Struct RetrievalSystem 

pub struct RetrievalSystem { /* private fields */ }

Retrieval system for querying the knowledge graph

Implementations

impl RetrievalSystem

pub fn new(config: &Config) -> Result<Self>

Create a new retrieval system

impl RetrievalSystem

pub fn with_parallel_processing( vector_store: Arc<dyn VectorStore>, embedding_generator: EmbeddingGenerator, parallel_processor: ParallelProcessor, ) -> Result<Self>

Create a new retrieval system with parallel processing support

pub async fn index_graph(&self, graph: &KnowledgeGraph) -> Result<()>

Index a knowledge graph for retrieval

pub fn initialize_enriched( &mut self, config: Option<EnrichedRetrievalConfig>, ) -> Result<()>

Initialize enriched metadata-aware retrieval system

pub fn query(&self, query: &str) -> Result<Vec<String>>

Query the system for relevant information

pub async fn hybrid_query( &mut self, query: &str, graph: &KnowledgeGraph, ) -> Result<Vec<SearchResult>>

Advanced hybrid query with strategy selection and hierarchical integration

pub async fn hybrid_query_with_trees( &mut self, query: &str, graph: &KnowledgeGraph, document_trees: &HashMap<DocumentId, DocumentTree>, ) -> Result<Vec<SearchResult>>

Hybrid query with access to document trees for hierarchical retrieval

pub async fn legacy_hybrid_query( &mut self, query: &str, graph: &KnowledgeGraph, ) -> Result<Vec<SearchResult>>

Query the system using hybrid retrieval (vector + graph) - legacy method

pub async fn add_embeddings_to_graph( &mut self, graph: &mut KnowledgeGraph, ) -> Result<()>

Add embeddings to chunks and entities in the graph with parallel processing

pub async fn batch_query( &mut self, queries: &[&str], graph: &KnowledgeGraph, ) -> Result<Vec<Vec<SearchResult>>>

Parallel batch query processing with optimized workload distribution Batch process multiple queries efficiently

pub fn analyze_query( &self, query: &str, graph: &KnowledgeGraph, ) -> Result<QueryAnalysis>

Analyze query to determine optimal retrieval strategy

pub async fn execute_adaptive_retrieval( &mut self, query: &str, query_embedding: &[f32], graph: &KnowledgeGraph, document_trees: &HashMap<DocumentId, DocumentTree>, analysis: &QueryAnalysis, ) -> Result<Vec<SearchResult>>

Execute adaptive retrieval based on query analysis

pub async fn comprehensive_search( &self, query_embedding: &[f32], graph: &KnowledgeGraph, ) -> Result<Vec<SearchResult>>

Comprehensive search that combines multiple retrieval strategies (legacy)

pub async fn vector_search( &mut self, query: &str, max_results: usize, ) -> Result<Vec<SearchResult>>

Vector-based search

pub fn graph_search( &self, query: &str, max_results: usize, ) -> Result<Vec<SearchResult>>

Graph-based search

pub fn public_hierarchical_search( &self, query: &str, max_results: usize, ) -> Result<Vec<SearchResult>>

Hierarchical search (public wrapper)

pub fn bm25_search( &self, query: &str, max_results: usize, ) -> Result<Vec<SearchResult>>

BM25-based search

pub fn get_statistics(&self) -> RetrievalStatistics

Get retrieval statistics

pub fn save_state_to_json(&self, file_path: &str) -> Result<()>

Save retrieval system state to JSON file