Reranking

Overview

Reranking is a two-stage retrieval approach where:

1. First Stage: Fast retrieval (bi-encoder, BM25) gets top-k candidates (e.g., 100 results)
2. Second Stage: Slower but more accurate model reorders these candidates

Why Reranking?

Initial Retrieval Limitations

• Bi-encoders: Fast but less accurate
• Encode query and document separately
• Limited interaction between query and document

Reranker Advantages

• Cross-Encoders: Process query-document pairs jointly
• Better Accuracy: Captures nuanced relevance signals
• Feasible: Only applied to small candidate set (10-100 docs)

Types of Rerankers

Cross-Encoder Models

• Joint encoding of query + document
• BERT-based: ms-marco-MiniLM, BGE-reranker
• Most accurate but slow
• ~10-100ms per query-doc pair

Learned Rerankers

• Lightweight models trained on retrieval data
• Faster than cross-encoders
• Examples: Cohere rerank, Jina reranker

Late Interaction

• ColBERT-style reranking
• Token-level interactions
• Balance of speed and accuracy

Reranking Process

Query: "How do transformers work?"

1. Initial Retrieval (bi-encoder):
   - Retrieve top-100 candidates
   - Fast (~10ms)

2. Reranking (cross-encoder):
   - Score each of 100 candidates
   - Reorder by relevance
   - Return top-10
   - Slower (~1s for 100 docs)

3. Final Results:
   - Highest quality top-10 results
   - Much better than initial retrieval alone

Performance Impact

Typical improvements:

• 10-30% better NDCG@10
• 15-40% better MRR (Mean Reciprocal Rank)
• Especially valuable for RAG quality

Popular Reranker Models

Open Source

• BGE-reranker-large: High quality, BAAI
• ms-marco-MiniLM-L-12-v2: Fast, Microsoft
• Jina reranker-v2: Multilingual support

Commercial APIs

• Cohere Rerank: State-of-the-art, multilingual
• Voyage AI Rerank: High performance
• OpenAI (via fine-tuned models)

Use Cases

• RAG Systems: Improve context quality for LLMs
• Search Engines: Better result ordering
• Question Answering: Find most relevant passages
• : Refine initial candidates

Implementation Example

# 1. Initial retrieval
candidates = vector_db.search(query_embedding, top_k=100)

# 2. Reranking
reranked = reranker.rank(
    query=query,
    documents=[c.text for c in candidates],
    top_k=10
)

# 3. Use top results
context = reranked[:5]

Trade-offs

Advantages

• Significantly better accuracy
• More nuanced relevance
• Handles complex queries better
• Improves RAG quality

Disadvantages

• Added latency
• Additional computational cost
• More complex pipeline
• Requires separate model/API

Best Practices

• Retrieve more candidates initially (50-200)
• Rerank to smaller final set (5-20)
• Use async/parallel processing
• Cache reranking results when possible
• Monitor latency budget
• A/B test impact on quality

Integration

Supported by:

• LangChain (Cohere, custom)
• LlamaIndex (multiple rerankers)
• Haystack (reranking nodes)
• Direct API calls (Cohere, Voyage)

Pricing

Varies by reranker API or self-hosted compute costs.