How can we integrate Google's Gemini Pro into our existing Python-based Data Science workflow?

All answers to this question.

Integrating Gemini into your Python workflow is very straightforward using the google-cloud-aiplatform library. One of Gemini’s biggest advantages for Data Science is the 1M+ token context window, which is perfect for your customer logs. Instead of complex RAG (Retrieval-Augmented Generation) pipelines for smaller datasets, you can often just feed the whole log file into the prompt. Regarding "Grounding," it is a game-changer for enterprise use. By connecting Gemini to your BigQuery instance or Google Search, the model cross-references its answers against your "ground truth" data. This significantly reduces hallucinations because the model is forced to cite its sources from your specific documentation.

   Answered 2024-01-15 by Susan Evans

How are you finding the latency of Gemini Pro compared to GPT-4, especially when you are processing those massive 1M token prompts?

   Answered 2024-01-17 by Anthony King

• The latency is higher for the massive prompts, obviously, but the "Time to First Token" is actually quite competitive. For our batch processing of logs, the extra few seconds of latency doesn't matter as much as the accuracy and the ability to process everything at once. We've found that Gemini is much better at following "system instructions" over long distances of text compared to what we were seeing previously. It feels like it 'remembers' the start of the document better.

     Commented 2024-01-18 by Joseph Wright

Use the Google AI Studio to prototype your prompts before moving to the Python SDK. It’s much faster to tweak the safety settings and temperature in the UI first.

   Answered 2024-01-19 by Nancy Young

• Nancy is right. Prototyping in AI Studio saves a lot of debugging time. Once you have the prompt right, you can just click "Get Code" to see the Python implementation.

     Commented 2024-01-20 by Charles Robinson