Chapter 3: Reranking and Filtering
What is Reranking?
Reranking uses a more powerful model to re-score initial retrieval results:
Cross-Encoder Reranking
Cross-encoders score query-document pairs directly:
from sentence_transformers import CrossEncoder
# Load reranker
reranker = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
# Initial retrieval
candidates = retriever.get_relevant_documents(query)
# Score pairs
pairs = [(query, doc.page_content) for doc in candidates]
scores = reranker.predict(pairs)
# Sort by reranked scores
reranked = sorted(zip(candidates, scores), key=lambda x: x[1], reverse=True)
Cohere Rerank
Production-ready reranking API:
import cohere
co = cohere.Client("your-api-key")
# Rerank documents
results = co.rerank(
model="rerank-english-v3.0",
query=query,
documents=[doc.page_content for doc in candidates],
top_n=5
)
# Get reranked documents
for result in results.results:
print(f"Score: {result.relevance_score}, Index: {result.index}")
Metadata Filtering
Filter before or after retrieval:
# Pre-retrieval filtering (more efficient)
retriever = vectorstore.as_retriever(
search_kwargs={
"k": 10,
"filter": {
"department": "HR",
"year": {"$gte": 2023}
}
}
)
# Post-retrieval filtering
results = retriever.get_relevant_documents(query)
filtered = [doc for doc in results if doc.metadata.get('verified')]
Complete Retrieval Pipeline
def advanced_retrieve(query, vectorstore, reranker, k=5):
# Step 1: Initial retrieval (more candidates)
candidates = vectorstore.similarity_search(query, k=50)
# Step 2: Rerank
pairs = [(query, doc.page_content) for doc in candidates]
scores = reranker.predict(pairs)
# Step 3: Sort and return top k
ranked = sorted(zip(candidates, scores), key=lambda x: x[1], reverse=True)
return [doc for doc, score in ranked[:k]]
Comparison
| Method | Latency | Quality | Cost |
|---|---|---|---|
| No reranking | Low | Good | Free |
| Cross-encoder | Medium | Better | Self-host |
| Cohere Rerank | Medium | Best | API cost |
Next Steps
Test your knowledge with the Module 5 Quiz!