Integrations with DataBricks

docs/docs/examples/llm/databricks.ipynb

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+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "2e33dced-e587-4397-81b3-d6606aa1738a",
+   "metadata": {},
+   "source": [
+    "# DataBricks\n",
+    "\n",
+    "Integrate with DataBricks LLMs APIs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c4105d3",
+   "metadata": {},
+   "source": [
+    "## Pre-requisites\n",
+    "\n",
+    "- [Databricks personal access token](https://docs.databricks.com/en/dev-tools/auth/pat.html) to query and access Databricks model serving endpoints.\n",
+    "\n",
+    "- [Databricks workspace](https://docs.databricks.com/en/workspace/index.html) in a [supported region](https://docs.databricks.com/en/machine-learning/model-serving/model-serving-limits.html#regions) for Foundation Model APIs pay-per-token."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "5863dde9-84a0-4c33-ad52-cc767442f63f",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "833bdb2b",
+   "metadata": {},
+   "source": [
+    "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4aff387e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "% pip install llama-index-llms-databricks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9bbbc106",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install llama-index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad297f19-998f-4485-aa2f-d67020058b7d",
+   "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.databricks import DataBricks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4eefec25",
+   "metadata": {},
+   "source": [
+    "\n",
+    "```bash\n",
+    "export DATABRICKS_API_KEY=<your api key>\n",
+    "export DATABRICKS_API_BASE=<your api serving endpoint>\n",
+    "```\n",
+    "\n",
+    "Alternatively, you can pass your API key and serving endpoint to the LLM when you init it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "152ced37-9a42-47be-9a39-4218521f5e72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = DataBricks(\n",
+    "    model=\"databricks-dbrx-instruct\",\n",
+    "    api_key=\"your_api_key\",\n",
+    "    api_base=\"https://[your-work-space].cloud.databricks.com/serving-endpoints/[your-serving-endpoint]\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "562455fe",
+   "metadata": {},
+   "source": [
+    "A list of available LLM models can be found [here](https://console.groq.com/docs/models)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d61b10bb-e911-47fb-8e84-19828cf224be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = llm.complete(\"Explain the importance of open source LLMs\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3bd14f4e-c245-4384-a471-97e4ddfcb40e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(response)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "3ba9503c-b440-43c6-a50c-676c79993813",
+   "metadata": {},
+   "source": [
+    "#### Call `chat` with a list of messages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee8a4a55-5680-4dc6-a44c-fc8ad7892f80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_index.core.llms import ChatMessage\n",
+    "\n",
+    "messages = [\n",
+    "    ChatMessage(\n",
+    "        role=\"system\", content=\"You are a pirate with a colorful personality\"\n",
+    "    ),\n",
+    "    ChatMessage(role=\"user\", content=\"What is your name\"),\n",
+    "]\n",
+    "resp = llm.chat(messages)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2a9bfe53-d15b-4e75-9d91-8c5d024f4eda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(resp)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "25ad1b00-28fc-4bcd-96c4-d5b35605721a",
+   "metadata": {},
+   "source": [
+    "### Streaming"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "13c641fa-345a-4dce-87c5-ab1f6dcf4757",
+   "metadata": {},
+   "source": [
+    "Using `stream_complete` endpoint "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06da1ef1-2f6b-497c-847b-62dd2df11491",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = llm.stream_complete(\"Explain the importance of open source LLMs\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1b851def-5160-46e5-a30c-5a3ef2356b79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for r in response:\n",
+    "    print(r.delta, end=\"\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "ca52051d-6b28-49d7-98f5-82e266a1c7a6",
+   "metadata": {},
+   "source": [
+    "Using `stream_chat` endpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe553190-52a9-436d-84ae-4dd99a1808f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_index.core.llms import ChatMessage\n",
+    "\n",
+    "messages = [\n",
+    "    ChatMessage(\n",
+    "        role=\"system\", content=\"You are a pirate with a colorful personality\"\n",
+    "    ),\n",
+    "    ChatMessage(role=\"user\", content=\"What is your name\"),\n",
+    "]\n",
+    "resp = llm.stream_chat(messages)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "154c503c-f893-4b6b-8a65-a9a27b636046",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for r in resp:\n",
+    "    print(r.delta, end=\"\")"
+   ]
+  }
+ ],
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+   "display_name": "Python 3 (ipykernel)",
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+   "codemirror_mode": {
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+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
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+ "nbformat": 4,
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+}