10 分钟内构建您的聊天机器人应用程序（Next.js、gpt4o 和 DenserRetriever）

npx create-next-app --example 
https://github.com/vercel/ai/tree/main/examples/next-langchain next-retriever

cd next-retriever
npm install

version: "3.5"

services:
  denserretriever:
    image: jotyy318/denserretriever
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8090/"]
      interval: 30s
      timeout: 20s
      retries: 3
    ports:
      - "8090:8090"

  elasticsearch:
    image: elasticsearch:8.13.4
    environment:
      - discovery.type=single-node
      - ES_JAVA_OPTS=-Xms1g -Xmx1g
      - xpack.security.enabled=false
    volumes:
      - ${DOCKER_VOLUME_DIRECTORY:-./docker-volume}:/usr/elasticsearch/data
    ports:
      - "9200:9200"
      - "9300:9300"

  etcd:
    container_name: milvus-etcd
    image: quay.io/coreos/etcd:v3.5.0
    environment:
      - ETCD_AUTO_COMPACTION_MODE=revision
      - ETCD_AUTO_COMPACTION_RETENTION=1000
      - ETCD_QUOTA_BACKEND_BYTES=4294967296
    volumes:
      - ${DOCKER_VOLUME_DIRECTORY:-./docker-volume}/volumes/etcd:/etcd
    command: etcd -advertise-client-urls=http://127.0.0.1:2379 -listen-client-urls http://0.0.0.0:2379 --data-dir /etcd

  minio:
    container_name: milvus-minio
    image: minio/minio:RELEASE.2020-12-03T00-03-10Z
    environment:
      MINIO_ACCESS_KEY: minioadmin
      MINIO_SECRET_KEY: minioadmin
    volumes:
      - ${DOCKER_VOLUME_DIRECTORY:-./docker-volume}/volumes/minio:/minio_data
    command: minio server /minio_data
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:9000/minio/health/live"]
      interval: 30s
      timeout: 20s
      retries: 3

  standalone:
    container_name: milvus-standalone
    image: milvusdb/milvus:v2.3.15
    command: ["milvus", "run", "standalone"]
    environment:
      ETCD_ENDPOINTS: etcd:2379
      MINIO_ADDRESS: minio:9000
    volumes:
      - ${DOCKER_VOLUME_DIRECTORY:-./docker-volume}/volumes/milvus:/var/lib/milvus
    ports:
      - "19530:19530"
    depends_on:
      - "etcd"
      - "minio"

networks:
  default:
    name: milvus

docker compose up -d

import { ChatOpenAI } from "@langchain/openai";
import { LangChainAdapter, Message, StreamingTextResponse } from "ai";
import { AIMessage, HumanMessage } from "langchain/schema";

export const dynamic = "force-dynamic";
export const maxDuration = 60;

function generatePrompt(query: string, passages: string[]): string {
  let prompt: string =
    "### Instructions:\n" +
    "The following context consists of an ordered list of sources. If you can find answers from the context, use the context to provide a long response. You MUST cite the context titles and source URLs strictly in Markdown format in your response. If you cannot find the answer from the sources, use your knowledge to come up with a reasonable answer and do not cite any sources. If the query asks to summarize the file or uploaded file, provide a summarization based on the provided sources. If the conversation involves casual talk or greetings, rely on your knowledge for an appropriate response.";

  prompt += `### Query:\n${query}\n`;

  if (passages.length > 0) {
    prompt += `\n### Context:\n${JSON.stringify(passages)}\n`;
  }

  prompt += "### Response:";

  return prompt;
}

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

  const model = new ChatOpenAI(
    {
      model: "gpt-4o",
    },
    {
      baseURL: process.env.OPENAI_API_BASE_URL,
    },
  );

  const query = messages[messages.length - 1].content;

  const { passages } = await fetch("http://127.0.0.1:8090/retrieve", {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      question: query,
    }),
  })
    .then((res) => {
      if (res.ok) {
        return res.json();
      } else {
        throw new Error("Failed to fetch");
      }
    })
    .catch((err) => {
      return { docs: [], passages: [] };
    });

  const prompt = generatePrompt(query, passages);

  const stream = await model.stream(
    messages.map((message) =>
      message.role == "user"
        ? new HumanMessage(prompt)
        : new AIMessage(message.content),
    ),
  );

  return new StreamingTextResponse(LangChainAdapter.toAIStream(stream));
}

cp .env.local.example .env.local