AI Applications: Hybrid Search

Term-based retrieval offers a faster and more cost-effective solution compared to embedding-based retrieval, making it a practical choice for initial implementations. Systems like BM25 and Elasticsearch are widely adopted, providing strong foundational baselines for more sophisticated retrieval methods. While embedding-based retrieval is computationally intensive, its performance can be fine-tuned over time to surpass term-based methods.

In a production setting, retrieval systems often employ a hybrid approach, combining term-based and embedding-based techniques. A common sequential pattern involves using a term-based system to quickly fetch broad candidates, followed by a more precise, computationally expensive method like k-nearest neighbors for reranking. This hybrid search strategy balances speed and accuracy, leveraging the strengths of both retrieval types to improve overall performance.

One notable advancement in hybrid search systems is the integration of context-aware reranking, which enhances the relevance of results by considering the user’s search history and behavior. This approach leverages machine learning models to dynamically adjust the importance of certain documents based on the context of previous queries, offering a more personalized search experience. By analyzing patterns and preferences from historical data, context-aware reranking can prioritize documents that are more likely to meet the user’s intent, even if they aren’t the most relevant based on the current query alone. This method not only improves the accuracy and user satisfaction of search results but also demonstrates the potential for hybrid search systems to evolve with continuous learning and adaptation.

Further Reading:

1. Weviate: Hybrid Search Explained

2. Context-aware Reranking with Utility Maximization for Recommendation

3. The Probabilistic Relevance Framework: BM25 and Beyond

4. Embedding-Based Retrieval: Our Journey and Learnings around Semantic Search at Faire