LangChain.js

npm install langchain @langchain/openai @langchain/anthropic

import { ChatOpenAI } from '@langchain/openai';
import { HumanMessage, SystemMessage } from '@langchain/core/messages';

const model = new ChatOpenAI({
  modelName: 'gpt-4o',
  temperature: 0.7
});

const response = await model.invoke([
  new SystemMessage('You are a helpful data science tutor.'),
  new HumanMessage('Explain gradient descent in simple terms.')
]);

console.log(response.content);

import { ChatPromptTemplate } from '@langchain/core/prompts';
import { StringOutputParser } from '@langchain/core/output_parsers';

const prompt = ChatPromptTemplate.fromMessages([
  ['system', 'You are an expert in {topic}. Be concise.'],
  ['human', '{question}']
]);

// LCEL: LangChain Expression Language
const chain = prompt.pipe(model).pipe(new StringOutputParser());

const result = await chain.invoke({
  topic: 'machine learning',
  question: 'What is overfitting?'
});

import { MemoryVectorStore } from 'langchain/vectorstores/memory';
import { OpenAIEmbeddings } from '@langchain/openai';
import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';

// 1. Split documents
const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: 1000, chunkOverlap: 200
});
const docs = await splitter.splitDocuments(documents);

// 2. Create vector store
const vectorStore = await MemoryVectorStore.fromDocuments(
  docs, new OpenAIEmbeddings()
);

// 3. Retrieve and generate
const retriever = vectorStore.asRetriever({ k: 3 });
const relevantDocs = await retriever.invoke('How does attention work?');

// 4. Pass context to LLM
const context = relevantDocs.map(d => d.pageContent).join('\n');
const answer = await chain.invoke({ context, question });

import { streamText } from 'ai';
import { openai } from '@ai-sdk/openai';

export async function POST(req: Request) {
  const { messages } = await req.json();

  const result = streamText({
    model: openai('gpt-4o'),
    system: 'You are a helpful assistant.',
    messages,
  });

  return result.toDataStreamResponse();
}