Refactor agents and tools (#91)

* Move tools to agents

* Move agents to dedicate place

* Remove subclassing BaseAgent from BaseTool
This commit is contained in:
Nguyen Trung Duc (john)
2023-11-30 09:52:08 +07:00
committed by GitHub
parent 4256030b4f
commit 8e3a1d193f
24 changed files with 126 additions and 124 deletions

View File

@@ -0,0 +1,66 @@
from typing import Any, List, Optional, Union
from kotaemon.base import BaseComponent
from kotaemon.llms import PromptTemplate
from ..base import BaseLLM
from ..output.base import BaseScratchPad
from .prompt import few_shot_solver_prompt, zero_shot_solver_prompt
class Solver(BaseComponent):
model: BaseLLM
prompt_template: Optional[PromptTemplate] = None
examples: Optional[Union[str, List[str]]] = None
def _compose_fewshot_prompt(self) -> str:
if self.examples is None:
return ""
if isinstance(self.examples, str):
return self.examples
else:
return "\n\n".join([e.strip("\n") for e in self.examples])
def _compose_prompt(self, instruction, plan_evidence) -> str:
"""
Compose the prompt from template, plan&evidence, examples and instruction.
"""
fewshot = self._compose_fewshot_prompt()
if self.prompt_template is not None:
if "fewshot" in self.prompt_template.placeholders:
return self.prompt_template.populate(
plan_evidence=plan_evidence, fewshot=fewshot, task=instruction
)
else:
return self.prompt_template.populate(
plan_evidence=plan_evidence, task=instruction
)
else:
if self.examples is not None:
return few_shot_solver_prompt.populate(
plan_evidence=plan_evidence, fewshot=fewshot, task=instruction
)
else:
return zero_shot_solver_prompt.populate(
plan_evidence=plan_evidence, task=instruction
)
def run(
self,
instruction: str,
plan_evidence: str,
output: BaseScratchPad = BaseScratchPad(),
) -> Any:
response = None
output.info("Running Solver")
output.debug(f"Instruction: {instruction}")
output.debug(f"Plan Evidence: {plan_evidence}")
prompt = self._compose_prompt(instruction, plan_evidence)
output.debug(f"Prompt: {prompt}")
try:
response = self.model(prompt)
output.info("Solver run successful.")
except ValueError:
output.error("Solver failed to retrieve response from LLM")
return response