Docs: Predibase Integration
diff --git a/docs/core_modules/model_modules/llms/modules.md b/docs/core_modules/model_modules/llms/modules.md
index 65d31b0360e05..82db5c5639043 100644
--- a/docs/core_modules/model_modules/llms/modules.md
+++ b/docs/core_modules/model_modules/llms/modules.md
@@ -42,6 +42,16 @@ maxdepth: 1
 
 ```
 
+## Predibase
+
+```{toctree}
+---
+maxdepth: 1
+---
+/examples/llm/predibase.ipynb
+
+```
+
 
 ## Replicate
 
diff --git a/docs/examples/llm/predibase.ipynb b/docs/examples/llm/predibase.ipynb
new file mode 100644
index 0000000000000..49a6ee265d366
--- /dev/null
+++ b/docs/examples/llm/predibase.ipynb
@@ -0,0 +1,198 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bf9f19f3",
+   "metadata": {},
+   "source": [
+    "# Predibase\n",
+    "\n",
+    "This notebook shows how you can use Predibase-hosted LLM's within Llamaindex. You can add [Predibase](https://predibase.com) to your existing Llamaindex worklow to: \n",
+    "1. Deploy and query pre-trained or custom open source LLM’s without the hassle\n",
+    "2. Operationalize an end-to-end Retrieval Augmented Generation (RAG) system\n",
+    "3. Fine-tune your own LLM in just a few lines of code\n",
+    "\n",
+    "## Getting Started\n",
+    "1. Sign up for a free Predibase account [here](https://predibase.com/free-trial)\n",
+    "2. Create an Account\n",
+    "3. Go to Settings > My profile and Generate a new API Token."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "79a726c5",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "!pip install llama-index --quiet\n",
+    "!pip install predibase --quiet\n",
+    "!pip install sentence-transformers --quiet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c2b0d5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"PREDIBASE_API_TOKEN\"] = \"{PREDIBASE_API_TOKEN}\"\n",
+    "from llama_index.llms import PredibaseLLM"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a602a2a",
+   "metadata": {},
+   "source": [
+    "## Flow 1: Query Predibase LLM directly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4baffaa2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = PredibaseLLM(model_name=\"llama-2-13b\", temperature=0.3, max_new_tokens=512)\n",
+    "# You can query any HuggingFace or fine-tuned LLM that's hosted on Predibase"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7039a65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = llm.complete(\"Can you recommend me a nice dry white wine?\")\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1112e828",
+   "metadata": {},
+   "source": [
+    "## Flow 2: Retrieval Augmented Generation (RAG) with Predibase LLM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cacff36a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext\n",
+    "from llama_index.embeddings import LangchainEmbedding\n",
+    "from langchain.embeddings import HuggingFaceEmbeddings\n",
+    "from sentence_transformers import SentenceTransformer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1edd41d1",
+   "metadata": {},
+   "source": [
+    "### Load Documents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5941151",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7df4407f",
+   "metadata": {},
+   "source": [
+    "### Configure Predibase LLM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dec73a6b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = PredibaseLLM(\n",
+    "    model_name=\"llama-2-13b\", temperature=0.3, max_new_tokens=400, context_window=1024\n",
+    ")\n",
+    "embed_model = LangchainEmbedding(HuggingFaceEmbeddings())\n",
+    "service_context = ServiceContext.from_defaults(\n",
+    "    chunk_size=1024, llm=llm, embed_model=embed_model\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a131a8e",
+   "metadata": {},
+   "source": [
+    "### Setup and Query Index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9b10269",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "index = VectorStoreIndex.from_documents(documents, service_context=service_context)\n",
+    "query_engine = index.as_query_engine()\n",
+    "response = query_engine.query(\"What did the author do growing up?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac73eb65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(response)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.12 ('.venv': venv)",
+   "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",
+   "version": "3.9.12"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "5ae9fa2777630f93d325d67fd0c37f7375ed1afcb20dd85f425eb8692a47ff3f"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

This PR adds the Predibase Integration to the Llamaindex docs under the LLM section. Screenshots below: 

<img width="1036" alt="Screen Shot 2023-08-01 at 4 56 32 PM" src="https://github.com/jerryjliu/llama_index/assets/32989166/b84297dc-49e8-45ab-8555-8525a60b0cf0">


<img width="1321" alt="Screen Shot 2023-08-01 at 4 56 44 PM" src="https://github.com/jerryjliu/llama_index/assets/32989166/6eeb6418-078a-4a12-b180-d5f6ca1c95fa">

