swarmauri_parser_textblob 0.11.0.dev1

Lightweight sentence and noun-phrase parsing package for Swarmauri built on TextBlob and NLTK.

Swarmauri Parser TextBlob

swarmauri_parser_textblob provides two Swarmauri text parsing components built on TextBlob: TextBlobSentenceParser for sentence segmentation and TextBlobNounParser for noun-phrase extraction. It is designed for lightweight NLP preprocessing before chunking, retrieval, classification, or agent workflows.

Why Use Swarmauri Parser TextBlob

• Split long passages into sentence-level documents for downstream processing.
• Extract noun phrases without introducing a larger transformer stack.
• Keep lightweight linguistic preprocessing aligned with the Swarmauri parser interface.
• Use simple NLP enrichment before embeddings, retrieval, or task routing.

FAQ

What parser classes are included?
TextBlobSentenceParser and TextBlobNounParser.

What does the sentence parser return?
A Document per detected sentence, with metadata indicating the parser.

What does the noun parser return?
A single Document containing the original text and a noun_phrases list in metadata.

Does it require NLTK resources?
Yes. The package downloads required NLTK corpora during initialization unless they are already present.

Features

• Sentence segmentation through TextBlobSentenceParser.
• Noun phrase extraction through TextBlobNounParser.
• Fits Swarmauri ingestion and preprocessing workflows using parser-style components.
• Useful for lightweight English NLP pipelines where a smaller dependency stack is preferred.
• Supports Python 3.10, 3.11, 3.12, 3.13, and 3.14.

Installation

uv add swarmauri_parser_textblob

pip install swarmauri_parser_textblob

Optional setup:

python -m textblob.download_corpora

Usage

Sentence parsing

from swarmauri_parser_textblob import TextBlobSentenceParser

parser = TextBlobSentenceParser()
documents = parser.parse("One more large chapula please. It should be extra spicy!")

for document in documents:
    print(document.content)

Noun phrase extraction

from swarmauri_parser_textblob import TextBlobNounParser

parser = TextBlobNounParser()
documents = parser.parse("One more large chapula please.")

print(documents[0].content)
print(documents[0].metadata["noun_phrases"])

Examples

Prepare sentence-level documents

from swarmauri_parser_textblob import TextBlobSentenceParser

parser = TextBlobSentenceParser()
sentences = parser.parse(
    "Swarmauri coordinates tools. It also routes data through composable components."
)

for sentence in sentences:
    print(sentence.metadata["parser"], sentence.content)

Extract noun phrases for downstream tagging

from swarmauri_parser_textblob import TextBlobNounParser

parser = TextBlobNounParser()
docs = parser.parse("The Swarmauri agent indexed a customer support knowledge base.")

print(docs[0].metadata["noun_phrases"])

Related Packages

• swarmauri_parser_entityrecognition
• swarmauri_tool_entityrecognition
• swarmauri_tool_sentimentanalysis
• swarmauri_parser_bertembedding

Swarmauri Foundations

• swarmauri
• swarmauri_core
• swarmauri_base
• swarmauri_standard

More Documentation

• TextBlob documentation
• NLTK documentation

Best Practices

• Pre-download NLTK corpora in CI, containers, or production images to avoid runtime setup costs.
• Use these parsers for lightweight English NLP tasks; domain-specific or multilingual corpora may require a different component.
• Combine sentence parsing with embeddings or vector stores when building retrieval-oriented pipelines.
• Treat noun phrase extraction as heuristic enrichment rather than a strict ontology or entity-linking system.

License

This project is licensed under the Apache-2.0 License.