const { v4 } = require("uuid"); const { writeResponseChunk, clientAbortedHandler, } = require("../../helpers/chat/responses"); const { NativeEmbedder } = require("../../EmbeddingEngines/native"); class AnthropicLLM { constructor(embedder = null, modelPreference = null) { if (!process.env.ANTHROPIC_API_KEY) throw new Error("No Anthropic API key was set."); // Docs: https://www.npmjs.com/package/@anthropic-ai/sdk const AnthropicAI = require("@anthropic-ai/sdk"); const anthropic = new AnthropicAI({ apiKey: process.env.ANTHROPIC_API_KEY, }); this.anthropic = anthropic; this.model = modelPreference || process.env.ANTHROPIC_MODEL_PREF || "claude-2.0"; this.limits = { history: this.promptWindowLimit() * 0.15, system: this.promptWindowLimit() * 0.15, user: this.promptWindowLimit() * 0.7, }; this.embedder = embedder ?? new NativeEmbedder(); this.defaultTemp = 0.7; } streamingEnabled() { return "streamGetChatCompletion" in this; } promptWindowLimit() { switch (this.model) { case "claude-instant-1.2": return 100_000; case "claude-2.0": return 100_000; case "claude-2.1": return 200_000; case "claude-3-opus-20240229": return 200_000; case "claude-3-sonnet-20240229": return 200_000; case "claude-3-haiku-20240307": return 200_000; case "claude-3-5-sonnet-20240620": return 200_000; default: return 100_000; // assume a claude-instant-1.2 model } } isValidChatCompletionModel(modelName = "") { const validModels = [ "claude-instant-1.2", "claude-2.0", "claude-2.1", "claude-3-opus-20240229", "claude-3-sonnet-20240229", "claude-3-haiku-20240307", "claude-3-5-sonnet-20240620", ]; return validModels.includes(modelName); } // Moderation can be done with Anthropic, but its not really "exact" so we skip it // https://docs.anthropic.com/claude/docs/content-moderation async isSafe(_input = "") { // Not implemented so must be stubbed return { safe: true, reasons: [] }; } constructPrompt({ systemPrompt = "", contextTexts = [], chatHistory = [], userPrompt = "", }) { const prompt = { role: "system", content: `${systemPrompt}${this.#appendContext(contextTexts)}`, }; return [prompt, ...chatHistory, { role: "user", content: userPrompt }]; } async getChatCompletion(messages = null, { temperature = 0.7 }) { if (!this.isValidChatCompletionModel(this.model)) throw new Error( `Anthropic chat: ${this.model} is not valid for chat completion!` ); try { const response = await this.anthropic.messages.create({ model: this.model, max_tokens: 4096, system: messages[0].content, // Strip out the system message messages: messages.slice(1), // Pop off the system message temperature: Number(temperature ?? this.defaultTemp), }); return response.content[0].text; } catch (error) { console.log(error); return error; } } async streamGetChatCompletion(messages = null, { temperature = 0.7 }) { if (!this.isValidChatCompletionModel(this.model)) throw new Error( `Anthropic chat: ${this.model} is not valid for chat completion!` ); const streamRequest = await this.anthropic.messages.stream({ model: this.model, max_tokens: 4096, system: messages[0].content, // Strip out the system message messages: messages.slice(1), // Pop off the system message temperature: Number(temperature ?? this.defaultTemp), }); return streamRequest; } handleStream(response, stream, responseProps) { return new Promise((resolve) => { let fullText = ""; const { uuid = v4(), sources = [] } = responseProps; // Establish listener to early-abort a streaming response // in case things go sideways or the user does not like the response. // We preserve the generated text but continue as if chat was completed // to preserve previously generated content. const handleAbort = () => clientAbortedHandler(resolve, fullText); response.on("close", handleAbort); stream.on("error", (event) => { const parseErrorMsg = (event) => { const error = event?.error?.error; if (!!error) return `Anthropic Error:${error?.type || "unknown"} ${ error?.message || "unknown error." }`; return event.message; }; writeResponseChunk(response, { uuid, sources: [], type: "abort", textResponse: null, close: true, error: parseErrorMsg(event), }); response.removeListener("close", handleAbort); resolve(fullText); }); stream.on("streamEvent", (message) => { const data = message; if ( data.type === "content_block_delta" && data.delta.type === "text_delta" ) { const text = data.delta.text; fullText += text; writeResponseChunk(response, { uuid, sources, type: "textResponseChunk", textResponse: text, close: false, error: false, }); } if ( message.type === "message_stop" || (data.stop_reason && data.stop_reason === "end_turn") ) { writeResponseChunk(response, { uuid, sources, type: "textResponseChunk", textResponse: "", close: true, error: false, }); response.removeListener("close", handleAbort); resolve(fullText); } }); }); } #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("") ); } async compressMessages(promptArgs = {}, rawHistory = []) { const { messageStringCompressor } = require("../../helpers/chat"); const compressedPrompt = await messageStringCompressor( this, promptArgs, rawHistory ); return compressedPrompt; } // 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); } } module.exports = { AnthropicLLM, };