Configuration reference

The following table describes all configuration options available for the Siren AI plugin. Options are bolded if they are required, though provider config options marked as required are only required if the associated provider is in use.

Option Description Type Default

siren-ai.enabled

Whether the plugin is enabled

boolean

true

siren-ai.provider

The provider to use

openai | azure | ollama | aws

OpenAI config

siren-ai.providerConfig.openai.connection.apiKey

OpenAI API key. This can be found in the API key page.

string

siren-ai.providerConfig.openai.connection.orgId

OpenAI organization ID.

string

siren-ai.providerConfig.openai.parameters.model

The OpenAI model to use. For a full list options, see here.

string

gpt-3.5-turbo

siren-ai.providerConfig.openai.parameters.temperature

See Temperature.

float (0.0-2.0)

siren-ai.providerConfig.openai.parameters.topP

See TopP.

float (0.0-1.0)

siren-ai.providerConfig.openai.parameters.timeout

LLM timeout in milliseconds.

integer (>0)

600000 (10 mins)

Azure OpenAI config

siren-ai.providerConfig.azure.connection.endpoint

Azure OpenAI endpoint. This can be found in the deployed Azure resource’s Keys and Endpoint page.

string

siren-ai.providerConfig.azure.connection.deploymentName

Azure OpenAI deployment name. This deployment determines the model used.

string

siren-ai.providerConfig.azure.connection.apiKey

Azure OpenAI API key. This can be found in the deployed Azure resource’s Keys and Endpoint page.

string

siren-ai.providerConfig.azure.parameters.temperature

See Temperature.

float (0.0-2.0)

siren-ai.providerConfig.azure.parameters.topP

See TopP.

float (0.0-1.0)

siren-ai.providerConfig.azure.parameters.timeout

LLM timeout in milliseconds.

integer (>0)

600000 (10 mins)

Ollama config

siren-ai.providerConfig.ollama.connection.host

The URL that the Ollama instance is listening on.

string

http://localhost:11434

siren-ai.providerConfig.ollama.parameters.model

Model to use. See here for a full list of available models.

string

siren-ai.providerConfig.ollama.parameters.temperature

See Temperature.

float (0.0-2.0)

siren-ai.providerConfig.ollama.parameters.topP

See TopP.

float (0.0-1.0)

siren-ai.providerConfig.ollama.parameters.timeout

LLM timeout in milliseconds.

integer (>0)

600000 (10 mins)

siren-ai.providerConfig.ollama.parameters.enableTools

Whether to enable tool use for Ollama. See note in Ollama configuration.

boolean

false

AWS Bedrock config

siren-ai.providerConfig.aws.connection.region

AWS region.

string

siren-ai.providerConfig.aws.connection.profile

AWS profile created locally.

string

siren-ai.providerConfig.aws.connection.credentials.accessKeyId

AWS access key ID. Can also be specified using AWS_ACCESS_KEY_ID.

string

siren-ai.providerConfig.aws.connection.credentials.secretAccessKey

AWS secret access key. Can also be specified using AWS_SECRET_ACCESS_KEY.

string

siren-ai.providerConfig.aws.connection.credentials.sessionToken

A security or session token to use with these credentials. Usually present for temporary credentials. Can also be specified using AWS_SESSION_TOKEN

string

siren-ai.providerConfig.aws.connection.credentials.credentialScope

AWS credential scope for this set of credentials.

string

siren-ai.providerConfig.aws.connection.credentials.accountId

AWS account ID.

string

siren-ai.providerConfig.aws.parameters.model

The model to use. See here for a full list of supported models.

string

siren-ai.providerConfig.aws.parameters.temperature

See Temperature.

float (0.0-2.0)

siren-ai.providerConfig.aws.parameters.topP

See TopP.

float (0.0-1.0)

siren-ai.providerConfig.aws.parameters.timeout

LLM timeout in milliseconds.

integer (>0)

undefined (no timeout)

LLM parameters

Temperature

The temperature parameter controls the randomness and creativity of the model’s output by adjusting the probability distribution used when selecting the next token.

A higher temperature value makes the model’s output more diverse and creative by giving less probable words a higher chance of being selected. Conversely, a lower temperature value makes the output more focused and predictable by favoring the most probable words. This parameter allows users to fine-tune the balance between creativity and coherence in the model’s responses, depending on the desired application.

Note: The configuration may accept a range of 0 to 2 but the valid range for temperature depends on the provider or model you are using. Some providers accept values between 0 and 1, while others support a wider range, typically 0 to 2. Always choose a temperature value that falls within the range supported by your selected provider. If this parameter is not defined, it will default to your provider’s default.

TopP

The topP parameter, also known as nucleus sampling, is used to control the diversity of the output generated by an LLM. It works by considering only the smallest set of top probable tokens whose cumulative probability exceeds the value of topP.

For example, if topP is set to 0.9, the model will only consider the top 90% of probable tokens for generating the next word, effectively filtering out the less likely options. This results in more diverse and creative outputs when topP is set closer to 1, as the model has a wider range of tokens to choose from. Conversely, setting topP closer to 0 makes the output more predictable and focused, as it limits the model to a smaller set of highly probable tokens.

If this parameter is not defined, it will default to your provider’s default.