Vector Embeddings on Demand

Encode text into vectors for semantic search and RAG pipelines.

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BGE M3

1024-dimensional vectors. Up to 8192 tokens per input, 245K total. Multilingual support. Ideal for RAG, semantic search, and similarity matching.

Model details

// 1536-dimensional vector

[
  0.0234, -0.0891, 0.1456, -0.0312, 0.0789, -0.1234, 0.0567, -0.0891,
  0.1123, -0.0456, 0.0891, -0.0234, 0.1567, -0.0789, 0.0345, -0.1012,
  0.0678, -0.0543, 0.1234, -0.0876, 0.0432, -0.1098, 0.0765, -0.0321,
  ... // 1536 dimensions total
]

Dimensions: 1536 Format: float32

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Vector Embeddings on Demand

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