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  1. Python Integrations
  2. INTPYTHON-546

Provide a Python library that can enable and simplify the development experience of working with multimodal data and embedding models

    • Type: Icon: Task Task
    • Resolution: Unresolved
    • Priority: Icon: Major - P3 Major - P3
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    • Affects Version/s: None
    • Component/s: AI/ML
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      Summary

      Users will have a way of indexing and searching against images and image metadata within their RAG workflows that leverages Atlas Vector Search, voyage-multimodal-3 embeddings, and s3 for image storage.

      Motivation
      Who is the affected end user?
      MongoDB Developers
      Head of Data Science
      ML Engineer
      AI Engineer

      How does this affect the end user?
      Dramatically simpler user experience, provides path to multimodal scalability

      Important Docs

      [Product Description

      https://docs.google.com/document/d/17SkDbUqn3RaI8d76EjLwKhhctvpMWZnnOcUb5pZkQKI/edit?tab=t.0]
      Epic Link

      Cast of Characters

      Engineering Lead: Steve Silvester
      Document Author: Henry Weller
      POCers: Casey Clements
      Product Owner: Henry Weller
      Program Manager:
      Stakeholders:

      Channels & Docs

      Slack Channel

      [Scope Document|some.url]

      [Technical Design Document|some.url]

            Assignee:
            Unassigned Unassigned
            Reporter:
            henry.weller@mongodb.com Henry Weller
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            Watchers:
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              Created:
              Updated:
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