Spaces:
Sleeping
Sleeping
File size: 1,398 Bytes
ad3baff e42ee9c ad3baff f8d7e03 e42ee9c df2761c d5d74f1 e42ee9c 49d0ec7 f8d7e03 27b0c70 e42ee9c ad3baff 8c79ae9 8464f47 ca94485 e42ee9c d56d7b0 017e4e7 8464f47 e42ee9c d56d7b0 e42ee9c 9495824 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 |
from smolagents import ToolCallingAgent, ActionStep, CodeAgent, DuckDuckGoSearchTool, LiteLLMModel
import os
import time
#import litellm
#litellm._turn_on_debug() # Enable debugging for litellm
# Set the API key for Sambanova
os.environ['SAMBANOVA_API_KEY'] = os.getenv('sambanova_token')
# Meta-Llama-3.1-405B-Instruct-8k
# Meta-Llama-3.1-70B-Instruct-8k
# Create the model with explicit provider specification
model = LiteLLMModel(
model_id="sambanova/Meta-Llama-3.1-405B-Instruct-8k",
max_tokens=2096,
temperature=0.5,
api_base="https://api.sambanova.ai/v1", # Specify the base URL for Sambanova, as in your working requests example
)
def my_paused(step_log: ActionStep, agent: ToolCallingAgent) -> None:
print('Paused 10sec.')
time.sleep(10.0)
return
# Create the agent
agent = ToolCallingAgent(
tools=[DuckDuckGoSearchTool()],
model=model,
step_callbacks=[my_paused],
)
# Когда запуск идет через streamlingt то ответы переводятс в json автоматом
#agent = CodeAgent(
# tools=[DuckDuckGoSearchTool()],
# model=model
#)
# Run the agent with error handling
try:
result = agent.run("Search for the best music recommendations for a party at the Wayne's mansion.")
print("\n++++\nResult:\n")
print(result)
except Exception as e:
print(f"\n++++\nError occurred:\n")
print(f"{e}") |