mirror of
https://github.com/BerriAI/litellm.git
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Add new videos endpoints
This commit is contained in:
Sameer Kankute
parent
79c787b85d
commit
c33889200a
1 changed files with 429 additions and 1 deletions
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@ -16,7 +16,15 @@ from litellm.proxy.common_utils.openai_endpoint_utils import (
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get_custom_llm_provider_from_request_query,
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)
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from litellm.proxy.image_endpoints.endpoints import batch_to_bytesio
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from litellm.types.videos.utils import decode_video_id_with_provider
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from litellm.proxy.video_endpoints.utils import (
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encode_character_id_in_response,
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extract_model_from_target_model_names,
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get_custom_provider_from_data,
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)
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from litellm.types.videos.utils import (
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decode_character_id_with_provider,
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decode_video_id_with_provider,
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)
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router = APIRouter()
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@ -504,3 +512,423 @@ async def video_remix(
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proxy_logging_obj=proxy_logging_obj,
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version=version,
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)
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@router.post(
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"/v1/videos/characters",
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dependencies=[Depends(user_api_key_auth)],
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response_class=ORJSONResponse,
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tags=["videos"],
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)
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@router.post(
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"/videos/characters",
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dependencies=[Depends(user_api_key_auth)],
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response_class=ORJSONResponse,
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tags=["videos"],
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)
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async def video_create_character(
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request: Request,
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fastapi_response: Response,
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video: UploadFile = File(...),
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user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
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):
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"""
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Create a character from an uploaded video file.
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Follows the OpenAI Videos API spec:
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https://platform.openai.com/docs/api-reference/videos/create-character
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Example:
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```bash
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curl -X POST "http://localhost:4000/v1/videos/characters" \
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-H "Authorization: Bearer sk-1234" \
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-F "video=@character_video.mp4" \
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-F "name=my_character"
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```
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"""
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from litellm.proxy.proxy_server import (
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general_settings,
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llm_router,
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proxy_config,
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proxy_logging_obj,
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select_data_generator,
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user_api_base,
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user_max_tokens,
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user_model,
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user_request_timeout,
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user_temperature,
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version,
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)
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data = await _read_request_body(request=request)
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video_file = await batch_to_bytesio([video])
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if video_file:
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data["video"] = video_file[0]
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target_model_name = extract_model_from_target_model_names(
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data.get("target_model_names")
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)
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if target_model_name and not data.get("model"):
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data["model"] = target_model_name
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custom_llm_provider = (
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get_custom_llm_provider_from_request_headers(request=request)
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or get_custom_llm_provider_from_request_query(request=request)
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or get_custom_provider_from_data(data=data)
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or "openai"
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)
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data["custom_llm_provider"] = custom_llm_provider
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processor = ProxyBaseLLMRequestProcessing(data=data)
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try:
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response = await processor.base_process_llm_request(
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request=request,
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fastapi_response=fastapi_response,
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user_api_key_dict=user_api_key_dict,
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route_type="avideo_create_character",
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proxy_logging_obj=proxy_logging_obj,
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llm_router=llm_router,
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general_settings=general_settings,
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proxy_config=proxy_config,
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select_data_generator=select_data_generator,
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model=None,
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user_model=user_model,
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user_temperature=user_temperature,
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user_request_timeout=user_request_timeout,
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user_max_tokens=user_max_tokens,
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user_api_base=user_api_base,
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version=version,
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)
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if target_model_name:
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hidden_params = getattr(response, "_hidden_params", {}) or {}
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provider_for_encoding = (
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hidden_params.get("custom_llm_provider")
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or custom_llm_provider
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or "openai"
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)
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model_id_for_encoding = hidden_params.get("model_id") or data.get("model")
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response = encode_character_id_in_response(
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response=response,
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custom_llm_provider=provider_for_encoding,
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model_id=model_id_for_encoding,
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)
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return response
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except Exception as e:
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raise await processor._handle_llm_api_exception(
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e=e,
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user_api_key_dict=user_api_key_dict,
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proxy_logging_obj=proxy_logging_obj,
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version=version,
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)
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@router.get(
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"/v1/videos/characters/{character_id}",
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dependencies=[Depends(user_api_key_auth)],
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response_class=ORJSONResponse,
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tags=["videos"],
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)
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@router.get(
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"/videos/characters/{character_id}",
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dependencies=[Depends(user_api_key_auth)],
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response_class=ORJSONResponse,
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tags=["videos"],
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)
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async def video_get_character(
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character_id: str,
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request: Request,
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fastapi_response: Response,
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user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
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):
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"""
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Retrieve a character by ID.
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Follows the OpenAI Videos API spec:
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https://platform.openai.com/docs/api-reference/videos/get-character
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Example:
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```bash
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curl -X GET "http://localhost:4000/v1/videos/characters/char_123" \
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-H "Authorization: Bearer sk-1234"
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```
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"""
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from litellm.proxy.proxy_server import (
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general_settings,
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llm_router,
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proxy_config,
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proxy_logging_obj,
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select_data_generator,
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user_api_base,
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user_max_tokens,
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user_model,
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user_request_timeout,
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user_temperature,
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version,
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)
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original_requested_character_id = character_id
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data: Dict[str, Any] = {"character_id": character_id}
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decoded = decode_character_id_with_provider(character_id)
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provider_from_id = decoded.get("custom_llm_provider")
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model_id_from_decoded = decoded.get("model_id")
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decoded_character_id = decoded.get("character_id")
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if decoded_character_id:
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data["character_id"] = decoded_character_id
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custom_llm_provider = (
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get_custom_llm_provider_from_request_headers(request=request)
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or get_custom_llm_provider_from_request_query(request=request)
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or await get_custom_llm_provider_from_request_body(request=request)
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or provider_from_id
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or "openai"
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)
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data["custom_llm_provider"] = custom_llm_provider
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if model_id_from_decoded and llm_router:
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resolved_model = llm_router.resolve_model_name_from_model_id(
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model_id_from_decoded
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)
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if resolved_model:
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data["model"] = resolved_model
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processor = ProxyBaseLLMRequestProcessing(data=data)
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try:
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response = await processor.base_process_llm_request(
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request=request,
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fastapi_response=fastapi_response,
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user_api_key_dict=user_api_key_dict,
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route_type="avideo_get_character",
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proxy_logging_obj=proxy_logging_obj,
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llm_router=llm_router,
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general_settings=general_settings,
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proxy_config=proxy_config,
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select_data_generator=select_data_generator,
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model=None,
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user_model=user_model,
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user_temperature=user_temperature,
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user_request_timeout=user_request_timeout,
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user_max_tokens=user_max_tokens,
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user_api_base=user_api_base,
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version=version,
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)
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if original_requested_character_id.startswith("character_"):
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provider_for_encoding = provider_from_id or custom_llm_provider or "openai"
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model_id_for_encoding = model_id_from_decoded
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response = encode_character_id_in_response(
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response=response,
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custom_llm_provider=provider_for_encoding,
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model_id=model_id_for_encoding,
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)
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return response
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except Exception as e:
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raise await processor._handle_llm_api_exception(
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e=e,
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user_api_key_dict=user_api_key_dict,
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proxy_logging_obj=proxy_logging_obj,
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version=version,
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)
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@router.post(
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"/v1/videos/edits",
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dependencies=[Depends(user_api_key_auth)],
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response_class=ORJSONResponse,
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tags=["videos"],
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)
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@router.post(
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"/videos/edits",
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dependencies=[Depends(user_api_key_auth)],
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response_class=ORJSONResponse,
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tags=["videos"],
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)
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async def video_edit(
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request: Request,
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fastapi_response: Response,
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user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
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):
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"""
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Create a video edit job.
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Follows the OpenAI Videos API spec:
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https://platform.openai.com/docs/api-reference/videos/create-edit
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Example:
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```bash
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curl -X POST "http://localhost:4000/v1/videos/edits" \
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-H "Authorization: Bearer sk-1234" \
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-H "Content-Type: application/json" \
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-d '{"prompt": "Make it brighter", "video": {"id": "video_123"}}'
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```
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"""
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from litellm.proxy.proxy_server import (
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general_settings,
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llm_router,
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proxy_config,
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proxy_logging_obj,
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select_data_generator,
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user_api_base,
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user_max_tokens,
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user_model,
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user_request_timeout,
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user_temperature,
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version,
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)
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body = await request.body()
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data = orjson.loads(body)
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# Extract video_id from nested video object
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video_ref = data.pop("video", {})
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video_id = video_ref.get("id", "") if isinstance(video_ref, dict) else ""
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data["video_id"] = video_id
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decoded = decode_video_id_with_provider(video_id)
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provider_from_id = decoded.get("custom_llm_provider")
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model_id_from_decoded = decoded.get("model_id")
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custom_llm_provider = (
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get_custom_llm_provider_from_request_headers(request=request)
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or get_custom_llm_provider_from_request_query(request=request)
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or get_custom_provider_from_data(data=data)
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or provider_from_id
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or "openai"
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)
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data["custom_llm_provider"] = custom_llm_provider
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if model_id_from_decoded and llm_router:
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resolved_model = llm_router.resolve_model_name_from_model_id(
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model_id_from_decoded
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)
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if resolved_model:
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data["model"] = resolved_model
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processor = ProxyBaseLLMRequestProcessing(data=data)
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try:
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return await processor.base_process_llm_request(
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request=request,
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fastapi_response=fastapi_response,
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user_api_key_dict=user_api_key_dict,
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route_type="avideo_edit",
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proxy_logging_obj=proxy_logging_obj,
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llm_router=llm_router,
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general_settings=general_settings,
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proxy_config=proxy_config,
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select_data_generator=select_data_generator,
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model=None,
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user_model=user_model,
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user_temperature=user_temperature,
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user_request_timeout=user_request_timeout,
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user_max_tokens=user_max_tokens,
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user_api_base=user_api_base,
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version=version,
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)
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except Exception as e:
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raise await processor._handle_llm_api_exception(
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e=e,
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user_api_key_dict=user_api_key_dict,
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proxy_logging_obj=proxy_logging_obj,
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version=version,
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)
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@router.post(
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"/v1/videos/extensions",
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dependencies=[Depends(user_api_key_auth)],
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response_class=ORJSONResponse,
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tags=["videos"],
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)
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@router.post(
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"/videos/extensions",
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dependencies=[Depends(user_api_key_auth)],
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response_class=ORJSONResponse,
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tags=["videos"],
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)
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async def video_extension(
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request: Request,
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fastapi_response: Response,
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user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
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):
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"""
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Create a video extension.
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Follows the OpenAI Videos API spec:
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https://platform.openai.com/docs/api-reference/videos/create-extension
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Example:
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```bash
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curl -X POST "http://localhost:4000/v1/videos/extensions" \
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-H "Authorization: Bearer sk-1234" \
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-H "Content-Type: application/json" \
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-d '{"prompt": "Continue the scene", "seconds": "5", "video": {"id": "video_123"}}'
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```
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"""
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from litellm.proxy.proxy_server import (
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general_settings,
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llm_router,
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proxy_config,
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proxy_logging_obj,
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select_data_generator,
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user_api_base,
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user_max_tokens,
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user_model,
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user_request_timeout,
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user_temperature,
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version,
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)
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body = await request.body()
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data = orjson.loads(body)
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# Extract video_id from nested video object
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video_ref = data.pop("video", {})
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video_id = video_ref.get("id", "") if isinstance(video_ref, dict) else ""
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data["video_id"] = video_id
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decoded = decode_video_id_with_provider(video_id)
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provider_from_id = decoded.get("custom_llm_provider")
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model_id_from_decoded = decoded.get("model_id")
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custom_llm_provider = (
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get_custom_llm_provider_from_request_headers(request=request)
|
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or get_custom_llm_provider_from_request_query(request=request)
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or get_custom_provider_from_data(data=data)
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or provider_from_id
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or "openai"
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)
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data["custom_llm_provider"] = custom_llm_provider
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if model_id_from_decoded and llm_router:
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resolved_model = llm_router.resolve_model_name_from_model_id(
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model_id_from_decoded
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)
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if resolved_model:
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data["model"] = resolved_model
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processor = ProxyBaseLLMRequestProcessing(data=data)
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try:
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return await processor.base_process_llm_request(
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request=request,
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fastapi_response=fastapi_response,
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user_api_key_dict=user_api_key_dict,
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route_type="avideo_extension",
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proxy_logging_obj=proxy_logging_obj,
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llm_router=llm_router,
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general_settings=general_settings,
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proxy_config=proxy_config,
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select_data_generator=select_data_generator,
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model=None,
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user_model=user_model,
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user_temperature=user_temperature,
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user_request_timeout=user_request_timeout,
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user_max_tokens=user_max_tokens,
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user_api_base=user_api_base,
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version=version,
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)
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except Exception as e:
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raise await processor._handle_llm_api_exception(
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e=e,
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user_api_key_dict=user_api_key_dict,
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proxy_logging_obj=proxy_logging_obj,
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version=version,
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)
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|
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