Source code for langchain_experimental.llm_bash.base

"""Chain that interprets a prompt and executes bash operations."""

from __future__ import annotations

import logging
import warnings
from typing import Any, Dict, List, Optional

from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.schema import BasePromptTemplate, OutputParserException
from langchain_core.callbacks.manager import CallbackManagerForChainRun
from langchain_core.language_models import BaseLanguageModel
from pydantic import ConfigDict, Field, model_validator

from langchain_experimental.llm_bash.bash import BashProcess
from langchain_experimental.llm_bash.prompt import PROMPT

logger = logging.getLogger(__name__)


[docs] class LLMBashChain(Chain): """Chain that interprets a prompt and executes bash operations. Example: .. code-block:: python from langchain.chains import LLMBashChain from langchain_community.llms import OpenAI llm_bash = LLMBashChain.from_llm(OpenAI()) """ llm_chain: LLMChain llm: Optional[BaseLanguageModel] = None """[Deprecated] LLM wrapper to use.""" input_key: str = "question" #: :meta private: output_key: str = "answer" #: :meta private: prompt: BasePromptTemplate = PROMPT """[Deprecated]""" bash_process: BashProcess = Field(default_factory=BashProcess) #: :meta private: model_config = ConfigDict( arbitrary_types_allowed=True, extra="forbid", ) @model_validator(mode="before") @classmethod def raise_deprecation(cls, values: Dict) -> Any: if "llm" in values: warnings.warn( "Directly instantiating an LLMBashChain with an llm is deprecated. " "Please instantiate with llm_chain or using the from_llm class method." ) if "llm_chain" not in values and values["llm"] is not None: prompt = values.get("prompt", PROMPT) values["llm_chain"] = LLMChain(llm=values["llm"], prompt=prompt) return values @model_validator(mode="before") @classmethod def validate_prompt(cls, values: Dict) -> Any: if values["llm_chain"].prompt.output_parser is None: raise ValueError( "The prompt used by llm_chain is expected to have an output_parser." ) return values @property def input_keys(self) -> List[str]: """Expect input key. :meta private: """ return [self.input_key] @property def output_keys(self) -> List[str]: """Expect output key. :meta private: """ return [self.output_key] def _call( self, inputs: Dict[str, Any], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, str]: _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() _run_manager.on_text(inputs[self.input_key], verbose=self.verbose) t = self.llm_chain.predict( question=inputs[self.input_key], callbacks=_run_manager.get_child() ) _run_manager.on_text(t, color="green", verbose=self.verbose) t = t.strip() try: parser = self.llm_chain.prompt.output_parser command_list = parser.parse(t) # type: ignore[union-attr] except OutputParserException as e: _run_manager.on_chain_error(e, verbose=self.verbose) raise e if self.verbose: _run_manager.on_text("\nCode: ", verbose=self.verbose) _run_manager.on_text( str(command_list), color="yellow", verbose=self.verbose ) output = self.bash_process.run(command_list) _run_manager.on_text("\nAnswer: ", verbose=self.verbose) _run_manager.on_text(output, color="yellow", verbose=self.verbose) return {self.output_key: output} @property def _chain_type(self) -> str: return "llm_bash_chain"
[docs] @classmethod def from_llm( cls, llm: BaseLanguageModel, prompt: BasePromptTemplate = PROMPT, **kwargs: Any, ) -> LLMBashChain: llm_chain = LLMChain(llm=llm, prompt=prompt) return cls(llm_chain=llm_chain, **kwargs)