{ "cells": [ { "attachments": {}, "cell_type": "markdown", "id": "ed47bb62", "metadata": {}, "source": [ "# Sentence Transformers Embeddings\n", "\n", "Let's generate embeddings using the [SentenceTransformers](https://www.sbert.net/) integration. SentenceTransformers is a python package that can generate text and image embeddings, originating from [Sentence-BERT](https://arxiv.org/abs/1908.10084)" ] }, { "cell_type": "code", "execution_count": 7, "id": "06c9f47d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", "To disable this warning, you can either:\n", "\t- Avoid using `tokenizers` before the fork if possible\n", "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" ] } ], "source": [ "!pip install sentence_transformers > /dev/null" ] }, { "cell_type": "code", "execution_count": 8, "id": "861521a9", "metadata": {}, "outputs": [], "source": [ "from langchain.embeddings import SentenceTransformerEmbeddings " ] }, { "cell_type": "code", "execution_count": 9, "id": "ff9be586", "metadata": {}, "outputs": [], "source": [ "embeddings = SentenceTransformerEmbeddings(model=\"all-MiniLM-L6-v2\")" ] }, { "cell_type": "code", "execution_count": 10, "id": "d0a98ae9", "metadata": {}, "outputs": [], "source": [ "text = \"This is a test document.\"" ] }, { "cell_type": "code", "execution_count": 11, "id": "5d6c682b", "metadata": {}, "outputs": [], "source": [ "query_result = embeddings.embed_query(text)" ] }, { "cell_type": "code", "execution_count": 12, "id": "bb5e74c0", "metadata": {}, "outputs": [], "source": [ "doc_result = embeddings.embed_documents([text, \"This is not a test document.\"])" ] }, { "cell_type": "code", "execution_count": null, "id": "aaad49f8", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.2" }, "vscode": { "interpreter": { "hash": "7377c2ccc78bc62c2683122d48c8cd1fb85a53850a1b1fc29736ed39852c9885" } } }, "nbformat": 4, "nbformat_minor": 5 }