feat: Qdrant Vector index support (#221)

This PR adds support for Qdrant - https://qdrant.tech/ to be used as a vector memory.

I've unit-tested the methods to confirm that they work as intended.

To run Qdrant

```
docker run -p 6333:6333 qdrant/qdrant
```

llama_stack/providers/adapters/memory/weaviate/weaviate.py

@@ -50,7 +50,9 @@ class WeaviateIndex(EmbeddingIndex):
         # TODO: make this async friendly
         collection.data.insert_many(data_objects)
 
-    async def query(self, embedding: NDArray, k: int) -> QueryDocumentsResponse:
+    async def query(
+        self, embedding: NDArray, k: int, score_threshold: float
+    ) -> QueryDocumentsResponse:
         collection = self.client.collections.get(self.collection_name)
 
         results = collection.query.near_vector(