Dev.to AI 🤖 Ai Tue, 16 Jun 2026 20:55:00 +0000 👁 0 📖 3 min read

Deploying Jina Serve Open-Source Neural Search and AI Serving Framework on Ubuntu 24.04

Jina Serve is an open-source framework for building neural search and multimodal AI applications, with a cloud-native runtime that handles dynamic batching, async streaming, and microservice orchestration. This guide deploys a Jina Flow with a custom text executor using Docker Compose, with Traefik handling automatic HTTPS in front of the gateway. By the end, you'll have a Jina Flow serving an /index and /search API securely at your domain.

Set Up the Directory Structure

1. Create the project directory structure:

$ mkdir -p ~/jina-serve/executor
$ cd ~/jina-serve

2. Create the executor module:

$ nano executor/executor.py

from jina import Executor, requests
from docarray import BaseDoc, DocList

class TextDoc(BaseDoc):
    text: str = ""

class TextProcessor(Executor):
    @requests(on="/index")
    def index(self, docs: DocList[TextDoc], **kwargs) -> DocList[TextDoc]:
        for doc in docs:
            if doc.text:
                doc.text = doc.text.upper()
        return docs

    @requests(on="/search")
    def search(self, docs: DocList[TextDoc], **kwargs) -> DocList[TextDoc]:
        return docs

3. Pin the executor's Python dependencies:

$ nano executor/requirements.txt

jina==3.34.0
docarray>=0.40.0

4. Wire the executor into Jina's loader:

$ nano executor/config.yml

jtype: TextProcessor
py_modules:
  - executor.py

5. Define the Flow:

$ nano flow.yml

jtype: Flow
with:
  protocol: http
  port: 8080

executors:
  - name: textprocessor
    uses: executor/config.yml

6. Build the container image with the Flow baked in:

$ nano Dockerfile

FROM jinaai/jina:3.34.0-py39-standard

WORKDIR /app

COPY executor/requirements.txt /app/executor/requirements.txt
RUN pip install --no-cache-dir -r /app/executor/requirements.txt

COPY executor /app/executor
COPY flow.yml /app/flow.yml

ENTRYPOINT ["jina", "flow", "--uses", "flow.yml"]

7. Create the environment file:

$ nano .env

DOMAIN=jina.example.com
LETSENCRYPT_EMAIL=[email protected]
JINA_LOG_LEVEL=INFO

Deploy with Docker Compose

1. Create the Compose manifest:

$ nano docker-compose.yml

services:
  traefik:
    image: traefik:v3.6
    container_name: traefik
    command:
      - "--providers.docker=true"
      - "--providers.docker.exposedbydefault=false"
      - "--entrypoints.web.address=:80"
      - "--entrypoints.websecure.address=:443"
      - "--entrypoints.web.http.redirections.entrypoint.to=websecure"
      - "--entrypoints.web.http.redirections.entrypoint.scheme=https"
      - "--certificatesresolvers.letsencrypt.acme.httpchallenge=true"
      - "--certificatesresolvers.letsencrypt.acme.httpchallenge.entrypoint=web"
      - "--certificatesresolvers.letsencrypt.acme.email=${LETSENCRYPT_EMAIL}"
      - "--certificatesresolvers.letsencrypt.acme.storage=/letsencrypt/acme.json"
    ports:
      - "80:80"
      - "443:443"
    volumes:
      - "/var/run/docker.sock:/var/run/docker.sock:ro"
      - "./letsencrypt:/letsencrypt"
    restart: unless-stopped

  jina:
    build:
      context: .
      dockerfile: Dockerfile
    container_name: jina-flow
    environment:
      - JINA_LOG_LEVEL=${JINA_LOG_LEVEL}
    expose:
      - "8080"
    labels:
      - "traefik.enable=true"
      - "traefik.http.routers.jina.rule=Host(`${DOMAIN}`)"
      - "traefik.http.routers.jina.entrypoints=websecure"
      - "traefik.http.routers.jina.tls.certresolver=letsencrypt"
      - "traefik.http.services.jina.loadbalancer.server.port=8080"
    restart: unless-stopped

2. Build and start the stack:

$ docker compose up -d --build

3. Verify the services and tail logs:

$ docker compose ps
$ docker compose logs

Verify the Gateway

1. Check the status endpoint:

$ curl https://jina.example.com/status

{"jina":{"jina":"3.34.0","docarray":"0.40.1"...

2. POST a document to /index:

$ curl -X POST https://jina.example.com/index \
    -H "Content-Type: application/json" \
    -d '{"data": [{"text": "hello world"}]}'

{"data":[{"id":null,"text":"HELLO WORLD"}],...}

3. POST to /search to see the passthrough endpoint:

$ curl -X POST https://jina.example.com/search \
    -H "Content-Type: application/json" \
    -d '{"data": [{"text": "test message"}]}'

4. Submit a batch of documents:

$ curl -X POST https://jina.example.com/index \
    -H "Content-Type: application/json" \
    -d '{"data": [{"text": "first"}, {"text": "second"}, {"text": "third"}]}'

Next Steps

Jina Serve is running and serving requests over HTTPS. From here you can:

Add more executors to the Flow for pre-processing, embedding, and ranking
Switch the runtime to GPU images for accelerated inference
Persist embeddings in Qdrant, Weaviate, or other vector stores

For the full guide with additional tips, visit the original article on Vultr Docs.

📰 Read the original article on Dev.to AI

Originally published by Dev.to AI. Aggregated on AIWithGhost for educational purposes — full credit and traffic to the original publisher.

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