API Reference

This page provides a quick reference of the most important types and methods in Laurus. For full details, generate the Rustdoc:

cargo doc --open

Engine

The central coordinator for all indexing and search operations.

Method	Description
`Engine::builder(storage, schema)`	Create an `EngineBuilder`
`engine.put_document(id, doc).await?`	Upsert a document (replace if ID exists)
`engine.add_document(id, doc).await?`	Add a document as a chunk (multiple chunks can share an ID)
`engine.delete_documents(id).await?`	Delete all documents/chunks by external ID
`engine.get_documents(id).await?`	Get all documents/chunks by external ID
`engine.search(request).await?`	Execute a search request
`engine.commit().await?`	Flush all pending changes to storage
`engine.stats()?`	Get index statistics

put_document vs add_document: put_document performs an upsert — if a document with the same external ID already exists, it is deleted and replaced. add_document always appends, allowing multiple document chunks to share the same external ID. See Schema & Fields — Indexing Documents for details.

EngineBuilder

Method	Description
`EngineBuilder::new(storage, schema)`	Create a builder with storage and schema
`.analyzer(Arc<dyn Analyzer>)`	Set the text analyzer (default: `StandardAnalyzer`)
`.embedder(Arc<dyn Embedder>)`	Set the vector embedder (optional)
`.build().await?`	Build the `Engine`

Schema

Defines document structure.

Method	Description
`Schema::builder()`	Create a `SchemaBuilder`

SchemaBuilder

Method	Description
`.add_text_field(name, TextOption)`	Add a full-text field
`.add_integer_field(name, IntegerOption)`	Add an integer field
`.add_float_field(name, FloatOption)`	Add a float field
`.add_boolean_field(name, BooleanOption)`	Add a boolean field
`.add_datetime_field(name, DateTimeOption)`	Add a datetime field
`.add_geo_field(name, GeoOption)`	Add a geographic field
`.add_bytes_field(name, BytesOption)`	Add a binary field
`.add_hnsw_field(name, HnswOption)`	Add an HNSW vector field
`.add_flat_field(name, FlatOption)`	Add a Flat vector field
`.add_ivf_field(name, IvfOption)`	Add an IVF vector field
`.add_default_field(name)`	Set a default search field
`.build()`	Build the `Schema`

Document

A collection of named field values.

Method	Description
`Document::builder()`	Create a `DocumentBuilder`
`doc.get(name)`	Get a field value by name
`doc.has_field(name)`	Check if a field exists
`doc.field_names()`	Get all field names

DocumentBuilder

Method	Description
`.add_text(name, value)`	Add a text field
`.add_integer(name, value)`	Add an integer field
`.add_float(name, value)`	Add a float field
`.add_boolean(name, value)`	Add a boolean field
`.add_datetime(name, value)`	Add a datetime field
`.add_vector(name, vec)`	Add a pre-computed vector
`.add_geo(name, lat, lon)`	Add a geographic point
`.add_bytes(name, data)`	Add binary data
`.build()`	Build the `Document`

Search

SearchRequestBuilder

Method	Description
`SearchRequestBuilder::new()`	Create a new builder
`.lexical_search_request(req)`	Set the lexical search component
`.vector_search_request(req)`	Set the vector search component
`.filter_query(query)`	Set a pre-filter query
`.fusion_algorithm(algo)`	Set the fusion algorithm (default: RRF)
`.limit(n)`	Maximum results (default: 10)
`.offset(n)`	Skip N results (default: 0)
`.build()`	Build the `SearchRequest`

LexicalSearchRequest

Method	Description
`LexicalSearchRequest::new(query)`	Create with a query
`LexicalSearchRequest::from_dsl(query_str)`	Create from a DSL query string
`.limit(n)`	Maximum results
`.load_documents(bool)`	Whether to load document content
`.min_score(f32)`	Minimum score threshold
`.timeout_ms(u64)`	Search timeout in milliseconds
`.parallel(bool)`	Enable parallel search
`.sort_by_field_asc(field)`	Sort by field ascending
`.sort_by_field_desc(field)`	Sort by field descending
`.sort_by_score()`	Sort by relevance score (default)
`.with_field_boost(field, boost)`	Add field-level boost

VectorSearchRequestBuilder

Method	Description
`VectorSearchRequestBuilder::new()`	Create a new builder
`.add_text(field, text)`	Add a text query for a field
`.add_vector(field, vector)`	Add a pre-computed query vector
`.add_bytes(field, bytes, mime)`	Add a binary payload (for multimodal)
`.limit(n)`	Maximum results
`.score_mode(VectorScoreMode)`	Score combination mode (WeightedSum, MaxSim)
`.min_score(f32)`	Minimum score threshold
`.field(name)`	Restrict search to a specific field
`.build()`	Build the request

SearchResult

Field	Type	Description
`id`	`String`	External document ID
`score`	`f32`	Relevance score
`document`	`Option<Document>`	Document content (if loaded)

FusionAlgorithm

Variant	Description
`RRF { k: f64 }`	Reciprocal Rank Fusion (default k=60.0)
`WeightedSum { lexical_weight, vector_weight }`	Linear combination of scores

Query Types (Lexical)

Query	Description	Example
`TermQuery::new(field, term)`	Exact term match	`TermQuery::new("body", "rust")`
`PhraseQuery::new(field, terms)`	Exact phrase	`PhraseQuery::new("body", vec!["machine".into(), "learning".into()])`
`BooleanQueryBuilder::new()`	Boolean combination	`.must(q1).should(q2).must_not(q3).build()`
`FuzzyQuery::new(field, term)`	Fuzzy match (default max_edits=2)	`FuzzyQuery::new("body", "programing").max_edits(1)`
`WildcardQuery::new(field, pattern)`	Wildcard	`WildcardQuery::new("file", "*.pdf")`
`NumericRangeQuery::new(...)`	Numeric range	See Lexical Search
`GeoQuery::within_radius(...)`	Geo radius	See Lexical Search
`SpanNearQuery::new(...)`	Proximity	See Lexical Search
`PrefixQuery::new(field, prefix)`	Prefix match	`PrefixQuery::new("body", "pro")`
`RegexpQuery::new(field, pattern)?`	Regex match	`RegexpQuery::new("body", "^pro.*ing$")?`

Query Parsers

Parser	Description
`QueryParser::new(analyzer)`	Parse lexical DSL queries
`VectorQueryParser::new(embedder)`	Parse vector DSL queries
`UnifiedQueryParser::new(lexical, vector)`	Parse hybrid DSL queries

Analyzers

Type	Description
`StandardAnalyzer`	RegexTokenizer + lowercase + stop words
`SimpleAnalyzer`	Tokenization only (no filtering)
`EnglishAnalyzer`	RegexTokenizer + lowercase + English stop words
`JapaneseAnalyzer`	Japanese morphological analysis
`KeywordAnalyzer`	No tokenization (exact match)
`PipelineAnalyzer`	Custom tokenizer + filter chain
`PerFieldAnalyzer`	Per-field analyzer dispatch

Embedders

Type	Feature Flag	Description
`CandleBertEmbedder`	`embeddings-candle`	Local BERT model
`OpenAIEmbedder`	`embeddings-openai`	OpenAI API
`CandleClipEmbedder`	`embeddings-multimodal`	Local CLIP model
`PrecomputedEmbedder`	(default)	Pre-computed vectors
`PerFieldEmbedder`	(default)	Per-field embedder dispatch

Storage

Type	Description
`MemoryStorage`	In-memory (non-durable)
`FileStorage`	File-system based (supports `use_mmap` for memory-mapped I/O)
`StorageFactory::create(config)`	Create from config

DataValue

Variant	Rust Type
`DataValue::Null`	—
`DataValue::Bool(bool)`	`bool`
`DataValue::Int64(i64)`	`i64`
`DataValue::Float64(f64)`	`f64`
`DataValue::Text(String)`	`String`
`DataValue::Bytes(Vec<u8>, Option<String>)`	`(data, mime_type)`
`DataValue::Vector(Vec<f32>)`	`Vec<f32>`
`DataValue::DateTime(DateTime<Utc>)`	`chrono::DateTime<Utc>`
`DataValue::Geo(f64, f64)`	`(latitude, longitude)`

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