Ranking

const results = await ns.search("product features", {
  topK: 50,
  rerank: true,
  rerankLimit: 10,
  rerankModel: "cohere:rerank-v4.0-pro",
});

results = client.search.execute(
    query="product features",
    top_k=50,
    rerank=True,
    rerank_limit=10,
    rerank_model="cohere:rerank-v4.0-pro",
)

const results = await ns.search(query, { topK: 20 });

// Apply weights based on document type
const weighted = results
  .map((result) => {
    let weight = 1.0;

    // Boost official documentation
    if (result.metadata?.category === "official-docs") {
      weight = 1.2;
    }

    // Penalize older content
    if (result.metadata?.year < 2023) {
      weight = 0.8;
    }

    return {
      ...result,
      weightedScore: result.score * weight,
    };
  })
  .sort((a, b) => b.weightedScore - a.weightedScore);

results = client.search.execute(query=query, top_k=20)

# Apply weights based on document type
weighted = []
for result in results.data:
    weight = 1.0

    # Boost official documentation
    if result.metadata.get("category") == "official-docs":
        weight = 1.2

    # Penalize older content
    if result.metadata.get("year", 2024) < 2023:
        weight = 0.8

    weighted.append({
        **result.__dict__,
        "weighted_score": result.score * weight,
    })

weighted.sort(key=lambda x: x["weighted_score"], reverse=True)

// 1. Get reranked results
const results = await ns.search(query, {
  topK: 50,
  rerank: true,
  rerankLimit: 20,
});

// 2. Apply custom weights
const final = results
  .map((result) => {
    let score = result.score;

    // Apply domain-specific weighting
    if (result.metadata?.docType === "faq") {
      score *= 1.1; // Boost FAQs
    }

    return { ...result, finalScore: score };
  })
  .sort((a, b) => b.finalScore - a.finalScore)
  .slice(0, 10);

# 1. Get reranked results
results = client.search.execute(
    query=query,
    top_k=50,
    rerank=True,
    rerank_limit=20,
)

# 2. Apply custom weights
final = []
for result in results.data:
    score = result.score

    # Apply domain-specific weighting
    if result.metadata.get("doc_type") == "faq":
        score *= 1.1  # Boost FAQs

    final.append({**result.__dict__, "final_score": score})

final.sort(key=lambda x: x["final_score"], reverse=True)
final = final[:10]