Files
lijiaoqiao/llm-gateway-competitors/litellm-wheel-src/litellm/llms/sambanova/embedding/transformation.py
2026-03-26 20:06:14 +08:00

140 lines
4.4 KiB
Python

"""
This is OpenAI compatible - no transformation is applied
"""
from typing import List, Optional, Union
import httpx
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.llms.base_llm.chat.transformation import BaseLLMException
from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig
from litellm.secret_managers.main import get_secret_str
from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues
from litellm.types.utils import EmbeddingResponse, Usage
from ..common_utils import SambaNovaError
class SambaNovaEmbeddingConfig(BaseEmbeddingConfig):
def __init__(self) -> None:
pass
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:
if api_base is None:
raise ValueError("api_base is required for SambaNova embeddings")
# Remove trailing slashes and ensure clean base URL
api_base = api_base.rstrip("/")
if not api_base.endswith("/embeddings"):
api_base = f"{api_base}/embeddings"
return api_base
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:
if api_key is None:
api_key = get_secret_str("SAMBANOVA_API_KEY")
default_headers = {
"Authorization": f"Bearer {api_key}",
"accept": "application/json",
"Content-Type": "application/json",
}
# If 'Authorization' is provided in headers, it overrides the default.
if "Authorization" in headers:
default_headers["Authorization"] = headers["Authorization"]
# Merge other headers, overriding any default ones except Authorization
return {**default_headers, **headers}
def get_supported_openai_params(self, model: str):
"""
Non additional params supported, placeholder method for future supported params
https://docs.sambanova.ai/cloud/api-reference/endpoints/embeddings-api
"""
return []
def map_openai_params(
self,
non_default_params: dict,
optional_params: dict,
model: str,
drop_params: bool,
):
"""
No transformation is applied - SambaNova is openai compatible
"""
supported_openai_params = self.get_supported_openai_params(model)
for param, value in non_default_params.items():
if param in supported_openai_params:
optional_params[param] = value
return optional_params
def transform_embedding_request(
self,
model: str,
input: AllEmbeddingInputValues,
optional_params: dict,
headers: dict,
) -> dict:
return {
"input": input,
"model": model,
**optional_params,
}
def transform_embedding_response(
self,
model: str,
raw_response: httpx.Response,
model_response: EmbeddingResponse,
logging_obj: LiteLLMLoggingObj,
api_key: Optional[str],
request_data: dict,
optional_params: dict,
litellm_params: dict,
) -> EmbeddingResponse:
try:
raw_response_json = raw_response.json()
except Exception:
raise SambaNovaError(
message=raw_response.text,
status_code=raw_response.status_code,
headers=raw_response.headers,
)
model_response.model = raw_response_json.get("model")
model_response.data = raw_response_json.get("data")
model_response.object = raw_response_json.get("object")
usage = Usage(
prompt_tokens=raw_response_json.get("usage", {}).get("prompt_tokens", 0),
total_tokens=raw_response_json.get("usage", {}).get("total_tokens", 0),
)
model_response.usage = usage
return model_response
def get_error_class(
self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
) -> BaseLLMException:
return SambaNovaError(
message=error_message, status_code=status_code, headers=headers
)