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Reranking applies a secondary ranking algorithm to improve the relevance order of search results. After the initial search returns results, the reranker analyzes the relationship between your query and each result to provide better ordering.

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How Reranking Works

Supermemory’s reranking process:
  1. Initial search returns results using standard semantic similarity
  2. Reranker model analyzes query-result pairs
  3. Scores are recalculated based on deeper semantic understanding
  4. Results are reordered by the new relevance scores
  5. Final results maintain the same structure but with improved ordering
The reranker is particularly effective at:
  • Understanding context and nuanced relationships
  • Handling ambiguous queries with multiple possible meanings
  • Improving precision for complex technical topics
  • Better ranking when results have similar initial scores

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Basic Reranking Comparison

  • TypeScript
  • Python
  • cURL
import Supermemory from 'supermemory';

const client = new Supermemory({
  apiKey: process.env.SUPERMEMORY_API_KEY!
});

// Search without reranking
const standardResults = await client.search.documents({
  q: "neural network optimization techniques",
  rerank: false,
  limit: 5
});

// Search with reranking
const rerankedResults = await client.search.documents({
  q: "neural network optimization techniques",
  rerank: true,
  limit: 5
});

console.log("Standard top result:", standardResults.results[0].score);
console.log("Reranked top result:", rerankedResults.results[0].score);
Sample Output Comparison: 
// Without reranking - results ordered by semantic similarity
{
  "results": [
    {
      "title": "Deep Learning Optimization Methods",
      "score": 0.82,
      "chunks": [
        {
          "content": "Various optimization algorithms like Adam, RMSprop, and SGD are used in neural network training...",
          "score": 0.79
        }
      ]
    },
    {
      "title": "Neural Network Training Techniques",
      "score": 0.81,
      "chunks": [
        {
          "content": "Batch normalization and dropout are common regularization techniques for neural networks...",
          "score": 0.78
        }
      ]
    }
  ],
  "timing": 145
}

// With reranking - results reordered by contextual relevance
{
  "results": [
    {
      "title": "Neural Network Training Techniques",
      "score": 0.89,  // Boosted by reranker
      "chunks": [
        {
          "content": "Batch normalization and dropout are common regularization techniques for neural networks...",
          "score": 0.85
        }
      ]
    },
    {
      "title": "Deep Learning Optimization Methods",
      "score": 0.86,  // Slightly adjusted
      "chunks": [
        {
          "content": "Various optimization algorithms like Adam, RMSprop, and SGD are used in neural network training...",
          "score": 0.83
        }
      ]
    }
  ],
  "timing": 267  // Additional ~120ms for reranking
}

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Complex Query Reranking

Reranking excels with complex, multi-faceted queries:
  • TypeScript
  • Python
  • cURL
const results = await client.search.documents({
  q: "sustainable machine learning carbon footprint energy efficiency",
  rerank: true,
  containerTags: ["research", "sustainability"],
  limit: 8
});

// Reranker understands the connection between:
// - Machine learning computational costs
// - Environmental impact of AI training
// - Energy-efficient model architectures
// - Green computing practices in ML
Sample Output: 
{
  "results": [
    {
      "documentId": "doc_green_ai",
      "title": "Green AI: Reducing the Carbon Footprint of Machine Learning",
      "score": 0.94,  // Highly relevant after reranking
      "chunks": [
        {
          "content": "Training large neural networks can consume as much energy as several cars over their lifetime. Sustainable ML practices focus on model efficiency, pruning, and quantization to reduce computational demands...",
          "score": 0.92,
          "isRelevant": true
        }
      ]
    },
    {
      "documentId": "doc_efficient_models",
      "title": "Energy-Efficient Neural Network Architectures",
      "score": 0.91,  // Boosted for strong topical relevance
      "chunks": [
        {
          "content": "MobileNets and EfficientNets are designed specifically for energy-constrained environments, achieving high accuracy with minimal computational overhead...",
          "score": 0.88,
          "isRelevant": true
        }
      ]
    }
  ],
  "total": 12,
  "timing": 298
}

​
Memory Search Reranking

Reranking also improves memory search results:
  • TypeScript
  • Python
  • cURL
const memoryResults = await client.search.memories({
  q: "explain transformer architecture attention mechanism",
  rerank: true,
  containerTag: "ai_notes",
  threshold: 0.6,
  limit: 5
});

// Reranker identifies memories that best explain
// the relationship between transformers and attention
Sample Output: 
{
  "results": [
    {
      "id": "mem_transformer_intro",
      "memory": "The transformer architecture revolutionized NLP by replacing recurrent layers with self-attention mechanisms. The attention mechanism allows the model to focus on different parts of the input sequence when processing each token, enabling parallel processing and better long-range dependency modeling.",
      "similarity": 0.93,  // Reranked higher for comprehensive explanation
      "title": "Transformer Architecture Overview",
      "metadata": {
        "topic": "deep-learning",
        "subtopic": "transformers"
      }
    },
    {
      "id": "mem_attention_detail",
      "memory": "Self-attention computes attention weights by taking dot products between query, key, and value vectors derived from the input embeddings. This allows each position to attend to all positions in the previous layer, capturing complex relationships in the data.",
      "similarity": 0.91,  // Boosted for technical detail
      "title": "Self-Attention Mechanism Details"
    }
  ],
  "total": 8,
  "timing": 198
}

​
Domain-Specific Reranking

Reranking understands domain-specific relationships:
  • TypeScript
  • Python
  • cURL
// Medical domain query
const medicalResults = await client.search.documents({
  q: "diabetes treatment insulin resistance metformin",
  rerank: true,
  filters: {
    AND: [
      { key: "domain", value: "medical", negate: false }
    ]
  },
  limit: 10
});

// Reranker understands medical relationships:
// - Diabetes types and treatments
// - Insulin resistance mechanisms
// - Metformin's role in diabetes management
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