This article was originally published on IBM Developer.
Elasticsearch has undergone a remarkable transformation from a simple keyword search engine to a sophisticated AI-powered search platform that combines traditional lexical search with modern vector-based techniques. This evolution has positioned Elasticsearch as a comprehensive retrieval platform capable of handling diverse data types including structured, unstructured, and vector data in real-time. Today's developers need to understand not just basic indexing and querying, but also how to leverage advanced features like language analyzers, dense vectors, and hybrid search to build cutting-edge applications.
The power of Elasticsearch lies in its ability to bridge the gap between traditional keyword search (Best Match 25, or BM25) and semantic vector search (AI-powered), creating a robust foundation for modern search experiences. In this comprehensive guide, we'll explore the practical aspects of implementing and optimizing Elasticsearch for contemporary use cases, with special emphasis on indexing strategies, analyzer configuration, and the powerful combination of lexical and semantic search known as hybrid search.
Text analysis in depth
At the heart of Elasticsearch's text processing capabilities lie analyzers, components that determine how text is split into tokens, normalized, and indexed. An analyzer consists of three main components:
- A tokenizer that splits text into tokens
- Zero or more token filters that modify these tokens
- Character filters that preprocess text before tokenization.
Learn more about the text analysis components in the Elastic docs.
Continue reading on IBM Developer to dive deeper into analyzers...
Vector search essentials
Vector search, also known as semantic search, represents a fundamental hift from traditional lexical matching. Instead of matching exact words, it finds content with similar meaning by representing data as dense vectors in a high-dimensional space. These vectors, called embeddings, are generated by machine learning models that capture semantic relationships between pieces of content.
Modern embedding models can represent various types of content as vectors, including text, images, events and more. Each dimension in the vector represents a feature or characteristic of the content, allowing similarity to be calculated based on semantic meaning rather than surface-level characteristics.
Continue reading on IBM Developer to dive deeper into vector search...
Implementing Hybrid Search
Hybrid search combines traditional full-text search with AI-powered semantic search, creating more powerful search experiences that serve a wider range of user needs. This approach is particularly effective because:
- Lexical search excels when users know the exact words they're looking for.
- Semantic search shines when users search for concepts or ideas not explicitly defined in documents.
- Hybrid search gives you the best of both worlds by blending precision with contextual understanding.
Continue reading on IBM Developer to dive deeper into vector search...
Practical implementation guide using Python
In this section, we’ll get hands-on with Elasticsearch using Python. To simplify development, Elasticsearch offers a dedicated Python SDK that streamlines tasks such as connecting to your cluster, creating indices, indexing documents, performing hybrid and regex searches, and ingesting data with embeddings. Each example builds on earlier concepts, showing how to translate theory into working code.
Continue reading on IBM Developer to walk through the Python code that implements each of these principles with Elasticsearch...
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