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chore: import upstream snapshot with attribution
2026-07-13 12:26:52 +08:00

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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_TOKEN=<your api key>\n",
"export DATABRICKS_SERVING_ENDPOINT=<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/\",\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=\"\")"
]
}
],
"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": 5
}