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Get Started Free →Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | -16% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -9% | 0% |
| case-01 | ✓→✓ | = Same ✓ | 11% | 0% |
| case-04 | ✓→✓ | = Same ✓ | -19% | 0% |
| case-05 | ✓→✓ | = Same ✓ | -20% | 0% |
Guide to selecting and optimizing embedding models for vector search applications.
| Model | Dimensions | Max Tokens | Best For | | -------------------------- | ---------- | ---------- | ----------------------------------- | | voyage-3-large | 1024 | 32000 | Claude apps (Anthropic recommended) | | voyage-3 | 1024 | 32000 | Claude apps, cost-effective | | voyage-code-3 | 1024 | 32000 | Code search | | voyage-finance-2 | 1024 | 32000 | Financial documents | | voyage-law-2 | 1024 | 32000 | Legal documents | | text-embedding-3-large | 3072 | 8191 | OpenAI apps, high accuracy | | text-embedding-3-small | 1536 | 8191 | OpenAI apps, cost-effective | | bge-large-en-v1.5 | 1024 | 512 | Open source, local deployment | | all-MiniLM-L6-v2 | 384 | 256 | Fast, lightweight | | multilingual-e5-large | 1024 | 512 | Multi-language |
Document → Chunking → Preprocessing → Embedding Model → Vector
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[Overlap, Size] [Clean, Normalize] [API/Local]Full template library and detailed worked examples live in references/details.md. Read that file when you need the concrete templates.
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