Improve Search Accuracy with Manticore's Fuzzy Search

Fuzzy search is a robust feature in Manticore Search that enables approximate string matching, helping users find relevant results even with misspellings or variations.*

*Available in preview release

What is Fuzzy Search

Fuzzy search allows matches to be found even when users make typos or enter slightly different terms. It’s ideal for situations where spelling errors or variations might hinder exact matches. In Manticore Search, enabling fuzzy search is simple — use the SQL/JSON search option fuzzy=1 or activate it.

When you need Fuzzy Search

Handle user input that includes typos or misspellings
Search for names or terms with multiple spellings
Process OCR-generated text that may contain errors
Search through user-generated content with possible spelling mistakes
Create a forgiving search interface to enhance user experience
Search for technical terms or jargon that users may mistype
Match similar but not identical phrases
Search multilingual content with variations in transliteration
Process search queries for products with complex or commonly misspelled names
Boost search recall when precision can be relaxed

Why Manticore Search is good for Fuzzy Search

It’s easy to implement with Manticore Search - just add fuzzy=1 to your SQL or JSON query.
Fuzzy search helps improve user experience by finding relevant results even when the input isn’t perfect.
It can increase the recall of your search results without significantly impacting precision.
Manticore’s fuzzy search is configurable, allowing you to adjust the fuzziness level to suit your needs.

How to get started

Install Manticore Search

Follow the installation instructions for your operating system
Alternatively, use Docker: docker pull manticoresearch/manticore:dev

Set up your Manticore Search table

Create a table
Configure full-text fields and attributes
Index your data using the appropriate method (SQL, JSON, CSV, etc.)

Implement fuzzy search

Using SQL syntax: Add fuzzy=1 to your SELECT clause
Using HTTP JSON interface: Include the fuzzy parameter in your search request

Fine-tune your fuzzy search

Experiment with different fuzziness levels to balance recall and precision
Adjust the fuzziness level distance if needed
Consider combining fuzzy search with other features like quorum matching or proximity search
Use highlighting to show where fuzzy matches occur in the results

Enjoy Fuzzy Search

Experience improved search results even with imperfect user input
Monitor search performance and user satisfaction
Feel free to create an issue if you encounter any problems
Consider our professional services for advanced implementations

Pros

✓ Easy to implement with a simple option in SQL or JSON queries

✓ Improves user experience by handling typos and misspellings

✓ Increases search recall without significantly impacting precision

✓ Configurable fuzziness level for fine-tuning

✓ Can be combined with other Manticore Search features for powerful, flexible searching

✓ Works well with both short queries and longer phrases

Cons

✗ May slightly increase search time compared to exact matching

✗ Can potentially return irrelevant results if not properly tuned

✗ Might require careful balancing with other search features for optimal results

✗ Not suitable for all types of data or search scenarios

Resources

Learn more about other use cases

Do not stop here when learning when you need Fuzzy Search and how Manticore Search can help you. There are many other use cases that you can explore.

Implement Fuzzy Search with Manticore Search

Try Manticore Search for implementing Fuzzy Search in your application today!

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