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How to build Google-like AI powered search using Python

How to build Google-like AI powered search using Python - AI Information Retrieval (IR) is a rapidly growing field of study. The aim of this research is to provide the most relevant search results based on the meaning of the query, not just the keywords used. Advanced studies typically use established deep neural networks, such as Google's BERT, and train them to determine the rank of search results. 

However, several challenges exist in this field, which will be discussed below. Creating a reliable and scalable semantic search engine is a formidable task, hence it's no surprise that Google is highly profitable.

Interested in building AI-powered search but don't know where to start?

Introducing Embeddings by Cohere AI

Embeddings are a way to represent the meaning of text in a numerical form. Cohere's model can convert texts to embeddings out-of-the-box.

Semantic search is based on the idea of understanding the meaning and context of the words used in a query, rather than just matching the exact keywords to the documents. It focuses on finding relevant results that match the user's intention, rather than matching the keywords.

Let's build a simple search example for finding twitter thread topics related to Y-Combinator. 

How to build Google-like AI-powered search using Python

Step-1: Go to the "Embed" Section in Cohere's Playground.

Here is the link - 👉

Step-2: Upload the dataset which you want to make searchable either in CSV form or upload it directly via Chohere's Playground UI.

Step-3: Visualize the embeddings in the playground to understand how semantic search works.

Semantically similar sentences would be dots closer to each other and vice-versa.

Step-4: Export the Python code from the Playground and paste it into an IDE.

Step-5: Add streamlit components to build a frontend for the search. Let's break it down what all we would need: 

1. We need users to be able to input their search query.

2. Cohere's model to convert text to embedding.

3. Finding similar sentences based on Cosine similarity.

This is how the final Streamlit application looks like: 

What are you waiting for?

Get started with building your AI-powered search using Cohere AI. 

That's all about How to build Google-like AI powered search using Python.

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