pipelines.extractor

Pipline for information extraction

Extractor

 Extractor (llm, prompt_template:Optional[str]=None, **kwargs)

Extractor applies a given prompt to each sentence or paragraph in a document and returns the results.

	Type	Default	Details
llm			An `onprem.LLM` object
prompt_template	Optional	None	A model specific prompt_template with a single placeholder named “{prompt}”. If supplied, overrides the `prompt_template` supplied to the `LLM` constructor.
kwargs

source

Extractor.apply

 Extractor.apply (ex_prompt_template:str, fpath:Optional[str]=None,
                  content:Optional[str]=None, unit:str='paragraph',
                  preproc_fn:Optional[Callable]=None,
                  filter_fn:Optional[Callable]=None,
                  clean_fn:Optional[Callable]=None,
                  pdf_pages:List[int]=[], maxchars=2048, stop:list=[],
                  pdf_use_unstructured:bool=False, **kwargs)

Apply the prompt to each unit (where a “unit” is either a paragraph or sentence) optionally filtered by filter_fn. Extra kwargs fed directly to langchain_community.document_loaders.pdf.UnstructuredPDFLoader when pdf_use_unstructured is True. Results are stored in a pandas.Dataframe.

	Type	Default	Details
ex_prompt_template	str		A prompt to apply to each `unit` in document. Should have a single variable, `{text}`
fpath	Optional	None	A path to to a single file of interest (e.g., a PDF or MS Word document). Mutually-exclusive with `content`.
content	Optional	None	Text content of a document of interest. Mutually-exclusive with `fpath`.
unit	str	paragraph	One of {‘sentence’, ‘paragraph’}.
preproc_fn	Optional	None	Function should accept a text string and returns a new preprocessed input.
filter_fn	Optional	None	A function that accepts a sentence or paragraph and returns `True` if prompt should be applied to it.
clean_fn	Optional	None	A function that accepts a sentence or paragraph and returns “cleaned” version of the text. (applied after `filter_fn`)
pdf_pages	List	[]	If `fpath` is a PDF document, only apply prompt to text on page numbers listed in `pdf_pages` (starts at 1).
maxchars	int	2048	units (i.e., paragraphs or sentences) larger than `maxchars` split.
stop	list	[]	list of characters to trigger the LLM to stop generating.
pdf_use_unstructured	bool	False	If True, use unstructured package to extract text from PDF.
kwargs

from onprem import LLM
from onprem.pipelines import Extractor

prompt_template = "<s>[INST] {prompt} [/INST]" # prompt template for Mistral
llm = LLM(model_url='https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/resolve/main/mistral-7b-instruct-v0.2.Q4_K_M.gguf', 
          n_gpu_layers=33,  # change based on your system
          verbose=False, mute_stream=True, 
          prompt_template=prompt_template)
extractor = Extractor(llm)

/home/amaiya/mambaforge/envs/llm/lib/python3.9/site-packages/langchain_core/language_models/llms.py:239: DeprecationWarning: callback_manager is deprecated. Please use callbacks instead.
  warnings.warn(

prompt = """Extract citations from the following sentences. Return #NA# if there are no citations in the text. Here are some examples:

[SENTENCE]:pretrained BERT text classifier (Devlin et al., 2018), models for sequence tagging (Lample et al., 2016)
[CITATIONS]:(Devlin et al., 2018), (Lample et al., 2016)
[SENTENCE]:Machine learning (ML) is a powerful tool.
[CITATIONS]:#NA#
[SENTENCE]:Following inspiration from a blog post by Rachel Thomas of fast.ai (Howard and Gugger, 2020), we refer to this as Augmented Machine Learning or AugML
[CITATIONS]:(Howard and Gugger, 2020)
[SENTENCE]:{text}
[CITATIONS]:"""

content = """
For instance, the fit_onecycle method employs a 1cycle policy (Smith, 2018). 
"""
df = extractor.apply(prompt, content=content, stop=['\n'])
assert df['Extractions'][0].strip().startswith('(Smith, 2018)')

content ="""In the case of text, this may involve language-specific preprocessing (e.g., tokenization)."""
df = extractor.apply(prompt, content=content, stop=['\n'])
assert df['Extractions'][0].strip().startswith('#NA#')