docs: add doc for enfer_core

enfer_core/src/document.rs

@@ -23,8 +23,8 @@ impl Metadata {
     }
 }
 
-
 impl Document {
+    /// Creates a new document from a string value.
     pub fn from(string_value: String) -> Self {
         Self {
             id: "".to_string(),
@@ -34,6 +34,16 @@ impl Document {
         }
     }
 
+    /// Creates a new document from a string value and metadata.
+    /// Example:
+    /// ```rust
+    /// use inference_core::{Document, Metadata};
+    ///
+    /// let mut metadata = Metadata::new();
+    /// metadata.metadata.insert("title".to_string(), "Hello world!".to_string());
+    /// let document = Document::from_with_metadata("Hello world!".to_string(), metadata);
+    /// assert_eq!(document.metadata.metadata.get("title").unwrap(), "Hello world!");
+    /// ```
     pub fn from_with_metadata(string_value: String, metadata: Metadata) -> Self {
         Self {
             id: "".to_string(),

enfer_core/src/embedding/semantic.rs

@@ -63,6 +63,16 @@ impl Semantic {
         Ok(semantic)
     }
 
+    /// Embeds a sequence of text into a vector of xxx floats. The xxx floats are the embedding of the sequence.
+    ///
+    /// Example:
+    /// ```rust
+    /// use inference_core::Semantic;
+    ///
+    /// let semantic = Semantic::new();
+    /// let embedding = semantic.embed("Hello world!");
+    /// assert_eq!(embedding.len(), 128); // if the model dimension is 128
+    /// ```
     pub fn embed(&self, sequence: &str) -> Result<Embedding, SemanticError> {
         let encoding = self.tokenizer.encode(sequence, true)
             .map_err(|_| SemanticError::TokenizeEncodeError)?;

enfer_core/src/similarity/cosine_similarity.rs

@@ -3,7 +3,10 @@ use crate::similarity::Similarity;
 
 pub struct CosineSimilarity;
 
+/// Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them.
+/// The cosine of 0° is 1, and it is less than 1 for any other angle.
 impl Similarity for CosineSimilarity {
+    /// Calculates the cosine similarity between two vectors.
     fn similarity_score(&self, vector_a: &Embedding, vector_b: &Embedding) -> f32 {
         if vector_a.len() != vector_b.len() {
             panic!(

enfer_core/src/store/embedding_store.rs

@@ -2,23 +2,23 @@ use crate::embedding::Embedding;
 use crate::similarity::EmbeddingMatch;
 
 pub trait EmbeddingStore<Embedded: Clone + Ord> {
-    // Adds an embedding to the store and returns its unique identifier.
+    /// Adds an embedding to the store and returns its unique identifier.
     fn add(&mut self, embedding: Embedding) -> String;
 
-    // Adds an embedding to the store with a specified identifier.
+    /// Adds an embedding to the store with a specified identifier.
     fn add_with_id(&mut self, id: String, embedding: Embedding);
 
-    // Adds an embedding to the store and associates it with the provided embedded data.
+    /// Adds an embedding to the store and associates it with the provided embedded data.
     fn add_with_embedded(&mut self, embedding: Embedding, embedded: Embedded) -> String;
 
-    // Adds a list of embeddings to the store and returns a list of unique identifiers.
+    /// Adds a list of embeddings to the store and returns a list of unique identifiers.
     fn add_all(&mut self, embeddings: Vec<Embedding>) -> Vec<String>;
 
-    // Adds a list of embeddings to the store and associates them with a list of embedded data.
+    /// Adds a list of embeddings to the store and associates them with a list of embedded data.
     fn add_all_with_embedded(&mut self, embeddings: Vec<Embedding>, embedded: Vec<Embedded>) -> Vec<String>;
 
-    // Find relevant embeddings in the store based on a reference embedding, with a maximum number of results.
-    // An optional minimum score can be specified to filter results.
+    /// Find relevant embeddings in the store based on a reference embedding, with a maximum number of results.
+    /// An optional minimum score can be specified to filter results.
     fn find_relevant(
         &self,
         reference_embedding: Embedding,

enfer_core/src/store/memory_store.rs

@@ -23,6 +23,20 @@ pub struct InMemoryEmbeddingStore {
     entries: Arc<Mutex<Vec<Entry>>>,
 }
 
+/// An in-memory implementation of the EmbeddingStore trait.
+///
+/// Example:
+/// ```rust
+/// use inference_core::{Document, InMemoryEmbeddingStore, Semantic};
+///
+/// let semantic = Semantic::new();
+/// let store = InMemoryEmbeddingStore::new();
+///
+/// let embedding = semantic.embed("Hello world!");
+/// let id = store.add("".to_string(), embedding, Document::from("Hello world!".to_string()));
+/// let matches = store.find_relevant(embedding, 10, 0.0);
+/// assert_eq!(matches.len(), 1);
+/// ```
 impl InMemoryEmbeddingStore {
     pub fn new() -> Self {
         InMemoryEmbeddingStore { entries: Arc::new(Mutex::new(vec![])) }