Kye
1 year ago
parent
be46bcf48f
commit
db43acbf7d
@ -1,28 +0,0 @@
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from swarms import HierarchicalSwarm
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swarm = HierarchicalSwarm(
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openai_api_key="key",
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model_type="openai",
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model_id="gpt-4",
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use_vectorstore=False,
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use_async=False,
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human_in_the_loop=False,
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logging_enabled=False,
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)
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# run the swarm with an objective
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result = swarm.run("Design a new car")
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# or huggingface
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swarm = HierarchicalSwarm(
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model_type="huggingface",
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model_id="tiaueu/falcon",
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use_vectorstore=True,
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embedding_size=768,
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use_async=False,
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human_in_the_loop=True,
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logging_enabled=False,
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)
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# Run the swarm with a particular objective
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result = swarm.run("Write a sci-fi short story")
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from swarms import Orchestrator, Worker
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# Instantiate the Orchestrator with 10 agents
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orchestrator = Orchestrator(
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Worker, agent_list=[Worker] * 10, task_queue=[]
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)
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# Agent 1 sends a message to Agent 2
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orchestrator.chat(
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sender_id=1, receiver_id=2, message="Hello, Agent 2!"
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)
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@ -1,7 +1,14 @@
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from swarms import swarm
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from swarms import Agent, OpenAIChat
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# Use the function
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api_key = "APIKEY"
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objective = "What is the capital of the UK?"
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result = swarm(api_key, objective)
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print(result) # Prints: "The capital of the UK is London."
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## Initialize the workflow
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agent = Agent(
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llm=OpenAIChat(),
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max_loops=1,
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autosave=True,
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dashboard=False,
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streaming_on=True,
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verbose=True,
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)
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# Run the workflow on a task
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agent("Find a chick fil a equivalent in hayes valley")
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from swarms import Orchestrator, Worker
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node = Worker(
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openai_api_key="",
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ai_name="Optimus Prime",
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)
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# Instantiate the Orchestrator with 10 agents
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orchestrator = Orchestrator(
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node, agent_list=[node] * 10, task_queue=[]
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)
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# Agent 7 sends a message to Agent 9
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orchestrator.chat(
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sender_id=7,
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receiver_id=9,
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message="Can you help me with this task?",
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)
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@ -1,19 +0,0 @@
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from ..swarms import HierarchicalSwarm
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# Retrieve your API key from the environment or replace with your actual key
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api_key = "sksdsds"
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# Initialize HierarchicalSwarm with your API key
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swarm = HierarchicalSwarm(openai_api_key=api_key)
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# Define an objective
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objective = """
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Please develop and serve a simple community web service.
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People can signup, login, post, comment.
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Post and comment should be visible at once.
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I want it to have neumorphism-style.
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The ports you can use are 4500 and 6500.
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"""
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# Run HierarchicalSwarm
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swarm.run(objective)
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@ -1,16 +0,0 @@
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from swarms import HierarchicalSwarm
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# Retrieve your API key from the environment or replace with your actual key
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api_key = ""
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# Initialize HierarchicalSwarm with your API key
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swarm = HierarchicalSwarm(api_key)
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# Define an objective
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objective = (
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"Find 20 potential customers for a HierarchicalSwarm based AI"
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" Agent automation infrastructure"
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)
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# Run HierarchicalSwarm
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swarm.run(objective)
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@ -1,19 +0,0 @@
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from swarms import HierarchicalSwarm
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# Retrieve your API key from the environment or replace with your actual key
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api_key = "sksdsds"
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# Initialize HierarchicalSwarm with your API key
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swarm = HierarchicalSwarm(openai_api_key=api_key)
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# Define an objective
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objective = """
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Please develop and serve a simple web TODO app.
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The user can list all TODO items and add or delete each TODO item.
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I want it to have neumorphism-style.
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The ports you can use are 4500 and 6500.
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"""
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# Run HierarchicalSwarm
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swarm.run(objective)
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from swarms.tools.tool import tool
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from swarms.tools.tool_func_doc_scraper import scrape_tool_func_docs
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@tool
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def search_api(query: str) -> str:
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"""Search API
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Args:
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query (str): _description_
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Returns:
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str: _description_
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"""
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print(f"Searching API for {query}")
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tool_docs = scrape_tool_func_docs(search_api)
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print(tool_docs)
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from swarms.models import OpenAIChat
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from swarms.structs.workflow import Workflow
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llm = OpenAIChat()
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workflow = Workflow(llm)
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from swarms.tools.tool import tool
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from swarms.tools.tool_func_doc_scraper import scrape_tool_func_docs
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# Define a tool by decorating a function with the tool decorator and providing a docstring
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@tool(return_direct=True)
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def search_api(query: str):
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"""Search the web for the query
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Args:
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query (str): _description_
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Returns:
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_type_: _description_
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"""
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return f"Search results for {query}"
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# Scrape the tool func docs to prepare for injection into the agent prompt
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out = scrape_tool_func_docs(search_api)
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print(out)
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@ -1,10 +0,0 @@
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from swarms import Workflow
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from swarms.models import ChatOpenAI
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workflow = Workflow(ChatOpenAI)
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workflow.add("What's the weather in miami")
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workflow.add("Provide details for {{ parent_output }}")
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workflow.add("Summarize the above information: {{ parent_output}}")
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workflow.run()
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