diff --git a/swarms/agents/agent.py b/swarms/agents/agent.py
new file mode 100644
index 00000000..59326190
--- /dev/null
+++ b/swarms/agents/agent.py
@@ -0,0 +1,150 @@
+#base toolset
+from swarms.agents.tools.agent_tools import *
+from langchain.tools import BaseTool
+
+from langchain.callbacks.manager import (
+ AsyncCallbackManagerForToolRun,
+ CallbackManagerForToolRun,
+)
+from typing import List, Any, Dict, Optional, Type
+from langchain.memory.chat_message_histories import FileChatMessageHistory
+
+import logging
+from pydantic import BaseModel, Extra
+
+from swarms.agents.models.hf import HuggingFaceLLM
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+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"""
+
+ def __init__(self, llm, tools, vectorstore):
+ if not llm or not tools or not vectorstore:
+ logging.error("llm, tools, and vectorstore cannot be None.")
+ raise ValueError("llm, tools, and vectorstore cannot be None.")
+
+ self.llm = llm
+ self.tools = tools
+ self.vectorstore = vectorstore
+ self.agent = None
+
+ def create_agent(self, ai_name="Swarm Agent AI Assistant", ai_role="Assistant", human_in_the_loop=False, search_kwargs={}, verbose=False):
+ logging.info("Creating agent in AgentNode")
+ try:
+ self.agent = AutoGPT.from_llm_and_tools(
+ ai_name=ai_name,
+ ai_role=ai_role,
+ tools=self.tools,
+ llm=self.llm,
+ memory=self.vectorstore.as_retriever(search_kwargs=search_kwargs),
+ human_in_the_loop=human_in_the_loop,
+ chat_history_memory=FileChatMessageHistory("chat_history.txt"),
+ )
+ # self.agent.chain.verbose = verbose
+ except Exception as e:
+ logging.error(f"Error while creating agent: {str(e)}")
+ raise e
+
+ def add_tool(self, tool: Tool):
+ if not isinstance(tool, Tool):
+ logging.error("Tool must be an instance of Tool.")
+ raise TypeError("Tool must be an instance of Tool.")
+
+ self.tools.append(tool)
+
+ 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:
+ logging.error("Prompt is empty.")
+ raise ValueError("Prompt is empty.")
+
+ try:
+ self.agent.run([f"{prompt}"])
+ return "Task completed by AgentNode"
+ except Exception as e:
+ logging.error(f"While running the agent: {str(e)}")
+ raise e
+
+
+class AgentNode:
+ def __init__(self, openai_api_key):
+ if not openai_api_key:
+ logging.error("OpenAI API key is not provided")
+ raise ValueError("openai_api_key cannot be None")
+
+ self.openai_api_key = openai_api_key
+
+ def initialize_llm(self, llm_class, temperature=0.5):
+ if not llm_class:
+ logging.error("llm_class cannot be none")
+ raise ValueError("llm_class cannot be None")
+
+ try:
+ return llm_class(openai_api_key=self.openai_api_key, temperature=temperature)
+ except Exception as e:
+ logging.error(f"Failed to initialize language model: {e}")
+ raise
+
+ def initialize_tools(self, llm_class):
+ if not llm_class:
+ logging.error("llm_class not cannot be none")
+ raise ValueError("llm_class cannot be none")
+ try:
+
+ logging.info('Creating AgentNode')
+ llm = self.initialize_llm(llm_class)
+ web_search = DuckDuckGoSearchRun()
+
+ tools = [
+ web_search,
+ WriteFileTool(root_dir=ROOT_DIR),
+ ReadFileTool(root_dir=ROOT_DIR),
+ process_csv,
+ WebpageQATool(qa_chain=load_qa_with_sources_chain(llm)),
+ ]
+ if not tools:
+ logging.error("Tools are not initialized")
+ raise ValueError("Tools are not initialized")
+ return tools
+ except Exception as e:
+ logging.error(f"Failed to initialize tools: {e}")
+
+ def initialize_vectorstore(self):
+ try:
+ embeddings_model = OpenAIEmbeddings(openai_api_key=self.openai_api_key)
+ embedding_size = 1536
+ index = faiss.IndexFlatL2(embedding_size)
+ return FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})
+ except Exception as e:
+ logging.error(f"Failed to initialize vector store: {e}")
+ raise
+
+ 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):
+ if not llm_class:
+ logging.error("llm_class cannot be None.")
+ raise ValueError("llm_class cannot be None.")
+ try:
+ agent_tools = self.initialize_tools(llm_class)
+ vectorstore = self.initialize_vectorstore()
+ agent = AgentNode(llm=self.initialize_llm(llm_class), tools=agent_tools, vectorstore=vectorstore)
+ 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)
+ return agent
+ except Exception as e:
+ logging.error(f"Failed to create agent node: {e}")
+ raise
+
+def agent(openai_api_key):
+ if not openai_api_key:
+ logging.error("OpenAI API key is not provided")
+ raise ValueError("OpenAI API key is required")
+ try:
+ initializer = AgentNodeInitializer(openai_api_key)
+ agent = initializer.create_agent()
+ return agent
+ except Exception as e:
+ logging.error(f"An error occured in agent: {e}")
+ raise
diff --git a/swarms/agents/models/hf.py b/swarms/agents/models/hf.py
index aca2d0c3..cd73d665 100644
--- a/swarms/agents/models/hf.py
+++ b/swarms/agents/models/hf.py
@@ -3,29 +3,7 @@ import logging
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
class HuggingFaceLLM:
- """
- A class that represents a Language Model (LLM) powered by HuggingFace Transformers.
-
- Attributes:
- model_id (str): ID of the pre-trained model in HuggingFace Model Hub.
- device (str): Device to load the model onto.
- max_length (int): Maximum length of the generated sequence.
- tokenizer: Instance of the tokenizer corresponding to the model.
- model: The loaded model instance.
- logger: Logger instance for the class.
- """
def __init__(self, model_id: str, device: str = None, max_length: int = 20, quantize: bool = False, quantization_config: dict = None):
- """
- Constructs all the necessary attributes for the HuggingFaceLLM object.
-
- Args:
- model_id (str): ID of the pre-trained model in HuggingFace Model Hub.
- device (str, optional): Device to load the model onto. Defaults to GPU if available, else CPU.
- max_length (int, optional): Maximum length of the generated sequence. Defaults to 20.
- quantize (bool, optional): Whether to apply quantization to the model. Defaults to False.
- quantization_config (dict, optional): Configuration for model quantization. Defaults to None,
- and a standard configuration will be used if quantize is True.
- """
self.logger = logging.getLogger(__name__)
self.device = device if device else ('cuda' if torch.cuda.is_available() else 'cpu')
self.model_id = model_id
@@ -50,17 +28,6 @@ class HuggingFaceLLM:
self.logger.error(f"Failed to load the model or the tokenizer: {e}")
raise
def generate_text(self, prompt_text: str, max_length: int = None):
- """
- Generates text based on the input prompt using the loaded model.
-
- Args:
- prompt_text (str): Input prompt to generate text from.
- max_length (int, optional): Maximum length of the generated sequence. Defaults to None,
- and the max_length set during initialization will be used.
-
- Returns:
- str: Generated text.
- """
max_length = max_length if max_length else self.max_length
try:
inputs = self.tokenizer.encode(prompt_text, return_tensors="pt").to(self.device)