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from . import backend_anthropic, backend_openai
from .utils import FunctionSpec, OutputType, PromptType, compile_prompt_to_md
def query(
system_message: PromptType | None,
user_message: PromptType | None,
model: str,
temperature: float | None = None,
max_tokens: int | None = None,
func_spec: FunctionSpec | None = None,
**model_kwargs,
) -> OutputType:
"""
General LLM query for various backends with a single system and user message.
Supports function calling for some backends.
Args:
system_message (PromptType | None): Uncompiled system message (will generate a message following the OpenAI/Anthropic format)
user_message (PromptType | None): Uncompiled user message (will generate a message following the OpenAI/Anthropic format)
model (str): string identifier for the model to use (e.g. "gpt-4-turbo")
temperature (float | None, optional): Temperature to sample at. Defaults to the model-specific default.
max_tokens (int | None, optional): Maximum number of tokens to generate. Defaults to the model-specific max tokens.
func_spec (FunctionSpec | None, optional): Optional FunctionSpec object defining a function call. If given, the return value will be a dict.
Returns:
OutputType: A string completion if func_spec is None, otherwise a dict with the function call details.
"""
model_kwargs = model_kwargs | {
"model": model,
"temperature": temperature,
"max_tokens": max_tokens,
}
# Handle models with beta limitations
# ref: https://platform.openai.com/docs/guides/reasoning/beta-limitations
if model.startswith("o1"):
if system_message:
user_message = system_message
system_message = None
model_kwargs["temperature"] = 1
query_func = backend_anthropic.query if "claude-" in model else backend_openai.query
output, req_time, in_tok_count, out_tok_count, info = query_func(
system_message=compile_prompt_to_md(system_message) if system_message else None,
user_message=compile_prompt_to_md(user_message) if user_message else None,
func_spec=func_spec,
**model_kwargs,
)
return output
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