Feature/integration nebius

docs/docs/api_reference/embeddings/nebius.md

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+::: llama_index.embeddings.nebius
+    options:
+      members:
+        - NebiusEmbedding

docs/docs/api_reference/llms/nebius.md

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+::: llama_index.llms.nebius
+    options:
+      members:
+        - NebiusLLM

docs/docs/api_reference/multi_modal_llms/nebius.md

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+::: llama_index.multi_modal_llms.nebius
+    options:
+      members:
+        - NebiusMultiModal

docs/docs/examples/embeddings/nebius.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/docs/examples/embeddings/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 Embeddings\n",
+    "\n",
+    "This notebook demonstrates how to use [Nebius AI Studio](https://studio.nebius.ai/) Embeddings with LlamaIndex. Nebius AI Studio implements all state-of-the-art embeddings models, 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-embeddings-nebius"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install llama-index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Insert your Nebius AI Studio key below. 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": [
+    "NEBIUS_API_KEY = \"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's get embeddings using Nebius AI Studio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_index.embeddings.nebius import NebiusEmbedding\n",
+    "\n",
+    "embed_model = NebiusEmbedding(api_key=NEBIUS_API_KEY)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Basic usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4096\n",
+      "[-0.002410888671875, 0.0083770751953125, -0.00542449951171875, 0.007366180419921875, -0.022216796875]\n"
+     ]
+    }
+   ],
+   "source": [
+    "text = \"Everyone loves justice at another person's expense\"\n",
+    "embeddings = embed_model.get_text_embedding(text)\n",
+    "assert len(embeddings) == 4096\n",
+    "print(len(embeddings), embeddings[:5], sep=\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Asynchronous usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4096\n",
+      "[-0.002410888671875, 0.0083770751953125, -0.00542449951171875, 0.007366180419921875, -0.022216796875]\n"
+     ]
+    }
+   ],
+   "source": [
+    "text = \"Everyone loves justice at another person's expense\"\n",
+    "embeddings = await embed_model.aget_text_embedding(text)\n",
+    "assert len(embeddings) == 4096\n",
+    "print(len(embeddings), embeddings[:5], sep=\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Batched usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.0003886222839355469, 0.0004887580871582031, 0.011199951171875]\n",
+      "[-0.003734588623046875, 0.01143646240234375, 0.008758544921875]\n",
+      "[0.005901336669921875, 0.005161285400390625, 0.00142669677734375]\n",
+      "[-0.00946807861328125, -0.0048675537109375, 0.004817962646484375]\n"
+     ]
+    }
+   ],
+   "source": [
+    "texts = [\n",
+    "    \"As the hours pass\",\n",
+    "    \"I will let you know\",\n",
+    "    \"That I need to ask\",\n",
+    "    \"Before I'm alone\",\n",
+    "]\n",
+    "\n",
+    "embeddings = embed_model.get_text_embedding_batch(texts)\n",
+    "assert len(embeddings) == 4\n",
+    "assert len(embeddings[0]) == 4096\n",
+    "print(*[x[:3] for x in embeddings], sep=\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Async batched usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.0003886222839355469, 0.0004887580871582031, 0.011199951171875]\n",
+      "[-0.003734588623046875, 0.01143646240234375, 0.008758544921875]\n",
+      "[0.005901336669921875, 0.005161285400390625, 0.00142669677734375]\n",
+      "[-0.00946807861328125, -0.0048675537109375, 0.004817962646484375]\n"
+     ]
+    }
+   ],
+   "source": [
+    "texts = [\n",
+    "    \"As the hours pass\",\n",
+    "    \"I will let you know\",\n",
+    "    \"That I need to ask\",\n",
+    "    \"Before I'm alone\",\n",
+    "]\n",
+    "\n",
+    "embeddings = await embed_model.aget_text_embedding_batch(texts)\n",
+    "assert len(embeddings) == 4\n",
+    "assert len(embeddings[0]) == 4096\n",
+    "print(*[x[:3] for x in embeddings], sep=\"\\n\")"
+   ]
+  }
+ ],
+ "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"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}