fix and refactor python examples (#770)

* fix: fix points selector bugs, refactor code

* fix: fix and refactor embeddings

* fix: fix and refactor frameworks

* refactor: refactor guides

* fix: fix and refactor aleph-alpha tutorial

* fix: fix and refactor tutorials

* refactoring: refactor quick-start

* fix: address review comments

* fix: replace remaining host

qdrant-landing/content/documentation/quick-start.md

@@ -39,7 +39,10 @@ Qdrant is now accessible:
 ```python
 from qdrant_client import QdrantClient
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
+```
+
+```typescript
 ```
 
 ```typescript
@@ -78,7 +81,7 @@ var client = new QdrantClient("localhost", 6334);
 You will be storing all of your vector data in a Qdrant collection. Let's call it `test_collection`. This collection will be using a dot product distance metric to compare vectors.
 
 ```python
-from qdrant_client.http.models import Distance, VectorParams
+from qdrant_client.models import Distance, VectorParams
 
 client.create_collection(
     collection_name="test_collection",
@@ -135,7 +138,7 @@ await client.CreateCollectionAsync(
 Let's now add a few vectors with a payload. Payloads are other data you want to associate with the vector:
 
 ```python
-from qdrant_client.http.models import PointStruct
+from qdrant_client.models import PointStruct
 
 operation_info = client.upsert(
     collection_name="test_collection",
@@ -524,7 +527,7 @@ See [payload and vector in the result](../concepts/search/#payload-and-vector-in
 We can narrow down the results further by filtering by payload. Let's find the closest results that include "London".
 
 ```python
-from qdrant_client.http.models import Filter, FieldCondition, MatchValue
+from qdrant_client.models import Filter, FieldCondition, MatchValue
 
 search_result = client.search(
     collection_name="test_collection",