Voyageai refactoring

[fzowl]

Voyageai refactoring:

• contextual model(s)
• remove default model value
• token counting and effective batching
• test with new reranker, rerank-2.5 as well
• more tests

[jit-ci]

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[Copilot]

Pull Request Overview

This PR refactors the VoyageAI integration to support contextual embedding models, improve token management, and enhance testing coverage. The changes include removing the default model value (requiring explicit model specification), implementing token-aware batching, adding support for the voyage-context-3 model, and updating tests to cover the new rerank-2.5 model.

Key Changes:

• Added token counting functionality and token-aware batching based on model-specific token limits
• Introduced support for contextualized embedding models (voyage-context-3) with automatic API endpoint detection
• Updated VoyageAI package dependency from 0.2.2 to 0.3.5

Reviewed Changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated 3 comments.

File	Description
tests/integration/test_vectorizers.py	Added VoyageAI-specific tests for token counting, context models, and batching; updated model references to voyage-3.5
tests/integration/test_rerankers.py	Enhanced reranker test fixture to test both rerank-lite-1 and rerank-2.5 models
redisvl/utils/vectorize/text/voyageai.py	Implemented token-aware batching, context model support, removed default model value, added token limits dictionary
pyproject.toml	Updated voyageai dependency version requirement from 0.2.2 to 0.3.5

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[Copilot]

Removing the default model value is a breaking API change. Existing code that instantiates VoyageAITextVectorizer without specifying a model will fail. Consider adding a deprecation warning in a previous release or documenting this as a breaking change in the release notes.

[Copilot]

The token-aware batching makes an additional API call to count_tokens() for every embed operation. This adds latency and API usage overhead. Consider adding a parameter to allow users to opt out of token-aware batching or implement local tokenization if the VoyageAI client supports it.

[fzowl]

count_token() is a low-latency local function.

[Copilot]

The batch_size parameter is marked as deprecated in the docstring, but the method signature still accepts it without any deprecation warning being raised. Consider adding a runtime deprecation warning when batch_size is provided, or remove the parameter entirely if it's no longer used.

[bsbodden]

@fzowl There are some failures in CI: The job failed due to mypy type errors in redisvl/utils/vectorize/text/voyageai.py:

• Line 302: "Client" has no attribute "contextualized_embed"
• Line 385: "AsyncClient" has no attribute "contextualized_embed"

Solution:
The mypy errors are caused by missing type annotations for contextualized_embed on the Client and AsyncClient classes from the voyageai package. To resolve this:

1. Ensure the voyageai package is up-to-date, as contextualized_embed may be a recent addition. Run:

   pip install --upgrade voyageai
   

2. If the method exists in runtime but mypy still complains, add type ignore comments for the relevant lines:

   response = self._client.contextualized_embed(  # type: ignore[attr-defined]
       inputs=[batch],
       model=self.model,
       input_type=input_type,
       **kwargs,
   )

   and

   response = await self._aclient.contextualized_embed(  # type: ignore[attr-defined]
       inputs=[batch],
       model=self.model,
       input_type=input_type,
       **kwargs,
   )

3. Alternatively, update your project's type stubs for voyageai or create a custom stub that includes contextualized_embed in both Client and AsyncClient.

This will resolve the mypy errors and allow the type checks to pass.

Relevant file: redisvl/utils/vectorize/text/voyageai.py (ref: 90340c52bfc47b4944b2095e4e1b492b80bf2507)