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"""
Interface for Anthropic's messages API
Use this to call LLMs in Anthropic /messages Request/Response format
This is an __init__.py file to allow the following interface
- litellm.messages.acreate
- litellm.messages.create
"""
from typing import AsyncIterator, Dict, Iterator, List, Optional, Union
from litellm.llms.anthropic.experimental_pass_through.messages.handler import (
anthropic_messages as _async_anthropic_messages,
)
from litellm.types.llms.anthropic_messages.anthropic_response import (
AnthropicMessagesResponse,
)
async def acreate(
max_tokens: int,
messages: List[Dict],
model: str,
metadata: Optional[Dict] = None,
stop_sequences: Optional[List[str]] = None,
stream: Optional[bool] = False,
system: Optional[str] = None,
temperature: Optional[float] = 1.0,
thinking: Optional[Dict] = None,
tool_choice: Optional[Dict] = None,
tools: Optional[List[Dict]] = None,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
**kwargs
) -> Union[AnthropicMessagesResponse, AsyncIterator]:
"""
Async wrapper for Anthropic's messages API
Args:
max_tokens (int): Maximum tokens to generate (required)
messages (List[Dict]): List of message objects with role and content (required)
model (str): Model name to use (required)
metadata (Dict, optional): Request metadata
stop_sequences (List[str], optional): Custom stop sequences
stream (bool, optional): Whether to stream the response
system (str, optional): System prompt
temperature (float, optional): Sampling temperature (0.0 to 1.0)
thinking (Dict, optional): Extended thinking configuration
tool_choice (Dict, optional): Tool choice configuration
tools (List[Dict], optional): List of tool definitions
top_k (int, optional): Top K sampling parameter
top_p (float, optional): Nucleus sampling parameter
**kwargs: Additional arguments
Returns:
Dict: Response from the API
"""
return await _async_anthropic_messages(
max_tokens=max_tokens,
messages=messages,
model=model,
metadata=metadata,
stop_sequences=stop_sequences,
stream=stream,
system=system,
temperature=temperature,
thinking=thinking,
tool_choice=tool_choice,
tools=tools,
top_k=top_k,
top_p=top_p,
**kwargs,
)
async def create(
max_tokens: int,
messages: List[Dict],
model: str,
metadata: Optional[Dict] = None,
stop_sequences: Optional[List[str]] = None,
stream: Optional[bool] = False,
system: Optional[str] = None,
temperature: Optional[float] = 1.0,
thinking: Optional[Dict] = None,
tool_choice: Optional[Dict] = None,
tools: Optional[List[Dict]] = None,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
**kwargs
) -> Union[AnthropicMessagesResponse, Iterator]:
"""
Async wrapper for Anthropic's messages API
Args:
max_tokens (int): Maximum tokens to generate (required)
messages (List[Dict]): List of message objects with role and content (required)
model (str): Model name to use (required)
metadata (Dict, optional): Request metadata
stop_sequences (List[str], optional): Custom stop sequences
stream (bool, optional): Whether to stream the response
system (str, optional): System prompt
temperature (float, optional): Sampling temperature (0.0 to 1.0)
thinking (Dict, optional): Extended thinking configuration
tool_choice (Dict, optional): Tool choice configuration
tools (List[Dict], optional): List of tool definitions
top_k (int, optional): Top K sampling parameter
top_p (float, optional): Nucleus sampling parameter
**kwargs: Additional arguments
Returns:
Dict: Response from the API
"""
raise NotImplementedError("This function is not implemented")
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