SurrealDB offers comprehensive support for vector embeddings, enabling powerful semantic search and machine learning capabilities across your data. Through integrations with leading embedding providers, you can easily store, index and query high-dimensional vectors alongside your regular data.
| Integration | Description |
|---|---|
| Anthropic | Integration with Anthropic’s embedding models for vector search functionality. |
| Bedrock | Integration with AWS Bedrock for embedding generation and vector search. |
| Gemini | Integration with Google’s Gemini embedding models for semantic search. |
| Jina Embeddings | Integration with Jina AI’s embedding models for vector search capabilities. |
| Llama Index | Integration with Llama Index for semantic search capabilities. |
| Mistral | Integration with Mistral AI’s embedding models for vector search capabilities. |
| MixedBread | Integration with MixedBread for domain-flexible sentence vectors and semantic search. |
| NVIDIA | Integration with NVIDIA’s embedding models for vector search capabilities. |
| Ollama | Integration with Ollama for local embedding generation and vector search. |
| OpenAI | Integration with OpenAI’s text embedding models for semantic search capabilities. |
| Upstage | Integration with Upstage’s embedding models for vector search capabilities. |
| Voyage | Integration with Voyage AI’s embedding models for semantic search capabilities. |