--- title: "Databricks" description: "import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';" type: skill canonical_url: https://claudary.paisolsolutions.com/skills/databricks source: "Claudary" difficulty: intermediate author: "Claude Code Knowledge Pack" date: 2026-07-10T11:19:33.067Z license: CC-BY-4.0 attribution: "Databricks — Claudary (https://claudary.paisolsolutions.com/skills/databricks)" --- # Databricks import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; ## Overview import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; # Databricks LiteLLM supports all models on Databricks :::tip **We support ALL Databricks models, just set `model=databricks/` as a prefix when sending litellm requests** ::: ## Authentication LiteLLM supports multiple authentication methods for Databricks, listed in order of preference: ### OAuth M2M (Recommended for Production) OAuth Machine-to-Machine authentication using Service Principal credentials is the **recommended method for production** deployments per Databricks Partner requirements. ```python import os from litellm import completion # Set OAuth credentials (Service Principal) os.environ["DATABRICKS_CLIENT_ID"] = "your-service-principal-application-id" os.environ["DATABRICKS_CLIENT_SECRET"] = "your-service-principal-secret" os.environ["DATABRICKS_API_BASE"] = "https://adb-xxx.azuredatabricks.net/serving-endpoints" response = completion( model="databricks/databricks-dbrx-instruct", messages=[{"role": "user", "content": "Hello!"}], ) ``` ### Personal Access Token (PAT) PAT authentication is supported for development and testing scenarios. ```python import os from litellm import completion os.environ["DATABRICKS_API_KEY"] = "dapi..." # Your Personal Access Token os.environ["DATABRICKS_API_BASE"] = "https://adb-xxx.azuredatabricks.net/serving-endpoints" response = completion( model="databricks/databricks-dbrx-instruct", messages=[{"role": "user", "content": "Hello!"}], ) ``` ### Databricks SDK Authentication (Automatic) If no credentials are provided, LiteLLM will use the Databricks SDK for automatic authentication. This supports OAuth, Azure AD, and other unified auth methods configured in your environment. ```python from litellm import completion # No environment variables needed - uses Databricks SDK unified auth # Requires: uv add databricks-sdk response = completion( model="databricks/databricks-dbrx-instruct", messages=[{"role": "user", "content": "Hello!"}], ) ``` ## Custom User-Agent for Partner Attribution If you're building a product on top of LiteLLM that integrates with Databricks, you can pass your own partner identifier for proper attribution in Databricks telemetry. The partner name will be prefixed to the LiteLLM user agent: ```python # Via parameter response = completion( model="databricks/databricks-dbrx-instruct", messages=[{"role": "user", "content": "Hello!"}], user_agent="mycompany/1.0.0", ) # Resulting User-Agent: mycompany_litellm/1.79.1 # Via environment variable os.environ["DATABRICKS_USER_AGENT"] = "mycompany/1.0.0" # Resulting User-Agent: mycompany_litellm/1.79.1 ``` | Input | Resulting User-Agent | |-------|---------------------| | (none) | `litellm/1.79.1` | | `mycompany/1.0.0` | `mycompany_litellm/1.79.1` | | `partner_product/2.5.0` | `partner_product_litellm/1.79.1` | | `acme` | `acme_litellm/1.79.1` | **Note:** The version from your custom user agent is ignored; LiteLLM's version is always used. ## Security LiteLLM automatically redacts sensitive information (tokens, secrets, API keys) from all debug logs to prevent credential leakage. This includes: - Authorization headers - API keys and tokens - Client secrets - Personal access tokens (PATs) ## Usage ### 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"}] ) ``` 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 user_agent: "mycompany/1.0.0" # Optional: for partner attribution ``` 2. Start the proxy ```bash $ litellm --config /path/to/config.yaml --debug ``` 3. Send Request to LiteLLM Proxy Server ```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) ``` ```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?" } ], }' ``` ## Passing additional params - max_tokens, temperature See all litellm.completion supported params [here](../completion/input.md#translated-openai-params) ```python # !uv add 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 ``` ## Usage - Thinking / `reasoning_content` LiteLLM translates OpenAI's `reasoning_effort` to Anthropic's `thinking` parameter. [Code](https://github.com/BerriAI/litellm/blob/23051d89dd3611a81617d84277059cd88b2df511/litellm/llms/anthropic/chat/transformation.py#L298) | reasoning_effort | thinking | | ---------------- | -------- | | "low" | "budget_tokens": 1024 | | "medium" | "budget_tokens": 2048 | | "high" | "budget_tokens": 4096 | Known Limitations: - Support for passing thinking blocks back to Claude [Issue](https://github.com/BerriAI/litellm/issues/9790) ```python from litellm import completion import os # set ENV variables (can also be passed in to .completion() - e.g. `api_base`, `api_key`) os.environ["DATABRICKS_API_KEY"] = "databricks key" os.environ["DATABRICKS_API_BASE"] = "databricks base url" resp = completion( model="databricks/databricks-claude-3-7-sonnet", messages=[{"role": "user", "content": "What is the capital of France?"}], reasoning_effort="low", ) ``` 1. Setup config.yaml ```yaml - model_name: claude-3-7-sonnet litellm_params: model: databricks/databricks-claude-3-7-sonnet api_key: os.environ/DATABRICKS_API_KEY api_base: os.environ/DATABRICKS_API_BASE ``` 2. Start proxy ```bash litellm --config /path/to/config.yaml ``` 3. Test it! ```bash curl http://0.0.0.0:4000/v1/chat/completions \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer " \\ -d '{ "model": "claude-3-7-sonnet", "messages": [{"role": "user", "content": "What is the capital of France?"}], "reasoning_effort": "low" }' ``` **Expected Response** ```python ModelResponse( id='chatcmpl-c542d76d-f675-4e87-8e5f-05855f5d0f5e', created=1740470510, model='claude-3-7-sonnet-20250219', object='chat.completion', system_fingerprint=None, choices=[ Choices( finish_reason='stop', index=0, message=Message( content="The capital of France is Paris.", role='assistant', tool_calls=None, function_call=None, provider_specific_fields={ 'citations': None, 'thinking_blocks': [ { 'type': 'thinking', 'thinking': 'The capital of France is Paris. This is a very straightforward factual question.', 'signature': 'EuYBCkQYAiJAy6...' } ] } ), thinking_blocks=[ { 'type': 'thinking', 'thinking': 'The capital of France is Paris. This is a very straightforward factual question.', 'signature': 'EuYBCkQYAiJAy6AGB...' } ], reasoning_content='The capital of France is Paris. This is a very straightforward factual question.' ) ], usage=Usage( completion_tokens=68, prompt_tokens=42, total_tokens=110, completion_tokens_details=None, prompt_tokens_details=PromptTokensDetailsWrapper( audio_tokens=None, cached_tokens=0, text_tokens=None, image_tokens=None ), cache_creation_input_tokens=0, cache_read_input_tokens=0 ) ) ``` ### Citations Anthropic models served through Databricks can return citation metadata. LiteLLM exposes these via `response.choices[0].message.provider_specific_fields["citations"]`. ### Pass `thinking` to Anthropic models You can also pass the `thinking` parameter to Anthropic models. You can also pass the `thinking` parameter to Anthropic models. ```python from litellm import completion import os # set ENV variables (can also be passed in to .completion() - e.g. `api_base`, `api_key`) os.environ["DATABRICKS_API_KEY"] = "databricks key" os.environ["DATABRICKS_API_BASE"] = "databricks base url" response = litellm.completion( model="databricks/databricks-claude-3-7-sonnet", messages=[{"role": "user", "content": "What is the capital of France?"}], thinking={"type": "enabled", "budget_tokens": 1024}, ) ``` ```bash curl http://0.0.0.0:4000/v1/chat/completions \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer $LITELLM_KEY" \\ -d '{ "model": "databricks/databricks-claude-3-7-sonnet", "messages": [{"role": "user", "content": "What is the capital of France?"}], "thinking": {"type": "enabled", "budget_tokens": 1024} }' ``` ## Supported Databricks Chat Completion Models :::tip **We support ALL Databricks models, just set `model=databricks/` as a prefix when sending litellm requests** ::: | Model Name | Command | |----------------------------|------------------------------------------------------------------| | databricks/databricks-claude-3-7-sonnet | `completion(model='databricks/databricks/databricks-claude-3-7-sonnet', messages=messages)` | | databricks-meta-llama-3-1-70b-instruct | `completion(model='databricks/databricks-meta-llama-3-1-70b-instruct', messages=messages)` | | databricks-meta-llama-3-1-405b-instruct | `completion(model='databricks/databricks-meta-llama-3-1-405b-instruct', messages=messages)` | | 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)` | ## Embedding Models ### Passing 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 # !uv add 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 Embedding Models :::tip **We support ALL Databricks models, just set `model=databricks/` as a prefix when sending litellm requests** ::: | 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)` | --- Source: [Claudary](https://claudary.paisolsolutions.com/skills/databricks) · https://claudary.paisolsolutions.com