693ed39de4
Since the only usage of prompt is within LLMs, it is reasonable to keep it within the LLM module. This way, it would be easier to discover module, and make the code base less complicated. Changes: * Move prompt components into llms * Bump version 0.3.1 * Make pip install dependencies in eager mode --------- Co-authored-by: ian <ian@cinnamon.is>
67 lines
2.3 KiB
Python
67 lines
2.3 KiB
Python
from typing import Any, List, Optional, Union
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from kotaemon.base import BaseComponent
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from kotaemon.llms import PromptTemplate
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from ..base import BaseLLM
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from ..output.base import BaseScratchPad
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from .prompt import few_shot_solver_prompt, zero_shot_solver_prompt
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class Solver(BaseComponent):
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model: BaseLLM
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prompt_template: Optional[PromptTemplate] = None
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examples: Optional[Union[str, List[str]]] = None
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def _compose_fewshot_prompt(self) -> str:
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if self.examples is None:
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return ""
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if isinstance(self.examples, str):
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return self.examples
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else:
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return "\n\n".join([e.strip("\n") for e in self.examples])
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def _compose_prompt(self, instruction, plan_evidence) -> str:
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"""
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Compose the prompt from template, plan&evidence, examples and instruction.
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"""
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fewshot = self._compose_fewshot_prompt()
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if self.prompt_template is not None:
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if "fewshot" in self.prompt_template.placeholders:
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return self.prompt_template.populate(
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plan_evidence=plan_evidence, fewshot=fewshot, task=instruction
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)
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else:
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return self.prompt_template.populate(
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plan_evidence=plan_evidence, task=instruction
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)
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else:
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if self.examples is not None:
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return few_shot_solver_prompt.populate(
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plan_evidence=plan_evidence, fewshot=fewshot, task=instruction
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)
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else:
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return zero_shot_solver_prompt.populate(
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plan_evidence=plan_evidence, task=instruction
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)
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def run(
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self,
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instruction: str,
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plan_evidence: str,
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output: BaseScratchPad = BaseScratchPad(),
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) -> Any:
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response = None
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output.info("Running Solver")
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output.debug(f"Instruction: {instruction}")
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output.debug(f"Plan Evidence: {plan_evidence}")
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prompt = self._compose_prompt(instruction, plan_evidence)
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output.debug(f"Prompt: {prompt}")
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try:
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response = self.model(prompt)
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output.info("Solver run successful.")
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except ValueError:
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output.error("Solver failed to retrieve response from LLM")
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return response
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