embedding-strategies
Guide to selecting and optimizing embedding models for vector search applications.
Embedding Strategies
Guide to selecting and optimizing embedding models for vector search applications.
Do not use this skill when
- The task is unrelated to embedding strategies
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open
resources/implementation-playbook.md.
Use this skill when
- Choosing embedding models for RAG
- Optimizing chunking strategies
- Fine-tuning embeddings for domains
- Comparing embedding model performance
- Reducing embedding dimensions
- Handling multilingual content
Core Concepts
1. Embedding Model Comparison
| Model | Dimensions | Max Tokens | Best For |
|---|---|---|---|
| text-embedding-3-large | 3072 | 8191 | High accuracy |
| text-embedding-3-small | 1536 | 8191 | Cost-effective |
| voyage-2 | 1024 | 4000 | Code, legal |
| bge-large-en-v1.5 | 1024 | 512 | Open source |
| all-MiniLM-L6-v2 | 384 | 256 | Fast, lightweight |
| multilingual-e5-large | 1024 | 512 | Multi-language |
2. Embedding Pipeline
Document → Chunking → Preprocessing → Embedding Model → Vector
↓
[Overlap, Size] [Clean, Normalize] [API/Local]Templ
Subscribers only
The full skill, its 1 bundled files and every download is included with every paid Complete AI plan.
Details
| Source | community |
|---|---|
| License | — |
| Risk label | critical ("critical" means the skill may run commands or touch files — read before use) |
| Files | SKILL.md |
| Added | 2026-02-27 |
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