> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mem0.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Zero Entropy

> Configure Zero Entropy neural reranking models in Mem0 with zerank-1 and zerank-1-small support.

[Zero Entropy](https://www.zeroentropy.dev) provides neural reranking models that significantly improve search relevance with fast performance.

## Models

Zero Entropy offers two reranking models:

* **`zerank-1`**: Flagship state-of-the-art reranker (non-commercial license)
* **`zerank-1-small`**: Open-source model (Apache 2.0 license)

## Installation

```bash theme={null}
pip install zeroentropy
```

## Configuration

```python Python theme={null}
from mem0 import Memory

config = {
    "vector_store": {
        "provider": "chroma",
        "config": {
            "collection_name": "my_memories",
            "path": "./chroma_db"
        }
    },
    "llm": {
        "provider": "openai",
        "config": {
            "model": "gpt-4o-mini"
        }
    },
    "rerank": {
        "provider": "zero_entropy",
        "config": {
            "model": "zerank-1",  # or "zerank-1-small"
            "api_key": "your-zero-entropy-api-key",  # or set ZERO_ENTROPY_API_KEY
            "top_k": 5
        }
    }
}

memory = Memory.from_config(config)
```

## Environment Variables

Set your API key as an environment variable:

```bash theme={null}
export ZERO_ENTROPY_API_KEY="your-api-key"
```

## Usage Example

```python Python theme={null}
import os
from mem0 import Memory

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

# Initialize memory with Zero Entropy reranker
config = {
    "vector_store": {"provider": "chroma"},
    "llm": {"provider": "openai", "config": {"model": "gpt-4o-mini"}},
    "rerank": {"provider": "zero_entropy", "config": {"model": "zerank-1"}}
}

memory = Memory.from_config(config)

# Add memories
messages = [
    {"role": "user", "content": "I love Italian pasta, especially carbonara"},
    {"role": "user", "content": "Japanese sushi is also amazing"},
    {"role": "user", "content": "I enjoy cooking Mediterranean dishes"}
]

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

# Search with reranking
results = memory.search("What Italian food does the user like?", filters={"user_id": "alice"})

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()
```

## Configuration Parameters

| Parameter | Description                                      | Type  | Default      |
| --------- | ------------------------------------------------ | ----- | ------------ |
| `model`   | Model to use: `"zerank-1"` or `"zerank-1-small"` | `str` | `"zerank-1"` |
| `api_key` | Zero Entropy API key                             | `str` | `None`       |
| `top_k`   | Maximum documents to return after reranking      | `int` | `None`       |

## Performance

* **Fast**: Optimized neural architecture for low latency
* **Accurate**: State-of-the-art relevance scoring
* **Cost-effective**: \~\$0.025/1M tokens processed

## Best Practices

1. **Model Selection**: Use `zerank-1` for best quality, `zerank-1-small` for faster processing
2. **Batch Size**: Process multiple queries together when possible
3. **Top-k Limiting**: Set reasonable `top_k` values (5-20) for best performance
4. **API Key Management**: Use environment variables for secure key storage