create-nodex/dist/templates/ai/common/typescript/ai-setup.js

CLI tool to create modern Node.js projects with TypeScript, AI capabilities, and more

import { ChatPromptTemplate } from "@langchain/core/prompts";
import { HumanMessage, SystemMessage, } from "@langchain/core/messages";
// This is a basic setup file for Langchain.
// You will need to configure the model and API keys based on your chosen provider.
// Example: Using OpenAI (remember to set your OPENAI_API_KEY environment variable)
// const model = new ChatOpenAI({
//   model: "gpt-4", // Or your preferred model
//   temperature: 0.7,
// });
/**
 * A simple function to demonstrate a chat completion.
 * @param modelInstance An instance of a Langchain chat model.
 * @param language The language to translate into.
 * @param text The text to translate.
 * @returns The translated text.
 */
export async function translateText(modelInstance, // Replace 'any' with the specific model type, e.g., ChatOpenAI
language, text) {
    const systemTemplate = "Translate the following from English into {language}.";
    const promptTemplate = ChatPromptTemplate.fromMessages([
        ["system", systemTemplate],
        ["user", "{text}"],
    ]);
    try {
        const chain = promptTemplate.pipe(modelInstance);
        const result = (await chain.invoke({ language, text }));
        return result?.content?.toString() || null;
    }
    catch (error) {
        console.error("Error during translation:", error);
        return null;
    }
}
// You can create more functions here to interact with your chosen LLM.
// For example, a function to have a more general conversation:
/**
 * A simple function to send a message to the LLM and get a response.
 * @param modelInstance An instance of a Langchain chat model.
 * @param userMessage The message from the user.
 * @param systemContext Optional system context to guide the LLM.
 * @returns The LLM's response.
 */
export async function askLLM(modelInstance, // Replace 'any' with the specific model type
userMessage, systemContext) {
    const messages = [];
    if (systemContext) {
        messages.push(new SystemMessage(systemContext));
    }
    messages.push(new HumanMessage(userMessage));
    try {
        const response = (await modelInstance.invoke(messages));
        return response?.content?.toString() || null;
    }
    catch (error) {
        console.error("Error interacting with LLM:", error);
        return null;
    }
}
console.log("Langchain AI setup file loaded. You can import these functions into your project.");
console.log("Remember to initialize your chosen model and pass it to the functions.");