Full-text Search vs. Vector Search: What's the Difference and When to Use Them?
When it comes to finding information, search technology has come a long way. Back in the day, we only had keyword searches, but now, thanks to machine learning, we have vector search, which helps us find information based on meaning, not just exact words. In this article, we'll break down the differences between full-text search and vector search, talk about their strengths, and look at when each one works best.
What is Full-text Search?
Full-text search is a classic method of finding information by matching the words you type in. It doesn't just look for exact matches but also supports features like prefix and infix searches, morphology processing (such as stemming and lemmatization), and even fuzzy searching methods like quorum or proximity search. This means full-text search can be surprisingly flexible—it helps you find what you're looking for even if you're not entirely sure of the exact phrasing.
One of the big advantages of full-text search is its speed and precision. Because it matches specific keywords and phrases, it can quickly narrow down to exactly what you want, especially when you're working with structured documents or databases that use defined terminology. With full-text search, you also know exactly which results match your query, making it easy to determine the total number of relevant results.
However, full-text search does have some limitations. It works best when you know the specific words involved, and even though there are ways to broaden the search (like fuzzy matching), it can still fall short if the vocabulary used doesn't align perfectly. For example, if you search for "car," it might not always bring up results that use "automobile," unless synonym handling has been explicitly added.
Understanding Vector Search (Semantic Search)
Vector search, sometimes called semantic search, is a newer, more advanced approach that uses machine learning. Instead of just focusing on keywords, it turns both your queries and documents into mathematical representations called vectors, also known as embeddings. By mapping words, phrases, and even entire documents into a high-dimensional space, vector search can understand the meaning and relationships between different terms.
For example, if you search for "car," vector search could also show you results for "automobile," "vehicle," or even "transport." This makes vector search more flexible and better at understanding what you're really looking for, especially with open-ended questions or natural language.
The strength of vector search is its ability to understand context. But how does it actually work, and what makes it different from what we're used to? This is perfect when you're not sure about the exact words or when you're dealing with more abstract language. However, one thing to keep in mind is that vector search, while powerful, requires more computational resources and can be harder to interpret why certain results were returned, as the underlying machine learning models are often quite complex. Additionally, vector search often uses methods like K-Nearest Neighbors (KNN), where you typically retrieve the top K results without always knowing the full set of matches.
Full-text vs. Vector Search: Pros and Cons
| Aspect | Full-text Search | Vector Search |
|---|---|---|
| Precision | High for exact keyword matches | Lower for specific keyword matches |
| Flexibility | Can handle prefixes, morphology, fuzzy matches | Very high, understands conceptual intent |
| Speed | Generally faster, less computation | Requires more computational power |
| Context Awareness | Limited, relies on literal and morphological matches | High, understands the meaning |
| Result Transparency | Easy to understand why results match | Harder to interpret, relies on complex models |
| Result Count | Exact result count available | Hard to determine, usually limited to top K results (e.g., KNN search) |
| Ideal Use Cases | Structured queries, known terms | Open-ended questions, abstract ideas |
Full-text search is quick, efficient, and precise when you have a specific term in mind. It's great for structured queries or when the vocabulary is pretty straightforward. On the flip side, vector search is best when you need help figuring out exactly how to phrase what you're looking for or when context matters more—like in recommendation systems, conversational search, or exploring new topics.
Real-world Use Cases
Full-text Search is best for things like:
Finding exact matches in documents.
Searching through structured databases where you know the exact terms.
Legal or technical searches where specific wording is key.
Vector Search is perfect for:
Recommending products or content based on what users have shown interest in.
Handling open-ended or natural language queries.
Creating personalized search experiences where understanding intent is more important than the exact words.
Imagine searching for "healthy snacks," and the search returns results like "mixed nuts," "fruit bars," and even "low-calorie granola." This is because vector search understands these items share a similar concept of healthiness, even if the exact term "healthy snacks" isn't mentioned.
Combining Full-text and Vector Search with Manticore Search
A lot of modern applications need a combination of both methods to get the best results. For example, a search engine might start with full-text search to narrow down the most relevant documents and then use vector search to rank them based on context.
Manticore Search gives you the best of both worlds. It has powerful full-text search features along with vector search, all in one package. This means you can get the precision of keyword-based searches while also benefiting from the flexibility and context awareness of vector search. Whether you're building a product recommendation system or just need a quick, efficient keyword search, Manticore Search has you covered.
From a technical perspective, implementing full-text search is often more straightforward, whereas vector search usually involves training or utilizing a pre-trained machine learning model like Word2Vec or BERT for embeddings.
Want to see how you can use both types of search in your projects? Try Manticore Search today and see how combining full-text and semantic search can help you build an intelligent, effective search solution.
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