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