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/concepts/collections.md

@@ -63,10 +63,9 @@ curl -X PUT http://localhost:6333/collections/test_collection1 \
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",
@@ -195,9 +194,8 @@ curl -X PUT http://localhost:6333/collections/test_collection2 \
 
 ```python
 from qdrant_client import QdrantClient
-from qdrant_client.http import models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",
@@ -325,10 +323,10 @@ curl -X PUT http://localhost:6333/collections/test_collection3 \
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",
@@ -487,10 +485,9 @@ curl -X PUT http://localhost:6333/collections/test_collection4 \
 
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",
@@ -1419,7 +1416,7 @@ curl -X GET http://localhost:6333/collections/test_collection2/aliases
 ```python
 from qdrant_client import QdrantClient
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.get_collection_aliases(collection_name="{collection_name}")
 ```
@@ -1472,7 +1469,7 @@ curl -X GET http://localhost:6333/aliases
 ```python
 from qdrant_client import QdrantClient
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.get_aliases()
 ```
@@ -1525,7 +1522,7 @@ curl -X GET http://localhost:6333/collections
 ```python
 from qdrant_client import QdrantClient
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.get_collections()
 ```

qdrant-landing/content/documentation/concepts/explore.md

@@ -36,10 +36,9 @@ POST /collections/{collection_name}/points/recommend
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.recommend(
     collection_name="{collection_name}",
@@ -455,12 +454,11 @@ POST /collections/{collection_name}/points/recommend/batch
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
-filter = models.Filter(
+filter_ = models.Filter(
     must=[
         models.FieldCondition(
             key="city",
@@ -473,9 +471,9 @@ filter = models.Filter(
 
 recommend_queries = [
     models.RecommendRequest(
-        positive=[100, 231], negative=[718], filter=filter, limit=3
+        positive=[100, 231], negative=[718], filter=filter_, limit=3
     ),
-    models.RecommendRequest(positive=[200, 67], negative=[300], filter=filter, limit=3),
+    models.RecommendRequest(positive=[200, 67], negative=[300], filter=filter_, limit=3),
 ]
 
 client.recommend_batch(collection_name="{collection_name}", requests=recommend_queries)
@@ -703,10 +701,9 @@ POST /collections/{collection_name}/points/discover
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 discover_queries = [
     models.DiscoverRequest(
@@ -906,10 +903,9 @@ POST /collections/{collection_name}/points/discover
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 discover_queries = [
     models.DiscoverRequest(

qdrant-landing/content/documentation/concepts/filtering.md

@@ -52,10 +52,9 @@ POST /collections/{collection_name}/points/scroll
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient(host="localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.scroll(
     collection_name="{collection_name}",
@@ -729,7 +728,7 @@ Example:
 ```
 
 ```python
-FieldCondition(
+models.FieldCondition(
     key="color",
     match=models.MatchAny(any=["black", "yellow"]),
 )
@@ -785,7 +784,7 @@ Example:
 ```
 
 ```python
-FieldCondition(
+models.FieldCondition(
     key="color",
     match=models.MatchExcept(**{"except": ["black", "yellow"]}),
 )

qdrant-landing/content/documentation/concepts/indexing.md

@@ -36,7 +36,7 @@ PUT /collections/{collection_name}/index
 ```python
 from qdrant_client import QdrantClient
 
-client = QdrantClient(host="localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_payload_index(
     collection_name="{collection_name}",
@@ -141,10 +141,9 @@ PUT /collections/{collection_name}/index
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient(host="localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_payload_index(
     collection_name="{collection_name}",
@@ -316,16 +315,15 @@ PUT /collections/{collection_name}/index
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient(host="localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_payload_index(
     collection_name="{collection_name}",
     field_name="name_of_the_field_to_index",
     field_schema=models.IntegerIndexParams(
-        type="integer",
+        type=models.IntegerIndexType.INTEGER,
         lookup=False,
         range=True,
     ),

qdrant-landing/content/documentation/concepts/payload.md

@@ -200,10 +200,9 @@ PUT /collections/{collection_name}/points
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient(host="localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.upsert(
     collection_name="{collection_name}",
@@ -768,9 +767,7 @@ POST /collections/{collection_name}/points/payload/clear
 ```python
 client.clear_payload(
     collection_name="{collection_name}",
-    points_selector=models.PointIdsList(
-        points=[0, 3, 100],
-    ),
+    points_selector=[0, 3, 100],
 )
 ```
 

qdrant-landing/content/documentation/concepts/points.md

@@ -82,10 +82,9 @@ PUT /collections/{collection_name}/points
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.upsert(
     collection_name="{collection_name}",
@@ -1212,9 +1211,7 @@ POST /collections/{collection_name}/points/vectors/delete
 ```python
 client.delete_vectors(
     collection_name="{collection_name}",
-    points_selector=models.PointIdsList(
-        points=[0, 3, 100],
-    ),
+    points=[0, 3, 100],
     vectors=["text", "image"],
 )
 ```
@@ -1955,7 +1952,7 @@ POST /collections/{collection_name}/points/batch
 
 ```python
 client.batch_update_points(
-    collection_name=collection_name,
+    collection_name="{collection_name}",
     update_operations=[
         models.UpsertOperation(
             upsert=models.PointsList(

qdrant-landing/content/documentation/concepts/search.md

@@ -83,10 +83,9 @@ POST /collections/{collection_name}/points/search
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.search(
     collection_name="{collection_name}",
@@ -250,9 +249,8 @@ POST /collections/{collection_name}/points/search
 
 ```python
 from qdrant_client import QdrantClient
-from qdrant_client.http import models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.search(
     collection_name="{collection_name}",
@@ -358,10 +356,9 @@ POST /collections/{collection_name}/points/search
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.search(
     collection_name="{collection_name}",
@@ -562,9 +559,8 @@ POST /collections/{collection_name}/points/search
 
 ```python
 from qdrant_client import QdrantClient
-from qdrant_client.http import models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.search(
     collection_name="{collection_name}",
@@ -656,10 +652,9 @@ POST /collections/{collection_name}/points/search
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.search(
     collection_name="{collection_name}",
@@ -808,12 +803,11 @@ POST /collections/{collection_name}/points/search/batch
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
-filter = models.Filter(
+filter_ = models.Filter(
     must=[
         models.FieldCondition(
             key="city",
@@ -825,8 +819,8 @@ filter = models.Filter(
 )
 
 search_queries = [
-    models.SearchRequest(vector=[0.2, 0.1, 0.9, 0.7], filter=filter, limit=3),
-    models.SearchRequest(vector=[0.5, 0.3, 0.2, 0.3], filter=filter, limit=3),
+    models.SearchRequest(vector=[0.2, 0.1, 0.9, 0.7], filter=filter_, limit=3),
+    models.SearchRequest(vector=[0.5, 0.3, 0.2, 0.3], filter=filter_, limit=3),
 ]
 
 client.search_batch(collection_name="{collection_name}", requests=search_queries)
@@ -1003,7 +997,7 @@ POST /collections/{collection_name}/points/search
 ```python
 from qdrant_client import QdrantClient
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.search(
     collection_name="{collection_name}",
@@ -1185,7 +1179,7 @@ POST /collections/{collection_name}/points/search/groups
 client.search_groups(
     collection_name="{collection_name}",
     # Same as in the regular search() API
-    query_vector=g,
+    query_vector=[1.1],
     # Grouping parameters
     group_by="document_id",  # Path of the field to group by
     limit=4,  # Max amount of groups

qdrant-landing/content/documentation/concepts/snapshots.md

@@ -51,7 +51,7 @@ POST /collections/{collection_name}/snapshots
 ```python
 from qdrant_client import QdrantClient
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_snapshot(collection_name="{collection_name}")
 ```
@@ -103,7 +103,7 @@ DELETE /collections/{collection_name}/snapshots/{snapshot_name}
 ```python
 from qdrant_client import QdrantClient
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.delete_snapshot(
     collection_name="{collection_name}", snapshot_name="{snapshot_name}"
@@ -155,7 +155,7 @@ GET /collections/{collection_name}/snapshots
 ```python
 from qdrant_client import QdrantClient
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.list_snapshots(collection_name="{collection_name}")
 ```
@@ -241,7 +241,7 @@ PUT /collections/{collection_name}/snapshots/recover
 ```python
 from qdrant_client import QdrantClient
 
-client = QdrantClient("qdrant-node-2", port=6333)
+client = QdrantClient(url="http://qdrant-node-2:6333")
 
 client.recover_snapshot(
     "{collection_name}",
@@ -326,7 +326,7 @@ PUT /collections/{collection_name}/snapshots/recover
 ```python
 from qdrant_client import QdrantClient, models
 
-client = QdrantClient("qdrant-node-2", port=6333)
+client = QdrantClient(url="http://qdrant-node-2:6333")
 
 client.recover_snapshot(
     "{collection_name}",
@@ -371,7 +371,7 @@ POST /snapshots
 ```python
 from qdrant_client import QdrantClient
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_full_snapshot()
 ```
@@ -421,7 +421,7 @@ DELETE /snapshots/{snapshot_name}
 ```python
 from qdrant_client import QdrantClient
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.delete_full_snapshot(snapshot_name="{snapshot_name}")
 ```

qdrant-landing/content/documentation/concepts/storage.md

@@ -56,7 +56,7 @@ PUT /collections/{collection_name}
 ```python
 from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",
@@ -168,7 +168,7 @@ PUT /collections/{collection_name}
 ```python
 from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",
@@ -293,7 +293,7 @@ PUT /collections/{collection_name}
 ```python
 from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",

qdrant-landing/content/documentation/embeddings/aleph-alpha.md

@@ -17,6 +17,7 @@ be done in the following way:
 
 ```python
 import qdrant_client
+from qdrant_client.models import Batch
 
 from aleph_alpha_client import (
     Prompt,
@@ -25,7 +26,6 @@ from aleph_alpha_client import (
     SemanticRepresentation,
     ImagePrompt
 )
-from qdrant_client.http.models import Batch
 
 aa_token = "<< your_token >>"
 model = "luminous-base"

qdrant-landing/content/documentation/embeddings/bedrock.md

@@ -35,7 +35,7 @@ bedrock_client = session.client(
     aws_secret_access_key="<YOUR_AWS_SECRET_KEY>",
 )
 
-qdrant_client = QdrantClient(location="http://localhost:6333")
+qdrant_client = QdrantClient(url="http://localhost:6333")
 
 qdrant_client.create_collection(
     "{collection_name}",

qdrant-landing/content/documentation/embeddings/cohere.md

@@ -18,8 +18,7 @@ The embeddings returned by co.embed API might be used directly in the Qdrant cli
 ```python
 import cohere
 import qdrant_client
-
-from qdrant_client.http.models import Batch
+from qdrant_client.models import Batch
 
 cohere_client = cohere.Client("<< your_api_key >>")
 qdrant_client = qdrant_client.QdrantClient()
@@ -55,12 +54,11 @@ documents with the Embed v3 model:
 ```python
 import cohere
 import qdrant_client
-
-from qdrant_client.http.models import Batch
+from qdrant_client.models import Batch
 
 cohere_client = cohere.Client("<< your_api_key >>")
-qdrant_client = qdrant_client.QdrantClient()
-qdrant_client.upsert(
+client = qdrant_client.QdrantClient()
+client.upsert(
     collection_name="MyCollection",
     points=Batch(
         ids=[1],
@@ -76,9 +74,9 @@ qdrant_client.upsert(
 Once the documents are indexed, you can search for the most relevant documents using the Embed v3 model:
 
 ```python
-qdrant_client.search(
+client.search(
     collection_name="MyCollection",
-    query=cohere_client.embed(
+    query_vector=cohere_client.embed(
         model="embed-english-v3.0",  # New Embed v3 model
         input_type="search_query",  # Input type for search queries
         texts=["The best vector database"],

qdrant-landing/content/documentation/embeddings/gemini.md

@@ -43,11 +43,13 @@ The following example shows how to embed a document with the `models/embedding-0
 ```python
 import google.generativeai as gemini_client
 from qdrant_client import QdrantClient
-from qdrant_client.http.models import Distance, PointStruct, VectorParams
+from qdrant_client.models import Distance, PointStruct, VectorParams
+
 collection_name = "example_collection"
 
 GEMINI_API_KEY = "YOUR GEMINI API KEY"  # add your key here
 
+client = QdrantClient(url="http://localhost:6333")
 gemini_client.configure(api_key=GEMINI_API_KEY)
 texts = [
     "Qdrant is a vector database that is compatible with Gemini.",
@@ -83,7 +85,7 @@ points = [
 ### Create Collection
 
 ```python
-search_client.create_collection(collection_name, vectors_config=
+client.create_collection(collection_name, vectors_config=
     VectorParams(
         size=768,
         distance=Distance.COSINE,
@@ -94,7 +96,7 @@ search_client.create_collection(collection_name, vectors_config=
 ### Add these into the collection
 
 ```python
-search_client.upsert(collection_name, points)
+client.upsert(collection_name, points)
 ```
 
 ## Searching for documents with Qdrant
@@ -102,7 +104,7 @@ search_client.upsert(collection_name, points)
 Once the documents are indexed, you can search for the most relevant documents using the same model with the `retrieval_query` task type:
 
 ```python
-search_client.search(
+client.search(
     collection_name=collection_name,
     query_vector=gemini_client.embed_content(
         model="models/embedding-001",

qdrant-landing/content/documentation/embeddings/jina-embeddings.md

@@ -16,8 +16,7 @@ To call their endpoint, all you need is an API key obtainable [here](https://jin
 import qdrant_client
 import requests
 
-from qdrant_client.http.models import Distance, VectorParams
-from qdrant_client.http.models import Batch
+from qdrant_client.models import Distance, VectorParams, Batch
 
 # Provide Jina API key and choose one of the available models.
 # You can get a free trial key here: https://jina.ai/embeddings/
@@ -43,8 +42,8 @@ embeddings = [d["embedding"] for d in response.json()["data"]]
 
 
 # Index the embeddings into Qdrant
-qdrant_client = qdrant_client.QdrantClient(":memory:")
-qdrant_client.create_collection(
+client = qdrant_client.QdrantClient(":memory:")
+client.create_collection(
     collection_name="MyCollection",
     vectors_config=VectorParams(size=EMBEDDING_SIZE, distance=Distance.DOT),
 )

qdrant-landing/content/documentation/embeddings/mistral.md

@@ -22,11 +22,12 @@ And then we set this up:
 ```python
 from mistralai.client import MistralClient
 from qdrant_client import QdrantClient
-from qdrant_client.http.models import PointStruct, VectorParams, Distance
+from qdrant_client.models import PointStruct, VectorParams, Distance
+
 collection_name = "example_collection"
 
 MISTRAL_API_KEY = "your_mistral_api_key"
-search_client = QdrantClient(":memory:")
+client = QdrantClient(":memory:")
 mistral_client = MistralClient(api_key=MISTRAL_API_KEY)
 texts = [
     "Qdrant is the best vector search engine!",
@@ -65,13 +66,12 @@ points = [
 ## Create a collection and Insert the documents
 
 ```python
-search_client.create_collection(collection_name, vectors_config=
-    VectorParams(
+client.create_collection(collection_name, vectors_config=VectorParams(
         size=1024,
         distance=Distance.COSINE,
     )
 )
-search_client.upsert(collection_name, points)
+client.upsert(collection_name, points)
 ```
 
 ## Searching for documents with Qdrant
@@ -79,7 +79,7 @@ search_client.upsert(collection_name, points)
 Once the documents are indexed, you can search for the most relevant documents using the same model with the `retrieval_query` task type:
 
 ```python
-search_client.search(
+client.search(
     collection_name=collection_name,
     query_vector=mistral_client.embeddings(
         model="mistral-embed", input=["What is the best to use for vector search scaling?"]

qdrant-landing/content/documentation/embeddings/nomic.md

@@ -30,8 +30,8 @@ output = embed.text(
     task_type="search_document",
 )
 
-qdrant_client = QdrantClient()
-qdrant_client.upsert(
+client = QdrantClient()
+client.upsert(
     collection_name="my-collection",
     points=models.Batch(
         ids=[1],
@@ -44,14 +44,14 @@ qdrant_client.upsert(
 
 ```python
 from fastembed import TextEmbedding
-from qdrant_client import QdrantClient, models
+from client import QdrantClient, models
 
 model = TextEmbedding("nomic-ai/nomic-embed-text-v1")
 
 output = model.embed(["Qdrant is the best vector database!"])
 
-qdrant_client = QdrantClient()
-qdrant_client.upsert(
+client = QdrantClient()
+client.upsert(
     collection_name="my-collection",
     points=models.Batch(
         ids=[1],
@@ -71,7 +71,7 @@ output = embed.text(
     task_type="search_query",
 )
 
-qdrant_client.search(
+client.search(
     collection_name="my-collection",
     query_vector=output["embeddings"][0],
 )
@@ -82,7 +82,7 @@ qdrant_client.search(
 ```python
 output = next(model.embed("What is the best vector database?"))
 
-qdrant_client.search(
+client.search(
     collection_name="my-collection",
     query_vector=output.tolist(),
 )

qdrant-landing/content/documentation/embeddings/nvidia.md

@@ -21,7 +21,7 @@ NVIDIA_API_KEY = "<YOUR_API_KEY>"
 
 nvidia_session = requests.Session()
 
-qdrant_client = QdrantClient(":memory:")
+client = QdrantClient(":memory:")
 
 headers = {
     "Authorization": f"Bearer {NVIDIA_API_KEY}",
@@ -89,7 +89,7 @@ let response_body = await response.json()
 ### Converting the model outputs to Qdrant points
 
 ```python
-from qdrant_client.http.models import PointStruct
+from qdrant_client.models import PointStruct
 
 points = [
     PointStruct(
@@ -120,14 +120,14 @@ from qdrant_client.models import VectorParams, Distance
 
 collection_name = "example_collection"
 
-qdrant_client.create_collection(
+client.create_collection(
     collection_name,
     vectors_config=VectorParams(
         size=1024,
         distance=Distance.COSINE,
     ),
 )
-qdrant_client.upsert(collection_name, points)
+client.upsert(collection_name, points)
 ```
 
 ```typescript
@@ -161,7 +161,7 @@ response_body = nvidia_session.post(
     NVIDIA_BASE_URL, headers=headers, json=payload
 ).json()
 
-qdrant_client.search(
+client.search(
     collection_name=collection_name,
     query_vector=response_body["data"][0]["embedding"],
 )

qdrant-landing/content/documentation/embeddings/openai.md

@@ -24,7 +24,7 @@ openai_client = openai.Client(
     api_key="<YOUR_API_KEY>"
 )
 
-qdrant_client = qdrant_client.QdrantClient(":memory:")
+client = qdrant_client.QdrantClient(":memory:")
 
 texts = [
     "Qdrant is the best vector search engine!",
@@ -39,13 +39,13 @@ The following example shows how to embed a document with the `text-embedding-3-s
 ```python
 embedding_model = "text-embedding-3-small"
 
-result = openai_client.embeddings.create(input= texts, model=embedding_model)
+result = openai_client.embeddings.create(input=texts, model=embedding_model)
 ```
 
 ### Converting the model outputs to Qdrant points
 
 ```python
-from qdrant_client.http.models import PointStruct
+from qdrant_client.models import PointStruct
 
 points = [
     PointStruct(
@@ -60,18 +60,18 @@ points = [
 ### Creating a collection to insert the documents
 
 ```python
-from qdrant_client.http.models import VectorParams, Distance
+from qdrant_client.models import VectorParams, Distance
 
 collection_name = "example_collection"
 
-qdrant_client.create_collection(
+client.create_collection(
     collection_name,
     vectors_config=VectorParams(
         size=1536,
         distance=Distance.COSINE,
     ),
 )
-qdrant_client.upsert(collection_name, points)
+client.upsert(collection_name, points)
 ```
 
 ## Searching for documents with Qdrant
@@ -79,7 +79,7 @@ qdrant_client.upsert(collection_name, points)
 Once the documents are indexed, you can search for the most relevant documents using the same model.
 
 ```python
-qdrant_client.search(
+client.search(
     collection_name=collection_name,
     query_vector=openai_client.embeddings.create(
         input=["What is the best to use for vector search scaling?"],

qdrant-landing/content/documentation/frameworks/airflow.md

@@ -69,7 +69,7 @@ ids = [32, 21, "b626f6a9-b14d-4af9-b7c3-43d8deb719a6"]
 payload = [{"meta": "data"}, {"meta": "data_2"}, {"meta": "data_3", "extra": "data"}]
 
 QdrantIngestOperator(
-    conn_id="qdrant_connection"
+    conn_id="qdrant_connection",
     task_id="qdrant_ingest",
     collection_name="<COLLECTION_NAME>",
     vectors=vectors,

qdrant-landing/content/documentation/frameworks/autogen.md

@@ -68,7 +68,7 @@ assistant = RetrieveAssistantAgent(
 # `chunk_token_size` is the chunk token size for the retrieve chat.
 # We use an in-memory QdrantClient instance here. Not recommended for production.
 
-ragproxyagent = QdrantRetrieveUserProxyAgent(
+rag_proxy_agent = QdrantRetrieveUserProxyAgent(
     name="qdrantagent",
     human_input_mode="NEVER",
     max_consecutive_auto_reply=10,
@@ -95,7 +95,7 @@ assistant.reset()
 
 # The query used below is for demonstration. It should usually be related to the docs made available to the agent
 code_problem = "How can I use FLAML to perform a classification task?"
-ragproxyagent.initiate_chat(assistant, problem=code_problem)
+rag_proxy_agent.initiate_chat(assistant, problem=code_problem)
 ```
 
 ## Next steps

qdrant-landing/content/documentation/frameworks/haystack.md

@@ -27,7 +27,7 @@ Scalar Quantization, you'd make that in the following way:
 
 ```python
 from qdrant_haystack.document_stores import QdrantDocumentStore
-from qdrant_client.http import models
+from qdrant_client import models
 
 document_store = QdrantDocumentStore(
     ":memory:",

qdrant-landing/content/documentation/frameworks/langchain.md

@@ -68,7 +68,8 @@ client is destroyed - usually at the end of your script/notebook.
 
 ```python
 qdrant = Qdrant.from_documents(
-    docs, embeddings, 
+    docs, 
+    embeddings, 
     location=":memory:",  # Local mode with in-memory storage only
     collection_name="my_documents",
 )
@@ -80,7 +81,8 @@ Local mode, without using the Qdrant server, may also store your vectors on disk
 
 ```python
 qdrant = Qdrant.from_documents(
-    docs, embeddings, 
+    docs, 
+    embeddings, 
     path="/tmp/local_qdrant",
     collection_name="my_documents",
 )

qdrant-landing/content/documentation/frameworks/pandas-ai.md

@@ -62,7 +62,8 @@ from pandasai.ee.vectorstores.qdrant import Qdrant
 qdrant = Qdrant(
     collection_name="<SOME_COLLECTION>",
     embedding_model="sentence-transformers/all-MiniLM-L6-v2",
-    location="http://localhost:6334",
+    url="http://localhost:6333",
+    grpc_port=6334,
     prefer_grpc=True
 )
 

qdrant-landing/content/documentation/frameworks/unstructured.md

@@ -38,7 +38,7 @@ unstructured-ingest \
   --verbose \
   qdrant \
   --collection-name "test" \
-  --location "http://localhost:6333" \
+  --url "http://localhost:6333" \
   --batch-size 80
 ```
 
@@ -66,7 +66,7 @@ from unstructured.ingest.runner.writers.qdrant import QdrantWriter
 def get_writer() -> Writer:
     return QdrantWriter(
         connector_config=SimpleQdrantConfig(
-            location="http://localhost:6333",
+            url="http://localhost:6333",
             collection_name="test",
         ),
         write_config=QdrantWriteConfig(batch_size=80),

qdrant-landing/content/documentation/guides/distributed_deployment.md

@@ -186,10 +186,9 @@ PUT /collections/{collection_name}
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",
@@ -337,10 +336,9 @@ PUT /collections/{collection_name}
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",
@@ -445,10 +443,9 @@ PUT /collections/{collection_name}/points
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.upsert(
     collection_name="{collection_name}",
@@ -662,10 +659,9 @@ PUT /collections/{collection_name}
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",
@@ -913,10 +909,9 @@ PUT /collections/{collection_name}
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",
@@ -1209,7 +1204,7 @@ client.upsert(
             [0.1, 0.1, 0.9],
         ],
     ),
-    ordering="strong",
+    ordering=models.WriteOrdering.STRONG,
 )
 ```
 

qdrant-landing/content/documentation/guides/multiple-partitions.md

@@ -221,7 +221,7 @@ POST /collections/{collection_name}/points/search
 ```python
 from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.search(
     collection_name="{collection_name}",
@@ -342,7 +342,7 @@ PUT /collections/{collection_name}
 ```python
 from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",

qdrant-landing/content/documentation/guides/optimize.md

@@ -42,10 +42,9 @@ PUT /collections/{collection_name}
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",
@@ -196,10 +195,9 @@ POST /collections/{collection_name}/points/search
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.search(
     collection_name="{collection_name}",
@@ -316,7 +314,7 @@ PUT /collections/{collection_name}
 ```python
 from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",
@@ -467,10 +465,9 @@ PUT /collections/{collection_name}
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",
@@ -620,7 +617,7 @@ POST /collections/{collection_name}/points/search
 ```python
 from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.search(
     collection_name="{collection_name}",
@@ -734,7 +731,7 @@ PUT /collections/{collection_name}
 ```python
 from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",
@@ -852,7 +849,7 @@ PUT /collections/{collection_name}
 ```python
 from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",

qdrant-landing/content/documentation/guides/quantization.md

@@ -167,10 +167,9 @@ PUT /collections/{collection_name}
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",
@@ -335,10 +334,9 @@ PUT /collections/{collection_name}
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",
@@ -475,10 +473,9 @@ PUT /collections/{collection_name}
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",
@@ -630,10 +627,9 @@ POST /collections/{collection_name}/points/search
 ```
 
 ```python
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
+from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.search(
     collection_name="{collection_name}",
@@ -784,7 +780,7 @@ POST /collections/{collection_name}/points/search
 ```python
 from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.search(
     collection_name="{collection_name}",
@@ -923,7 +919,7 @@ PUT /collections/{collection_name}
 ```python
 from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",
@@ -1077,7 +1073,7 @@ POST /collections/{collection_name}/points/search
 ```python
 from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.search(
     collection_name="{collection_name}",
@@ -1200,7 +1196,7 @@ PUT /collections/{collection_name}
 ```python
 from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",

qdrant-landing/content/documentation/guides/security.md

@@ -63,8 +63,7 @@ curl \
 from qdrant_client import QdrantClient
 
 client = QdrantClient(
-    url="https://localhost",
-    port=6333,
+    url="https://localhost:6333",
     api_key="your_secret_api_key_here",
 )
 ```
@@ -186,8 +185,7 @@ curl -X GET https://localhost:6333
 from qdrant_client import QdrantClient
 
 client = QdrantClient(
-    url="https://localhost",
-    port=6333,
+    url="https://localhost:6333",
 )
 ```
 

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",

qdrant-landing/content/documentation/tutorials/aleph-alpha-search.md

@@ -70,7 +70,7 @@ from aleph_alpha_client import (
 from glob import glob
 
 ids, vectors, payloads = [], [], []
-async with AsyncClient(token=aa_token) as client:
+async with AsyncClient(token=aa_token) as aa_client:
     for i, image_path in enumerate(glob("./val2017/*.jpg")):
         # Convert the JPEG file into the embedding by calling
         # Aleph Alpha API
@@ -82,7 +82,7 @@ async with AsyncClient(token=aa_token) as client:
             "compress_to_size": 128,
         }
         query_request = SemanticEmbeddingRequest(**query_params)
-        query_response = await client.semantic_embed(request=query_request, model=model)
+        query_response = await aa_client.semantic_embed(request=query_request, model=model)
 
         # Finally store the id, vector and the payload
         ids.append(i)
@@ -96,17 +96,17 @@ Add all created embeddings, along with their ids and payloads into the `COCO` co
 
 ```python
 import qdrant_client
-from qdrant_client.http.models import Batch, VectorParams, Distance
+from qdrant_client.models import Batch, VectorParams, Distance
 
-qdrant_client = qdrant_client.QdrantClient()
-qdrant_client.recreate_collection(
+client = qdrant_client.QdrantClient()
+client.recreate_collection(
     collection_name="COCO",
     vectors_config=VectorParams(
         size=len(vectors[0]),
         distance=Distance.COSINE,
     ),
 )
-qdrant_client.upsert(
+client.upsert(
     collection_name="COCO",
     points=Batch(
         ids=ids,
@@ -126,7 +126,7 @@ text queries and reverse image search. Assume you want to find images similar to
 With the following code snippet create its vector embedding and then perform the lookup in Qdrant:
 
 ```python
-async with AsyncCliet(token=aa_token) as client:
+async with AsyncCliet(token=aa_token) as aa_client:
     prompt = ImagePrompt.from_file("query.jpg")
     prompt = Prompt.from_image(prompt)
 
@@ -136,9 +136,9 @@ async with AsyncCliet(token=aa_token) as client:
         "compress_to_size": 128,
     }
     query_request = SemanticEmbeddingRequest(**query_params)
-    query_response = await client.semantic_embed(request=query_request, model=model)
+    query_response = await aa_client.semantic_embed(request=query_request, model=model)
 
-    results = qdrant.search(
+    results = client.search(
         collection_name="COCO",
         query_vector=query_response.embedding,
         limit=3,
@@ -156,16 +156,16 @@ and Spanish. Your search is not only multimodal, but also multilingual, without
 ```python
 text = "Surfing"
 
-async with AsyncClient(token=aa_token) as client:
+async with AsyncClient(token=aa_token) as aa_client:
     query_params = {
         "prompt": Prompt.from_text(text),
         "representation": SemanticRepresentation.Symmetric,
         "compres_to_size": 128,
     }
     query_request = SemanticEmbeddingRequest(**query_params)
-    query_response = await client.semantic_embed(request=query_request, model=model)
+    query_response = await aa_client.semantic_embed(request=query_request, model=model)
 
-    results = qdrant.search(
+    results = client.search(
         collection_name="COCO",
         query_vector=query_response.embedding,
         limit=3,

qdrant-landing/content/documentation/tutorials/bulk-upload.md

@@ -37,7 +37,7 @@ PUT /collections/{collection_name}
 ```python
 from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",
@@ -78,7 +78,7 @@ PATCH /collections/{collection_name}
 ```python
 from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.update_collection(
     collection_name="{collection_name}",
@@ -138,7 +138,7 @@ PUT /collections/{collection_name}
 ```python
 from qdrant_client import QdrantClient, models
 
-client = QdrantClient("localhost", port=6333)
+client = QdrantClient(url="http://localhost:6333")
 
 client.create_collection(
     collection_name="{collection_name}",

qdrant-landing/content/documentation/tutorials/cohere-rag-connector.md

@@ -42,11 +42,11 @@ actions to perform.
 ```python
 from qdrant_client import QdrantClient, models
 
-qdrant_client = QdrantClient(
+client = QdrantClient(
     "https://my-cluster.cloud.qdrant.io:6333", 
     api_key="my-api-key",
 )
-qdrant_client.create_collection(
+client.create_collection(
     collection_name="personal-notes",
     vectors_config=models.VectorParams(
         size=1024,
@@ -120,7 +120,7 @@ response = cohere_client.embed(
     input_type="search_document",
 )
 
-qdrant_client.upload_points(
+client.upload_points(
     collection_name="personal-notes",
     points=[
         models.PointStruct(
@@ -183,7 +183,7 @@ from typing import Annotated
 
 app = FastAPI()
 
-def qdrant_client() -> QdrantClient:
+def client() -> QdrantClient:
     return QdrantClient(config.QDRANT_URL, api_key=config.QDRANT_API_KEY)
 
 def cohere_client() -> cohere.Client:
@@ -192,7 +192,7 @@ def cohere_client() -> cohere.Client:
 @app.post("/search")
 def search(
     query: SearchQuery,
-    qdrant_client: Annotated[QdrantClient, Depends(qdrant_client)],
+    client: Annotated[QdrantClient, Depends(client)],
     cohere_client: Annotated[cohere.Client, Depends(cohere_client)],
 ) -> SearchResults:
     response = cohere_client.embed(
@@ -200,7 +200,7 @@ def search(
         model="embed-multilingual-v3.0",
         input_type="search_query",
     )
-    results = qdrant_client.search(
+    results = client.search(
         collection_name="personal-notes",
         query_vector=response.embeddings[0],
         limit=2,

qdrant-landing/content/documentation/tutorials/create-snapshot.md

@@ -75,9 +75,9 @@ We used the streaming mode, so the dataset is not loaded into memory. Instead, w
 
 ```python
 for payload in dataset:
-    id = payload.pop("id")
+    id_ = payload.pop("id")
     vector = payload.pop("vector")
-    print(id, vector, payload)
+    print(id_, vector, payload)
 ```
 
 A single payload looks like this:
@@ -114,10 +114,10 @@ Calculating the embeddings is usually a bottleneck of the vector search pipeline
 ```python
 ids, vectors, payloads = [], [], []
 for payload in dataset:
-    id = payload.pop("id")
+    id_ = payload.pop("id")
     vector = payload.pop("vector")
 
-    ids.append(id)
+    ids.append(id_)
     vectors.append(vector)
     payloads.append(payload)
 

qdrant-landing/content/documentation/tutorials/neural-search-fastembed.md

@@ -106,7 +106,7 @@ Now you need to write a script to upload all startup data and vectors into the s
 # Import client library
 from qdrant_client import QdrantClient
 
-qdrant_client = QdrantClient("http://localhost:6333")
+client = QdrantClient("http://localhost:6333")
 ```
 
 3. Select model to encode your data.
@@ -114,16 +114,16 @@ qdrant_client = QdrantClient("http://localhost:6333")
 You will be using a pre-trained model called `sentence-transformers/all-MiniLM-L6-v2`.
 
 ```python
-qdrant_client.set_model("sentence-transformers/all-MiniLM-L6-v2")
+client.set_model("sentence-transformers/all-MiniLM-L6-v2")
 ```
 
 
 4. Related vectors need to be added to a collection. Create a new collection for your startup vectors.
 
 ```python
-qdrant_client.recreate_collection(
+client.recreate_collection(
     collection_name="startups",
-    vectors_config=qdrant_client.get_fastembed_vector_params(),
+    vectors_config=client.get_fastembed_vector_params(),
 )
 ```
 

qdrant-landing/content/documentation/tutorials/neural-search.md

@@ -146,13 +146,13 @@ Now you need to write a script to upload all startup data and vectors into the s
 from qdrant_client import QdrantClient
 from qdrant_client.models import VectorParams, Distance
 
-qdrant_client = QdrantClient("http://localhost:6333")
+client = QdrantClient("http://localhost:6333")
 ```
 
 3. Related vectors need to be added to a collection. Create a new collection for your startup vectors.
 
 ```python
-qdrant_client.recreate_collection(
+client.recreate_collection(
     collection_name="startups",
     vectors_config=VectorParams(size=384, distance=Distance.COSINE),
 )
@@ -186,7 +186,7 @@ vectors = np.load("./startup_vectors.npy")
 5. Upload the data
 
 ```python
-qdrant_client.upload_collection(
+client.upload_collection(
     collection_name="startups",
     vectors=vectors,
     payload=payload,

qdrant-landing/content/documentation/tutorials/retrieval-quality.md

@@ -105,10 +105,10 @@ after receiving the response from the `upsert` endpoint. **As long as the indexi
 the exact search**. We have to wait until the indexing is finished to be sure that the approximate search is performed.
 
 ```python
-client.upload_records(
+client.upload_points(  # upload_points is available as of qdrant-client v1.7.1
     collection_name="arxiv-titles-instructorxl-embeddings",
-    records=[
-        models.Record(
+    points=[
+        models.PointStruct(
             id=item["id"],
             vector=item["vector"],
             payload=item,

qdrant-landing/content/documentation/tutorials/search-beginners.md

@@ -147,7 +147,7 @@ documents = [
 You need to tell Qdrant where to store embeddings. This is a basic demo, so your local computer will use its memory as temporary storage.
 
 ```python
-qdrant = QdrantClient(":memory:")
+client = QdrantClient(":memory:")
 ```
 
 ## 4. Create a collection
@@ -155,7 +155,7 @@ qdrant = QdrantClient(":memory:")
 All data in Qdrant is organized by collections. In this case, you are storing books, so we are calling it `my_books`.
 
 ```python
-qdrant.recreate_collection(
+client.recreate_collection(
     collection_name="my_books",
     vectors_config=models.VectorParams(
         size=encoder.get_sentence_embedding_dimension(),  # Vector size is defined by used model
@@ -176,7 +176,7 @@ qdrant.recreate_collection(
 Tell the database to upload `documents` to the `my_books` collection. This will give each record an id and a payload. The payload is just the metadata from the dataset.
 
 ```python
-qdrant.upload_points(
+client.upload_points(
     collection_name="my_books",
     points=[
         models.PointStruct(
@@ -192,7 +192,7 @@ qdrant.upload_points(
 Now that the data is stored in Qdrant, you can ask it questions and receive semantically relevant results.
 
 ```python
-hits = qdrant.search(
+hits = client.search(
     collection_name="my_books",
     query_vector=encoder.encode("alien invasion").tolist(),
     limit=3,
@@ -216,7 +216,7 @@ The search engine shows three of the most likely responses that have to do with
 How about the most recent book from the early 2000s?
 
 ```python
-hits = qdrant.search(
+hits = client.search(
     collection_name="my_books",
     query_vector=encoder.encode("alien invasion").tolist(),
     query_filter=models.Filter(