Examples

The laurus/examples/ directory contains runnable examples demonstrating different features of the library.

Running Examples

# Run an example without feature flags
cargo run --example <name>

# Run an example with a feature flag
cargo run --example <name> --features <flag>

A minimal example showing the basic workflow: create storage, define a schema, build an engine, index documents, and search.

cargo run --example quickstart

Demonstrates: In-memory storage, TextOption, TermQuery, LexicalSearchRequest.

Comprehensive example of all lexical query types, using both the Builder API and the QueryParser DSL.

cargo run --example lexical_search

Demonstrates: TermQuery, PhraseQuery, FuzzyQuery, WildcardQuery, NumericRangeQuery, GeoQuery, BooleanQuery, SpanQuery.

Vector search with a mock embedder, including filtered vector search and DSL syntax.

cargo run --example vector_search

Demonstrates: PerFieldEmbedder, VectorSearchRequestBuilder, filtered search, DSL syntax (field:~"query").

Combining lexical and vector search with different fusion algorithms.

cargo run --example hybrid_search

Demonstrates: Lexical-only, vector-only, and hybrid search. Both RRF and WeightedSum fusion algorithms. Builder API and DSL.

Vector search using real BERT embeddings via Hugging Face Candle. The model is downloaded automatically on first run (~80 MB).

cargo run --example search_with_candle --features embeddings-candle

Requires: embeddings-candle feature flag.

Demonstrates: CandleBertEmbedder with sentence-transformers/all-MiniLM-L6-v2 (384 dimensions).

Vector search using the OpenAI Embeddings API.

export OPENAI_API_KEY=your-api-key
cargo run --example search_with_openai --features embeddings-openai

Requires: embeddings-openai feature flag, OPENAI_API_KEY environment variable.

Demonstrates: OpenAIEmbedder with text-embedding-3-small (1536 dimensions).

Multimodal (text + image) search using a CLIP model.

cargo run --example multimodal_search --features embeddings-multimodal

Requires: embeddings-multimodal feature flag.

Demonstrates: CandleClipEmbedder, indexing images from the filesystem, text-to-image and image-to-image queries.

Demonstrates the SynonymGraphFilter for token expansion during analysis.

cargo run --example synonym_graph_filter

Demonstrates: Synonym dictionary creation, synonym-based token expansion, boost application, token position and position_length attributes.

The common.rs file provides shared utilities used by the examples:

memory_storage() – Create an in-memory storage instance
per_field_analyzer() – Create a PerFieldAnalyzer with KeywordAnalyzer for specific fields
MockEmbedder – A mock Embedder implementation for testing vector search without a real model