Kye
2 years ago
parent
000ae415e1
commit
cf04413a01
@ -1,191 +0,0 @@
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#base toolset
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from swarms.agents.tools.agent_tools import *
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from swarms.utils.logger import logger
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from langchain.tools import BaseTool
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from typing import List, Any, Optional
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from langchain.memory.chat_message_histories import FileChatMessageHistory
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import logging
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from swarms.agents.models.hf import HuggingFaceLLM
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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class AgentNodeInitializer:
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"""Useful for spawning autonomous agent instances to accomplish complex tasks."""
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def __init__(self,
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llm: Optional[Any] = None,
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tools: Optional[List[BaseTool]] = None,
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vectorstore: Optional[List[Any]] = None,
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temperature: float = 0.5,
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model_type: Optional[str] = None,
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human_in_the_loop: bool = True,
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model_id: Optional[str] = None,
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embedding_size: int = 8192,
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system_prompt: Optional[str] = None,
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max_iterations: Optional[int] = None,
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agent_name: Optional[str] = None,
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agent_role: Optional[str] = None,
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verbose: bool = False,
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openai_api_key: Optional[str] = None):
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if not openai_api_key and (model_type is None or model_type.lower() == 'openai'):
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raise ValueError("OpenAI API key cannot be None when model_type is 'openai'")
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self.llm = llm or self.initialize_llm(model_type, model_id, openai_api_key, temperature)
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self.tools = tools or []
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self.vectorstore = vectorstore or []
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self.temperature = temperature
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self.model_type = model_type
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self.human_in_the_loop = human_in_the_loop
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self.model_id = model_id
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self.embedding_size = embedding_size
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self.system_prompt = system_prompt
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self.agent_name = agent_name
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self.agent_role = agent_role
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self.verbose = verbose
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self.openai_api_key = openai_api_key
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self.agent = None
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self.initialize_agent()
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def initialize_llm(self, model_type: str, model_id: str, openai_api_key: str, temperature: float):
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try:
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if model_type.lower() == 'openai':
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return ChatOpenAI(openai_api_key=openai_api_key, temperature=temperature)
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elif model_type.lower() == 'huggingface':
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return HuggingFaceLLM(model_id=model_id, temperature=temperature)
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else:
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raise ValueError("Invalid model_type. It should be either 'openai' or 'huggingface'")
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except Exception as e:
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logger.error(f"Failed to initialize language model: {e}")
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raise e
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def initialize_agent(self):
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try:
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self.agent = AutoGPT.from_llm_and_tools(
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ai_name=self.agent_name,
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ai_role=self.agent_role,
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tools=self.tools,
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llm=self.llm,
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memory=self.vectorstore.as_retriever(search_kwargs={}),
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human_in_the_loop=self.human_in_the_loop,
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chat_history_memory=FileChatMessageHistory("chat_history.txt"),
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verbose=self.verbose,
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)
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except Exception as e:
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logger.error(f"Error while creating agent: {str(e)}")
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raise e
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def add_tool(self, tool: BaseTool):
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if not isinstance(tool, BaseTool):
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logger.error("Tool must be an instance of BaseTool.")
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raise TypeError("Tool must be an instance of BaseTool.")
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self.tools.append(tool)
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def run(self, prompt: str) -> str:
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if not prompt:
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logger.error("Prompt is empty.")
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raise ValueError("Prompt is empty.")
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try:
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self.agent.run([f"{prompt}"])
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return "Task completed by AgentNode"
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except Exception as e:
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logger.error(f"While running the agent: {str(e)}")
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raise e
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class AgentNode:
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def __init__(self, openai_api_key):
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if not openai_api_key:
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logging.error("OpenAI API key is not provided")
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raise ValueError("openai_api_key cannot be None")
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self.openai_api_key = openai_api_key
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def initialize_llm(self, llm_class):
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"""
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Init LLM
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Params:
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llm_class(class): The Language model class. Default is OpenAI.
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temperature (float): The Temperature for the language model. Default is 0.5
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"""
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try:
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# Initialize language model
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if self.llm_class == 'openai' or OpenAI:
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return llm_class(openai_api_key=self.openai_api_key, temperature=self.temperature)
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elif self.model_type == "huggingface":
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return HuggingFaceLLM(model_id=self.model_id, temperature=self.temperature)
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except Exception as e:
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logging.error(f"Failed to initialize language model: {e}")
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def initialize_tools(self, llm_class):
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if not llm_class:
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logging.error("llm_class not cannot be none")
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raise ValueError("llm_class cannot be none")
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try:
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logging.info('Creating AgentNode')
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llm = self.initialize_llm(llm_class)
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web_search = DuckDuckGoSearchRun()
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tools = [
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web_search,
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WriteFileTool(root_dir=ROOT_DIR),
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ReadFileTool(root_dir=ROOT_DIR),
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process_csv,
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WebpageQATool(qa_chain=load_qa_with_sources_chain(llm)),
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]
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if not tools:
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logging.error("Tools are not initialized")
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raise ValueError("Tools are not initialized")
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return tools
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except Exception as e:
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logging.error(f"Failed to initialize tools: {e}")
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def initialize_vectorstore(self):
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try:
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embeddings_model = OpenAIEmbeddings(openai_api_key=self.openai_api_key)
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index = faiss.IndexFlatL2(self.embedding_size)
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return FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})
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except Exception as e:
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logging.error(f"Failed to initialize vector store: {e}")
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raise
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def create_agent(self, llm_class=ChatOpenAI, ai_name="Swarm Agent AI Assistant", ai_role="Assistant", human_in_the_loop=False, search_kwargs={}, verbose=False):
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if not llm_class:
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logging.error("llm_class cannot be None.")
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raise ValueError("llm_class cannot be None.")
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try:
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agent_tools = self.initialize_tools(llm_class)
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vectorstore = self.initialize_vectorstore()
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agent = AgentNode(llm=self.initialize_llm(llm_class), tools=agent_tools, vectorstore=vectorstore)
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agent.create_agent(ai_name=ai_name, ai_role=ai_role, human_in_the_loop=human_in_the_loop, search_kwargs=search_kwargs, verbose=verbose)
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return agent
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except Exception as e:
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logging.error(f"Failed to create agent node: {e}")
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raise
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def agent(openai_api_key, objective):
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if not objective or not isinstance(objective, str):
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logging.error("Invalid objective")
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raise ValueError("A valid objective is required")
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if not openai_api_key:
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logging.error("OpenAI API key is not provided")
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raise ValueError("OpenAI API key is required")
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try:
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initializer = AgentNodeInitializer(openai_api_key)
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agent = initializer.create_agent()
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agent = agent.run(objective)
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return agent
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except Exception as e:
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logging.error(f"An error occured in agent: {e}")
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raise
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