Vector Use Case Catalog: A RAG Knowledge Base Implementation - Vector Intern Talks

Eric Edem Dzeha and Nikki Smith's project focuses on the development and implementation of a Retrieval-Augmented Generation (RAG) solution tailored for Vector Institute’s diverse range of AI projects. This system efficiently catalogs and provides easy access to use cases from past projects, ensuring that relevant information can be retrieved on demand. The project involved structuring textual data from various sources, developing static and dynamic use case cards, and integrating these elements into a user-friendly interface. Key technologies include ChromaDB for data storage, Cohere for LLM-based query processing, and feature extraction for static card generation. The outcome is a comprehensive tool that streamlines project reference, supports research, and enhances operational efficiency.
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