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Documentation Index

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Manage connections to external LLM providers (OpenAI, Azure OpenAI, AWS Bedrock, Vertex AI, Anthropic, and others) for use within the Arize platform.
The ai_integrations client methods are currently in ALPHA. The API may change without notice. A one-time warning is emitted on first use.

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Key Capabilities

  • List AI integrations with optional filtering by space
  • Retrieve integration details by ID
  • Create integrations for any supported LLM provider
  • Update integration settings without replacing the entire resource
  • Delete integrations

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Supported Providers

Provider valueDescription
openAIOpenAI
azureOpenAIAzure OpenAI
awsBedrockAWS Bedrock
vertexAIGoogle Vertex AI
anthropicAnthropic
geminiGoogle Gemini
nvidiaNimNVIDIA NIM
customCustom provider

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List AI Integrations

List all AI integrations you have access to, with optional filtering by space or name. 
resp = client.ai_integrations.list(
    space="your-space-name-or-id",  # optional
    name="openai",                  # optional substring filter
    limit=50,
)

for integration in resp.ai_integrations:
    print(integration.id, integration.name, integration.provider)
For details on pagination, field introspection, and data conversion (to dict/JSON/DataFrame), see Response Objects.

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Create an AI Integration

Integration names must be unique within the account.

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OpenAI

from arize.ai_integrations.types import AiIntegrationProvider

integration = client.ai_integrations.create(
    name="my-openai",
    provider=AiIntegrationProvider.OPENAI,
    api_key="sk-...",
    model_names=["gpt-4o", "gpt-4o-mini"],
)

print(integration.id)

​
Azure OpenAI

from arize.ai_integrations.types import AiIntegrationProvider

integration = client.ai_integrations.create(
    name="my-azure-openai",
    provider=AiIntegrationProvider.AZUREOPENAI,
    api_key="your-azure-key",
    base_url="https://your-resource.openai.azure.com/",
    model_names=["gpt-4o"],
)

​
AWS Bedrock

For AWS Bedrock, provider_metadata must include the "aws" kind discriminator and a role_arn. 
from arize.ai_integrations.types import AiIntegrationProvider

integration = client.ai_integrations.create(
    name="my-bedrock",
    provider=AiIntegrationProvider.AWSBEDROCK,
    provider_metadata={
        "kind": "aws",
        "role_arn": "arn:aws:iam::123456789012:role/my-role",
    },
    model_names=["anthropic.claude-3-5-sonnet-20241022-v2:0"],
)

​
Vertex AI

For Vertex AI, provider_metadata must include the "gcp" kind discriminator and project_id, location, and project_access_label. 
from arize.ai_integrations.types import AiIntegrationProvider

integration = client.ai_integrations.create(
    name="my-vertex-ai",
    provider=AiIntegrationProvider.VERTEXAI,
    provider_metadata={
        "kind": "gcp",
        "project_id": "my-gcp-project",
        "location": "us-central1",
        "project_access_label": "my-label",
    },
    model_names=["gemini-2.0-flash"],
)

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Get an AI Integration

Retrieve a specific integration by name or ID. 
integration = client.ai_integrations.get(integration="your-integration-name-or-id")

print(integration.id, integration.name)

​
Update an AI Integration

Only the fields you pass are updated — omitted fields are left unchanged. To explicitly clear a nullable field (e.g. api_key), pass None. 
integration = client.ai_integrations.update(
    integration="your-integration-name-or-id",
    name="updated-name",
    model_names=["gpt-4o", "gpt-4o-mini", "o3-mini"],
)

print(integration.name)

​
Delete an AI Integration

Delete an integration by name or ID. This operation is irreversible. 
client.ai_integrations.delete(integration="your-integration-name-or-id")

print("Integration deleted")