Build your own AI Powered Neural Search Engine using Jina.AI neural search solutions

Introduction

Good evening, folks! Today, we're going to introduce something new and exciting—building your own AI-powered search engine using Jina.AI. Joining us are Jinnah and Susanna. Susanna from Jina is here to walk us through the process.

What You're Going to Learn Today

Jina helps you quickly bootstrap your first prototype using pre-trained models. You can achieve some fancy functionalities, like capturing queries from users and returning relevant content with ease. Susanna will explain the steps, data structures, and provide examples on how to build your neural search engine.

Introduction to Neural Search

Search technology is ubiquitous today. However, we're focusing on neural search—a newer approach that utilizes neural networks to enable advanced search capabilities. Unlike traditional search, where you have to define all possible synonyms and details, neural search uses vectors to handle different modalities like text, images, audio, and video. This makes it more robust and easier to handle complex queries.

Benefits of Neural Search

• Multi-modality: Combine text, audio, video, and image data.
• Cross-Modality: Query across different types of data.
• Reduced Complexity: Easier to manage than traditional search by leveraging vectors.

Connecting Neural Search with Jina.AI

Data Flow

1. Indexing: Process and encode data into vectors.
2. Querying: Encode the user query and compare with stored vectors.

Steps in Jina

1. Crafting: Split the data into smaller pieces.
2. Encoding: Convert these pieces into vectors.
3. Indexing: Store these vectors.
4. Querying: Find the closest matching vectors to the query.

Jina.AI Components

1. Flow: Manages the different stages like crafting and encoding.
2. Pods: Perform specific tasks like encoding.
3. Executors: Specialized component within Pods to handle particular tasks.

Creating Your Flow

Here’s a simple flow example using Python to add encoding and indexing Pods: