Mastering Semantic Search: A Comprehensive Cohere Tutorial

Understanding Semantic Search: A Comprehensive Introduction

What is semantic search, you ask? Well, let's dive into this intriguing concept. Semantic search is the ability of computers to search by meaning, transcending the usual keyword matching search. It's like having a conversation with your search engine, where it understands not just what you're asking, but why you're asking it.

This is where the magic of natural language processing, artificial intelligence, and machine learning come into play. They work together to comprehend the user's query, the context of the query, and the user's intent. Semantic search examines the relationship between words, or the meaning of words, to provide more accurate and relevant search results than traditional keyword searches.

Practical Applications of Semantic Search

Now, semantic search engines aren't just a cool concept; they have many practical applications. For instance, have you ever noticed StackOverflow's "similar questions" feature? That's powered by a semantic search engine. Semantic search engines can also be used to build a private search engine for internal documents or records.

Building Your Own Semantic Search Tool with Cohere

But how do you build such a tool? This is where our Cohere tutorial comes into play. We'll show you how to build a basic semantic search engine using Cohere. This tutorial covers the usage of an archive of questions to embed, search with an index, and perform nearest neighbour search. By the end, you'll visualize the results based on the embeddings, and you'll be well-equipped to either build a Cohere app or simply learn how to use Cohere effectively.

Getting Started with Cohere

To start this Cohere AI tutorial, we will utilize the example data provided by Cohere. Follow these steps:

1. Install the Necessary Libraries: Ensure you have the proper libraries needed for the tutorial.
2. Create a New Notebook: Create a new notebook or Python file and import the necessary libraries.

Getting the Archive of Questions

Next, we will retrieve the archive of questions from Cohere. This archive is the TREC dataset, which is a collection of questions with categories. We will use the load_dataset function from the datasets library to load the dataset.

Embedding the Archive of Questions

Now we can embed the questions using Cohere. We will utilize the embed function from the Cohere library to embed the questions. This should only take a few seconds to generate one thousand embeddings of this length.

Building an Index for Nearest Neighbour Search

With our embeddings ready, we can create an index and perform a nearest neighbour search. We will use the AnnoyIndex function from the Annoy library. The optimization problem of finding the point in a given set that is closest (or most similar) to a specified point is known as nearest neighbour search.

Finding Neighbours in the Dataset

We can use the index we built to find the nearest neighbours of both existing questions and new questions that we embed. If we're solely interested in measuring the similarities between the questions in the dataset (rather than external queries), a straightforward approach is to calculate the similarities between every pair of embeddings we have.

Finding Neighbours of a User Query

Additionally, we can use embedding to find the nearest neighbours of a user query. By embedding the query, we can measure its similarity with items in the dataset and identify the closest neighbours.

Visualization: Unleashing the Power of Semantic Search

As we wrap up this introductory guide on semantic search using sentence embeddings, it's clear that the journey is just beginning. When constructing a search product, there are additional factors to consider. For instance, handling lengthy texts or training to optimize the embeddings for a specific purpose are crucial steps in the process.

This Cohere tutorial has set the foundation, but the world of semantic search is vast and ripe for exploration. Feel free to dive in, experiment with other datasets, and push the boundaries of what's possible. Whether you're aiming to build a Cohere app, seeking a comprehensive tutorial, or curious about how to use Cohere, the path forward is filled with exciting opportunities.

Join the Excitement: Test Your Skills

If you want to test what you have learned, you can join our AI hackathons. Identify a problem around you and build a Cohere app to fix it.