A new wave of AI has emerged, changing the way businesses use internal and external data in existing workflows. The combination of Large Language Models (LLMs) and Embedding Models (models that can create high dimensional vectors from unstructured data) now enable you to make sense of data of any type.
With >80% of information being unstructured (think text, documents, images, video files, etc.), search is moving beyond just the keyword, with vector embeddings helping to contextualize all of this data, even when your end users might not know what they’re looking for. So how can your business utilize vector search and the power of LLMs to extend your own corporate knowledge set and increase relevant results?
Join our expert Rashi Yadav, Solutions Architect at MongoDB for a discussion on these tectonic trends to find out
- What Vector Search is and how AI plays a role in making sense of unstructured data.
- How to create vector embeddings to increase relevance by harnessing the power of LLMs.
- Various approaches to storing and retrieving vectors.
- Real world examples of vector use cases, AI integrations, and results.
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