438 lines
13 KiB
Python
438 lines
13 KiB
Python
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"""
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Anthropic Skills API endpoints - /v1/skills
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"""
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from typing import Optional
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import orjson
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from fastapi import APIRouter, Depends, Request, Response
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from litellm.proxy._types import UserAPIKeyAuth
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from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
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from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing
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from litellm.proxy.common_utils.http_parsing_utils import (
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convert_upload_files_to_file_data,
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get_form_data,
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)
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from litellm.types.llms.anthropic_skills import (
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DeleteSkillResponse,
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ListSkillsResponse,
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Skill,
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)
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router = APIRouter()
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@router.post(
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"/v1/skills",
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tags=["[beta] Anthropic Skills API"],
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dependencies=[Depends(user_api_key_auth)],
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response_model=Skill,
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)
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async def create_skill(
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fastapi_response: Response,
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request: Request,
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custom_llm_provider: Optional[str] = "anthropic",
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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 new skill on Anthropic.
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Requires `?beta=true` query parameter.
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Model-based routing (for multi-account support):
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- Pass model via header: `x-litellm-model: claude-account-1`
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- Pass model via query: `?model=claude-account-1`
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- Pass model via form field: `model=claude-account-1`
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Example usage:
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```bash
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# Basic usage
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curl -X POST "http://localhost:4000/v1/skills?beta=true" \
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-H "Content-Type: multipart/form-data" \
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-H "Authorization: Bearer your-key" \
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-F "display_title=My Skill" \
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-F "files[]=@skill.zip"
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# With model-based routing
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curl -X POST "http://localhost:4000/v1/skills?beta=true" \
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-H "Content-Type: multipart/form-data" \
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-H "Authorization: Bearer your-key" \
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-H "x-litellm-model: claude-account-1" \
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-F "display_title=My Skill" \
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-F "files[]=@skill.zip"
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```
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Returns: Skill object with id, display_title, etc.
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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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# Read form data and convert UploadFile objects to file data tuples
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form_data = await get_form_data(request)
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data = await convert_upload_files_to_file_data(form_data)
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# Extract model for routing (header > query > body)
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model = (
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data.get("model")
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or request.query_params.get("model")
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or request.headers.get("x-litellm-model")
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)
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if model:
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data["model"] = model
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if "custom_llm_provider" not in data:
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data["custom_llm_provider"] = custom_llm_provider
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# Process request using ProxyBaseLLMRequestProcessing
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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="acreate_skill",
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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=data.get("model"),
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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.get(
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"/v1/skills",
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tags=["[beta] Anthropic Skills API"],
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dependencies=[Depends(user_api_key_auth)],
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response_model=ListSkillsResponse,
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)
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async def list_skills(
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fastapi_response: Response,
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request: Request,
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limit: Optional[int] = 10,
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after_id: Optional[str] = None,
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before_id: Optional[str] = None,
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custom_llm_provider: Optional[str] = "anthropic",
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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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List skills on Anthropic.
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Requires `?beta=true` query parameter.
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Model-based routing (for multi-account support):
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- Pass model via header: `x-litellm-model: claude-account-1`
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- Pass model via query: `?model=claude-account-1`
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- Pass model via body: `{"model": "claude-account-1"}`
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Example usage:
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```bash
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# Basic usage
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curl "http://localhost:4000/v1/skills?beta=true&limit=10" \
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-H "Authorization: Bearer your-key"
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# With model-based routing
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curl "http://localhost:4000/v1/skills?beta=true&limit=10" \
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-H "Authorization: Bearer your-key" \
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-H "x-litellm-model: claude-account-1"
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```
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Returns: ListSkillsResponse with list of skills
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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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# Read request body
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body = await request.body()
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data = orjson.loads(body) if body else {}
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# Use query params if not in body
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if "limit" not in data and limit is not None:
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data["limit"] = limit
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if "after_id" not in data and after_id is not None:
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data["after_id"] = after_id
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if "before_id" not in data and before_id is not None:
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data["before_id"] = before_id
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# Extract model for routing (header > query > body)
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model = (
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data.get("model")
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or request.query_params.get("model")
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or request.headers.get("x-litellm-model")
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)
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if model:
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data["model"] = model
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# Set custom_llm_provider: body > query param > default
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if "custom_llm_provider" not in data:
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data["custom_llm_provider"] = custom_llm_provider
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# Process request using ProxyBaseLLMRequestProcessing
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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="alist_skills",
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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=data.get("model"),
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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.get(
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"/v1/skills/{skill_id}",
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tags=["[beta] Anthropic Skills API"],
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dependencies=[Depends(user_api_key_auth)],
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response_model=Skill,
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)
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async def get_skill(
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skill_id: str,
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fastapi_response: Response,
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request: Request,
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custom_llm_provider: Optional[str] = "anthropic",
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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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Get a specific skill by ID from Anthropic.
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Requires `?beta=true` query parameter.
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Model-based routing (for multi-account support):
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- Pass model via header: `x-litellm-model: claude-account-1`
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- Pass model via query: `?model=claude-account-1`
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- Pass model via body: `{"model": "claude-account-1"}`
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Example usage:
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```bash
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# Basic usage
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curl "http://localhost:4000/v1/skills/skill_123?beta=true" \
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-H "Authorization: Bearer your-key"
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# With model-based routing
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curl "http://localhost:4000/v1/skills/skill_123?beta=true" \
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-H "Authorization: Bearer your-key" \
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-H "x-litellm-model: claude-account-1"
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```
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Returns: Skill object
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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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# Read request body
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body = await request.body()
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data = orjson.loads(body) if body else {}
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# Set skill_id from path parameter
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data["skill_id"] = skill_id
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# Extract model for routing (header > query > body)
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model = (
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data.get("model")
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or request.query_params.get("model")
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or request.headers.get("x-litellm-model")
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)
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if model:
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data["model"] = model
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# Set custom_llm_provider: body > query param > default
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if "custom_llm_provider" not in data:
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data["custom_llm_provider"] = custom_llm_provider
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# Process request using ProxyBaseLLMRequestProcessing
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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="aget_skill",
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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=data.get("model"),
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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.delete(
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"/v1/skills/{skill_id}",
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tags=["[beta] Anthropic Skills API"],
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dependencies=[Depends(user_api_key_auth)],
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response_model=DeleteSkillResponse,
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)
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async def delete_skill(
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skill_id: str,
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fastapi_response: Response,
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request: Request,
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custom_llm_provider: Optional[str] = "anthropic",
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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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Delete a skill by ID from Anthropic.
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Requires `?beta=true` query parameter.
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Note: Anthropic does not allow deleting skills with existing versions.
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Model-based routing (for multi-account support):
|
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- Pass model via header: `x-litellm-model: claude-account-1`
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|
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- Pass model via query: `?model=claude-account-1`
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- Pass model via body: `{"model": "claude-account-1"}`
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|
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Example usage:
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|
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```bash
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|
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# Basic usage
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curl -X DELETE "http://localhost:4000/v1/skills/skill_123?beta=true" \
|
||
|
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-H "Authorization: Bearer your-key"
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# With model-based routing
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curl -X DELETE "http://localhost:4000/v1/skills/skill_123?beta=true" \
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||
|
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-H "Authorization: Bearer your-key" \
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||
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-H "x-litellm-model: claude-account-1"
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```
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Returns: DeleteSkillResponse with type="skill_deleted"
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"""
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|
from litellm.proxy.proxy_server import (
|
||
|
|
general_settings,
|
||
|
|
llm_router,
|
||
|
|
proxy_config,
|
||
|
|
proxy_logging_obj,
|
||
|
|
select_data_generator,
|
||
|
|
user_api_base,
|
||
|
|
user_max_tokens,
|
||
|
|
user_model,
|
||
|
|
user_request_timeout,
|
||
|
|
user_temperature,
|
||
|
|
version,
|
||
|
|
)
|
||
|
|
|
||
|
|
# Read request body
|
||
|
|
body = await request.body()
|
||
|
|
data = orjson.loads(body) if body else {}
|
||
|
|
|
||
|
|
# Set skill_id from path parameter
|
||
|
|
data["skill_id"] = skill_id
|
||
|
|
|
||
|
|
# Extract model for routing (header > query > body)
|
||
|
|
model = (
|
||
|
|
data.get("model")
|
||
|
|
or request.query_params.get("model")
|
||
|
|
or request.headers.get("x-litellm-model")
|
||
|
|
)
|
||
|
|
if model:
|
||
|
|
data["model"] = model
|
||
|
|
|
||
|
|
# Set custom_llm_provider: body > query param > default
|
||
|
|
if "custom_llm_provider" not in data:
|
||
|
|
data["custom_llm_provider"] = custom_llm_provider
|
||
|
|
|
||
|
|
# Process request using ProxyBaseLLMRequestProcessing
|
||
|
|
processor = ProxyBaseLLMRequestProcessing(data=data)
|
||
|
|
try:
|
||
|
|
return await processor.base_process_llm_request(
|
||
|
|
request=request,
|
||
|
|
fastapi_response=fastapi_response,
|
||
|
|
user_api_key_dict=user_api_key_dict,
|
||
|
|
route_type="adelete_skill",
|
||
|
|
proxy_logging_obj=proxy_logging_obj,
|
||
|
|
llm_router=llm_router,
|
||
|
|
general_settings=general_settings,
|
||
|
|
proxy_config=proxy_config,
|
||
|
|
select_data_generator=select_data_generator,
|
||
|
|
model=data.get("model"),
|
||
|
|
user_model=user_model,
|
||
|
|
user_temperature=user_temperature,
|
||
|
|
user_request_timeout=user_request_timeout,
|
||
|
|
user_max_tokens=user_max_tokens,
|
||
|
|
user_api_base=user_api_base,
|
||
|
|
version=version,
|
||
|
|
)
|
||
|
|
except Exception as e:
|
||
|
|
raise await processor._handle_llm_api_exception(
|
||
|
|
e=e,
|
||
|
|
user_api_key_dict=user_api_key_dict,
|
||
|
|
proxy_logging_obj=proxy_logging_obj,
|
||
|
|
version=version,
|
||
|
|
)
|