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  1. Python Driver
  2. PYTHON-4743

Spike -- Investigate pipeline refinements for AI/ML integrations across driver teams

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    • Type: Icon: Task Task
    • Resolution: Duplicate
    • Priority: Icon: Unknown Unknown
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    • Python Drivers
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      1. What would you like to communicate to the user about this feature?
      2. Would you like the user to see examples of the syntax and/or executable code and its output?
      3. Which versions of the driver/connector does this apply to?

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      1. What would you like to communicate to the user about this feature? 2. Would you like the user to see examples of the syntax and/or executable code and its output? 3. Which versions of the driver/connector does this apply to?

      Context

      Document how each driver team has implemented their AI/ML integrations. Note any similarities/commonalities in issues across all implementations.

      Key points to capture:

      • Type of MongoDB server used for testing (local/cloud)
      • Type of LLM used for testing (Homemade Mock Embedder / Paid for OPENAI_API_KEY)
      • How often regression are caught
      • All the libraries our teams have integrated with within a driver team
      • How often tests get run within the integrated repository (and outside of it)
      • Where and if there are any "implementation best practices" for their team
      • Findings on how requests surface
      • Findings on how integrations get prioritized/triaged
      • Findings on how teams deal with external PRs
      • ...etc

      Definition of done

      Documentation of investigation findings. Potential follow-ups.

      Pitfalls

      What should the implementer watch out for? What are the risks?

            Assignee:
            jib.adegunloye@mongodb.com Jib Adegunloye
            Reporter:
            jib.adegunloye@mongodb.com Jib Adegunloye
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              Created:
              Updated:
              Resolved: