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import json
from typing import TYPE_CHECKING, Any, AsyncIterator, Iterator, List, Optional, Union
import httpx
from litellm.litellm_core_utils.prompt_templates.common_utils import (
convert_content_list_to_str,
)
from litellm.llms.base_llm.base_model_iterator import FakeStreamResponseIterator
from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
from litellm.types.llms.openai import AllMessageValues
from litellm.types.utils import (
ChatCompletionToolCallChunk,
ChatCompletionUsageBlock,
Choices,
GenericStreamingChunk,
Message,
ModelResponse,
Usage,
)
from litellm.utils import token_counter
from ..common_utils import ClarifaiError
if TYPE_CHECKING:
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
LoggingClass = LiteLLMLoggingObj
else:
LoggingClass = Any
class ClarifaiConfig(BaseConfig):
"""
Reference: https://clarifai.com/meta/Llama-2/models/llama2-70b-chat
"""
max_tokens: Optional[int] = None
temperature: Optional[int] = None
top_k: Optional[int] = None
def __init__(
self,
max_tokens: Optional[int] = None,
temperature: Optional[int] = None,
top_k: Optional[int] = None,
) -> None:
locals_ = locals()
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:
return [
"temperature",
"max_tokens",
]
def map_openai_params(
self,
non_default_params: dict,
optional_params: dict,
model: str,
drop_params: bool,
) -> dict:
for param, value in non_default_params.items():
if param == "temperature":
optional_params["temperature"] = value
elif param == "max_tokens":
optional_params["max_tokens"] = value
return optional_params
def _completions_to_model(self, prompt: str, optional_params: dict) -> dict:
params = {}
if temperature := optional_params.get("temperature"):
params["temperature"] = temperature
if max_tokens := optional_params.get("max_tokens"):
params["max_tokens"] = max_tokens
return {
"inputs": [{"data": {"text": {"raw": prompt}}}],
"model": {"output_info": {"params": params}},
}
def _convert_model_to_url(self, model: str, api_base: str):
user_id, app_id, model_id = model.split(".")
return f"{api_base}/users/{user_id}/apps/{app_id}/models/{model_id}/outputs"
def transform_request(
self,
model: str,
messages: List[AllMessageValues],
optional_params: dict,
litellm_params: dict,
headers: dict,
) -> dict:
prompt = " ".join(convert_content_list_to_str(message) for message in messages)
## Load Config
config = self.get_config()
for k, v in config.items():
if k not in optional_params:
optional_params[k] = v
data = self._completions_to_model(
prompt=prompt, optional_params=optional_params
)
return data
def validate_environment(
self,
headers: dict,
model: str,
messages: List[AllMessageValues],
optional_params: dict,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
) -> dict:
headers = {
"accept": "application/json",
"content-type": "application/json",
}
if api_key:
headers["Authorization"] = f"Bearer {api_key}"
return headers
def get_error_class(
self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
) -> BaseLLMException:
return ClarifaiError(message=error_message, status_code=status_code)
def transform_response(
self,
model: str,
raw_response: httpx.Response,
model_response: ModelResponse,
logging_obj: LoggingClass,
request_data: dict,
messages: List[AllMessageValues],
optional_params: dict,
litellm_params: dict,
encoding: str,
api_key: Optional[str] = None,
json_mode: Optional[bool] = None,
) -> ModelResponse:
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 = raw_response.json()
except httpx.HTTPStatusError as e:
raise ClarifaiError(
message=str(e),
status_code=raw_response.status_code,
)
except Exception as e:
raise ClarifaiError(
message=str(e),
status_code=422,
)
# print(completion_response)
try:
choices_list = []
for idx, item in enumerate(completion_response["outputs"]):
if len(item["data"]["text"]["raw"]) > 0:
message_obj = Message(content=item["data"]["text"]["raw"])
else:
message_obj = Message(content=None)
choice_obj = Choices(
finish_reason="stop",
index=idx + 1, # check
message=message_obj,
)
choices_list.append(choice_obj)
model_response.choices = choices_list # type: ignore
except Exception as e:
raise ClarifaiError(
message=str(e),
status_code=422,
)
# Calculate Usage
prompt_tokens = token_counter(model=model, messages=messages)
completion_tokens = len(
encoding.encode(model_response["choices"][0]["message"].get("content"))
)
model_response.model = model
setattr(
model_response,
"usage",
Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
),
)
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,
) -> Any:
return ClarifaiModelResponseIterator(
model_response=streaming_response,
json_mode=json_mode,
)
class ClarifaiModelResponseIterator(FakeStreamResponseIterator):
def __init__(
self,
model_response: Union[Iterator[str], AsyncIterator[str], ModelResponse],
json_mode: Optional[bool] = False,
):
super().__init__(
model_response=model_response,
json_mode=json_mode,
)
def chunk_parser(self, chunk: dict) -> GenericStreamingChunk:
try:
text = ""
tool_use: Optional[ChatCompletionToolCallChunk] = None
is_finished = False
finish_reason = ""
usage: Optional[ChatCompletionUsageBlock] = None
provider_specific_fields = None
text = (
chunk.get("outputs", "")[0]
.get("data", "")
.get("text", "")
.get("raw", "")
)
index: int = 0
return GenericStreamingChunk(
text=text,
tool_use=tool_use,
is_finished=is_finished,
finish_reason=finish_reason,
usage=usage,
index=index,
provider_specific_fields=provider_specific_fields,
)
except json.JSONDecodeError:
raise ValueError(f"Failed to decode JSON from chunk: {chunk}")
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