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 +1,83 @@
from typing import Any, List, Optional, Union
from kotaemon.base import BaseComponent
from kotaemon.llms import PromptTemplate
from ..base import BaseLLM, BaseTool
from ..output.base import BaseScratchPad
from .prompt import few_shot_planner_prompt, zero_shot_planner_prompt
class Planner(BaseComponent):
model: BaseLLM
prompt_template: Optional[PromptTemplate] = None
examples: Optional[Union[str, List[str]]] = None
plugins: List[BaseTool]
def _compose_worker_description(self) -> str:
"""
Compose the worker prompt from the workers.
Example:
toolname1[input]: tool1 description
toolname2[input]: tool2 description
"""
prompt = ""
try:
for worker in self.plugins:
prompt += f"{worker.name}[input]: {worker.description}\n"
except Exception:
raise ValueError("Worker must have a name and description.")
return prompt
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) -> str:
"""
Compose the prompt from template, worker description, examples and instruction.
"""
worker_desctription = self._compose_worker_description()
fewshot = self._compose_fewshot_prompt()
if self.prompt_template is not None:
if "fewshot" in self.prompt_template.placeholders:
return self.prompt_template.populate(
tool_description=worker_desctription,
fewshot=fewshot,
task=instruction,
)
else:
return self.prompt_template.populate(
tool_description=worker_desctription, task=instruction
)
else:
if self.examples is not None:
return few_shot_planner_prompt.populate(
tool_description=worker_desctription,
fewshot=fewshot,
task=instruction,
)
else:
return zero_shot_planner_prompt.populate(
tool_description=worker_desctription, task=instruction
)
def run(self, instruction: str, output: BaseScratchPad = BaseScratchPad()) -> Any:
response = None
output.info("Running Planner")
prompt = self._compose_prompt(instruction)
output.debug(f"Prompt: {prompt}")
try:
response = self.model(prompt)
self.log_progress(".planner", response=response)
output.info("Planner run successful.")
except ValueError as e:
output.error("Planner failed to retrieve response from LLM")
raise ValueError("Planner failed to retrieve response from LLM") from e
return response