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Project: Semantic Search Engine

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Description

In this project, you will build a semantic search engine that uses embeddings to provide more relevant search results based on the meaning of the query, rather than just keyword matching. This project will help you understand the use of embeddings and vector search techniques.

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Project Prompt

  • Develop a backend to generate embeddings from text data using a pre-trained model.
  • Create a frontend interface where users can input search queries.
  • Implement a search algorithm that uses embeddings to find semantically similar results.
  • Provide features like filtering and sorting based on relevance.

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Getting Started

  1. Choose an appropriate pre-trained embeddings model (e.g., BERT, Word2Vec).
  2. Set up a backend service to compute and store embeddings for your dataset.
  3. Implement a vector search algorithm to retrieve and rank results based on semantic similarity.
  4. Develop a frontend application for users to input queries and view search results.
  5. Test the search engine with various types of queries to ensure accuracy and performance.

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Deliverable

A semantic search engine that returns relevant results based on the meaning of user queries, with a user-friendly interface for inputting search queries and viewing results.