mirror of
https://github.com/hwchase17/langchain
synced 2024-11-11 19:11:02 +00:00
a0c2281540
```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()
```
129 lines
4.4 KiB
Python
129 lines
4.4 KiB
Python
"""Chain that interprets a prompt and executes bash operations."""
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from __future__ import annotations
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import logging
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import warnings
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from typing import Any, Dict, List, Optional
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from langchain.chains.base import Chain
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from langchain.chains.llm import LLMChain
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from langchain.schema import BasePromptTemplate, OutputParserException
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from langchain_core.callbacks.manager import CallbackManagerForChainRun
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from langchain_core.language_models import BaseLanguageModel
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from langchain_experimental.llm_bash.bash import BashProcess
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from langchain_experimental.llm_bash.prompt import PROMPT
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from langchain_experimental.pydantic_v1 import Extra, Field, root_validator
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logger = logging.getLogger(__name__)
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class LLMBashChain(Chain):
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"""Chain that interprets a prompt and executes bash operations.
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Example:
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.. code-block:: python
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from langchain.chains import LLMBashChain
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from langchain_community.llms import OpenAI
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llm_bash = LLMBashChain.from_llm(OpenAI())
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"""
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llm_chain: LLMChain
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llm: Optional[BaseLanguageModel] = None
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"""[Deprecated] LLM wrapper to use."""
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input_key: str = "question" #: :meta private:
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output_key: str = "answer" #: :meta private:
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prompt: BasePromptTemplate = PROMPT
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"""[Deprecated]"""
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bash_process: BashProcess = Field(default_factory=BashProcess) #: :meta private:
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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@root_validator(pre=True)
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def raise_deprecation(cls, values: Dict) -> Dict:
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if "llm" in values:
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warnings.warn(
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"Directly instantiating an LLMBashChain with an llm is deprecated. "
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"Please instantiate with llm_chain or using the from_llm class method."
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)
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if "llm_chain" not in values and values["llm"] is not None:
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prompt = values.get("prompt", PROMPT)
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values["llm_chain"] = LLMChain(llm=values["llm"], prompt=prompt)
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return values
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# TODO: move away from `root_validator` since it is deprecated in pydantic v2
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# and causes mypy type-checking failures (hence the `type: ignore`)
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@root_validator # type: ignore[call-overload]
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def validate_prompt(cls, values: Dict) -> Dict:
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if values["llm_chain"].prompt.output_parser is None:
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raise ValueError(
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"The prompt used by llm_chain is expected to have an output_parser."
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)
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return values
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@property
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def input_keys(self) -> List[str]:
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"""Expect input key.
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:meta private:
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"""
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return [self.input_key]
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@property
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def output_keys(self) -> List[str]:
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"""Expect output key.
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:meta private:
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"""
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return [self.output_key]
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def _call(
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self,
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inputs: Dict[str, Any],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Dict[str, str]:
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_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
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_run_manager.on_text(inputs[self.input_key], verbose=self.verbose)
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t = self.llm_chain.predict(
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question=inputs[self.input_key], callbacks=_run_manager.get_child()
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)
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_run_manager.on_text(t, color="green", verbose=self.verbose)
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t = t.strip()
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try:
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parser = self.llm_chain.prompt.output_parser
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command_list = parser.parse(t) # type: ignore[union-attr]
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except OutputParserException as e:
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_run_manager.on_chain_error(e, verbose=self.verbose)
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raise e
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if self.verbose:
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_run_manager.on_text("\nCode: ", verbose=self.verbose)
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_run_manager.on_text(
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str(command_list), color="yellow", verbose=self.verbose
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)
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output = self.bash_process.run(command_list)
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_run_manager.on_text("\nAnswer: ", verbose=self.verbose)
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_run_manager.on_text(output, color="yellow", verbose=self.verbose)
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return {self.output_key: output}
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@property
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def _chain_type(self) -> str:
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return "llm_bash_chain"
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@classmethod
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def from_llm(
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cls,
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llm: BaseLanguageModel,
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prompt: BasePromptTemplate = PROMPT,
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**kwargs: Any,
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) -> LLMBashChain:
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llm_chain = LLMChain(llm=llm, prompt=prompt)
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return cls(llm_chain=llm_chain, **kwargs)
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