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/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(