Voyage AI

Embedding model provider. Offers high-dimensional text embedding APIs used in semantic and vector search pipelines.

Referenced in Erik Hatcher’s Hybrid Search Blueprint Series Semantic Boosting as the embedding provider for query vectorization (voyage-4-large, 2048 dimensions, dot product similarity).

Models

• voyage-3-large — used for pre-embedding documents in the embedded_movies example
• voyage-4-large — used for query embedding at query time

People

• Erik Hatcher — used Voyage AI in Semantic Boosting demo

Articles

• Hybrid Search Blueprint Series Semantic Boosting
• Hev meets Jev — rerank-3 is the strongest model in that three-corpus BEIR comparison, at 0.504 mean nDCG@10 and $0.50 per 1,000 queries, with p50/p95 of 185 ms / 287 ms; it edges out an untuned general decision model (Jev, 0.501) by a hair on quality and price, while keeping a much tighter latency tail

Key Concepts

• Dense Vector Retrieval
• Semantic Boosting
• Embeddings