Embedding Strategies

When to use this skill

Use Embedding Strategies when an AI agent needs a reusable SKILL.md workflow for this job: 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.

When not to use it

Skip Embedding Strategies when the task is outside the coding category, or when a more specific skill in this directory already covers the same workflow with clearer triggers.

How to install

1. Personal install: create ~/.claude/skills/embedding-strategies/SKILL.md (and any bundled scripts) so Claude Code, Claude Desktop, and compatible agents can load it in every project.
2. Project install: commit the same folder at .claude/skills/embedding-strategies/ so teammates get the skill with the repo.
3. Restart the agent session after copying files so it re-scans the skills directory, then ask for the task in words that match the skill description.

Full install guide for Claude, Cursor, and Codex

What this skill does

# 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 (2026)

| 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 |

### 2. Embedding Pipeline

``` Document → Chunking → Preprocessing → Embedding Model → Vector ↓ [Overlap, Size] [Clean, Normalize] [API/Local] ```

## Templates and detailed worked examples

Full template library and detailed worked examples live in `references/details.md`. Read that file when you need the concrete templates.

## Best Practices

### Do's

- **Match model to use case**: Code vs prose vs multilingual - **Chunk thoughtfully**: Preserve semantic boundaries - **Normalize embeddings**: For cosine similarity search - **Batch requests**: More efficient than one-by-one - **Cache embeddings**: Avoid recomputing for static content - **Use Voyage AI for Claude apps**: Recommended by Anthropic

### Don'ts

- **Don't ignore token limits**: Truncation loses information - **Don't mix embedding models**: Incompatible vector spaces - **Don't skip preprocessing**: Garbage in, garbage out - **Don't over-chunk**: Lose important context - **Don't forget metadata**: Essential for filtering and debugging

Intended uses

• Choosing embedding models for RAG
• Optimizing chunking strategies
• Fine-tuning embeddings for domains
• Comparing embedding model performance
• Reducing embedding dimensions
• Handling multilingual content