Build your own Search Engine API with Flask and Eden AI Embeddings

This tutorial provides a comprehensive guide to building a search engine in Python using text embeddings, specifically leveraging Eden AI's API and Flask. By converting text into numerical embeddings, users can measure the similarity between different pieces of text, enabling the creation of a search API that ranks results based on similarity scores. The process involves preparing a dataset, using Eden AI to generate embeddings, and deploying a REST API with Flask. The tutorial includes detailed instructions on setting up a virtual environment, applying embeddings to the dataset, calculating cosine similarity, and constructing a Flask application to handle search queries. The guide highlights the benefits of using Eden AI for managing multiple AI models under a unified API, making it suitable for production environments due to its reliability features, such as fallback routing and centralized monitoring.

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