Summary This is another component in SmartAnswer and enhanced LLM interface.
In this blog post, we introduce a wrapper class, FaissDB, which integrates FAISS with SQLite or any database to manage document embeddings and enable efficient similarity search. This approach combines FAISS’s vector search capabilities with the storage and querying power of a database, making it ideal for applications such as Retrieval-Augmented Generation (RAG) and recommendation systems.
It builds up this tool PaperSearch.
Summary Searching through massive datasets efficiently is a challenge, whether in image retrieval, recommendation systems, or semantic search. Faiss (Facebook AI Similarity Search) is a powerful open-source library developed by Meta to handle high-dimensional similarity search at scale.
It’s particularly well-suited for tasks like:
Image search: Finding visually similar images in a large database. Recommendation systems: Recommending items (products, movies, etc.) to users based on their preferences. Semantic search: Finding documents or text passages that are semantically similar to a given query.
Summary Imagine you have a dataset of customer profiles. How can you group similar customers together to tailor marketing campaigns? This is where K-Means clustering comes into play.
K-Means is a popular unsupervised learning algorithm used for clustering data points into distinct groups based on their similarities. It is widely used in various domains such as customer segmentation, image compression, and anomaly detection.
In this blog post, we’ll cover how K-Means works and demonstrate its implementation in Python using scikit-learn.