In this updated code, I've removed the verbose parameter from BossNode and BabyAGI.from_llm as it's not mentioned in the BabyAGI setup code you shared. I've also removed the assert isinstance(llm, dict) check, as llm is expected to be an instance of OpenAI or a similar class, not a dictionary.

NewTools
Kye 2 years ago
parent 059ca8367e
commit 3cf1ff8206

@ -3,38 +3,20 @@ from pydantic import ValidationError
# ---------- Boss Node ----------
class BossNode:
def __init__(self, llm, vectorstore, task_execution_chain, verbose, max_iterations):
def __init__(self, llm, vectorstore, agent_executor, max_iterations):
self.llm = llm
self.vectorstore = vectorstore
self.task_execution_chain = task_execution_chain
self.verbose = verbose
self.agent_executor = agent_executor
self.max_iterations = max_iterations
try:
# Ensure llm is a dictionary before passing it to BabyAGI
assert isinstance(llm, dict), "llm should be a dictionary."
self.baby_agi = BabyAGI.from_llm(
llm=self.llm,
vectorstore=self.vectorstore,
task_execution_chain=self.task_execution_chain,
verbose=self.verbose,
task_execution_chain=self.agent_executor,
max_iterations=self.max_iterations,
)
except ValidationError as e:
print(f"Validation Error while initializing BabyAGI: {e}")
except Exception as e:
print(f"Unexpected Error while initializing BabyAGI: {e}")
def create_task(self, objective):
try:
task = {"objective": objective}
return task
except Exception as e:
print(f"Unexpected Error while creating a task: {e}")
def execute_task(self, task):
try:
self.baby_agi(task)
except Exception as e:
print(f"Unexpected Error while executing a task: {e}")
print(f"Unexpected Error while initializing BabyAGI: {e}")

@ -12,9 +12,9 @@ class Swarms:
def __init__(self, openai_api_key):
self.openai_api_key = openai_api_key
def initialize_llm(self, llm_class):
def initialize_llm(self, llm_class, temperature=0.5):
# Initialize language model
return llm_class(openai_api_key=self.openai_api_key)
return llm_class(openai_api_key=self.openai_api_key, temperature=temperature)
def initialize_tools(self, llm_class):
llm = self.initialize_llm(llm_class)

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