How to build a language processing pipeline using AI with Redis

The Launchpad App created a language processing machine learning pipeline to weed out confirmation bias in medical literature, utilizing Redis as the data fabric. A knowledge graph was created using RedisGears and RedisGraph to store entities, concepts, and relationships between them. The pipeline uses the Aho-Corasick algorithm to match incoming sentences into pairs of nodes and present sentences as edges in a graph. The system is designed to promote diversity of opinion and prevent confirmation bias in medical professionals' diagnoses. The knowledge graph can be visualized as a graph structure, highlighting each entity's properties along with their relationships. The pipeline uses RedisGears and RedisGraph to process information using RedisGears and stores it in RedisGraph. The system also utilizes the BERT model for summarization and question answering tasks. The Redis Knowledge Graph is designed to create knowledge graphs based on long and detailed queries, allowing users to navigate through medical literature seamlessly without suffering from confirmation bias.

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