diff --git a/docs/modules/utils/combine_docs_examples/embeddings.ipynb b/docs/modules/utils/combine_docs_examples/embeddings.ipynb index 686afe6d..fde4f4d1 100644 --- a/docs/modules/utils/combine_docs_examples/embeddings.ipynb +++ b/docs/modules/utils/combine_docs_examples/embeddings.ipynb @@ -77,7 +77,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "42f76e43", "metadata": {}, @@ -138,7 +137,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "id": "ed47bb62", "metadata": {}, @@ -196,11 +194,79 @@ "source": [ "doc_result = embeddings.embed_documents([text])" ] + }, + { + "cell_type": "markdown", + "id": "fff4734f", + "metadata": {}, + "source": [ + "## TensorflowHub\n", + "Let's load the TensorflowHub Embedding class." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f822104b", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.embeddings import TensorflowHubEmbeddings" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "bac84e46", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-01-30 23:53:01.652176: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-01-30 23:53:34.362802: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + } + ], + "source": [ + "embeddings = TensorflowHubEmbeddings()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4790d770", + "metadata": {}, + "outputs": [], + "source": [ + "text = \"This is a test document.\"" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f556dcdb", + "metadata": {}, + "outputs": [], + "source": [ + "query_result = embeddings.embed_query(text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90f0db94", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "cohere", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -214,7 +280,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.10.9" }, "vscode": { "interpreter": { diff --git a/langchain/embeddings/__init__.py b/langchain/embeddings/__init__.py index 972b7712..c66434e4 100644 --- a/langchain/embeddings/__init__.py +++ b/langchain/embeddings/__init__.py @@ -6,6 +6,7 @@ from langchain.embeddings.cohere import CohereEmbeddings from langchain.embeddings.huggingface import HuggingFaceEmbeddings from langchain.embeddings.huggingface_hub import HuggingFaceHubEmbeddings from langchain.embeddings.openai import OpenAIEmbeddings +from langchain.embeddings.tensorflow_hub import TensorflowHubEmbeddings logger = logging.getLogger(__name__) @@ -14,6 +15,7 @@ __all__ = [ "HuggingFaceEmbeddings", "CohereEmbeddings", "HuggingFaceHubEmbeddings", + "TensorflowHubEmbeddings", ] diff --git a/langchain/embeddings/tensorflow_hub.py b/langchain/embeddings/tensorflow_hub.py new file mode 100644 index 00000000..25e63949 --- /dev/null +++ b/langchain/embeddings/tensorflow_hub.py @@ -0,0 +1,70 @@ +"""Wrapper around TensorflowHub embedding models.""" +from typing import Any, List + +from pydantic import BaseModel, Extra + +from langchain.embeddings.base import Embeddings + +DEFAULT_MODEL_URL = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3" + + +class TensorflowHubEmbeddings(BaseModel, Embeddings): + """Wrapper around tensorflow_hub embedding models. + + To use, you should have the ``tensorflow_text`` python package installed. + + Example: + .. code-block:: python + + from langchain.embeddings import TensorflowHubEmbeddings + url = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3" + tf = TensorflowHubEmbeddings(model_url=url) + """ + + embed: Any #: :meta private: + model_url: str = DEFAULT_MODEL_URL + """Model name to use.""" + + def __init__(self, **kwargs: Any): + """Initialize the tensorflow_hub and tensorflow_text.""" + super().__init__(**kwargs) + try: + import tensorflow_hub + import tensorflow_text # noqa + + self.embed = tensorflow_hub.load(self.model_url) + except ImportError as e: + raise ValueError( + "Could not import some python packages." "Please install them." + ) from e + + class Config: + """Configuration for this pydantic object.""" + + extra = Extra.forbid + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + """Compute doc embeddings using a TensorflowHub embedding model. + + Args: + texts: The list of texts to embed. + + Returns: + List of embeddings, one for each text. + """ + texts = list(map(lambda x: x.replace("\n", " "), texts)) + embeddings = self.embed(texts).numpy() + return embeddings.tolist() + + def embed_query(self, text: str) -> List[float]: + """Compute query embeddings using a TensorflowHub embedding model. + + Args: + text: The text to embed. + + Returns: + Embeddings for the text. + """ + text = text.replace("\n", " ") + embedding = self.embed(text).numpy()[0] + return embedding.tolist() diff --git a/poetry.lock b/poetry.lock index dc2adb7c..18e67dc1 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,5 +1,17 @@ # This file is automatically @generated by Poetry and should not be 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python-versions = ">=3.8.1,<4.0" -content-hash = "bed5e0cb4cfa8b6173dd9574982bd9154ebc61721704002b85474af3c2b675ca" +content-hash = "fdf51cf2f138653a8e32f1c43a2dcbbcca76f74534afc9dc4a7879c58282100a" diff --git a/pyproject.toml b/pyproject.toml index d093d5b3..7b5351e2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,6 +35,7 @@ google-api-python-client = {version = "2.70.0", optional = true} wolframalpha = {version = "5.0.0", optional = true} qdrant-client = {version = "^0.11.7", optional = true} dataclasses-json = "^0.5.7" +tensorflow-text = {version = "^2.11.0", optional = true, python = "^3.10, <3.12"} [tool.poetry.group.docs.dependencies] autodoc_pydantic = "^1.8.0" @@ -81,7 +82,7 @@ playwright = "^1.28.0" [tool.poetry.extras] llms = ["cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "torch", "transformers"] -all = ["cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "elasticsearch", "google-search-results", "faiss-cpu", "sentence_transformers", 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test_tensorflowhub_embedding_documents() -> None: + """Test tensorflowhub embeddings.""" + documents = ["foo bar"] + embedding = TensorflowHubEmbeddings() + output = embedding.embed_documents(documents) + assert len(output) == 1 + assert len(output[0]) == 512 + + +def test_tensorflowhub_embedding_query() -> None: + """Test tensorflowhub embeddings.""" + document = "foo bar" + embedding = TensorflowHubEmbeddings() + output = embedding.embed_query(document) + assert len(output) == 512