Optional callbacksCallbacks for this call and any sub-calls (eg. a Chain calling an LLM). Tags are passed to all callbacks, metadata is passed to handle*Start callbacks.
Optional configurableRuntime values for attributes previously made configurable on this Runnable, or sub-Runnables.
Optional logprobsAn integer that specifies how many top token log probabilities are included in the response for each token generation step.
Optional metadataMetadata for this call and any sub-calls (eg. a Chain calling an LLM). Keys should be strings, values should be JSON-serializable.
Optional repetitionA number that controls the diversity of generated text by reducing the likelihood of repeated sequences. Higher values decrease repetition.
{1}
Optional runName for the tracer run for this call. Defaults to the name of the class.
Optional safetyRun an LLM-based input-output safeguard model on top of any model.
Optional signalAbort signal for this call. If provided, the call will be aborted when the signal is aborted.
Optional stopStop tokens to use for this call. If not provided, the default stop tokens for the model will be used.
Optional tagsTags for this call and any sub-calls (eg. a Chain calling an LLM). You can use these to filter calls.
Optional temperatureA decimal number that determines the degree of randomness in the response. A value of 1 will always yield the same output. A temperature less than 1 favors more correctness and is appropriate for question answering or summarization. A value greater than 1 introduces more randomness in the output.
{0.7}
Optional timeoutTimeout for this call in milliseconds.
Optional topKThe topK parameter is used to limit the number of choices for the next predicted word or token.
It specifies the maximum number of tokens to consider at each step, based on their probability of occurrence.
This technique helps to speed up the generation process and can improve the quality of the generated text by focusing on the most likely options.
{50}
Optional topPThe topP (nucleus) parameter is used to dynamically adjust the number of choices for each predicted token based on the cumulative probabilities.
It specifies a probability threshold, below which all less likely tokens are filtered out.
This technique helps to maintain diversity and generate more fluent and natural-sounding text.
{0.7}
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The name of the model to query.