embeddings

# Embeddings

Convert text to dense vector representations for semantic search and similarity.

## Quick Reference

```python
from openai import OpenAI

client = OpenAI()

# Single text embedding
response = client.embeddings.create(
    model="text-embedding-3-small",
    input="Your text here"
)
vector = response.data[0].embedding  # 1536 dimensions
```

```python
# Batch embedding (efficient)
texts = ["text1", "text2", "text3"]
response = client.embeddings.create(
    model="text-embedding-3-small",
    input=texts
)
vectors = [item.embedding for item in response.data]
```

## Model Selection

| Model | Dims | Cost | Use Case |
|-------|------|------|----------|
| `text-embedding-3-small` | 1536 | $0.02/1M | General purpose |
| `text-embedding-3-large` | 3072 | $0.13/1M | High accuracy |
| `nomic-embed-text` (Ollama) | 768 | Free | Local/CI |

## Chunking Strategy

```python
def chunk_text(text: str, chunk_size: int = 512, overlap: int = 50) -> list[str]:
    """Split text into overlapping chunks for embedding."""
    words = text.split()
    chunks = []

    for i in range(0, len(words), chunk_size - overlap):
        chunk = " ".join(words[i:i + chunk_size])
        if chunk:
            chunks.append(chunk)

    return chunks
```

**Guidelines:**
- Chunk size: 256-1024 tokens (512 typical)
- Overlap: 10-20% for context continuity
- Include metadata (title, source) with chunks

## Similarity Calculation

```python
import numpy as np

def cosine_similarity(a: list[float], b: list[float]) -> float:
    """Calculate cosine similarity between two vectors."""
    a, b = np.array(a), np.array(b)
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

# Usage
similarity = cosine_similarity(vector1, vector2)
# 1.0 = identical, 0.0 = orthogonal, -1.0 = opposite
```

## Key Decisions

- **Dimension reduction**: Can truncate `text-embedding-3-large` to 1536 dims
- **Normalization**: Most models return normalized vectors
- **Batch size**: 100-500 texts per API ca