Improving the VoyageAI integration

[fzowl]

Adding contextual model support
Counting the tokens and batching
Tests

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Note

Upgrades the VoyageAI embedding stack for performance and model coverage.

• Adds token-aware batching and count_tokens, with per-model token limits and unified embedding flow (regular, contextual via contextualized_embed, and multimodal via multimodal_embed)
• Introduces helpers for model-type detection and batch building; sets large batch size; removes default model; makes api_key and model required in types
• Updates provider wiring (client init hints) and removes default model from VoyageAIProvider
• Adds extensive tests covering standard/contextual/multimodal models, custom dimensions, batching behavior, token limits, and edge cases
• Refreshes docs (EN/KO/PT-BR) to recommend voyage-4, adds advanced config examples and model overview, and updates RAG Tool config options

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Bug: Default Model Removal Breaks Existing Configurations

Removing the default value for the model field breaks existing code that relies on the previous voyage-2 default. Users who don't explicitly specify a model in their VoyageAI embedder configuration will now encounter validation errors when the provider is instantiated, whereas their code worked before this change.

lib/crewai/src/crewai/rag/embeddings/providers/voyageai/voyageai_provider.py#L17-L21

crewAI/lib/crewai/src/crewai/rag/embeddings/providers/voyageai/voyageai_provider.py

Lines 17 to 21 in da99de1

	)
	model: str = Field(
	description="Model to use for embeddings",
	validation_alias="EMBEDDINGS_VOYAGEAI_MODEL",
	)

 

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[fzowl]

@lorenzejay Can you please take a look?

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