Embedding Strategies

---
name: embedding-strategies
description: Guide to selecting and optimizing embedding models for vector search applications. 
category: Document Processing
source: antigravity
tags: [python, markdown, api, ai, template, document, rag]
url: https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/embedding-strategies
---


# 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]
```

## Templates

### Template 1: OpenAI Embeddings

```python
from openai import OpenAI
from typing import List
import numpy as np

client = OpenAI()

def get_embeddings(
    texts: List[str],
    model: str = "text-embedding-3-small",
    dimensions: int = None
) -> List[List[float]]:
    """Get embeddings from OpenAI."""
    # Handle batching for large lists
    batch_size = 100
    all_embeddings = []

    for i in range(0, len(texts), batch_size):
        batch = texts[i:i + batch_size]

        kwargs = {"input": batch, "model": model}
        if dimensions:
            kwargs["dimensions"] = dimensions

        response = client.embeddings.create(**kwargs)
        embeddings = [item.embedding for item in response.data]
        all_embeddings.extend(embeddings)

    return all_embeddings


def get_embedding(text: str, **kwargs) -> List[float]:
    """Get single embedding."""
    return get_embeddings([text], **kwargs)[0]


# Dimension reduction with OpenAI
def get_reduced_embedding(text: str, dimensions: int = 512) -> List[float]:
    """Get embedding with reduced dimensions (Matryoshka)."""
    return get_embedding(
        text,
        model="text-embedding-3-small",
        dimensions=dimensions
    )
```

### Template 2: Local Embeddings with Sentence Transformers

```python
from sentence_transformers import SentenceTransformer
from typing import List, Optional
import numpy as np

class LocalEmbedder:
    """Local embedding with sentence-transformers."""

    def __init__(
        self,
        model_name: str = "BAAI/bge-large-en-v1.5",
        device: str = "cpu",
        query_prefix: str = ""
    ):
        self.model = SentenceTransformer(model_name, device=device)
        self.query_prefix = query_prefix  # Choose from the selected model card.

    def embed(
        self,
        texts: List[str],
        normalize: bool = True,
        show_progress: bool = False
    ) -> np.ndarray:
        """Embed texts with optional normalization."""
        embeddings = self.model.encode(
            texts,
            normalize_embeddings=normalize,
            show_progress_bar=show_progress,
            convert_to_numpy=True
        )
        return embeddings

    def embed_query(self, query: str) -> np.ndarray:
        """Embed a query with the explicitly configured model prefix."""
        return self.embed([self.query_prefix + query])[0]

    def embed_documents(self, documents: List[str]) -> np.ndarray:
        """Embed documents for indexing."""
        return self.embed(documents)


# E5 model with instructions
class E5Embedder:
    def __init__(self, model_name: str = "intfloat/multilingual-e5-large"):
        self.model = SentenceTransformer(model_name)

    def embed_query(self, query: str) -> np.ndarray:
        return self.model.encode(f"query: {query}")

    def embed_document(self, document: str) -> np.ndarray:
        return self.model.encode(f"passage: {document}")
```

### Template 3: Chunking Strategies

```python
from typing import List, Tuple
import re

def chunk_by_tokens(
    text: str,
    chunk_size: int = 512,
    chunk_overlap: int = 50,
    tokenizer=None
) -> List[str]:
    """Chunk text by token count."""
    if not isinstance(chunk_size, int) or not isinstance(chunk_overlap, int):
        raise ValueError("chunk size and overlap must be integers")
    if chunk_size <= 0 or not 0 <= chunk_overlap < chunk_size:
        raise ValueError("require chunk_size > 0 and 0 <= overlap < chunk_size")
    if tokenizer is None:
        import tiktoken
        tokenizer = tiktoken.get_encoding("cl100k_base")

    tokens =