Arctic Embed
Arctic Embed offers high-performance, open-source embedding models optimized for retrieval tasks, suitable for various enterprise applications. It balances efficiency and accuracy, topping MTEB retrieval benchmarks.
3 Steps
- 1
Model Selection: Choose the appropriate Arctic Embed model size based on your performance and resource constraints. Smaller models (22M parameters) offer faster inference, while larger models (334M parameters) provide higher accuracy.
- 2
Text Embedding Generation: Use the selected Arctic Embed model to generate embeddings for your text data. This involves loading the model and passing your text through it.
- 3
Similarity Search: Implement a similarity search using the generated embeddings. Common methods include cosine similarity or dot product. Use a vector database for efficient search at scale.
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