Imagine searching for a piece of information online, but the results just don't seem right—they're either irrelevant or miss exactly what you were looking for. This scenario is surprisingly common, even with advanced search technologies. The reason? Most search systems rely on either keyword-based searches (exact word matches) or semantic searches (based on meaning), but rarely combine both effectively.
Today, many organizations are beginning to adopt semantic or vector-based search methods because they offer powerful context understanding and meaning-driven results. However, relying solely on either semantic search or keyword search can limit the effectiveness and relevance of search results.
What Exactly Is Hybrid Search?
Keyword Search (Lexical Search): This traditional search type focuses on finding exact word matches. It is precise but rigid—if the query does not match the indexed terms exactly, relevant content may be missed.
Vector Search (Semantic Search): This newer approach uses AI models to understand the meaning behind words and phrases, retrieving content that's contextually similar, even if the exact keywords don't match.
Hybrid search brings together the best of both worlds. It performs both keyword and vector searches simultaneously and then intelligently merges and re-ranks the results, providing a balanced and comprehensive set of answers.
Why Does Hybrid Search Matter?
When users search, they expect accurate, relevant, and comprehensive results:
- Better Relevance: Delivers precise matches while also capturing relevant content that keyword-only search might overlook.
- Greater Coverage: It overcomes the limitations of semantic search, ensuring precise matches aren’t ignored.
- Enhanced User Experience: Users receive results that better match their intent, significantly improving their overall satisfaction.
In short, hybrid search isn't just a nice-to-have feature—it's essential for modern search systems aiming for the highest quality and accuracy.
Let's dive deeper into why neither keyword nor vector search alone can fully meet users' expectations.
Why Keyword Search or Vector Search Alone Isn't Enough
To see why hybrid search is crucial, let's first understand the limitations of each individual method.
Keyword Search: Precise but Limited
Keyword-based searches are great when you know exactly what you’re looking for. They rely on matching the exact words from the user's query to the content stored in the database. However, this method has significant limitations:
Exact Match Requirement: Slightly different wording or synonyms won’t match, causing relevant results to be missed.
No Context Understanding: Keyword search can’t differentiate nuances or user intent behind the queries, resulting in irrelevant or incomplete results.
Example Scenario:
A user searches for "fix slow Wi-Fi," but the documentation uses terms like "improve wireless internet speed" or "solving connectivity issues." Pure keyword search might not show these relevant results.
Vector Search: Smart but Sometimes Too Broad
Vector search solves many of keyword search's issues by understanding the meaning and context behind queries through AI models. However, it’s not without its own drawbacks:
Overgeneralization:
Because vector searches rely on semantic similarity, they may retrieve documents that appear relevant but lack the precise details needed to answer the query.Ambiguity: It might return results that are "semantically related" but not exactly what the user intended—especially problematic in domains with specialized terminology.
Hybrid Search: Best of Both Worlds
Hybrid search addresses the limitations of both methods by combining them:
- Comprehensive Results: Combines exact keyword matches and semantically relevant results.
- Improved Relevance: Ensures finding precise documents even if phrasing slightly varies.
- Better User Satisfaction: Users quickly find the information they actually need, greatly enhancing their overall experience.
Real-World Example
Consider a search for "company travel expense policy."
Keyword-only search:
Might only return documents titled "travel expense policy" or "expense reimbursement rules," missing relevant content under headings like "business trip procedures" or "transportation and lodging guidelines."Semantic-only search:
May return broader topics such as "company spending policies" or "employee benefits," which relate loosely but don’t address the specific topic of travel expenses.Hybrid search:
Combines the accuracy of keyword matches with the flexibility of semantic understanding, surfacing both exact policy documents and related content like procedural guides or reimbursement forms in one coherent result list.
The Current Situation (Solr/Lucene Limitations)
Apache Solr and Lucene both support keyword searches (e.g., BM25 scoring) and vector searches (e.g., approximate nearest neighbor queries). However, neither currently supports advanced hybrid search out-of-the-box.
Key limitations are:
No Intelligent Score Fusion:
Keyword and vector searches can be executed separately, but there is no native mechanism to merge or re-rank results effectively.Score Imbalance:
Keyword search scores (BM25) and vector search scores (cosine similarity) are calculated on different scales, making simple summation unreliable without normalization.Loss of Detailed Scores:
Without intelligent merging, you lose insight into why documents were ranked, reducing trust and control over results.
Hybrid retrieval remains an area of interest in the community. For example, an effort to implement Reciprocal Rank Fusion (RRF) natively in Solr was initiated under JIRA issue SOLR-17319. However, development of that contribution is currently inactive and would require renewed community or commercial support to proceed.
How Hybrid Search Methods Can Help
Hybrid search methods are already in use at industry-leading platforms worldwide, demonstrating proven effectiveness in production environments. Here's a simple overview of the most effective techniques:
1. Reciprocal Rank Fusion (RRF): Quick and Simple
What It Is:
- Combines ranked lists based on position rather than raw score.
- Prioritizes documents that appear highly in either keyword or semantic rankings.
How it works:
- Rank positions from both keyword and semantic results are combined using the following formula:
Where:RRF(d) = 1 / (k + rank_keyword(d)) + 1 / (k + rank_semantic(d))rank_keyword(d)andrank_semantic(d)are the document's positions in each result list.kis a constant (commonly 60) to reduce the influence of lower-ranked results.
Why RRF?
- No score normalization or tuning required.
- Easy, fast implementation, great for quickly improving search quality.
Ideal Scenario:
- Immediate and noticeable search improvements with minimal effort.
2. Weighted Fusion (Convex Combination): Precision and Control
What It Is:
- Combines scores from keyword and semantic search using adjustable weights.
- Allows each method to contribute based on its relative importance.
How It Works:
- The final score for each document is computed as a weighted average of the two methods:
Where:HybridScore(d) = α × SemanticScore(d) + (1 − α) × KeywordScore(d)αis a value between 0 and 1 that controls the influence of semantic search.(1 − α)gives the corresponding weight to keyword search.- Scores must be normalized before applying this method to ensure consistency.
In simple terms:
- Adjust the weight to emphasize semantic or keyword scores.
- Fine-tune based on user feedback or evaluation data.
Why Weighted Fusion?
- Enables precise control over ranking behavior.
- Provides significantly improved results, especially when the system matures and evolves.
Ideal Scenario:
- When evaluation data is available and fine-tuning is desired for higher-quality results.
3. Advanced: Learning to Rank (Machine Learning)
What It Is:
Machine-learning-based ranking approach. The model learns optimal ways to rank results by analyzing past user interactions or explicitly labeled data.
In simple terms:
- Machine learning continuously optimizes the ranking of search results based on real user interaction data.
- Dynamically adapts to evolving user expectations and behaviors.
Why consider LTR?
- Provides a highly personalized search experience, continuously improving relevance and accuracy.
Ideal Scenario:
- Mature businesses with ample user data and a strong focus on continuous search improvement.
Our Vision: Hybrid Search at KandaSearch
At Rondhuit, we're continuously looking to improve the capabilities of our KandaSearch platform. Currently, KandaSearch provides powerful keyword and semantic search capabilities, but we recognize the potential of hybrid search to further enhance the overall search experience.
Hybrid search methods, as discussed, are not yet part of KandaSearch. However, their integration is under active consideration as part of ongoing development planning. Our goal is to provide a roadmap towards adopting these advanced techniques, supporting the ongoing improvement of search functionality and relevance.
We suggest the following phased approach once these capabilities become available:
Phase 1: Immediate Results (RRF)
- Quick implementation to see immediate improvements in search quality and user satisfaction.
Phase 2: Precision and Customization (Weighted Fusion)
- Refine and optimize search results using user feedback.
Phase 3: Continuous Improvement (Learning to Rank)
- Advanced machine learning models to dynamically optimize search results as usage patterns and content evolve.
Importance of Hybrid Search in Enterprise Applications
In environments where information coverage, precision, and user satisfaction are critical, hybrid search presents clear advantages:
- Broader Retrieval Coverage: Captures varied ways of expressing the same concept.
- Improved Ranking Consistency: Reduces missed results due to vocabulary mismatch or overgeneralized matches.
- Scalability: Adapts to evolving datasets and query complexity.
Summary
| Search Type | Strengths | Limitations |
|---|---|---|
| Keyword Search | Fast, precise, interpretable | Misses synonyms, no context understanding |
| Semantic Search | Context-aware, flexible phrasing | May return overly broad or imprecise results |
| Hybrid Search | Combines both for balanced retrieval | Requires merging logic and normalization |
Hybrid search is recognized as a key innovation in modern retrieval systems. It is particularly valuable for enterprise platforms that must balance accuracy with flexibility.
Next Steps
The integration of hybrid search into KandaSearch is currently under active review. Updates on development progress and release planning will be shared as the roadmap evolves.
For inquiries about KandaSearch or to express interest in hybrid search capabilities, please use the contact form:
As the expectations for search continue to grow, hybrid search represents a forward-looking enhancement—balancing accuracy, adaptability, and trust. Its inclusion in KandaSearch will mark a natural evolution in delivering smarter, more responsive search solutions.