Zero Entropy - Mem0

Zero Entropy

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)

TypeScript (self-hosted)

The TypeScript OSS SDK (mem0ai/oss) ships the Zero Entropy reranker under the same provider name as Python, zero_entropy. It reads the key from config or ZERO_ENTROPY_API_KEY and defaults to the zerank-1 model.

pnpm add zeroentropy

import { Memory } from "mem0ai/oss";

const memory = new Memory({
  reranker: {
    provider: "zero_entropy",
    config: {
      apiKey: process.env.ZERO_ENTROPY_API_KEY,
      // model: "zerank-1",   // default (or "zerank-1-small")
      topK: 5,
    },
  },
});

const results = await memory.search("What Italian food does the user like?", {
  filters: { userId: "alice" },
  rerank: true,
});

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

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

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