Chunking Pipeline for Retrieval-Augmented Generation

This internship revolves around creating a Chunking API to enable seamless integration of data into vector databases, facilitating the development of Retrieval-Augmented Generation (RAG) pipelines. Organizations work with data in various formats—text, images, PDFs, and more—and this data often needs to be processed and transformed into meaningful chunks for AI retrieval systems.

The focus of this internship is to design and develop a robust, scalable chunking pipeline capable of handling diverse data formats. You will contribute to advancing how businesses process and utilize data for RAG-powered solutions, leveraging tools like Unstructured, Textract, Tesseract, OpenAI API, and vector databases.

We encourage your creative input throughout the project. As a startup, we thrive on innovation, and your work will have a direct impact on our development. You'll also have the opportunity to explore and refine your skills in a dynamic environment that prioritizes learning and experimentation.

Read more about this intership here.