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import argparse
import os
import subprocess
import sys
try:
from importlib.metadata import distribution, PackageNotFoundError
assert distribution('aider-chat') is not None
have_aider = True
except (PackageNotFoundError, AssertionError):
have_aider = False
def install_aider():
if not have_aider:
subprocess.check_call([sys.executable, "-m", "pip", "install", "aider-chat>=0.59.0"])
print("Successfully installed aider-chat.")
def main():
# Install aider-chat if not already installed
try:
import aider
except ImportError:
print("aider-chat not found. Installing...")
install_aider()
# Now we can safely import from aider
from aider.coders import Coder
from aider.models import Model
from aider.io import InputOutput
default_max_time = int(os.getenv('H2OGPT_AGENT_OPENAI_TIMEOUT', "120"))
parser = argparse.ArgumentParser(description="Aider Coding Tool")
parser.add_argument("--model", type=str, help="Model to use for coding assistance")
parser.add_argument("--files", nargs="+", required=False, help="Files to work on")
parser.add_argument("--output_dir", type=str, default="aider_output", help="Directory for output files")
parser.add_argument("--prompt", "--query", type=str, required=True, help="Prompt or query for the coding task")
parser.add_argument("--max_time", type=int, default=default_max_time, help="Maximum time in seconds for API calls")
parser.add_argument("--verbose", action="store_true", help="Show verbose output")
args = parser.parse_args()
# Ensure output directory exists
os.makedirs(args.output_dir, exist_ok=True)
# Set up OpenAI-like client
base_url = os.getenv('H2OGPT_OPENAI_BASE_URL')
assert base_url is not None, "H2OGPT_OPENAI_BASE_URL environment variable is not set"
server_api_key = os.getenv('H2OGPT_OPENAI_API_KEY', 'EMPTY')
from openai import OpenAI
client = OpenAI(base_url=base_url, api_key=server_api_key, timeout=args.max_time)
# Set environment variables for Aider
os.environ['OPENAI_API_KEY'] = server_api_key
os.environ['OPENAI_API_BASE'] = base_url
# Set up InputOutput with streaming enabled
io = InputOutput(
yes=True,
chat_history_file=os.path.join(args.output_dir, "chat_history.txt"),
pretty=True,
)
# Determine which model to use
if args.model:
selected_model = args.model
elif os.getenv('H2OGPT_AGENT_OPENAI_MODEL'):
selected_model = os.getenv('H2OGPT_AGENT_OPENAI_MODEL')
else:
# Only fetch the model list if we need to use the default
model_list = client.models.list()
selected_model = model_list.data[0].id
print(f"Using model: {selected_model}")
# Set up Model
main_model = Model(selected_model)
# Set up Coder with streaming enabled
coder = Coder.create(
main_model=main_model,
fnames=args.files if args.files else [],
io=io,
stream=True,
use_git=False,
edit_format="diff"
#edit_format="whole" # required for weaker models
)
# Run the prompt
output = coder.run(args.prompt)
# Save the output
output_file = os.path.join(args.output_dir, "aider_output.txt")
with open(output_file, "w") as f:
f.write(output)
if args.verbose:
print(f"Task completed. Output saved to {output_file}")
if __name__ == "__main__":
main()
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