Understanding Elasticsearch Semantic Search and Embeddings

What is Semantic Search?

Semantic search aims to improve search accuracy by understanding the context and intent behind user queries, rather than relying solely on keyword matching.

Key Components of Elasticsearch Semantic Search

1. Embeddings: These are dense numerical vectors that represent text. An embedding model converts raw text into these vectors, capturing semantic meaning.
2. Inference: During the document ingestion phase, embeddings are generated for each document. When a user searches, their query is also converted into an embedding for comparison.
3. Document Ingestion: This involves cleaning the text and optionally breaking it into smaller, searchable passages. This ensures that relevant information is easily retrievable.
4. Vector Indexing: Elasticsearch uses fields like dense_vector to store the text, its metadata, and the corresponding embeddings. This allows for efficient searching.
5. kNN Vector Search: This method retrieves nearby vectors in the embedding space to find semantically similar passages, improving the relevance of search results.

End-to-End Architecture

The flow of data in semantic search can be summarized as:

• Raw documents → Embedding generation → Vector indexing → User queries → Semantic retrieval.

Trade-offs in System Design

When designing a semantic search system, consider:

• Relevance: How well the results match user intent.
• Latency: The time taken to return results.
• Cost: Resources required for storage and computation.
• Model Consistency: Ensuring that the embedding model remains effective over time.

Model Upgrades

Changing embedding models or dimensions may require re-embedding and reindexing of documents to maintain search quality.

Memory Trick

EMBED: Convert text into vectors
INDEX: Store vectors with text and metadata
RETRIEVE: Find semantically similar content

Understanding these concepts is crucial for designing effective search systems that can handle complex queries and provide relevant results.