litellm/docs/my-website/docs/providers/databricks.md

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# 🆕 Databricks

LiteLLM supports all models on Databricks


## Usage

<Tabs>
<TabItem value="sdk" label="SDK">

### ENV VAR
```python
import os 
os.environ["DATABRICKS_API_KEY"] = ""
os.environ["DATABRICKS_API_BASE"] = ""
```

### Example Call

```python
from litellm import completion
import os
## set ENV variables
os.environ["DATABRICKS_API_KEY"] = "databricks key"
os.environ["DATABRICKS_API_BASE"] = "databricks base url" # e.g.: https://adb-3064715882934586.6.azuredatabricks.net/serving-endpoints

# Databricks dbrx-instruct call
response = completion(
    model="databricks/databricks-dbrx-instruct", 
    messages = [{ "content": "Hello, how are you?","role": "user"}]
)
```

</TabItem>
<TabItem value="proxy" label="PROXY">

1. Add models to your config.yaml

  ```yaml
  model_list:
    - model_name: dbrx-instruct
      litellm_params:
        model: databricks/databricks-dbrx-instruct
        api_key: os.environ/DATABRICKS_API_KEY
        api_base: os.environ/DATABRICKS_API_BASE
  ```



2. Start the proxy 

  ```bash
  $ litellm --config /path/to/config.yaml --debug
  ```

3. Send Request to LiteLLM Proxy Server

  <Tabs>

  <TabItem value="openai" label="OpenAI Python v1.0.0+">

  ```python
  import openai
  client = openai.OpenAI(
      api_key="sk-1234",             # pass litellm proxy key, if you're using virtual keys
      base_url="http://0.0.0.0:4000" # litellm-proxy-base url
  )

  response = client.chat.completions.create(
      model="dbrx-instruct",
      messages = [
        {
            "role": "system",
            "content": "Be a good human!"
        },
        {
            "role": "user",
            "content": "What do you know about earth?"
        }
    ]
  )

  print(response)
  ```

  </TabItem>

  <TabItem value="curl" label="curl">

  ```shell
  curl --location 'http://0.0.0.0:4000/chat/completions' \
      --header 'Authorization: Bearer sk-1234' \
      --header 'Content-Type: application/json' \
      --data '{
      "model": "dbrx-instruct",
      "messages": [
        {
            "role": "system",
            "content": "Be a good human!"
        },
        {
            "role": "user",
            "content": "What do you know about earth?"
        }
        ],
  }'
  ```
  </TabItem>

  </Tabs>


</TabItem>

</Tabs>

## Passing additional params - max_tokens, temperature 
See all litellm.completion supported params [here](../completion/input.md#translated-openai-params)

```python
# !pip install litellm
from litellm import completion
import os
## set ENV variables
os.environ["DATABRICKS_API_KEY"] = "databricks key"
os.environ["DATABRICKS_API_BASE"] = "databricks api base"

# databricks dbrx call
response = completion(
    model="databricks/databricks-dbrx-instruct", 
    messages = [{ "content": "Hello, how are you?","role": "user"}],
    max_tokens=20,
    temperature=0.5
)
```

**proxy**

```yaml
  model_list:
    - model_name: llama-3
      litellm_params:
        model: databricks/databricks-meta-llama-3-70b-instruct
        api_key: os.environ/DATABRICKS_API_KEY
        max_tokens: 20
        temperature: 0.5
```

## Passings Databricks specific params - 'instruction'

For embedding models, databricks lets you pass in an additional param 'instruction'. [Full Spec](https://github.com/BerriAI/litellm/blob/43353c28b341df0d9992b45c6ce464222ebd7984/litellm/llms/databricks.py#L164)


```python
# !pip install litellm
from litellm import embedding
import os
## set ENV variables
os.environ["DATABRICKS_API_KEY"] = "databricks key"
os.environ["DATABRICKS_API_BASE"] = "databricks url"

# Databricks bge-large-en call
response = litellm.embedding(
      model="databricks/databricks-bge-large-en",
      input=["good morning from litellm"],
      instruction="Represent this sentence for searching relevant passages:",
  )
```

**proxy**

```yaml
  model_list:
    - model_name: bge-large
      litellm_params:
        model: databricks/databricks-bge-large-en
        api_key: os.environ/DATABRICKS_API_KEY
        api_base: os.environ/DATABRICKS_API_BASE
        instruction: "Represent this sentence for searching relevant passages:"
```


## Supported Databricks Chat Completion Models 

| Model Name                 | Command                                                          |
|----------------------------|------------------------------------------------------------------|
| databricks-dbrx-instruct    | `completion(model='databricks/databricks-dbrx-instruct', messages=messages)`   | 
| databricks-meta-llama-3-70b-instruct    | `completion(model='databricks/databricks-meta-llama-3-70b-instruct', messages=messages)`   | 
| databricks-llama-2-70b-chat    | `completion(model='databricks/databricks-llama-2-70b-chat', messages=messages)`   | 
| databricks-mixtral-8x7b-instruct    | `completion(model='databricks/databricks-mixtral-8x7b-instruct', messages=messages)`   | 
| databricks-mpt-30b-instruct    | `completion(model='databricks/databricks-mpt-30b-instruct', messages=messages)`   | 
| databricks-mpt-7b-instruct    | `completion(model='databricks/databricks-mpt-7b-instruct', messages=messages)`   | 

## Supported Databricks Embedding Models 

| Model Name                 | Command                                                          |
|----------------------------|------------------------------------------------------------------|
| databricks-bge-large-en    | `embedding(model='databricks/databricks-bge-large-en', messages=messages)`   |
| databricks-gte-large-en    | `embedding(model='databricks/databricks-gte-large-en', messages=messages)`   |