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 …
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Chapters (15)
Multi-modal hybrid search
1:05
Multi-modal hybrid search in e-commerce
5:14
How do we construct multi-modal embeddings
7:05
Difference between sparse and dense vectors
9:43
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
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Faiss - Introduction to Similarity Search
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Angular App Setup With Material - Stoic Q&A #5
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Why are there so many Tokenization methods in HF Transformers?
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Choosing Indexes for Similarity Search (Faiss in Python)
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Locality Sensitive Hashing (LSH) for Search with Shingling + MinHashing (Python)
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How LSH Random Projection works in search (+Python)
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IndexLSH for Fast Similarity Search in Faiss
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Faiss - Vector Compression with PQ and IVFPQ (in Python)
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Product Quantization for Vector Similarity Search (+ Python)
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How to Build a Bert WordPiece Tokenizer in Python and HuggingFace
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