chore: initial snapshot for gitea/github upload
This commit is contained in:
Your Name
@@ -0,0 +1,182 @@
|
||||
"""
|
||||
Translate from OpenAI's `/v1/chat/completions` to VLLM's `/v1/chat/completions`
|
||||
"""
|
||||
|
||||
from typing import Any, Coroutine, List, Literal, Optional, Tuple, Union, cast, overload
|
||||
|
||||
from litellm.litellm_core_utils.prompt_templates.common_utils import (
|
||||
_get_image_mime_type_from_url,
|
||||
)
|
||||
from litellm.litellm_core_utils.prompt_templates.factory import _parse_mime_type
|
||||
from litellm.secret_managers.main import get_secret_str
|
||||
from litellm.types.llms.openai import (
|
||||
AllMessageValues,
|
||||
ChatCompletionFileObject,
|
||||
ChatCompletionVideoObject,
|
||||
ChatCompletionVideoUrlObject,
|
||||
)
|
||||
|
||||
from ....utils import _remove_additional_properties, _remove_strict_from_schema
|
||||
from ...openai.chat.gpt_transformation import OpenAIGPTConfig
|
||||
|
||||
|
||||
class HostedVLLMChatConfig(OpenAIGPTConfig):
|
||||
def get_supported_openai_params(self, model: str) -> List[str]:
|
||||
params = super().get_supported_openai_params(model)
|
||||
params.extend(["reasoning_effort", "thinking"])
|
||||
return params
|
||||
|
||||
def map_openai_params(
|
||||
self,
|
||||
non_default_params: dict,
|
||||
optional_params: dict,
|
||||
model: str,
|
||||
drop_params: bool,
|
||||
) -> dict:
|
||||
_tools = non_default_params.pop("tools", None)
|
||||
if _tools is not None:
|
||||
# remove 'additionalProperties' from tools
|
||||
_tools = _remove_additional_properties(_tools)
|
||||
# remove 'strict' from tools
|
||||
_tools = _remove_strict_from_schema(_tools)
|
||||
if _tools is not None:
|
||||
non_default_params["tools"] = _tools
|
||||
|
||||
# Handle thinking parameter - convert Anthropic-style to OpenAI-style reasoning_effort
|
||||
# vLLM is OpenAI-compatible, so it understands reasoning_effort, not thinking
|
||||
# Reference: https://github.com/BerriAI/litellm/issues/19761
|
||||
thinking = non_default_params.pop("thinking", None)
|
||||
if thinking is not None and isinstance(thinking, dict):
|
||||
if thinking.get("type") == "enabled":
|
||||
# Only convert if reasoning_effort not already set
|
||||
if "reasoning_effort" not in non_default_params:
|
||||
budget_tokens = thinking.get("budget_tokens", 0)
|
||||
# Map budget_tokens to reasoning_effort level
|
||||
# Same logic as Anthropic adapter (translate_anthropic_thinking_to_reasoning_effort)
|
||||
if budget_tokens >= 10000:
|
||||
non_default_params["reasoning_effort"] = "high"
|
||||
elif budget_tokens >= 5000:
|
||||
non_default_params["reasoning_effort"] = "medium"
|
||||
elif budget_tokens >= 2000:
|
||||
non_default_params["reasoning_effort"] = "low"
|
||||
else:
|
||||
non_default_params["reasoning_effort"] = "minimal"
|
||||
|
||||
return super().map_openai_params(
|
||||
non_default_params, optional_params, model, drop_params
|
||||
)
|
||||
|
||||
def _get_openai_compatible_provider_info(
|
||||
self, api_base: Optional[str], api_key: Optional[str]
|
||||
) -> Tuple[Optional[str], Optional[str]]:
|
||||
api_base = api_base or get_secret_str("HOSTED_VLLM_API_BASE") # type: ignore
|
||||
dynamic_api_key = (
|
||||
api_key or get_secret_str("HOSTED_VLLM_API_KEY") or "fake-api-key"
|
||||
) # vllm does not require an api key
|
||||
return api_base, dynamic_api_key
|
||||
|
||||
def _is_video_file(self, content_item: ChatCompletionFileObject) -> bool:
|
||||
"""
|
||||
Check if the file is a video
|
||||
|
||||
- format: video/<extension>
|
||||
- file_data: base64 encoded video data
|
||||
- file_id: infer mp4 from extension
|
||||
"""
|
||||
file = content_item.get("file", {})
|
||||
format = file.get("format")
|
||||
file_data = file.get("file_data")
|
||||
file_id = file.get("file_id")
|
||||
if content_item.get("type") != "file":
|
||||
return False
|
||||
if format and format.startswith("video/"):
|
||||
return True
|
||||
elif file_data:
|
||||
mime_type = _parse_mime_type(file_data)
|
||||
if mime_type and mime_type.startswith("video/"):
|
||||
return True
|
||||
elif file_id:
|
||||
mime_type = _get_image_mime_type_from_url(file_id)
|
||||
if mime_type and mime_type.startswith("video/"):
|
||||
return True
|
||||
return False
|
||||
|
||||
def _convert_file_to_video_url(
|
||||
self, content_item: ChatCompletionFileObject
|
||||
) -> ChatCompletionVideoObject:
|
||||
file = content_item.get("file", {})
|
||||
file_id = file.get("file_id")
|
||||
file_data = file.get("file_data")
|
||||
|
||||
if file_id:
|
||||
return ChatCompletionVideoObject(
|
||||
type="video_url", video_url=ChatCompletionVideoUrlObject(url=file_id)
|
||||
)
|
||||
elif file_data:
|
||||
return ChatCompletionVideoObject(
|
||||
type="video_url", video_url=ChatCompletionVideoUrlObject(url=file_data)
|
||||
)
|
||||
raise ValueError("file_id or file_data is required")
|
||||
|
||||
@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]]]:
|
||||
"""
|
||||
Support translating:
|
||||
- video files from file_id or file_data to video_url
|
||||
- thinking_blocks on assistant messages to content blocks
|
||||
"""
|
||||
for message in messages:
|
||||
if message["role"] == "assistant":
|
||||
thinking_blocks = message.pop("thinking_blocks", None) # type: ignore
|
||||
if thinking_blocks:
|
||||
new_content: list = [
|
||||
{"type": block["type"], "thinking": block.get("thinking", "")}
|
||||
if block.get("type") == "thinking"
|
||||
else {"type": block["type"], "data": block.get("data", "")}
|
||||
for block in thinking_blocks
|
||||
]
|
||||
existing_content = message.get("content")
|
||||
if isinstance(existing_content, str):
|
||||
new_content.append({"type": "text", "text": existing_content})
|
||||
elif isinstance(existing_content, list):
|
||||
new_content.extend(existing_content)
|
||||
message["content"] = new_content # type: ignore
|
||||
elif message["role"] == "user":
|
||||
message_content = message.get("content")
|
||||
if message_content and isinstance(message_content, list):
|
||||
replaced_content_items: List[
|
||||
Tuple[int, ChatCompletionFileObject]
|
||||
] = []
|
||||
for idx, content_item in enumerate(message_content):
|
||||
if content_item.get("type") == "file":
|
||||
content_item = cast(ChatCompletionFileObject, content_item)
|
||||
if self._is_video_file(content_item):
|
||||
replaced_content_items.append((idx, content_item))
|
||||
for idx, content_item in replaced_content_items:
|
||||
message_content[idx] = self._convert_file_to_video_url(
|
||||
content_item
|
||||
)
|
||||
if is_async:
|
||||
return super()._transform_messages(
|
||||
messages, model, is_async=cast(Literal[True], True)
|
||||
)
|
||||
else:
|
||||
return super()._transform_messages(
|
||||
messages, model, is_async=cast(Literal[False], False)
|
||||
)
|
||||
Reference in New Issue
Block a user