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Developer Builds Semantic FAQ Bot Using Sentence Embeddings and Cosine Similarity

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A developer working through a self-directed AI and machine learning curriculum has built a semantic FAQ chatbot as their fourth project. Unlike traditional FAQ bots that require exact keyword matches, the system converts questions into 384-dimensional numerical vectors using the Sentence Transformer model all-MiniLM-L6-v2. Cosine similarity is then used to find the closest matching FAQ entry, allowing the bot to handle paraphrased queries, abbreviations, and casually worded questions. A confidence threshold ensures the bot only responds when it has a reliable match, avoiding incorrect answers. The developer plans future upgrades including FAISS integration, a FastAPI backend, a Streamlit interface, and eventual evolution into a Retrieval-Augmented Generation chatbot.

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Developer Builds Semantic FAQ Bot Using Sentence Embeddings and Cosine Similarity · ShortSingh