Reciprocal rank fusion
Reciprocal rank fusion is a rank-aggregation technique that combines ranked lists by summing reciprocal-rank contributions for each item across the lists.
AI Agents and Applications
Reciprocal Rank Fusion is a result-merging method that scores documents by summing reciprocal rank contributions across multiple ranked lists.
- How it is used
- Improves RAG retrieval when multiple query formulations or heterogeneous retrieval methods produce complementary ranked lists.
- How deep it goes
- Explains the scoring formula, cumulative reranking, Python implementation, LangChain composition, and final prompt integration.
- Examples
- Demonstrates five query variations, vector-store retrieval, top-three selection, and a Brighton travel question answered through a RAG chain.
- What you can do after
- Build RAG fusion with LangChain by generating queries, retrieving results, applying RRF, selecting top documents, and synthesizing an answer.
Read how AI Agents and Applications explains reciprocal rank fusion →
AI-Powered Search
Reciprocal rank fusion is a rank-based result-combination algorithm that merges rankings from multiple retrieval systems using reciprocal-rank contributions.
- How it is used
- Combines lexical and vector retrieval results into a hybrid ranking.
- How deep it goes
- Explains the scoring loop, aggregation, sorting, and k trade-off, while stopping short of formal evaluation.
- Examples
- Uses the queries "singin' in the rain" and "the hobbit" with lexical, vector, and hybrid result comparisons.
- What you can do after
- Apply it via a Python function or hybrid-search API to merge ranked retrieval results.
Read how AI-Powered Search explains reciprocal rank fusion →
AI Agents in Action, Second Edition
Reciprocal rank fusion combines ranked result lists by summing the reciprocal of each result's rank offset across the lists.
- How it is used
- Combines vector and keyword result lists into ranked context for hybrid search and retrieval-augmented agents.
- How deep it goes
- Defines the rank-only formula, explains its score-independence, and stops short of implementation code for the fusion function.
- Examples
- Uses parallel vector and keyword result lists, a typical constant of 60, and an agent selecting separate search tools.
- What you can do after
- Apply it via hybrid search to merge incompatible ranked outputs into grounded, comprehensive retrieval context.
Read how AI Agents in Action, Second Edition explains reciprocal rank fusion →
Related concepts
- retrieval rankinguses · Retrieval ranking is the process of ordering retrieved candidates according to their estimated relevance or usefulness to a query.
- hybrid searchused by · Hybrid search is an information-retrieval technique that combines multiple retrieval methods or relevance signals to find or rank relevant results.
- lexical searchuses · Lexical search retrieves or ranks items by matching query terms or other surface-text features against indexed content, often using sparse representations and statistical weighting.
- similarity searchuses · Similarity search is a retrieval technique that finds or ranks items by comparing their representations with a query according to a similarity or distance measure.
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