Searching for relevant information in vast repositories of unstructured text can be a challenge. This article explains a Python-based approach to implementing an efficient document search system using FAISS (Facebook AI Similarity Search) for Vector DB and sentence embeddings, which can be useful in applications like chatbots, document retrieval, and natural language understanding.
In this guide, we will break down how to use FAISS in combination with sentence transformers library to create a semantic search solution that can effectively locate related documents based on a user query. For example, this could be used in a customer support system to find