diff --git a/docs/examples/embeddings/nomic.ipynb b/docs/examples/embeddings/nomic.ipynb
new file mode 100644
index 0000000000000..3ad2641c63b1f
--- /dev/null
+++ b/docs/examples/embeddings/nomic.ipynb
@@ -0,0 +1,485 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/nomic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Nomic Embedding\n",
+    "\n",
+    "Nomic has released v1.5 🪆🪆🪆 is capable of variable sized embeddings with matryoshka learning and an 8192 context, embedding dimensions between 64 and 768.\n",
+    "\n",
+    "In this notebook, we will explore using Nomic v1.5 embedding at different dimensions."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -U llama-index llama-index-embeddings-nomic"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setup API Keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nomic_api_key = \"<NOMIC API KEY>\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import nest_asyncio\n",
+    "\n",
+    "nest_asyncio.apply()\n",
+    "\n",
+    "from llama_index.embeddings.nomic import NomicEmbedding"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### With dimension at 128"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embed_model = NomicEmbedding(\n",
+    "    api_key=nomic_api_key,\n",
+    "    dimensionality=128,\n",
+    "    model_name=\"nomic-embed-text-v1.5\",\n",
+    ")\n",
+    "\n",
+    "embedding = embed_model.get_text_embedding(\"Nomic Embeddings\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "128\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(embedding))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.05569458, 0.057922363, -0.30126953, -0.09832764, 0.05947876]"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "embedding[:5]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### With dimension at 256"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embed_model = NomicEmbedding(\n",
+    "    api_key=nomic_api_key,\n",
+    "    dimensionality=256,\n",
+    "    model_name=\"nomic-embed-text-v1.5\",\n",
+    ")\n",
+    "\n",
+    "embedding = embed_model.get_text_embedding(\"Nomic Embeddings\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "256\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(embedding))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.044708252, 0.04650879, -0.24182129, -0.07897949, 0.04776001]"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "embedding[:5]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### With dimension at 768"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embed_model = NomicEmbedding(\n",
+    "    api_key=nomic_api_key,\n",
+    "    dimensionality=768,\n",
+    "    model_name=\"nomic-embed-text-v1.5\",\n",
+    ")\n",
+    "\n",
+    "embedding = embed_model.get_text_embedding(\"Nomic Embeddings\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "768\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(embedding))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.027282715, 0.028381348, -0.14758301, -0.048187256, 0.029144287]"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "embedding[:5]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### You can still use v1 Nomic Embeddings\n",
+    "\n",
+    "It has 768 fixed embedding dimensions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embed_model = NomicEmbedding(\n",
+    "    api_key=nomic_api_key, model_name=\"nomic-embed-text-v1\"\n",
+    ")\n",
+    "\n",
+    "embedding = embed_model.get_text_embedding(\"Nomic Embeddings\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "768\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(embedding))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.0059013367, 0.03744507, 0.0035305023, -0.047180176, 0.0154418945]"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "embedding[:5]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's Build end to end RAG pipeline with Nomic v1.5 Embedding.\n",
+    "\n",
+    "We will use OpenAI for Generation step."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Set Embedding model and llm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_index.core import settings\n",
+    "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader\n",
+    "from llama_index.llms.openai import OpenAI\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"<YOUR OPENAI API KEY>\"\n",
+    "\n",
+    "embed_model = NomicEmbedding(\n",
+    "    api_key=nomic_api_key,\n",
+    "    dimensionality=128,\n",
+    "    model_name=\"nomic-embed-text-v1.5\",\n",
+    ")\n",
+    "\n",
+    "llm = OpenAI(model=\"gpt-3.5-turbo\")\n",
+    "\n",
+    "settings.llm = llm\n",
+    "settings.embed_model = embed_model"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Download Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2024-02-16 18:37:03--  https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt\n",
+      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 2606:50c0:8001::154, 2606:50c0:8003::154, 2606:50c0:8000::154, ...\n",
+      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|2606:50c0:8001::154|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 75042 (73K) [text/plain]\n",
+      "Saving to: 'data/paul_graham/paul_graham_essay.txt'\n",
+      "\n",
+      "data/paul_graham/pa 100%[===================>]  73.28K  --.-KB/s    in 0.02s   \n",
+      "\n",
+      "2024-02-16 18:37:03 (3.87 MB/s) - 'data/paul_graham/paul_graham_essay.txt' saved [75042/75042]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!mkdir -p 'data/paul_graham/'\n",
+    "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "documents = SimpleDirectoryReader(\"./data/paul_graham\").load_data()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Index creation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "index = VectorStoreIndex.from_documents(documents)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Query Engine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_engine = index.as_query_engine()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The author, growing up, worked on writing and programming. They wrote short stories and also tried writing programs on an IBM 1401 computer. Later, they got a microcomputer and started programming more extensively, writing simple games and a word processor.\n"
+     ]
+    }
+   ],
+   "source": [
+    "response = query_engine.query(\"what did author do growing up?\")\n",
+    "print(response)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "llama",
+   "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"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "b1d2a638b53f4d7129cb7686d8e3b97ae1d80a593a1618479f60cef5591ea888"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/module_guides/models/embeddings.md b/docs/module_guides/models/embeddings.md
index 5aaf6dfad8754..85d284b0f0a18 100644
--- a/docs/module_guides/models/embeddings.md
+++ b/docs/module_guides/models/embeddings.md
@@ -233,4 +233,5 @@ maxdepth: 1
 /examples/embeddings/text_embedding_inference.ipynb
 /examples/embeddings/together.ipynb
 /examples/embeddings/voyageai.ipynb
+/examples/embeddings/nomic.ipynb
 ```