feat: Add SpaCy Processor for Enhanced NLP Support in Quivr #3468
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- Introduced SpaCyProcessor to handle various file formats (PDF, DOCX, TXT, CSV) - Supports recursive text splitting for chunked processing - Applies spaCy NLP pipeline for tokenization and entity recognition on file content
…sing - Added support for PDF, DOCX, TXT, and CSV file extraction using fitz (PyMuPDF) and python-docx. - Integrated spaCy NLP pipeline for entity recognition and sentence tokenization. - Configured RecursiveCharacterTextSplitter for document chunking with metadata (chunk size, entities, sentences). - Added error handling and logging for robust file processing and validation. - Improved support for asynchronous text extraction, enabling efficient file reading.
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Very nice PR! We'll take a closer look :) @AmineDiro |
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Description
This pull request introduces the SpaCyProcessor class to handle various text file types (PDF, DOCX, TXT, and CSV) and perform NLP processing using spaCy. This addition includes:
Key Features:
Motivation:
This feature adds support for spaCy NLP processing to enable richer text analysis and processing across various file types. The processor now efficiently handles different file formats, extracts meaningful text, and applies NLP, making it easier to work with structured document data in downstream applications.
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