Hybrid Search: Bridging the Gap in Vector Databases
In the realm of Artificial Intelligence, particularly within vector databases and Retrieval Augmented Generation (RAG) systems, achieving optimal search results is paramount. While pure vector search excels at semantic similarity, it can sometimes miss relevant results that are not semantically close but are important due to keyword matching or structured data attributes. Hybrid search emerges as a powerful solution, combining the strengths of different search methodologies to deliver more comprehensive and accurate retrieval.
Understanding the Need for Hybrid Search
Traditional search methods, like keyword-based search (e.g., BM25), are excellent at finding exact matches and are highly interpretable. Vector search, on the other hand, leverages embeddings to understand the meaning or context of a query, enabling it to find semantically related documents even if they don't share exact keywords. However, relying solely on one method can lead to limitations. Keyword search might miss nuanced semantic relationships, while vector search might overlook important terms or struggle with very specific queries.
Hybrid search combines keyword and vector search for superior retrieval.
By integrating both exact term matching and semantic understanding, hybrid search ensures that queries capture both explicit keywords and underlying meaning, leading to more relevant results.
Hybrid search aims to mitigate the individual weaknesses of keyword and vector search. It typically involves executing a query against both a keyword index and a vector index. The results from each are then combined and re-ranked using a fusion algorithm. This approach allows users to benefit from the precision of keyword matching and the contextual understanding of vector embeddings simultaneously.
Key Components of Hybrid Search
At its core, hybrid search involves several key components:
- Keyword Indexing: Traditional inverted indexes are used to store and search for exact terms.
- Vector Indexing: Vector databases store high-dimensional embeddings of data, allowing for similarity searches.
- Query Decomposition: The user's query is often processed to extract keywords and generate embeddings.
- Fusion Algorithms: Techniques like Reciprocal Rank Fusion (RRF) or weighted averaging are used to combine and re-rank results from different search methods.
Keyword search and vector search.
Fusion Techniques: Merging Search Results
The effectiveness of hybrid search heavily relies on how the results from keyword and vector searches are combined. Common fusion techniques include:
| Technique | Description | Pros | Cons |
|---|---|---|---|
| Weighted Sum | Assigns a weight to the score of each result from keyword and vector searches and sums them. | Simple to implement. | Requires careful tuning of weights; can be sensitive to score ranges. |
| Reciprocal Rank Fusion (RRF) | Combines rankings by considering the reciprocal of the rank position of each item, giving more importance to higher-ranked items. | Robust to different scoring scales; prioritizes items that appear high in multiple lists. | Slightly more complex than weighted sum. |
| Interleaving | Alternates results from different search methods in the final ranked list. | Provides a balanced mix of results. | Can be less optimal if one method consistently outperforms the other. |
Reciprocal Rank Fusion (RRF) is a popular choice for hybrid search because it effectively merges ranked lists without requiring scores to be normalized, making it robust to different retrieval systems.
Hybrid Search in RAG Systems
In the context of RAG, hybrid search plays a crucial role in the retrieval phase. When a user asks a question, the RAG system can use hybrid search to fetch relevant documents from its knowledge base. This ensures that the LLM receives a more comprehensive set of contextually relevant information, leading to more accurate and informative generated responses. For instance, a query like 'What are the side effects of ibuprofen?' would benefit from both keyword matching for 'ibuprofen' and 'side effects', and semantic understanding to capture related medical terms or patient experiences.
Visualizing the hybrid search process: A query is processed, sent to both a keyword index (e.g., BM25) and a vector index (e.g., ANN). Results from both are then fused using an algorithm like RRF, producing a final ranked list of documents. This combined list is then passed to the LLM for context.
Text-based content
Library pages focus on text content
Benefits and Considerations
The primary benefit of hybrid search is improved retrieval accuracy and relevance. It caters to a wider range of query types, from precise keyword searches to nuanced semantic queries. However, implementing hybrid search requires managing two different indexing systems and carefully tuning the fusion algorithm, which can add complexity to the system architecture.
Managing two indexing systems and tuning the fusion algorithm.
Learning Resources
This blog post provides a clear explanation of what hybrid search is, why it's important, and how it works in practice, particularly within the context of vector databases.
An in-depth look at Reciprocal Rank Fusion, a popular algorithm for merging results from different search systems, explaining its mechanics and benefits.
This article contrasts vector search and keyword search, highlighting their strengths and weaknesses, which sets the stage for understanding the need for hybrid approaches.
A practical guide from Qdrant on how to implement hybrid search, covering the underlying concepts and providing code examples.
This resource details the components of a RAG pipeline, including the retrieval stage where hybrid search is often employed.
Provides foundational knowledge about vector databases, which are essential for understanding the vector search component of hybrid search.
A foundational paper or lecture notes explaining the BM25 algorithm, a common keyword-based search technique.
Explores the differences and synergies between semantic and keyword search, providing context for hybrid approaches.
Discusses the practical applications and benefits of hybrid search in enterprise AI solutions.
An overview of vector search and its growing importance, setting the context for why hybrid approaches are becoming necessary.