docs: Query API snippets with Python (#1095)

Co-authored-by: generall <andrey@vasnetsov.com>

qdrant-landing/content/documentation/tutorials/async-api.md

@@ -66,11 +66,11 @@ async def main():
     )
 
     # Search for nearest neighbors
-    points = await client.search(
+    points = await client.query_points(
         collection_name="my_collection",
-        query_vector=[0.9, 0.1, 0.1, 0.5],
+        query=[0.9, 0.1, 0.1, 0.5],
         limit=2,
-    )
+    ).points
 
     # Your async code using AsyncQdrantClient might be put here
     # ...

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

@@ -325,13 +325,12 @@ Fortunately, this model should continue to provide the results you need.</aside>
 ```python
 query = "How do I count points in a collection?"
 
-hits = client.search(
+hits = client.query_points(
     "qdrant-sources",
-    query_vector=(
-        "text", nlp_model.encode(query).tolist()
-    ),
+    query=nlp_model.encode(query).tolist(),
+    using="text",
     limit=5,
-)
+).points
 ```
 
 Now, review the results. The following table lists the module, the file name
@@ -349,13 +348,12 @@ the file.
 It seems we were able to find some relevant code structures. Let's try the same with the code embeddings:
 
 ```python
-hits = client.search(
+hits = client.query_points(
     "qdrant-sources",
-    query_vector=(
-        "code", code_model.encode(query).tolist()
-    ),
+    query=code_model.encode(query).tolist(),
+    using="code",
     limit=5,
-)
+).points
 ```
 
 Output:
@@ -374,27 +372,25 @@ different aspects of the codebase. We can use both models to query the collectio
 and then combine the results to get the most relevant code snippets, from a single batch request.
 
 ```python
-results = client.search_batch(
+responses = client.query_batch_points(
     "qdrant-sources",
     requests=[
-        models.SearchRequest(
-            vector=models.NamedVector(
-                name="text",
-                vector=nlp_model.encode(query).tolist()
-            ),
+        models.QueryRequest(
+            query=nlp_model.encode(query).tolist(),
+            using="text",
             with_payload=True,
             limit=5,
         ),
-        models.SearchRequest(
-            vector=models.NamedVector(
-                name="code",
-                vector=code_model.encode(query).tolist()
-            ),
+        models.QueryRequest(
+            query=code_model.encode(query).tolist(),
+            using="code",
             with_payload=True,
             limit=5,
         ),
     ]
 )
+
+results = [response.points for response in responses]
 ```
 
 Output:

qdrant-landing/content/documentation/tutorials/collaborative-filtering.md

@@ -195,14 +195,12 @@ From the uploaded list of movies with ratings, we can perform a search in Qdrant
 
 ```python
 # Perform the search
-results = qdrant_client.search(
+results = qdrant_client.query_points(
     collection_name=collection_name,
-    query_vector=NamedSparseVector(
-        name="ratings",
-        vector=to_vector(my_ratings)
-    ),
+    query=to_vector(my_ratings),
+    using="ratings",
     limit=20
-)
+).points
 ```
 
 Now we can find the movies liked by the other similar users, but we haven't seen yet.

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

@@ -226,12 +226,12 @@ def search(self, text: str):
     vector = self.model.encode(text).tolist()
 
     # Use `vector` for search for closest vectors in the collection
-    search_result = self.qdrant_client.search(
+    search_result = self.qdrant_client.query_points(
         collection_name=self.collection_name,
-        query_vector=vector,
+        query=vector,
         query_filter=None,  # If you don't want any filters for now
         limit=5,  # 5 the most closest results is enough
-    )
+    ).points
     # `search_result` contains found vector ids with similarity scores along with the stored payload
     # In this function you are interested in payload only
     payloads = [hit.payload for hit in search_result]
@@ -260,12 +260,12 @@ from qdrant_client.models import Filter
         }]
     })
 
-    search_result = self.qdrant_client.search(
+    search_result = self.qdrant_client.query_points(
         collection_name=self.collection_name,
-        query_vector=vector,
+        query=vector,
         query_filter=city_filter,
         limit=5
-    )
+    ).points
     ...
 ```
 

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

@@ -137,20 +137,20 @@ values of `k`.
 def avg_precision_at_k(k: int):
     precisions = []
     for item in test_dataset:
-        ann_result = client.search(
+        ann_result = client.query_points(
             collection_name="arxiv-titles-instructorxl-embeddings",
-            query_vector=item["vector"],
+            query=item["vector"],
             limit=k,
-        )
+        ).points
     
-        knn_result = client.search(
+        knn_result = client.query_points(
             collection_name="arxiv-titles-instructorxl-embeddings",
-            query_vector=item["vector"],
+            query=item["vector"],
             limit=k,
             search_params=models.SearchParams(
                 exact=True,  # Turns on the exact search mode
             ),
-        )
+        ).points
 
         # We can calculate the precision@k by comparing the ids of the search results
         ann_ids = set(item.id for item in ann_result)

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

@@ -192,11 +192,12 @@ client.upload_points(
 Now that the data is stored in Qdrant, you can ask it questions and receive semantically relevant results.
 
 ```python
-hits = client.search(
+hits = client.query_points(
     collection_name="my_books",
-    query_vector=encoder.encode("alien invasion").tolist(),
+    query=encoder.encode("alien invasion").tolist(),
     limit=3,
-)
+).points
+
 for hit in hits:
     print(hit.payload, "score:", hit.score)
 ```
@@ -216,14 +217,15 @@ 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 = client.search(
+hits = client.query_points(
     collection_name="my_books",
-    query_vector=encoder.encode("alien invasion").tolist(),
+    query=encoder.encode("alien invasion").tolist(),
     query_filter=models.Filter(
         must=[models.FieldCondition(key="year", range=models.Range(gte=2000))]
     ),
     limit=1,
-)
+).points
+
 for hit in hits:
     print(hit.payload, "score:", hit.score)
 ```