Getting Started with LangChain: A Language Model API

Introduction

In this post, we explore LangChain—a powerful library designed to help you build language model applications. LangChain streamlines the process of chaining together various components such as prompts, LLMs, and data sources, enabling you to create complex workflows effortlessly. Whether you're building a chatbot, translation tool, or an AI assistant, LangChain provides the building blocks you need.

To learn more, check out the official LangChain Introduction and Concepts pages.

Step 1: Setting Up the Environment

First things first: install LangChain along with any required dependencies. You can use npm or yarn:

npm install langchain openai

Also, ensure your OPENAI_API_KEY is stored as an environment variable. This key is essential if you plan to use OpenAI’s language models.

Step 2: Creating a Prompt and Chain

LangChain allows you to create customizable prompts that can be chained together with LLMs. In this example, we build a simple translation chain that converts English text to French.

import { OpenAI } from 'langchain/llms/openai';
import { LLMChain } from 'langchain/chains';
import { PromptTemplate } from 'langchain/prompts';

// Define a prompt template for translation
const prompt = new PromptTemplate({
  template: "Translate the following English text to French: {text}",
  inputVariables: ["text"],
});

// Initialize the language model (using OpenAI)
const llm = new OpenAI({
  openAIApiKey: process.env.OPENAI_API_KEY,
  modelName: "text-davinci-003",
});

// Create the chain by combining the prompt and the language model
const chain = new LLMChain({ llm, prompt });

// Function to translate text using the chain
export async function translateText(text: string) {
  const response = await chain.call({ text });
  return response.text;
}

// Example usage:
translateText("Hello, how are you?").then((result) => {
  console.log("Translation:", result);
});

This chain accepts an input text, passes it through the prompt template, and returns the French translation. LangChain handles the formatting and communication with the LLM, letting you focus on building your workflow.

Step 3: Building a Complex Chain

LangChain isn’t limited to simple, one-step chains. You can create multi-step chains that process data through several stages. For example, you can first summarize a text and then translate that summary.

import { OpenAI } from 'langchain/llms/openai';
import { LLMChain, SequentialChain } from 'langchain/chains';
import { PromptTemplate } from 'langchain/prompts';

// Initialize the language model
const llm = new OpenAI({
  openAIApiKey: process.env.OPENAI_API_KEY,
  modelName: "text-davinci-003",
});

// Step 1: Summarize the text
const summaryPrompt = new PromptTemplate({
  template: "Summarize the following text: {text}",
  inputVariables: ["text"],
});
const summaryChain = new LLMChain({ llm, prompt: summaryPrompt });

// Step 2: Translate the summary to French
const translationPrompt = new PromptTemplate({
  template: "Translate the following summary to French: {summary}",
  inputVariables: ["summary"],
});
const translationChain = new LLMChain({ llm, prompt: translationPrompt });

// Combine the two chains sequentially
const combinedChain = new SequentialChain({
  chains: [summaryChain, translationChain],
  inputVariables: ["text"],
  outputVariables: ["summary", "translation"],
});

// Function to process text through the combined chain
export async function summarizeAndTranslate(text: string) {
  const result = await combinedChain.call({ text });
  return result.translation;
}

// Example usage:
summarizeAndTranslate("LangChain helps developers build applications powered by large language models.")
  .then((translation) => {
    console.log("Translated Summary:", translation);
  });

Step 4: Putting It All Together

Finally, let’s combine the steps into a complete script. This script will:

Initialize the LangChain environment
Create a prompt template and configure the language model
Build a chain to perform a task (translation in our example)
Run the chain and output the result

import { OpenAI } from 'langchain/llms/openai';
import { LLMChain } from 'langchain/chains';
import { PromptTemplate } from 'langchain/prompts';

async function main() {
  // Initialize the language model
  const llm = new OpenAI({
    openAIApiKey: process.env.OPENAI_API_KEY,
    modelName: "text-davinci-003",
  });

  // Create the prompt for translation
  const prompt = new PromptTemplate({
    template: "Translate the following English text to French: {text}",
    inputVariables: ["text"],
  });

  // Build the chain
  const chain = new LLMChain({ llm, prompt });

  // Input text to be translated
  const inputText = "Hello, how are you?";

  // Execute the chain
  const response = await chain.call({ text: inputText });
  console.log("Translation:", response.text);
}

main().catch(console.error);

Step 5: Advanced Debugging & Error Handling

In production, robust error handling is a must. Wrap your chain executions in try/catch blocks to gracefully handle any unexpected issues. This not only improves the user experience but also helps in debugging when things go wrong.

import { OpenAI } from 'langchain/llms/openai';
import { LLMChain } from 'langchain/chains';
import { PromptTemplate } from 'langchain/prompts';

async function safeTranslate(text: string) {
  const prompt = new PromptTemplate({
    template: "Translate the following English text to French: {text}",
    inputVariables: ["text"],
  });
  const llm = new OpenAI({
    openAIApiKey: process.env.OPENAI_API_KEY,
    modelName: "text-davinci-003",
  });
  const chain = new LLMChain({ llm, prompt });

  try {
    const response = await chain.call({ text });
    return response.text;
  } catch (error) {
    console.error("Error during translation:", error);
    // Return a fallback message or handle the error as needed
    return "Translation error. Please try again later.";
  }
}

// Example usage:
safeTranslate("This is a test for error handling.").then(console.log);

Step 6: Integrating External Data Sources

LangChain isn’t just for text transformations—it can also be used to process data fetched from external APIs. In the following example, we retrieve weather data and then use LangChain to generate a concise summary.

import { OpenAI } from 'langchain/llms/openai';
import { LLMChain } from 'langchain/chains';
import { PromptTemplate } from 'langchain/prompts';

async function getWeatherSummary(location: string) {
  // Fetch weather data from an external API
  const response = await fetch(
    `https://api.weatherapi.com/v1/current.json?key=${process.env.WEATHER_API_KEY}&q=${location}`
  );
  const weatherData = await response.json();

  // Create a prompt to summarize the weather data
  const prompt = new PromptTemplate({
    template: "Given the following weather data: {data}, provide a brief summary of the current weather conditions.",
    inputVariables: ["data"],
  });

  const llm = new OpenAI({
    openAIApiKey: process.env.OPENAI_API_KEY,
    modelName: "text-davinci-003",
  });
  const chain = new LLMChain({ llm, prompt });

  const result = await chain.call({ data: JSON.stringify(weatherData) });
  return result.text;
}

// Example usage:
getWeatherSummary("New York").then((summary) => {
  console.log("Weather Summary:", summary);
});

Step 7: Testing Your Chains

As your chains grow more complex, writing tests becomes essential. Ensure your functions behave as expected using your preferred testing framework. Below is a simple Jest test for our translation chain.

// translateText.test.ts
import { translateText } from './chain';

describe("translateText", () => {
  it("should return a French translation for 'Good morning'", async () => {
    const result = await translateText("Good morning");
    // The translation should contain "bonjour" (case-insensitive)
    expect(result.toLowerCase()).toContain("bonjour");
  });
});

Best Practices & Gotchas

Keep Secrets Secure: Store your OPENAI_API_KEY and other sensitive credentials in environment variables or secure vaults.
Simplify First: Begin with simple chains before moving to multi-step, complex workflows.
Error Handling & Retries: Use try/catch blocks to gracefully handle API errors and consider implementing retries for transient issues.
Rate Limits & Throttling: Be aware of API rate limits. Optimize your chains to minimize the number of calls, or implement throttling when necessary.
Prompt Engineering: Experiment with your prompt templates. Small tweaks can yield significantly different results.
Input Sanitization: Validate and sanitize inputs to avoid issues such as prompt injection.
Testing: Write tests for your chain functions to ensure consistent behavior as your code evolves.
Performance Optimization: Monitor latency and costs, and consider caching frequent responses.
Consult the Docs: Check out the How-To Guides for advanced configurations and use cases.

Conclusion

LangChain empowers you to create sophisticated language model applications with minimal overhead. From simple translations to multi-step, externally integrated chains, the possibilities are vast.

This expanded guide has taken you through setting up your environment, building basic and complex chains, and even diving into advanced topics like error handling and testing. As you experiment and build, remember to consult the documentation and iterate on your prompt designs.

Happy coding and may your chains be ever robust!