diff --git a/endpoints/OAI/utils/embeddings.py b/endpoints/OAI/utils/embeddings.py
index cf5b799..1ce611c 100644
--- a/endpoints/OAI/utils/embeddings.py
+++ b/endpoints/OAI/utils/embeddings.py
@@ -7,37 +7,41 @@ typing/pydantic classes moved into this file,
 embeddings function declared async.
 """
 
-import asyncio
-import os
 import base64
 import pathlib
-from loguru import logger
 import numpy as np
-from transformers import AutoModel
+from loguru import logger
 
 from common import config
-from common.utils import unwrap
 from endpoints.OAI.types.embedding import (
     EmbeddingObject,
     EmbeddingsRequest,
     EmbeddingsResponse,
+    UsageInfo,
 )
 
+# Conditionally import infinity embeddings engine
+# Required so the logger doesn't take over tabby's logging handlers
+try:
+    from infinity_emb import EngineArgs, AsyncEmbeddingEngine
+
+    has_infinity_emb = True
+except ImportError:
+    has_infinity_emb = False
+
 
 embeddings_model = None
 
 
 def load_embedding_model(model_path: pathlib.Path, device: str):
-    try:
-        from infinity_emb import EngineArgs, AsyncEmbeddingEngine
-    except ModuleNotFoundError:
+    if not has_infinity_emb:
         logger.error(
             "Skipping embeddings because infinity-emb is not installed.\n"
             "Please run the following command in your environment "
             "to install extra packages:\n"
             "pip install -U .[extras]"
         )
-        raise ModuleNotFoundError from None
+        raise ModuleNotFoundError
 
     global embeddings_model
     try:
@@ -76,30 +80,22 @@ async def embeddings(data: EmbeddingsRequest) -> dict:
         embeddings, usage = await embeddings_model.embed(data.input)
 
         # OAI expects a return of base64 if the input is base64
-        if data.encoding_format == "base64":
-            embedding_data = [
-                {
-                    "object": "embedding",
-                    "embedding": float_list_to_base64(emb),
-                    "index": n,
-                }
-                for n, emb in enumerate(embeddings)
-            ]
-        else:
-            embedding_data = [
-                {"object": "embedding", "embedding": emb.tolist(), "index": n}
-                for n, emb in enumerate(embeddings)
-            ]
+        embedding_data = [
+            EmbeddingObject(
+                embedding=float_list_to_base64(emb)
+                if data.encoding_format == "base64"
+                else emb.tolist(),
+                index=n,
+            )
+            for n, emb in enumerate(embeddings)
+        ]
+
+        response = EmbeddingsResponse(
+            data=embedding_data,
+            model=model_path.name,
+            usage=UsageInfo(prompt_tokens=usage, total_tokens=usage),
+        )
 
-        response = {
-            "object": "list",
-            "data": embedding_data,
-            "model": model_path.name,
-            "usage": {
-                "prompt_tokens": usage,
-                "total_tokens": usage,
-            },
-        }
         return response