Choose an Embedding Model
Evaluate embedding models on representative queries: exact names, acronyms, code, multilingual text, domain vocabulary, paraphrases, and long passages. Compare Recall@K, ranking metrics, dimensions, latency, throughput, price, input limits, deployment choices, and data-handling constraints.
Keep a gold set of queries and relevant source IDs. Change one factor at a time. When switching models, use a versioned index or migration because vectors from incompatible spaces generally cannot be mixed.
Implementation example
Compare models on:
- Recall@K / MRR
- Domain and language coverage
- Latency and throughput
- Cost per indexed token/query
- Data handling and operational fit💡 Choose using task-specific evaluation, not only marketing claims or vector dimensions.
Key Takeaways
- Inspect the relevant pipeline stage instead of guessing.
- Preserve source provenance, versions, and access scope.
- Measure quality on representative queries.
Knowledge Check
Why test embedding models on your own corpus?