Reranking

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);
```

client = Supermemory(api_key=os.environ.get("SUPERMEMORY_API_KEY"))

# Search without reranking
standard_results = client.search.documents(
    q="neural network optimization techniques",
    rerank=False,
    limit=5
)

# Search with reranking
reranked_results = client.search.documents(
    q="neural network optimization techniques",
    rerank=True,
    limit=5
)

print("Standard top result:", standard_results.results[0].score)
print("Reranked top result:", reranked_results.results[0].score)
```

# With reranking
echo "Reranked results:"
curl -X POST "https://api.supermemory.ai/v3/search" \
  -H "Authorization: Bearer $SUPERMEMORY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "q": "neural network optimization techniques",
    "rerank": true,
    "limit": 3
  }' | jq '.results[0] | {title, score}'
```

// 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
}

// Reranker understands the connection between:
// - Machine learning computational costs
// - Environmental impact of AI training
// - Energy-efficient model architectures
// - Green computing practices in ML
```

# Reranker understands the connection between:
# - Machine learning computational costs
# - Environmental impact of AI training
# - Energy-efficient model architectures
# - Green computing practices in ML
```

{
  "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
}

// Reranker identifies memories that best explain
// the relationship between transformers and attention
```

# Reranker identifies memories that best explain
# the relationship between transformers and attention
```

{
  "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
}

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

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