What is hybrid search, and how does it work?

Hybrid search is a form of searching that combines keyword search (BM25 and other sparse techniques) and semantic search (vector embeddings). This means that a hybrid search engine will typically execute these two forms of searching simultaneously. The first form of searching will focus on finding matching keywords, whereas the second will focus on matching intent and meaning. 

The final output will be obtained by combining, deduplicating, and re-ranking these two forms of searching using a combined scoring technology, such as weighted scoring and Reciprocal Rank Fusion. The final output will be fed into other AI systems to produce more accurate and complete responses.

How does a hybrid search engine differ from traditional keyword or vector-only search?

A hybrid search engine is unique due to its ability to overcome the limitations of both keyword and vector-only search. Keyword search is very accurate but lacks flexibility. Vector search, on the other hand, is very flexible but lacks accuracy. The hybrid search combines these two. 

The hybrid ensures no results are left out while still understanding the user’s intent. This is important for enterprises, where queries may include both structured and unstructured content. The hybrid combines these two, ensuring more accurate, context-aware results.

Why does hybrid search matter specifically for enterprise AI search? 

The enterprise-level artificial intelligence-based search engine must process a wide variety of queries, ranging from natural-language queries to very specific ones, such as product codes, legal clauses, customer IDs, etc. In this scenario, hybrid search becomes particularly important, as it can handle both extremes simultaneously. 

In many cases, enterprises use their own language and terminology, making it difficult to find specific data. In such cases, semantic search may not always accurately interpret queries, whereas hybrid search can provide the much-needed accuracy and contextual relevance. This is important for building trust among users and AI-based systems at large.

What role does hybrid search play in RAG (Retrieval-Augmented Generation) pipelines?

Hybrid search in RAG pipelines serves as the retrieval layer that generates the output provided to the language model. The importance of hybrid search lies in its direct impact on the generation of accurate responses (grounded responses) by providing quality context to the model. Hybrid Search retrieves documents based on keyword and/or semantic searches, returning only the most relevant and complete set. 

Enterprise environments have found that hybrid search yields grounded responses with a significant reduction in hallucinations and an increase in accuracy. In addition, hybrid search is important because AI models cannot produce speculative answers, but can only answer questions based on verified internal data.

What are the key business benefits of adopting AI search with hybrid search capabilities?

Using an AI-powered hybrid search delivers many business benefits. First, it can improve the relevance of overall search results, so users can access the required information without having to rephrase their queries. Second, it can help boost business productivity across departments that require access to precise documents, including legal teams, as well as customer support teams that need access to various types of content.

Thirdly, it can increase trust in AI systems because they can consistently deliver accurate results. Hybrid search can also maximize the business value of existing data by ensuring that both structured and unstructured data are accessible. This can lead to better decision-making, a better customer experience, and a greater return on investment in AI systems.

What should enterprises look for when evaluating a hybrid search solution?

When assessing a hybrid search solution, enterprises should look for performance and flexibility. Some key features in this regard include advanced re-ranking techniques for effective use of both keyword and vector search, high performance for real-time applications, and seamless connectivity to existing infrastructure such as databases, data lakes, and artificial intelligence systems. 

Real-time indexing is another significant factor to consider. At the same time, fine-tuning is a feature that needs to be considered. This allows the enterprise to fine-tune the weighting of both semantic and keyword search. Solutions that offer a unified architecture for both search types are recommended.