CustomLLMModel
Properties
This is the starting state for the conversation for the assistant role.
This determines whether we detect user's emotion while they speak and send it as an additional info to model.
Default `false` because the model is usually good at understanding the user's emotion from text.
This is the starting state for the conversation for the function role.
This determines whether metadata is sent in requests to the custom provider.
off will not send any metadata. Payload will look like { messages }
variable will send assistant.metadata as a variable on the payload. Payload will look like { messages, metadata }
destructured will send assistant.metadata fields directly on the payload. Payload will look like { messages, ...metadata }
Further,
variable and destructured will send call, phoneNumber, and customer objects in the payload.
Default is
variable.
This sets how many turns at the start of the conversation to use a smaller, faster model from the same provider before switching to the primary model. Example, gpt-3.5-turbo if provider is openai. Default is 0.
This is the starting state for the conversation for the system role.
This is the temperature that will be used for calls.
This is the starting state for the conversation for the tool role.
This is the starting state for the conversation for the user role.
Functions
This is the function definition of the tool. For endCall, transferCall, and dtmf tools, this is auto-filled based on tool-specific fields like tool.destinations. But, even in those cases, you can provide a custom function definition for advanced use cases. An example of an advanced use case is if you want to customize the message that's spoken for endCall tool. You can specify a function where it returns an argument "reason". Then, in messages array, you can have many "request-complete" messages. One of these messages will be triggered if the messages[].conditions matches the "reason" argument.
These are the options for the knowledge base.