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	-
DeepSeek	-	Text	-
DigitalOcean	Text	Text	-
FriendliAI	-	Text	-
Google	Text, Multimodal	Text	-
Hugging Face	Text	-	-
Jina AI	Text, Multimodal	-	Reranker
Mistral	Text	Text	-
Morph	Text	-	-
NVIDIA	Text, Multimodal	Text	Reranker
OctoAI (Deprecated)	Text	Text	-
OpenAI	Text	Text	-
Azure OpenAI	Text	Text	-
TwelveLabs	Multimodal	-	-
Voyage AI	Text, Multimodal	-	Reranker
Weaviate	Text, Multimodal	-	-
xAI	-	Text	-

Enable all API-based modules

All API-based model integrations are available by default starting with Weaviate v1.33.

To opt out, for example in an air-gapped or otherwise restricted deployment, set the API_BASED_MODULES_DISABLED environment variable to true. Weaviate then loads only the modules that you list in ENABLE_MODULES. This variable was added in v1.33.

For releases before v1.33, enable all API-based modules by setting the ENABLE_API_BASED_MODULES environment variable to true. Weaviate stopped reading that variable in v1.33.

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	-

Embedding Model Evaluation & Selection

Embedding models are the heart of vector search. Learn how to evaluate and select appropriate embedding models for your use case.

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.

%%{init: {
  'theme': 'base',
  'themeVariables': {
    'primaryColor': '#4a5568',
    'primaryTextColor': '#2d3748',
    'primaryBorderColor': '#718096',
    'lineColor': '#718096',
    'secondaryColor': '#f7fafc',
    'tertiaryColor': '#edf2f7'
  }
}}%%

flowchart LR
    %% Style definitions
    classDef systemBox fill:#f7fafc,stroke:#3182ce,stroke-width:2px,color:#2d3748
    classDef weaviateBox fill:#f7fafc,stroke:#2d3748,stroke-width:2px,color:#2d3748
    classDef providerBox fill:#f7fafc,stroke:#48bb78,stroke-width:2px,color:#2d3748
    classDef component fill:white,stroke:#718096,stroke-width:1.5px,color:#2d3748,rx:6

    %% Model Provider section (leftmost)
    subgraph provider["Model Provider"]
        inference["🤖 Inference API /<br> Local Model"]
    end

    %% Weaviate section (middle)
    subgraph weaviate["Weaviate"]
        vectorizer["🔌 Model Provider<br> Integration"]
        core["⚡️ Data & vector store"]
    end

    %% User System (bottom)
    subgraph user["User System"]
        data["📄 Data"]
    end

    %% Connections
    data -->|"1\. Insert objects"| core
    core -->|"2\. Request vector"| vectorizer
    vectorizer -->|"3\. Request vector"| inference
    inference -->|"4\. Vector"| vectorizer
    vectorizer -->|"5\. Vector"| core

    %% Apply styles
    class user systemBox
    class weaviate weaviateBox
    class provider providerBox
    class data,core,vectorizer,inference component

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

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