Practice Bank: Embedding Model Decisions
Use starter.py to choose embedding strategies for realistic production scenarios.
Scenario 1: Support paraphrase
General neural embeddings improve meaning match.
Scenario 2: Error-code-heavy docs
Hybrid search protects exact identifiers.
Scenario 3: HR private corpus
Privacy may favor local or approved hosted models.
Scenario 4: Multilingual users
Model must be evaluated across languages.
Scenario 5: Legal corpus
Domain-specific evals matter more than generic benchmarks.
Scenario 6: Low-latency chat
Query embedding latency and caching matter.
Scenario 7: Large re-index
Rebuild cost and parallel index release matter.
Scenario 8: Provider outage
Sparse/local fallback keeps search alive.
Scenario 9: Domain acronyms
Evaluate vocabulary and acronym behavior.
Scenario 10: Model migration
Shadow testing prevents blind production swaps.