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Language support is a shortlist criterion; retrieval quality must be measured on representative questions and source fragments.
Choose embeddings for Russian-language retrieval by building a small search test on your documents. A model card’s Russian-language support helps shortlist candidates, but does not guarantee performance on internal terminology, abbreviations or mixed Russian-English material. Evaluate useful retrieval before treating a larger model or dimension as an improvement.
Prepare questions and relevant evidence
Write representative questions and identify the fragments that would help answer them. Include ordinary formulations, technical terms and questions using different wording from the source. Keep a small set separate from any prompt or configuration tuning so the final comparison has an honest control.
A useful test also includes questions with no supporting answer in the corpus. The system should not receive credit for returning a superficially similar but irrelevant passage. Preserve document versions and labels so another reviewer can understand why a result is considered useful.
Keep preparation comparable
Record chunking, input length, normalization and the query or document prompts required by each candidate. Follow model-specific guidance, but label differences rather than pretending every configuration is identical. Short questions and long passages may have different encoding roles in an asymmetric search task.
Use the same corpus versions and evaluation questions. If an input exceeds the supported length, record truncation or a changed chunking strategy. Otherwise a model may appear worse simply because important evidence never reached its encoder.
Select metrics for user behavior
| Search scenario | What to check |
|---|---|
| One short answer in a manual | Relevant evidence among the first results |
| Several related sources | Coverage of useful candidates |
| A list read in order | Relevance and ordering near the top |
Choose evaluation tools and thresholds that match the intended behavior. Save per-question outcomes, not only one aggregate score. A mixed corpus may reveal a failure concentrated in abbreviations or cross-language phrasing even when the average looks acceptable.
Add resource measurements and review difficult cases
Measure encoding throughput, GPU memory, vector size and query latency for the tested setup. A larger representation can increase storage and search requirements without improving the particular corpus. Keep the quality and resource results together so the trade-off is visible.
Inspect failures manually: ambiguous names, inflected terms, mixed scripts and fragments missing context. Separate corpus-preparation problems from ranking errors. That supports an indexing or serving pilot based on specific language needs, without assuming any model is universally best for Russian documents.
Official references: sbert.net — README, sbert.net — evaluation.
Related guides: Batch text embeddings on your own GPU, Reranking in RAG: how many candidates should you rescore?.