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.orgId

OpenAI organization ID.

string

siren-ai.providerConfig.openai.connection.apiKey

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

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 (0.0-1.0)

0.5

siren-ai.providerConfig.openai.parameters.topP

See TopP.

float (0.0-1.0)

1.0

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 (0.0-1.0)

0.5

siren-ai.providerConfig.azure.parameters.topP

See TopP.

float (0.0-1.0)

1.0

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 (0.0-1.0)

0.5

siren-ai.providerConfig.ollama.parameters.topP

See TopP.

float (0.0-1.0)

1.0

siren-ai.providerConfig.ollama.parameters.numCtx

See Context window (Ollama only).

integer

4096

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 (0.0-1.0)

0.5

siren-ai.providerConfig.aws.parameters.topP

See TopP.

float (0.0-1.0)

1.0

LLM parameters

Temperature

The temperature parameter is a value between 0 and 1 that is used to control the randomness and creativity of the generated output. It works by adjusting the probability distribution of the next word in the sequence. A higher temperature value (closer to 1) 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 (closer to 0) 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.

This value defaults to 0.5 as it provides a good balance between creativity and coherence. For a more in-depth description of this parameter, see here.

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.

This value defaults to 1 as it means that the model will consider all possible tokens for generating the next word, which ensures that the output is as diverse as possible. For a more in-depth description of this parameter, see here.

Context window (Ollama only)

The numCtx parameter is used to control the size of the context that the Ollama model considers when generating a response. A larger numCtx value allows the model to consider more information from the input text, which can lead to more coherent and contextually relevant responses. However, increasing this value also increases the computational resources and time required to generate the response, so it is important to balance the context window size with the available resources.

This value defaults to 4096 as it provides a good balance between context size and computational efficiency.