Step-by-Step Guide to Choosing the Best Embedding Model for Your Application

Vector embeddings are crucial in modern search and Retrieval-Augmented Generation (RAG) applications as they capture semantic meaning of data objects like text and represent them in numerical arrays. Weaviate, a vector database that stores these embeddings and retrieves data based on vector search, has numerous integrations with various model providers and their embedding models. To choose the right embedding model for your application, you need to consider several factors such as use case, baseline model selection, task-specific performance, model size and memory usage, embedding dimensions, max tokens, and evaluation of the model on your specific dataset. The Massive Text Embedding Benchmark (MTEB) Leaderboard can be a useful starting point for this process. Once you have selected an appropriate model, it's crucial to evaluate its performance on your own dataset as benchmarks may not accurately represent the data you are dealing with. You can do this by preparing a small hand-labeled dataset and using Weaviate vector database to generate and store corresponding vector embeddings. Then, run queries and compare results against desired outputs for each query. Finally, after building an initial pipeline with a small baseline model, experiment with different embedding models to see if you can improve performance beyond your initial choice. Fine-tuning the model is optional but may be necessary for last bits of performance enhancement.