langchain/docs/modules/models/text_embedding/examples/minimax.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MiniMax\n",
    "\n",
    "[MiniMax](https://api.minimax.chat/document/guides/embeddings?id=6464722084cdc277dfaa966a) offers an embeddings service.\n",
    "\n",
    "This example goes over how to use LangChain to interact with MiniMax Inference for text embedding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-24T15:13:15.397075Z",
     "start_time": "2023-05-24T15:13:15.387540Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"MINIMAX_GROUP_ID\"] = \"MINIMAX_GROUP_ID\"\n",
    "os.environ[\"MINIMAX_API_KEY\"] = \"MINIMAX_API_KEY\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-24T15:13:17.176956Z",
     "start_time": "2023-05-24T15:13:15.399076Z"
    }
   },
   "outputs": [],
   "source": [
    "from langchain.embeddings import MiniMaxEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-24T15:13:17.193751Z",
     "start_time": "2023-05-24T15:13:17.182053Z"
    }
   },
   "outputs": [],
   "source": [
    "embeddings = MiniMaxEmbeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-24T15:13:17.844903Z",
     "start_time": "2023-05-24T15:13:17.198751Z"
    }
   },
   "outputs": [],
   "source": [
    "query_text = \"This is a test query.\"\n",
    "query_result = embeddings.embed_query(query_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-24T15:13:18.605339Z",
     "start_time": "2023-05-24T15:13:17.845906Z"
    }
   },
   "outputs": [],
   "source": [
    "document_text = \"This is a test document.\"\n",
    "document_result = embeddings.embed_documents([document_text])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-24T15:13:18.620432Z",
     "start_time": "2023-05-24T15:13:18.608335Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cosine similarity between document and query: 0.1573236279277012\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "query_numpy = np.array(query_result)\n",
    "document_numpy = np.array(document_result[0])\n",
    "similarity = np.dot(query_numpy, document_numpy) / (np.linalg.norm(query_numpy)*np.linalg.norm(document_numpy))\n",
    "print(f\"Cosine similarity between document and query: {similarity}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}