Model provider integrations

Weaviate integrates with a variety of self-hosted and API-based models from a range of providers.

This enables an enhanced developed experience, such as the ability to:

• Import objects directly into Weaviate without having to manually specify embeddings, and
• Build an integrated retrieval augmented generation (RAG) pipeline with generative AI models.

Model provider integrations

API-based

Model provider	Embeddings	Generative AI	Others
Anthropic	-	Text	-
Anyscale	-	Text	-
AWS	Text	Text
Cohere	Text, Multimodal	Text	Reranker
Contextual AI	-	Text	Reranker
Databricks	Text	Text	-
FriendliAI	-	Text	-
Google	Text, Multimodal	Text	-
Hugging Face	Text	-	-
Jina AI	Text, Multimodal	-	Reranker
Mistral	Text	Text	-
NVIDIA	Text, Multimodal	Text	Reranker
OctoAI (Deprecated)	Text	Text	-
OpenAI	Text	Text	-
Azure OpenAI	Text	Text	-
Voyage AI	Text, Multimodal	-	Reranker
Weaviate	Text	-	-
xAI	-	Text	-

Enable all API-based modules

All API-based model integrations are available by default starting with Weaviate v1.33. For older versions, you can enable them all by setting the ENABLE_API_BASED_MODULES environment variable to true.

Locally hosted

Model provider	Embeddings	Generative AI	Others
GPT4All (Deprecated)	Text (Deprecated)	-	-
Hugging Face	Text, Multimodal (CLIP)	-	Reranker
KubeAI	Text	-	-
Model2vec	Text	-	-
Meta ImageBind	Multimodal	-	-
Ollama	Text	Text	-

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How does Weaviate generate embeddings?

When a model provider integration for embeddings is enabled, Weaviate automatically generates embeddings for objects that are added to the database.

This is done by providing the source data to the integration provider, which then returns the embeddings to Weaviate. The embeddings are then stored in the Weaviate Database.

Weaviate generates embeddings for objects as follows:

• Selects properties with text or text[] data types unless they are configured to be skipped
• Sorts properties in alphabetical (a-z) order before concatenating values
• Prepends the collection name if configured

Case sensitivity

For Weaviate versions before v1.27, the string created above is lowercased before being sent to the model provider. Starting in v1.27, the string is sent as is.

If you prefer the text to be lowercased, you can do so by setting the LOWERCASE_VECTORIZATION_INPUT environment variable. The text is always lowercased for the text2vec-contextionary integration.

Questions and feedback

If you have any questions or feedback, let us know in the user forum.

Technical questions

If you have questions feel free to post on our Community forum.

Documentation feedback

Leave feedback by opening a GitHub issue.