worker agent

main
Kye 2 years ago
parent 18ad70a5aa
commit ce9d17b508

@ -3,7 +3,7 @@ from setuptools import setup, find_packages
setup( setup(
name = 'swarms', name = 'swarms',
packages = find_packages(exclude=[]), packages = find_packages(exclude=[]),
version = '0.1.7', version = '0.1.8',
license='MIT', license='MIT',
description = 'Swarms - Pytorch', description = 'Swarms - Pytorch',
author = 'Kye Gomez', author = 'Kye Gomez',

@ -53,55 +53,65 @@ class MultiModalVisualAgentTool(BaseTool):
embeddings_model = OpenAIEmbeddings(openai_api_key="")
embedding_size = 1536
index = faiss.IndexFlatL2(embedding_size)
vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})
class WorkerAgent:
def __init__(self, objective: str, api_key: str):
self.objective = objective
self.api_key = api_key
self.worker = self.create_agent_worker()
def create_agent_worker(self):
os.environ['OPENAI_API_KEY'] = self.api_key
llm = ChatOpenAI(model_name="gpt-4", temperature=1.0)
embeddings_model = OpenAIEmbeddings()
embedding_size = 1536
index = faiss.IndexFlatL2(embedding_size)
vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})
query_website_tool = WebpageQATool(qa_chain=load_qa_with_sources_chain(llm)) query_website_tool = WebpageQATool(qa_chain=load_qa_with_sources_chain(llm))
web_search = DuckDuckGoSearchRun()
# !pip install duckduckgo_search multimodal_agent = MultiModalVisualAgent()
web_search = DuckDuckGoSearchRun() multimodal_agent_tool = MultiModalVisualAgentTool(multimodal_agent)
tools = [
web_search,
WriteFileTool(root_dir="./data"),
ReadFileTool(root_dir="./data"),
#MM CHILD AGENT multimodal_agent_tool,
multimodal_agent = MultiModalVisualAgent() process_csv,
query_website_tool,
Terminal,
#
multimodal_agent_tool = MultiModalVisualAgentTool(MultiModalVisualAgent)
tools = [ CodeWriter,
CodeEditor
web_search, ]
WriteFileTool(root_dir="./data"),
ReadFileTool(root_dir="./data"), agent_worker = AutoGPT.from_llm_and_tools(
process_csv, ai_name="WorkerX",
ai_role="Assistant",
tools=tools,
llm=llm,
memory=vectorstore.as_retriever(search_kwargs={"k": 8}),
human_in_the_loop=True,
)
agent_worker.chain.verbose = True
return agent_worker
# multimodal_agent_tool, # objective = "Your objective here"
# api_key = "Your OpenAI API key here"
# worker_agent = WorkerAgent(objective, api_key)
query_website_tool,
Terminal,
CodeWriter,
CodeEditor
# HumanInputRun(), # Activate if you want the permit asking for help from the human
]
agent_worker = AutoGPT.from_llm_and_tools(
ai_name="WorkerX",
ai_role="Assistant",
tools=tools,
llm=llm,
memory=vectorstore.as_retriever(search_kwargs={"k": 8}),
human_in_the_loop=True, # Set to True if you want to add feedback at each step.
)
agent_worker.chain.verbose = True
# worker_agent = agent_worker # worker_agent = agent_worker
# tree_of_thoughts_prompt = """ # tree_of_thoughts_prompt = """

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