mirror of
https://github.com/Mintplex-Labs/anything-llm.git
synced 2024-09-11 23:03:25 +02:00
243 lines
6.8 KiB
JavaScript
243 lines
6.8 KiB
JavaScript
const { OpenAiEmbedder } = require("../../EmbeddingEngines/openAi");
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const { chatPrompt } = require("../../chats");
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class OpenAiLLM {
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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.OPEN_AI_KEY) throw new Error("No OpenAI API key was set.");
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const config = new Configuration({
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apiKey: process.env.OPEN_AI_KEY,
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});
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this.openai = new OpenAIApi(config);
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this.model =
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modelPreference || process.env.OPEN_MODEL_PREF || "gpt-3.5-turbo";
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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 OpenAiLLM - falling back to OpenAiEmbedder for embedding!"
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);
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this.embedder = !embedder ? new OpenAiEmbedder() : embedder;
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this.defaultTemp = 0.7;
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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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switch (this.model) {
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case "gpt-3.5-turbo":
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return 4096;
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case "gpt-3.5-turbo-1106":
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return 16385;
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case "gpt-4":
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return 8192;
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case "gpt-4-1106-preview":
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return 128000;
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case "gpt-4-turbo-preview":
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return 128000;
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case "gpt-4-32k":
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return 32000;
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default:
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return 4096; // assume a fine-tune 3.5
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}
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}
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async isValidChatCompletionModel(modelName = "") {
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const validModels = [
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"gpt-4",
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"gpt-3.5-turbo",
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"gpt-3.5-turbo-1106",
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"gpt-4-1106-preview",
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"gpt-4-turbo-preview",
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"gpt-4-32k",
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];
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const isPreset = validModels.some((model) => modelName === model);
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if (isPreset) return true;
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const model = await this.openai
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.retrieveModel(modelName)
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.then((res) => res.data)
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.catch(() => null);
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return !!model;
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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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const prompt = {
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role: "system",
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content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
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};
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return [prompt, ...chatHistory, { role: "user", content: userPrompt }];
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}
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async isSafe(input = "") {
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const { flagged = false, categories = {} } = await this.openai
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.createModeration({ input })
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.then((json) => {
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const res = json.data;
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if (!res.hasOwnProperty("results"))
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throw new Error("OpenAI moderation: No results!");
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if (res.results.length === 0)
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throw new Error("OpenAI moderation: No results length!");
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return res.results[0];
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})
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.catch((error) => {
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throw new Error(
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`OpenAI::CreateModeration failed with: ${error.message}`
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);
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});
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if (!flagged) return { safe: true, reasons: [] };
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const reasons = Object.keys(categories)
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.map((category) => {
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const value = categories[category];
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if (value === true) {
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return category.replace("/", " or ");
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} else {
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return null;
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}
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})
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.filter((reason) => !!reason);
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return { safe: false, reasons };
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}
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async sendChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
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if (!(await this.isValidChatCompletionModel(this.model)))
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throw new Error(
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`OpenAI chat: ${this.model} is not valid for chat completion!`
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);
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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("OpenAI chat: No results!");
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if (res.choices.length === 0)
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throw new Error("OpenAI 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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`OpenAI::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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if (!(await this.isValidChatCompletionModel(this.model)))
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throw new Error(
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`OpenAI chat: ${this.model} is not valid for chat completion!`
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);
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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 streamRequest;
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}
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async getChatCompletion(messages = null, { temperature = 0.7 }) {
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if (!(await this.isValidChatCompletionModel(this.model)))
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throw new Error(
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`OpenAI chat: ${this.model} is not valid for chat completion!`
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);
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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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if (!(await this.isValidChatCompletionModel(this.model)))
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throw new Error(
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`OpenAI chat: ${this.model} is not valid for chat completion!`
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);
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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 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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OpenAiLLM,
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};
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