two version of R2R are here HEAD master

2 files changed, 190 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/databricks/chat/handler.py b/.venv/lib/python3.12/site-packages/litellm/llms/databricks/chat/handler.py
new file mode 100644
index 00000000..abb71474
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/databricks/chat/handler.py
@@ -0,0 +1,84 @@
+"""
+Handles the chat completion request for Databricks
+"""
+
+from typing import Callable, List, Optional, Union, cast
+
+from httpx._config import Timeout
+
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from litellm.types.llms.openai import AllMessageValues
+from litellm.types.utils import CustomStreamingDecoder
+from litellm.utils import ModelResponse
+
+from ...openai_like.chat.handler import OpenAILikeChatHandler
+from ..common_utils import DatabricksBase
+from .transformation import DatabricksConfig
+
+
+class DatabricksChatCompletion(OpenAILikeChatHandler, DatabricksBase):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    def completion(
+        self,
+        *,
+        model: str,
+        messages: list,
+        api_base: str,
+        custom_llm_provider: str,
+        custom_prompt_dict: dict,
+        model_response: ModelResponse,
+        print_verbose: Callable,
+        encoding,
+        api_key: Optional[str],
+        logging_obj,
+        optional_params: dict,
+        acompletion=None,
+        litellm_params=None,
+        logger_fn=None,
+        headers: Optional[dict] = None,
+        timeout: Optional[Union[float, Timeout]] = None,
+        client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+        custom_endpoint: Optional[bool] = None,
+        streaming_decoder: Optional[CustomStreamingDecoder] = None,
+        fake_stream: bool = False,
+    ):
+        messages = DatabricksConfig()._transform_messages(
+            messages=cast(List[AllMessageValues], messages), model=model
+        )
+        api_base, headers = self.databricks_validate_environment(
+            api_base=api_base,
+            api_key=api_key,
+            endpoint_type="chat_completions",
+            custom_endpoint=custom_endpoint,
+            headers=headers,
+        )
+
+        if optional_params.get("stream") is True:
+            fake_stream = DatabricksConfig()._should_fake_stream(optional_params)
+        else:
+            fake_stream = False
+
+        return super().completion(
+            model=model,
+            messages=messages,
+            api_base=api_base,
+            custom_llm_provider=custom_llm_provider,
+            custom_prompt_dict=custom_prompt_dict,
+            model_response=model_response,
+            print_verbose=print_verbose,
+            encoding=encoding,
+            api_key=api_key,
+            logging_obj=logging_obj,
+            optional_params=optional_params,
+            acompletion=acompletion,
+            litellm_params=litellm_params,
+            logger_fn=logger_fn,
+            headers=headers,
+            timeout=timeout,
+            client=client,
+            custom_endpoint=True,
+            streaming_decoder=streaming_decoder,
+            fake_stream=fake_stream,
+        )
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/databricks/chat/transformation.py b/.venv/lib/python3.12/site-packages/litellm/llms/databricks/chat/transformation.py
new file mode 100644
index 00000000..94e02034
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/databricks/chat/transformation.py
@@ -0,0 +1,106 @@
+"""
+Translates from OpenAI's `/v1/chat/completions` to Databricks' `/chat/completions`
+"""
+
+from typing import List, Optional, Union
+
+from pydantic import BaseModel
+
+from litellm.litellm_core_utils.prompt_templates.common_utils import (
+    handle_messages_with_content_list_to_str_conversion,
+    strip_name_from_messages,
+)
+from litellm.types.llms.openai import AllMessageValues
+from litellm.types.utils import ProviderField
+
+from ...openai_like.chat.transformation import OpenAILikeChatConfig
+
+
+class DatabricksConfig(OpenAILikeChatConfig):
+    """
+    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 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",
+        ]
+
+    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)