Faiss: Efficient Similarity Search and Clustering of Dense Vectors
FAISS, short for Facebook AI Similarity Search, is an open-source library created by Facebook AI Research (FAIR) to facilitate efficient similarity search and clustering of high-dimensional vectors. It is designed to excel in situations with extensive datasets and high-dimensional feature vectors, which are frequently encountered in tasks like image and text retrieval, recommendation systems, and natural language processing.
In this video we are going to explore about this vector DB with in-depth intuition..
Code:
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https://github.com/SatadruMukherjee/D...
Reference Links:
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https://github.com/facebookresearch/f...
https://github.com/facebookresearch/f...
Prerequisite:
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Working with FAISS Vector DB
Implementing FAISS: Vector Similarity Search for Recommendations
Working with FAISS for Similarity Search
Faiss: Efficient Similarity Search and Clustering of Dense Vectors
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