chore: add huggingface provider and examples

crates/stratum-embeddings/examples/huggingface_usage.rs

@@ -1,8 +1,6 @@
 use std::time::Duration;
 
-use stratum_embeddings::{
+use stratum_embeddings::{EmbeddingOptions, HuggingFaceModel, HuggingFaceProvider, MemoryCache};
-    EmbeddingOptions, HuggingFaceModel, HuggingFaceProvider, MemoryCache,
-};
 use tracing::info;
 
 #[tokio::main]
@@ -48,10 +46,7 @@ async fn main() -> Result<(), Box<dyn std::error::Error>> {
     let provider = HuggingFaceProvider::new(api_key, custom_model)?;
     let embedding = provider.embed(text, &options).await?;
 
-    info!(
+    info!("Custom model embedding: {} dimensions", embedding.len());
-        "Custom model embedding: {} dimensions",
-        embedding.len()
-    );
 
     // Example 4: Batch embeddings (sequential requests to HF API)
     info!("\n4. Batch embedding (sequential API calls)");
@@ -74,9 +69,8 @@ async fn main() -> Result<(), Box<dyn std::error::Error>> {
     // Example 5: Using with cache
     info!("\n5. Demonstrating cache effectiveness");
     let cache = MemoryCache::new(1000, Duration::from_secs(300));
-    let service = stratum_embeddings::EmbeddingService::new(
+    let service = stratum_embeddings::EmbeddingService::new(HuggingFaceProvider::bge_small()?)
-        HuggingFaceProvider::bge_small()?
+        .with_cache(cache);
-    ).with_cache(cache);
 
     let cached_options = EmbeddingOptions::default_with_cache();
 
@@ -91,7 +85,10 @@ async fn main() -> Result<(), Box<dyn std::error::Error>> {
     let _ = service.embed(text, &cached_options).await?;
     let second_duration = start.elapsed();
     info!("Second call (cache hit): {:?}", second_duration);
-    info!("Speedup: {:.2}x", first_duration.as_secs_f64() / second_duration.as_secs_f64());
+    info!(
+        "Speedup: {:.2}x",
+        first_duration.as_secs_f64() / second_duration.as_secs_f64()
+    );
     info!("Cache size: {}", service.cache_size());
 
     // Example 6: Normalized embeddings for similarity search
@@ -117,7 +114,10 @@ async fn main() -> Result<(), Box<dyn std::error::Error>> {
     info!("Query: '{}'", query);
     info!("Similarity with doc1 ('{}'): {:.4}", doc1, sim1);
     info!("Similarity with doc2 ('{}'): {:.4}", doc2, sim2);
-    info!("Most similar: {}", if sim1 > sim2 { "doc1" } else { "doc2" });
+    info!(
+        "Most similar: {}",
+        if sim1 > sim2 { "doc1" } else { "doc2" }
+    );
 
     info!("\n=== Demo Complete ===");
 

crates/stratum-embeddings/src/providers/huggingface.rs

@@ -26,7 +26,8 @@ pub enum HuggingFaceModel {
     BgeLarge,
     /// sentence-transformers/all-MiniLM-L6-v2 - 384 dimensions, fast
     AllMiniLm,
-    /// sentence-transformers/all-mpnet-base-v2 - 768 dimensions, strong baseline
+    /// sentence-transformers/all-mpnet-base-v2 - 768 dimensions, strong
+    /// baseline
     AllMpnet,
     /// Custom model with model ID and dimensions
     Custom(String, usize),
@@ -101,9 +102,13 @@ enum HFResponse {
 
 #[cfg(feature = "huggingface-provider")]
 impl HuggingFaceProvider {
-    const BASE_URL: &'static str = "https://api-inference.huggingface.co/pipeline/feature-extraction";
+    const BASE_URL: &'static str =
+        "https://api-inference.huggingface.co/pipeline/feature-extraction";
 
-    pub fn new(api_key: impl Into<String>, model: HuggingFaceModel) -> Result<Self, EmbeddingError> {
+    pub fn new(
+        api_key: impl Into<String>,
+        model: HuggingFaceModel,
+    ) -> Result<Self, EmbeddingError> {
         let api_key = api_key.into();
         if api_key.is_empty() {
             return Err(EmbeddingError::ConfigError(