mirror of
https://github.com/hwchase17/langchain
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480626dc99
…tch]: import models from community ran ```bash git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g" git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g" git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g" git checkout master libs/langchain/tests/unit_tests/llms git checkout master libs/langchain/tests/unit_tests/chat_models git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py make format cd libs/langchain; make format cd ../experimental; make format cd ../core; make format ```
73 lines
2.3 KiB
Python
73 lines
2.3 KiB
Python
"""Experimental implementation of RELLM wrapped LLM."""
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from __future__ import annotations
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from typing import TYPE_CHECKING, Any, List, Optional, cast
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from langchain.callbacks.manager import CallbackManagerForLLMRun
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from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
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from langchain_community.llms.utils import enforce_stop_tokens
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from langchain_experimental.pydantic_v1 import Field, root_validator
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if TYPE_CHECKING:
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import rellm
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from regex import Pattern as RegexPattern
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else:
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try:
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from regex import Pattern as RegexPattern
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except ImportError:
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pass
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def import_rellm() -> rellm:
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"""Lazily import rellm."""
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try:
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import rellm
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except ImportError:
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raise ImportError(
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"Could not import rellm python package. "
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"Please install it with `pip install rellm`."
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)
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return rellm
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class RELLM(HuggingFacePipeline):
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"""RELLM wrapped LLM using HuggingFace Pipeline API."""
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regex: RegexPattern = Field(..., description="The structured format to complete.")
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max_new_tokens: int = Field(
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default=200, description="Maximum number of new tokens to generate."
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)
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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 check_rellm_installation(cls, values: dict) -> dict:
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import_rellm()
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return values
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def _call(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> str:
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rellm = import_rellm()
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from transformers import Text2TextGenerationPipeline
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pipeline = cast(Text2TextGenerationPipeline, self.pipeline)
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text = rellm.complete_re(
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prompt,
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self.regex,
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tokenizer=pipeline.tokenizer,
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model=pipeline.model,
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max_new_tokens=self.max_new_tokens,
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)
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if stop is not None:
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# This is a bit hacky, but I can't figure out a better way to enforce
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# stop tokens when making calls to huggingface_hub.
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text = enforce_stop_tokens(text, stop)
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return text
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