How can we evaluate different embedding models to decide which yields the best retrieval performance for our specific RAG use case?

To evaluate embedding models for a RAG (Retrieval-Augmented Generation) system, you need a structured approach that tests how well each model retrieves contextually relevant information for your specific data. Start by defining a benchmark dataset that mirrors your real-world use case. This dataset should include sample queries and a curated set of documents or passages that are known to be relevant to those queries. For example, if your RAG system is designed for legal document retrieval, your benchmark should include queries like “What constitutes breach of contract?” paired with relevant legal texts. Use standard retrieval metrics such as recall@k (how many relevant documents are in the top k results) and precision@k (how many of the top k results are relevant) to quantify performance. Tools like the TREC evaluation toolkit or custom scripts can automate these calculations.

Next, compare models by running them against your benchmark. Popular open-source models like Sentence-BERT, OpenAI’s text-embedding-ada-002, or domain-specific options (e.g., BioBERT for medical texts) should be tested. For each model, encode your documents and queries into embeddings, then use a similarity metric like cosine similarity to rank documents. To ensure fairness, use the same vector database (e.g., FAISS or Annoy) and search parameters (e.g., k=10 for top results) across all tests. For example, you might find that Sentence-BERT achieves 85% recall@5 on technical documentation but struggles with slang-heavy queries, while OpenAI’s model performs better on diverse language but is slower. Consider using hybrid approaches, like reranking initial results with a cross-encoder (e.g., a MiniLM model), to improve accuracy.

Finally, factor in practical constraints. Evaluate inference speed, especially if your system requires real-time responses. Smaller models (e.g., All-MiniLM-L6-v2) may trade slight accuracy losses for faster performance. Check computational requirements: some models require GPUs, while others run efficiently on CPUs. Also, assess scalability—models that compress embeddings (e.g., using quantization) reduce storage costs. For instance, a model producing 384-dimensional vectors might save 60% storage compared to a 1024-dimensional model with minimal performance drop. Continuously update your benchmark as your data evolves, and retest models periodically to ensure they adapt to new query patterns or document types.