Integration: Document Lemmatizer
A lemmatizing node for documents which can potentially reduce token use by up to 30%.
Lemmatization
Lemmatization is a text pre-processing technique used in natural language processing (NLP) models to break a word down to its root meaning to identify similarities. For example, a lemmatization algorithm would reduce the word better to its root word, or lemme, good.
This node can be placed within a pipeline to lemmatize documents returned by a Retriever, prior to adding them as context to a prompt (for a PromptNode or similar). The process of lemmatizing the document content can potentially reduce the amount of tokens used by up to 30%, without drastically affecting the meaning of the document.
Before Lemmatization:
After Lemmatization:
Installation
Run pip install haystack-lemmatize-node to install the latest available release.
Usage
Include it in your pipeline - example as follows:
import logging
import re
from datasets import load_dataset
from haystack.document_stores import InMemoryDocumentStore
from haystack.nodes import PromptNode, PromptTemplate, AnswerParser, BM25Retriever
from haystack.pipelines import Pipeline
from haystack_lemmatize_node import LemmatizeDocuments
logging.basicConfig(format="%(levelname)s - %(name)s - %(message)s", level=logging.WARNING)
logging.getLogger("haystack").setLevel(logging.INFO)
document_store = InMemoryDocumentStore(use_bm25=True)
dataset = load_dataset("bilgeyucel/seven-wonders", split="train")
document_store.write_documents(dataset)
retriever = BM25Retriever(document_store=document_store, top_k=2)
lfqa_prompt = PromptTemplate(
name="lfqa",
prompt_text="Given the context please answer the question using your own words. Generate a comprehensive, summarized answer. If the information is not included in the provided context, reply with 'Provided documents didn't contain the necessary information to provide the answer'\n\nContext: {documents}\n\nQuestion: {query} \n\nAnswer:",
output_parser=AnswerParser(),
)
prompt_node = PromptNode(
model_name_or_path="text-davinci-003",
default_prompt_template=lfqa_prompt,
max_length=500,
api_key="sk-OPENAIKEY",
)
lemmatize = LemmatizeDocuments() # you can pass the `base_lang=XX` argument here too, where XX is a language as listed here: https://pypi.org/project/simplemma/
pipe = Pipeline()
pipe.add_node(component=retriever, name="Retriever", inputs=["Query"])
pipe.add_node(component=lemmatize, name="Lemmatize", inputs=["Retriever"])
pipe.add_node(component=prompt_node, name="prompt_node", inputs=["Lemmatize"])
query = "What does the Rhodes Statue look like?"
output = pipe.run(query)
print(output['answers'][0].answer)
Caveats
Sometimes lemmatization can be slow for large document content, but in the world of AI where we can potentially wait 30+ seconds for an LLM to respond (hello GPT-4), what’s a couple more seconds?