Hybrid search explained: Full-text meets vector search

You've probably hit this before: searching for "authentication middleware" returns exact function names but misses related security docs. Or searching "database connection timeout" finds the error code but nothing about fixing it.

Hybrid search solves this by running keyword and semantic retrieval in parallel, so you get precise matches and conceptually related results in one query.

This article covers why hybrid search outperforms single-method approaches, how the ranking algorithms work, and how to build it with Redis.

What is hybrid search?

Hybrid search is a retrieval method that combines full-text search and semantic search in a single query, running both simultaneously and merging the results. Instead of choosing between exact keyword matching or conceptual similarity, you get both.

Full-text search (also called keyword or lexical search) matches the exact terms in your query against an index of document text. If you search for "authentication middleware," it finds documents containing those specific words.

Semantic search takes a different approach—it converts your query into a vector embedding and compares it against document embeddings to find conceptually similar content, even when the exact words don't appear.

Both methods have limitations on their own. Full-text search misses relevant results when users phrase queries differently than the source text, and it treats synonyms as completely unrelated terms. Semantic search captures conceptual relationships but can struggle with precise identifiers like product codes, API names, and technical terminology that need exact matching.

Hybrid search overcomes these limitations by running both methods in parallel. A dev searching for "authentication middleware" gets exact function name matches alongside conceptually related security docs. A support engineer looking for "database connection timeout" finds the exact error code and semantically similar troubleshooting guides that might not contain those exact terms.

Why hybrid search beats single-method retrieval

AI/ML apps struggle when they rely on single retrieval methods. Pure vector search understands that "postgresql" and "database" are conceptually related but lacks lexical precision for technical identifiers, product names, and exact terminology. Pure keyword search sees those same terms as completely different with no semantic understanding.

Your apps need both capabilities to handle the full range of user queries. And that’s exactly what hybrid search gets you. Here's why hybrid search matters in practice:

• Faster queries: You can achieve much faster throughput with hybrid search compared to single-method approaches, without sacrificing accuracy.
• Better precision: If you're building Retrieval Augmented Generation (RAG) systems for legal docs, medical records, or technical docs, combining dense retrievers with keyword-based sparse search gets you better precision and recall. Hybrid approaches capture both the precise terminology those domains require and the semantic relationships that pure keyword search misses.
• Specialized domains: User queries in production systems vary dramatically. Some users know exact product names or error codes. Others describe symptoms or desired outcomes without knowing technical terminology. Hybrid search handles both query types without forcing you to build separate retrieval pipelines or teach users how to query your system "correctly."
• Works across languages: Dense vectors capture semantic meaning that transcends language boundaries through multilingual embedding models, while sparse vectors ensure you don't lose critical language-specific terms, proper nouns, and technical terminology. This combination helps when keyword search fails across languages but pure semantic search misses culturally or linguistically specific terms.

These benefits compound in production. Faster retrieval lowers infrastructure costs, better relevance reduces failed queries, and you replace multiple specialized systems with one unified implementation. If your AI/ML app needs fresh data—agent memory systems, real-time RAG, apps where context changes frequently—you'll also want infrastructure that makes vectors immediately searchable after insertion without batch reindexing.

How hybrid search works

Hybrid search runs two retrieval methods in parallel—BM25 (Best Match 25) for lexical matching and vector search for semantic matching—then fuses the results. Understanding the basics helps you tune performance and troubleshoot relevance issues.

BM25 handles keyword matching

BM25 (Best Match 25) is the algorithm behind most keyword searches. It scores documents based on how often your search terms appear, while accounting for document length and how common each term is across your entire corpus. A term that appears in every document (like "the") gets a low weight, while a rare term that appears multiple times in a specific document gets a high weight.

Vector search finds semantic matches

Vector search converts your query into an embedding—a list of numbers that represents its meaning—and finds documents with similar embeddings. Two pieces of text can have high similarity even if they share no words, as long as they're about the same concept.

The most common similarity metrics are cosine similarity (measures the angle between vectors), dot product (faster for normalized vectors), and Euclidean distance (measures straight-line distance). Most embedding models are trained with dot product, so that's usually the best choice.

Reciprocal Rank Fusion merges the results

Once you have ranked results from both BM25 and vector search, you need to combine them. Reciprocal Rank Fusion (RRF) is the simplest approach—it scores each document based on its position in each result list using the formula 1/(rank + 60), then adds up the scores.

For example, if a document ranks #3 in vector search and #1 in BM25:

• Vector score: 1/(60+3) = 0.0159
• BM25 score: 1/(60+1) = 0.0164
• Combined: 0.0323

Documents that appear in both result sets naturally bubble up to the top. RRF works well out of the box without tuning, which is why it's the default choice for most implementations. If you need more control, you can use weighted scoring instead—but that requires normalizing the raw scores and tuning the weights for your specific use case.

When to use hybrid search

Hybrid search works best when your users search with a mix of exact terms and natural language, or when your content contains both precise identifiers and conceptual information. Here are some of the most common applications.

RAG & LLM apps

RAG systems need to retrieve the right context for every query, even when users give wildly varied prompts. Some users paste error messages verbatim, while others describe what they're trying to do in plain language. Hybrid search handles both without requiring you to preprocess queries or guess which retrieval method will work better. The keyword component catches exact matches on technical terms, while vector search pulls in conceptually relevant context that improves LLM response quality.

E-commerce product search

Shoppers search differently depending on what they know. Someone looking for "Nike Air Max 90 white" expects exact matches, but someone searching "comfortable running shoes for flat feet" needs semantic understanding. Hybrid search returns the specific SKU when users know what they want, and relevant alternatives when they're browsing by description. You also avoid the frustration of zero-result pages when users misspell brand names or use non-standard terminology.

Customer support & knowledge bases

Support queries like "error 5012 app won't load" or "billing issue after upgrading" often combine exact identifiers with vague descriptions. Hybrid search matches the error code to known issues while also surfacing troubleshooting guides that describe similar symptoms in different words. This reduces ticket escalation and helps users find answers without knowing the exact terminology your docs use.

Code search & developer tools

Devs search for function names, class definitions, and API endpoints alongside conceptual queries like "how to handle authentication" or "examples of rate limiting." Hybrid search returns exact matches on handleAuth() or RateLimiter while also finding relevant code patterns, README sections, and documentation that explain the concepts without using those exact terms.

Limitations of hybrid search

Hybrid search is a powerful addition to many apps, but it isn't always the right choice. Consider these trade-offs before you add hybrid search to your search stack:

• Higher resource usage: You're maintaining two indexes (full-text and vector), which means more storage and memory usage.
• Added latency: There’s more overhead when you’re running two searches in parallel and fusing results. For most implementations the difference is milliseconds, but latency-sensitive apps need to account for it.
• Tuning complexity: It takes some experimenting to balance keyword and semantic results. While RRF works well out of the box, if you need weighted scoring, you'll spend time tuning parameters for your specific dataset and query patterns.
• Overkill for simple use cases: If your users only search by exact product IDs or your content is highly structured, pure keyword search is often enough. Similarly, if your queries are always conceptual with no exact-match requirements, vector search alone is probably simpler.

These trade-offs are manageable for most production apps, especially when you use infrastructure that handles both indexing methods natively. The key is knowing when the relevance improvements justify the added complexity.

Build hybrid search with Redis

Redis Query Engine handles hybrid search by combining vector search and full-text search in a single query. You get sub-millisecond retrieval, BM25 ranking for keywords, and the ability to add filters like categories, numeric ranges, and geographic bounds, all in one atomic operation.

For RAG systems, Redis works as both your retrieval layer and agent memory store. You can set TTLs to automatically expire old memories, which helps manage context windows without manual cleanup. And since Redis also handles caching and session storage, you don't need separate systems for semantic caching, conversation context, and vector search.

Try Redis for free to build your first hybrid search index, or book a demo to see how Redis handles your specific retrieval needs.