마이그레이션 가이드

from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQAChain

chain = RetrievalQAChain.from_chain_type(
    llm=ChatOpenAI(model="gpt-4"),
    retriever=vectorstore.as_retriever(),
)
result = chain.run("질문")

import { generateText, tool } from 'ai'
import { z } from 'zod'

const result = await generateText({
  model: 'openai/gpt-5.4',
  messages: [{ role: 'user', content: '질문' }],
  tools: {
    searchDocs: tool({
      description: '문서를 검색합니다.',
      inputSchema: z.object({ query: z.string() }),
      execute: async ({ query }) => {
        const docs = await vectorStore.search(query)
        return docs.map((d) => d.content).join('\n')
      },
    }),
  },
})

from langgraph.graph import StateGraph

graph = StateGraph(AgentState)
graph.add_node("classify", classify_node)
graph.add_node("research", research_node)
graph.add_node("respond", respond_node)
graph.add_conditional_edges("classify", route_fn)

export async function agentWorkflow(input: AgentInput) {
  'use workflow'

  const classification = await classify(input)

  if (classification.route === 'research') {
    const research = await researchStep(classification)
    return respond(research)
  }

  return respond(classification)
}

async function classify(input: AgentInput) {
  'use step'
  return classifyInput(input)
}

async function researchStep(classification: Classification) {
  'use step'
  return runResearch(classification)
}

async function respond(input: Classification | ResearchResult) {
  'use step'
  return generateResponse(input)
}