feat: (re-)enable Databricks inference adapter (#3500)

# What does this PR do?

add/enable the Databricks inference adapter

Databricks inference adapter was broken, closes #3486 

- remove deprecated completion / chat_completion endpoints
- enable dynamic model listing w/o refresh, listing is not async
- use SecretStr instead of str for token
- backward incompatible change: for consistency with databricks docs,
env DATABRICKS_URL -> DATABRICKS_HOST and DATABRICKS_API_TOKEN ->
DATABRICKS_TOKEN
- databricks urls are custom per user/org, add special recorder handling
for databricks urls
- add integration test --setup databricks
- enable chat completions tests
- enable embeddings tests
- disable n > 1 tests
- disable embeddings base64 tests
- disable embeddings dimensions tests

note: reasoning models, e.g. gpt oss, fail because databricks has a
custom, incompatible response format

## Test Plan

ci and 

```
./scripts/integration-tests.sh --stack-config server:ci-tests --setup databricks --subdirs inference --pattern openai
```

note: databricks needs to be manually added to the ci-tests distro for
replay testing

docs/source/providers/inference/remote_databricks.md

@@ -9,13 +9,13 @@ Databricks inference provider for running models on Databricks' unified analytic
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `url` | `<class 'str'>` | No |  | The URL for the Databricks model serving endpoint |
-| `api_token` | `<class 'str'>` | No |  | The Databricks API token |
+| `api_token` | `<class 'pydantic.types.SecretStr'>` | No |  | The Databricks API token |
 
 ## Sample Configuration
 
 ```yaml
-url: ${env.DATABRICKS_URL:=}
+url: ${env.DATABRICKS_HOST:=}
-api_token: ${env.DATABRICKS_API_TOKEN:=}
+api_token: ${env.DATABRICKS_TOKEN:=}
 
 ```
 

llama_stack/providers/registry/inference.py

@@ -142,7 +142,7 @@ def available_providers() -> list[ProviderSpec]:
             api=Api.inference,
             adapter_type="databricks",
             provider_type="remote::databricks",
-            pip_packages=[],
+            pip_packages=["databricks-sdk"],
             module="llama_stack.providers.remote.inference.databricks",
             config_class="llama_stack.providers.remote.inference.databricks.DatabricksImplConfig",
             description="Databricks inference provider for running models on Databricks' unified analytics platform.",

llama_stack/providers/remote/inference/databricks/__init__.py

@@ -5,10 +5,11 @@
 # the root directory of this source tree.
 
 from .config import DatabricksImplConfig
-from .databricks import DatabricksInferenceAdapter
 
 
 async def get_adapter_impl(config: DatabricksImplConfig, _deps):
+    from .databricks import DatabricksInferenceAdapter
+
     assert isinstance(config, DatabricksImplConfig), f"Unexpected config type: {type(config)}"
     impl = DatabricksInferenceAdapter(config)
     await impl.initialize()

llama_stack/providers/remote/inference/databricks/config.py

@@ -6,7 +6,7 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
 from llama_stack.schema_utils import json_schema_type
 
@@ -17,16 +17,16 @@ class DatabricksImplConfig(BaseModel):
         default=None,
         description="The URL for the Databricks model serving endpoint",
     )
-    api_token: str = Field(
+    api_token: SecretStr = Field(
-        default=None,
+        default=SecretStr(None),
         description="The Databricks API token",
     )
 
     @classmethod
     def sample_run_config(
         cls,
-        url: str = "${env.DATABRICKS_URL:=}",
+        url: str = "${env.DATABRICKS_HOST:=}",
-        api_token: str = "${env.DATABRICKS_API_TOKEN:=}",
+        api_token: str = "${env.DATABRICKS_TOKEN:=}",
         **kwargs: Any,
     ) -> dict[str, Any]:
         return {

llama_stack/providers/remote/inference/databricks/databricks.py

@@ -4,23 +4,26 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from collections.abc import AsyncGenerator
+from collections.abc import AsyncIterator
+from typing import Any
 
-from openai import OpenAI
+from databricks.sdk import WorkspaceClient
 
 from llama_stack.apis.common.content_types import (
     InterleavedContent,
     InterleavedContentItem,
 )
 from llama_stack.apis.inference import (
-    ChatCompletionRequest,
     ChatCompletionResponse,
+    ChatCompletionResponseStreamChunk,
+    CompletionResponse,
+    CompletionResponseStreamChunk,
     EmbeddingsResponse,
     EmbeddingTaskType,
     Inference,
     LogProbConfig,
     Message,
-    OpenAIEmbeddingsResponse,
+    OpenAICompletion,
     ResponseFormat,
     SamplingParams,
     TextTruncation,
@@ -29,49 +32,50 @@ from llama_stack.apis.inference import (
     ToolDefinition,
     ToolPromptFormat,
 )
-from llama_stack.models.llama.sku_types import CoreModelId
+from llama_stack.apis.models import Model, ModelType
+from llama_stack.log import get_logger
 from llama_stack.providers.utils.inference.model_registry import (
-    ModelRegistryHelper,
+    ProviderModelEntry,
-    build_hf_repo_model_entry,
-)
-from llama_stack.providers.utils.inference.openai_compat import (
-    OpenAIChatCompletionToLlamaStackMixin,
-    OpenAICompletionToLlamaStackMixin,
-    get_sampling_options,
-    process_chat_completion_response,
-    process_chat_completion_stream_response,
-)
-from llama_stack.providers.utils.inference.prompt_adapter import (
-    chat_completion_request_to_prompt,
 )
+from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 from .config import DatabricksImplConfig
 
-SAFETY_MODELS_ENTRIES = []
+logger = get_logger(name=__name__, category="inference::databricks")
 
-# https://docs.databricks.com/aws/en/machine-learning/model-serving/foundation-model-overview
+
-MODEL_ENTRIES = [
+# source: https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models
-    build_hf_repo_model_entry(
+EMBEDDING_MODEL_ENTRIES = {
-        "databricks-meta-llama-3-1-70b-instruct",
+    "databricks-gte-large-en": ProviderModelEntry(
-        CoreModelId.llama3_1_70b_instruct.value,
+        provider_model_id="databricks-gte-large-en",
+        metadata={
+            "embedding_dimension": 1024,
+            "context_length": 8192,
+        },
     ),
-    build_hf_repo_model_entry(
+    "databricks-bge-large-en": ProviderModelEntry(
-        "databricks-meta-llama-3-1-405b-instruct",
+        provider_model_id="databricks-bge-large-en",
-        CoreModelId.llama3_1_405b_instruct.value,
+        metadata={
+            "embedding_dimension": 1024,
+            "context_length": 512,
+        },
     ),
-] + SAFETY_MODELS_ENTRIES
+}
 
 
 class DatabricksInferenceAdapter(
-    ModelRegistryHelper,
+    OpenAIMixin,
     Inference,
-    OpenAIChatCompletionToLlamaStackMixin,
-    OpenAICompletionToLlamaStackMixin,
 ):
     def __init__(self, config: DatabricksImplConfig) -> None:
-        ModelRegistryHelper.__init__(self, model_entries=MODEL_ENTRIES)
         self.config = config
 
+    def get_api_key(self) -> str:
+        return self.config.api_token.get_secret_value()
+
+    def get_base_url(self) -> str:
+        return f"{self.config.url}/serving-endpoints"
+
     async def initialize(self) -> None:
         return
 
@@ -80,72 +84,54 @@ class DatabricksInferenceAdapter(
 
     async def completion(
         self,
-        model: str,
+        model_id: str,
         content: InterleavedContent,
         sampling_params: SamplingParams | None = None,
         response_format: ResponseFormat | None = None,
         stream: bool | None = False,
         logprobs: LogProbConfig | None = None,
-    ) -> AsyncGenerator:
+    ) -> CompletionResponse | AsyncIterator[CompletionResponseStreamChunk]:
+        raise NotImplementedError()
+
+    async def openai_completion(
+        self,
+        model: str,
+        prompt: str | list[str] | list[int] | list[list[int]],
+        best_of: int | None = None,
+        echo: bool | None = None,
+        frequency_penalty: float | None = None,
+        logit_bias: dict[str, float] | None = None,
+        logprobs: bool | None = None,
+        max_tokens: int | None = None,
+        n: int | None = None,
+        presence_penalty: float | None = None,
+        seed: int | None = None,
+        stop: str | list[str] | None = None,
+        stream: bool | None = None,
+        stream_options: dict[str, Any] | None = None,
+        temperature: float | None = None,
+        top_p: float | None = None,
+        user: str | None = None,
+        guided_choice: list[str] | None = None,
+        prompt_logprobs: int | None = None,
+        suffix: str | None = None,
+    ) -> OpenAICompletion:
         raise NotImplementedError()
 
     async def chat_completion(
         self,
-        model: str,
+        model_id: str,
         messages: list[Message],
         sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
         tools: list[ToolDefinition] | None = None,
         tool_choice: ToolChoice | None = ToolChoice.auto,
         tool_prompt_format: ToolPromptFormat | None = None,
+        response_format: ResponseFormat | None = None,
         stream: bool | None = False,
         logprobs: LogProbConfig | None = None,
         tool_config: ToolConfig | None = None,
-    ) -> AsyncGenerator:
+    ) -> ChatCompletionResponse | AsyncIterator[ChatCompletionResponseStreamChunk]:
-        if sampling_params is None:
+        raise NotImplementedError()
-            sampling_params = SamplingParams()
-        request = ChatCompletionRequest(
-            model=model,
-            messages=messages,
-            sampling_params=sampling_params,
-            tools=tools or [],
-            stream=stream,
-            logprobs=logprobs,
-            tool_config=tool_config,
-        )
-
-        client = OpenAI(base_url=self.config.url, api_key=self.config.api_token)
-        if stream:
-            return self._stream_chat_completion(request, client)
-        else:
-            return await self._nonstream_chat_completion(request, client)
-
-    async def _nonstream_chat_completion(
-        self, request: ChatCompletionRequest, client: OpenAI
-    ) -> ChatCompletionResponse:
-        params = self._get_params(request)
-        r = client.completions.create(**params)
-        return process_chat_completion_response(r, request)
-
-    async def _stream_chat_completion(self, request: ChatCompletionRequest, client: OpenAI) -> AsyncGenerator:
-        params = self._get_params(request)
-
-        async def _to_async_generator():
-            s = client.completions.create(**params)
-            for chunk in s:
-                yield chunk
-
-        stream = _to_async_generator()
-        async for chunk in process_chat_completion_stream_response(stream, request):
-            yield chunk
-
-    def _get_params(self, request: ChatCompletionRequest) -> dict:
-        return {
-            "model": request.model,
-            "prompt": chat_completion_request_to_prompt(request, self.get_llama_model(request.model)),
-            "stream": request.stream,
-            **get_sampling_options(request.sampling_params),
-        }
 
     async def embeddings(
         self,
@@ -157,12 +143,39 @@ class DatabricksInferenceAdapter(
     ) -> EmbeddingsResponse:
         raise NotImplementedError()
 
-    async def openai_embeddings(
+    async def list_models(self) -> list[Model] | None:
-        self,
+        self._model_cache = {}  # from OpenAIMixin
-        model: str,
+        ws_client = WorkspaceClient(host=self.config.url, token=self.get_api_key())  # TODO: this is not async
-        input: str | list[str],
+        endpoints = ws_client.serving_endpoints.list()
-        encoding_format: str | None = "float",
+        for endpoint in endpoints:
-        dimensions: int | None = None,
+            model = Model(
-        user: str | None = None,
+                provider_id=self.__provider_id__,
-    ) -> OpenAIEmbeddingsResponse:
+                provider_resource_id=endpoint.name,
-        raise NotImplementedError()
+                identifier=endpoint.name,
+            )
+            if endpoint.task == "llm/v1/chat":
+                model.model_type = ModelType.llm  # this is redundant, but informative
+            elif endpoint.task == "llm/v1/embeddings":
+                if endpoint.name not in EMBEDDING_MODEL_ENTRIES:
+                    logger.warning(f"No metadata information available for embedding model {endpoint.name}, skipping.")
+                    continue
+                model.model_type = ModelType.embedding
+                model.metadata = EMBEDDING_MODEL_ENTRIES[endpoint.name].metadata
+            else:
+                logger.warning(f"Unknown model type, skipping: {endpoint}")
+                continue
+
+            self._model_cache[endpoint.name] = model
+
+        return list(self._model_cache.values())
+
+    async def register_model(self, model: Model) -> Model:
+        if not await self.check_model_availability(model.provider_resource_id):
+            raise ValueError(f"Model {model.provider_resource_id} is not available in Databricks workspace.")
+        return model
+
+    async def unregister_model(self, model_id: str) -> None:
+        pass
+
+    async def should_refresh_models(self) -> bool:
+        return False

llama_stack/testing/inference_recorder.py

@@ -267,6 +267,10 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
         raise ValueError(f"Unknown client type: {client_type}")
 
     url = base_url.rstrip("/") + endpoint
+    # Special handling for Databricks URLs to avoid leaking workspace info
+    # e.g. https://adb-1234567890123456.7.cloud.databricks.com -> https://...cloud.databricks.com
+    if "cloud.databricks.com" in url:
+        url = "__databricks__" + url.split("cloud.databricks.com")[-1]
     method = "POST"
     headers = {}
     body = kwargs

tests/integration/inference/test_openai_completion.py

@@ -98,6 +98,7 @@ def skip_if_doesnt_support_n(client_with_models, model_id):
         #  the entered value was 2. Update the candidateCount value and try again.', 'status': 'INVALID_ARGUMENT'}
         "remote::tgi",  # TGI ignores n param silently
         "remote::together",  # `n` > 1 is not supported when streaming tokens. Please disable `stream`
+        "remote::databricks",  # Bad request: parameter "n" must be equal to 1 for streaming mode
     ):
         pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support n param.")
 
@@ -110,7 +111,6 @@ def skip_if_model_doesnt_support_openai_chat_completion(client_with_models, mode
         "inline::vllm",
         "remote::bedrock",
         "remote::cerebras",
-        "remote::databricks",
         "remote::runpod",
         "remote::watsonx",  # watsonx returns 404 when hitting the /openai/v1 endpoint
     ):

tests/integration/inference/test_openai_embeddings.py

@@ -42,6 +42,7 @@ def skip_if_model_doesnt_support_encoding_format_base64(client, model_id):
     provider = provider_from_model(client, model_id)
     if provider.provider_type in (
         "remote::together",  # param silently ignored, always returns floats
+        "remote::databricks",  # param silently ignored, always returns floats
         "remote::fireworks",  # param silently ignored, always returns list of floats
     ):
         pytest.skip(f"Model {model_id} hosted by {provider.provider_type} does not support encoding_format='base64'.")
@@ -52,6 +53,8 @@ def skip_if_model_doesnt_support_variable_dimensions(client_with_models, model_i
     if provider.provider_type in (
         "remote::together",  # returns 400
         "inline::sentence-transformers",
+        # Error code: 400 - {'error_code': 'BAD_REQUEST', 'message': 'Bad request: json: unknown field "dimensions"\n'}
+        "remote::databricks",
     ):
         pytest.skip(
             f"Model {model_id} hosted by {provider.provider_type} does not support variable output embedding dimensions."
@@ -75,7 +78,6 @@ def skip_if_model_doesnt_support_openai_embeddings(client, model_id):
         "inline::meta-reference",
         "remote::bedrock",
         "remote::cerebras",
-        "remote::databricks",
         "remote::runpod",
         "remote::sambanova",
         "remote::tgi",

tests/integration/recordings/responses/121a72d1c4cf.json

@@ -0,0 +1,728 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "__databricks__/serving-endpoints/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "databricks-meta-llama-3-3-70b-instruct",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Hello, world!"
+        }
+      ],
+      "stream": true
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "databricks-meta-llama-3-3-70b-instruct"
+  },
+  "response": {
+    "body": [
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl_7268e4ee-3b8e-461e-80dc-608e76f3801d",
+          "choices": [
+            {
+              "delta": {
+                "content": "",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": null
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1758326500,
+          "model": "meta-llama-3.3-70b-instruct-121024",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": null,
+          "usage": {
+            "completion_tokens": 3,
+            "prompt_tokens": 14,
+            "total_tokens": 17,
+            "completion_tokens_details": null,
+            "prompt_tokens_details": null
+          }
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl_7268e4ee-3b8e-461e-80dc-608e76f3801d",
+          "choices": [
+            {
+              "delta": {
+                "content": "Hello! ",
+                "function_call": null,
+                "refusal": null,
+                "role": null,
+                "tool_calls": null
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1758326500,
+          "model": "meta-llama-3.3-70b-instruct-121024",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": null,
+          "usage": {
+            "completion_tokens": 3,
+            "prompt_tokens": 14,
+            "total_tokens": 17,
+            "completion_tokens_details": null,
+            "prompt_tokens_details": null
+          }
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl_7268e4ee-3b8e-461e-80dc-608e76f3801d",
+          "choices": [
+            {
+              "delta": {
+                "content": "It's ",
+                "function_call": null,
+                "refusal": null,
+                "role": null,
+                "tool_calls": null
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1758326500,
+          "model": "meta-llama-3.3-70b-instruct-121024",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": null,
+          "usage": {
+            "completion_tokens": 5,
+            "prompt_tokens": 14,
+            "total_tokens": 19,
+            "completion_tokens_details": null,
+            "prompt_tokens_details": null
+          }
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl_7268e4ee-3b8e-461e-80dc-608e76f3801d",
+          "choices": [
+            {
+              "delta": {
+                "content": "nice ",
+                "function_call": null,
+                "refusal": null,
+                "role": null,
+                "tool_calls": null
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1758326500,
+          "model": "meta-llama-3.3-70b-instruct-121024",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": null,
+          "usage": {
+            "completion_tokens": 6,
+            "prompt_tokens": 14,
+            "total_tokens": 20,
+            "completion_tokens_details": null,
+            "prompt_tokens_details": null
+          }
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl_7268e4ee-3b8e-461e-80dc-608e76f3801d",
+          "choices": [
+            {
+              "delta": {
+                "content": "to ",
+                "function_call": null,
+                "refusal": null,
+                "role": null,
+                "tool_calls": null
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1758326500,
+          "model": "meta-llama-3.3-70b-instruct-121024",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": null,
+          "usage": {
+            "completion_tokens": 7,
+            "prompt_tokens": 14,
+            "total_tokens": 21,
+            "completion_tokens_details": null,
+            "prompt_tokens_details": null
+          }
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl_7268e4ee-3b8e-461e-80dc-608e76f3801d",
+          "choices": [
+            {
+              "delta": {
+                "content": "meet ",
+                "function_call": null,
+                "refusal": null,
+                "role": null,
+                "tool_calls": null
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1758326500,
+          "model": "meta-llama-3.3-70b-instruct-121024",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": null,
+          "usage": {
+            "completion_tokens": 8,
+            "prompt_tokens": 14,
+            "total_tokens": 22,
+            "completion_tokens_details": null,
+            "prompt_tokens_details": null
+          }
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl_7268e4ee-3b8e-461e-80dc-608e76f3801d",
+          "choices": [
+            {
+              "delta": {
+                "content": "you. ",
+                "function_call": null,
+                "refusal": null,
+                "role": null,
+                "tool_calls": null
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1758326500,
+          "model": "meta-llama-3.3-70b-instruct-121024",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": null,
+          "usage": {
+            "completion_tokens": 10,
+            "prompt_tokens": 14,
+            "total_tokens": 24,
+            "completion_tokens_details": null,
+            "prompt_tokens_details": null
+          }
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl_7268e4ee-3b8e-461e-80dc-608e76f3801d",
+          "choices": [
+            {
+              "delta": {
+                "content": "Is ",
+                "function_call": null,
+                "refusal": null,
+                "role": null,
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tests/integration/recordings/responses/249b7f0ddde6.json

@@ -0,0 +1,56 @@
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tests/integration/recordings/responses/28648cf8d421.json

@@ -0,0 +1,56 @@
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tests/integration/recordings/responses/29585e055e6f.json

@@ -0,0 +1,56 @@
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tests/integration/recordings/responses/3ef0f9aab128.json

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+      "stream": true
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tests/integration/recordings/responses/e509387fc329.json

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tests/integration/recordings/responses/ecf6f0c51485.json

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tests/integration/suites.py

@@ -108,6 +108,14 @@ SETUP_DEFINITIONS: dict[str, Setup] = {
             "embedding_model": "together/togethercomputer/m2-bert-80M-32k-retrieval",
         },
     ),
+    "databricks": Setup(
+        name="databricks",
+        description="Databricks models",
+        defaults={
+            "text_model": "databricks/databricks-meta-llama-3-3-70b-instruct",
+            "embedding_model": "databricks/databricks-bge-large-en",
+        },
+    ),
     "fireworks": Setup(
         name="fireworks",
         description="Fireworks provider with a text model",