mirror of
https://github.com/aljazceru/gpt-engineer.git
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19a4c10b6e
* Added LangChain integration * Fixed issue created by git checkin process * Added ':' to characters to remove from end of file path * Tested initial migration to LangChain, removed comments and logging used for debugging * Tested initial migration to LangChain, removed comments and logging used for debugging * Converted camelCase to snake_case * Turns out we need the exception handling * Testing Hugging Face Integrations via LangChain * Added LangChain loadable models * Renames "qa" prompt to "clarify", since it's used in the "clarify" step, asking for clarification * Fixed loading model yaml files * Fixed streaming * Added modeldir cli option * Fixed typing * Fixed interaction with token logging * Fix spelling + dependency issues + typing * Fix spelling + tests * Removed unneeded logging which caused test to fail * Cleaned up code * Incorporated feedback - deleted unnecessary functions & logger.info - used LangChain ChatLLM instead of LLM to naturally communicate with gpt-4 - deleted loading model from yaml file, as LC doesn't offer this for ChatModels * Update gpt_engineer/steps.py Co-authored-by: Anton Osika <anton.osika@gmail.com> * Incorporated feedback - Fixed failing test - Removed parsing complexity by using # type: ignore - Replace every ocurence of ai.last_message_content with its content * Fixed test * Update gpt_engineer/steps.py --------- Co-authored-by: H <holden.robbins@gmail.com> Co-authored-by: Anton Osika <anton.osika@gmail.com>
68 lines
1.8 KiB
Python
68 lines
1.8 KiB
Python
import logging
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from pathlib import Path
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import typer
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from gpt_engineer.ai import AI, fallback_model
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from gpt_engineer.collect import collect_learnings
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from gpt_engineer.db import DB, DBs, archive
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from gpt_engineer.learning import collect_consent
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from gpt_engineer.steps import STEPS, Config as StepsConfig
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app = typer.Typer()
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@app.command()
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def main(
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project_path: str = typer.Argument("projects/example", help="path"),
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model: str = typer.Argument("gpt-4", help="model id string"),
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temperature: float = 0.1,
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steps_config: StepsConfig = typer.Option(
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StepsConfig.DEFAULT, "--steps", "-s", help="decide which steps to run"
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),
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verbose: bool = typer.Option(False, "--verbose", "-v"),
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):
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logging.basicConfig(level=logging.DEBUG if verbose else logging.INFO)
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model = fallback_model(model)
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ai = AI(
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model_name=model,
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temperature=temperature,
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)
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input_path = Path(project_path).absolute()
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memory_path = input_path / "memory"
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workspace_path = input_path / "workspace"
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archive_path = input_path / "archive"
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dbs = DBs(
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memory=DB(memory_path),
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logs=DB(memory_path / "logs"),
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input=DB(input_path),
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workspace=DB(workspace_path),
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preprompts=DB(Path(__file__).parent / "preprompts"),
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archive=DB(archive_path),
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)
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if steps_config not in [
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StepsConfig.EXECUTE_ONLY,
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StepsConfig.USE_FEEDBACK,
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StepsConfig.EVALUATE,
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]:
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archive(dbs)
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steps = STEPS[steps_config]
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for step in steps:
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messages = step(ai, dbs)
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dbs.logs[step.__name__] = AI.serialize_messages(messages)
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if collect_consent():
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collect_learnings(model, temperature, steps, dbs)
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dbs.logs["token_usage"] = ai.format_token_usage_log()
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if __name__ == "__main__":
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app()
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