fix: restore server builds and stabilize Rust CI

Cargo.toml

@@ -58,8 +58,8 @@ serde-wasm-bindgen = { version = "0.6", optional = true }
 js-sys = { version = "0.3", optional = true }
 web-sys = { version = "0.3", optional = true, features = ["console", "Window", "Storage", "IdbFactory", "IdbDatabase", "IdbObjectStore", "IdbTransaction"] }
 console_error_panic_hook = { version = "0.1", optional = true }
-parquet = { version = "57", optional = true }
-arrow = { version = "57", optional = true }
+parquet = { version = "59", optional = true }
+arrow = { version = "59", optional = true }
 candle-core = { version = "0.9", optional = true }
 candle-nn = { version = "0.9", optional = true }
 candle-transformers = { version = "0.9", optional = true }

benches/openai_embeddings_bench.rs

@@ -6,7 +6,7 @@
 //
 // Run with: cargo bench --features "embeddings,openai-embeddings" --bench openai_embeddings_bench
 
-use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
+use criterion::{BenchmarkId, Criterion, Throughput, black_box, criterion_group, criterion_main};
 use vecstore::embeddings::openai_backend::{OpenAIEmbedding, OpenAIModel};
 
 // Helper to create embedder

benches/rag_benchmarks.rs

@@ -1,9 +1,9 @@
-use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
+use criterion::{BenchmarkId, Criterion, Throughput, black_box, criterion_group, criterion_main};
 use std::collections::HashMap;
 use vecstore::{
+    Metadata, Query, VecStore,
     rag_utils::MultiQueryRetrieval,
     text_splitter::{RecursiveCharacterTextSplitter, TextSplitter},
-    Metadata, Query, VecStore,
 };
 
 // Mock embedding function for benchmarks

benches/simd_bench.rs

@@ -1,4 +1,4 @@
-use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
+use criterion::{BenchmarkId, Criterion, Throughput, black_box, criterion_group, criterion_main};
 use vecstore::simd::*;
 
 fn generate_random_vector(dim: usize) -> Vec<f32> {

benches/vecstore_bench.rs

@@ -1,6 +1,6 @@
-use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
+use criterion::{BenchmarkId, Criterion, Throughput, black_box, criterion_group, criterion_main};
 use std::collections::HashMap;
-use vecstore::{make_record, FilterExpr, FilterOp, Metadata, Query, VecStore};
+use vecstore::{FilterExpr, FilterOp, Metadata, Query, VecStore, make_record};
 
 fn setup_store_with_data(n: usize, dim: usize) -> (tempfile::TempDir, VecStore) {
     let temp_dir = tempfile::tempdir().unwrap();

benches/wasm_hnsw_bench.rs

@@ -5,15 +5,15 @@
 //
 // Run with: cargo bench --bench wasm_hnsw_bench
 
-use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
+use criterion::{BenchmarkId, Criterion, Throughput, black_box, criterion_group, criterion_main};
 use rand::Rng;
-use vecstore::store::wasm_hnsw::WasmHnsw;
 use vecstore::Distance;
+use vecstore::store::wasm_hnsw::WasmHnsw;
 
 fn generate_random_vectors(n: usize, dim: usize) -> Vec<Vec<f32>> {
     let mut rng = rand::thread_rng();
     (0..n)
-        .map(|_| (0..dim).map(|_| rng.gen::<f32>()).collect())
+        .map(|_| (0..dim).map(|_| rng.r#gen::<f32>()).collect())
         .collect()
 }
 

examples/01_basic_rag.rs

@@ -12,8 +12,8 @@
 use anyhow::Result;
 use std::collections::HashMap;
 use vecstore::{
-    text_splitter::{RecursiveCharacterTextSplitter, TextSplitter},
     Metadata, Query, VecStore,
+    text_splitter::{RecursiveCharacterTextSplitter, TextSplitter},
 };
 
 fn main() -> Result<()> {

examples/03_web_scraping_rag.rs

@@ -11,8 +11,8 @@
 use anyhow::Result;
 use std::collections::HashMap;
 use vecstore::{
-    text_splitter::{RecursiveCharacterTextSplitter, TextSplitter},
     Metadata, Query, VecStore,
+    text_splitter::{RecursiveCharacterTextSplitter, TextSplitter},
 };
 
 fn main() -> Result<()> {

examples/04_code_search.rs

@@ -10,8 +10,8 @@
 use anyhow::Result;
 use std::collections::HashMap;
 use vecstore::{
-    text_splitter::{CodeTextSplitter, TextSplitter},
     Metadata, Query, VecStore,
+    text_splitter::{CodeTextSplitter, TextSplitter},
 };
 
 fn main() -> Result<()> {

examples/06_reranking_pipeline.rs

@@ -11,8 +11,8 @@
 use anyhow::Result;
 use std::collections::HashMap;
 use vecstore::{
-    text_splitter::{RecursiveCharacterTextSplitter, TextSplitter},
     Metadata, Neighbor, Query, VecStore,
+    text_splitter::{RecursiveCharacterTextSplitter, TextSplitter},
 };
 
 fn main() -> Result<()> {

examples/07_multi_query_rag.rs

@@ -10,9 +10,9 @@
 use anyhow::Result;
 use std::collections::HashMap;
 use vecstore::{
+    Metadata, Query, VecStore,
     rag_utils::MultiQueryRetrieval,
     text_splitter::{RecursiveCharacterTextSplitter, TextSplitter},
-    Metadata, Query, VecStore,
 };
 
 fn main() -> Result<()> {

examples/08_conversation_rag.rs

@@ -8,9 +8,9 @@
 use anyhow::Result;
 use std::collections::HashMap;
 use vecstore::{
+    Metadata, Query, VecStore,
     rag_utils::ConversationMemory,
     text_splitter::{RecursiveCharacterTextSplitter, TextSplitter},
-    Metadata, Query, VecStore,
 };
 
 fn main() -> Result<()> {

examples/09_evaluation_demo.rs

@@ -9,8 +9,8 @@
 use anyhow::Result;
 use std::collections::HashMap;
 use vecstore::{
-    text_splitter::{RecursiveCharacterTextSplitter, TextSplitter},
     Metadata, Query, VecStore,
+    text_splitter::{RecursiveCharacterTextSplitter, TextSplitter},
 };
 
 fn main() -> Result<()> {
@@ -50,7 +50,10 @@ fn main() -> Result<()> {
             "What is VecStore?",
             "VecStore is a high-performance vector database built in Rust.",
         ),
-        ("How fast is VecStore?", "Rust avoids the Python interpreter in tight loops."),
+        (
+            "How fast is VecStore?",
+            "Rust avoids the Python interpreter in tight loops.",
+        ),
     ];
 
     let mut total_score = 0.0;

examples/10_production_rag.rs

@@ -12,8 +12,8 @@ use anyhow::{Context, Result};
 use std::collections::HashMap;
 use std::time::Instant;
 use vecstore::{
-    text_splitter::{RecursiveCharacterTextSplitter, TextSplitter},
     Metadata, Query, VecDatabase,
+    text_splitter::{RecursiveCharacterTextSplitter, TextSplitter},
 };
 
 fn main() -> Result<()> {

examples/bulk_migration_demo.rs

@@ -10,8 +10,8 @@
 use anyhow::Result;
 use std::fs;
 use tempfile::TempDir;
-use vecstore::bulk_migration::{ChromaDBMigration, MigrationConfig, PineconeMigration};
 use vecstore::VecStore;
+use vecstore::bulk_migration::{ChromaDBMigration, MigrationConfig, PineconeMigration};
 
 fn main() -> Result<()> {
     println!("🔄 VecStore Bulk Migration Demo\n");

examples/compression.rs

@@ -17,8 +17,8 @@
 //! ```
 
 use vecstore::compression::{
-    decode_rle, encode_rle, encode_varint, CompressedNeighborList,
-    CompressionConfig, CompressionLevel, CompressionStats,
+    CompressedNeighborList, CompressionConfig, CompressionLevel, CompressionStats, decode_rle,
+    encode_rle, encode_varint,
 };
 
 fn main() {

examples/dim_reduction_demo.rs

@@ -7,7 +7,7 @@
 //! - Reconstruct original vectors
 
 use anyhow::Result;
-use vecstore::dim_reduction::{ReductionStats, PCA};
+use vecstore::dim_reduction::{PCA, ReductionStats};
 
 fn generate_high_dim_data() -> Vec<Vec<f32>> {
     let mut vectors = Vec::new();

examples/distance_metrics_demo.rs

@@ -12,8 +12,8 @@
 
 use std::collections::HashMap;
 use vecstore::{
-    hamming_distance_simd, jaccard_distance_simd, manhattan_distance_simd, Distance, Metadata,
-    VecStore,
+    Distance, Metadata, VecStore, hamming_distance_simd, jaccard_distance_simd,
+    manhattan_distance_simd,
 };
 
 fn main() -> anyhow::Result<()> {

examples/distributed_indexing.rs

@@ -23,8 +23,8 @@
 //! ```
 
 use vecstore::distributed::{
-    ConsistencyLevel, ConsistentHashRing, DistributedConfig, DistributedStore,
-    ReplicationStrategy, ShardingStrategy,
+    ConsistencyLevel, ConsistentHashRing, DistributedConfig, DistributedStore, ReplicationStrategy,
+    ShardingStrategy,
 };
 
 fn main() {

examples/embedding_integration_demo.rs

@@ -17,8 +17,8 @@
 
 #[cfg(feature = "embeddings")]
 use vecstore::{
-    embeddings::{EmbeddingCollection, SimpleEmbedder, TextEmbedder},
     Metadata, VecDatabase,
+    embeddings::{EmbeddingCollection, SimpleEmbedder, TextEmbedder},
 };
 
 #[cfg(feature = "embeddings")]

examples/fuzzy_search.rs

@@ -18,8 +18,8 @@
 //! ```
 
 use vecstore::fuzzy::{
-    damerau_levenshtein_distance, levenshtein_distance, similarity_score, suggest_corrections,
-    BKTree, FuzzyMatcher,
+    BKTree, FuzzyMatcher, damerau_levenshtein_distance, levenshtein_distance, similarity_score,
+    suggest_corrections,
 };
 
 fn main() {

examples/gpu_acceleration.rs

@@ -294,10 +294,10 @@ fn main() {
                 };
 
                 println!("{:<20}  {:>12.2}  {:>14.2}x", name, duration_ms, speedup);
-            }
+            },
             Err(_) => {
                 println!("{:<20}  {:>12}  {:>15}", name, "N/A", "N/A");
-            }
+            },
         }
     }
 

examples/graph_visualization.rs

@@ -143,14 +143,14 @@ fn main() -> Result<()> {
             println!("   2. Open graph.json in a D3.js visualization tool");
             println!("   3. Load graph_cytoscape.json in Cytoscape.js");
             println!("   4. Read graph_stats.txt for detailed metrics");
-        }
+        },
         Err(e) => {
             println!("⚠️  Graph visualization not available: {}", e);
             println!("\nNote: Graph visualization is currently only supported for WASM builds.");
             println!("To use visualization:");
             println!("  1. Build with: cargo build --target wasm32-unknown-unknown");
             println!("  2. Or use the WASM-based backend");
-        }
+        },
     }
 
     Ok(())

examples/langchain_rag_pipeline.rs

@@ -11,8 +11,8 @@
 use anyhow::Result;
 use vecstore::error::VecStoreError;
 use vecstore::{
-    print_health_report, Document, HealthChecker, LangChainVectorStore, RetrieverConfig, VecStore,
-    VectorStoreRetriever,
+    Document, HealthChecker, LangChainVectorStore, RetrieverConfig, VecStore, VectorStoreRetriever,
+    print_health_report,
 };
 
 fn main() -> Result<()> {

examples/metadata_indexing_demo.rs

@@ -183,7 +183,9 @@ fn main() -> Result<()> {
     println!("   • Range queries: O(log N) instead of O(N)");
     println!("   • Equality queries: O(1) instead of O(N)");
     println!("   • Compound queries: Fast intersection of index results");
-    println!("   • Potential speedup: orders of magnitude for large datasets (measure on your workload)");
+    println!(
+        "   • Potential speedup: orders of magnitude for large datasets (measure on your workload)"
+    );
 
     println!("\n📝 Use Cases:");
     println!("   • E-commerce: Filter by price, category, rating");

examples/namespace_demo.rs

@@ -201,7 +201,7 @@ fn main() -> anyhow::Result<()> {
     match result {
         Err(e) if e.to_string().contains("not active") => {
             println!("   ✅ Write rejected: {}", e);
-        }
+        },
         _ => println!("   ⚠️  Expected error but got: {:?}", result),
     }
 

examples/openai_basic.rs

@@ -34,7 +34,7 @@ async fn main() -> Result<()> {
             eprintln!("\nPlease set your OpenAI API key:");
             eprintln!("  export OPENAI_API_KEY='your-api-key-here'\n");
             std::process::exit(1);
-        }
+        },
     };
 
     println!("✓ API key found\n");

examples/openai_cost_tracking.rs

@@ -35,7 +35,7 @@ async fn main() -> Result<()> {
             eprintln!("\nPlease set your OpenAI API key:");
             eprintln!("  export OPENAI_API_KEY='your-api-key-here'\n");
             std::process::exit(1);
-        }
+        },
     };
 
     println!("✓ API key found\n");
@@ -378,6 +378,8 @@ fn format_number(n: usize) -> String {
 #[cfg(not(all(feature = "embeddings", feature = "openai-embeddings")))]
 fn main() {
     eprintln!("This example requires the 'embeddings' and 'openai-embeddings' features.");
-    eprintln!("Run with: cargo run --example openai_cost_tracking --features \"embeddings,openai-embeddings\"");
+    eprintln!(
+        "Run with: cargo run --example openai_cost_tracking --features \"embeddings,openai-embeddings\""
+    );
     std::process::exit(1);
 }

examples/openai_rag.rs

@@ -38,21 +38,42 @@ async fn main() -> Result<()> {
             eprintln!("\nPlease set your OpenAI API key:");
             eprintln!("  export OPENAI_API_KEY='your-api-key-here'\n");
             std::process::exit(1);
-        }
+        },
     };
 
     println!("✓ API key found\n");
 
     // 2. Create sample documents to index
     println!("Creating sample documents...");
     let documents = vec![
-        ("rust_intro", "Rust is a systems programming language that runs blazingly fast, prevents segfaults, and guarantees thread safety. It accomplishes these goals without using a garbage collector."),
-        ("rust_ownership", "Rust's ownership system is its most unique feature. The ownership rules ensure memory safety without needing a garbage collector. Each value has a variable that's its owner, and there can only be one owner at a time."),
-        ("rust_cargo", "Cargo is Rust's build system and package manager. It handles building your code, downloading dependencies, and building those dependencies. Most Rustaceans use Cargo to manage their Rust projects."),
-        ("python_intro", "Python is a high-level, interpreted programming language known for its simplicity and readability. It supports multiple programming paradigms and has a comprehensive standard library."),
-        ("python_django", "Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design. It follows the model-template-views architectural pattern and includes an ORM."),
-        ("javascript_intro", "JavaScript is a versatile programming language primarily used for web development. It runs in browsers and enables interactive web pages. Node.js allows JavaScript to run on servers too."),
-        ("vecstore_intro", "VecStore is a high-performance vector database written in Rust. It provides efficient similarity search using HNSW indexing, supports multiple embedding backends, and offers both Rust and Python APIs."),
+        (
+            "rust_intro",
+            "Rust is a systems programming language that runs blazingly fast, prevents segfaults, and guarantees thread safety. It accomplishes these goals without using a garbage collector.",
+        ),
+        (
+            "rust_ownership",
+            "Rust's ownership system is its most unique feature. The ownership rules ensure memory safety without needing a garbage collector. Each value has a variable that's its owner, and there can only be one owner at a time.",
+        ),
+        (
+            "rust_cargo",
+            "Cargo is Rust's build system and package manager. It handles building your code, downloading dependencies, and building those dependencies. Most Rustaceans use Cargo to manage their Rust projects.",
+        ),
+        (
+            "python_intro",
+            "Python is a high-level, interpreted programming language known for its simplicity and readability. It supports multiple programming paradigms and has a comprehensive standard library.",
+        ),
+        (
+            "python_django",
+            "Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design. It follows the model-template-views architectural pattern and includes an ORM.",
+        ),
+        (
+            "javascript_intro",
+            "JavaScript is a versatile programming language primarily used for web development. It runs in browsers and enables interactive web pages. Node.js allows JavaScript to run on servers too.",
+        ),
+        (
+            "vecstore_intro",
+            "VecStore is a high-performance vector database written in Rust. It provides efficient similarity search using HNSW indexing, supports multiple embedding backends, and offers both Rust and Python APIs.",
+        ),
     ];
 
     println!("✓ Created {} documents", documents.len());

examples/performance_monitoring.rs

@@ -7,7 +7,7 @@
 //! - Resource utilization
 
 use anyhow::Result;
-use vecstore::{print_health_report, BenchmarkConfig, Benchmarker, HealthChecker, VecStore};
+use vecstore::{BenchmarkConfig, Benchmarker, HealthChecker, VecStore, print_health_report};
 
 fn main() -> Result<()> {
     println!("📊 VecStore Performance Monitoring Example\n");

examples/phrase_matching_demo.rs

@@ -8,7 +8,6 @@
 //!
 //! Run with: cargo run --example phrase_matching_demo
 
-
 // Mock TextIndex for demonstration (in practice, use VecStore's internal TextIndex)
 fn main() {
     println!("=== VecStore Phrase Matching Demo ===\n");