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Code Search Using Retrieval Augmented Generation

Retrieval Augmented Generation (RAG) is becoming a popular paradigm for bridging the knowledge gap between pre-trained Large Language models and other data sources. For developer productivity, several code copilots help with code completion. Code Search is an age-old problem that can be rethought in the age of RAG. Imagine you are trying to contribute to a new code base (a GitHub repository) for a beginner task. Knowing which file to change and where to make the change can be time-consuming. We've all been there. You're enthusiastic about contributing to a new GitHub repository but overwhelmed. Which file do you modify? Where do you start? For newcomers, the maze of a new codebase can be truly daunting.

Retrieval Augmented Generation for Code Search

The technical solution consists of 2 parts.

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Cross-Pollination for Creativity Leveraging LLMs

Large Language models (LLMs) are used for creative tasks such as story writing, poetry, and script writing for plays. There are several GPT-based wrapper tools for advertising slogan creation, generating plot lines, and music compositions. Let's explore how to use LLMs to identify research gaps in a field and leverage the ideas of other fields to inspire new ideas. 

Problem Statement

Researchers need inspiration when they are stuck on a problem. It's common for researchers to get fixated on a particular hypothesis or approach. The vast amount of information can be overwhelming. It is a struggle in itself to sift through the information and identify a potential new path. Interdisciplinary collaboration is often challenging with researchers on both sides not familiar with the jargon of the two fields.