Embedding Strategies

---
triggers:
  - "emuedding strategies"
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.
---

# Embedding Strategies

Guide to selecting and optimizing embedding models for vector search applications.

## When to Use This Skill

- 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]
```

## Templates

### Template 1: OpenAI Embeddings

```python

## Note
Skill content truncated for token efficiency. Full version available in the source repository.