Skip to main content

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 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

pip install zeroentropy

​
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-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: 
export ZERO_ENTROPY_API_KEY="your-api-key"

​
Usage Example

Python
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

ParameterDescriptionTypeDefault
modelModel to use: "zerank-1" or "zerank-1-small"str"zerank-1"
api_keyZero Entropy API keystrNone
top_kMaximum documents to return after rerankingintNone

​
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