Embedding Strategies
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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.
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Frontmatter — 2 properties
| name | embedding-strategies |
|---|---|
| description | 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. |
| 1 | --- |
| 2 | name: embedding-strategies |
| 3 | description: 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. |
| 4 | ---A5 — No allowed-tools declared — no way to tell what this skill may touch |
| 5 | |
| 6 | # Embedding Strategies |
| 7 | |
| 8 | Guide to selecting and optimizing embedding models for vector search applications. |
| 9 | |
| 10 | ## When to Use This Skill |
| 11 | |
| 12 | - Choosing embedding models for RAG |
| 13 | - Optimizing chunking strategies |
| 14 | - Fine-tuning embeddings for domains |
| 15 | - Comparing embedding model performance |
| 16 | - Reducing embedding dimensions |
| 17 | - Handling multilingual content |
| 18 | |
| 19 | ## Core Concepts |
| 20 | |
| 21 | ### 1. Embedding Model Comparison (2026) |
| 22 | |
| 23 | | Model | Dimensions | Max Tokens | Best For | |
| 24 | | -------------------------- | ---------- | ---------- | ----------------------------------- | |
| 25 | | **voyage-3-large** | 1024 | 32000 | Claude apps (Anthropic recommended) | |
| 26 | | **voyage-3** | 1024 | 32000 | Claude apps, cost-effective | |
| 27 | | **voyage-code-3** | 1024 | 32000 | Code search | |
| 28 | | **voyage-finance-2** | 1024 | 32000 | Financial documents | |
| 29 | | **voyage-law-2** | 1024 | 32000 | Legal documents | |
| 30 | | **text-embedding-3-large** | 3072 | 8191 | OpenAI apps, high accuracy | |
| 31 | | **text-embedding-3-small** | 1536 | 8191 | OpenAI apps, cost-effective | |
| 32 | | **bge-large-en-v1.5** | 1024 | 512 | Open source, local deployment | |
| 33 | | **all-MiniLM-L6-v2** | 384 | 256 | Fast, lightweight | |
| 34 | | **multilingual-e5-large** | 1024 | 512 | Multi-language | |
| 35 | |
| 36 | ### 2. Embedding Pipeline |
| 37 | |
| 38 | ``` |
| 39 | Document → Chunking → Preprocessing → Embedding Model → Vector |
| 40 | ↓ |
| 41 | [Overlap, Size] [Clean, Normalize] [API/Local] |
| 42 | ``` |
| 43 | |
| 44 | ## Templates and detailed worked examples |
| 45 | |
| 46 | Full template library and detailed worked examples live in `references/details.md`. Read that file when you need the concrete templates. |
| 47 | |
| 48 | ## Best Practices |
| 49 | |
| 50 | ### Do's |
| 51 | |
| 52 | - **Match model to use case**: Code vs prose vs multilingual |
| 53 | - **Chunk thoughtfully**: Preserve semantic boundaries |
| 54 | - **Normalize embeddings**: For cosine similarity search |
| 55 | - **Batch requests**: More efficient than one-by-one |
| 56 | - **Cache embeddings**: Avoid recomputing for static content |
| 57 | - **Use Voyage AI for Claude apps**: Recommended by Anthropic |
| 58 | |
| 59 | ### Don'ts |
| 60 | |
| 61 | - **Don't ignore token limits**: Truncation loses information |
| 62 | - **Don't mix embedding models**: Incompatible vector spaces |
| 63 | - **Don't skip preprocessing**: Garbage in, garbage out |
| 64 | - **Don't over-chunk**: Lose important context |
| 65 | - **Don't forget metadata**: Essential for filtering and debugging |
| 66 |
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