Source code for langchain_experimental.llms.lmformatenforcer_decoder

"""Experimental implementation of lm-format-enforcer wrapped LLM."""
from __future__ import annotations

from typing import TYPE_CHECKING, Any, List, Optional

from langchain.schema import LLMResult
from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
from langchain_core.callbacks.manager import CallbackManagerForLLMRun

from langchain_experimental.pydantic_v1 import Field

if TYPE_CHECKING:
    import lmformatenforcer


[docs]def import_lmformatenforcer() -> lmformatenforcer: """Lazily import of the lmformatenforcer package.""" try: import lmformatenforcer except ImportError: raise ImportError( "Could not import lmformatenforcer python package. " "Please install it with `pip install lm-format-enforcer`." ) return lmformatenforcer
[docs]class LMFormatEnforcer(HuggingFacePipeline): """LMFormatEnforcer wrapped LLM using HuggingFace Pipeline API. This pipeline is experimental and not yet stable. """ json_schema: Optional[dict] = Field( description="The JSON Schema to complete.", default=None ) regex: Optional[str] = Field( description="The regular expression to complete.", default=None ) def _generate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: lmformatenforcer = import_lmformatenforcer() import lmformatenforcer.integrations.transformers as hf_integration # We integrate lmformatenforcer by adding a prefix_allowed_tokens_fn. # It has to be done on each call, because the prefix function is stateful. if "prefix_allowed_tokens_fn" in self.pipeline._forward_params: raise ValueError( "prefix_allowed_tokens_fn param is forbidden with LMFormatEnforcer." ) has_json_schema = self.json_schema is not None has_regex = self.regex is not None if has_json_schema == has_regex: raise ValueError( "You must specify exactly one of json_schema or a regex, but not both." ) if has_json_schema: parser = lmformatenforcer.JsonSchemaParser(self.json_schema) else: parser = lmformatenforcer.RegexParser(self.regex) prefix_function = hf_integration.build_transformers_prefix_allowed_tokens_fn( self.pipeline.tokenizer, parser ) self.pipeline._forward_params["prefix_allowed_tokens_fn"] = prefix_function result = super()._generate( prompts, stop=stop, run_manager=run_manager, **kwargs, ) del self.pipeline._forward_params["prefix_allowed_tokens_fn"] return result