mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-26 03:04:13 +00:00
311432ca17
* refactor(fireworks_ai/): inherit from openai like base config refactors fireworks ai to use a common config * test: fix import in test * refactor(watsonx/): refactor watsonx to use llm base config refactors chat + completion routes to base config path * fix: fix linting error * test: fix test * fix: fix test
101 lines
3.3 KiB
Python
101 lines
3.3 KiB
Python
"""
|
|
OpenAI-like chat completion transformation
|
|
"""
|
|
|
|
import types
|
|
from typing import List, Optional, Tuple, Union
|
|
|
|
import httpx
|
|
from pydantic import BaseModel
|
|
|
|
import litellm
|
|
from litellm.secret_managers.main import get_secret_str
|
|
from litellm.types.llms.openai import AllMessageValues, ChatCompletionAssistantMessage
|
|
from litellm.types.utils import ModelResponse
|
|
|
|
from ....utils import _remove_additional_properties, _remove_strict_from_schema
|
|
from ...OpenAI.chat.gpt_transformation import OpenAIGPTConfig
|
|
|
|
|
|
class OpenAILikeChatConfig(OpenAIGPTConfig):
|
|
def _get_openai_compatible_provider_info(
|
|
self,
|
|
api_base: Optional[str],
|
|
api_key: Optional[str],
|
|
model: Optional[str] = None,
|
|
) -> Tuple[Optional[str], Optional[str]]:
|
|
api_base = api_base or get_secret_str("OPENAI_LIKE_API_BASE") # type: ignore
|
|
dynamic_api_key = (
|
|
api_key or get_secret_str("OPENAI_LIKE_API_KEY") or ""
|
|
) # vllm does not require an api key
|
|
return api_base, dynamic_api_key
|
|
|
|
@staticmethod
|
|
def _convert_tool_response_to_message(
|
|
message: ChatCompletionAssistantMessage, json_mode: bool
|
|
) -> ChatCompletionAssistantMessage:
|
|
"""
|
|
if json_mode is true, convert the returned tool call response to a content with json str
|
|
|
|
e.g. input:
|
|
|
|
{"role": "assistant", "tool_calls": [{"id": "call_5ms4", "type": "function", "function": {"name": "json_tool_call", "arguments": "{\"key\": \"question\", \"value\": \"What is the capital of France?\"}"}}]}
|
|
|
|
output:
|
|
|
|
{"role": "assistant", "content": "{\"key\": \"question\", \"value\": \"What is the capital of France?\"}"}
|
|
"""
|
|
if not json_mode:
|
|
return message
|
|
|
|
_tool_calls = message.get("tool_calls")
|
|
|
|
if _tool_calls is None or len(_tool_calls) != 1:
|
|
return message
|
|
|
|
message["content"] = _tool_calls[0]["function"].get("arguments") or ""
|
|
message["tool_calls"] = None
|
|
|
|
return message
|
|
|
|
@staticmethod
|
|
def _transform_response(
|
|
model: str,
|
|
response: httpx.Response,
|
|
model_response: ModelResponse,
|
|
stream: bool,
|
|
logging_obj: litellm.litellm_core_utils.litellm_logging.Logging, # type: ignore
|
|
optional_params: dict,
|
|
api_key: Optional[str],
|
|
data: Union[dict, str],
|
|
messages: List,
|
|
print_verbose,
|
|
encoding,
|
|
json_mode: bool,
|
|
custom_llm_provider: str,
|
|
base_model: Optional[str],
|
|
) -> ModelResponse:
|
|
response_json = response.json()
|
|
logging_obj.post_call(
|
|
input=messages,
|
|
api_key="",
|
|
original_response=response_json,
|
|
additional_args={"complete_input_dict": data},
|
|
)
|
|
|
|
if json_mode:
|
|
for choice in response_json["choices"]:
|
|
message = OpenAILikeChatConfig._convert_tool_response_to_message(
|
|
choice.get("message"), json_mode
|
|
)
|
|
choice["message"] = message
|
|
|
|
returned_response = ModelResponse(**response_json)
|
|
|
|
returned_response.model = (
|
|
custom_llm_provider + "/" + (returned_response.model or "")
|
|
)
|
|
|
|
if base_model is not None:
|
|
returned_response._hidden_params["model"] = base_model
|
|
return returned_response
|