langchain/libs/experimental/langchain_experimental/llms/lmformatenforcer_decoder.py
Leonid Ganeline 3f6bf852ea
experimental: docstrings update (#18048)
Added missed docstrings. Formatted docsctrings to the consistent format.
2024-02-23 21:24:16 -05:00

84 lines
2.8 KiB
Python

"""Experimental implementation of lm-format-enforcer wrapped LLM."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, List, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.schema import LLMResult
from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
from langchain_experimental.pydantic_v1 import Field
if TYPE_CHECKING:
import lmformatenforcer
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
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