diff --git a/semantic_router/index/qdrant.py b/semantic_router/index/qdrant.py
index 531eb345b561ce884f9a96829bb7a7906211c6a3..cc1ddaefe702dd56e22330db48516abbe4387f17 100644
--- a/semantic_router/index/qdrant.py
+++ b/semantic_router/index/qdrant.py
@@ -5,7 +5,7 @@ from pydantic.v1 import Field
 
 from semantic_router.index.base import BaseIndex
 
-DEFAULT_COLLECTION_NAME = "semantic-router-collection"
+DEFAULT_COLLECTION_NAME = "semantic-router-index"
 DEFAULT_UPLOAD_BATCH_SIZE = 100
 SCROLL_SIZE = 1000
 SR_UTTERANCE_PAYLOAD_KEY = "sr_utterance"
@@ -15,7 +15,7 @@ SR_ROUTE_PAYLOAD_KEY = "sr_route"
 class QdrantIndex(BaseIndex):
     "The name of the collection to use"
 
-    collection_name: str = Field(
+    index_name: str = Field(
         default=DEFAULT_COLLECTION_NAME,
         description=f"The name of the Qdrant collection to use. Defaults to '{DEFAULT_COLLECTION_NAME}'",
     )
@@ -67,11 +67,11 @@ class QdrantIndex(BaseIndex):
         default=None,
         description="Options to be passed to the low-level Qdrant GRPC client, if used.",
     )
-    size: Union[int, None] = Field(
+    dimensions: Union[int, None] = Field(
         default=None,
         description="Embedding dimensions. Defaults to the embedding length of the configured encoder.",
     )
-    distance: str = Field(
+    metric: str = Field(
         default="Cosine", description="Distance metric to use for similarity search."
     )
     collection_options: Optional[Dict[str, Any]] = Field(
@@ -115,17 +115,17 @@ class QdrantIndex(BaseIndex):
         from qdrant_client import QdrantClient, models
 
         self.client: QdrantClient
-        if not self.client.collection_exists(self.collection_name):
+        if not self.client.collection_exists(self.index_name):
             if not self.dimensions:
                 raise ValueError(
                     "Cannot create a collection without specifying the dimensions."
                 )
 
             self.client.create_collection(
-                collection_name=self.collection_name,
+                collection_name=self.index_name,
                 vectors_config=models.VectorParams(
                     size=self.dimensions,
-                    distance=self.distance,  # type: ignore
+                    distance=self.metric,  # type: ignore
                 ),
                 **self.collection_options,
             )
@@ -147,7 +147,7 @@ class QdrantIndex(BaseIndex):
 
         # UUIDs are autogenerated by qdrant-client if not provided explicitly
         self.client.upload_collection(
-            self.collection_name,
+            self.index_name,
             vectors=embeddings,
             payload=payloads,
             batch_size=batch_size,
@@ -168,7 +168,7 @@ class QdrantIndex(BaseIndex):
         stop_scrolling = False
         while not stop_scrolling:
             records, next_offset = self.client.scroll(
-                self.collection_name,
+                self.index_name,
                 limit=SCROLL_SIZE,
                 offset=next_offset,
                 with_payload=True,
@@ -191,7 +191,7 @@ class QdrantIndex(BaseIndex):
         from qdrant_client import models
 
         self.client.delete(
-            self.collection_name,
+            self.index_name,
             points_selector=models.Filter(
                 must=[
                     models.FieldCondition(
@@ -203,7 +203,7 @@ class QdrantIndex(BaseIndex):
         )
 
     def describe(self) -> dict:
-        collection_info = self.client.get_collection(self.collection_name)
+        collection_info = self.client.get_collection(self.index_name)
 
         return {
             "type": self.type,
@@ -213,14 +213,14 @@ class QdrantIndex(BaseIndex):
 
     def query(self, vector: np.ndarray, top_k: int = 5) -> Tuple[np.ndarray, List[str]]:
         results = self.client.search(
-            self.collection_name, query_vector=vector, limit=top_k, with_payload=True
+            self.index_name, query_vector=vector, limit=top_k, with_payload=True
         )
         scores = [result.score for result in results]
         route_names = [result.payload["sr_route"] for result in results]
         return np.array(scores), route_names
 
     def delete_index(self):
-        self.client.delete_collection(self.collection_name)
+        self.client.delete_collection(self.index_name)
 
     def __len__(self):
-        return self.client.get_collection(self.collection_name).points_count
+        return self.client.get_collection(self.index_name).points_count