114 lines
3.4 KiB
JavaScript
114 lines
3.4 KiB
JavaScript
const { toChunks, maximumChunkLength } = require("../../helpers");
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class LocalAiEmbedder {
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constructor() {
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if (!process.env.EMBEDDING_BASE_PATH)
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throw new Error("No embedding base path was set.");
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if (!process.env.EMBEDDING_MODEL_PREF)
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throw new Error("No embedding model was set.");
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this.className = "LocalAiEmbedder";
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const { OpenAI: OpenAIApi } = require("openai");
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this.model = process.env.EMBEDDING_MODEL_PREF;
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this.openai = new OpenAIApi({
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baseURL: process.env.EMBEDDING_BASE_PATH,
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apiKey: process.env.LOCAL_AI_API_KEY ?? null,
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});
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// Limit of how many strings we can process in a single pass to stay with resource or network limits
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this.maxConcurrentChunks = 50;
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this.embeddingMaxChunkLength = maximumChunkLength();
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this.log(
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`Initialized with ${this.model} - Max Size: ${this.embeddingMaxChunkLength}` +
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(this.outputDimensions
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? ` - Output Dimensions: ${this.outputDimensions}`
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: " Assuming default output dimensions")
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);
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}
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log(text, ...args) {
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console.log(`\x1b[36m[${this.className}]\x1b[0m ${text}`, ...args);
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}
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get outputDimensions() {
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if (
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process.env.EMBEDDING_OUTPUT_DIMENSIONS &&
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!isNaN(process.env.EMBEDDING_OUTPUT_DIMENSIONS) &&
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process.env.EMBEDDING_OUTPUT_DIMENSIONS > 0
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)
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return parseInt(process.env.EMBEDDING_OUTPUT_DIMENSIONS);
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return null;
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}
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async embedTextInput(textInput) {
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const result = await this.embedChunks(
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Array.isArray(textInput) ? textInput : [textInput]
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);
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return result?.[0] || [];
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}
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async embedChunks(textChunks = []) {
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const embeddingRequests = [];
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for (const chunk of toChunks(textChunks, this.maxConcurrentChunks)) {
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embeddingRequests.push(
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new Promise((resolve) => {
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this.openai.embeddings
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.create({
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model: this.model,
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input: chunk,
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dimensions: this.outputDimensions,
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})
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.then((result) => {
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resolve({ data: result?.data, error: null });
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})
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.catch((e) => {
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e.type =
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e?.response?.data?.error?.code ||
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e?.response?.status ||
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"failed_to_embed";
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e.message = e?.response?.data?.error?.message || e.message;
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resolve({ data: [], error: e });
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});
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})
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);
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}
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const { data = [], error = null } = await Promise.all(
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embeddingRequests
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).then((results) => {
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// If any errors were returned from LocalAI abort the entire sequence because the embeddings
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// will be incomplete.
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const errors = results
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.filter((res) => !!res.error)
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.map((res) => res.error)
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.flat();
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if (errors.length > 0) {
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let uniqueErrors = new Set();
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errors.map((error) =>
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uniqueErrors.add(`[${error.type}]: ${error.message}`)
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);
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return {
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data: [],
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error: Array.from(uniqueErrors).join(", "),
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};
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}
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return {
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data: results.map((res) => res?.data || []).flat(),
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error: null,
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};
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});
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if (!!error) throw new Error(`LocalAI Failed to embed: ${error}`);
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return data.length > 0 &&
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data.every((embd) => embd.hasOwnProperty("embedding"))
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? data.map((embd) => embd.embedding)
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: null;
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}
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}
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module.exports = {
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LocalAiEmbedder,
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};
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