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85 lines
3.3 KiB
Python
85 lines
3.3 KiB
Python
"""Experiment with different models."""
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from typing import List, Optional, Sequence, Union
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from langchain.agents.agent import Agent
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from langchain.chains.base import Chain
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from langchain.chains.llm import LLMChain
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from langchain.printing import print_text, get_color_mapping
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from langchain.llms.base import LLM
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from langchain.prompts.prompt import PromptTemplate
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class ModelLaboratory:
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"""Experiment with different models."""
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def __init__(
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self, chains: Sequence[Union[Chain, Agent]], names: Optional[List[str]] = None
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):
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"""Initialize with chains to experiment with.
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Args:
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chains: list of chains to experiment with.
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"""
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for chain in chains:
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if not isinstance(chain, (Chain, Agent)):
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raise ValueError(
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"ModelLaboratory should now be initialized with Chains or Agents. "
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"If you want to initialize with LLMs, use the `from_llms` method "
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"instead (`ModelLaboratory.from_llms(...)`)"
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)
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if isinstance(chain, Chain):
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if len(chain.input_keys) != 1:
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raise ValueError(
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"Currently only support chains with one input variable, "
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f"got {chain.input_keys}"
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)
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if len(chain.output_keys) != 1:
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raise ValueError(
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"Currently only support chains with one output variable, "
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f"got {chain.output_keys}"
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)
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if names is not None:
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if len(names) != len(chains):
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raise ValueError("Length of chains does not match length of names.")
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self.chains = chains
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chain_range = [str(i) for i in range(len(self.chains))]
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self.chain_colors = get_color_mapping(chain_range)
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self.names = names
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@classmethod
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def from_llms(
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cls, llms: List[LLM], prompt: Optional[PromptTemplate] = None
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) -> "ModelLaboratory":
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"""Initialize with LLMs to experiment with and optional prompt.
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Args:
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llms: list of LLMs to experiment with
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prompt: Optional prompt to use to prompt the LLMs. Defaults to None.
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If a prompt was provided, it should only have one input variable.
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"""
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if prompt is None:
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prompt = PromptTemplate(input_variables=["_input"], template="{_input}")
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chains = [LLMChain(llm=llm, prompt=prompt) for llm in llms]
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names = [str(llm) for llm in llms]
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return cls(chains, names=names)
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def compare(self, text: str) -> None:
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"""Compare model outputs on an input text.
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If a prompt was provided with starting the laboratory, then this text will be
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fed into the prompt. If no prompt was provided, then the input text is the
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entire prompt.
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Args:
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text: input text to run all models on.
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"""
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print(f"\033[1mInput:\033[0m\n{text}\n")
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for i, chain in enumerate(self.chains):
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if self.names is not None:
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name = self.names[i]
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else:
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name = str(chain)
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print_text(name, end="\n")
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output = chain.run(text)
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print_text(output, color=self.chain_colors[str(i)], end="\n\n")
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