mirror of
https://github.com/tcsenpai/agenticSeek.git
synced 2025-06-06 11:05:26 +00:00
97 lines
3.2 KiB
Python
97 lines
3.2 KiB
Python
import os
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import sys
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import torch
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from transformers import pipeline
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from sources.agents.agent import Agent
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from sources.agents.code_agent import CoderAgent
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from sources.agents.casual_agent import CasualAgent
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from sources.agents.planner_agent import PlannerAgent
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from sources.utility import pretty_print
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class AgentRouter:
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"""
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AgentRouter is a class that selects the appropriate agent based on the user query.
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"""
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def __init__(self, agents: list, model_name: str = "facebook/bart-large-mnli"):
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self.model = model_name
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self.pipeline = pipeline("zero-shot-classification",
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model=self.model)
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self.agents = agents
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self.labels = [agent.role for agent in agents]
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def get_device(self) -> str:
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if torch.backends.mps.is_available():
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return "mps"
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elif torch.cuda.is_available():
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return "cuda:0"
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else:
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return "cpu"
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def classify_text(self, text: str, threshold: float = 0.5) -> list:
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"""
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Classify the text into labels (agent roles).
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Args:
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text (str): The text to classify
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threshold (float, optional): The threshold for the classification.
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Returns:
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list: The list of agents and their scores
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"""
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first_sentence = None
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for line in text.split("\n"):
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first_sentence = line.strip()
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break
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if first_sentence is None:
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first_sentence = text
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result = self.pipeline(first_sentence, self.labels, threshold=threshold)
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return result
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def select_agent(self, text: str) -> Agent:
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"""
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Select the appropriate agent based on the text.
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Args:
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text (str): The text to select the agent from
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Returns:
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Agent: The selected agent
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"""
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if len(self.agents) == 0 or len(self.labels) == 0:
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return self.agents[0]
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result = self.classify_text(text)
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for agent in self.agents:
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if result["labels"][0] == agent.role:
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pretty_print(f"Selected agent: {agent.agent_name}", color="warning")
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return agent
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return None
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if __name__ == "__main__":
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agents = [
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CoderAgent("deepseek-r1:14b", "agent1", "../prompts/coder_agent.txt", "server"),
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CasualAgent("deepseek-r1:14b", "agent2", "../prompts/casual_agent.txt", "server"),
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PlannerAgent("deepseek-r1:14b", "agent3", "../prompts/planner_agent.txt", "server")
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]
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router = AgentRouter(agents)
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texts = ["""
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Write a python script to check if the device on my network is connected to the internet
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""",
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"""
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Hey could you search the web for the latest news on the stock market ?
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""",
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"""
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hey can you give dating advice ?
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""",
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"""
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Make a cool game to illustrate the current relation between USA and europe
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"""
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]
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for text in texts:
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print(text)
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results = router.classify_text(text)
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for result in results:
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print(result["label"], "=>", result["score"])
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agent = router.select_agent(text)
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print("Selected agent role:", agent.role)
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