docs auto-sync

qdrant-landing/content/documentation/filtering.md

@@ -60,11 +60,11 @@ client.scroll(
         must=[
             models.FieldCondition(
                 key="city", 
-                match=models.Match(value="London"),
+                match=models.MatchValue(value="London"),
             ),
             models.FieldCondition(
                 key="color",
-                match=models.Match(value="red"),
+                match=models.MatchValue(value="red"),
             ),
         ]
     ),
@@ -108,11 +108,11 @@ client.scroll(
         should=[
             models.FieldCondition(
                 key="city", 
-                match=models.Match(value="London"),
+                match=models.MatchValue(value="London"),
             ),
             models.FieldCondition(
                 key="color", 
-                match=models.Match(value="red"),
+                match=models.MatchValue(value="red"),
             ),
         ]
     ),
@@ -159,11 +159,11 @@ client.scroll(
         must_not=[
             models.FieldCondition(
                 key="city", 
-                match=models.Match(value="London")
+                match=models.MatchValue(value="London")
             ),
             models.FieldCondition(
                 key="color", 
-                match=models.Match(value="red")
+                match=models.MatchValue(value="red")
             ),
         ]
     ),
@@ -210,13 +210,13 @@ client.scroll(
         must=[
             models.FieldCondition(
                 key="city", 
-                match=models.Match(value="London")
+                match=models.MatchValue(value="London")
             ),
         ],
         must_not=[
             models.FieldCondition(
                 key="color", 
-                match=models.Match(value="red")
+                match=models.MatchValue(value="red")
             ),
         ],
     ),
@@ -263,11 +263,11 @@ client.scroll(
                 must=[
                     models.FieldCondition(
                         key="city", 
-                        match=models.Match(value="London")
+                        match=models.MatchValue(value="London")
                     ),
                     models.FieldCondition(
                         key="color", 
-                        match=models.Match(value="red")
+                        match=models.MatchValue(value="red")
                     ),
                 ],
             ),

qdrant-landing/content/documentation/points.md

@@ -467,7 +467,7 @@ client.scroll(
         must=[
             models.FieldCondition(
                 key="color", 
-                match=models.Match(value="red")
+                match=models.MatchValue(value="red")
             ),
         ]
     ),
@@ -510,3 +510,58 @@ Python client:
 ```
  -->
 
+## Counting points
+
+*Avalable since v0.8.4*
+
+Sometimes it can be useful to know how many points fit the filter conditions without doing a real search.
+
+Among others, for example, we can highlight the following scenarios: 
+
+* Evaluation of results size for faceted search
+* Determining the number of pages for pagination
+* Debugging the query execution speed
+
+
+REST API ([Schema](https://qdrant.github.io/qdrant/redoc/index.html#operation/scroll_points)):
+
+```http
+POST /collections/{collection_name}/points/count
+
+{
+    "filter": {
+        "must": [
+            {
+                "key": "color",
+                "match": {
+                    "value": "red"
+                }
+            }
+        ]
+    },
+    "exact": true
+}
+```
+
+```python
+client.scroll(
+    collection_name="{collection_name}", 
+    scroll_filter=models.Filter(
+        must=[
+            models.FieldCondition(
+                key="color", 
+                match=models.MatchValue(value="red")
+            ),
+        ]
+    ),
+    exact=True,
+)
+```
+
+Returns number of counts mathcing given filtering conditions:
+
+```json
+{
+    "count": 3811
+}
+```

qdrant-landing/content/documentation/search.md

@@ -78,7 +78,7 @@ POST /collections/{collection_name}/points/search
         "hnsw_ef": 128
     },
     "vector": [0.2, 0.1, 0.9, 0.7],
-    "top": 3
+    "limit": 3
 }
 ```
 
@@ -104,12 +104,12 @@ client.search(
         hnsw_ef=128
     ),
     query_vector=[0.2, 0.1, 0.9, 0.7],
-    top=3,
+    limit=3,
 )
 ```
 
 In this example, we are looking for vectors similar to vector `[0.2, 0.1, 0.9, 0.7]`. 
-Parameter `top` specifies the amount of most similar results we would like to retrieve.
+Parameter `limit` (or its alias - `top`) specifies the amount of most similar results we would like to retrieve.
 
 Values under the key `params` specify custom parameters for the search.
 Currently, it could be:
@@ -145,7 +145,7 @@ It will exclude all results with a score worse than the given.
 
 <aside role="status">This parameter may exclude lower or higher scores depending on the used metric. For example, higher scores of Euclidean metric are considered more distant and, therefore, will be excluded.</aside>
 
-### Payload in vector in the result
+### Payload and vector in the result
 
 By default, retrieval methods do not return any stored information.
 Additional parameters `with_vector` and `with_payload` could alter this behavior.
@@ -229,7 +229,7 @@ POST /collections/{collection_name}/points/recommend
   },
   "negative": [718],
   "positive": [100, 231],
-  "top": 10
+  "limit": 10
 }
 ```
 
@@ -248,7 +248,7 @@ client.recommend(
     ),
     negative=[718],
     positive=[100, 231],
-    top=10,
+    limit=10,
 )
 ```
 
@@ -265,3 +265,46 @@ Example result of this API would be
   "time": 0.001
 }
 ```
+
+## Pagination
+
+*Avalable since v0.8.3*
+
+Search and recommendation APIs allow to skip first results of the search and return only the result starting from some specified offset:
+
+Example:
+
+```http
+POST /collections/{collection_name}/points/search
+
+{
+    "vector": [0.2, 0.1, 0.9, 0.7],
+    "with_vector": true,
+    "with_payload": true,
+    "limit": 10,
+    "offset": 100
+}
+```
+
+```python
+client.search(
+    collection_name="{collection_name}",
+    query_vector=[0.2, 0.1, 0.9, 0.7],
+    with_vector=True,
+    with_payload=True,
+    limit=10,
+    offset=100
+)
+```
+
+Is equvalent to retrieving 11th page with 10 records per page.
+
+<aside role="alert">Large offset values may cause performance issues</aside>
+
+Vector-based retrieval in general and HNSW index in particular, are not designed to be paginated.
+It is impossible to retrieve Nth closest vector without retrieving the first N vectors first.
+
+However, using the offset parameter saves the resources by reducing network traffic and the number of times the storage is accessed.
+
+Using an `offset` parameter, will require to internally retrieve `offset + limit` points, but only access payload and vector from the storage those points which are going to be actually returned.
+

qdrant-landing/content/documentation/snapshots.md

@@ -3,6 +3,8 @@ title: Snapshots
 weight: 51
 ---
 
+*avalable since v0.8.4*
+
 Snapshots are performed on a per collection basis and consist in a `tar` archive file containing the necessary data to restore the collection at the time of the snapshot.
 
 This feature can be used to archive data or easily replicate an existing deployment.