Project Objectives

• Client Goals: The NHIR Institute needed an innovative AI-powered search tool and research assistant to enhance user interaction with their document archives and improve access to scientific data.
• Our Mission: To develop an advanced search system using Retrieval-Augmented Generation (RAG) that allows users to engage with the NHIR archives through AI-driven search and research tools.

Project Scope

• AI-Powered Search: Developed an AI search tool and research assistant, incorporating the RAG design pattern for efficient and accurate information retrieval.
• Document Ingestion Pipeline: Built a pipeline to convert the extensive NHIR document archive into a vector database to support semantic search capabilities.
• Multiple Search Interfaces: Enabled traditional search, AI question-and-answer, and AI chatbot features to offer users flexible and interactive research options.
• Timeline: 3 Months (including design, development, and testing).

Challenges Faced

• Large Data Handling: Managing the ingestion and indexing of large volumes of archive documents for efficient AI search.
• Response Quality vs Speed: Balancing the need for high-quality AI responses with maintaining fast search speeds.

Solutions Provided

• Retrieval-Augmented Generation (RAG): Implemented RAG to provide contextually accurate answers, complete with citations and source links.
• User Interaction: Enhanced user engagement by offering a natural language interface and personal AI research assistant, improving accessibility and interaction with the archives.
• Improved Information Access: Delivered AI-driven answers with links to relevant documents, allowing users to efficiently find and verify information.

‍