{ "cells": [ { "cell_type": "markdown", "id": "1c0cf975", "metadata": {}, "source": [ "# Jina\n", "\n", "Let's load the Jina Embedding class." ] }, { "cell_type": "code", "execution_count": 2, "id": "d94c62b4", "metadata": {}, "outputs": [], "source": [ "from langchain.embeddings import JinaEmbeddings" ] }, { "cell_type": "code", "execution_count": null, "id": "523a09e3", "metadata": {}, "outputs": [], "source": [ "embeddings = JinaEmbeddings(\n", " jina_auth_token=jina_auth_token, model_name=\"ViT-B-32::openai\"\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "b212bd5a", "metadata": {}, "outputs": [], "source": [ "text = \"This is a test document.\"" ] }, { "cell_type": "code", "execution_count": null, "id": "57db66bd", "metadata": {}, "outputs": [], "source": [ "query_result = embeddings.embed_query(text)" ] }, { "cell_type": "code", "execution_count": null, "id": "b790fd09", "metadata": {}, "outputs": [], "source": [ "doc_result = embeddings.embed_documents([text])" ] }, { "cell_type": "markdown", "id": "6f3607a0", "metadata": {}, "source": [ "In the above example, `ViT-B-32::openai`, OpenAI's pretrained `ViT-B-32` model is used. For a full list of models, see [here](https://cloud.jina.ai/user/inference/model/63dca9df5a0da83009d519cd)." ] }, { "cell_type": "code", "execution_count": null, "id": "cd5f148e", "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": "3.9.1" } }, "nbformat": 4, "nbformat_minor": 5 }