Introduction
Finding the right information at the right time is the difference between a good user experience and a frustrating one. But with the evolution of search technology – from simple keyword matching to AI-driven semantic understanding – choosing the right search approach has become both more powerful and more complex.
Elasticsearch supports multiple search techniques, each designed for different data types, query patterns, and business goals. The challenge isn’t whether Elasticsearch can handle your use case; it’s knowing which approach (or combination of approaches) will deliver the best results.
In this guide, we break down the five fundamental search approaches available in Elasticsearch, explain when each one shines, and show you how to combine them into a search strategy that actually works.
1. Full-Text Search: The Foundation You Can't Skip
Full-text search is where most Elasticsearch journeys begin – and for good reason. It’s the bedrock of every search experience, handling the everyday queries your users already know how to write.
How It Works
At its core, full-text search uses BM25 – Elasticsearch’s default scoring algorithm – to rank documents based on how well they match query terms. When a document is indexed, Elasticsearch runs it through an analyzer that breaks text into individual tokens (words), normalizes them (lowercasing, stemming), and builds an inverted index for blazing-fast lookups.
When a user searches for “running shoes,” Elasticsearch doesn’t just find exact matches. Through analysis, it can also match “run,” “runner,” and “shoes” with proper relevance scoring.
Key Capabilities
- Match queries for flexible keyword searches with built-in typo tolerance
- Phrase queries for exact sequence matching (e.g., “machine learning engineer”)
- Multi-match queries to search across multiple fields simultaneously with field-level boosting
- Synonym support so “laptop,” “notebook,” and “portable computer” all return the same results
- Relevance tuning through field weights, function scores, and custom analyzers
When to Use Full-Text Search
Full-text search is the right choice when your users know – at least roughly – what terms to search for. It excels at:
- Website and documentation search where users type keywords
- Product catalogs with structured attributes like names, descriptions, and categories
- Log analysis where you’re searching for specific error codes or messages
- Compliance and legal search where exact term matching matters
An Important Note
Don’t underestimate full-text search. One of Elasticsearch’s greatest strengths is that you can start with a well-tuned BM25 setup and achieve excellent results without any machine learning infrastructure. Many organizations find that properly configured full-text search – with good analyzers, synonyms, and relevance tuning – handles 80% or more of their search needs.
2. AI-Powered Search: Understanding Meaning, Not Just Words
Traditional search has a blind spot: it can’t understand meaning. A user searching for “affordable family dining” won’t match a restaurant described as “budget-friendly meals for kids and parents” – because none of the words overlap. AI-powered search closes that gap.
How It Works
AI-powered search transforms both queries and documents into numerical vectors (embeddings) that represent their semantic meaning. Documents that are conceptually similar end up close together in vector space, even when they share no words in common.
Elasticsearch supports two flavors of vector search:
Dense Vectors use models like BERT or sentence transformers to generate fixed-length arrays of floating-point numbers. Every dimension carries information, making them powerful for capturing nuanced meaning. Elasticsearch uses approximate k-nearest neighbor (kNN) algorithms to search these efficiently at scale.
Sparse Vectors take a different approach. Elastic’s own ELSER (Elastic Learned Sparse EncodeR) model expands text into semantically related terms and assigns weights. The result is a sparse vector – mostly zeros, with meaningful non-zero values tied to interpretable terms. Sparse vectors are resource-efficient, require no fine-tuning, and work well in zero-shot scenarios.
The semantic_text Field
Elasticsearch now offers a semantic_text field type that dramatically simplifies AI-powered search. It handles embedding generation and inference automatically at ingest time, meaning you can index documents like a regular text field and run semantic queries without configuring ingest pipelines or managing models manually.
When to Use AI-Powered Search
AI-powered search is the right choice when:
- Users write natural language queries instead of keywords (“How do I fix a leaking faucet?”)
- Your content uses varied terminology for the same concepts
- You’re building recommendation engines or content discovery features
- You need to handle multilingual search where concepts transcend language barriers
- You’re implementing RAG (Retrieval Augmented Generation) to ground LLM responses in your data
3. Hybrid Search: The Best of Both Worlds
Here’s the reality: keyword search and semantic search each have strengths the other lacks. Full-text search is precise when users know the exact terms. Semantic search understands intent when they don’t. Hybrid search combines both – and it’s rapidly becoming the recommended default for production search applications.
How It Works
A hybrid search query executes both a lexical (BM25) search and a vector (semantic) search in parallel, then merges the two result sets into a single ranked list.
The merging is the critical part. Elasticsearch supports two primary fusion strategies:
Reciprocal Rank Fusion (RRF) is a rank-based approach. Rather than trying to normalize the raw scores from two very different scoring systems, RRF simply looks at the rank position of each document in each result list and computes a combined rank. It’s simple, effective, and requires no tuning.
Linear Combination (Convex Combination) blends the actual scores from each search method using configurable weights. This gives you fine-grained control – you might weight BM25 at 0.3 and semantic at 0.7 for a concept-heavy knowledge base, or flip the ratio for a product catalog where exact terms matter more.
The semantic_text Hybrid Workflow
With the semantic_text field, you can set up hybrid search with minimal configuration. Map a semantic_text field for embeddings and a standard text field for full-text search, then use Elasticsearch’s retriever API to combine them in a single query – no external orchestration required.
When to Use Hybrid Search
Hybrid search should be your default consideration when:
- Your queries range from precise keywords to vague natural language
- You need to handle product searches where SKUs and descriptions coexist
- You’re building RAG applications where retrieval quality directly impacts AI output
- Your content is diverse – mixing structured data, long-form text, and technical documentation
- You can’t predict whether users will search by exact term or by concept
Real-World Example
Consider a real estate platform. A user searches for “quiet 2-bedroom apartment near a park.” Lexical search catches “2-bedroom” and “apartment” precisely. Semantic search understands that “quiet” implies residential neighborhoods and that “near a park” is a proximity concept – potentially matching listings that mention “garden views” or “green spaces” without saying “park.” Hybrid search delivers both, ranked together.
4. Ranking and Reranking: Polishing Your Results
Retrieval is only half the battle. Even after finding the right documents, their order determines whether users find what they need on the first page or give up.
Elasticsearch provides a layered ranking system that goes far beyond default BM25 scores.
First-Stage Ranking: BM25 and Vector Scores
Every search starts with a first-stage retrieval that produces an initial ranked list. For full-text search, BM25 scores documents based on term frequency, inverse document frequency, and document length normalization. For vector search, similarity scores (cosine, dot product, or L2) determine the ranking.
These first-stage methods are fast and efficient, but they can be improved.
Reciprocal Rank Fusion (RRF)
When combining results from multiple retrieval methods (the essence of hybrid search), RRF merges rank positions rather than raw scores. This elegantly sidesteps the problem of normalizing scores from completely different scoring systems.
Semantic Reranking
Semantic reranking is the most powerful second-stage technique available. It uses a cross-encoder model – which simultaneously processes both the query and a document to compute a relevance score – to re-order the top-k results from your initial retrieval.
Elasticsearch supports semantic reranking through the Inference API, with options including:
Elastic Rerank – Elastic’s own cross-encoder model, available as a preconfigured endpoint
Third-party models from Cohere, Google Vertex AI, Jina AI, or custom models uploaded via Eland
Hugging Face models imported directly into your cluster
The retriever API makes this composable: define your first-stage retrieval, then wrap it in a text_similarity_reranker that sends the top results to a reranking model – all in a single API call.
Learning to Rank (LTR)
For organizations with abundant training data and sophisticated relevance requirements, Learning to Rank trains a machine learning model (typically gradient boosted decision trees like XGBoost’s LambdaMART) to learn optimal ranking from features like click-through rates, ratings, and query-document signals.
LTR is powerful but requires more investment: you need labeled training data (judgment lists), feature engineering, and ongoing model retraining as user behavior evolves.
When to Invest in Reranking
- Semantic reranking is a quick win for any existing search – it can dramatically improve relevance without reindexing or restructuring your data
- LTR makes sense for e-commerce, media, and publishing platforms where you have click data and the business impact of improved ranking justifies the investment
5. Geospatial Search: When Location Is Everything
For applications where physical location matters, Elasticsearch offers native geospatial search capabilities that integrate seamlessly with all other search approaches.
How It Works
Elasticsearch supports geospatial data types (geo_point for coordinates, geo_shape for polygons and complex geometries) and provides specialized queries for:
- Distance-based search – find everything within 5 km of a given location
- Bounding box queries – search within a rectangular region
- Polygon queries – search within custom geographic boundaries
- Spatial relationships – find shapes that intersect, contain, or are within other shapes
Combining Geospatial with Other Approaches
Geospatial search truly shines when combined with hybrid search. Consider an Airbnb-style application: semantic search understands that “cozy place near the best nightlife” maps to listings in entertainment districts, while geospatial filtering ensures results are actually within the user’s preferred area. Lexical search catches specific amenity mentions, and distance sorting puts the closest results first.
When to Use Geospatial Search
- Ride-sharing and delivery applications with proximity-based matching
- Real estate platforms with location-aware property search
- Retail and restaurant finders with “near me” functionality
- Logistics and fleet management with route and zone-based queries
- IoT and asset tracking applications with spatial boundaries
Choosing the Right Approach: A Decision Framework
There’s no single “best” search approach – the right choice depends on your data, your users, and your business goals. Here’s a practical framework:
If your users… | Start with… | Then consider adding… |
Search with specific keywords or codes | Full-text search | Synonym support, relevance tuning |
Ask questions in natural language | AI-powered (semantic) search | Hybrid search for precision |
Do both of the above | Hybrid search | Semantic reranking for top-k refinement |
Need location-aware results | Geospatial search | Combine with hybrid for rich filtering |
Have high-value search where ranking matters | Any retrieval method | Semantic reranking or LTR |
The Recommended Path
For most organizations, we recommend this progression:
- Start with full-text search. Get your analyzers, mappings, and basic relevance tuning right. This alone solves many search problems.
- Add semantic search when keyword limits appear. Use semantic_text fields to minimize infrastructure complexity.
- Move to hybrid search as your default query strategy, using RRF or linear combination to merge results.
- Layer in semantic reranking to polish the top results without restructuring your pipeline.
- Introduce geospatial or LTR when your use case demands it.
This incremental approach lets you improve search quality at each step while keeping complexity manageable.
How Qavi Tech Can Help
At Qavi Tech, we’ve spent over a decade helping organizations build high-performance search experiences with Elasticsearch. As a certified Elastic partner, we bring deep expertise in:
- Search architecture and strategy – helping you select and implement the right combination of search approaches for your data and users
- Elasticsearch optimization – tuning analyzers, mappings, relevance scoring, and cluster performance for production workloads
- AI-powered search implementation – deploying vector search, semantic search, and RAG systems with Elasticsearch
- Migration services – moving from Solr, Splunk, or legacy search systems to modern Elasticsearch deployments
Whether you’re building your first search feature or optimizing an enterprise-scale search platform, we provide the consulting, implementation, and ongoing support to help you get it right.
Ready to build a better search experience?
Team Qavi Tech