import json import re import subprocess from enum import Enum from typing import List from termcolor import colored from gpt_engineer.ai import AI from gpt_engineer.chat_to_files import to_files from gpt_engineer.db import DBs from gpt_engineer.learning import human_input def setup_sys_prompt(dbs: DBs) -> str: return ( dbs.preprompts["generate"] + "\nUseful to know:\n" + dbs.preprompts["philosophy"] ) def get_prompt(dbs: DBs) -> str: """While we migrate we have this fallback getter""" assert ( "prompt" in dbs.input or "main_prompt" in dbs.input ), "Please put your prompt in the file `prompt` in the project directory" if "prompt" not in dbs.input: print( colored("Please put the prompt in the file `prompt`, not `main_prompt", "red") ) print() return dbs.input["main_prompt"] return dbs.input["prompt"] # All steps below have the signature Step def simple_gen(ai: AI, dbs: DBs) -> List[dict]: """Run the AI on the main prompt and save the results""" messages = ai.start(setup_sys_prompt(dbs), get_prompt(dbs)) to_files(messages[-1]["content"], dbs.workspace) return messages def clarify(ai: AI, dbs: DBs) -> List[dict]: """ Ask the user if they want to clarify anything and save the results to the workspace """ messages = [ai.fsystem(dbs.preprompts["qa"])] user_input = get_prompt(dbs) while True: messages = ai.next(messages, user_input) if messages[-1]["content"].strip() == "Nothing more to clarify.": break if messages[-1]["content"].strip().lower().startswith("no"): print("Nothing more to clarify.") break print() user_input = input('(answer in text, or "c" to move on)\n') print() if not user_input or user_input == "c": print("(letting gpt-engineer make its own assumptions)") print() messages = ai.next( messages, "Make your own assumptions and state them explicitly before starting", ) print() return messages user_input += ( "\n\n" "Is anything else unclear? If yes, only answer in the form:\n" "{remaining unclear areas} remaining questions.\n" "{Next question}\n" 'If everything is sufficiently clear, only answer "Nothing more to clarify.".' ) print() return messages def gen_spec(ai: AI, dbs: DBs) -> List[dict]: """ Generate a spec from the main prompt + clarifications and save the results to the workspace """ messages = [ ai.fsystem(setup_sys_prompt(dbs)), ai.fsystem(f"Instructions: {dbs.input['prompt']}"), ] messages = ai.next(messages, dbs.preprompts["spec"]) dbs.memory["specification"] = messages[-1]["content"] return messages def respec(ai: AI, dbs: DBs) -> List[dict]: messages = json.loads(dbs.logs[gen_spec.__name__]) messages += [ai.fsystem(dbs.preprompts["respec"])] messages = ai.next(messages) messages = ai.next( messages, ( "Based on the conversation so far, please reiterate the specification for " "the program. " "If there are things that can be improved, please incorporate the " "improvements. " "If you are satisfied with the specification, just write out the " "specification word by word again." ), ) dbs.memory["specification"] = messages[-1]["content"] return messages def gen_unit_tests(ai: AI, dbs: DBs) -> List[dict]: """ Generate unit tests based on the specification, that should work. """ messages = [ ai.fsystem(setup_sys_prompt(dbs)), ai.fuser(f"Instructions: {dbs.input['prompt']}"), ai.fuser(f"Specification:\n\n{dbs.memory['specification']}"), ] messages = ai.next(messages, dbs.preprompts["unit_tests"]) dbs.memory["unit_tests"] = messages[-1]["content"] to_files(dbs.memory["unit_tests"], dbs.workspace) return messages def gen_clarified_code(ai: AI, dbs: DBs) -> List[dict]: """Takes clarification and generates code""" messages = json.loads(dbs.logs[clarify.__name__]) messages = [ ai.fsystem(setup_sys_prompt(dbs)), ] + messages[1:] messages = ai.next(messages, dbs.preprompts["use_qa"]) to_files(messages[-1]["content"], dbs.workspace) return messages def gen_code(ai: AI, dbs: DBs) -> List[dict]: # get the messages from previous step messages = [ ai.fsystem(setup_sys_prompt(dbs)), ai.fuser(f"Instructions: {dbs.input['prompt']}"), ai.fuser(f"Specification:\n\n{dbs.memory['specification']}"), ai.fuser(f"Unit tests:\n\n{dbs.memory['unit_tests']}"), ] messages = ai.next(messages, dbs.preprompts["use_qa"]) to_files(messages[-1]["content"], dbs.workspace) return messages def execute_entrypoint(ai: AI, dbs: DBs) -> List[dict]: command = dbs.workspace["run.sh"] print("Do you want to execute this code?") print() print(command) print() print('If yes, press enter. Otherwise, type "no"') print() if input() not in ["", "y", "yes"]: print("Ok, not executing the code.") return [] print("Executing the code...") print() print( colored( "Note: If it does not work as expected, consider running the code" + " in another way than above.", "green", ) ) print() print("You can press ctrl+c *once* to stop the execution.") print() p = subprocess.Popen("bash run.sh", shell=True, cwd=dbs.workspace.path) try: p.wait() except KeyboardInterrupt: print() print("Stopping execution.") print("Execution stopped.") p.kill() print() return [] def gen_entrypoint(ai: AI, dbs: DBs) -> List[dict]: messages = ai.start( system=( "You will get information about a codebase that is currently on disk in " "the current folder.\n" "From this you will answer with code blocks that includes all the necessary " "unix terminal commands to " "a) install dependencies " "b) run all necessary parts of the codebase (in parallel if necessary).\n" "Do not install globally. Do not use sudo.\n" "Do not explain the code, just give the commands.\n" "Do not use placeholders, use example values (like . for a folder argument) " "if necessary.\n" ), user="Information about the codebase:\n\n" + dbs.workspace["all_output.txt"], ) print() regex = r"```\S*\n(.+?)```" matches = re.finditer(regex, messages[-1]["content"], re.DOTALL) dbs.workspace["run.sh"] = "\n".join(match.group(1) for match in matches) return messages def use_feedback(ai: AI, dbs: DBs): messages = [ ai.fsystem(setup_sys_prompt(dbs)), ai.fuser(f"Instructions: {dbs.input['prompt']}"), ai.fassistant(dbs.workspace["all_output.txt"]), ai.fsystem(dbs.preprompts["use_feedback"]), ] messages = ai.next(messages, dbs.input["feedback"]) to_files(messages[-1]["content"], dbs.workspace) return messages def fix_code(ai: AI, dbs: DBs): code_output = json.loads(dbs.logs[gen_code.__name__])[-1]["content"] messages = [ ai.fsystem(setup_sys_prompt(dbs)), ai.fuser(f"Instructions: {dbs.input['prompt']}"), ai.fuser(code_output), ai.fsystem(dbs.preprompts["fix_code"]), ] messages = ai.next(messages, "Please fix any errors in the code above.") to_files(messages[-1]["content"], dbs.workspace) return messages def human_review(ai: AI, dbs: DBs): review = human_input() dbs.memory["review"] = review.to_json() # type: ignore return [] class Config(str, Enum): DEFAULT = "default" BENCHMARK = "benchmark" SIMPLE = "simple" TDD = "tdd" TDD_PLUS = "tdd+" CLARIFY = "clarify" RESPEC = "respec" EXECUTE_ONLY = "execute_only" USE_FEEDBACK = "use_feedback" # Different configs of what steps to run STEPS = { Config.DEFAULT: [ clarify, gen_clarified_code, gen_entrypoint, execute_entrypoint, human_review, ], Config.BENCHMARK: [simple_gen, gen_entrypoint], Config.SIMPLE: [simple_gen, gen_entrypoint, execute_entrypoint], Config.TDD: [ gen_spec, gen_unit_tests, gen_code, gen_entrypoint, execute_entrypoint, human_review, ], Config.TDD_PLUS: [ gen_spec, gen_unit_tests, gen_code, fix_code, gen_entrypoint, execute_entrypoint, human_review, ], Config.CLARIFY: [ clarify, gen_clarified_code, gen_entrypoint, execute_entrypoint, human_review, ], Config.RESPEC: [ gen_spec, respec, gen_unit_tests, gen_code, fix_code, gen_entrypoint, execute_entrypoint, human_review, ], Config.USE_FEEDBACK: [use_feedback, gen_entrypoint, execute_entrypoint, human_review], Config.EXECUTE_ONLY: [execute_entrypoint], } # Future steps that can be added: # run_tests_and_fix_files # execute_entrypoint_and_fix_files_if_it_results_in_error