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Prerequisites

The Starter-Kit comes with the OpenAI API integration of Lanchain from the start. To use LangChain, you need to:
  • Get your OpenAI API key from the OpenAI API website.

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Setup

  1. Add your OpenAI API key to the .env file.
.env
OPENAI_API_KEY=your-openai-api-key
  1. The Starter-Kit already includes the necessary configuration for LangChain in src/lib/ai/langchain.ts, if you have selected the LangChain Provider via the CLI. This file initializes the OpenAI language model client.

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Usage

To use LangChain in your application, you can create an API route that interacts with the language model. Here’s an example of how to set up a route that uses the OpenAI model for text generation: 
import { kv } from '@vercel/kv';
import { llm } from '@/lib/ai/langchain';
import { Ratelimit } from '@upstash/ratelimit';
import { NextRequest, NextResponse } from 'next/server';
import { PromptTemplate } from "@langchain/core/prompts";// Allow streaming responses up to 30 seconds

export const maxDuration = 30;

// Create Rate limit
const ratelimit = new Ratelimit({
    redis: kv,
    limiter: Ratelimit.fixedWindow(5, '30s'),
});

export async function POST(req: NextRequest) {
    // call ratelimit with request ip
    const { success, remaining } = await ratelimit.limit(req.ip ?? 'ip');

    // block the request if unsuccessful
    if (!success) return new Response('Rate limit exceeded', { status: 429 });

    const { topic } = await req.json();

    try {
        // Create a prompt template
        const template = "Write a short paragraph about {topic}.";
        const prompt = PromptTemplate.fromTemplate(template);

        // Generate the text
        const formattedPrompt = await prompt.format({ topic });
        const result = await llm.call(formattedPrompt);

        return NextResponse.json({ generatedText: result });
    } catch (error) {
        console.error('Error generating text:', error);
        return NextResponse.json({ error: 'Failed to generate text' }, { status: 500 });
    }
}

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Advanced Usage

LangChain offers many powerful features beyond simple text generation. Here are a few examples:

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Chains

Chains allow you to combine multiple steps of processing. For example, you can create a chain that generates a question and then answers it: 
import { LLMChain } from "langchain/chains";
import { loadDocuments } from "@langchain/core";
import { llm } from "@/lib/ai/langchain";

const documents = await loadDocuments("path/to/your/documents.txt");

const qaChain = new LLMChain({
	llm,
	prompt: {
		template:
			"Question: {question}\nAnswer the question based on the provided context:\n{context}",
		inputVariables: ["question", "context"],
	},
});

const answer = await qaChain.invoke({
	question: "What is the main topic of the documents?",
	context: documents.pageContent.join("\n\n"),
});
console.log({ answer });

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Agents

Agents can use tools to gather information and make decisions. Here’s a simple example using a calculator tool: 
import { initializeAgentExecutorWithOptions } from "langchain/agents";
import { Calculator } from "@langchain/community/tools/calculator";
import { llm } from "@/lib/ai/langchain";

const tools = [new Calculator()];

const executor = await initializeAgentExecutorWithOptions(tools, llm, {
	agentType: "zero-shot-react-description",
	verbose: true,
});

const input = `What is the square root of 256 multiplied by 2?`;

const result = await executor.invoke({ input });

console.log(`Got output ${JSON.stringify(result, null, 2)}`);

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Model Selection

If you want to use a different model, you can change the model or provider in the src/lib/ai/langchain.ts file. You can explore the available models on the Langchain Website. See the Langchain documentation for more information on the available models and their usage.