Vertex AI V1 API - Class Google::Cloud::AIPlatform::V1::GenerationConfig (v0.61.0)

Reference documentation and code samples for the Vertex AI V1 API class Google::Cloud::AIPlatform::V1::GenerationConfig.

Generation config.

Inherits

Object

Extended By

Google::Protobuf::MessageExts::ClassMethods

Includes

Google::Protobuf::MessageExts

Methods

#candidate_count

def candidate_count() -> ::Integer

Returns

(::Integer) — Optional. Number of candidates to generate.

#candidate_count=

def candidate_count=(value) -> ::Integer

Parameter

value (::Integer) — Optional. Number of candidates to generate.

Returns

(::Integer) — Optional. Number of candidates to generate.

#frequency_penalty

def frequency_penalty() -> ::Float

Returns

(::Float) — Optional. Frequency penalties.

#frequency_penalty=

def frequency_penalty=(value) -> ::Float

Parameter

value (::Float) — Optional. Frequency penalties.

Returns

(::Float) — Optional. Frequency penalties.

#logprobs

def logprobs() -> ::Integer

Returns

(::Integer) — Optional. Logit probabilities.

#logprobs=

def logprobs=(value) -> ::Integer

Parameter

value (::Integer) — Optional. Logit probabilities.

Returns

(::Integer) — Optional. Logit probabilities.

#max_output_tokens

def max_output_tokens() -> ::Integer

Returns

(::Integer) — Optional. The maximum number of output tokens to generate per message.

#max_output_tokens=

def max_output_tokens=(value) -> ::Integer

Parameter

value (::Integer) — Optional. The maximum number of output tokens to generate per message.

Returns

(::Integer) — Optional. The maximum number of output tokens to generate per message.

#presence_penalty

def presence_penalty() -> ::Float

Returns

(::Float) — Optional. Positive penalties.

#presence_penalty=

def presence_penalty=(value) -> ::Float

Parameter

value (::Float) — Optional. Positive penalties.

Returns

(::Float) — Optional. Positive penalties.

#response_logprobs

def response_logprobs() -> ::Boolean

Returns

(::Boolean) — Optional. If true, export the logprobs results in response.

#response_logprobs=

def response_logprobs=(value) -> ::Boolean

Parameter

value (::Boolean) — Optional. If true, export the logprobs results in response.

Returns

(::Boolean) — Optional. If true, export the logprobs results in response.

#response_mime_type

def response_mime_type() -> ::String

Returns

(::String) —
Optional. Output response mimetype of the generated candidate text. Supported mimetype:
- text/plain: (default) Text output.
- application/json: JSON response in the candidates. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature.

#response_mime_type=

def response_mime_type=(value) -> ::String

Parameter

value (::String) —
Optional. Output response mimetype of the generated candidate text. Supported mimetype:
- text/plain: (default) Text output.
- application/json: JSON response in the candidates. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature.

Returns

(::String) —
Optional. Output response mimetype of the generated candidate text. Supported mimetype:
- text/plain: (default) Text output.
- application/json: JSON response in the candidates. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature.

#response_schema

def response_schema() -> ::Google::Cloud::AIPlatform::V1::Schema

Returns

(::Google::Cloud::AIPlatform::V1::Schema) — Optional. The Schema object allows the definition of input and output data types. These types can be objects, but also primitives and arrays. Represents a select subset of an OpenAPI 3.0 schema object. If set, a compatible response_mime_type must also be set. Compatible mimetypes: application/json: Schema for JSON response.

#response_schema=

def response_schema=(value) -> ::Google::Cloud::AIPlatform::V1::Schema

Parameter

value (::Google::Cloud::AIPlatform::V1::Schema) — Optional. The Schema object allows the definition of input and output data types. These types can be objects, but also primitives and arrays. Represents a select subset of an OpenAPI 3.0 schema object. If set, a compatible response_mime_type must also be set. Compatible mimetypes: application/json: Schema for JSON response.

Returns

(::Google::Cloud::AIPlatform::V1::Schema) — Optional. The Schema object allows the definition of input and output data types. These types can be objects, but also primitives and arrays. Represents a select subset of an OpenAPI 3.0 schema object. If set, a compatible response_mime_type must also be set. Compatible mimetypes: application/json: Schema for JSON response.

#routing_config

def routing_config() -> ::Google::Cloud::AIPlatform::V1::GenerationConfig::RoutingConfig

Returns

(::Google::Cloud::AIPlatform::V1::GenerationConfig::RoutingConfig) — Optional. Routing configuration.

#routing_config=

def routing_config=(value) -> ::Google::Cloud::AIPlatform::V1::GenerationConfig::RoutingConfig

Parameter

value (::Google::Cloud::AIPlatform::V1::GenerationConfig::RoutingConfig) — Optional. Routing configuration.

Returns

(::Google::Cloud::AIPlatform::V1::GenerationConfig::RoutingConfig) — Optional. Routing configuration.

#seed

def seed() -> ::Integer

Returns

(::Integer) — Optional. Seed.

#seed=

def seed=(value) -> ::Integer

Parameter

value (::Integer) — Optional. Seed.

Returns

(::Integer) — Optional. Seed.

#stop_sequences

def stop_sequences() -> ::Array<::String>

Returns

(::Array<::String>) — Optional. Stop sequences.

#stop_sequences=

def stop_sequences=(value) -> ::Array<::String>

Parameter

value (::Array<::String>) — Optional. Stop sequences.

Returns

(::Array<::String>) — Optional. Stop sequences.

#temperature

def temperature() -> ::Float

Returns

(::Float) — Optional. Controls the randomness of predictions.

#temperature=

def temperature=(value) -> ::Float

Parameter

value (::Float) — Optional. Controls the randomness of predictions.

Returns

(::Float) — Optional. Controls the randomness of predictions.

#top_k

def top_k() -> ::Float

Returns

(::Float) — Optional. If specified, top-k sampling will be used.

#top_k=

def top_k=(value) -> ::Float

Parameter

value (::Float) — Optional. If specified, top-k sampling will be used.

Returns

(::Float) — Optional. If specified, top-k sampling will be used.

#top_p

def top_p() -> ::Float

Returns

(::Float) — Optional. If specified, nucleus sampling will be used.

#top_p=

def top_p=(value) -> ::Float

Parameter

value (::Float) — Optional. If specified, nucleus sampling will be used.

Returns

(::Float) — Optional. If specified, nucleus sampling will be used.