ids: Optional[list[int]]) -> None:
+def test_gpu_cuda_device_ids(device_ids):
     docs = ["hello world", "flag embedding"]
     device_id = device_ids[0] if device_ids else 0
     embedding_model = TextEmbedding(
@@ -171,7 +170,7 @@ def test_gpu_cuda_device_ids(device_ids: Optional[list[int]]) -> None:
 @pytest.mark.parametrize(
     "device_ids,parallel", [(None, None), (None, 2), ([1], None), ([1], 1), ([1], 2), ([0, 1], 2)]
 )
-def test_multi_gpu_parallel_inference(device_ids: Optional[list[int]], parallel: int) -> None:
+def test_multi_gpu_parallel_inference(device_ids, parallel):
     docs = ["hello world", "flag embedding"] * 100
     batch_size = 5
 

tests/test_sparse_embeddings.py

@@ -5,10 +5,10 @@ import numpy as np
 
 from fastembed.sparse.bm25 import Bm25
 from fastembed.sparse.sparse_text_embedding import SparseTextEmbedding
-from tests.utils import delete_model_cache, should_test_model
+from tests.utils import delete_model_cache
 
 CANONICAL_COLUMN_VALUES = {
-    "prithivida/Splade_PP_en_v1": {
+    "prithvida/Splade_PP_en_v1": {
         "indices": [
             2040,
             2047,
@@ -49,41 +49,28 @@ CANONICAL_COLUMN_VALUES = {
 docs = ["Hello World"]
 
 
-@pytest.mark.parametrize("model_name", ["prithivida/Splade_PP_en_v1"])
-def test_batch_embedding(model_name: str) -> None:
+def test_batch_embedding():
     is_ci = os.getenv("CI")
     docs_to_embed = docs * 10
 
-    model = SparseTextEmbedding(model_name=model_name)
-    result = next(iter(model.embed(docs_to_embed, batch_size=6)))
-    expected_result = CANONICAL_COLUMN_VALUES[model_name]
-    assert result.indices.tolist() == expected_result["indices"]
+    for model_name, expected_result in CANONICAL_COLUMN_VALUES.items():
+        model = SparseTextEmbedding(model_name=model_name)
+        result = next(iter(model.embed(docs_to_embed, batch_size=6)))
+        assert result.indices.tolist() == expected_result["indices"]
 
-    for i, value in enumerate(result.values):
-        assert pytest.approx(value, abs=0.001) == expected_result["values"][i]
-    if is_ci:
-        delete_model_cache(model.model._model_dir)
+        for i, value in enumerate(result.values):
+            assert pytest.approx(value, abs=0.001) == expected_result["values"][i]
+        if is_ci:
+            delete_model_cache(model.model._model_dir)
 
 
-@pytest.mark.parametrize("model_name", ["prithivida/Splade_PP_en_v1"])
-def test_single_embedding(model_name: str) -> None:
+def test_single_embedding():
     is_ci = os.getenv("CI")
-    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
-
-    for model_desc in SparseTextEmbedding._list_supported_models():
-        if (
-            model_desc.model not in CANONICAL_COLUMN_VALUES
-        ):  # attention models and bm25 are also parts of
-            # SparseTextEmbedding, however, they have their own tests
-            continue
-        if not should_test_model(model_desc, model_name, is_ci, is_manual):
-            continue
-
+    for model_name, expected_result in CANONICAL_COLUMN_VALUES.items():
         model = SparseTextEmbedding(model_name=model_name)
 
         passage_result = next(iter(model.embed(docs, batch_size=6)))
         query_result = next(iter(model.query_embed(docs)))
-        expected_result = CANONICAL_COLUMN_VALUES[model_name]
         for result in [passage_result, query_result]:
             assert result.indices.tolist() == expected_result["indices"]
 
@@ -93,10 +80,9 @@ def test_single_embedding(model_name: str) -> None:
             delete_model_cache(model.model._model_dir)
 
 
-@pytest.mark.parametrize("model_name", ["prithivida/Splade_PP_en_v1"])
-def test_parallel_processing(model_name: str) -> None:
+def test_parallel_processing():
     is_ci = os.getenv("CI")
-    model = SparseTextEmbedding(model_name=model_name)
+    model = SparseTextEmbedding(model_name="prithivida/Splade_PP_en_v1")
     docs = ["hello world", "flag embedding"] * 30
     sparse_embeddings_duo = list(model.embed(docs, batch_size=10, parallel=2))
     sparse_embeddings_all = list(model.embed(docs, batch_size=10, parallel=0))
@@ -125,7 +111,7 @@ def test_parallel_processing(model_name: str) -> None:
 
 
 @pytest.fixture
-def bm25_instance() -> None:
+def bm25_instance():
     ci = os.getenv("CI", True)
     model = Bm25("Qdrant/bm25", language="english")
     yield model
@@ -133,7 +119,7 @@ def bm25_instance() -> None:
         delete_model_cache(model._model_dir)
 
 
-def test_stem_with_stopwords_and_punctuation(bm25_instance: Bm25) -> None:
+def test_stem_with_stopwords_and_punctuation(bm25_instance):
     # Setup
     bm25_instance.stopwords = {"the", "is", "a"}
     bm25_instance.punctuation = {".", ",", "!"}
@@ -149,7 +135,7 @@ def test_stem_with_stopwords_and_punctuation(bm25_instance: Bm25) -> None:
     assert result == expected, f"Expected {expected}, but got {result}"
 
 
-def test_stem_case_insensitive_stopwords(bm25_instance: Bm25) -> None:
+def test_stem_case_insensitive_stopwords(bm25_instance):
     # Setup
     bm25_instance.stopwords = {"the", "is", "a"}
     bm25_instance.punctuation = {".", ",", "!"}
@@ -165,29 +151,11 @@ def test_stem_case_insensitive_stopwords(bm25_instance: Bm25) -> None:
     assert result == expected, f"Expected {expected}, but got {result}"
 
 
-@pytest.mark.parametrize("disable_stemmer", [True, False])
-def test_disable_stemmer_behavior(disable_stemmer: bool) -> None:
-    # Setup
-    model = Bm25("Qdrant/bm25", language="english", disable_stemmer=disable_stemmer)
-    model.stopwords = {"the", "is", "a"}
-    model.punctuation = {".", ",", "!"}
-
-    # Test data
-    tokens = ["The", "quick", "brown", "fox", "is", "a", "test", "sentence", ".", "!"]
-
-    # Execute
-    result = model._stem(tokens)
-
-    # Assert
-    if disable_stemmer:
-        expected = ["quick", "brown", "fox", "test", "sentence"]  # no stemming, lower case only
-    else:
-        expected = ["quick", "brown", "fox", "test", "sentenc"]
-    assert result == expected, f"Expected {expected}, but got {result}"
-
-
-@pytest.mark.parametrize("model_name", ["prithivida/Splade_PP_en_v1"])
-def test_lazy_load(model_name: str) -> None:
+@pytest.mark.parametrize(
+    "model_name",
+    ["prithivida/Splade_PP_en_v1"],
+)
+def test_lazy_load(model_name):
     is_ci = os.getenv("CI")
     model = SparseTextEmbedding(model_name=model_name, lazy_load=True)
     assert not hasattr(model.model, "model")

tests/test_text_cross_encoder.py

@@ -4,39 +4,29 @@ import numpy as np
 import pytest
 
 from fastembed.rerank.cross_encoder import TextCrossEncoder
-from tests.utils import delete_model_cache, should_test_model
+from tests.utils import delete_model_cache
 
 CANONICAL_SCORE_VALUES = {
     "Xenova/ms-marco-MiniLM-L-6-v2": np.array([8.500708, -2.541011]),
     "Xenova/ms-marco-MiniLM-L-12-v2": np.array([9.330912, -2.0380247]),
     "BAAI/bge-reranker-base": np.array([6.15733337, -3.65939403]),
-    "jinaai/jina-reranker-v1-tiny-en": np.array([2.5911, 0.1122]),
-    "jinaai/jina-reranker-v1-turbo-en": np.array([1.8295, -2.8908]),
-    "jinaai/jina-reranker-v2-base-multilingual": np.array([1.6533, -1.6455]),
 }
 
 
-@pytest.mark.parametrize("model_name", ["Xenova/ms-marco-MiniLM-L-6-v2"])
-def test_rerank(model_name: str) -> None:
+def test_rerank():
     is_ci = os.getenv("CI")
-    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
 
-    for model_desc in TextCrossEncoder._list_supported_models():
-        if not should_test_model(model_desc, model_name, is_ci, is_manual):
+    for model_desc in TextCrossEncoder.list_supported_models():
+        if not is_ci and model_desc["size_in_GB"] > 1:
             continue
 
+        model_name = model_desc["model"]
         model = TextCrossEncoder(model_name=model_name)
 
         query = "What is the capital of France?"
         documents = ["Paris is the capital of France.", "Berlin is the capital of Germany."]
         scores = np.array(list(model.rerank(query, documents)))
 
-        pairs = [(query, doc) for doc in documents]
-        scores2 = np.array(list(model.rerank_pairs(pairs)))
-        assert np.allclose(
-            scores, scores2, atol=1e-5
-        ), f"Model: {model_name}, Scores: {scores}, Scores2: {scores2}"
-
         canonical_scores = CANONICAL_SCORE_VALUES[model_name]
         assert np.allclose(
             scores, canonical_scores, atol=1e-3
@@ -45,8 +35,11 @@ def test_rerank(model_name: str) -> None:
             delete_model_cache(model.model._model_dir)
 
 
-@pytest.mark.parametrize("model_name", ["Xenova/ms-marco-MiniLM-L-6-v2"])
-def test_batch_rerank(model_name: str) -> None:
+@pytest.mark.parametrize(
+    "model_name",
+    ["Xenova/ms-marco-MiniLM-L-6-v2", "Xenova/ms-marco-MiniLM-L-12-v2", "BAAI/bge-reranker-base"],
+)
+def test_batch_rerank(model_name):
     is_ci = os.getenv("CI")
 
     model = TextCrossEncoder(model_name=model_name)
@@ -55,12 +48,6 @@ def test_batch_rerank(model_name: str) -> None:
     documents = ["Paris is the capital of France.", "Berlin is the capital of Germany."] * 50
     scores = np.array(list(model.rerank(query, documents, batch_size=10)))
 
-    pairs = [(query, doc) for doc in documents]
-    scores2 = np.array(list(model.rerank_pairs(pairs)))
-    assert np.allclose(
-        scores, scores2, atol=1e-5
-    ), f"Model: {model_name}, Scores: {scores}, Scores2: {scores2}"
-
     canonical_scores = np.tile(CANONICAL_SCORE_VALUES[model_name], 50)
 
     assert scores.shape == canonical_scores.shape, f"Unexpected shape for model {model_name}"
@@ -71,8 +58,11 @@ def test_batch_rerank(model_name: str) -> None:
         delete_model_cache(model.model._model_dir)
 
 
-@pytest.mark.parametrize("model_name", ["Xenova/ms-marco-MiniLM-L-6-v2"])
-def test_lazy_load(model_name: str) -> None:
+@pytest.mark.parametrize(
+    "model_name",
+    ["Xenova/ms-marco-MiniLM-L-6-v2"],
+)
+def test_lazy_load(model_name):
     is_ci = os.getenv("CI")
     model = TextCrossEncoder(model_name=model_name, lazy_load=True)
     assert not hasattr(model.model, "model")
@@ -83,24 +73,3 @@ def test_lazy_load(model_name: str) -> None:
 
     if is_ci:
         delete_model_cache(model.model._model_dir)
-
-
-@pytest.mark.parametrize("model_name", ["Xenova/ms-marco-MiniLM-L-6-v2"])
-def test_rerank_pairs_parallel(model_name: str) -> None:
-    is_ci = os.getenv("CI")
-
-    model = TextCrossEncoder(model_name=model_name)
-    query = "What is the capital of France?"
-    documents = ["Paris is the capital of France.", "Berlin is the capital of Germany."] * 10
-    pairs = [(query, doc) for doc in documents]
-    scores_parallel = np.array(list(model.rerank_pairs(pairs, parallel=2, batch_size=10)))
-    scores_sequential = np.array(list(model.rerank_pairs(pairs, batch_size=10)))
-    assert np.allclose(
-        scores_parallel, scores_sequential, atol=1e-5
-    ), f"Model: {model_name}, Scores (Parallel): {scores_parallel}, Scores (Sequential): {scores_sequential}"
-    canonical_scores = CANONICAL_SCORE_VALUES[model_name]
-    assert np.allclose(
-        scores_parallel[: len(canonical_scores)], canonical_scores, atol=1e-3
-    ), f"Model: {model_name}, Scores (Parallel): {scores_parallel}, Expected: {canonical_scores}"
-    if is_ci:
-        delete_model_cache(model.model._model_dir)

tests/test_text_multitask_embeddings.py

@@ -1,246 +0,0 @@
-import os
-
-import numpy as np
-import pytest
-
-from fastembed import TextEmbedding
-from fastembed.text.multitask_embedding import JinaEmbeddingV3, Task
-from tests.utils import delete_model_cache
-
-
-CANONICAL_VECTOR_VALUES = {
-    "jinaai/jina-embeddings-v3": [
-        {
-            "task_id": Task.RETRIEVAL_QUERY,
-            "vectors": np.array(
-                [
-                    [0.0623, -0.0402, 0.1706, -0.0143, 0.0617],
-                    [-0.1064, -0.0733, 0.0353, 0.0096, 0.0667],
-                ]
-            ),
-        },
-        {
-            "task_id": Task.RETRIEVAL_PASSAGE,
-            "vectors": np.array(
-                [
-                    [0.0513, -0.0247, 0.1751, -0.0075, 0.0679],
-                    [-0.0987, -0.0786, 0.09, 0.0087, 0.0577],
-                ]
-            ),
-        },
-        {
-            "task_id": Task.SEPARATION,
-            "vectors": np.array(
-                [
-                    [0.094, -0.1065, 0.1305, 0.0547, 0.0556],
-                    [0.0315, -0.1468, 0.065, 0.0568, 0.0546],
-                ]
-            ),
-        },
-        {
-            "task_id": Task.CLASSIFICATION,
-            "vectors": np.array(
-                [
-                    [0.0606, -0.0877, 0.1384, 0.0065, 0.0722],
-                    [-0.0502, -0.119, 0.032, 0.0514, 0.0689],
-                ]
-            ),
-        },
-        {
-            "task_id": Task.TEXT_MATCHING,
-            "vectors": np.array(
-                [
-                    [0.0911, -0.0341, 0.1305, -0.026, 0.0576],
-                    [-0.1432, -0.05, 0.0133, 0.0464, 0.0789],
-                ]
-            ),
-        },
-    ]
-}
-docs = ["Hello World", "Follow the white rabbit."]
-
-
-@pytest.mark.parametrize("dim,model_name", [(1024, "jinaai/jina-embeddings-v3")])
-def test_batch_embedding(dim: int, model_name: str):
-    is_ci = os.getenv("CI")
-    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
-    if is_ci and not is_manual:
-        pytest.skip("Skipping multitask models in CI non-manual mode")
-
-    docs_to_embed = docs * 10
-    default_task = Task.RETRIEVAL_PASSAGE
-
-    model = TextEmbedding(model_name=model_name)
-
-    embeddings = list(model.embed(documents=docs_to_embed, batch_size=6))
-    embeddings = np.stack(embeddings, axis=0)
-
-    assert embeddings.shape == (len(docs_to_embed), dim)
-
-    canonical_vector = CANONICAL_VECTOR_VALUES[model_name][default_task]["vectors"]
-    assert np.allclose(
-        embeddings[: len(docs), : canonical_vector.shape[1]], canonical_vector, atol=1e-4
-    ), model_name
-
-    if is_ci:
-        delete_model_cache(model.model._model_dir)
-
-
-def test_single_embedding():
-    is_ci = os.getenv("CI")
-    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
-    if is_ci and not is_manual:
-        pytest.skip("Skipping multitask models in CI non-manual mode")
-
-    for model_desc in JinaEmbeddingV3._list_supported_models():
-        # todo: once we add more models, we should not test models >1GB size locally
-        model_name = model_desc.model
-        dim = model_desc.dim
-
-        model = TextEmbedding(model_name=model_name)
-
-        for task in CANONICAL_VECTOR_VALUES[model_name]:
-            print(f"evaluating {model_name} task_id: {task['task_id']}")
-
-            embeddings = list(model.embed(documents=docs, task_id=task["task_id"]))
-            embeddings = np.stack(embeddings, axis=0)
-
-            assert embeddings.shape == (len(docs), dim)
-
-            canonical_vector = task["vectors"]
-            assert np.allclose(
-                embeddings[: len(docs), : canonical_vector.shape[1]], canonical_vector, atol=1e-4
-            ), model_desc.model
-
-        if is_ci:
-            delete_model_cache(model.model._model_dir)
-
-
-def test_single_embedding_query():
-    is_ci = os.getenv("CI")
-    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
-    if is_ci and not is_manual:
-        pytest.skip("Skipping multitask models in CI non-manual mode")
-
-    task_id = Task.RETRIEVAL_QUERY
-
-    for model_desc in JinaEmbeddingV3._list_supported_models():
-        # todo: once we add more models, we should not test models >1GB size locally
-        model_name = model_desc.model
-        dim = model_desc.dim
-
-        model = TextEmbedding(model_name=model_name)
-
-        print(f"evaluating {model_name} query_embed task_id: {task_id}")
-
-        embeddings = list(model.query_embed(query=docs))
-        embeddings = np.stack(embeddings, axis=0)
-
-        assert embeddings.shape == (len(docs), dim)
-
-        canonical_vector = CANONICAL_VECTOR_VALUES[model_name][task_id]["vectors"]
-        assert np.allclose(
-            embeddings[: len(docs), : canonical_vector.shape[1]], canonical_vector, atol=1e-4
-        ), model_desc.model
-
-        if is_ci:
-            delete_model_cache(model.model._model_dir)
-
-
-def test_single_embedding_passage():
-    is_ci = os.getenv("CI")
-    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
-    if is_ci and not is_manual:
-        pytest.skip("Skipping multitask models in CI non-manual mode")
-
-    task_id = Task.RETRIEVAL_PASSAGE
-
-    for model_desc in JinaEmbeddingV3._list_supported_models():
-        # todo: once we add more models, we should not test models >1GB size locally
-
-        model_name = model_desc.model
-        dim = model_desc.dim
-
-        model = TextEmbedding(model_name=model_name)
-
-        print(f"evaluating {model_name} passage_embed task_id: {task_id}")
-
-        embeddings = list(model.passage_embed(texts=docs))
-        embeddings = np.stack(embeddings, axis=0)
-
-        assert embeddings.shape == (len(docs), dim)
-
-        canonical_vector = CANONICAL_VECTOR_VALUES[model_name][task_id]["vectors"]
-        assert np.allclose(
-            embeddings[: len(docs), : canonical_vector.shape[1]], canonical_vector, atol=1e-4
-        ), model_desc.model
-
-        if is_ci:
-            delete_model_cache(model.model._model_dir)
-
-
-@pytest.mark.parametrize("dim,model_name", [(1024, "jinaai/jina-embeddings-v3")])
-def test_parallel_processing(dim: int, model_name: str):
-    is_ci = os.getenv("CI")
-    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
-    if is_ci and not is_manual:
-        pytest.skip("Skipping in CI non-manual mode")
-
-    docs = ["Hello World", "Follow the white rabbit."] * 10
-
-    model = TextEmbedding(model_name=model_name)
-
-    task_id = Task.SEPARATION
-    embeddings_1 = list(model.embed(docs, batch_size=10, parallel=None, task_id=task_id))
-    embeddings_1 = np.stack(embeddings_1, axis=0)
-
-    embeddings_2 = list(model.embed(docs, batch_size=10, parallel=1, task_id=task_id))
-    embeddings_2 = np.stack(embeddings_2, axis=0)
-
-    assert embeddings_1.shape[0] == len(docs) and embeddings_1.shape[-1] == dim
-    assert np.allclose(embeddings_1, embeddings_2, atol=1e-4)
-
-    canonical_vector = CANONICAL_VECTOR_VALUES[model_name][task_id]["vectors"]
-    assert np.allclose(embeddings_2[:2, : canonical_vector.shape[1]], canonical_vector, atol=1e-4)
-
-    if is_ci:
-        delete_model_cache(model.model._model_dir)
-
-
-def test_task_assignment():
-    is_ci = os.getenv("CI")
-    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
-
-    if is_ci and not is_manual:
-        pytest.skip("Skipping in CI non-manual mode")
-
-    for model_desc in JinaEmbeddingV3._list_supported_models():
-        # todo: once we add more models, we should not test models >1GB size locally
-        model_name = model_desc.model
-
-        model = TextEmbedding(model_name=model_name)
-
-        for i, task_id in enumerate(Task):
-            _ = list(model.embed(documents=docs, batch_size=1, task_id=i))
-            assert model.model.current_task_id == task_id
-
-        if is_ci:
-            delete_model_cache(model.model._model_dir)
-
-
-@pytest.mark.parametrize("model_name", ["jinaai/jina-embeddings-v3"])
-def test_lazy_load(model_name: str):
-    is_ci = os.getenv("CI")
-    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
-
-    if is_ci and not is_manual:
-        pytest.skip("Skipping in CI non-manual mode")
-
-    model = TextEmbedding(model_name=model_name, lazy_load=True)
-    assert not hasattr(model.model, "model")
-
-    list(model.embed(docs))
-    assert hasattr(model.model, "model")
-
-    if is_ci:
-        delete_model_cache(model.model._model_dir)

tests/test_text_onnx_embeddings.py

@@ -1,11 +1,10 @@
 import os
-import platform
 
 import numpy as np
 import pytest
 
 from fastembed.text.text_embedding import TextEmbedding
-from tests.utils import delete_model_cache, should_test_model
+from tests.utils import delete_model_cache
 
 CANONICAL_VECTOR_VALUES = {
     "BAAI/bge-small-en": np.array([-0.0232, -0.0255, 0.0174, -0.0639, -0.0006]),
@@ -32,27 +31,25 @@ CANONICAL_VECTOR_VALUES = {
         [-0.034478, 0.03102, 0.00673, 0.02611, -0.039362]
     ),
     "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2": np.array(
-        [0.0361, 0.1862, 0.2776, 0.2461, -0.1904]
+        [0.0094, 0.0184, 0.0328, 0.0072, -0.0351]
     ),
-    "intfloat/multilingual-e5-large": np.array([0.4544, -0.0968, 0.1054, -1.3753, 0.1500]),
+    "intfloat/multilingual-e5-large": np.array([0.0098, 0.0045, 0.0066, -0.0354, 0.0070]),
     "sentence-transformers/paraphrase-multilingual-mpnet-base-v2": np.array(
-        [0.0047, 0.1334, -0.0102, 0.0714, 0.1930]
+        [-0.01341097, 0.0416553, -0.00480805, 0.02844842, 0.0505299]
     ),
     "jinaai/jina-embeddings-v2-small-en": np.array([-0.0455, -0.0428, -0.0122, 0.0613, 0.0015]),
     "jinaai/jina-embeddings-v2-base-en": np.array([-0.0332, -0.0509, 0.0287, -0.0043, -0.0077]),
     "jinaai/jina-embeddings-v2-base-de": np.array([-0.0085, 0.0417, 0.0342, 0.0309, -0.0149]),
     "jinaai/jina-embeddings-v2-base-code": np.array([0.0145, -0.0164, 0.0136, -0.0170, 0.0734]),
-    "jinaai/jina-embeddings-v2-base-zh": np.array([0.0381, 0.0286, -0.0231, 0.0052, -0.0151]),
-    "jinaai/jina-embeddings-v2-base-es": np.array([-0.0108, -0.0092, -0.0373, 0.0171, -0.0301]),
     "nomic-ai/nomic-embed-text-v1": np.array([0.3708, 0.2031, -0.3406, -0.2114, -0.3230]),
     "nomic-ai/nomic-embed-text-v1.5": np.array(
         [-0.15407836, -0.03053198, -3.9138033, 0.1910364, 0.13224715]
     ),
     "nomic-ai/nomic-embed-text-v1.5-Q": np.array(
-        [0.0802303, 0.3700881, -4.3053818, 0.4431803, -0.271572]
+        [-0.12525563, 0.38030425, -3.961622, 0.04176439, -0.0758301]
     ),
     "thenlper/gte-large": np.array(
-        [-0.00986551, -0.00018734, 0.00605892, -0.03289612, -0.0387564],
+        [-0.01920587, 0.00113156, -0.00708992, -0.00632304, -0.04025577]
     ),
     "mixedbread-ai/mxbai-embed-large-v1": np.array(
         [0.02295546, 0.03196154, 0.016512, -0.04031524, -0.0219634]
@@ -65,45 +62,37 @@ CANONICAL_VECTOR_VALUES = {
     ),
     "snowflake/snowflake-arctic-embed-l": np.array([0.0189, -0.0673, 0.0183, 0.0124, 0.0146]),
     "Qdrant/clip-ViT-B-32-text": np.array([0.0083, 0.0103, -0.0138, 0.0199, -0.0069]),
-    "thenlper/gte-base": np.array([0.0038, 0.0355, 0.0181, 0.0092, 0.0654]),
-    "jinaai/jina-clip-v1": np.array([-0.0862, -0.0101, -0.0056, 0.0375, -0.0472]),
 }
 
-MULTI_TASK_MODELS = ["jinaai/jina-embeddings-v3"]
 
-
-@pytest.mark.parametrize("model_name", ["BAAI/bge-small-en-v1.5"])
-def test_embedding(model_name: str) -> None:
+def test_embedding():
     is_ci = os.getenv("CI")
-    is_mac = platform.system() == "Darwin"
-    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
 
-    for model_desc in TextEmbedding._list_supported_models():
-        if model_desc.model in MULTI_TASK_MODELS or (
-            is_mac and model_desc.model == "nomic-ai/nomic-embed-text-v1.5-Q"
-        ):
-            continue
-        if not should_test_model(model_desc, model_name, is_ci, is_manual):
+    for model_desc in TextEmbedding.list_supported_models():
+        if not is_ci and model_desc["size_in_GB"] > 1:
             continue
 
-        dim = model_desc.dim
+        dim = model_desc["dim"]
 
-        model = TextEmbedding(model_name=model_desc.model)
+        model = TextEmbedding(model_name=model_desc["model"])
         docs = ["hello world", "flag embedding"]
         embeddings = list(model.embed(docs))
         embeddings = np.stack(embeddings, axis=0)
         assert embeddings.shape == (2, dim)
 
-        canonical_vector = CANONICAL_VECTOR_VALUES[model_desc.model]
+        canonical_vector = CANONICAL_VECTOR_VALUES[model_desc["model"]]
         assert np.allclose(
             embeddings[0, : canonical_vector.shape[0]], canonical_vector, atol=1e-3
-        ), model_desc.model
+        ), model_desc["model"]
         if is_ci:
             delete_model_cache(model.model._model_dir)
 
 
-@pytest.mark.parametrize("n_dims,model_name", [(384, "BAAI/bge-small-en-v1.5")])
-def test_batch_embedding(n_dims: int, model_name: str) -> None:
+@pytest.mark.parametrize(
+    "n_dims,model_name",
+    [(384, "BAAI/bge-small-en-v1.5"), (768, "jinaai/jina-embeddings-v2-base-en")],
+)
+def test_batch_embedding(n_dims, model_name):
     is_ci = os.getenv("CI")
     model = TextEmbedding(model_name=model_name)
 
@@ -111,13 +100,16 @@ def test_batch_embedding(n_dims: int, model_name: str) -> None:
     embeddings = list(model.embed(docs, batch_size=10))
     embeddings = np.stack(embeddings, axis=0)
 
-    assert embeddings.shape == (len(docs), n_dims)
+    assert embeddings.shape == (200, n_dims)
     if is_ci:
         delete_model_cache(model.model._model_dir)
 
 
-@pytest.mark.parametrize("n_dims,model_name", [(384, "BAAI/bge-small-en-v1.5")])
-def test_parallel_processing(n_dims: int, model_name: str) -> None:
+@pytest.mark.parametrize(
+    "n_dims,model_name",
+    [(384, "BAAI/bge-small-en-v1.5"), (768, "jinaai/jina-embeddings-v2-base-en")],
+)
+def test_parallel_processing(n_dims, model_name):
     is_ci = os.getenv("CI")
     model = TextEmbedding(model_name=model_name)
 
@@ -131,7 +123,7 @@ def test_parallel_processing(n_dims: int, model_name: str) -> None:
     embeddings_3 = list(model.embed(docs, batch_size=10, parallel=0))
     embeddings_3 = np.stack(embeddings_3, axis=0)
 
-    assert embeddings.shape == (len(docs), n_dims)
+    assert embeddings.shape == (200, n_dims)
     assert np.allclose(embeddings, embeddings_2, atol=1e-3)
     assert np.allclose(embeddings, embeddings_3, atol=1e-3)
 
@@ -139,8 +131,11 @@ def test_parallel_processing(n_dims: int, model_name: str) -> None:
         delete_model_cache(model.model._model_dir)
 
 
-@pytest.mark.parametrize("model_name", ["BAAI/bge-small-en-v1.5"])
-def test_lazy_load(model_name: str) -> None:
+@pytest.mark.parametrize(
+    "model_name",
+    ["BAAI/bge-small-en-v1.5"],
+)
+def test_lazy_load(model_name):
     is_ci = os.getenv("CI")
     model = TextEmbedding(model_name=model_name, lazy_load=True)
     assert not hasattr(model.model, "model")

tests/type_stub.py

@@ -1,56 +0,0 @@
-from fastembed import TextEmbedding, LateInteractionTextEmbedding, SparseTextEmbedding
-from fastembed.sparse.bm25 import Bm25
-from fastembed.rerank.cross_encoder import TextCrossEncoder
-
-
-text_embedder = TextEmbedding(cache_dir="models")
-late_interaction_embedder = LateInteractionTextEmbedding(model_name="", cache_dir="models")
-reranker = TextCrossEncoder(model_name="", cache_dir="models")
-sparse_embedder = SparseTextEmbedding(model_name="", cache_dir="models")
-bm25_embedder = Bm25(
-    model_name="",
-    k=1.0,
-    b=1.0,
-    avg_len=1.0,
-    language="",
-    token_max_length=1,
-    disable_stemmer=False,
-    specific_model_path="models",
-)
-
-text_embedder.list_supported_models()
-text_embedder.embed(documents=[""], batch_size=1, parallel=1)
-text_embedder.embed(documents="", parallel=None, task_id=1)
-text_embedder.query_embed(query=[""], batch_size=1, parallel=1)
-text_embedder.query_embed(query="", parallel=None)
-text_embedder.passage_embed(texts=[""], batch_size=1, parallel=1)
-text_embedder.passage_embed(texts=[""], parallel=None)
-
-late_interaction_embedder.list_supported_models()
-late_interaction_embedder.embed(documents=[""], batch_size=1, parallel=1)
-late_interaction_embedder.embed(documents="", parallel=None)
-late_interaction_embedder.query_embed(query=[""], batch_size=1, parallel=1)
-late_interaction_embedder.query_embed(query="", parallel=None)
-late_interaction_embedder.passage_embed(texts=[""], batch_size=1, parallel=1)
-late_interaction_embedder.passage_embed(texts=[""], parallel=None)
-
-reranker.list_supported_models()
-reranker.rerank(query="", documents=[""], batch_size=1, parallel=1)
-reranker.rerank(query="", documents=[""], parallel=None)
-reranker.rerank_pairs(pairs=[("", "")], batch_size=1, parallel=1)
-reranker.rerank_pairs(pairs=[("", "")], parallel=None)
-
-sparse_embedder.list_supported_models()
-sparse_embedder.embed(documents=[""], batch_size=1, parallel=1)
-sparse_embedder.embed(documents="", batch_size=1, parallel=None)
-sparse_embedder.query_embed(query=[""], batch_size=1, parallel=1)
-sparse_embedder.query_embed(query="", batch_size=1, parallel=None)
-sparse_embedder.passage_embed(texts=[""], batch_size=1, parallel=1)
-sparse_embedder.passage_embed(texts=[""], batch_size=1, parallel=None)
-
-bm25_embedder.list_supported_models()
-bm25_embedder.embed(documents=[""], batch_size=1, parallel=1)
-bm25_embedder.embed(documents="", batch_size=1, parallel=None)
-bm25_embedder.query_embed(query=[""], batch_size=1, parallel=1)
-bm25_embedder.query_embed(query="", batch_size=1, parallel=None)
-bm25_embedder.raw_embed(documents=[""])

tests/utils.py

@@ -1,11 +1,7 @@
 import shutil
-import traceback
 
 from pathlib import Path
-from types import TracebackType
-from typing import Union, Callable, Any, Type, Optional
-
-from fastembed.common.model_description import BaseModelDescription
+from typing import Union
 
 
 def delete_model_cache(model_dir: Union[str, Path]) -> None:
@@ -18,16 +14,6 @@ def delete_model_cache(model_dir: Union[str, Path]) -> None:
     Args:
         model_dir (Union[str, Path]): The path to the model cache directory.
     """
-
-    def on_error(
-        func: Callable[..., Any],
-        path: str,
-        exc_info: tuple[Type[BaseException], BaseException, TracebackType],
-    ) -> None:
-        print("Failed to remove: ", path)
-        print("Exception: ", exc_info)
-        traceback.print_exception(*exc_info)
-
     if isinstance(model_dir, str):
         model_dir = Path(model_dir)
 
@@ -35,33 +21,4 @@ def delete_model_cache(model_dir: Union[str, Path]) -> None:
         model_dir = model_dir.parent.parent
 
     if model_dir.exists():
-        # todo: PermissionDenied is raised on blobs removal in Windows, with blobs > 2GB
-        shutil.rmtree(model_dir, onerror=on_error)
-
-
-def should_test_model(
-    model_desc: BaseModelDescription,
-    autotest_model_name: str,
-    is_ci: Optional[str],
-    is_manual: bool,
-):
-    """Determine if a model should be tested based on environment
-
-    Tests can be run either in ci or locally.
-    Testing all models each time in ci is too long.
-    The testing scheme in ci and on a local machine are different, therefore, there are 3 possible scenarious.
-    1) Run lightweight tests in ci:
-        - test only one model that has been manually chosen as a representative for a certain class family
-    2) Run heavyweight (manual) tests in ci:
-        - test all models
-        Running tests in ci each time is too expensive, however, it's fine to run it one time with a manual dispatch
-    3) Run tests locally:
-        - test all models, which are not too heavy, since network speed might be a bottleneck
-
-    """
-    if not is_ci:
-        if model_desc.size_in_GB > 1:
-            return False
-    elif not is_manual and model_desc.model != autotest_model_name:
-        return False
-    return True
+        shutil.rmtree(model_dir)