Upgrade text embedding from text-embedding-004 to text-embedding-preview-0815

[MrCsabaToth]

At the latest Gemini Unplugged show when I was the guest speaker (https://www.linkedin.com/events/7234841228205268993/comments/) CHANDRA drew my attention that there's a new embedding model in a preview state. He said 008 I think he meant the text-embedding-preview-0815. It seems that it's also has 768 dimensionality.

This change will require a data migration. The dimensionality is the same, so no schema change is needed.

[MrCsabaToth]

Calling the preview embedding:

curl -X POST \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "Content-Type: application/json; charset=utf-8" \
    -d @request.json \
    "https://us-central1-aiplatform.googleapis.com/v1/projects/duet-ai-roadshow-415022/locations/us-central1/publishers/google/models/text-embedding-preview-0815:predict"

payload (request.json):

{
  "instances": [
    {
      "task_type": "RETRIEVAL_DOCUMENT",
      "title": "document title",
      "content": "I would like embeddings for this text!"
    }
  ]
}

response:

{
  "predictions": [
    {
      "embeddings": {
        "statistics": {
          "truncated": false,
          "token_count": 13
        },
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      }
    }
  ],
  "metadata": {
    "billableCharacterCount": 45
  }
}

[MrCsabaToth]

For comparison, here is the call of the 004 embedding (with the same payload):

curl -X POST
     -H "Authorization: Bearer $(gcloud auth print-access-token)"
     -H "Content-Type: application/json; charset=utf-8"
     -d @request.json
     "https://us-central1-aiplatform.googleapis.com/v1/projects/duet-ai-roadshow-415022/locations/us-central1/publishers/google/models/text-embedding-004:predict"

{
  "predictions": [
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        "statistics": {
          "token_count": 13,
          "truncated": false
        },
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  "metadata": {
    "billableCharacterCount": 45
  }
}

[MrCsabaToth]

Since embedding-004 the API supports outputDimensionality reduction, it's part of the parameter section of the payload:

{
  "instances": [
    { "content": "TEXT",
      "task_type": "TASK_TYPE",
      "title": "TITLE"
    },
  ],
  "parameters": {
    "autoTruncate": AUTO_TRUNCATE,
    "outputDimensionality": OUTPUT_DIMENSIONALITY
  }
}

According to doc the "reduction" is a simple truncation:

Used to specify output embedding size. If set, output embeddings will be truncated to the size specified.

Note that a code example suggests reduction to 256. Note also that autoTruncate is on by default.

[MrCsabaToth]

Since the app is in alpha I will skip the data migration this time. Later when we'll need that the migration could mean a lot of embedding calls (with long chat history), which possibly even need to be throttled to not hit any quotas. We can use batch inference to cut down the number of requests, but that has a limit as well (max number of texts in the array). We cannot do that during app startup, this would rather be in the settings and once the user switches the embedding model an alert could verify the intent and then a progress bar view would accompany the process.

[MrCsabaToth]

I'll decide if I take advantage of the dimensionality truncation. A reduction to 256 dimensions would cut the storage size in 1/3rd (768 / 3 = 256) and also the retrieval processing time as well. If we go with the truncation we'd definitely benefit from a reranking #39

[MrCsabaToth]

Looks like the reduction works with any arbitrary number, I tried 64. I also realize that this is not available on the Gemini Dart API yet google-gemini/generative-ai-dart#208, however the workaround can be to perform the truncation ourselves until the support, since it's a simple truncation.

[MrCsabaToth]

I ended up opening a separate ticket for dimensionality reduction: #47
My idea is that since we'd need to perform the reduction manually anyway, we should rather fold the dimensions instead of truncating. That will be a simple procedure only containing 512 floating point addition operations per embedding.

[MrCsabaToth]

I prefer multilingual model #48 over this upgrade