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Add sample code to measure latency on TextEmbeddingModel

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Assessment

Difficulty
3/5
Estimated time
Half a day
Newbie friendliness
35/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Stale
Tech stack
python
Domain
machine-learning

Research direction

No file or test is named. Start by locating existing TextEmbeddingModel samples in the repository and follow their entry points and conventions. Done means a new sample sends the given text N times, measures each request, and reports the 50th, 95th, and 99th percentile latencies.

Written by the indexing model from the issue text.

Description

priority: p3 samples triage me type: feature request

Thanks for stopping by to let us know something could be better!

PLEASE READ: If you have a support contract with Google, please create an issue in the support console instead of filing on GitHub. This will ensure a timely response.

The issue you're having must be related to a file in this repository. We are unable to provide assistance for issues unrelated to samples in this repository.

Please include as much information as possible:

Is your feature request related to a problem? Please describe.

Customer is looking for an authoritative way to properly measure the latency on a given model of TextEmbeddingModel

Describe the solution you'd like.

I would like to contribute by adding a new file with a code to send a given text N times, measure the time on each request and grouping them in percentiles 50, 95 and 99.

Describe alternatives you've considered.

I have not thought on any other alternative.

Additional context.

This is useful for those early adapters who want to make sure and confirm that this is indeed a reliable solution for the enterprise.

Making sure to follow these steps will guarantee the quickest resolution possible.

Thanks!

Dominant language
Jupyter Notebook
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Forks
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Avg merge
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Merged PRs (30d)
8

Getting set up

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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