node-llama-cpp
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Run AI models locally on your machine with node.js bindings for llama.cpp. Enforce a JSON schema on the model output on the generation level
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JavaScript
import { AsyncDisposeAggregator, EventRelay, splitText, withLock } from "lifecycle-utils";
import { tokenizeInput } from "../utils/tokenizeInput.js";
import { resolveBeginningTokenToPrepend, resolveEndTokenToAppend } from "../utils/tokenizerUtils.js";
import { isRankingTemplateValid, parseRankingTemplate } from "../gguf/insights/GgufInsights.js";
/**
* @see [Reranking Documents](https://node-llama-cpp.withcat.ai/guide/embedding#reranking) tutorial
*/
export class LlamaRankingContext {
/** @internal */ _llamaContext;
/** @internal */ _template;
/** @internal */ _sequence;
/** @internal */ _disposeAggregator = new AsyncDisposeAggregator();
onDispose = new EventRelay();
constructor({ _llamaContext, _template }) {
this._llamaContext = _llamaContext;
this._template = _template;
this._sequence = this._llamaContext.getSequence();
this._disposeAggregator.add(this._llamaContext.onDispose.createListener(() => {
void this._disposeAggregator.dispose();
}));
this._disposeAggregator.add(this.onDispose.dispatchEvent);
this._disposeAggregator.add(async () => {
await this._llamaContext.dispose();
});
}
/**
* Get the ranking score for a document for a query.
*
* A ranking score is a number between 0 and 1 representing the probability that the document is relevant to the query.
* @returns a ranking score between 0 and 1 representing the probability that the document is relevant to the query.
*/
async rank(query, document) {
const resolvedInput = this._getEvaluationInput(query, document);
if (resolvedInput.length > this._llamaContext.contextSize)
throw new Error("The input length exceed the context size. " +
`Try to increase the context size to at least ${resolvedInput.length + 1} ` +
"or use another model that supports longer contexts.");
return this._evaluateRankingForInput(resolvedInput);
}
/**
* Get the ranking scores for all the given documents for a query.
*
* A ranking score is a number between 0 and 1 representing the probability that the document is relevant to the query.
* @returns an array of ranking scores between 0 and 1 representing the probability that the document is relevant to the query.
*/
async rankAll(query, documents) {
const resolvedTokens = documents.map((document) => this._getEvaluationInput(query, document));
const maxInputTokensLength = resolvedTokens.reduce((max, tokens) => Math.max(max, tokens.length), 0);
if (maxInputTokensLength > this._llamaContext.contextSize)
throw new Error("The input lengths of some of the given documents exceed the context size. " +
`Try to increase the context size to at least ${maxInputTokensLength + 1} ` +
"or use another model that supports longer contexts.");
else if (resolvedTokens.length === 0)
return [];
return await Promise.all(resolvedTokens.map((tokens) => this._evaluateRankingForInput(tokens)));
}
/**
* Get the ranking scores for all the given documents for a query and sort them by score from highest to lowest.
*
* A ranking score is a number between 0 and 1 representing the probability that the document is relevant to the query.
*/
async rankAndSort(query, documents) {
const scores = await this.rankAll(query, documents);
return documents
.map((document, index) => ({ document: document, score: scores[index] }))
.sort((a, b) => b.score - a.score);
}
async dispose() {
await this._disposeAggregator.dispose();
}
/** @hidden */
[Symbol.asyncDispose]() {
return this.dispose();
}
get disposed() {
return this._llamaContext.disposed;
}
get model() {
return this._llamaContext.model;
}
/** @internal */
_getEvaluationInput(query, document) {
if (this._template != null) {
const resolvedInput = splitText(this._template, ["{{query}}", "{{document}}"])
.flatMap((item) => {
if (typeof item === "string")
return this._llamaContext.model.tokenize(item, true, "trimLeadingSpace");
else if (item.separator === "{{query}}")
return tokenizeInput(query, this._llamaContext.model.tokenizer, "trimLeadingSpace", false);
else if (item.separator === "{{document}}")
return tokenizeInput(document, this._llamaContext.model.tokenizer, "trimLeadingSpace", false);
else
void item;
void item;
return [];
});
const beginningTokens = resolveBeginningTokenToPrepend(this.model.vocabularyType, this.model.tokens);
const endToken = resolveEndTokenToAppend(this.model.vocabularyType, this.model.tokens);
if (beginningTokens != null && resolvedInput.at(0) !== beginningTokens)
resolvedInput.unshift(beginningTokens);
if (endToken != null && resolvedInput.at(-1) !== endToken)
resolvedInput.unshift(endToken);
return resolvedInput;
}
if (this.model.tokens.eos == null && this.model.tokens.sep == null)
throw new Error("Computing rankings is not supported for this model.");
const resolvedQuery = tokenizeInput(query, this._llamaContext.model.tokenizer, "trimLeadingSpace", false);
const resolvedDocument = tokenizeInput(document, this._llamaContext.model.tokenizer, "trimLeadingSpace", false);
if (resolvedQuery.length === 0 && resolvedDocument.length === 0)
return [];
const resolvedInput = [
...(this.model.tokens.bos == null ? [] : [this.model.tokens.bos]),
...resolvedQuery,
...(this.model.tokens.eos == null ? [] : [this.model.tokens.eos]),
...(this.model.tokens.sep == null ? [] : [this.model.tokens.sep]),
...resolvedDocument,
...(this.model.tokens.eos == null ? [] : [this.model.tokens.eos])
];
return resolvedInput;
}
/** @internal */
_evaluateRankingForInput(input) {
if (input.length === 0)
return Promise.resolve(0);
return withLock([this, "evaluate"], async () => {
await this._sequence.eraseContextTokenRanges([{
start: 0,
end: this._sequence.nextTokenIndex
}]);
const iterator = this._sequence.evaluate(input, { _noSampling: true });
// eslint-disable-next-line @typescript-eslint/no-unused-vars
for await (const token of iterator) {
break; // only generate one token to get embeddings
}
const embedding = this._llamaContext._ctx.getEmbedding(input.length, 1);
if (embedding.length === 0)
return 0;
const logit = embedding[0];
const probability = logitToSigmoid(logit);
return probability;
});
}
/** @internal */
static async _create({ _model }, { contextSize, batchSize, threads = 6, createSignal, template, ignoreMemorySafetyChecks }) {
const resolvedTemplate = template ?? parseRankingTemplate(_model.fileInfo.metadata?.tokenizer?.["chat_template.rerank"]);
if (_model.tokens.eos == null && _model.tokens.sep == null) {
if (!isRankingTemplateValid(resolvedTemplate)) {
if (resolvedTemplate === _model.fileInfo.metadata?.tokenizer?.["chat_template.rerank"])
throw new Error("The model's builtin template is invalid. It must contain both {query} and {document} placeholders.");
else
throw new Error("The provided template is invalid. It must contain both {{query}} and {{document}} placeholders.");
}
else if (resolvedTemplate == null)
throw new Error("Computing rankings is not supported for this model.");
}
if (_model.fileInsights.hasEncoder && _model.fileInsights.hasDecoder)
throw new Error("Computing rankings is not supported for encoder-decoder models.");
if (!_model.fileInsights.supportsRanking)
throw new Error("Computing rankings is not supported for this model.");
const llamaContext = await _model.createContext({
contextSize,
batchSize,
threads,
createSignal,
ignoreMemorySafetyChecks,
_embeddings: true,
_ranking: true
});
return new LlamaRankingContext({
_llamaContext: llamaContext,
_template: resolvedTemplate
});
}
}
function logitToSigmoid(logit) {
return 1 / (1 + Math.exp(-logit));
}
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