mirror of
https://github.com/Mintplex-Labs/anything-llm.git
synced 2024-09-22 04:10:10 +02:00
186 lines
5.5 KiB
JavaScript
186 lines
5.5 KiB
JavaScript
const { NativeEmbedder } = require("../../EmbeddingEngines/native");
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const { OpenAiEmbedder } = require("../../EmbeddingEngines/openAi");
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const { chatPrompt } = require("../../chats");
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class HuggingFaceLLM {
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constructor(embedder = null, _modelPreference = null) {
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const { Configuration, OpenAIApi } = require("openai");
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if (!process.env.HUGGING_FACE_LLM_ENDPOINT)
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throw new Error("No HuggingFace Inference Endpoint was set.");
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if (!process.env.HUGGING_FACE_LLM_API_KEY)
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throw new Error("No HuggingFace Access Token was set.");
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const config = new Configuration({
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basePath: `${process.env.HUGGING_FACE_LLM_ENDPOINT}/v1`,
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apiKey: process.env.HUGGING_FACE_LLM_API_KEY,
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});
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this.openai = new OpenAIApi(config);
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// When using HF inference server - the model param is not required so
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// we can stub it here. HF Endpoints can only run one model at a time.
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// We set to 'tgi' so that endpoint for HF can accept message format
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this.model = "tgi";
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this.limits = {
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history: this.promptWindowLimit() * 0.15,
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system: this.promptWindowLimit() * 0.15,
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user: this.promptWindowLimit() * 0.7,
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};
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if (!embedder)
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console.warn(
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"No embedding provider defined for HuggingFaceLLM - falling back to Native for embedding!"
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);
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this.embedder = !embedder ? new OpenAiEmbedder() : new NativeEmbedder();
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this.defaultTemp = 0.2;
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}
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#appendContext(contextTexts = []) {
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if (!contextTexts || !contextTexts.length) return "";
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return (
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"\nContext:\n" +
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contextTexts
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.map((text, i) => {
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return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`;
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})
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.join("")
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);
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}
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streamingEnabled() {
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return "streamChat" in this && "streamGetChatCompletion" in this;
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}
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promptWindowLimit() {
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const limit = process.env.HUGGING_FACE_LLM_TOKEN_LIMIT || 4096;
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if (!limit || isNaN(Number(limit)))
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throw new Error("No HuggingFace token context limit was set.");
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return Number(limit);
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}
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async isValidChatCompletionModel(_ = "") {
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return true;
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}
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constructPrompt({
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systemPrompt = "",
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contextTexts = [],
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chatHistory = [],
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userPrompt = "",
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}) {
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// System prompt it not enabled for HF model chats
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const prompt = {
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role: "user",
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content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
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};
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const assistantResponse = {
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role: "assistant",
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content: "Okay, I will follow those instructions",
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};
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return [
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prompt,
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assistantResponse,
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...chatHistory,
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{ role: "user", content: userPrompt },
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];
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}
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async isSafe(_input = "") {
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// Not implemented so must be stubbed
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return { safe: true, reasons: [] };
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}
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async sendChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
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const textResponse = await this.openai
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.createChatCompletion({
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model: this.model,
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temperature: Number(workspace?.openAiTemp ?? this.defaultTemp),
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n: 1,
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messages: await this.compressMessages(
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{
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systemPrompt: chatPrompt(workspace),
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userPrompt: prompt,
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chatHistory,
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},
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rawHistory
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),
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})
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.then((json) => {
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const res = json.data;
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if (!res.hasOwnProperty("choices"))
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throw new Error("HuggingFace chat: No results!");
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if (res.choices.length === 0)
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throw new Error("HuggingFace chat: No results length!");
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return res.choices[0].message.content;
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})
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.catch((error) => {
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throw new Error(
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`HuggingFace::createChatCompletion failed with: ${error.message}`
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);
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});
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return textResponse;
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}
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async streamChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
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const streamRequest = await this.openai.createChatCompletion(
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{
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model: this.model,
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stream: true,
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temperature: Number(workspace?.openAiTemp ?? this.defaultTemp),
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n: 1,
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messages: await this.compressMessages(
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{
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systemPrompt: chatPrompt(workspace),
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userPrompt: prompt,
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chatHistory,
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},
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rawHistory
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),
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},
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{ responseType: "stream" }
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);
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return { type: "huggingFaceStream", stream: streamRequest };
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}
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async getChatCompletion(messages = null, { temperature = 0.7 }) {
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const { data } = await this.openai.createChatCompletion({
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model: this.model,
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messages,
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temperature,
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});
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if (!data.hasOwnProperty("choices")) return null;
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return data.choices[0].message.content;
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}
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async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
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const streamRequest = await this.openai.createChatCompletion(
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{
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model: this.model,
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stream: true,
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messages,
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temperature,
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},
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{ responseType: "stream" }
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);
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return { type: "huggingFaceStream", stream: streamRequest };
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}
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// Simple wrapper for dynamic embedder & normalize interface for all LLM implementations
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async embedTextInput(textInput) {
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return await this.embedder.embedTextInput(textInput);
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}
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async embedChunks(textChunks = []) {
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return await this.embedder.embedChunks(textChunks);
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}
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async compressMessages(promptArgs = {}, rawHistory = []) {
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const { messageArrayCompressor } = require("../../helpers/chat");
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const messageArray = this.constructPrompt(promptArgs);
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return await messageArrayCompressor(this, messageArray, rawHistory);
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}
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}
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module.exports = {
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HuggingFaceLLM,
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};
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