Added simple example (#31)

crates/glowrs/examples/simple.rs

@@ -0,0 +1,45 @@
+use glowrs::{Device, Error, PoolingStrategy, SentenceTransformer};
+use std::process::ExitCode;
+
+fn main() -> Result<ExitCode, Error> {
+    let sentences = vec![
+        "The cat sits outside",
+        "A man is playing guitar",
+        "I love pasta",
+        "The new movie is awesome",
+        "The cat plays in the garden",
+        "A woman watches TV",
+        "The new movie is so great",
+        "Do you like pizza?",
+        "The cat sits",
+    ];
+    let device = Device::Cpu;
+    let encoder =
+        SentenceTransformer::from_repo_string("Snowflake/snowflake-arctic-embed-xs", &device)?;
+
+    let pooling_strategy = PoolingStrategy::Mean;
+    let embeddings = encoder.encode_batch(sentences.clone(), false, pooling_strategy)?;
+    println!("Embeddings: {:?}", embeddings);
+
+    let (n_sentences, _) = embeddings.dims2()?;
+    let mut similarities = Vec::with_capacity(n_sentences * (n_sentences - 1) / 2);
+
+    for i in 0..n_sentences {
+        let e_i = embeddings.get(i)?;
+        for j in (i + 1)..n_sentences {
+            let e_j = embeddings.get(j)?;
+            let sum_ij = (&e_i * &e_j)?.sum_all()?.to_scalar::<f32>()?;
+            let sum_i2 = (&e_i * &e_i)?.sum_all()?.to_scalar::<f32>()?;
+            let sum_j2 = (&e_j * &e_j)?.sum_all()?.to_scalar::<f32>()?;
+            let cosine_similarity = sum_ij / (sum_i2 * sum_j2).sqrt();
+            similarities.push((cosine_similarity, i, j))
+        }
+    }
+
+    similarities.sort_by(|u, v| v.0.total_cmp(&u.0));
+    for &(score, i, j) in similarities[..5].iter() {
+        println!("score: {score:.2} '{}' '{}'", sentences[i], sentences[j])
+    }
+
+    Ok(ExitCode::SUCCESS)
+}

crates/glowrs/src/lib.rs

@@ -3,6 +3,7 @@
 mod error;
 mod exports;
 pub mod model;
+
 pub use exports::*;
 
 pub use crate::error::Error;