Semantic Search with Manticore Search

Unlock the Power of Semantic Search with Vector Embeddings in Manticore.

What is Semantic Search

Semantic Search goes beyond simple keyword matching by understanding the context and meaning behind search queries. By leveraging vector embeddings, it captures the semantic relationships in text to deliver more accurate and relevant results. Manticore Search allows you to implement Semantic Search seamlessly by vectorizing your data externally and storing the resulting vectors within Manticore.

When you need Semantic Search

Searching for conceptually similar documents
Implementing natural language understanding in search
Improving search relevance beyond keyword matching
Handling queries with synonyms or related concepts
Searching multilingual content
Implementing recommendation systems
Enhancing e-commerce product search
Improving content discovery in large document collections
Implementing question-answering systems
Enhancing chatbot or virtual assistant capabilities

Why Manticore Search is good for Semantic Search

Manticore Search supports storing and searching vector embeddings, enabling Semantic Search capabilities.
By using external tools or libraries to generate vector embeddings, you have the freedom to choose the best, cutting-edge models and techniques that suit your data and needs.
Manticore’s optimized vector search ensures fast and efficient comparisons for semantic similarity.
For even more robust search solutions, you can seamlessly combine vector search with traditional full-text search for a hybrid approach.

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

Prepare your data for Semantic Search

Choose a vectorization method (e.g., Word2Vec, BERT, FastText)
Use an external tool or library to generate vector embeddings for your text data
Ensure your vectors are in a format compatible with Manticore Search

Set up your Manticore Search table

Define your table schema, including a field for vector embeddings
Configure the vector field with appropriate dimensions
Index your data, including the pre-generated vector embeddings

Implement Semantic Search functionality

Use Manticore’s vector search capabilities to find similar documents
Implement a client-side SQL or JSON request to query Manticore Search
Handle the response and display semantically relevant results to the user

Fine-tune your Semantic Search

Experiment with different vector similarity metrics (e.g., cosine, dot product)
Combine vector search with traditional full-text search for hybrid solutions
Adjust relevance scoring to balance between semantic and keyword matching

Enjoy Semantic Search

Experience improved search relevance with Manticore Search’s Semantic Search capabilities
Feel free to create an issue in case of any problems
Also, check out the professional services we provide for you

Pros

✓ Improved search relevance by understanding context and meaning

✓ Ability to find conceptually similar documents even without exact keyword matches

✓ Flexible integration with various vectorization methods

✓ Can be combined with traditional full-text search for hybrid solutions

✓ Efficient vector search capabilities in Manticore Search

✓ Supports both SQL and JSON interfaces

Cons

✗ Requires external vectorization of data before indexing

✗ May require more storage space due to vector embeddings

✗ Potential increase in indexing time due to vector generation

✗ May require fine-tuning to balance between semantic and keyword relevance

Resources

Learn more about other use cases

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

Implement Semantic Search with Manticore Search

Enhance your search capabilities with Manticore Search's Semantic Search features today!

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