run-llama--llama_index/docs/examples/llm/nebius.ipynb

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   "source": [
    "<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/nebius.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nebius LLMs\n",
    "\n",
    "This notebook demonstrates how to use LLMs from [Nebius AI Studio](https://studio.nebius.ai/) with LlamaIndex. Nebius AI Studio implements all state-of-the-art LLMs available for commercial use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's install LlamaIndex and dependencies of Nebius AI Studio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install llama-index-llms-nebius llama-index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upload your Nebius AI Studio key from system variables below or simply insert it. You can get it by registering for free at [Nebius AI Studio](https://auth.eu.nebius.com/ui/login) and issuing the key at [API Keys section](https://studio.nebius.ai/settings/api-keys).\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "NEBIUS_API_KEY = os.getenv(\"NEBIUS_API_KEY\")  # NEBIUS_API_KEY = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n"
     ]
    }
   ],
   "source": [
    "from llama_index.llms.nebius import NebiusLLM\n",
    "\n",
    "llm = NebiusLLM(\n",
    "    api_key=NEBIUS_API_KEY, model=\"meta-llama/Llama-3.3-70B-Instruct-fast\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Call `complete` with a prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Netherlands! Amsterdam is indeed the capital and largest city of the Netherlands.\n"
     ]
    }
   ],
   "source": [
    "response = llm.complete(\"Amsterdam is the capital of \")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Call `chat` with a list of messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "assistant: WALL-E is a small waste-collecting robot and the main character in the 2008 Pixar animated film of the same name.\n"
     ]
    }
   ],
   "source": [
    "from llama_index.core.llms import ChatMessage\n",
    "\n",
    "messages = [\n",
    "    ChatMessage(role=\"system\", content=\"You are a helpful AI assistant.\"),\n",
    "    ChatMessage(\n",
    "        role=\"user\",\n",
    "        content=\"Answer briefly: who is Wall-e?\",\n",
    "    ),\n",
    "]\n",
    "response = llm.chat(messages)\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Streaming"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using `stream_complete` endpoint "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Netherlands! Amsterdam is indeed the capital and largest city of the Netherlands."
     ]
    }
   ],
   "source": [
    "response = llm.stream_complete(\"Amsterdam is the capital of \")\n",
    "for r in response:\n",
    "    print(r.delta, end=\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using `stream_chat` with a list of messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WALL-E is a small waste-collecting robot and the main character in the 2008 Pixar animated film of the same name."
     ]
    }
   ],
   "source": [
    "from llama_index.core.llms import ChatMessage\n",
    "\n",
    "messages = [\n",
    "    ChatMessage(role=\"system\", content=\"You are a helpful AI assistant.\"),\n",
    "    ChatMessage(\n",
    "        role=\"user\",\n",
    "        content=\"Answer briefly: who is Wall-e?\",\n",
    "    ),\n",
    "]\n",
    "response = llm.stream_chat(messages)\n",
    "for r in response:\n",
    "    print(r.delta, end=\"\")"
   ]
  }
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