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83 lines
2.2 KiB
83 lines
2.2 KiB
"""
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pip3 install -U swarms
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pip3 install -U chromadb
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task -> Understanding Agent [understands the problem better] -> Summarize of the conversation -> research agent that has access to internt perplexity -> final rag agent
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# Todo
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- Use better llm -- gpt4, claude, gemini
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- Make better system prompt
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- Populate the vector database with q/a of past history
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"""
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from swarms import Agent, llama3Hosted, AgentRearrange
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from pydantic import BaseModel
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from swarms_memory import ChromaDB
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# Initialize the language model agent (e.g., GPT-3)
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llm = llama3Hosted(max_tokens=3000)
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# Initialize Memory
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memory = ChromaDB(output_dir="swarm_mechanic", n_results=2, verbose=True)
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# Output
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class EvaluatorOuputSchema(BaseModel):
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evaluation: str = None
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question_for_user: str = None
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# Initialize agents for individual tasks
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agent1 = Agent(
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agent_name="Summary ++ Hightlighter Agent",
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system_prompt="Generate a simple, direct, and reliable summary of the input task alongside the highlights",
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llm=llm,
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max_loops=1,
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)
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# Point out that if their are details that can be added
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# What do you mean? What lights do you have turned on.
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agent2 = Agent(
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agent_name="Evaluator",
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system_prompt="Summarize and evaluate the summary and the users demand, always be interested in learning more about the situation with extreme precision.",
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llm=llm,
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max_loops=1,
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list_base_models=[EvaluatorOuputSchema],
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)
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# research_agent = Agent(
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# agent_name="Research Agent",
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# system_prompt="Summarize and evaluate the summary and the users demand, always be interested in learning more about the situation with extreme precision.",
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# llm=llm,
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# max_loops=1,
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# tool = [webbrowser]
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# )
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agent3 = Agent(
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agent_name="Summarizer Agent",
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system_prompt="Summarize the entire history of the interaction",
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llm=llm,
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max_loops=1,
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long_term_memory=memory,
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)
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# Task
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task = "Car Model: S-Class, Car Year: 2020, Car Mileage: 10000, all my service lights are on, what should i do?"
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# Swarm
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swarm = AgentRearrange(
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agents=[agent1, agent2, agent3],
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flow=f"{agent1.agent_name} -> {agent2.agent_name} -> {agent3.agent_name}",
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memory_system=memory,
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)
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# Task
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out = swarm.run(task)
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print(out)
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