diff --git a/rig-fastembed/examples/vector_search_local.rs b/rig-fastembed/examples/vector_search_local.rs
new file mode 100644
index 00000000..794e0b87
--- /dev/null
+++ b/rig-fastembed/examples/vector_search_local.rs
@@ -0,0 +1,105 @@
+use rig::{
+    embeddings::{EmbeddingsBuilder, Embedding},
+    vector_store::{in_memory_store::InMemoryVectorStore, VectorStoreIndex},
+    Embed, OneOrMany,
+};
+use fastembed::{
+    read_file_to_bytes, EmbeddingModel as FastembedModel, InitOptionsUserDefined, TextEmbedding as FastembedTextEmbedding,
+    TokenizerFiles, UserDefinedEmbeddingModel, Pooling,
+};
+use rig_fastembed::{
+    EmbeddingModel
+};
+use serde::{Deserialize, Serialize};
+use std::path::Path;
+use std::sync::Arc;
+
+// Shape of data that needs to be RAG'ed.
+// The definition field will be used to generate embeddings.
+#[derive(Embed, Clone, Deserialize, Debug, Serialize, Eq, PartialEq, Default)]
+struct WordDefinition {
+    id: String,
+    word: String,
+    #[embed]
+    definitions: Vec<String>,
+}
+
+#[tokio::main]
+async fn main() -> Result<(), anyhow::Error> {
+    // Get model info
+    let test_model_info = FastembedTextEmbedding::get_model_info(&FastembedModel::AllMiniLML6V2).unwrap();
+    
+    // Set up model directory
+    let model_dir = Path::new("./models/Qdrant--all-MiniLM-L6-v2-onnx/snapshots");
+    println!("Loading model from: {:?}", model_dir);
+
+    // Load model files
+    let onnx_file = read_file_to_bytes(&model_dir.join("model.onnx"))
+        .expect("Could not read model.onnx file");
+
+    let tokenizer_files = TokenizerFiles {
+        tokenizer_file: read_file_to_bytes(&model_dir.join("tokenizer.json"))
+            .expect("Could not read tokenizer.json"),
+        config_file: read_file_to_bytes(&model_dir.join("config.json"))
+            .expect("Could not read config.json"),
+        special_tokens_map_file: read_file_to_bytes(&model_dir.join("special_tokens_map.json"))
+            .expect("Could not read special_tokens_map.json"),
+        tokenizer_config_file: read_file_to_bytes(&model_dir.join("tokenizer_config.json"))
+            .expect("Could not read tokenizer_config.json"),
+    };
+
+    // Create embedding model
+    let user_defined_model = UserDefinedEmbeddingModel::new(onnx_file, tokenizer_files)
+        .with_pooling(Pooling::Mean);
+
+    let embedding_model = EmbeddingModel::new_from_user_defined(user_defined_model, 384, &test_model_info);
+
+    // Create documents
+    let documents = vec![
+        WordDefinition {
+            id: "doc0".to_string(),
+            word: "flurbo".to_string(),
+            definitions: vec![
+                "A green alien that lives on cold planets.".to_string(),
+                "A fictional digital currency that originated in the animated series Rick and Morty.".to_string()
+            ]
+        },
+        WordDefinition {
+            id: "doc1".to_string(),
+            word: "glarb-glarb".to_string(),
+            definitions: vec![
+                "An ancient tool used by the ancestors of the inhabitants of planet Jiro to farm the land.".to_string(),
+                "A fictional creature found in the distant, swampy marshlands of the planet Glibbo in the Andromeda galaxy.".to_string()
+            ]
+        },
+        WordDefinition {
+            id: "doc2".to_string(),
+            word: "linglingdong".to_string(),
+            definitions: vec![
+                "A term used by inhabitants of the sombrero galaxy to describe humans.".to_string(),
+                "A rare, mystical instrument crafted by the ancient monks of the Nebulon Mountain Ranges on the planet Quarm.".to_string()
+            ]
+        },
+    ];
+
+    // Create embeddings using EmbeddingsBuilder
+    let embeddings = EmbeddingsBuilder::new(embedding_model.clone())
+        .documents(documents)?
+        .build()
+        .await?;
+
+    // Create vector store
+    let vector_store = InMemoryVectorStore::from_documents_with_id_f(embeddings, |doc| doc.id.clone());
+    let index = vector_store.index(embedding_model);
+
+    let results = index
+        .top_n::<WordDefinition>("I need to buy something in a fictional universe. What type of money can I use for this?", 1)
+        .await?
+        .into_iter()
+        .map(|(score, id, doc)| (score, id, doc.word))
+        .collect::<Vec<_>>();
+
+    println!("Results: {:?}", results);
+
+    Ok(())
+}
diff --git a/rig-fastembed/src/lib.rs b/rig-fastembed/src/lib.rs
index 94e5e002..8f2d18d0 100644
--- a/rig-fastembed/src/lib.rs
+++ b/rig-fastembed/src/lib.rs
@@ -1,7 +1,13 @@
 use std::sync::Arc;
 
 pub use fastembed::EmbeddingModel as FastembedModel;
-use fastembed::{InitOptions, TextEmbedding};
+use fastembed::{
+    InitOptions,
+    TextEmbedding,
+    InitOptionsUserDefined,
+    UserDefinedEmbeddingModel,
+    ModelInfo,
+};
 use rig::{
     embeddings::{self, EmbeddingError, EmbeddingsBuilder},
     Embed,
@@ -87,6 +93,21 @@ impl EmbeddingModel {
             ndims,
         }
     }
+
+    pub fn new_from_user_defined(user_defined_model: UserDefinedEmbeddingModel, ndims: usize, model_info: &ModelInfo<FastembedModel>) -> Self {
+        let fastembed_embedding_model = TextEmbedding::try_new_from_user_defined(
+            user_defined_model,
+            InitOptionsUserDefined::default(),
+        ).unwrap();
+
+        let embedder = Arc::new(fastembed_embedding_model);
+
+        Self {
+            embedder,
+            model: model_info.model.to_owned(),
+            ndims,
+        }
+    }
 }
 
 impl embeddings::EmbeddingModel for EmbeddingModel {