diff --git a/inference_core/Cargo.toml b/inference_core/Cargo.toml
index d31645c..3e24541 100644
--- a/inference_core/Cargo.toml
+++ b/inference_core/Cargo.toml
@@ -12,6 +12,7 @@ tokenizers = { version = "0.14.1", default-features = false, features = ["progre
 ndarray = "0.15.6"
 
 uniffi = { version = "0.25" }
+uuid = { version = "1.5.0", features = ["v4"] }
 
 [lib]
 crate-type = ["lib", "cdylib"]
diff --git a/inference_core/src/cosine_similarity.rs b/inference_core/src/cosine_similarity.rs
new file mode 100644
index 0000000..c33dea1
--- /dev/null
+++ b/inference_core/src/cosine_similarity.rs
@@ -0,0 +1,33 @@
+use crate::embedding::Embedding;
+use crate::similarity::Similarity;
+
+pub struct CosineSimilarity;
+
+impl Similarity for CosineSimilarity {
+    fn similarity_score(&self, vector_a: &Embedding, vector_b: &Embedding) -> f32 {
+        if vector_a.len() != vector_b.len() {
+            panic!(
+                "Length of vector a ({}) must be equal to the length of vector b ({})",
+                vector_a.len(),
+                vector_b.len()
+            );
+        }
+
+        let dot_product: f32 = vector_a
+            .iter()
+            .zip(vector_b.iter())  // Use vector_b.iter() directly
+            .map(|(a, b)| a * b)
+            .sum();
+
+        let norm_a: f32 = vector_a.iter().map(|x| x * x).sum();
+        let norm_b: f32 = vector_b.iter().map(|x| x * x).sum();
+
+        dot_product / (f32::sqrt(norm_a) * f32::sqrt(norm_b))
+    }
+}
+
+impl CosineSimilarity {
+    pub fn between(embedding: &Embedding, reference_embedding: &Embedding) -> f32 {
+        CosineSimilarity.similarity_score(embedding, reference_embedding)
+    }
+}
diff --git a/inference_core/src/document.rs b/inference_core/src/document.rs
index 4da73fb..54bc0f8 100644
--- a/inference_core/src/document.rs
+++ b/inference_core/src/document.rs
@@ -1,17 +1,26 @@
 use std::collections::HashMap;
 use crate::embedding::Embedding;
 
-struct Document {
+pub struct Document {
     id: String,
     metadata: Metadata,
     text: String,
     vector: Embedding,
 }
 
-struct Metadata {
+pub struct Metadata {
     metadata: HashMap<String, String>,
 }
 
+impl Metadata {
+    pub fn new() -> Self {
+        Self {
+            metadata: HashMap::new(),
+        }
+    }
+}
+
+
 impl Document {
     fn from(string_value: String) -> Self {
         Self {
diff --git a/inference_core/src/embedding_match.rs b/inference_core/src/embedding_match.rs
new file mode 100644
index 0000000..0728ce2
--- /dev/null
+++ b/inference_core/src/embedding_match.rs
@@ -0,0 +1,42 @@
+use std::cmp::Ordering;
+use crate::embedding::Embedding;
+
+#[derive(Debug, Clone)]
+pub struct EmbeddingMatch<Embedded: Clone + Ord> {
+    score: f32,
+    embedding_id: String,
+    embedding: Embedding,
+    embedded: Embedded,
+}
+
+impl<Embedded: Clone + Ord> EmbeddingMatch<Embedded> {
+    pub(crate) fn new(score: f32, embedding_id: String, embedding: Embedding, embedded: Embedded) -> Self {
+        EmbeddingMatch {
+            score,
+            embedding_id,
+            embedding,
+            embedded,
+        }
+    }
+}
+
+impl<Embedded: Clone + Ord> PartialOrd for EmbeddingMatch<Embedded> {
+    fn partial_cmp(&self, other: &Self) -> Option<std::cmp::Ordering> {
+        self.score.partial_cmp(&other.score)
+    }
+}
+
+impl<Embedded: Clone + Ord> PartialEq for EmbeddingMatch<Embedded> {
+    fn eq(&self, other: &Self) -> bool {
+        self.score == other.score
+    }
+}
+
+impl<Embedded: Clone + Ord> Ord for EmbeddingMatch<Embedded> {
+    fn cmp(&self, other: &Self) -> Ordering {
+        self.score.partial_cmp(&other.score).unwrap_or(Ordering::Equal)
+    }
+}
+
+impl<Embedded: Clone + Ord> Eq for EmbeddingMatch<Embedded> {
+}
\ No newline at end of file
diff --git a/inference_core/src/embedding_store.rs b/inference_core/src/embedding_store.rs
new file mode 100644
index 0000000..3a5f09c
--- /dev/null
+++ b/inference_core/src/embedding_store.rs
@@ -0,0 +1,28 @@
+use crate::embedding::Embedding;
+use crate::embedding_match::EmbeddingMatch;
+
+trait EmbeddingStore<Embedded: Clone + Ord> {
+    // 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.
+    fn add_with_id(&mut self, id: String, embedding: Embedding);
+
+    // 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.
+    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.
+    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.
+    fn find_relevant(
+        &self,
+        reference_embedding: Embedding,
+        max_results: usize,
+        min_score: f32,
+    ) -> Vec<EmbeddingMatch<Embedded>>;
+}
diff --git a/inference_core/src/lib.rs b/inference_core/src/lib.rs
index aaa082a..32e7fe0 100644
--- a/inference_core/src/lib.rs
+++ b/inference_core/src/lib.rs
@@ -2,6 +2,11 @@ pub mod embed;
 pub mod memory_store;
 pub mod document;
 pub mod embedding;
+pub mod embedding_store;
+mod embedding_match;
+mod similarity;
+mod relevance_score;
+mod cosine_similarity;
 
 fn hello_name(name: String) -> String {
     format!("Hello {}", name)
diff --git a/inference_core/src/memory_store.rs b/inference_core/src/memory_store.rs
index e69de29..39efea9 100644
--- a/inference_core/src/memory_store.rs
+++ b/inference_core/src/memory_store.rs
@@ -0,0 +1,85 @@
+use std::collections::BinaryHeap;
+
+use crate::cosine_similarity::CosineSimilarity;
+use crate::embedding::Embedding;
+use crate::embedding_match::EmbeddingMatch;
+use crate::relevance_score::RelevanceScore;
+
+
+#[derive(Clone)]
+struct Entry<Embedded: Clone + Ord> {
+    id: String,
+    embedding: Embedding,
+    embedded: Option<Embedded>,
+}
+
+impl<Embedded: Clone + Ord> Entry<Embedded> {
+    fn new(id: String, embedding: Embedding, embedded: Option<Embedded>) -> Self {
+        Entry { id, embedding, embedded }
+    }
+}
+
+pub struct InMemoryEmbeddingStore<Embedded: Clone + Ord> {
+    entries: Vec<Entry<Embedded>>,
+}
+
+// Implement methods for InMemoryEmbeddingStore
+impl<Embedded: Clone + Ord> InMemoryEmbeddingStore<Embedded> {
+    fn new() -> Self {
+        InMemoryEmbeddingStore { entries: Vec::new() }
+    }
+
+    fn add(&mut self, embedding: Embedding) -> String {
+        let id = uuid::Uuid::new_v4().to_string();
+        self.add_with_id(id.clone(), embedding);
+        id
+    }
+
+    fn add_with_id(&mut self, id: String, embedding: Embedding) {
+        self.add_with_embedded(id, embedding, None);
+    }
+
+    fn add_with_embedded(&mut self, id: String, embedding: Embedding, embedded: Option<Embedded>) -> String {
+        let entry = Entry::new(id.clone(), embedding, embedded);
+        self.entries.push(entry);
+        id
+    }
+
+    fn add_all(&mut self, embeddings: Vec<Embedding>) -> Vec<String> {
+        embeddings
+            .into_iter()
+            .map(|embedding| self.add(embedding))
+            .collect()
+    }
+
+    fn add_all_with_embedded(&mut self, embeddings: Vec<Embedding>, embedded: Vec<Embedded>) -> Vec<String> {
+        assert_eq!(embeddings.len(), embedded.len(), "The list of embeddings and embedded must have the same size");
+
+        embeddings
+            .into_iter()
+            .zip(embedded)
+            .map(|(embedding, embedded)| self.add_with_embedded(uuid::Uuid::new_v4().to_string(), embedding, Some(embedded)))
+            .collect()
+    }
+
+    fn find_relevant(&self, reference_embedding: Embedding, max_results: usize, min_score: f32) -> Vec<EmbeddingMatch<Embedded>> {
+        let mut matches = BinaryHeap::new();
+
+        for entry in &self.entries {
+            let cosine_similarity = CosineSimilarity::between(&entry.embedding, &reference_embedding);
+            let score = RelevanceScore::from_cosine_similarity(cosine_similarity);
+
+            if score >= min_score {
+                matches.push(EmbeddingMatch::new(score, entry.id.clone(), entry.embedding.clone(), entry.embedded.clone().unwrap()));
+
+                if matches.len() > max_results {
+                    matches.pop();
+                }
+            }
+        }
+
+        let mut result: Vec<_> = matches.into_sorted_vec();
+        result.reverse();
+        result
+    }
+}
diff --git a/inference_core/src/relevance_score.rs b/inference_core/src/relevance_score.rs
new file mode 100644
index 0000000..2c6a3e3
--- /dev/null
+++ b/inference_core/src/relevance_score.rs
@@ -0,0 +1,7 @@
+pub struct RelevanceScore;
+
+impl RelevanceScore {
+    pub fn from_cosine_similarity(cosine_similarity: f32) -> f32 {
+        (cosine_similarity + 1.0) / 2.0
+    }
+}
diff --git a/inference_core/src/similarity.rs b/inference_core/src/similarity.rs
new file mode 100644
index 0000000..a91777c
--- /dev/null
+++ b/inference_core/src/similarity.rs
@@ -0,0 +1,5 @@
+use crate::embedding::Embedding;
+
+pub trait Similarity {
+    fn similarity_score(&self, set1: &Embedding, set2: &Embedding) -> f32;
+}