Top-K Retrieval
Top-K retrieval refers to the process of returning the K most relevant or closest results from a dataset in response to a query. In vector search and machine learning, it’s a fundamental operation used to surface the best-matching items based on a similarity score or distance metric.
Purpose
Rather than scanning and ranking the entire dataset, Top-K retrieval lets you efficiently pull a small, relevant subset—ideal for LLMs, recommendations, and search systems.
Key Concepts
- K: The number of top results to retrieve (e.g., Top-5, Top-10).
- Similarity Metric: Determines how closeness is measured (e.g., cosine similarity, dot product, Euclidean distance).
- Ranking: Results are sorted by relevance or distance in descending/ascending order.
Use Cases
- Semantic Search: Return the top-K similar documents for a query vector.
- RAG Pipelines: Feed top-K context chunks into an LLM.
- Recommender Systems: Recommend top-K products or content based on user embedding.
- Classification: Use top-K class probabilities in NLP or vision tasks.
Considerations
- Too low K? Risk missing relevant info.
- Too high K? May include noise, increase latency, and cost.
- Choose based on use case precision/recall trade-offs.
Example
Query vector: Embedding of “how to train a neural network”
- Database: 100,000 embedded docs
- Top-K: 5
- Vector DB returns 5 documents most similar to the query
- These are used as LLM context or shown to the user