const { AzureOpenAiEmbedder } = require("../../EmbeddingEngines/azureOpenAi"); const { chatPrompt } = require("../../chats"); class AzureOpenAiLLM { constructor(embedder = null, _modelPreference = null) { const { OpenAIClient, AzureKeyCredential } = require("@azure/openai"); if (!process.env.AZURE_OPENAI_ENDPOINT) throw new Error("No Azure API endpoint was set."); if (!process.env.AZURE_OPENAI_KEY) throw new Error("No Azure API key was set."); this.openai = new OpenAIClient( process.env.AZURE_OPENAI_ENDPOINT, new AzureKeyCredential(process.env.AZURE_OPENAI_KEY) ); this.model = process.env.OPEN_MODEL_PREF; this.limits = { history: this.promptWindowLimit() * 0.15, system: this.promptWindowLimit() * 0.15, user: this.promptWindowLimit() * 0.7, }; if (!embedder) console.warn( "No embedding provider defined for AzureOpenAiLLM - falling back to AzureOpenAiEmbedder for embedding!" ); this.embedder = !embedder ? new AzureOpenAiEmbedder() : embedder; this.defaultTemp = 0.7; } #appendContext(contextTexts = []) { if (!contextTexts || !contextTexts.length) return ""; return ( "\nContext:\n" + contextTexts .map((text, i) => { return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`; }) .join("") ); } streamingEnabled() { return "streamChat" in this && "streamGetChatCompletion" in this; } // Sure the user selected a proper value for the token limit // could be any of these https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models#gpt-4-models // and if undefined - assume it is the lowest end. promptWindowLimit() { return !!process.env.AZURE_OPENAI_TOKEN_LIMIT ? Number(process.env.AZURE_OPENAI_TOKEN_LIMIT) : 4096; } isValidChatCompletionModel(_modelName = "") { // The Azure user names their "models" as deployments and they can be any name // so we rely on the user to put in the correct deployment as only they would // know it. return true; } constructPrompt({ systemPrompt = "", contextTexts = [], chatHistory = [], userPrompt = "", }) { const prompt = { role: "system", content: `${systemPrompt}${this.#appendContext(contextTexts)}`, }; return [prompt, ...chatHistory, { role: "user", content: userPrompt }]; } async isSafe(_input = "") { // Not implemented by Azure OpenAI so must be stubbed return { safe: true, reasons: [] }; } async sendChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) { if (!this.model) throw new Error( "No OPEN_MODEL_PREF ENV defined. This must the name of a deployment on your Azure account for an LLM chat model like GPT-3.5." ); const messages = await this.compressMessages( { systemPrompt: chatPrompt(workspace), userPrompt: prompt, chatHistory, }, rawHistory ); const textResponse = await this.openai .getChatCompletions(this.model, messages, { temperature: Number(workspace?.openAiTemp ?? this.defaultTemp), n: 1, }) .then((res) => { if (!res.hasOwnProperty("choices")) throw new Error("AzureOpenAI chat: No results!"); if (res.choices.length === 0) throw new Error("AzureOpenAI chat: No results length!"); return res.choices[0].message.content; }) .catch((error) => { console.log(error); throw new Error( `AzureOpenAI::getChatCompletions failed with: ${error.message}` ); }); return textResponse; } async streamChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) { if (!this.model) throw new Error( "No OPEN_MODEL_PREF ENV defined. This must the name of a deployment on your Azure account for an LLM chat model like GPT-3.5." ); const messages = await this.compressMessages( { systemPrompt: chatPrompt(workspace), userPrompt: prompt, chatHistory, }, rawHistory ); const stream = await this.openai.streamChatCompletions( this.model, messages, { temperature: Number(workspace?.openAiTemp ?? this.defaultTemp), n: 1, } ); return { type: "azureStream", stream }; } async getChatCompletion(messages = [], { temperature = 0.7 }) { if (!this.model) throw new Error( "No OPEN_MODEL_PREF ENV defined. This must the name of a deployment on your Azure account for an LLM chat model like GPT-3.5." ); const data = await this.openai.getChatCompletions(this.model, messages, { temperature, }); if (!data.hasOwnProperty("choices")) return null; return data.choices[0].message.content; } async streamGetChatCompletion(messages = [], { temperature = 0.7 }) { if (!this.model) throw new Error( "No OPEN_MODEL_PREF ENV defined. This must the name of a deployment on your Azure account for an LLM chat model like GPT-3.5." ); const stream = await this.openai.streamChatCompletions( this.model, messages, { temperature, n: 1, } ); return { type: "azureStream", stream }; } // Simple wrapper for dynamic embedder & normalize interface for all LLM implementations async embedTextInput(textInput) { return await this.embedder.embedTextInput(textInput); } async embedChunks(textChunks = []) { return await this.embedder.embedChunks(textChunks); } async compressMessages(promptArgs = {}, rawHistory = []) { const { messageArrayCompressor } = require("../../helpers/chat"); const messageArray = this.constructPrompt(promptArgs); return await messageArrayCompressor(this, messageArray, rawHistory); } } module.exports = { AzureOpenAiLLM, };