Rerankers enhance the quality of search results by re-ordering the initial retrieval results using more sophisticated scoring mechanisms. They act as a secondary ranking layer that can significantly improve the relevance of retrieved memories.

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How Rerankers Work

  1. Initial Retrieval: Vector search returns candidate memories based on semantic similarity
  2. Reranking: The reranker evaluates and re-scores these candidates using more complex criteria
  3. Final Results: Returns the top-k memories with improved relevance ordering

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Benefits

  • Improved Precision: Better ranking of relevant memories
  • Context Awareness: More sophisticated understanding of query-memory relationships
  • Performance: Can improve results without changing the underlying vector store

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Supported Rerankers

Mem0 supports several reranker models:

Cohere

Sentence Transformer

Hugging Face

LLM Reranker

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Usage

Rerankers are configured as part of the memory configuration: 
from mem0 import Memory

config = {
    "reranker": {
        "provider": "cohere",
        "config": {
            "api_key": "your-api-key",
            "top_n": 10
        }
    }
}

memory = Memory.from_config(config)
For detailed configuration options, see the Config page.