Project Name

Transforming the Unstructured Data into Structured Data for Bringing Better Insights

Python, Postman, ELK


Our client was a leading Networking company that works on enhancing the user experience through the implementation of a question-answering chatbot leveraging Language model (LMM) capabilities. The primary aim of them was to work on unstructured text data extraction from a diverse document format that includes PDFs, Word, Excel, and PowerPoint (PPTX) files, and transform them into structured data.

python-overview (1)


python-challenges (1)
  • Facing issues in accurately identifying tables within the documents and structured data from them.
  • Each document type exhibited unique patterns making it challenging to establish a uniform extraction approach.
  • The data present in the Excel sheets was not organized, which gave a significant challenge.

Our Solution

Our team provided a robust approach to our client that included:

  • First, we utilized the MuPDF library for the accurate extraction of data. This helps in identifying the titles and headers based on specific patterns.
  • Then, our team implemented the various preprocessing steps to ensure data accuracy and leverage the python-pptx library to extract data.
  • This helps to identify the elements within slides that are associated with attributes like tables, placeholders, text, hyperlinks, etc.
  • We reuse the code developed for PDF extraction with Word files that share similar characteristics and apply the same title and header identification patterns and preprocessing steps for further queries.
  • At last, our team employed libraries like openpyxl for data extraction. This allows our client to identify rows and columns to extract the right information that accommodates the irregular data organization.

Data Flow Diagram

python-dataflow-diagram (1)


At last, our Python data extraction solution successfully extracted structured data, and categorized information into distinct columns such as titles, headers, content, page numbers, hyperlinks, and tokens. With our implementation, it becomes possible to solve challenges while extracting unstructured data and also improve the user experience through the development of a question-answered chatbot. Our tailored solution helps to ensure data accuracy and efficiency in transforming the unstructured data into the right structured format. Moreover, our solution aligns directly with our client’s objectives of leveraging LLMs to enhance user interactions and information retrieval.

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