chore: initial public snapshot for github upload

llm-gateway-competitors/litellm-wheel-src/litellm/llms/sambanova/chat.py

@@ -0,0 +1,131 @@
+"""
+Sambanova Chat Completions API
+
+this is OpenAI compatible - no translation needed / occurs
+"""
+
+from typing import Any, Coroutine, List, Literal, Optional, Union, overload
+
+from litellm.litellm_core_utils.prompt_templates.common_utils import (
+    handle_messages_with_content_list_to_str_conversion,
+)
+from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
+from litellm.types.llms.openai import AllMessageValues
+
+
+class SambanovaConfig(OpenAIGPTConfig):
+    """
+    Reference: https://docs.sambanova.ai/cloud/api-reference/
+
+    Below are the parameters:
+    """
+
+    max_tokens: Optional[int] = None
+    temperature: Optional[int] = None
+    top_p: Optional[int] = None
+    top_k: Optional[int] = None
+    stop: Optional[Union[str, list]] = None
+    stream: Optional[bool] = None
+    stream_options: Optional[dict] = None
+    tool_choice: Optional[str] = None
+    response_format: Optional[dict] = None
+    tools: Optional[list] = None
+
+    def __init__(
+        self,
+        max_tokens: Optional[int] = None,
+        response_format: Optional[dict] = None,
+        stop: Optional[str] = None,
+        stream: Optional[bool] = None,
+        stream_options: Optional[dict] = None,
+        temperature: Optional[float] = None,
+        top_p: Optional[float] = None,
+        top_k: Optional[int] = None,
+        tool_choice: Optional[str] = None,
+        tools: Optional[list] = 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_supported_openai_params(self, model: str) -> list:
+        """
+        Get the supported OpenAI params for the given model
+
+        """
+        from litellm.utils import supports_function_calling
+
+        params = [
+            "max_completion_tokens",
+            "max_tokens",
+            "response_format",
+            "stop",
+            "stream",
+            "stream_options",
+            "temperature",
+            "top_p",
+            "top_k",
+        ]
+
+        if supports_function_calling(model, custom_llm_provider="sambanova"):
+            params.append("tools")
+            params.append("tool_choice")
+            params.append("parallel_tool_calls")
+
+        return params
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        """
+        map max_completion_tokens param to max_tokens
+        """
+        supported_openai_params = self.get_supported_openai_params(model=model)
+        for param, value in non_default_params.items():
+            if param == "max_completion_tokens":
+                optional_params["max_tokens"] = value
+            elif param in supported_openai_params:
+                optional_params[param] = value
+        return optional_params
+
+    @overload
+    def _transform_messages(
+        self, messages: List[AllMessageValues], model: str, is_async: Literal[True]
+    ) -> Coroutine[Any, Any, List[AllMessageValues]]:
+        ...
+
+    @overload
+    def _transform_messages(
+        self,
+        messages: List[AllMessageValues],
+        model: str,
+        is_async: Literal[False] = False,
+    ) -> List[AllMessageValues]:
+        ...
+
+    def _transform_messages(
+        self, messages: List[AllMessageValues], model: str, is_async: bool = False
+    ) -> Union[List[AllMessageValues], Coroutine[Any, Any, List[AllMessageValues]]]:
+        """
+        Transform messages to handle content list conversion.
+
+        SambaNova API doesn't support content as a list - only string content.
+        This converts content lists like [{"type": "text", "text": "..."}] to strings.
+        """
+
+        async def _async_transform():
+            return handle_messages_with_content_list_to_str_conversion(messages)
+
+        if is_async:
+            return _async_transform()
+        messages = handle_messages_with_content_list_to_str_conversion(messages)
+        return messages