Add MiniMax embeddings (#5174)

- Add support for MiniMax embeddings

Doc: [MiniMax
embeddings](https://api.minimax.chat/document/guides/embeddings?id=6464722084cdc277dfaa966a)

---------

Co-authored-by: Archon <archongum@outlook.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>

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

@@ -0,0 +1,145 @@
+{
+ "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
+}