---
title: Databricks
 | Developer Documentation
---

Integrate with Databricks LLMs APIs.

## Pre-requisites

- [Databricks personal access token](https://docs.databricks.com/en/dev-tools/auth/pat.html) to query and access Databricks model serving endpoints.

- [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.

## Setup

If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

```
% pip install llama-index-llms-databricks
```

```
!pip install llama-index
```

```
from llama_index.llms.databricks import Databricks
```

```
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.
```

Terminal window

```
export DATABRICKS_TOKEN=<your api key>
export DATABRICKS_SERVING_ENDPOINT=<your api serving endpoint>
```

Alternatively, you can pass your API key and serving endpoint to the LLM when you init it:

```
llm = Databricks(
    model="databricks-dbrx-instruct",
    api_key="your_api_key",
    api_base="https://[your-work-space].cloud.databricks.com/serving-endpoints/",
)
```

A list of available LLM models can be found [here](https://console.groq.com/docs/models).

```
response = llm.complete("Explain the importance of open source LLMs")
```

```
print(response)
```

#### Call `chat` with a list of messages

```
from llama_index.core.llms import ChatMessage


messages = [
    ChatMessage(
        role="system", content="You are a pirate with a colorful personality"
    ),
    ChatMessage(role="user", content="What is your name"),
]
resp = llm.chat(messages)
```

```
print(resp)
```

### Streaming

Using `stream_complete` endpoint

```
response = llm.stream_complete("Explain the importance of open source LLMs")
```

```
for r in response:
    print(r.delta, end="")
```

Using `stream_chat` endpoint

```
from llama_index.core.llms import ChatMessage


messages = [
    ChatMessage(
        role="system", content="You are a pirate with a colorful personality"
    ),
    ChatMessage(role="user", content="What is your name"),
]
resp = llm.stream_chat(messages)
```

```
for r in resp:
    print(r.delta, end="")
```