Supercharge eCommerce Search: OpenAI's CLIP, BM25, and Python
We build a multi-modal hybrid search engine for ecommerce using OpenAI's CLIP, BM25, Pinecone vector database, and Python. The search engine processes text and image-based queries and can produce better results than traditional methods. The search engine allows users to search and retrieve data using both text and visual queries, which is especially useful in e-commerce domains where users have a range of search queries, from specific product searches to image-based searches for related items. By using CLIP and BM25, the search engine can process both text and image-based queries, providing users with a comprehensive search experience. Additionally, Pinecone vector database and Python allow for easy indexing, storage, and retrieval of data, making it possible to handle large volumes of data in real time. 📌 Example notebook: https://github.com/pinecone-io/examples/blob/master/learn/search/hybrid-search/ecommerce-search/ecommerce-search.ipynb 🎙️ AI Dev Studio: https://aurelio.ai/ 👾 Discord: https://discord.gg/c5QtDB9RAP 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 00:00 Multi-modal hybrid search 01:05 Multi-modal hybrid search in e-commerce 05:14 How do we construct multi-modal embeddings 07:05 Difference between sparse and dense vectors 09:43 E-commerce search in Python 11:11 Connect to Pinecone vector db 12:04 Creating a Pinecone index 13:45 Data preparation 16:32 Creating BM25 sparse vectors 19:33 Creating dense vectors with sentence transformers 20:26 Indexing everything in Pinecone 24:41 Making hybrid queries 26:01 Mixing dense vs sparse with alpha 32:11 Adding product metadata filtering 34:13 Final thoughts on search
