diff --git a/main.py b/main.py
index ff890b2..9069559 100644
--- a/main.py
+++ b/main.py
@@ -7,6 +7,10 @@ from math import ceil
 
 import chromadb
 
+from chromadb.utils.embedding_functions.ollama_embedding_function import (
+    OllamaEmbeddingFunction,
+)
+
 client = chromadb.EphemeralClient()
 collection = client.create_collection(name="docs")
 
@@ -29,6 +33,11 @@ class Chunk:
 
 class Chunker:
     def __init__(self) -> None:
+        self.embedding_fx = OllamaEmbeddingFunction(
+            url="http://localhost:11434",
+            model_name="mxbai-embed-large",
+        )
+
         pass
 
     def chunk_document(self, document: str, chunk_size: int = 300) -> list[Chunk]:
@@ -47,15 +56,20 @@ class Chunker:
             )
             text_chunk = document[curr_pos:to_pos]
 
+            embedding = self.embedding_fx([text_chunk])
             collection.add(
                 ids=[str(doc_uuid) + ":" + str(i)],
                 documents=[text_chunk],
+                embeddings=embedding,
             )
 
         return chunks
 
 
-# Setup
+embedding_fx = OllamaEmbeddingFunction(
+    url="http://localhost:11434",
+    model_name="mxbai-embed-large",
+)
 
 # Step 1: Get the text
 ppngx = PaperlessNGXService()
@@ -71,8 +85,8 @@ for text in texts:
 
 # Ask
 input = "How many teeth has Simba had removed?"
-response = ollama.embed(model="mxbai-embed-large", input=input)
-results = collection.query(query_texts=[input], n_results=1)
+embeddings = embedding_fx(input=[input])
+results = collection.query(query_texts=[input], query_embeddings=embeddings)
 print(results)
 # Generate
 output = ollama.generate(