@ -16,83 +16,93 @@ from swarms.agents.models.hf import HuggingFaceLLM
logging . basicConfig ( level = logging . INFO , format = ' %(asctime)s - %(levelname)s - %(message)s ' ) logging . basicConfig ( level = logging . INFO , format = ' %(asctime)s - %(levelname)s - %(message)s ' )
class AgentNodeInitializer : class AgentNodeInitializer :
""" Useful for when you need to spawn an autonomous agent instance as a agent to accomplish complex tasks, it can search the internet or spawn child multi-modality models to process and generate images and text or audio and so on """
""" Useful for spawning autonomous agent instances to accomplish complex tasks. """
def __init__ ( self ,
def __init__ ( self ,
llm ,
llm : Optional [ Any ] = None ,
tools ,
tools : Optional [ List [ BaseTool ] ] = None ,
vectorstore ,
vectorstore : Optional [ List [ Any ] ] = None ,
temperature ,
temperature : float = 0.5 ,
model_type : str = None ,
model_type : Optional [ str ] = None ,
human_in_the_loop = True ,
human_in_the_loop : bool = True ,
model_id : str = None ,
model_id : Optional [ str ] = None ,
embedding_size : int = 8192 ,
embedding_size : int = 8192 ,
system_prompt : str = None ,
system_prompt : Optional [ str ] = None ,
max_iterations : int = None ) :
max_iterations : Optional [ int ] = None ,
agent_name : Optional [ str ] = None ,
agent_role : Optional [ str ] = None ,
if not llm or not tools or not vectorstore :
verbose : bool = False ,
logging . error ( " llm, tools, and vectorstore cannot be None. " )
openai_api_key : Optional [ str ] = None ) :
raise ValueError ( " llm, tools, and vectorstore cannot be None. " )
if not openai_api_key and ( model_type is None or model_type . lower ( ) == ' openai ' ) :
self . llm = llm
raise ValueError ( " OpenAI API key cannot be None when model_type is ' openai ' " )
self . tools = tools
self . vectorstore = vectorstore
self . llm = llm or self . initialize_llm ( model_type , model_id , openai_api_key , temperature )
self . tools = tools or [ ]
self . vectorstore = vectorstore or [ ]
self . agent = None
self . temperature = temperature
self . temperature = temperature
self . model_type = model_type
self . model_type = model_type
self . human_in_the_loop = human_in_the_loop
self . human_in_the_loop = human_in_the_loop
self . prompt = prompt
self . model_id = model_id
self . model_id = model_id
self . embedding_size = embedding_size
self . embedding_size = embedding_size
self . system_prompt system_prompt
self . system_prompt = system_prompt
self . agent_name = agent_name
self . agent_role = agent_role
self . verbose = verbose
self . openai_api_key = openai_api_key
self . agent = None
self . initialize_agent ( )
def initialize_llm ( self , model_type : str , model_id : str , openai_api_key : str , temperature : float ) :
try :
if model_type . lower ( ) == ' openai ' :
return ChatOpenAI ( openai_api_key = openai_api_key , temperature = temperature )
elif model_type . lower ( ) == ' huggingface ' :
return HuggingFaceLLM ( model_id = model_id , temperature = temperature )
else :
raise ValueError ( " Invalid model_type. It should be either ' openai ' or ' huggingface ' " )
except Exception as e :
logger . error ( f " Failed to initialize language model: { e } " )
raise e
def create_agent ( self , ai_name = " Swarm Agent AI Assistant " , ai_role = " Assistant " , human_in_the_loop = True , search_kwargs = { } , verbose = False ) :
def initialize_agent ( self ) :
logging . info ( " Creating agent in AgentNode " )
try :
try :
self . agent = AutoGPT . from_llm_and_tools (
self . agent = AutoGPT . from_llm_and_tools (
ai_name = ai_name ,
ai_name = self . agent ,
ai_role = ai_role ,
ai_role = self . agent ,
tools = self . tools ,
tools = self . tools ,
llm = self . llm ,
llm = self . llm ,
memory = self . vectorstore . as_retriever ( search_kwargs = search_kwargs ) ,
memory = self . vectorstore . as_retriever ( search_kwargs = { } ) ,
human_in_the_loop = human_in_the_loop ,
human_in_the_loop = self . human_in_the_loop ,
chat_history_memory = FileChatMessageHistory ( " chat_history.txt " ) ,
chat_history_memory = FileChatMessageHistory ( " chat_history.txt " ) ,
verbose = self . verbose ,
)
)
# self.agent.chain.verbose = verbose
except Exception as e :
except Exception as e :
logg ing . error ( f " Error while creating agent: { str ( e ) } " )
logg er . error ( f " Error while creating agent: { str ( e ) } " )
raise e
raise e
def add_tool ( self , tool : Tool ) :
def add_tool ( self , tool : BaseTool ) :
if not isinstance ( tool , Tool ) :
if not isinstance ( tool , BaseTool ) :
logging . error ( " Tool must be an instance of Tool. " )
logger . error ( " Tool must be an instance of BaseTool. " )
raise TypeError ( " Tool must be an instance of Tool. " )
raise TypeError ( " Tool must be an instance of BaseTool. " )
self . tools . append ( tool )
self . tools . append ( tool )
def run ( self , prompt : str ) - > str :
def run ( self , prompt : str ) - > str :
if not isinstance ( prompt , str ) :
logging . error ( " Prompt must be a string. " )
raise TypeError ( " Prompt must be a string. " )
if not prompt :
if not prompt :
logg ing . error ( " Prompt is empty. " )
logger . error ( " Prompt is empty. " )
raise ValueError ( " Prompt is empty. " )
raise ValueError ( " Prompt is empty. " )
try :
try :
self . agent . run ( [ f " { prompt } " ] )
self . agent . run ( [ f " { prompt } " ] )
return " Task completed by AgentNode "
return " Task completed by AgentNode "
except Exception as e :
except Exception as e :
logg ing . error ( f " While running the agent: { str ( e ) } " )
logg er . error ( f " While running the agent: { str ( e ) } " )
raise e
raise e
@ -121,7 +131,6 @@ class AgentNode:
except Exception as e :
except Exception as e :
logging . error ( f " Failed to initialize language model: { e } " )
logging . error ( f " Failed to initialize language model: { e } " )
def initialize_tools ( self , llm_class ) :
def initialize_tools ( self , llm_class ) :
if not llm_class :
if not llm_class :
logging . error ( " llm_class not cannot be none " )
logging . error ( " llm_class not cannot be none " )
Kye
1 year ago