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import argparse
import os
import time
from src.open_strawberry import get_defaults, manage_conversation
def parse_arguments(model, system_prompt, next_prompts, num_turns, show_next, final_prompt,
num_turns_final_mod, show_cot, verbose):
parser = argparse.ArgumentParser(description="Open Strawberry Conversation Manager")
parser.add_argument("--show_next", action="store_true", default=show_next, help="Show all messages")
parser.add_argument("--verbose", action="store_true", default=verbose, help="Show usage information")
parser.add_argument("--system_prompt", type=str, default=system_prompt, help="Custom system prompt")
parser.add_argument("--num_turns_final_mod", type=int, default=num_turns_final_mod,
help="Number of turns before final prompt")
parser.add_argument("--num_turns", type=int, default=num_turns,
help="Number of turns before pausing for continuation")
parser.add_argument("--model", type=str, default=model, help="Model to use for conversation")
parser.add_argument("--initial_prompt", type=str, default='', help="Initial prompt. If empty, then ask user.")
parser.add_argument("--expected_answer", type=str, default='', help="Expected answer. If empty, then ignore.")
parser.add_argument("--next_prompts", type=str, nargs="+", default=next_prompts, help="Next prompts")
parser.add_argument("--final_prompt", type=str, default=final_prompt, help="Final prompt")
parser.add_argument("--temperature", type=float, default=0.3, help="Temperature for the model")
parser.add_argument("--max_tokens", type=int, default=1024, help="Maximum tokens for the model")
parser.add_argument("--seed", type=int, default=0, help="Random seed, 0 means random seed")
parser.add_argument("--show_cot", type=bool, default=show_cot, help="Whether to show detailed Chain of Thoughts")
return parser.parse_args()
def go_cli():
(model, system_prompt, initial_prompt, expected_answer,
next_prompts, num_turns, show_next, final_prompt,
temperature, max_tokens, num_turns_final_mod,
show_cot, verbose) = get_defaults()
args = parse_arguments(model, system_prompt, next_prompts, num_turns, show_next, final_prompt,
num_turns_final_mod, show_cot, verbose)
if args.initial_prompt == '':
initial_prompt_query = input("Enter the initial prompt (hitting enter will use default initial_prompt)\n\n")
if initial_prompt_query not in ['', '\n', '\r\n']:
initial_prompt_chosen = initial_prompt_query
else:
initial_prompt_chosen = initial_prompt
else:
initial_prompt_chosen = args.initial_prompt
generator = manage_conversation(model=args.model,
system=args.system_prompt,
initial_prompt=initial_prompt_chosen,
next_prompts=args.next_prompts,
final_prompt=args.final_prompt,
num_turns_final_mod=args.num_turns_final_mod,
num_turns=args.num_turns,
temperature=args.temperature,
max_tokens=args.max_tokens,
seed=args.seed,
secrets=dict(os.environ),
cli_mode=True)
response = ''
conversation_history = []
try:
step = 1
while True:
chunk = next(generator)
if 'role' in chunk and chunk['role'] == 'assistant':
response += chunk['content']
if 'turn_title' in chunk and chunk['turn_title']:
step_time = f' in time {str(int(chunk["thinking_time"]))}s'
acum_time = f' in total {str(int(chunk["total_thinking_time"]))}s'
extra = '\n\n' if show_cot else ''
extra2 = '**' if show_cot else ''
extra3 = ' ' if show_cot else ''
print(
f'{extra}{extra2}{extra3}Completed Step {step}: {chunk["content"]}{step_time}{acum_time}{extra3}{extra2}{extra}')
step += 1
elif 'final' in chunk and chunk['final']:
if '\n' in chunk['content'] or '\r' in chunk['content']:
print(f'\n\nFinal Answer:\n\n {chunk["content"]}')
else:
print('\n\nFinal Answer:\n\n**', chunk['content'], '**\n\n')
elif show_cot:
print(chunk['content'], end='')
if 'chat_history' in chunk:
conversation_history = chunk['chat_history']
elif 'role' in chunk and chunk['role'] == 'user':
if not chunk['initial'] and not show_next:
if show_cot:
print('\n\n')
continue
print('\n', end='') # finish assistant
print('\nUser: ', chunk['content'], end='\n\n')
print('\nAssistant:\n\n ')
time.sleep(0.001)
except StopIteration as e:
pass
if verbose:
print("Conversation history:", conversation_history)
if expected_answer and expected_answer in conversation_history[-1]['content']:
print("\n\nGot Expected answer!")
if not show_cot:
print("**FULL RESPONSE:**\n\n")
print(response)
return response
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