diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index d040f89a..8a8833c4 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -1,8 +1,8 @@ -name: ci +name: ci on: push: branches: - - master + - master - main permissions: contents: write @@ -14,7 +14,7 @@ jobs: - uses: actions/setup-python@v4 with: python-version: 3.x - - run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV + - run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV - uses: actions/cache@v3 with: key: mkdocs-material-${{ env.cache_id }} diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 9968eebd..0f4b73ab 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,12 +1,9 @@ repos: -- repo: https://github.com/pre-commit/pre-commit-hooks - rev: v3.2.0 - hooks: - - id: trailing-whitespace - - id: end-of-file-fixer - - id: check-yaml - - id: check-added-large-files -- repo: https://github.com/psf/black - rev: 23.7.0 - hooks: - - id: black + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.1.13 + hooks: + - id: ruff + types_or: [ python, pyi, jupyter ] + args: [ --fix ] + - id: ruff-format + types_or: [ python, pyi, jupyter ] diff --git a/README.md b/README.md index b4826d7b..27e3cfdb 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ The default embedding supports "query" and "passage" prefixes for the input text ## 🚀 Installation -To install the FastEmbed library, pip works: +To install the FastEmbed library, pip works: ```bash pip install fastembed @@ -36,8 +36,8 @@ documents: List[str] = [ "passage: This is an example passage.", "fastembed is supported by and maintained by Qdrant." # You can leave out the prefix but it's recommended ] -embedding_model = Embedding(model_name="BAAI/bge-base-en", max_length=512) -embeddings: List[np.ndarray] = list(embedding_model.embed(documents)) # Note the list() call - this is a generator +embedding_model = Embedding(model_name="BAAI/bge-base-en", max_length=512) +embeddings: List[np.ndarray] = list(embedding_model.embed(documents)) # Note the list() call - this is a generator ``` ## Usage with Qdrant @@ -48,7 +48,7 @@ Installation with Qdrant Client in Python: pip install qdrant-client[fastembed] ``` -Might have to use ```pip install 'qdrant-client[fastembed]'``` on zsh. +Might have to use ```pip install 'qdrant-client[fastembed]'``` on zsh. ```python from qdrant_client import QdrantClient diff --git a/docs/examples/FastEmbed_vs_HF_Comparison.ipynb b/docs/examples/FastEmbed_vs_HF_Comparison.ipynb index d842cb82..5b7a9eac 100644 --- a/docs/examples/FastEmbed_vs_HF_Comparison.ipynb +++ b/docs/examples/FastEmbed_vs_HF_Comparison.ipynb @@ -20,10 +20,8 @@ "outputs": [], "source": [ "import time\n", - "from pathlib import Path\n", - "from typing import Any, Callable, List, Tuple\n", + "from typing import Callable, List, Tuple\n", "\n", - "import numpy as np\n", "import torch.nn.functional as F\n", "from fastembed.embedding import DefaultEmbedding\n", "import matplotlib.pyplot as plt\n", @@ -116,6 +114,7 @@ " sentence_embeddings = F.normalize(sentence_embeddings)\n", " return sentence_embeddings\n", "\n", + "\n", "hf = HF(model_id=\"BAAI/bge-small-en-v1.5\")\n", "hf.embed(documents).shape" ] @@ -165,6 +164,8 @@ ], "source": [ "import types\n", + "\n", + "\n", "def calculate_time_stats(embed_func: Callable, documents: list, k: int) -> Tuple[float, float, float]:\n", " times = []\n", " for _ in range(k):\n", @@ -181,6 +182,7 @@ " # Returning mean, max, and min time for the call\n", " return (sum(times) / k, max(times), min(times))\n", "\n", + "\n", "hf_stats = calculate_time_stats(hf.embed, documents, k=2)\n", "print(f\"Huggingface Transformers (Average, Max, Min): {hf_stats}\")\n", "fst_stats = calculate_time_stats(embedding_model.embed, documents, k=2)\n", @@ -289,6 +291,7 @@ " \"\"\"\n", " return F.cosine_similarity(embeddings1, embeddings2).mean().item()\n", "\n", + "\n", "calculate_cosine_similarity(hf.embed(documents), Tensor(list(embedding_model.embed(documents))))" ] }, diff --git a/docs/examples/Supported_Models.ipynb b/docs/examples/Supported_Models.ipynb index df9bccd7..18b64899 100644 --- a/docs/examples/Supported_Models.ipynb +++ b/docs/examples/Supported_Models.ipynb @@ -1,5 +1,15 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -141,12 +151,10 @@ } ], "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", "from fastembed.embedding import Embedding\n", "import pandas as pd\n", - "pd.set_option('display.max_colwidth', None)\n", + "\n", + "pd.set_option(\"display.max_colwidth\", None)\n", "pd.DataFrame(Embedding.list_supported_models())" ] } diff --git a/docs/examples/Usage_With_Qdrant.ipynb b/docs/examples/Usage_With_Qdrant.ipynb index d60095a3..91d79861 100644 --- a/docs/examples/Usage_With_Qdrant.ipynb +++ b/docs/examples/Usage_With_Qdrant.ipynb @@ -47,8 +47,6 @@ "outputs": [], "source": [ "from typing import List\n", - "import numpy as np\n", - "from fastembed.embedding import FlagEmbedding as Embedding\n", "from qdrant_client import QdrantClient" ] }, @@ -170,12 +168,7 @@ "ids = [42, 2]\n", "\n", "# Use the new add method\n", - "client.add(\n", - " collection_name=\"demo_collection\",\n", - " documents=docs,\n", - " metadata=metadata,\n", - " ids=ids\n", - ")" + "client.add(collection_name=\"demo_collection\", documents=docs, metadata=metadata, ids=ids)" ] }, { @@ -199,10 +192,7 @@ } ], "source": [ - "search_result = client.query(\n", - " collection_name=\"demo_collection\",\n", - " 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)" ] }, diff --git a/docs/experimental/Binary Quantization from Scratch.ipynb b/docs/experimental/Binary Quantization from Scratch.ipynb index cf491a9c..cb4f5c8d 100644 --- a/docs/experimental/Binary Quantization from Scratch.ipynb +++ b/docs/experimental/Binary Quantization from Scratch.ipynb @@ -46,7 +46,6 @@ "source": [ "import numpy as np\n", "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", "from tqdm import tqdm" ] }, @@ -305,9 +304,11 @@ "sampling_rate = [1, 2, 3, 5]\n", "results = []\n", "\n", + "\n", "def mean_accuracy(number_of_samples, limit, sampling_rate):\n", " return np.mean([accuracy(i, limit=limit, oversampling=sampling_rate) for i in range(number_of_samples)])\n", "\n", + "\n", "for i in tqdm(sampling_rate):\n", " for j in tqdm(limits):\n", " result = {\"sampling_rate\": i, \"limit\": j, \"recall\": mean_accuracy(number_of_samples, j, i)}\n", diff --git a/docs/experimental/Binary Quantization with Qdrant.ipynb b/docs/experimental/Binary Quantization with Qdrant.ipynb index 956943a8..89e11eea 100644 --- a/docs/experimental/Binary Quantization with Qdrant.ipynb +++ b/docs/experimental/Binary Quantization with Qdrant.ipynb @@ -34,10 +34,8 @@ "outputs": [], "source": [ "import pandas as pd\n", - "import uuid\n", "from qdrant_client import QdrantClient\n", - "from qdrant_client.http import models\n", - "from qdrant_client.http.models import PointStruct" + "from qdrant_client.http import models" ] }, { @@ -71,6 +69,7 @@ ], "source": [ "import datasets\n", + "\n", "dataset = datasets.load_dataset(\"KShivendu/dbpedia-entities-openai-1M\", split=\"train[0:100000]\")" ] }, @@ -133,10 +132,8 @@ } ], "source": [ - "from qdrant_client import QdrantClient\n", - "\n", "# client = QdrantClient(\n", - "# url=\"https://2aaa9439-b209-4ba6-8beb-d0b61dbd9388.us-east-1-0.aws.cloud.qdrant.io:6333\", \n", + "# url=\"https://2aaa9439-b209-4ba6-8beb-d0b61dbd9388.us-east-1-0.aws.cloud.qdrant.io:6333\",\n", "# api_key=\"FCF8_ADVuSRrtNGeg_rBJvAMJecEDgQhzuXMZGW8F7OzvaC9wYOPeQ\",\n", "# prefer_grpc=True\n", "# )\n", @@ -175,12 +172,10 @@ "bs = 10000\n", "for i in range(0, len(dataset), bs):\n", " client.upload_collection(\n", - " collection_name=collection_name, \n", - " ids=range(i, i+bs),\n", - " vectors=dataset[i:i+bs][\"openai\"],\n", - " payload=[\n", - " {\"text\": x} for x in dataset[i:i+bs][\"text\"]\n", - " ],\n", + " collection_name=collection_name,\n", + " ids=range(i, i + bs),\n", + " vectors=dataset[i : i + bs][\"openai\"],\n", + " payload=[{\"text\": x} for x in dataset[i : i + bs][\"text\"]],\n", " parallel=10,\n", " )" ] @@ -203,10 +198,7 @@ ], "source": [ "client.update_collection(\n", - " collection_name=f\"{collection_name}\",\n", - " optimizer_config=models.OptimizersConfigDiff(\n", - " indexing_threshold=20000\n", - " )\n", + " collection_name=f\"{collection_name}\", optimizer_config=models.OptimizersConfigDiff(indexing_threshold=20000)\n", ")" ] }, @@ -289,6 +281,7 @@ "source": [ "import random\n", "from random import randint\n", + "\n", "random.seed(37)\n", "\n", "query_indices = [randint(0, len(dataset)) for _ in range(100)]\n", @@ -304,7 +297,10 @@ "source": [ "## Add Gaussian noise to any vector\n", "import numpy as np\n", + "\n", "np.random.seed(37)\n", + "\n", + "\n", "def add_noise(vector, noise=0.05):\n", " return vector + noise * np.random.randn(*vector.shape)" ] @@ -959,6 +955,8 @@ ], "source": [ "import time\n", + "\n", + "\n", "def correct(results, text):\n", " result_texts = [x.payload[\"text\"] for x in results]\n", " return text in result_texts\n", @@ -977,7 +975,7 @@ " rescore=rescore,\n", " oversampling=oversampling,\n", " )\n", - " )\n", + " ),\n", " )\n", " correct_results += correct(results, text)\n", " return correct_results\n", @@ -996,14 +994,16 @@ " start = time.time()\n", " correct_results = count_correct(query_dataset, limit=limit, oversampling=oversampling, rescore=rescore)\n", " end = time.time()\n", - " results.append({\n", - " \"limit\": limit,\n", - " \"oversampling\": oversampling,\n", - " \"rescore\": rescore,\n", - " \"correct\": correct_results,\n", - " \"total queries\": len(query_dataset[\"text\"]),\n", - " \"time\": end - start,\n", - " })\n", + " results.append(\n", + " {\n", + " \"limit\": limit,\n", + " \"oversampling\": oversampling,\n", + " \"rescore\": rescore,\n", + " \"correct\": correct_results,\n", + " \"total queries\": len(query_dataset[\"text\"]),\n", + " \"time\": end - start,\n", + " }\n", + " )\n", "\n", "results_df = pd.DataFrame(results)\n", "results_df" diff --git a/docs/index.md b/docs/index.md index 90652e1b..969b1a5d 100644 --- a/docs/index.md +++ b/docs/index.md @@ -15,7 +15,7 @@ The default embedding supports "query" and "passage" prefixes for the input text ## 🚀 Installation -To install the FastEmbed library, pip works: +To install the FastEmbed library, pip works: ```bash pip install fastembed @@ -32,8 +32,8 @@ documents: List[str] = [ "passage: This is an example passage.", "fastembed is supported by and maintained by Qdrant." # You can leave out the prefix but it's recommended ] -embedding_model = Embedding(model_name="BAAI/bge-base-en", max_length=512) -embeddings: List[np.ndarray] = embedding_model.embed(documents) # If you use +embedding_model = Embedding(model_name="BAAI/bge-base-en", max_length=512) +embeddings: List[np.ndarray] = embedding_model.embed(documents) # If you use ``` ## Usage with Qdrant @@ -44,7 +44,7 @@ Installation with Qdrant Client in Python: pip install qdrant-client[fastembed] ``` -Might have to use ```pip install 'qdrant-client[fastembed]'``` on zsh. +Might have to use ```pip install 'qdrant-client[fastembed]'``` on zsh. ```python from qdrant_client import QdrantClient @@ -73,4 +73,4 @@ search_result = client.query( query_text="This is a query document" ) print(search_result) -``` \ No newline at end of file +``` diff --git a/docs/overrides/main.html b/docs/overrides/main.html index 5f111b35..1c0fd5b4 100644 --- a/docs/overrides/main.html +++ b/docs/overrides/main.html @@ -5,7 +5,7 @@ {% include ".icons/material/download.svg" %} -{% endif %} +{% endif %} {{ super() }} @@ -24,4 +24,4 @@ href="https://cloud.qdrant.io?utm_source=twitter&utm_medium=website&utm_campaign=fastembed">Qdrant Cloud to get started with vector search! -{% endblock %} \ No newline at end of file +{% endblock %} diff --git a/fastembed/embedding.py b/fastembed/embedding.py index f642c65e..2edd6797 100644 --- a/fastembed/embedding.py +++ b/fastembed/embedding.py @@ -3,7 +3,6 @@ import shutil import tarfile import tempfile - from abc import ABC, abstractmethod from itertools import islice from multiprocessing import get_all_start_methods @@ -76,11 +75,11 @@ def load_tokenizer(cls, model_dir: Path, max_length: int = 512) -> Tokenizer: return tokenizer def __init__( - self, - path: Path, - model_name: str, - max_length: int = 512, - max_threads: int = None, + self, + path: Path, + model_name: str, + max_length: int = 512, + max_threads: int = None, ): self.path = path self.model_name = model_name @@ -134,10 +133,10 @@ def onnx_embed(self, documents: List[str]) -> Tuple[np.ndarray, np.ndarray]: class EmbeddingWorker(Worker): def __init__( - self, - path: Path, - model_name: str, - max_length: int = 512, + self, + path: Path, + model_name: str, + max_length: int = 512, ): self.model = EmbeddingModel(path=path, model_name=model_name, max_length=max_length, max_threads=1) @@ -189,56 +188,56 @@ def list_supported_models(cls) -> List[Dict[str, Union[str, Union[int, float]]]] "model": "BAAI/bge-small-en", "dim": 384, "description": "Fast English model", - "size_in_GB": 0.2 + "size_in_GB": 0.2, }, { "model": "BAAI/bge-small-en-v1.5", "dim": 384, "description": "Fast and Default English model", - "size_in_GB": 0.13 + "size_in_GB": 0.13, }, { "model": "BAAI/bge-small-zh-v1.5", "dim": 512, "description": "Fast and recommended Chinese model", - "size_in_GB": 0.1 + "size_in_GB": 0.1, }, { "model": "BAAI/bge-base-en", "dim": 768, "description": "Base English model", - "size_in_GB": 0.5 + "size_in_GB": 0.5, }, { "model": "BAAI/bge-base-en-v1.5", "dim": 768, "description": "Base English model, v1.5", - "size_in_GB": 0.44 + "size_in_GB": 0.44, }, { "model": "sentence-transformers/all-MiniLM-L6-v2", "dim": 384, "description": "Sentence Transformer model, MiniLM-L6-v2", - "size_in_GB": 0.09 + "size_in_GB": 0.09, }, { "model": "intfloat/multilingual-e5-large", "dim": 1024, "description": "Multilingual model, e5-large. Recommend using this model for non-English languages", - "size_in_GB": 2.24 + "size_in_GB": 2.24, }, { "model": "jinaai/jina-embeddings-v2-base-en", "dim": 768, "description": " English embedding model supporting 8192 sequence length", - "size_in_GB": 0.55 + "size_in_GB": 0.55, }, { "model": "jinaai/jina-embeddings-v2-small-en", "dim": 512, "description": " English embedding model supporting 8192 sequence length", - "size_in_GB": 0.13 - } + "size_in_GB": 0.13, + }, ] @classmethod @@ -310,7 +309,9 @@ def download_files_from_huggingface(cls, repod_id: str, cache_dir: Optional[str] from huggingface_hub import snapshot_download return snapshot_download( - repo_id=repod_id, ignore_patterns=["model.safetensors", "pytorch_model.bin"], cache_dir=cache_dir + repo_id=repod_id, + ignore_patterns=["model.safetensors", "pytorch_model.bin"], + cache_dir=cache_dir, ) @classmethod @@ -405,7 +406,7 @@ def retrieve_model_hf(self, model_name: str, cache_dir: str) -> Path: """ assert ( - "/" in model_name + "/" in model_name ), "model_name must be in the format / e.g. jinaai/jina-embeddings-v2-small-en" return Path(self.download_files_from_huggingface(repod_id=model_name, cache_dir=cache_dir)) @@ -451,11 +452,11 @@ class FlagEmbedding(Embedding): """ def __init__( - self, - model_name: str = "BAAI/bge-small-en-v1.5", - max_length: int = 512, - cache_dir: str = None, - threads: int = None, + self, + model_name: str = "BAAI/bge-small-en-v1.5", + max_length: int = 512, + cache_dir: str = None, + threads: int = None, ): """ Args: @@ -480,11 +481,10 @@ def __init__( self._model_dir = self.retrieve_model_gcs(model_name, cache_dir) self._max_length = max_length - self.model = EmbeddingModel(self._model_dir, self.model_name, max_length=max_length, - max_threads=threads) + self.model = EmbeddingModel(self._model_dir, self.model_name, max_length=max_length, max_threads=threads) def embed( - self, documents: Union[str, Iterable[str]], batch_size: int = 256, parallel: int = None + self, documents: Union[str, Iterable[str]], batch_size: int = 256, parallel: int = None ) -> Iterable[np.ndarray]: """ Encode a list of documents into list of embeddings. @@ -536,7 +536,7 @@ def list_supported_models(cls) -> List[Dict[str, Union[str, Union[int, float]]]] Lists the supported models. """ # jina models are not supported by this class - return [model for model in super().list_supported_models() if not model['model'].startswith('jinaai')] + return [model for model in super().list_supported_models() if not model["model"].startswith("jinaai")] class DefaultEmbedding(FlagEmbedding): @@ -548,11 +548,11 @@ class DefaultEmbedding(FlagEmbedding): """ def __init__( - self, - model_name: str = "BAAI/bge-small-en-v1.5", - max_length: int = 512, - cache_dir: Optional[str] = None, - threads: Optional[int] = None, + self, + model_name: str = "BAAI/bge-small-en-v1.5", + max_length: int = 512, + cache_dir: Optional[str] = None, + threads: Optional[int] = None, ): super().__init__(model_name, max_length=max_length, cache_dir=cache_dir, threads=threads) @@ -571,11 +571,11 @@ def embed(self, texts, batch_size: int = 256, parallel: int = None): class JinaEmbedding(Embedding): def __init__( - self, - model_name: str = "jinaai/jina-embeddings-v2-base-en", - max_length: int = 512, - cache_dir: str = None, - threads: int = None, + self, + model_name: str = "jinaai/jina-embeddings-v2-base-en", + max_length: int = 512, + cache_dir: str = None, + threads: int = None, ): """ Args: @@ -599,11 +599,10 @@ def __init__( self._model_dir = self.retrieve_model_hf(model_name, cache_dir) self._max_length = max_length - self.model = EmbeddingModel(self._model_dir, self.model_name, max_length=max_length, - max_threads=threads) + self.model = EmbeddingModel(self._model_dir, self.model_name, max_length=max_length, max_threads=threads) def embed( - self, documents: Union[str, Iterable[str]], batch_size: int = 256, parallel: int = None + self, documents: Union[str, Iterable[str]], batch_size: int = 256, parallel: int = None ) -> Iterable[np.ndarray]: """ Encode a list of documents into list of embeddings. @@ -653,7 +652,7 @@ def list_supported_models(cls) -> List[Dict[str, Union[str, Union[int, float]]]] Lists the supported models. """ # only jina models are supported by this class - return [model for model in Embedding.list_supported_models() if model['model'].startswith('jinaai')] + return [model for model in Embedding.list_supported_models() if model["model"].startswith("jinaai")] @staticmethod def mean_pooling(model_output, attention_mask): diff --git a/fastembed/parallel_processor.py b/fastembed/parallel_processor.py index 9df2eec9..9a0fb043 100644 --- a/fastembed/parallel_processor.py +++ b/fastembed/parallel_processor.py @@ -7,7 +7,7 @@ from multiprocessing.process import BaseProcess from multiprocessing.sharedctypes import Synchronized as BaseValue from queue import Empty -from typing import Any, Dict, Iterable, List, Optional, Type, Tuple +from typing import Any, Dict, Iterable, List, Optional, Tuple, Type # Single item should be processed in less than: processing_timeout = 10 * 60 # seconds diff --git a/fooling_around/01_ONNX_Port.ipynb b/fooling_around/01_ONNX_Port.ipynb index 81f43606..a13d129f 100644 --- a/fooling_around/01_ONNX_Port.ipynb +++ b/fooling_around/01_ONNX_Port.ipynb @@ -27,7 +27,6 @@ "from transformers import AutoTokenizer, AutoModel\n", "\n", "from optimum.onnxruntime import AutoOptimizationConfig, ORTModelForFeatureExtraction, ORTOptimizer\n", - "from optimum.onnxruntime.configuration import OptimizationConfig\n", "from optimum.pipelines import pipeline\n", "import torch.nn.functional as F" ] @@ -149,7 +148,9 @@ "metadata": {}, "outputs": [], "source": [ - "onnx_quant_embed = pipeline(\"feature-extraction\", model=model, accelerator=\"ort\", tokenizer=tokenizer,return_tensors=True)" + "onnx_quant_embed = pipeline(\n", + " \"feature-extraction\", model=model, accelerator=\"ort\", tokenizer=tokenizer, return_tensors=True\n", + ")" ] }, { @@ -159,9 +160,8 @@ "metadata": {}, "outputs": [], "source": [ - "import torch\n", "embeddings = onnx_quant_embed(inputs=english_texts)\n", - "F.normalize(embeddings[4])[:,0], english_texts[4], len(embeddings), len(english_texts)" + "F.normalize(embeddings[4])[:, 0], english_texts[4], len(embeddings), len(english_texts)" ] }, { @@ -171,7 +171,6 @@ "metadata": {}, "outputs": [], "source": [ - "\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", @@ -256,6 +255,7 @@ "\n", "save_dir = Path(\"../local_cache/fast-bge-small-en-v1.5\")\n", "\n", + "\n", "def compress(directory_path):\n", " directory_path = Path(directory_path)\n", " assert directory_path.exists(), f\"{directory_path} does not exist\"\n", @@ -304,9 +304,9 @@ } ], "source": [ - "import os\n", "from google.cloud import storage\n", "\n", + "\n", "def upload(bucket_name, source_file_path):\n", " storage_client = storage.Client(project=\"main\")\n", " bucket = storage_client.bucket(bucket_name)\n", diff --git a/fooling_around/Throughput_Across_Models.ipynb b/fooling_around/Throughput_Across_Models.ipynb index 279dfbea..6cfa31c5 100644 --- a/fooling_around/Throughput_Across_Models.ipynb +++ b/fooling_around/Throughput_Across_Models.ipynb @@ -20,14 +20,11 @@ "outputs": [], "source": [ "import time\n", - "from pathlib import Path\n", - "from typing import Any, Callable, List, Tuple\n", + "from typing import Callable, List, Tuple\n", "\n", - "import numpy as np\n", "import torch.nn.functional as F\n", - "from fastembed.embedding import DefaultEmbedding, Embedding\n", + "from fastembed.embedding import Embedding\n", "import matplotlib.pyplot as plt\n", - "from torch import Tensor\n", "from transformers import AutoModel, AutoTokenizer" ] }, @@ -126,6 +123,7 @@ " sentence_embeddings = F.normalize(sentence_embeddings)\n", " return sentence_embeddings\n", "\n", + "\n", "hf = HF(model_id=model_id)\n", "hf.embed(documents).shape" ] @@ -175,6 +173,8 @@ ], "source": [ "import types\n", + "\n", + "\n", "def calculate_time_stats(embed_func: Callable, documents: list, k: int) -> Tuple[float, float, float]:\n", " times = []\n", " for _ in range(k):\n", @@ -191,6 +191,7 @@ " # Returning mean, max, and min time for the call\n", " return (sum(times) / k, max(times), min(times))\n", "\n", + "\n", "hf_stats = calculate_time_stats(hf.embed, documents, k=2)\n", "print(f\"Huggingface Transformers (Average, Max, Min): {hf_stats}\")\n", "fst_stats = calculate_time_stats(embedding_model.embed, documents, k=2)\n", diff --git a/mkdocs.yml b/mkdocs.yml index fdc35997..ff995904 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -2,7 +2,7 @@ site_name: FastEmbed site_url: https://qdrant.github.io/fastembed/ site_author: Nirant Kasliwal repo_url: https://github.com/qdrant/fastembed/ -repo_name: qdrant/fastembed +repo_name: qdrant/fastembed remote_branch: gh-pages remote_name: origin @@ -33,11 +33,11 @@ theme: # Text color for primary color text: "#ffffff" - 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Comparing the performance of Huggingface's 🤗 Transformers and ⚡ FastEmbed️ on a simple task on the following machine: Apple M2 Max, 32 GB RAM -# +# # ## 📦 Imports -# +# # Importing the necessary libraries for this comparison. # %% import time -from pathlib import Path -from typing import Any, Callable, List, Tuple +from typing import Callable, List, Tuple -import numpy as np +import matplotlib.pyplot as plt import torch.nn.functional as F -from torch import Tensor from transformers import AutoModel, AutoTokenizer from fastembed.embedding import DefaultEmbedding # %% [markdown] # ## 📖 Data -# +# # data is a list of strings, each string is a document. # %% @@ -43,9 +41,10 @@ # %% [markdown] # ## Setting up 🤗 Huggingface -# +# # We'll be using the [Huggingface Transformers](https://huggingface.co/transformers/) with PyTorch library to generate embeddings. We'll be using the same model across both libraries for a fair(er?) comparison. + # %% class HF: """ @@ -64,12 +63,13 @@ def embed(self, texts: List[str]): sentence_embeddings = F.normalize(sentence_embeddings) return sentence_embeddings + hf = HF(model_id="BAAI/bge-small-en") hf.embed(documents).shape # %% [markdown] # ## Setting up ⚡️FastEmbed -# +# # Sorry, don't have a lot to set up here. We'll be using the default model, which is Flag Embedding, same as the Huggingface model. # %% @@ -77,18 +77,19 @@ def embed(self, texts: List[str]): # %% [markdown] # ## 📊 Comparison -# +# # We'll be comparing the following metrics: Minimum, Maximum, Mean, across k runs. Let's write a function to do that: -# +# # ### 🚀 Calculating Stats + # %% def calculate_time_stats(embed_func: Callable, documents: list, k: int) -> Tuple[float, float, float]: times = [] for _ in range(k): # Timing the embed_func call start_time = time.time() - embeddings = embed_func(documents) + embed_func(documents) end_time = time.time() times.append(end_time - start_time) @@ -96,15 +97,13 @@ def calculate_time_stats(embed_func: Callable, documents: list, k: int) -> Tuple # Returning mean, max, and min time for the call return (sum(times) / k, max(times), min(times)) + # %% hf_stats = calculate_time_stats(hf.embed, documents, k=2) print(f"Huggingface Transformers (Average, Max, Min): {hf_stats}") fst_stats = calculate_time_stats(lambda x: list(embedding_model.embed(x)), documents, k=2) print(f"FastEmbed (Average, Max, Min): {fst_stats}") -# %% -import matplotlib.pyplot as plt - # %% def plot_character_per_second_comparison( @@ -141,5 +140,3 @@ def plot_character_per_second_comparison( plot_character_per_second_comparison(hf_stats, fst_stats, documents) - - diff --git a/tests/test_onnx_embeddings.py b/tests/test_onnx_embeddings.py index 45373ef0..c9af9e10 100644 --- a/tests/test_onnx_embeddings.py +++ b/tests/test_onnx_embeddings.py @@ -1,6 +1,7 @@ import os -import pytest + import numpy as np +import pytest from fastembed.embedding import DefaultEmbedding, JinaEmbedding @@ -17,7 +18,7 @@ } -@pytest.mark.parametrize('embedding_class', [DefaultEmbedding, JinaEmbedding]) +@pytest.mark.parametrize("embedding_class", [DefaultEmbedding, JinaEmbedding]) def test_embedding(embedding_class): is_ubuntu_ci = os.getenv("IS_UBUNTU_CI") @@ -34,10 +35,10 @@ def test_embedding(embedding_class): assert embeddings.shape == (2, dim) 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"] + assert np.allclose(embeddings[0, : canonical_vector.shape[0]], canonical_vector, atol=1e-3), model_desc["model"] -@pytest.mark.parametrize('n_dims,embedding_class', [(384, DefaultEmbedding), (768, JinaEmbedding)]) +@pytest.mark.parametrize("n_dims,embedding_class", [(384, DefaultEmbedding), (768, JinaEmbedding)]) def test_batch_embedding(n_dims, embedding_class): model = embedding_class() @@ -48,7 +49,7 @@ def test_batch_embedding(n_dims, embedding_class): assert embeddings.shape == (200, n_dims) -@pytest.mark.parametrize('n_dims,embedding_class', [(384, DefaultEmbedding), (768, JinaEmbedding)]) +@pytest.mark.parametrize("n_dims,embedding_class", [(384, DefaultEmbedding), (768, JinaEmbedding)]) def test_parallel_processing(n_dims, embedding_class): model = embedding_class()