# Cohere Embed — Multilingual AI Embeddings API

> Generate high-quality multilingual embeddings for search and RAG. Cohere Embed v3 supports 100+ languages with specialized modes for documents, queries, and classification.

## Install

Paste the prompt below into your AI tool:

## Quick Use

```bash
pip install cohere
```

```python
import cohere

co = cohere.ClientV2(api_key="...")

# Generate embeddings
response = co.embed(
    texts=["What is machine learning?", "How does AI work?"],
    model="embed-v4.0",
    input_type="search_document",
    embedding_types=["float"],
)

print(len(response.embeddings.float_[0]))  # 1024 dimensions
```

## What is Cohere Embed?

Cohere Embed is a multilingual embedding API that converts text into high-dimensional vectors for semantic search, RAG, and classification. Version 4.0 supports 100+ languages, offers specialized input types (document vs. query), and includes built-in compression for storage efficiency. It consistently ranks among the top embedding models on the MTEB benchmark.

**Answer-Ready**: Cohere Embed v4.0 generates multilingual embeddings for search and RAG. Top MTEB benchmark scores, 100+ languages, specialized input types (document/query/classification). Binary and int8 compression for 32x storage savings. Production API with generous free tier.

**Best for**: Teams building multilingual search or RAG pipelines. **Works with**: Any vector database, LangChain, LlamaIndex. **Setup time**: Under 2 minutes.

## Core Features

### 1. Input Types

```python
# Different modes optimize for different tasks
docs = co.embed(texts=[...], input_type="search_document")    # For indexing
queries = co.embed(texts=[...], input_type="search_query")     # For searching
classify = co.embed(texts=[...], input_type="classification")  # For classification
cluster = co.embed(texts=[...], input_type="clustering")       # For clustering
```

### 2. Compression (32x Savings)

```python
response = co.embed(
    texts=["Hello world"],
    model="embed-v4.0",
    input_type="search_document",
    embedding_types=["float", "int8", "ubinary"],
)

# float: 1024 x 4 bytes = 4KB per vector
# int8:  1024 x 1 byte  = 1KB per vector (4x savings)
# binary: 1024 / 8 bytes = 128B per vector (32x savings)
```

### 3. Multilingual (100+ Languages)

```python
# Same model handles all languages — no separate models needed
texts = [
    "What is AI?",           # English
    "AI 是什么？",            # Chinese
    "AIとは何ですか？",       # Japanese
    "Was ist KI?",           # German
]
response = co.embed(texts=texts, model="embed-v4.0", input_type="search_document")
# Cross-lingual similarity works automatically
```

### 4. Batch Processing

```python
# Embed up to 96 texts per request
all_embeddings = []
for batch in chunks(documents, 96):
    response = co.embed(texts=batch, model="embed-v4.0", input_type="search_document")
    all_embeddings.extend(response.embeddings.float_)
```

## Cohere Embed vs Alternatives

| Model | Dimensions | Languages | MTEB Score | Compression |
|-------|-----------|-----------|------------|-------------|
| Cohere Embed v4.0 | 1024 | 100+ | Top 3 | float/int8/binary |
| OpenAI text-embedding-3-large | 3072 | 50+ | Top 5 | Matryoshka |
| Voyage AI v3 | 1024 | 20+ | Top 5 | No |
| BGE-M3 (open source) | 1024 | 100+ | Good | No |

## Pricing

| Tier | Embeddings/mo | Price |
|------|---------------|-------|
| Free | 1M | $0 |
| Production | Pay-as-you-go | $0.1/M tokens |

## FAQ

**Q: How does it compare to OpenAI embeddings?**
A: Comparable quality on MTEB, better multilingual support, and built-in binary compression for significant storage savings.

**Q: Can I use it with Pinecone/Qdrant/Weaviate?**
A: Yes, generate embeddings with Cohere and store in any vector database.

**Q: Is there an open-source alternative?**
A: BGE-M3 and E5-Mistral are strong open-source options, but require self-hosting.

## Source & Thanks

> Created by [Cohere](https://cohere.com).
>
> [cohere.com/embed](https://cohere.com/embed) — Multilingual embedding API

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## Quick Start

```bash
pip install cohere
```

Generate high-quality multilingual vector embeddings in three lines.

## What is Cohere Embed?

Cohere Embed is a multilingual embedding API that converts text into high-dimensional vectors for semantic search, RAG, and classification. Supports 100+ languages and ranks top on MTEB.

**In one sentence**: Multilingual embedding API — 100+ languages, MTEB Top 3, supports 32x storage compression, dedicated document/query/classification modes, free tier up to 1M/month.

**For**: Teams building multilingual search or RAG.

## Core Features

### 1. Input Types
Four optimized modes: document, query, classification, clustering.

### 2. Compression
Three compression levels (float/int8/binary) saving up to 32x storage.

### 3. Multilingual
Single model for 100+ languages — cross-language similarity works out of the box.

## FAQ

**Q: How does it compare to OpenAI embeddings?**
A: Comparable quality, better multilingual, built-in compression saves storage.

**Q: Open-source alternatives?**
A: BGE-M3 and E5-Mistral — but require self-hosting.

## Source & Thanks

> [cohere.com/embed](https://cohere.com/embed) — multilingual embedding API

---
Source: https://tokrepo.com/en/workflows/cohere-embed-multilingual-ai-embeddings-api-dde04e91
Author: Cohere