run-llama--llama_index/docs/examples/embeddings/netmind.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Netmind AI Embeddings\n",
    "\n",
    "This notebook shows how to use `Netmind AI` for embeddings.\n",
    "\n",
    "Visit https://www.netmind.ai/ and sign up to get an API key."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install llama-index-embeddings-netmind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install llama-index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can set the API key in the embeddings or env\n",
    "# import os\n",
    "# os.environ[\"NETMIND_API_KEY\"] = \"your-api-key\"\n",
    "\n",
    "from llama_index.embeddings.netmind import NetmindEmbedding\n",
    "\n",
    "embed_model = NetmindEmbedding(\n",
    "    model_name=\"BAAI/bge-m3\", api_key=\"your-api-key\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = embed_model.get_text_embedding(\"hello world\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1024\n"
     ]
    }
   ],
   "source": [
    "print(len(embeddings))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.04039396345615387, 0.03703497350215912, -0.02897450141608715, 0.016117244958877563, -0.03569157049059868]\n"
     ]
    }
   ],
   "source": [
    "print(embeddings[:5])"
   ]
  }
 ],
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