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From Jaccard to SBERT: A Comprehensive Guide to Vector Similarity Search Techniques
- Similarity search methods have been researched since the 1970s in the field of NLP.
- Jaccard similarity and w-shingling are two traditional methods for text similarity search.
- TF-IDF, BM25, and Sentence BERT are popular vector-based methods for similarity search.
- TF-IDF is used to compare the importance of each word to a particular document by looking at a large set of documents.
- BM25 is an optimized version of TF-IDF that uses adjustable hyperparameters to improve results.
- Sentence BERT uses BERT to generate vector embeddings of sentences that are all of the same size and excel at semantic search.
- All of these methods can be implemented on actual problems using libraries like FAISS or vector databases like Postgres pgvector, Qdrant, ChromaDB, and Pinecone.
- Cosine similarity is used to compare vector embeddings of sentences and calculate the similarity between two sentences, ranging from -1 to 1.
- Journey through this article gave an extensive knowledge of different similarity search methods.
- The field of NLP has come a long way in text similarity search since the 1970s.
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