LangChain - Intro

With LangChain, you can automate the execution of actions, such as:

• Automatically performing tasks, like reservations
• Generating reports, articles, blog posts, or marketing materials
• Automatically extracting key information from documents, such as contracts, scientific articles, or financial reports
• Summarizing long documents or articles
• Integrating with existing tools: connecting to databases like Qdrant, Pinecone, Redis, or libraries such as OpenAI, Hugging Face, or other models

Create the Project Directory

First, create the project directory and navigate into it:

mkdir langchain-tutorial
cd langchain-tutorial

Initialize the project with a package.json file:

npm init -y

This will generate a default package.json file. You will replace its content with the provided one in the next step.

Install TypeScript and the necessary types for Node.js:

npm install --save-dev typescript @types/node

Install ts-node and nodemon for Development. These tools help you run TypeScript files directly and automatically restart the server when files change:

npm install --save-dev ts-node nodemon

Install Express, a popular Node.js web framework, and its TypeScript definitions:

npm install express
npm install --save-dev @types/express

Install the langchain package and the OpenAI integration:

npm install langchain @langchain/openai

Install dotenv for environment variable management:

npm install dotenv

Create a tsconfig.json file to configure TypeScript:

npx tsc --init

Create the necessary directories and a basic index.ts file:

mkdir src
touch src/index.ts

Chain Invoke

This code sets up and runs a simple LangChain-based application that generates a joke using the OpenAI GPT-3.5-turbo model. It demonstrates how to:

• Load environment variables.
• Create and configure an OpenAI language model.
• Define a prompt template with a placeholder.
• Create a processing chain by piping the prompt into the model.
• Invoke the chain with a specific input and log the generated response.

╔════════════════════════════════════════════════════════════════════════════╗
║                         🌟 EDUCATIONAL EXAMPLE 🌟                         ║
║                                                                            ║
║  📌 This is a minimal and short working example for educational purposes.  ║
║  ⚠️ Not optimized for production!                                          ║
║                                                                            ║
║  📦 Versions Used:                                                         ║
║    - "@langchain/core": "^0.3.38"                                          ║
║    - "@langchain/openai": "^0.4.2"                                         ║
║                                                                            ║
║  🔄 Note: LangChain is transitioning from a monolithic structure to a      ║
║      modular package structure. Ensure compatibility with future updates.  ║
╚════════════════════════════════════════════════════════════════════════════╝

// src/index.ts
import { ChatOpenAI } from "@langchain/openai";
import * as dotenv from 'dotenv'
import { ChatPromptTemplate } from "@langchain/core/prompts"

dotenv.config()

async function main() {
  // Create model
  const model = new ChatOpenAI({
    openAIApiKey: process.env.OPENAI_API_KEY!,
    modelName: 'gpt-3.5-turbo',
    temperature: 0.7
  });

  const prompt = ChatPromptTemplate.fromTemplate("You are a comedian. Tell a joke based on the following word {input}");

  // Create chain
  const chain = prompt.pipe(model)

  // Call chain
  const response = await chain.invoke({
    input: "cat"
  })

  console.log(response)
}

main().catch(console.error);

• openAIApiKey: This gets the OpenAI API key from environment variables.
• modelName: Specifies the model to use (in this case, gpt-3.5-turbo).
• temperature: Sets the creativity of the responses. A value of 0.7 means moderately creative responses.
• Create Chain:The prompt template is piped into the model using the pipe method. This creates a processing chain where the formatted prompt is directly fed into the language model.
• Invoke Chain:The chain is called (invoked) with the input "cat". This means the input word "cat" will replace the {input} placeholder in the prompt template. The formatted prompt is then processed by the model to generate a response.
• Run main Function:The main function is called and any errors are caught and logged to the console using console.error.

Streaming AI Responses in the Terminal with LangChain

╔════════════════════════════════════════════════════════════════════════════╗
║                         🌟 EDUCATIONAL EXAMPLE 🌟                         ║
║                                                                            ║
║  📌 This is a minimal and short working example for educational purposes.  ║
║  ⚠️ Not optimized for production!                                          ║
║                                                                            ║
║  📦 Versions Used:                                                         ║
║    - "@langchain/core": "^0.3.38"                                          ║
║    - "@langchain/openai": "^0.4.2"                                         ║
║                                                                            ║
║  🔄 Note: LangChain is transitioning from a monolithic structure to a      ║
║      modular package structure. Ensure compatibility with future updates.  ║
╚════════════════════════════════════════════════════════════════════════════╝

// src/index.ts
import { ChatOpenAI } from "@langchain/openai"; // used to create the model
import dotenv from "dotenv";  // used to load environment variables

dotenv.config(); // load environment variables from .env file

const model = new ChatOpenAI({ // create the model with key and model name, and temperature
  openAIApiKey: process.env.OPENAI_API_KEY!,
  modelName: "gpt-3.5-turbo",
  temperature: 0.7,
})

async function run() {
  const response = await model.stream("What are quarks in quantum physics?") // send the question to the model

  process.stdout.write("🤖 AI: ") // Start response on the same line
  for await (const chunk of response) { // for each chunk of the response
      // checks if the content is an array, and joins the array elements with an empty string, or converts the content to a string
      process.stdout.write(Array.isArray(chunk.content) ? chunk.content.join("") : String(chunk.content) 
    )
  }

  process.stdout.write("
") // Move to a new line after completion
}

run().catch(console.error); // run the function and catch any errors

This script demonstrates how to interact with OpenAI’s GPT-3.5-turbo model using LangChain while streaming responses dynamically in the terminal. Unlike a standard API call that waits for the entire response before displaying it, streaming allows the response to appear word by word, simulating real-time typing.

How It Works

• Model Initialization The script initializes a ChatOpenAI model with the specified API key, model name, and temperature (which controls randomness in responses). The API key is loaded securely from environment variables using dotenv.
• Streaming the AI Response The model.stream() function sends a question to the AI model, requesting information about quarks in quantum physics. Instead of waiting for the full response, each chunk of data is processed as soon as it arrives.
• Efficient Terminal Output Handling process.stdout.write("🤖 AI: ") ensures that the response starts on the same line, rather than jumping to a new one.The for-await loop processes the response chunk by chunk, checking whether the content is an array or a string. The AI's words appear in real-time, creating a smooth user experience.
• Error Handling The script includes .catch(console.error) to catch and display errors if something goes wrong.

Summary

This code snippet is a simple demonstration of how to interact with OpenAI's language models using the LangChain framework. It loads an API key from an environment variable, sends a prompt to the OpenAI model, and then streams the response back, printing each chunk of the response (in this case, a poem about AI) to the console in real-time. This approach is particularly useful for handling long responses or when you want to process data as it arrives rather than waiting for the entire response.

Chain Batch

╔════════════════════════════════════════════════════════════════════════════╗
║                         🌟 EDUCATIONAL EXAMPLE 🌟                         ║
║                                                                            ║
║  📌 This is a minimal and short working example for educational purposes.  ║
║  ⚠️ Not optimized for production!                                          ║
║                                                                            ║
║  📦 Versions Used:                                                         ║
║    - "@langchain/core": "^0.3.38"                                          ║
║    - "@langchain/openai": "^0.4.2"                                         ║
║                                                                            ║
║  🔄 Note: LangChain is transitioning from a monolithic structure to a      ║
║      modular package structure. Ensure compatibility with future updates.  ║
╚════════════════════════════════════════════════════════════════════════════╝

// src/index.ts
import { ChatOpenAI } from "@langchain/openai";
import dotenv from 'dotenv';

dotenv.config();

const model = new ChatOpenAI({
  openAIApiKey: process.env.OPENAI_API_KEY!
})

async function run() {
  const response = await model.batch(["Hello", "How are you ?"])
  console.log(response)
}

run().catch(console.error)

• async function run(): This defines an asynchronous function named run. The async keyword allows the function to use the await keyword, which is used to wait for asynchronous operations to complete.
• model.batch(["Hello", "How are you ?"]): The batch method sends multiple inputs (in this case, ["Hello", "How are you ?"]) to the language model at once and returns their responses. It's used for processing multiple prompts efficiently in one go.
• await: The await keyword is used to pause the execution of the run function until the batch method returns a response. This makes sure the code doesn't move on to the next line until the responses are received.

This code is a simple example of how to use the LangChain library with OpenAI's language models. It imports necessary modules, loads environment variables, creates an instance of the ChatOpenAI class with an API key, and sends a batch of prompts to the model, printing out the responses.

Additional Methods

In LangChain, different methods allow users to interact with models in various ways. These methods control how models process input and return output—synchronously, in batches, or via streaming. Here are the most common methods:

• model.invoke – Calls the model synchronously with a single input.
• model.batch – Processes multiple inputs in a batch (parallel execution).
• model.stream – Streams output token by token, useful for real-time applications.
• model.generate – Generates multiple completions for a given prompt with controlled parameters (e.g., temperature, max tokens).

LangChain Patterns & Concepts

In addition to the standard execution methods, LangChain provides additional patterns and utilities for building complex workflows:

• Runnable Chains – LangChain allows pipelining models and tools together, forming multi-step workflows.
• Retrieval-Augmented Generation (RAG) – Commonly used for knowledge-augmented responses, combining LLMs with databases like Pinecone or Qdrant.
• Memory & Context Handling – Using ConversationSummaryMemory and ConversationTokenBufferMemory, LLMs can remember past interactions.

Conclusions

LangChain offers a powerful framework for building AI-powered applications that are dynamic, context-aware, and personalized. From setting up your project to leveraging advanced chaining methods, LangChain simplifies the process of integrating AI into your projects. Whether you're using synchronous calls, batch processing, or real-time streaming, LangChain provides the tools needed to unlock the full potential of models like OpenAI's GPT.

By following this guide, you now have a solid understanding of how to get started with LangChain, including setting up your environment, understanding prompt structures, and working with advanced techniques like chaining and batch processing. These skills will help you build smarter, more efficient applications capable of handling complex queries and delivering tailored user experiences.

As you move forward, don't stop experimenting. Explore additional methods, integrate LangChain into different workflows, and push the boundaries of what AI-powered applications can achieve. LangChain's flexibility and ease of use make it an essential tool for developers who want to innovate and stay ahead in the rapidly evolving world of AI.