Create Your Own AI Meme Search Engine using Jina

Introduction

Introduction

In this workshop, we'll be building a search engine for memes using Jina, an open-source neural search framework. Jyoti and Alex from Jina AI will guide you through how to create, deploy, and understand a meme search engine using both text and images.

Overview

The session was hosted by Jyoti and Alex from Jina AI. They discussed the power of neural search, how it leverages AI models, and how it can be implemented using Jina's open-source tools. This article will present the step-by-step tutorial presented in the workshop, including basic setup and deployment.

Detailed Steps

Initial Setup

First, we prepare the environment by importing the necessary libraries and setting up our basic configurations:

import warnings
warnings.filterwarnings('ignore')
import os
from google.colab import files
import json
from jina import Document, DocumentArray, Flow
from jina.types.document.generators import from_csv

Downloading the Dataset

We use a data set from Kaggle called the Image Flip Meme dataset:

!wget 'https://raw.githubusercontent.com/alexcg1/jina-meme-search/master/data/memes.json' -P data/

Loading the Data

A custom function to load the JSON data:

def load_data(filepath, max_docs):
    memes = DocumentArray()
    with open(filepath, 'r') as file:
        raw_meme_data = json.load(file)
        for meme in raw_meme_data[:max_docs]:
            doc = Document(
                  text=f"(meme['template']): (meme['caption_text'])",
                  tags=meme
                )
            memes.append(doc)
    return memes

Load and shuffle the data:

docs = load_data('data/memes.json', 50, True)

Setting Up the Flow

Next, we need to create a Jina flow that will process the data through an encoder and an indexer:

f = (Flow()
    .add(uses='jinahub://TransformerTorchEncoder')
    .add(uses='jinahub://SimpleIndexer', 
         uses_with=('index_file_name': 'index'), 
         install_requirements=True)
)

Indexing the Data

We process the data through the flow to index it:

with f:
    f.post(on='index', inputs=docs, show_progress=True)

Searching Through the Index

We create a simple search function to send queries to the flow:

def query_search(text):
    query_doc = Document(text=text)
    with f:
        result = f.search(inputs=query_doc, return_results=True)
    return result

Example query:

search_results = query_search('school')

Displaying Results

Finally, we use matplotlib to plot the search results:

import matplotlib.pyplot as plt
import requests
from PIL import Image
from io import BytesIO

def show_results(results):
    fig, axs = plt.subplots(1, len(results), figsize=(20,10))
    for ax, res in zip(axs, results):
        response = requests.get(res.tags['image_url'])
        img = Image.open(BytesIO(response.content))
        ax.imshow(img)
        ax.axis('off')
        ax.set_title(res.text)
    plt.show()

## Introduction
show_results(search_results)

Conclusion

In this workshop, we covered the installation and setup process for using Jina for neural search. The example demonstrated how you can create a meme search engine using text-based embeddings. From setting up the environment to processing the data and indexing it for search, you should now have a basic understanding of how neural search works using Jina.

Keywords

• Jina AI
• Neural Search
• Meme Search Engine
• Document Array
• Indexing
• Query Search
• Flow
• TransformerTorchEncoder
• SimpleIndexer

FAQ

Q1: Can I use Jina for data types other than text and images?

A1: Yes, Jina supports multiple data types including text, images, audio, video, and even advanced types like 3D mesh.

Q2: What is the advantage of using Jina over traditional search engines?

A2: Jina uses neural networks to understand the semantic meaning of data, offering more accurate and meaningful search results compared to traditional search engines that rely on keyword matching.

Q3: How does Jina handle dependencies for different machine learning models?

A3: Jina uses Docker to sandbox different environments, thus preventing dependency clashes and ensuring smooth operation across various models.

Q4: Is it possible to fine-tune the model used in the search engine?

A4: Yes, you can fine-tune the models using Jina's FineTuner which allows for specialized tuning of models to better handle specific types of data.

Q5: What do I do if I run into issues while using Jina?

A5: You can always reach out to the Jina community on Slack, or open an issue on their GitHub repository. The team is very responsive and happy to help with any problems you encounter.