> ## Documentation Index
> Fetch the complete documentation index at: https://docs.praison.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Cohere Embeddings

> Generate embeddings using Cohere's embed models

## Overview

Cohere provides high-quality embedding models optimized for search, classification, and clustering with excellent multilingual support.

## Quick Start

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import embedding

result = embedding(
    input="Hello world",
    model="cohere/embed-english-v3.0"
)
print(f"Dimensions: {len(result.embeddings[0])}")
```

## CLI Usage

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai embed "Hello world" --model cohere/embed-english-v3.0
```

## Setup

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
export COHERE_API_KEY="your-cohere-api-key"
```

## Available Models

| Model                             | Dimensions | Languages | Use Case             |
| --------------------------------- | ---------- | --------- | -------------------- |
| `cohere/embed-v4.0`               | 1024       | 100+      | Latest, best quality |
| `cohere/embed-english-v3.0`       | 1024       | English   | High quality English |
| `cohere/embed-english-light-v3.0` | 384        | English   | Fast, cost-effective |
| `cohere/embed-multilingual-v3.0`  | 1024       | 100+      | Multilingual support |

## Input Types

Cohere supports different input types for optimal embeddings:

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import embedding

# For search queries
result = embedding(
    input="What is machine learning?",
    model="cohere/embed-english-v3.0",
    input_type="search_query"
)

# For documents to be searched
result = embedding(
    input="Machine learning is a subset of AI...",
    model="cohere/embed-english-v3.0",
    input_type="search_document"
)
```

## Batch Embeddings

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import embedding

documents = [
    "First document content",
    "Second document content",
    "Third document content"
]
result = embedding(
    input=documents,
    model="cohere/embed-english-v3.0"
)
print(f"Generated {len(result.embeddings)} embeddings")
```

## Multilingual Example

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import embedding

texts = [
    "Hello world",           # English
    "Bonjour le monde",      # French
    "Hola mundo",            # Spanish
    "こんにちは世界"          # Japanese
]
result = embedding(
    input=texts,
    model="cohere/embed-multilingual-v3.0"
)
```

## Pricing

| Model                    | Price per 1M tokens |
| ------------------------ | ------------------- |
| embed-v4.0               | \$0.10              |
| embed-english-v3.0       | \$0.10              |
| embed-english-light-v3.0 | \$0.10              |
| embed-multilingual-v3.0  | \$0.10              |

## Related

* [Embedding Providers Overview](/docs/embeddings/index)
* [Voyage AI Embeddings](/docs/embeddings/providers/voyage)