The author embarked on a two-month project to build a web search engine from scratch, driven by the desire for higher quality, more intelligent search results than current keyword-based systems. This involved deep dives into various computer science domains, leveraging neural embeddings for semantic search, and overcoming significant infrastructure and data processing challenges. The project resulted in a functional demo focusing on quality content and user experience. ✨
Article Points:
1
Neural embeddings enable superior semantic search over keyword matching.
2
Semantic text extraction and contextual chunking are crucial for quality.
3
RocksDB and sharding provided scalable, high-performance storage.
4
GPU inference was optimized for cost-effectiveness and utilization.
5
Low latency and user experience were prioritized through various optimizations.
6
Future search engines should focus on quality indexing and agentic search.
Motivation
Quality Content Focus
Human-level Intelligence
Core Technology
Neural Embeddings
Semantic Search
Data Pipeline
HTML Normalization
Contextual Chunking
Statement Chaining
Infrastructure
RocksDB for Storage
Sharding for Scale
Optimized GPU Inference
Performance
Low Latency Priority
Server-Side Rendering
Cloudflare Argo
Future Outlook