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"""
Translates from OpenAI's `/v1/chat/completions` to Databricks' `/chat/completions`
"""
from typing import (
TYPE_CHECKING,
Any,
AsyncIterator,
Iterator,
List,
Optional,
Tuple,
Union,
cast,
)
import httpx
from pydantic import BaseModel
from litellm.constants import RESPONSE_FORMAT_TOOL_NAME
from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import (
_handle_invalid_parallel_tool_calls,
_should_convert_tool_call_to_json_mode,
)
from litellm.litellm_core_utils.prompt_templates.common_utils import (
handle_messages_with_content_list_to_str_conversion,
strip_name_from_messages,
)
from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator
from litellm.types.llms.anthropic import AllAnthropicToolsValues
from litellm.types.llms.databricks import (
AllDatabricksContentValues,
DatabricksChoice,
DatabricksFunction,
DatabricksResponse,
DatabricksTool,
)
from litellm.types.llms.openai import (
AllMessageValues,
ChatCompletionRedactedThinkingBlock,
ChatCompletionThinkingBlock,
ChatCompletionToolChoiceFunctionParam,
ChatCompletionToolChoiceObjectParam,
)
from litellm.types.utils import (
ChatCompletionMessageToolCall,
Choices,
Message,
ModelResponse,
ModelResponseStream,
ProviderField,
Usage,
)
from ...anthropic.chat.transformation import AnthropicConfig
from ...openai_like.chat.transformation import OpenAILikeChatConfig
from ..common_utils import DatabricksBase, DatabricksException
if TYPE_CHECKING:
from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
LiteLLMLoggingObj = _LiteLLMLoggingObj
else:
LiteLLMLoggingObj = Any
class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig):
"""
Reference: https://docs.databricks.com/en/machine-learning/foundation-models/api-reference.html#chat-request
"""
max_tokens: Optional[int] = None
temperature: Optional[int] = None
top_p: Optional[int] = None
top_k: Optional[int] = None
stop: Optional[Union[List[str], str]] = None
n: Optional[int] = None
def __init__(
self,
max_tokens: Optional[int] = None,
temperature: Optional[int] = None,
top_p: Optional[int] = None,
top_k: Optional[int] = None,
stop: Optional[Union[List[str], str]] = None,
n: Optional[int] = None,
) -> None:
locals_ = locals().copy()
for key, value in locals_.items():
if key != "self" and value is not None:
setattr(self.__class__, key, value)
@classmethod
def get_config(cls):
return super().get_config()
def get_required_params(self) -> List[ProviderField]:
"""For a given provider, return it's required fields with a description"""
return [
ProviderField(
field_name="api_key",
field_type="string",
field_description="Your Databricks API Key.",
field_value="dapi...",
),
ProviderField(
field_name="api_base",
field_type="string",
field_description="Your Databricks API Base.",
field_value="https://adb-..",
),
]
def validate_environment(
self,
headers: dict,
model: str,
messages: List[AllMessageValues],
optional_params: dict,
litellm_params: dict,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
) -> dict:
api_base, headers = self.databricks_validate_environment(
api_base=api_base,
api_key=api_key,
endpoint_type="chat_completions",
custom_endpoint=False,
headers=headers,
)
# Ensure Content-Type header is set
headers["Content-Type"] = "application/json"
return headers
def get_complete_url(
self,
api_base: Optional[str],
api_key: Optional[str],
model: str,
optional_params: dict,
litellm_params: dict,
stream: Optional[bool] = None,
) -> str:
api_base = self._get_api_base(api_base)
complete_url = f"{api_base}/chat/completions"
return complete_url
def get_supported_openai_params(self, model: Optional[str] = None) -> list:
return [
"stream",
"stop",
"temperature",
"top_p",
"max_tokens",
"max_completion_tokens",
"n",
"response_format",
"tools",
"tool_choice",
"reasoning_effort",
"thinking",
]
def convert_anthropic_tool_to_databricks_tool(
self, tool: Optional[AllAnthropicToolsValues]
) -> Optional[DatabricksTool]:
if tool is None:
return None
return DatabricksTool(
type="function",
function=DatabricksFunction(
name=tool["name"],
parameters=cast(dict, tool.get("input_schema") or {}),
),
)
def _map_openai_to_dbrx_tool(self, model: str, tools: List) -> List[DatabricksTool]:
# if not claude, send as is
if "claude" not in model:
return tools
# if claude, convert to anthropic tool and then to databricks tool
anthropic_tools = self._map_tools(tools=tools)
databricks_tools = [
cast(DatabricksTool, self.convert_anthropic_tool_to_databricks_tool(tool))
for tool in anthropic_tools
]
return databricks_tools
def map_response_format_to_databricks_tool(
self,
model: str,
value: Optional[dict],
optional_params: dict,
is_thinking_enabled: bool,
) -> Optional[DatabricksTool]:
if value is None:
return None
tool = self.map_response_format_to_anthropic_tool(
value, optional_params, is_thinking_enabled
)
databricks_tool = self.convert_anthropic_tool_to_databricks_tool(tool)
return databricks_tool
def map_openai_params(
self,
non_default_params: dict,
optional_params: dict,
model: str,
drop_params: bool,
replace_max_completion_tokens_with_max_tokens: bool = True,
) -> dict:
is_thinking_enabled = self.is_thinking_enabled(non_default_params)
mapped_params = super().map_openai_params(
non_default_params, optional_params, model, drop_params
)
if "tools" in mapped_params:
mapped_params["tools"] = self._map_openai_to_dbrx_tool(
model=model, tools=mapped_params["tools"]
)
if (
"max_completion_tokens" in non_default_params
and replace_max_completion_tokens_with_max_tokens
):
mapped_params["max_tokens"] = non_default_params[
"max_completion_tokens"
] # most openai-compatible providers support 'max_tokens' not 'max_completion_tokens'
mapped_params.pop("max_completion_tokens", None)
if "response_format" in non_default_params and "claude" in model:
_tool = self.map_response_format_to_databricks_tool(
model,
non_default_params["response_format"],
mapped_params,
is_thinking_enabled,
)
if _tool is not None:
self._add_tools_to_optional_params(
optional_params=optional_params, tools=[_tool]
)
optional_params["json_mode"] = True
if not is_thinking_enabled:
_tool_choice = ChatCompletionToolChoiceObjectParam(
type="function",
function=ChatCompletionToolChoiceFunctionParam(
name=RESPONSE_FORMAT_TOOL_NAME
),
)
optional_params["tool_choice"] = _tool_choice
optional_params.pop(
"response_format", None
) # unsupported for claude models - if json_schema -> convert to tool call
if "reasoning_effort" in non_default_params and "claude" in model:
optional_params["thinking"] = AnthropicConfig._map_reasoning_effort(
non_default_params.get("reasoning_effort")
)
optional_params.pop("reasoning_effort", None)
## handle thinking tokens
self.update_optional_params_with_thinking_tokens(
non_default_params=non_default_params, optional_params=mapped_params
)
return mapped_params
def _should_fake_stream(self, optional_params: dict) -> bool:
"""
Databricks doesn't support 'response_format' while streaming
"""
if optional_params.get("response_format") is not None:
return True
return False
def _transform_messages(
self, messages: List[AllMessageValues], model: str
) -> List[AllMessageValues]:
"""
Databricks does not support:
- content in list format.
- 'name' in user message.
"""
new_messages = []
for idx, message in enumerate(messages):
if isinstance(message, BaseModel):
_message = message.model_dump(exclude_none=True)
else:
_message = message
new_messages.append(_message)
new_messages = handle_messages_with_content_list_to_str_conversion(new_messages)
new_messages = strip_name_from_messages(new_messages)
return super()._transform_messages(messages=new_messages, model=model)
@staticmethod
def extract_content_str(
content: Optional[AllDatabricksContentValues],
) -> Optional[str]:
if content is None:
return None
if isinstance(content, str):
return content
elif isinstance(content, list):
content_str = ""
for item in content:
if item["type"] == "text":
content_str += item["text"]
return content_str
else:
raise Exception(f"Unsupported content type: {type(content)}")
@staticmethod
def extract_reasoning_content(
content: Optional[AllDatabricksContentValues],
) -> Tuple[
Optional[str],
Optional[
List[
Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]
]
],
]:
"""
Extract and return the reasoning content and thinking blocks
"""
if content is None:
return None, None
thinking_blocks: Optional[
List[
Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]
]
] = None
reasoning_content: Optional[str] = None
if isinstance(content, list):
for item in content:
if item["type"] == "reasoning":
for sum in item["summary"]:
if reasoning_content is None:
reasoning_content = ""
reasoning_content += sum["text"]
thinking_block = ChatCompletionThinkingBlock(
type="thinking",
thinking=sum["text"],
signature=sum["signature"],
)
if thinking_blocks is None:
thinking_blocks = []
thinking_blocks.append(thinking_block)
return reasoning_content, thinking_blocks
def _transform_dbrx_choices(
self, choices: List[DatabricksChoice], json_mode: Optional[bool] = None
) -> List[Choices]:
transformed_choices = []
for choice in choices:
## HANDLE JSON MODE - anthropic returns single function call]
tool_calls = choice["message"].get("tool_calls", None)
if tool_calls is not None:
_openai_tool_calls = []
for _tc in tool_calls:
_openai_tc = ChatCompletionMessageToolCall(**_tc) # type: ignore
_openai_tool_calls.append(_openai_tc)
fixed_tool_calls = _handle_invalid_parallel_tool_calls(
_openai_tool_calls
)
if fixed_tool_calls is not None:
tool_calls = fixed_tool_calls
translated_message: Optional[Message] = None
finish_reason: Optional[str] = None
if tool_calls and _should_convert_tool_call_to_json_mode(
tool_calls=tool_calls,
convert_tool_call_to_json_mode=json_mode,
):
# to support response_format on claude models
json_mode_content_str: Optional[str] = (
str(tool_calls[0]["function"].get("arguments", "")) or None
)
if json_mode_content_str is not None:
translated_message = Message(content=json_mode_content_str)
finish_reason = "stop"
if translated_message is None:
## get the content str
content_str = DatabricksConfig.extract_content_str(
choice["message"]["content"]
)
## get the reasoning content
(
reasoning_content,
thinking_blocks,
) = DatabricksConfig.extract_reasoning_content(
choice["message"].get("content")
)
translated_message = Message(
role="assistant",
content=content_str,
reasoning_content=reasoning_content,
thinking_blocks=thinking_blocks,
tool_calls=choice["message"].get("tool_calls"),
)
if finish_reason is None:
finish_reason = choice["finish_reason"]
translated_choice = Choices(
finish_reason=finish_reason,
index=choice["index"],
message=translated_message,
logprobs=None,
enhancements=None,
)
transformed_choices.append(translated_choice)
return transformed_choices
def transform_response(
self,
model: str,
raw_response: httpx.Response,
model_response: ModelResponse,
logging_obj: LiteLLMLoggingObj,
request_data: dict,
messages: List[AllMessageValues],
optional_params: dict,
litellm_params: dict,
encoding: Any,
api_key: Optional[str] = None,
json_mode: Optional[bool] = None,
) -> ModelResponse:
## LOGGING
logging_obj.post_call(
input=messages,
api_key=api_key,
original_response=raw_response.text,
additional_args={"complete_input_dict": request_data},
)
## RESPONSE OBJECT
try:
completion_response = DatabricksResponse(**raw_response.json()) # type: ignore
except Exception as e:
response_headers = getattr(raw_response, "headers", None)
raise DatabricksException(
message="Unable to get json response - {}, Original Response: {}".format(
str(e), raw_response.text
),
status_code=raw_response.status_code,
headers=response_headers,
)
model_response.model = completion_response["model"]
model_response.id = completion_response["id"]
model_response.created = completion_response["created"]
setattr(model_response, "usage", Usage(**completion_response["usage"]))
model_response.choices = self._transform_dbrx_choices( # type: ignore
choices=completion_response["choices"],
json_mode=json_mode,
)
return model_response
def get_model_response_iterator(
self,
streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse],
sync_stream: bool,
json_mode: Optional[bool] = False,
):
return DatabricksChatResponseIterator(
streaming_response=streaming_response,
sync_stream=sync_stream,
json_mode=json_mode,
)
class DatabricksChatResponseIterator(BaseModelResponseIterator):
def __init__(
self,
streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse],
sync_stream: bool,
json_mode: Optional[bool] = False,
):
super().__init__(streaming_response, sync_stream)
self.json_mode = json_mode
self._last_function_name = None # Track the last seen function name
def chunk_parser(self, chunk: dict) -> ModelResponseStream:
try:
translated_choices = []
for choice in chunk["choices"]:
tool_calls = choice["delta"].get("tool_calls")
if tool_calls and self.json_mode:
# 1. Check if the function name is set and == RESPONSE_FORMAT_TOOL_NAME
# 2. If no function name, just args -> check last function name (saved via state variable)
# 3. Convert args to json
# 4. Convert json to message
# 5. Set content to message.content
# 6. Set tool_calls to None
from litellm.constants import RESPONSE_FORMAT_TOOL_NAME
from litellm.llms.base_llm.base_utils import (
_convert_tool_response_to_message,
)
# Check if this chunk has a function name
function_name = tool_calls[0].get("function", {}).get("name")
if function_name is not None:
self._last_function_name = function_name
# If we have a saved function name that matches RESPONSE_FORMAT_TOOL_NAME
# or this chunk has the matching function name
if (
self._last_function_name == RESPONSE_FORMAT_TOOL_NAME
or function_name == RESPONSE_FORMAT_TOOL_NAME
):
# Convert tool calls to message format
message = _convert_tool_response_to_message(tool_calls)
if message is not None:
if message.content == "{}": # empty json
message.content = ""
choice["delta"]["content"] = message.content
choice["delta"]["tool_calls"] = None
elif tool_calls:
for _tc in tool_calls:
if _tc.get("function", {}).get("arguments") == "{}":
_tc["function"]["arguments"] = "" # avoid invalid json
# extract the content str
content_str = DatabricksConfig.extract_content_str(
choice["delta"].get("content")
)
# extract the reasoning content
(
reasoning_content,
thinking_blocks,
) = DatabricksConfig.extract_reasoning_content(
choice["delta"]["content"]
)
choice["delta"]["content"] = content_str
choice["delta"]["reasoning_content"] = reasoning_content
choice["delta"]["thinking_blocks"] = thinking_blocks
translated_choices.append(choice)
return ModelResponseStream(
id=chunk["id"],
object="chat.completion.chunk",
created=chunk["created"],
model=chunk["model"],
choices=translated_choices,
)
except KeyError as e:
raise DatabricksException(
message=f"KeyError: {e}, Got unexpected response from Databricks: {chunk}",
status_code=400,
)
except Exception as e:
raise e
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