Vector Search with Manticore Search

Achieve Next-Level Search Accuracy with Vector Search in Manticore.

What is Vector Search

Vector Search enables finding items by comparing their vector representations, making it ideal for tasks such as semantic search, recommendation systems, and image similarity detection. Manticore Search offers built-in support for vector search, making it simple to integrate this powerful feature into your applications.

When you need Vector Search

Implementing semantic search for natural language queries
Building recommendation systems for products or content
Searching for similar images or audio files
Detecting duplicate or near-duplicate content
Analyzing similarities in user behavior or preferences
Implementing facial recognition systems
Clustering similar documents or data points
Enhancing search results with contextual understanding
Implementing anomaly detection in time-series data
Performing multi-modal search combining text and images

Why Manticore Search is good for Vector Search

Manticore Search offers native support for vector search, eliminating the need for additional specialized tools.
It supports multiple distance metrics like cosine, dot product, and L2 distance for flexible similarity calculations.
Manticore’s vector search can be combined with traditional full-text search for powerful hybrid queries.
The implementation is highly optimized for performance, even with large-scale vector datasets.

How to get started

Install Manticore Search

Visit the official Manticore Search website: https://manticoresearch.com/
Follow the installation instructions for your operating system
Alternatively, use Docker: docker pull manticoresearch/manticore

Set up your Manticore Search index with vector fields

Create your table schema including vector fields
Specify the dimension of your vectors and the distance metric to use
Index your data, including both text and vector representations

Implement vector search functionality

Use the KNN clause with vector fields to perform similarity searches
Combine vector search with full-text search for hybrid queries
Implement a client-side HTTP request to query Manticore Search

Fine-tune your vector search

Experiment with different distance metrics to find the best fit for your data
Use query-time parameters to adjust search behavior
Implement post-processing steps if needed, such as re-ranking results

Enjoy Vector Search

Experience powerful similarity-based search with Manticore Search
Feel free to create an issue if you encounter any problems
Consider our professional services for advanced implementations

Pros

✓ Native support for vector search without additional tools

✓ Multiple distance metrics available for flexibility

✓ Can be combined with full-text search for hybrid queries

✓ Highly optimized for performance with large vector datasets

✓ Seamless integration with existing Manticore Search features

✓ Supports both SQL and JSON interfaces

Cons

✗ Requires pre-computed vector representations of your data

✗ May require more storage space compared to traditional text-only indexes

✗ Performance can degrade with very high-dimensional vectors

Resources

Learn more about other use cases

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

Install Manticore Search to use Vector Search

Try Manticore Search for implementing advanced Vector Search in your applications today!

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