Semantic Car Search:
A Vector-Based Approach
Leveraging High-Dimensional Embeddings for Intelligent Automotive Discovery
Abstract
We present a novel semantic search system for automotive data, leveraging state-of-the-art vector embeddings to enable intelligent query understanding beyond traditional keyword matching. Our approach indexes 22,180 vehicle models into a 1,536-dimensional vector space using a high-performance transformer model.
The system achieves a 75.6% validation accuracy on 897 test queries, demonstrating robust performance in matching user intent to relevant vehicles. Through efficient batch processing and local vector comparisons, we reduce search latency from ~7 minutes to ~2 seconds for bulk operations.
1. Introduction
1.1 Motivation
Traditional automotive search systems rely on exact string matching, failing to capture semantic relationships between queries. A user searching for "Beamer M3" should find BMW M3 results, yet keyword-based systems struggle with:
- Brand synonyms (Beamer → BMW, Merco → Mercedes)
- Model variations (911 Turbo → 911 Turbo S)
- Typographical errors (Ferari → Ferrari)
- Cross-language queries (voiture sportive → sports car)
1.2 Problem Statement
Given a database of 22,180 automotive models and user queries in natural language, design a system that:
- Understands semantic intent beyond literal text
- Scales efficiently for real-time search
- Maintains accuracy across diverse query patterns
- Minimizes computational cost
2. Methodology
2.1 Vector Embeddings
We employ a state-of-the-art transformer model to transform textual car descriptions into dense 1,536-dimensional vectors.
2.2 Similarity Computation
Semantic similarity between a query Q and document D is computed using cosine similarity in the embedded space. Since the embeddings are pre-normalized, this simplifies to a dot product, enabling rapid batch comparisons via NumPy matrix operations.
3. Key Metrics
4. Interactive Demonstration
Explore the 22,180-vehicle embedding space reduced to 2D via PCA. Search for any car model to see semantic clustering in action.
5. Experimental Results
Match Distribution
Confidence Levels
| Approach | Time (897 queries) | Cost |
|---|---|---|
| Individual API Calls | ~7 min | ~$0.25 |
| Our Approach (Batch + Local) | ~2s | ~$0.00 |
6. Conclusion & Future Work
We successfully demonstrated a production-ready semantic search system for automotive data, achieving 75.6% validation accuracy while reducing search latency by 210× compared to naive approaches.
Future Directions
- Multimodal Search: Incorporate vehicle images via CLIP embeddings
- Fine-tuning: Domain-specific embedding models for automotive terminology
- Real-time Updates: Incremental indexing for new vehicle releases