node-llama-cpp

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Type Alias: LLamaChatCompletePromptOptions ​

ts
type LLamaChatCompletePromptOptions = {
  maxTokens: LLamaChatPromptOptions["maxTokens"];
  stopOnAbortSignal: LLamaChatPromptOptions["stopOnAbortSignal"];
  onTextChunk: LLamaChatPromptOptions["onTextChunk"];
  onToken: LLamaChatPromptOptions["onToken"];
  signal: LLamaChatPromptOptions["signal"];
  temperature: LLamaChatPromptOptions["temperature"];
  minP: LLamaChatPromptOptions["minP"];
  topK: LLamaChatPromptOptions["topK"];
  topP: LLamaChatPromptOptions["topP"];
  seed: LLamaChatPromptOptions["seed"];
  trimWhitespaceSuffix: LLamaChatPromptOptions["trimWhitespaceSuffix"];
  evaluationPriority: LLamaChatPromptOptions["evaluationPriority"];
  repeatPenalty: LLamaChatPromptOptions["repeatPenalty"];
  tokenBias: LLamaChatPromptOptions["tokenBias"];
  customStopTriggers: LLamaChatPromptOptions["customStopTriggers"];
  grammar: LlamaGrammar;
  functions: ChatSessionModelFunctions;
  documentFunctionParams: boolean;
};

Defined in: evaluator/LlamaChatSession/LlamaChatSession.ts:208

Type declaration ​

maxTokens? ​

ts
optional maxTokens: LLamaChatPromptOptions["maxTokens"];

Generate a completion for the given user prompt up to the given number of tokens.

Defaults to 256 or half the context size, whichever is smaller.

stopOnAbortSignal? ​

ts
optional stopOnAbortSignal: LLamaChatPromptOptions["stopOnAbortSignal"];

When a completion already started being generated and then the given signal is aborted, the generation will stop and the completion will be returned as-is instead of throwing an error.

Defaults to false.

onTextChunk? ​

ts
optional onTextChunk: LLamaChatPromptOptions["onTextChunk"];

Called as the model generates a completion with the generated text chunk.

Useful for streaming the generated completion as it's being generated.

onToken? ​

ts
optional onToken: LLamaChatPromptOptions["onToken"];

Called as the model generates a completion with the generated tokens.

Preferably, you'd want to use onTextChunk instead of this.

signal? ​

ts
optional signal: LLamaChatPromptOptions["signal"];

temperature? ​

ts
optional temperature: LLamaChatPromptOptions["temperature"];

minP? ​

ts
optional minP: LLamaChatPromptOptions["minP"];

topK? ​

ts
optional topK: LLamaChatPromptOptions["topK"];

topP? ​

ts
optional topP: LLamaChatPromptOptions["topP"];

seed? ​

ts
optional seed: LLamaChatPromptOptions["seed"];

trimWhitespaceSuffix? ​

ts
optional trimWhitespaceSuffix: LLamaChatPromptOptions["trimWhitespaceSuffix"];

evaluationPriority? ​

ts
optional evaluationPriority: LLamaChatPromptOptions["evaluationPriority"];

repeatPenalty? ​

ts
optional repeatPenalty: LLamaChatPromptOptions["repeatPenalty"];

tokenBias? ​

ts
optional tokenBias: LLamaChatPromptOptions["tokenBias"];

customStopTriggers? ​

ts
optional customStopTriggers: LLamaChatPromptOptions["customStopTriggers"];

grammar? ​

ts
optional grammar: LlamaGrammar;

functions? ​

ts
optional functions: ChatSessionModelFunctions;

Functions are not used by the model here, but are used for keeping the instructions given to the model about the functions in the current context state, to avoid context shifts.

It's best to provide the same functions that were used for the previous prompt here.

documentFunctionParams? ​

ts
optional documentFunctionParams: boolean;

Functions are not used by the model here, but are used for keeping the instructions given to the model about the functions in the current context state, to avoid context shifts.

It's best to provide the same value that was used for the previous prompt here.