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Document Processing Pipeline Using docAI Toolkit

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Problem

Document-centric workflows often require extraction, segmentation, text cleaning, OCR, and embedding preparation before feeding into AI models.
Teams tend to stitch together custom scripts, ad-hoc processing, or partial library support, resulting in inconsistent pipelines.


Objective

Provide a clean, modular toolkit for building AI-ready document processing flows, usable for:

  • local processing
  • RAG pipelines
  • search/indexing systems
  • document analytics


Solution Overview

The docAI toolkit provides utilities to support:
  • Document loading
  • Page and text splitting
  • Preprocessing (cleaning, normalization)
  • Optional OCR using external engines
  • Preparation for embedding or ML-based processing

Repository:
https://github.com/2pk03/docai
PyPI:
https://pypi.org/project/docai-toolkit/


Architecture and Technologies

  • Python toolkit
  • Modular utility functions
  • Supports Markdown, text and document conversion
  • Hooks for integrating with embedding frameworks or ML endpoints


Implementation Notes

  • Focus on simplicity: deterministic functions rather than pipelines.
  • Can be used locally, in batch, or as part of larger workflows.
  • Works well as a building block for downstream systems such as indexers or AI-driven classifiers.


Example Workflow (Generic Business Use Case)

  1. Source documents ingested (PDF, DOCX, Markdown).
  2. docAI splits and preprocesses text.
  3. Cleaned chunks sent to embedding model or classification module.
  4. Results indexed for search or analytics.


Benefits

  • Reduces the need for bespoke glue code.
  • Provides a predictable interface for common document tasks.
  • Easy to integrate into production systems or RAG solutions.


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