Kye Gomez
8 months ago
parent
2cf86acd6d
commit
36492c1e0f
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"""
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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 playground.memory.chromadb_example 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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# Perplexity Agent
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# def webbrowser(query: str):
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# # put your logic here
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# return query
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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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@ -1,15 +0,0 @@
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from swarms.memory import ChromaDB
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# Initialize the memory
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chroma = ChromaDB(
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metric="cosine",
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limit_tokens=1000,
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verbose=True,
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)
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# Add text
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text = "This is a test"
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chroma.add(text)
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# Search for similar text
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similar_text = chroma.query(text)
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@ -0,0 +1,73 @@
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import timeit
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from typing import Callable, Iterable, List, Optional, TypeVar
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T = TypeVar("T")
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R = TypeVar("R")
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def optimized_loop(
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data: Iterable[T],
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operation: Callable[[T], R],
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condition: Optional[Callable[[T], bool]] = None,
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) -> List[R]:
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"""
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Perform an optimized loop over the input data, applying an operation to each element.
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Optionally, filter elements based on a condition before applying the operation.
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Args:
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data (Iterable[T]): The input data to be processed. Can be any iterable type.
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operation (Callable[[T], R]): The operation to be applied to each element.
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condition (Optional[Callable[[T], bool]]): An optional condition to filter elements before applying the operation.
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Returns:
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List[R]: The result of applying the operation to the filtered elements.
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"""
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if condition is not None:
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return [operation(x) for x in data if condition(x)]
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else:
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return [operation(x) for x in data]
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# Sample data, operation, and condition for benchmarking
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data = list(range(1000000))
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operation = lambda x: x * x
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condition = lambda x: x % 2 == 0
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# Define a traditional loop for comparison
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def traditional_loop(data: Iterable[int]) -> List[int]:
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result = []
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for x in data:
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if x % 2 == 0:
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result.append(x * x)
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return result
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# Define a benchmarking function
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def benchmark():
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# Time the execution of the optimized loop
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optimized_time = timeit.timeit(
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stmt="optimized_loop(data, operation, condition)",
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setup="from __main__ import optimized_loop, data, operation, condition",
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globals=globals(),
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number=10,
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)
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print(f"Optimized loop execution time: {optimized_time:.4f} seconds")
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# Time the execution of the traditional loop for comparison
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traditional_time = timeit.timeit(
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stmt="traditional_loop(data)",
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setup="from __main__ import traditional_loop, data",
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globals=globals(),
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number=10,
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)
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print(
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f"Traditional loop execution time: {traditional_time:.4f} seconds"
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)
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# Run the benchmark
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if __name__ == "__main__":
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benchmark()
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