⚡ Quick AnswerHybrid search combines information retrieval techniques to form one ranked list of results, most commonly the results from a lexical search and the results from a semantic search. The search technique of a Hybrid search is to combine the best of both the precise results from a search that uses keywords, and the results that understand the meaning of a query. |
What Is Hybrid Search?
The approach of hybrid search is to combine the retrieval methods in order to achieve the best results in terms of relevance. The most common combination is that of lexical search and semantic search. The first approach searches for words and phrases of a given query while the second one looks for results based on their meaning.
Lexical search methods are best suited to exact terms, product names, technical phrases, etc. – for which Semantic Search will often return irrelevant results due to them not containing the search term exactly. However, natural language search queries, which contain much more redundancy than querying by exact term, are best served by a Semantic Search approach.
When running different retrieval methods, these results are then combined and ranked in a single ranking list, which allows for the use of exact matching as well as of semantic understanding of the search terms.
How Does Hybrid Search Work?
First, results from different search methods are retrieved. Then, these results are compared or combined. Finally, a unified ranking list of documents is produced.
Lexical Search
First, the same query is searched for in the indexed documents using lexical search methods (like BM25). In this case, the score for each document is based on parameters like term frequency in the document and the global distribution of the terms in the corpus.
This retrieval approach excels when searching for exact product names, technical terms, and even more structured search queries. All of these search scenarios have users who know exactly what they are looking for.
Semantic Search
The core idea behind semantic search is to search for meaning, not terms. For this, an embedding model (e.g. DistilBERT, fasttext) is used to embed the search query as well as documents in a high dimensional vector space. Documents that are semantically similar to the search query are then found by calculating the cosine similarity between the vectors.
This means that semantic search is perfect for natural-language questions, very vague queries and all kinds of queries where the user is trying to find information about an idea, rather than specific words or terms.
Combining and Ranking Results
Combining the results of both retrieval methods (lexical search and semantic search) is one of the biggest challenges in building hybrid search systems. This is because the scores from both types of retrieval methods typically will not be comparable (i.e. on the same scale). Therefore, a fusion method is typically applied to the ranked lists of both retrieval methods to produce a single ranked list of documents.
Two of the most common methods for ranking hybrid search results are Reciprocal Rank Fusion (RRF) and a weighted or linear combination of the results from the individual retrieval methods.
What Are the Benefits of Hybrid Search?
Hybrid search is good because it balances the good points of the different search methods.
A hybrid search environment can thus exploit the strengths of both retrieval techniques: lexical search in particular is very precise for exact terms, whereas semantic search is able to identify relevant documents even for queries worded very differently from the corresponding documents.
A further advantage of hybrid search is that it enables users to use search terms more naturally: keywords, terms and phrases that describe a search requirement in more detail, and even full questions and statements that describe a requirement that users are looking for information about.
The nature of information retrieved for AI applications greatly affects the quality of information that the AI can use for subsequent processes, thus better search information is used as context for AI assistants and for retrieval-augmented generation (RAG).
What Are the Applications of Hybrid Search?
E-commerce Search
To make the search more flexible for shopping online, hybrid search can also be used in e-commerce applications such as online shopping platforms or retail websites. The search function can support search queries containing specific product attributes and the intended use or features of products that the user is looking for.
Enterprise Search
Many organizations today store a multitude of information (such as policies, reports, technical documentation, knowledge articles etc.) within large collections of documents. Such information can often be searched for by entering exact keywords or natural language queries.
Knowledge and Document Search
The hybrid search function is useful for searching large collections of documents such as knowledge. The keyword search function can be used to find documents containing specific terminology. The semantic search function can find documents that contain equivalent information even if the language is slightly different.
AI Assistants and RAG
Hybrid search methods can serve as a search layer for AI assistants and RAG systems. Retrieval can combine information that was searched for using exact terms and meaning-based search terms.
This is particularly relevant in cases in which the knowledge base contains many technical terms, product identifiers, names etc. for which exact matching is important in addition to good semantic understanding.
Hybrid Search vs. Semantic Search
| Feature | Hybrid Search | Semantic Search |
| Search approach | Combines multiple retrieval methods | Primarily searches based on meaning |
| Keyword matching | Can use exact keyword matching | Not dependent on exact keyword matching |
| Semantic understanding | Uses semantic retrieval as one component | Core part of the retrieval process |
| Precision | Can preserve precision from lexical search | Can be less precise for some exact-term queries |
| Query handling | Works with both exact and natural-language queries | Strong for descriptive and natural-language queries |
| Retrieval methods | Can combine lexical, semantic, dense, sparse, or other methods | Typically uses embeddings or semantic representations |
| Result ranking | Requires fusion of multiple result sets | Usually ranks results using semantic similarity |
| Best suited for | Applications needing both precision and semantic relevance | Applications focused on meaning-based retrieval |
Real-World Examples of Hybrid Search
For a product search engine it’s possible to combine keyword matching with semantic retrieval. For instance, in a search for “comfortable dress shoes for office” exact matching for terms “dress shoes” could be combined with semantic retrieval for concepts like “comfort”, “cushioned soles”, etc. related to the comfortable shoes.
Enterprise knowledge search: This would include an employee searching for a specific policy name (lexical search), but also searching for information on a company procedure (semantic retrieval of documents even when worded differently from search query).
This also applies to the search in AI-powered knowledge systems. A search pipeline first uses lexical search to retrieve results based on the keyword search. Then vector-based search is used on the retrieved documents to get the relevant information. This information is then fed to the rest of the AI system.
The use of Hybrid Search is not limited to text. Other types of information such as geospatial information, images, and more can be searched using different retrieval approaches and then fused together to meet the user’s needs.
Frequently Asked Questions
A hybrid search system in AI uses more than one retrieval method, typically a combination of lexical search and semantic search in order to provide the best possible results to the AI system such as assistants and RAG (Retrieve And Generate) applications.
Hybrid search can use a combination of signals such as to improve search results compared to using semantic search alone. For example, hybrid search can use lexical search in combination with semantic search.
Hybrid search improves the retrieval stage of RAG applications. This search method combines the precision of exact keyword search with the semantic similarity of the information retrieved. This is especially important if both the terminology used and the context of the information to be retrieved are important.
Reciprocal Rank Fusion (RRF) is a ranking method that uses Reciprocal Rank Fusion to combine the ranked result lists of multiple retrieval methods into one ranked result list of maximum quality. It does not compare raw scores of different types and thus is more robust than other ranking methods for fusion of results from different retrieval methods.
Yes, hybrid search can be built of more than two search types such as lexical search + semantic search + dense-vector search + sparse-vector search + image search + more.