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