mirror of
https://github.com/hwchase17/langchain
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```python """python scripts/update_mypy_ruff.py""" import glob import tomllib from pathlib import Path import toml import subprocess import re ROOT_DIR = Path(__file__).parents[1] def main(): for path in glob.glob(str(ROOT_DIR / "libs/**/pyproject.toml"), recursive=True): print(path) with open(path, "rb") as f: pyproject = tomllib.load(f) try: pyproject["tool"]["poetry"]["group"]["typing"]["dependencies"]["mypy"] = ( "^1.10" ) pyproject["tool"]["poetry"]["group"]["lint"]["dependencies"]["ruff"] = ( "^0.5" ) except KeyError: continue with open(path, "w") as f: toml.dump(pyproject, f) cwd = "/".join(path.split("/")[:-1]) completed = subprocess.run( "poetry lock --no-update; poetry install --with typing; poetry run mypy . --no-color", cwd=cwd, shell=True, capture_output=True, text=True, ) logs = completed.stdout.split("\n") to_ignore = {} for l in logs: if re.match("^(.*)\:(\d+)\: error:.*\[(.*)\]", l): path, line_no, error_type = re.match( "^(.*)\:(\d+)\: error:.*\[(.*)\]", l ).groups() if (path, line_no) in to_ignore: to_ignore[(path, line_no)].append(error_type) else: to_ignore[(path, line_no)] = [error_type] print(len(to_ignore)) for (error_path, line_no), error_types in to_ignore.items(): all_errors = ", ".join(error_types) full_path = f"{cwd}/{error_path}" try: with open(full_path, "r") as f: file_lines = f.readlines() except FileNotFoundError: continue file_lines[int(line_no) - 1] = ( file_lines[int(line_no) - 1][:-1] + f" # type: ignore[{all_errors}]\n" ) with open(full_path, "w") as f: f.write("".join(file_lines)) subprocess.run( "poetry run ruff format .; poetry run ruff --select I --fix .", cwd=cwd, shell=True, capture_output=True, text=True, ) if __name__ == "__main__": main() ```
19 lines
867 B
Python
19 lines
867 B
Python
"""**Autonomous agents** in the Langchain experimental package include
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[AutoGPT](https://github.com/Significant-Gravitas/AutoGPT),
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[BabyAGI](https://github.com/yoheinakajima/babyagi),
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and [HuggingGPT](https://arxiv.org/abs/2303.17580) agents that
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interact with language models autonomously.
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These agents have specific functionalities like memory management,
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task creation, execution chains, and response generation.
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They differ from ordinary agents by their autonomous decision-making capabilities,
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memory handling, and specialized functionalities for tasks and response.
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"""
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from langchain_experimental.autonomous_agents.autogpt.agent import AutoGPT
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from langchain_experimental.autonomous_agents.baby_agi.baby_agi import BabyAGI
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from langchain_experimental.autonomous_agents.hugginggpt.hugginggpt import HuggingGPT
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__all__ = ["BabyAGI", "AutoGPT", "HuggingGPT"]
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