Skip to main content
Cohere provides enterprise-grade reranking models with excellent multilingual support and production-ready performance.

​
Models

Cohere offers several reranking models:
  • rerank-english-v3.0: Latest English reranker with best performance
  • rerank-multilingual-v3.0: Multilingual support for global applications
  • rerank-english-v2.0: Previous generation English reranker

​
Installation

pip install cohere

​
Configuration

Python
from mem0 import Memory

config = {
    "vector_store": {
        "provider": "chroma",
        "config": {
            "collection_name": "my_memories",
            "path": "./chroma_db"
        }
    },
    "llm": {
        "provider": "openai",
        "config": {
            "model": "gpt-4.1-nano-2025-04-14"
        }
    },
    "reranker": {
        "provider": "cohere",
        "config": {
            "model": "rerank-english-v3.0",
            "api_key": "your-cohere-api-key",  # or set COHERE_API_KEY
            "top_k": 5,
            "return_documents": False,
            "max_chunks_per_doc": None
        }
    }
}

memory = Memory.from_config(config)

​
Environment Variables

Set your API key as an environment variable: 
export COHERE_API_KEY="your-api-key"

​
Usage Example

Python
import os
from mem0 import Memory

# Set API key
os.environ["COHERE_API_KEY"] = "your-api-key"

# Initialize memory with Cohere reranker
config = {
    "vector_store": {"provider": "chroma"},
    "llm": {"provider": "openai", "config": {"model": "gpt-4o-mini"}},
    "rerank": {
        "provider": "cohere",
        "config": {
            "model": "rerank-english-v3.0",
            "top_k": 3
        }
    }
}

memory = Memory.from_config(config)

# Add memories
messages = [
    {"role": "user", "content": "I work as a data scientist at Microsoft"},
    {"role": "user", "content": "I specialize in machine learning and NLP"},
    {"role": "user", "content": "I enjoy playing tennis on weekends"}
]

memory.add(messages, user_id="bob")

# Search with reranking
results = memory.search("What is the user's profession?", user_id="bob")

for result in results['results']:
    print(f"Memory: {result['memory']}")
    print(f"Vector Score: {result['score']:.3f}")
    print(f"Rerank Score: {result['rerank_score']:.3f}")
    print()

​
Multilingual Support

For multilingual applications, use the multilingual model:
Python
config = {
    "rerank": {
        "provider": "cohere",
        "config": {
            "model": "rerank-multilingual-v3.0",
            "top_k": 5
        }
    }
}

​
Configuration Parameters

ParameterDescriptionTypeDefault
modelCohere rerank model to usestr"rerank-english-v3.0"
api_keyCohere API keystrNone
top_kMaximum documents to returnintNone
return_documentsWhether to return document textsboolFalse
max_chunks_per_docMaximum chunks per documentintNone

​
Features

  • High Quality: Enterprise-grade relevance scoring
  • Multilingual: Support for 100+ languages
  • Scalable: Production-ready with high throughput
  • Reliable: SLA-backed service with 99.9% uptime

​
Best Practices

  1. Model Selection: Use rerank-english-v3.0 for English, rerank-multilingual-v3.0 for other languages
  2. Batch Processing: Process multiple queries efficiently
  3. Error Handling: Implement retry logic for production systems
  4. Monitoring: Track reranking performance and costs