Embedding Recommendations
Hokai uses embeddings and semantic search to power Smart Match and directory search. This article explains how it works at a high level.
What Are Embeddings?
Embeddings are vector representations of text. Similar concepts map to nearby vectors. "AI coding assistant" and "developer copilot" are close in embedding space; "AI coding assistant" and "social media scheduler" are farther apart.
How Hokai Uses Them
- Smart Match — Your query and context are embedded. We match against tool descriptions, categories, and capabilities. The best matches are ranked and returned.
- Directory search — Search queries are embedded and matched against tool profiles.
Validation
Recommendations are validated against our tools database. We do not hallucinate tools. Every recommended tool exists in our directory with real data.
The Bottom Line
Embeddings help us find relevant tools. Validation ensures they're real. The result is a ranked stack that fits your context.