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146 lines
3.3 KiB
Plaintext
146 lines
3.3 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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"source": [
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"# MiniMax\n",
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"\n",
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"[MiniMax](https://api.minimax.chat/document/guides/embeddings?id=6464722084cdc277dfaa966a) offers an embeddings service.\n",
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"\n",
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"This example goes over how to use LangChain to interact with MiniMax Inference for text embedding."
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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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"end_time": "2023-05-24T15:13:15.397075Z",
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"start_time": "2023-05-24T15:13:15.387540Z"
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}
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},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"os.environ[\"MINIMAX_GROUP_ID\"] = \"MINIMAX_GROUP_ID\"\n",
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"os.environ[\"MINIMAX_API_KEY\"] = \"MINIMAX_API_KEY\""
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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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"end_time": "2023-05-24T15:13:17.176956Z",
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"start_time": "2023-05-24T15:13:15.399076Z"
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}
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},
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"outputs": [],
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"source": [
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"from langchain.embeddings import MiniMaxEmbeddings"
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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": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-05-24T15:13:17.193751Z",
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"start_time": "2023-05-24T15:13:17.182053Z"
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}
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},
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"outputs": [],
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"source": [
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"embeddings = MiniMaxEmbeddings()"
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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": 4,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-05-24T15:13:17.844903Z",
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"start_time": "2023-05-24T15:13:17.198751Z"
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}
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},
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"outputs": [],
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"source": [
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"query_text = \"This is a test query.\"\n",
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"query_result = embeddings.embed_query(query_text)"
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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": 5,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-05-24T15:13:18.605339Z",
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"start_time": "2023-05-24T15:13:17.845906Z"
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}
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},
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"outputs": [],
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"source": [
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"document_text = \"This is a test document.\"\n",
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"document_result = embeddings.embed_documents([document_text])"
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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": 6,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-05-24T15:13:18.620432Z",
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"start_time": "2023-05-24T15:13:18.608335Z"
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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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"Cosine similarity between document and query: 0.1573236279277012\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"\n",
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"query_numpy = np.array(query_result)\n",
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"document_numpy = np.array(document_result[0])\n",
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"similarity = np.dot(query_numpy, document_numpy) / (np.linalg.norm(query_numpy)*np.linalg.norm(document_numpy))\n",
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"print(f\"Cosine similarity between document and query: {similarity}\")"
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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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"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": "3.11.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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