Source code for langchain.model_laboratory

"""Experiment with different models."""

from __future__ import annotations

from typing import List, Optional, Sequence

from langchain_core.language_models.llms import BaseLLM
from langchain_core.prompts.prompt import PromptTemplate
from langchain_core.utils.input import get_color_mapping, print_text

from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain

[docs]class ModelLaboratory: """Experiment with different models."""
[docs] def __init__(self, chains: Sequence[Chain], names: Optional[List[str]] = None): """Initialize with chains to experiment with. Args: chains: list of chains to experiment with. """ for chain in chains: if not isinstance(chain, Chain): raise ValueError( "ModelLaboratory should now be initialized with Chains. " "If you want to initialize with LLMs, use the `from_llms` method " "instead (`ModelLaboratory.from_llms(...)`)" ) if len(chain.input_keys) != 1: raise ValueError( "Currently only support chains with one input variable, " f"got {chain.input_keys}" ) if len(chain.output_keys) != 1: raise ValueError( "Currently only support chains with one output variable, " f"got {chain.output_keys}" ) if names is not None: if len(names) != len(chains): raise ValueError("Length of chains does not match length of names.") self.chains = chains chain_range = [str(i) for i in range(len(self.chains))] self.chain_colors = get_color_mapping(chain_range) self.names = names
[docs] @classmethod def from_llms( cls, llms: List[BaseLLM], prompt: Optional[PromptTemplate] = None ) -> ModelLaboratory: """Initialize with LLMs to experiment with and optional prompt. Args: llms: list of LLMs to experiment with prompt: Optional prompt to use to prompt the LLMs. Defaults to None. If a prompt was provided, it should only have one input variable. """ if prompt is None: prompt = PromptTemplate(input_variables=["_input"], template="{_input}") chains = [LLMChain(llm=llm, prompt=prompt) for llm in llms] names = [str(llm) for llm in llms] return cls(chains, names=names)
[docs] def compare(self, text: str) -> None: """Compare model outputs on an input text. If a prompt was provided with starting the laboratory, then this text will be fed into the prompt. If no prompt was provided, then the input text is the entire prompt. Args: text: input text to run all models on. """ print(f"\033[1mInput:\033[0m\n{text}\n") # noqa: T201 for i, chain in enumerate(self.chains): if self.names is not None: name = self.names[i] else: name = str(chain) print_text(name, end="\n") output = print_text(output, color=self.chain_colors[str(i)], end="\n\n")