@Generated(value="com.amazonaws:awsjavasdkcodegenerator") public class InferenceConfiguration extends Object implements Serializable, Cloneable, StructuredPojo
Contains inference parameters to use when the agent invokes a foundation model in the part of the agent sequence
defined by the promptType
. For more information, see Inference parameters for foundation
models.
Constructor and Description 

InferenceConfiguration() 
Modifier and Type  Method and Description 

InferenceConfiguration 
clone() 
boolean 
equals(Object obj) 
Integer 
getMaximumLength()
The maximum number of tokens to allow in the generated response.

List<String> 
getStopSequences()
A list of stop sequences.

Float 
getTemperature()
The likelihood of the model selecting higherprobability options while generating a response.

Integer 
getTopK()
While generating a response, the model determines the probability of the following token at each point of
generation.

Float 
getTopP()
While generating a response, the model determines the probability of the following token at each point of
generation.

int 
hashCode() 
void 
marshall(ProtocolMarshaller protocolMarshaller)
Marshalls this structured data using the given
ProtocolMarshaller . 
void 
setMaximumLength(Integer maximumLength)
The maximum number of tokens to allow in the generated response.

void 
setStopSequences(Collection<String> stopSequences)
A list of stop sequences.

void 
setTemperature(Float temperature)
The likelihood of the model selecting higherprobability options while generating a response.

void 
setTopK(Integer topK)
While generating a response, the model determines the probability of the following token at each point of
generation.

void 
setTopP(Float topP)
While generating a response, the model determines the probability of the following token at each point of
generation.

String 
toString()
Returns a string representation of this object.

InferenceConfiguration 
withMaximumLength(Integer maximumLength)
The maximum number of tokens to allow in the generated response.

InferenceConfiguration 
withStopSequences(Collection<String> stopSequences)
A list of stop sequences.

InferenceConfiguration 
withStopSequences(String... stopSequences)
A list of stop sequences.

InferenceConfiguration 
withTemperature(Float temperature)
The likelihood of the model selecting higherprobability options while generating a response.

InferenceConfiguration 
withTopK(Integer topK)
While generating a response, the model determines the probability of the following token at each point of
generation.

InferenceConfiguration 
withTopP(Float topP)
While generating a response, the model determines the probability of the following token at each point of
generation.

public void setMaximumLength(Integer maximumLength)
The maximum number of tokens to allow in the generated response.
maximumLength
 The maximum number of tokens to allow in the generated response.public Integer getMaximumLength()
The maximum number of tokens to allow in the generated response.
public InferenceConfiguration withMaximumLength(Integer maximumLength)
The maximum number of tokens to allow in the generated response.
maximumLength
 The maximum number of tokens to allow in the generated response.public List<String> getStopSequences()
A list of stop sequences. A stop sequence is a sequence of characters that causes the model to stop generating the response.
public void setStopSequences(Collection<String> stopSequences)
A list of stop sequences. A stop sequence is a sequence of characters that causes the model to stop generating the response.
stopSequences
 A list of stop sequences. A stop sequence is a sequence of characters that causes the model to stop
generating the response.public InferenceConfiguration withStopSequences(String... stopSequences)
A list of stop sequences. A stop sequence is a sequence of characters that causes the model to stop generating the response.
NOTE: This method appends the values to the existing list (if any). Use
setStopSequences(java.util.Collection)
or withStopSequences(java.util.Collection)
if you want
to override the existing values.
stopSequences
 A list of stop sequences. A stop sequence is a sequence of characters that causes the model to stop
generating the response.public InferenceConfiguration withStopSequences(Collection<String> stopSequences)
A list of stop sequences. A stop sequence is a sequence of characters that causes the model to stop generating the response.
stopSequences
 A list of stop sequences. A stop sequence is a sequence of characters that causes the model to stop
generating the response.public void setTemperature(Float temperature)
The likelihood of the model selecting higherprobability options while generating a response. A lower value makes the model more likely to choose higherprobability options, while a higher value makes the model more likely to choose lowerprobability options.
temperature
 The likelihood of the model selecting higherprobability options while generating a response. A lower
value makes the model more likely to choose higherprobability options, while a higher value makes the
model more likely to choose lowerprobability options.public Float getTemperature()
The likelihood of the model selecting higherprobability options while generating a response. A lower value makes the model more likely to choose higherprobability options, while a higher value makes the model more likely to choose lowerprobability options.
public InferenceConfiguration withTemperature(Float temperature)
The likelihood of the model selecting higherprobability options while generating a response. A lower value makes the model more likely to choose higherprobability options, while a higher value makes the model more likely to choose lowerprobability options.
temperature
 The likelihood of the model selecting higherprobability options while generating a response. A lower
value makes the model more likely to choose higherprobability options, while a higher value makes the
model more likely to choose lowerprobability options.public void setTopK(Integer topK)
While generating a response, the model determines the probability of the following token at each point of
generation. The value that you set for topK
is the number of mostlikely candidates from which the
model chooses the next token in the sequence. For example, if you set topK
to 50, the model selects
the next token from among the top 50 most likely choices.
topK
 While generating a response, the model determines the probability of the following token at each point of
generation. The value that you set for topK
is the number of mostlikely candidates from
which the model chooses the next token in the sequence. For example, if you set topK
to 50,
the model selects the next token from among the top 50 most likely choices.public Integer getTopK()
While generating a response, the model determines the probability of the following token at each point of
generation. The value that you set for topK
is the number of mostlikely candidates from which the
model chooses the next token in the sequence. For example, if you set topK
to 50, the model selects
the next token from among the top 50 most likely choices.
topK
is the number of mostlikely candidates from
which the model chooses the next token in the sequence. For example, if you set topK
to 50,
the model selects the next token from among the top 50 most likely choices.public InferenceConfiguration withTopK(Integer topK)
While generating a response, the model determines the probability of the following token at each point of
generation. The value that you set for topK
is the number of mostlikely candidates from which the
model chooses the next token in the sequence. For example, if you set topK
to 50, the model selects
the next token from among the top 50 most likely choices.
topK
 While generating a response, the model determines the probability of the following token at each point of
generation. The value that you set for topK
is the number of mostlikely candidates from
which the model chooses the next token in the sequence. For example, if you set topK
to 50,
the model selects the next token from among the top 50 most likely choices.public void setTopP(Float topP)
While generating a response, the model determines the probability of the following token at each point of
generation. The value that you set for Top P
determines the number of mostlikely candidates from
which the model chooses the next token in the sequence. For example, if you set topP
to 80, the
model only selects the next token from the top 80% of the probability distribution of next tokens.
topP
 While generating a response, the model determines the probability of the following token at each point of
generation. The value that you set for Top P
determines the number of mostlikely candidates
from which the model chooses the next token in the sequence. For example, if you set topP
to
80, the model only selects the next token from the top 80% of the probability distribution of next tokens.public Float getTopP()
While generating a response, the model determines the probability of the following token at each point of
generation. The value that you set for Top P
determines the number of mostlikely candidates from
which the model chooses the next token in the sequence. For example, if you set topP
to 80, the
model only selects the next token from the top 80% of the probability distribution of next tokens.
Top P
determines the number of mostlikely candidates
from which the model chooses the next token in the sequence. For example, if you set topP
to
80, the model only selects the next token from the top 80% of the probability distribution of next
tokens.public InferenceConfiguration withTopP(Float topP)
While generating a response, the model determines the probability of the following token at each point of
generation. The value that you set for Top P
determines the number of mostlikely candidates from
which the model chooses the next token in the sequence. For example, if you set topP
to 80, the
model only selects the next token from the top 80% of the probability distribution of next tokens.
topP
 While generating a response, the model determines the probability of the following token at each point of
generation. The value that you set for Top P
determines the number of mostlikely candidates
from which the model chooses the next token in the sequence. For example, if you set topP
to
80, the model only selects the next token from the top 80% of the probability distribution of next tokens.public String toString()
toString
in class Object
Object.toString()
public InferenceConfiguration clone()
public void marshall(ProtocolMarshaller protocolMarshaller)
StructuredPojo
ProtocolMarshaller
.marshall
in interface StructuredPojo
protocolMarshaller
 Implementation of ProtocolMarshaller
used to marshall this object's data.