diff --git a/docs/examples/embeddings/nomic.ipynb b/docs/examples/embeddings/nomic.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3ad2641c63b1f1001b2956783e923aace89ba123
--- /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 5aaf6dfad8754394f2fcac373553dd3c0f459a3f..85d284b0f0a18dcacd057bd9f8ccb8ef187a227c 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
```