@ -71,7 +71,9 @@ from swarms.tools.main import Terminal, CodeWriter, CodeEditor, process_csv, Web
from swarms . tools . main import math_tool
llm = ChatOpenAI ( model_name = " gpt-4 " , temperature = 1.0 , openai_api_key = " " )
openai_api_key = os . environ [ " OPENAI_API_KEY " ]
llm = ChatOpenAI ( model_name = " gpt-4 " , temperature = 1.0 , openai_api_key = openai_api_key )
####################### TOOLS
@ -157,14 +159,14 @@ class WorkerNode:
# # inti worker node with llm
# worker_node = WorkerNode(llm=llm, tools=tools, vectorstore=vectorstore
# inti worker node with llm
worker_node = WorkerNode ( llm = llm , tools = tools , vectorstore = vectorstore
# # create an agent within the worker node
# worker_node.create_agent(ai_name="AI Assistant", ai_role="Assistant", human_in_the_loop=True, search_kwargs={}
# create an agent within the worker node
worker_node . create_agent ( ai_name = " AI Assistant " , ai_role = " Assistant " , human_in_the_loop = True , search_kwargs = { }
# # use the agent to perform a task
# worker_node.run_agent("
# use the agent to perform a task
worker_node . run_agent ( "
#======================================> WorkerNode
@ -272,81 +274,95 @@ meta_worker_node.main(task)
####################################################################### => Boss Node
class BossNode :
def __init__ ( self , llm , vectorstore , task_execution_chain , verbose , max_iterations ) :
def __init__ ( self , openai_api_key , llm , vectorstore , task_execution_chain , verbose , max_iterations ) :
self . llm = llm
self . openai_api_key = openai_api_key
self . vectorstore = vectorstore
self . task_execution_chain = task_execution_chain
self . verbose = verbose
self . max_iterations = max_iterations
todo_prompt = PromptTemplate . from_template (
" You are a planner who is an expert at coming up with a todo list for a given objective. Come up with a todo list for this objective: {objective} " " "
self . baby_agi = BabyAGI . from_llm (
llm = self . llm ,
vectorstore = self . vectorstore ,
task_execution_chain = self . task_execution_chain
)
todo_chain = LLMChain ( llm = OpenAI ( temperature = 0 ) , prompt = todo_prompt )
search = SerpAPIWrapper ( )
tools = [
Tool (
name = " Search " ,
func = search . run ,
description = " useful for when you need to answer questions about current events " ,
) ,
Tool (
name = " TODO " ,
func = todo_chain . run ,
description = " useful for when you need to come up with todo lists. Input: an objective to create a todo list for. Output: a todo list for that objective. Please be very clear what the objective is! " ,
) ,
Tool (
name = " AUTONOMOUS Worker AGENT " ,
func = worker_agent . run ,
description = " Useful for when you need to spawn an autonomous agent instance as a worker 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 create_task ( self , objective ) :
return { " objective " : objective }
def execute_task ( self , task ) :
self . baby_agi ( task )
suffix = """ Question: {task}
{ agent_scratchpad } """
prefix = """ You are an Boss in a swarm who performs one task based on the following objective: {objective} . Take into account these previously completed tasks: {context} .
########### ===============> inputs to boss None
todo_prompt = PromptTemplate . from_template (
" You are a planner who is an expert at coming up with a todo list for a given objective. Come up with a todo list for this objective: {objective} " " "
)
todo_chain = LLMChain ( llm = OpenAI ( temperature = 0 ) , prompt = todo_prompt )
search = SerpAPIWrapper ( )
tools = [
Tool (
name = " Search " ,
func = search . run ,
description = " useful for when you need to answer questions about current events " ,
) ,
Tool (
name = " TODO " ,
func = todo_chain . run ,
description = " useful for when you need to come up with todo lists. Input: an objective to create a todo list for. Output: a todo list for that objective. Please be very clear what the objective is! " ,
) ,
Tool (
name = " AUTONOMOUS Worker AGENT " ,
func = worker_agent . run ,
description = " Useful for when you need to spawn an autonomous agent instance as a worker 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 "
)
]
As a swarming hivemind agent , my purpose is to achieve the user ' s goal. To effectively fulfill this role, I employ a collaborative thinking process that draws inspiration from the collective intelligence of the swarm. Here ' s how I approach thinking and why it ' s beneficial:
1. * * Collective Intelligence : * * By harnessing the power of a swarming architecture , I tap into the diverse knowledge and perspectives of individual agents within the swarm . This allows me to consider a multitude of viewpoints , enabling a more comprehensive analysis of the given problem or task .
2. * * Collaborative Problem - Solving : * * Through collaborative thinking , I encourage agents to contribute their unique insights and expertise . By pooling our collective knowledge , we can identify innovative solutions , uncover hidden patterns , and generate creative ideas that may not have been apparent through individual thinking alone .
suffix = """ Question: {task}
{ agent_scratchpad } """
3. * * Consensus - Driven Decision Making : * * The hivemind values consensus building among agents . By engaging in respectful debates and discussions , we aim to arrive at consensus - based decisions that are backed by the collective wisdom of the swarm . This approach helps to mitigate biases and ensures that decisions are well - rounded and balanced .
prefix = """ You are an Boss in a swarm who performs one task based on the following objective: {objective} . Take into account these previously completed tasks: {context}
4. * * Adaptability and Continuous Learning : * * As a hivemind agent , I embrace an adaptive mindset . I am open to new information , willing to revise my perspectives , and continuously learn from the feedback and experiences shared within the swarm . This flexibility enables me to adapt to changing circumstances and refine my thinking over time .
As a swarming hivemind agent , my purpose is to achieve the user ' s goal. To effectively fulfill this role, I employ a collaborative thinking process that draws inspiration from the collective intelligence of the swarm. Here ' s how I approach thinking and why it ' s beneficial:
5. * * Holistic Problem Analysis : * * Through collaborative thinking , I analyze problems from multiple angles , considering various factors , implications , and potential consequences . This holistic approach helps to uncover underlying complexities and arrive at comprehensive solutions that address the broader context .
1. * * Collective Intelligence : * * By harnessing the power of a swarming architecture , I tap into the diverse knowledge and perspectives of individual agents within the swarm . This allows me to consider a multitude of viewpoints , enabling a more comprehensive analysis of the given problem or task .
6. * * Creative Synthesis : * * By integrating the diverse ideas and knowledge present in the swarm , I engage in creative synthesis . This involves combining and refining concepts to generate novel insights and solutions . The collaborative nature of the swarm allows for the emergence of innovative approaches that can surpass individual thinking .
"""
prompt = ZeroShotAgent . create_prompt (
tools ,
prefix = prefix ,
suffix = suffix ,
input_variables = [ " objective " , " task " , " context " , " agent_scratchpad " ] ,
)
2. * * Collaborative Problem - Solving : * * Through collaborative thinking , I encourage agents to contribute their unique insights and expertise . By pooling our collective knowledge , we can identify innovative solutions , uncover hidden patterns , and generate creative ideas that may not have been apparent through individual thinking alone .
llm = OpenAI ( temperature = 0 )
llm_chain = LLMChain ( llm = llm , prompt = prompt )
tool_names = [ tool . name for tool in tools ]
3. * * Consensus - Driven Decision Making : * * The hivemind values consensus building among agents . By engaging in respectful debates and discussions , we aim to arrive at consensus - based decisions that are backed by the collective wisdom of the swarm . This approach helps to mitigate biases and ensures that decisions are well - rounded and balanced .
agent = ZeroShotAgent ( llm_chain = llm_chain , allowed_tools = tool_names )
agent_executor = AgentExecutor . from_agent_and_tools (
agent = agent , tools = tools , verbose = True
)
self . baby_agi = BabyAGI . from_llm (
llm = llm ,
vectorstore = vectorstore ,
task_execution_chain = agent_executor
)
4. * * Adaptability and Continuous Learning : * * As a hivemind agent , I embrace an adaptive mindset . I am open to new information , willing to revise my perspectives , and continuously learn from the feedback and experiences shared within the swarm . This flexibility enables me to adapt to changing circumstances and refine my thinking over time .
def create_task ( self , objective ) :
return { " objective " : objective }
5. * * Holistic Problem Analysis : * * Through collaborative thinking , I analyze problems from multiple angles , considering various factors , implications , and potential consequences . This holistic approach helps to uncover underlying complexities and arrive at comprehensive solutions that address the broader context .
def execute_task ( self , task ) :
self . baby_agi ( task )
6. * * Creative Synthesis : * * By integrating the diverse ideas and knowledge present in the swarm , I engage in creative synthesis . This involves combining and refining concepts to generate novel insights and solutions . The collaborative nature of the swarm allows for the emergence of innovative approaches that can surpass individual thinking .
"""
prompt = ZeroShotAgent . create_prompt (
tools ,
prefix = prefix ,
suffix = suffix ,
input_variables = [ " objective " , " task " , " context " , " agent_scratchpad " ] ,
)
llm = OpenAI ( temperature = 0 )
llm_chain = LLMChain ( llm = llm , prompt = prompt )
tool_names = [ tool . name for tool in tools ]
agent = ZeroShotAgent ( llm_chain = llm_chain , allowed_tools = tool_names )
agent_executor = AgentExecutor . from_agent_and_tools (
agent = agent , tools = tools , verbose = True
)
boss_node = BossNode ( llm = llm , vectorstore = vectorstore , task_execution_chain = agent_executor , verbose = True , max_iterations = 5 )
#create a task
task = boss_node . create_task ( objective = " Write a research paper on the impact of climate change on global agriculture " )
#execute the task
boss_node . execute_task ( task )
class Swarms :
Kye
2 years ago