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presentation

Continuously Updating Vector Embeddings for Gen AI Apps

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The solution uses MongoDB Atlas Stream Processing and Atlas Vector Search to continuously update vector embeddings with data received from an Apache Kafka data pipeline. Our Senior Consulting Engineer, David Sanchez walks developers through continuously updating, storing, and searching embeddings with a single interface. And it shows why the MongoDB document data model is so well suited to stream processing.

The webinar details:

  • How to set up and configure the environment.
  • How to create vector search indices in Atlas.
  • How to create a private and scalable embedding generator system using purpose-built LLMs.
  • How to interactively run semantic queries.

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