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74 lines
1.4 KiB
74 lines
1.4 KiB
# Import the OpenAIChat model and the Agent struct
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import os
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from swarms import (
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Agent,
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OpenAIChat,
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SwarmNetwork,
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Anthropic,
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TogetherLLM,
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)
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from swarms.memory import ChromaDB
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from dotenv import load_dotenv
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# load the environment variables
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load_dotenv()
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# Initialize the ChromaDB
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memory = ChromaDB()
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# Initialize the language model
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llm = OpenAIChat(
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temperature=0.5,
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)
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# Initialize the Anthropic
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anthropic = Anthropic(max_tokens=3000)
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# TogeterLM
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together_llm = TogetherLLM(
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together_api_key=os.getenv("TOGETHER_API_KEY"), max_tokens=3000
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)
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## Initialize the workflow
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agent = Agent(
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llm=anthropic,
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max_loops=1,
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agent_name="Social Media Manager",
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long_term_memory=memory,
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)
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agent2 = Agent(
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llm=llm,
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max_loops=1,
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agent_name=" Product Manager",
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long_term_memory=memory,
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)
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agent3 = Agent(
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llm=together_llm,
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max_loops=1,
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agent_name="SEO Manager",
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long_term_memory=memory,
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)
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# Load the swarmnet with the agents
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swarmnet = SwarmNetwork(
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agents=[agent, agent2, agent3], logging_enabled=True
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)
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# List the agents in the swarm network
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out = swarmnet.list_agents()
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print(out)
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# Run the workflow on a task
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out = swarmnet.run_single_agent(
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agent2.id, "Generate a 10,000 word blog on health and wellness."
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)
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print(out)
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# # Run all the agents in the swarm network on a task
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# out = swarmnet.run_many_agents(
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# f"Summarize the blog and create a social media post: {out}"
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# )
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# print(out)
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