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Description: Volcano Ark is an enterprise-grade large-model service platform for developers, providing a full range of functions and services such as model training, inference, evaluation, fine-tuning. You can visit its homepage at https://www.volcengine.com/docs/82379/1099455 for details. This change could help developers use the platform for embedding. Issue: None Dependencies: volcengine Tag maintainer: @baskaryan Twitter handle: @hinnnnnnnnnnnns --------- Co-authored-by: lujingxuansc <lujingxuansc@bytedance.com>
124 lines
3.1 KiB
Plaintext
124 lines
3.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"# Volc Engine\n",
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"\n",
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"This notebook provides you with a guide on how to load the Volcano Embedding class.\n",
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"\n",
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"\n",
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"## API Initialization\n",
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"\n",
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"To use the LLM services based on [VolcEngine](https://www.volcengine.com/docs/82379/1099455), you have to initialize these parameters:\n",
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"\n",
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"You could either choose to init the AK,SK in environment variables or init params:\n",
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"\n",
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"```base\n",
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"export VOLC_ACCESSKEY=XXX\n",
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"export VOLC_SECRETKEY=XXX\n",
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"```"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"start_time": "2023-12-14T03:05:29.857798Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"embed_documents result:\n",
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" [0.02929673343896866, -0.009310632012784481, -0.060323506593704224, 0.0031018739100545645, -0.002218986628577113, -0.0023125179577618837, -0.04864659160375595, -2.062115163425915e-05]\n",
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" [0.01987231895327568, -0.026041055098176003, -0.08395249396562576, 0.020043574273586273, -0.028862033039331436, 0.004629664588719606, -0.023107370361685753, -0.0342753604054451]\n"
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]
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}
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],
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"source": [
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"\"\"\"For basic init and call\"\"\"\n",
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"import os\n",
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"\n",
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"from langchain_community.embeddings import VolcanoEmbeddings\n",
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"\n",
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"os.environ[\"VOLC_ACCESSKEY\"] = \"\"\n",
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"os.environ[\"VOLC_SECRETKEY\"] = \"\"\n",
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"\n",
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"embed = VolcanoEmbeddings(volcano_ak=\"\", volcano_sk=\"\")\n",
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"print(\"embed_documents result:\")\n",
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"res1 = embed.embed_documents([\"foo\", \"bar\"])\n",
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"for r in res1:\n",
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" print(\"\", r[:8])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"ExecuteTime": {
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"start_time": "2023-12-14T03:05:29.859276Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"embed_query result:\n",
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" [0.01987231895327568, -0.026041055098176003, -0.08395249396562576, 0.020043574273586273, -0.028862033039331436, 0.004629664588719606, -0.023107370361685753, -0.0342753604054451]\n"
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]
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}
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],
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"source": [
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"print(\"embed_query result:\")\n",
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"res2 = embed.embed_query(\"foo\")\n",
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"print(\"\", r[:8])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"start_time": "2023-12-14T03:05:29.860282Z"
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}
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.18"
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},
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"vscode": {
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"interpreter": {
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"hash": "6fa70026b407ae751a5c9e6bd7f7d482379da8ad616f98512780b705c84ee157"
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"nbformat": 4,
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"nbformat_minor": 4
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