Source code for langchain_experimental.llms.rellm_decoder

"""Experimental implementation of RELLM wrapped LLM."""
from __future__ import annotations

from typing import TYPE_CHECKING, Any, List, Optional, cast

from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
from langchain_community.llms.utils import enforce_stop_tokens
from langchain_core.callbacks.manager import CallbackManagerForLLMRun

from langchain_experimental.pydantic_v1 import Field, root_validator

    import rellm
    from regex import Pattern as RegexPattern
        from regex import Pattern as RegexPattern
    except ImportError:

[docs]def import_rellm() -> rellm: """Lazily import of the rellm package.""" try: import rellm except ImportError: raise ImportError( "Could not import rellm python package. " "Please install it with `pip install rellm`." ) return rellm
[docs]class RELLM(HuggingFacePipeline): """RELLM wrapped LLM using HuggingFace Pipeline API.""" regex: RegexPattern = Field(..., description="The structured format to complete.") max_new_tokens: int = Field( default=200, description="Maximum number of new tokens to generate." ) # TODO: move away from `root_validator` since it is deprecated in pydantic v2 # and causes mypy type-checking failures (hence the `type: ignore`) @root_validator # type: ignore[call-overload] def check_rellm_installation(cls, values: dict) -> dict: import_rellm() return values def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: rellm = import_rellm() from transformers import Text2TextGenerationPipeline pipeline = cast(Text2TextGenerationPipeline, self.pipeline) text = rellm.complete_re( prompt, self.regex, tokenizer=pipeline.tokenizer, model=pipeline.model, max_new_tokens=self.max_new_tokens, ) if stop is not None: # This is a bit hacky, but I can't figure out a better way to enforce # stop tokens when making calls to huggingface_hub. text = enforce_stop_tokens(text, stop) return text