Building a Hybrid Semantic Search Engine from Scratch: A Deep Dive into TF-IDF, GloVe, and Dual GPU…

2026-03-24 · Source: Data Science on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing, Data Science & Analytics · Depth: Advanced, quick

Summary

This article details the construction of a hybrid semantic search engine from scratch, combining traditional keyword matching with semantic understanding. The system processes 8,470 customer support tickets, each with descriptions, types, priority levels, and channels, to return relevant past tickets based on user queries. It integrates TF-IDF for keyword-based search and GloVe embeddings for semantic meaning, implemented using only base PyTorch and NumPy without high-level libraries like scikit-learn. The hybrid approach significantly improves search relevance, particularly for queries requiring intent understanding, achieving a Precision@5 of 99% in correctly identifying ticket types within the top 5 results, outperforming pure keyword search in examples like "I need money-related help" versus "billing inquiry."

Key takeaway

For NLP Engineers building customer support systems, integrating hybrid search is crucial for understanding user intent beyond exact keywords. Your system can achieve 99% Precision@5 by combining TF-IDF for keyword matching with GloVe embeddings for semantic understanding, leading to more accurate and relevant ticket retrieval. Consider implementing this approach with base PyTorch and NumPy to maintain granular control and optimize performance.

Key insights

Hybrid search combining TF-IDF and GloVe embeddings significantly improves intent-based query relevance over keyword-only methods.

Principles

• Combine keyword and semantic search for robust results.
• Intent understanding enhances search precision.

Method

Build a search system using base PyTorch and NumPy, integrating TF-IDF for keyword matching and GloVe for semantic embeddings to process customer support tickets.

In practice

• Implement TF-IDF for initial keyword filtering.
• Generate GloVe embeddings for semantic similarity.
• Optimize for dual GPU processing.

Topics

• Hybrid Semantic Search
• TF-IDF
• GloVe Embeddings
• Customer Support Systems
• PyTorch

Best for: Machine Learning Engineer, NLP Engineer, Data Scientist

Related on AIssential

• How I Built a Smart Ticket Search System Using PyTorch and GloVe — Combining TF-IDF with GloVe embeddings creates a robust hybrid search for semantic and keyword matching.
• When I started this assignment I honestly had no idea where to begin. — Combining TF-IDF and GloVe embeddings improves semantic search accuracy for customer support tickets.
• Build your own Hybrid Search — Hybrid search combining BM25 and semantic embeddings dramatically improves relevance for complex queries over large datasets.
• From TF-IDF to Transformers: Implementing Four Generations of Semantic Search — Semantic search evolved from explicit rules to learned contextual representations, balancing interpretability with semantic depth.
• The Engine Behind Information Retrieval: Exploring TF-IDF — TF-IDF quantifies word importance by balancing local frequency with global rarity, enabling effective information retrieval.
• Rethinking Enterprise Search: How Cortex Search Turns Data into Business Impact — Hybrid retrieval combines keyword and semantic search to enhance enterprise data accessibility and AI application grounding.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Science on Medium.