fix: resolve rolegraph example clippy failures in CI

Author: AlexMikhalev

crates/terraphim_rolegraph/examples/graph_embeddings_tutorial.rs

@@ -42,7 +42,7 @@ use terraphim_types::{
 /// When you search for "consensus algorithms", the graph traverses from the
 /// matched node to connected nodes, finding documents that mention related
 /// concepts like "raft", "leader election", etc.
-
+///
 /// Build the initial thesaurus (basic learning concepts)
 fn build_initial_thesaurus() -> Thesaurus {
     let mut thesaurus = Thesaurus::new("Initial Learnings".to_string());
@@ -129,7 +129,7 @@ fn build_enhanced_thesaurus() -> Thesaurus {
 /// PART 2: Sample Learning Documents
 /// ============================================================================
 /// These represent notes captured from technical books, courses, and research.
-
+///
 fn create_learning_documents() -> Vec<Document> {
     vec![
         Document {
@@ -247,7 +247,7 @@ Prevents use-after-free, double-free, and data races."#
 /// ============================================================================
 /// PART 3: Demonstrating Graph Embedding and Indexing
 /// ============================================================================
-
+///
 async fn demonstrate_embedding(
     rolegraph: &mut RoleGraph,
     docs: &[Document],
@@ -292,7 +292,7 @@ async fn demonstrate_embedding(
 /// ============================================================================
 /// This is the key demonstration: how adding domain-specific terms improves
 /// retrieval quality.
-
+///
 async fn compare_rankings(
     initial_graph: &RoleGraph,
     enhanced_graph: &RoleGraph,
@@ -372,7 +372,7 @@ async fn compare_rankings(
 /// PART 5: Connectivity Analysis
 /// ============================================================================
 /// Shows how graph connectivity indicates semantic coherence
-
+///
 fn demonstrate_connectivity(rolegraph: &RoleGraph, queries: &[&str]) {
     println!("\n🕸️  Semantic Connectivity Analysis");
     println!("   (Checks if query terms are connected in the knowledge graph)");

crates/terraphim_rolegraph/examples/learning_via_negativa.rs

@@ -16,7 +16,6 @@
 //! 2. Create correction knowledge graph (wrong -> right)
 //! 3. Use replace tool to suggest corrections
 
-use std::collections::HashMap;
 use terraphim_rolegraph::RoleGraph;
 use terraphim_types::{
     Document, DocumentType, NormalizedTerm, NormalizedTermValue, RoleName, Thesaurus,
@@ -314,6 +313,7 @@ async fn main() -> Result<(), Box<dyn std::error::Error>> {
     println!("\n📝 Captured Failed Command:");
     println!("   Wrong:   {}", failed.wrong_command);
     println!("   Error:   {}", failed.error_output);
+    println!("   Correct: {}", failed.correct_command);
     println!("   Context: {}", failed.context);
 
     // Build correction knowledge graphs
@@ -368,7 +368,7 @@ async fn main() -> Result<(), Box<dyn std::error::Error>> {
             println!("  Initial: No correction found");
         } else {
             println!("  Initial: {} results", initial_results.len());
-            for (doc_id, doc) in initial_results.iter().take(1) {
+            for (doc_id, _doc) in initial_results.iter().take(1) {
                 println!("    -> {}", doc_id);
             }
         }
@@ -394,6 +394,8 @@ async fn main() -> Result<(), Box<dyn std::error::Error>> {
         }
     }
 
+    demonstrate_correction(&enhanced_graph, &failed.wrong_command).await;
+
     // Summary
     println!("\n================================================================================");
     println!("  Summary: Learning via Negativa");