const { AzureOpenAiEmbedder } = require("../../EmbeddingEngines/azureOpenAi"); const { writeResponseChunk, clientAbortedHandler, } = require("../../helpers/chat/responses"); 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 ${i}]\n\n`; }) .join("") ); } streamingEnabled() { return "streamGetChatCompletion" in this; } // Sure the user selected a proper value for the token limit // could be any of these https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models#gpt-4-models // and if undefined - assume it is the lowest end. promptWindowLimit() { return !!process.env.AZURE_OPENAI_TOKEN_LIMIT ? Number(process.env.AZURE_OPENAI_TOKEN_LIMIT) : 4096; } isValidChatCompletionModel(_modelName = "") { // The Azure user names their "models" as deployments and they can be any name // so we rely on the user to put in the correct deployment as only they would // know it. return true; } constructPrompt({ systemPrompt = "", contextTexts = [], chatHistory = [], userPrompt = "", }) { const prompt = { role: "system", content: `${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 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 stream; } handleStream(response, stream, responseProps) { const { uuid = uuidv4(), sources = [] } = responseProps; return new Promise(async (resolve) => { let fullText = ""; // Establish listener to early-abort a streaming response // in case things go sideways or the user does not like the response. // We preserve the generated text but continue as if chat was completed // to preserve previously generated content. const handleAbort = () => clientAbortedHandler(resolve, fullText); response.on("close", handleAbort); for await (const event of stream) { for (const choice of event.choices) { const delta = choice.delta?.content; if (!delta) continue; fullText += delta; writeResponseChunk(response, { uuid, sources: [], type: "textResponseChunk", textResponse: delta, close: false, error: false, }); } } writeResponseChunk(response, { uuid, sources, type: "textResponseChunk", textResponse: "", close: true, error: false, }); response.removeListener("close", handleAbort); resolve(fullText); }); } // 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, };