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 can generate embeddings automatically from text or accept vectors generated by an external model, then search them natively and combine the results with full-text search.
When to use 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.
- You can use Manticore auto-embeddings to vectorize text during indexing and querying, or supply vectors from an external model when you need full control over the embedding pipeline.
- 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 between Manticore auto-embeddings and vectors generated by an external model
- For auto-embeddings, select a supported local or remote embedding model in the table definition
- For external embeddings, ensure the vector dimensions and format match the table schema
Set up your Manticore Search table
- Define a table with a vector field or a model-backed auto-embedding field
- Configure the distance metric and either vector dimensions or model settings
- Index text directly with auto-embeddings, or provide precomputed vectors
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
Cons
Learn more about other use cases
Explore the other Manticore Search use cases to see how one engine handles full-text, vector, hybrid, autocomplete, and log-search workloads.
Implement Semantic Search with Manticore Search
Enhance your search capabilities with Manticore Search's Semantic Search features today!
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