Auto-generated code for main

elasticsearch/_async/client/__init__.py

@@ -4745,7 +4745,8 @@ async def search(
             limit the impact of the search on the cluster in order to limit the number
             of concurrent shard requests.
         :param min_score: The minimum `_score` for matching documents. Documents with
-            a lower `_score` are not included in the search results.
+            a lower `_score` are not included in search results and results collected
+            by aggregations.
         :param pit: Limit the search to a point in time (PIT). If you provide a PIT,
             you cannot specify an `<index>` in the request path.
         :param post_filter: Use the `post_filter` parameter to filter search results.

elasticsearch/_async/client/async_search.py

@@ -401,7 +401,7 @@ async def submit(
             limit the impact of the search on the cluster in order to limit the number
             of concurrent shard requests
         :param min_score: Minimum _score for matching documents. Documents with a lower
-            _score are not included in the search results.
+            _score are not included in search results and results collected by aggregations.
         :param pit: Limits the search to a point in time (PIT). If you provide a PIT,
             you cannot specify an <index> in the request path.
         :param post_filter:

elasticsearch/_async/client/fleet.py

@@ -430,7 +430,7 @@ async def search(
         :param lenient:
         :param max_concurrent_shard_requests:
         :param min_score: Minimum _score for matching documents. Documents with a lower
-            _score are not included in the search results.
+            _score are not included in search results and results collected by aggregations.
         :param pit: Limits the search to a point in time (PIT). If you provide a PIT,
             you cannot specify an <index> in the request path.
         :param post_filter:

elasticsearch/_sync/client/__init__.py

@@ -4743,7 +4743,8 @@ def search(
             limit the impact of the search on the cluster in order to limit the number
             of concurrent shard requests.
         :param min_score: The minimum `_score` for matching documents. Documents with
-            a lower `_score` are not included in the search results.
+            a lower `_score` are not included in search results and results collected
+            by aggregations.
         :param pit: Limit the search to a point in time (PIT). If you provide a PIT,
             you cannot specify an `<index>` in the request path.
         :param post_filter: Use the `post_filter` parameter to filter search results.

elasticsearch/_sync/client/async_search.py

@@ -401,7 +401,7 @@ def submit(
             limit the impact of the search on the cluster in order to limit the number
             of concurrent shard requests
         :param min_score: Minimum _score for matching documents. Documents with a lower
-            _score are not included in the search results.
+            _score are not included in search results and results collected by aggregations.
         :param pit: Limits the search to a point in time (PIT). If you provide a PIT,
             you cannot specify an <index> in the request path.
         :param post_filter:

elasticsearch/_sync/client/fleet.py

@@ -430,7 +430,7 @@ def search(
         :param lenient:
         :param max_concurrent_shard_requests:
         :param min_score: Minimum _score for matching documents. Documents with a lower
-            _score are not included in the search results.
+            _score are not included in search results and results collected by aggregations.
         :param pit: Limits the search to a point in time (PIT). If you provide a PIT,
             you cannot specify an <index> in the request path.
         :param post_filter:

elasticsearch/dsl/query.py

@@ -1084,7 +1084,7 @@ class Knn(Query):
     :arg similarity: The minimum similarity for a vector to be considered
         a match
     :arg rescore_vector: Apply oversampling and rescoring to quantized
-        vectors *
+        vectors
     :arg boost: Floating point number used to decrease or increase the
         relevance scores of the query. Boost values are relative to the
         default value of 1.0. A boost value between 0 and 1.0 decreases

elasticsearch/dsl/types.py

@@ -324,31 +324,58 @@ class DenseVectorIndexOptions(AttrDict[Any]):
         `int4_flat` index types.
     :arg ef_construction: The number of candidates to track while
         assembling the list of nearest neighbors for each new node.  Only
-        applicable to `hnsw`, `int8_hnsw`, and `int4_hnsw` index types.
-        Defaults to `100` if omitted.
+        applicable to `hnsw`, `int8_hnsw`, `bbq_hnsw`, and `int4_hnsw`
+        index types. Defaults to `100` if omitted.
     :arg m: The number of neighbors each node will be connected to in the
-        HNSW graph.  Only applicable to `hnsw`, `int8_hnsw`, and
-        `int4_hnsw` index types. Defaults to `16` if omitted.
+        HNSW graph.  Only applicable to `hnsw`, `int8_hnsw`, `bbq_hnsw`,
+        and `int4_hnsw` index types. Defaults to `16` if omitted.
+    :arg rescore_vector: The rescore vector options. This is only
+        applicable to `bbq_hnsw`, `int4_hnsw`, `int8_hnsw`, `bbq_flat`,
+        `int4_flat`, and `int8_flat` index types.
     """
 
     type: Union[
-        Literal["flat", "hnsw", "int4_flat", "int4_hnsw", "int8_flat", "int8_hnsw"],
+        Literal[
+            "bbq_flat",
+            "bbq_hnsw",
+            "flat",
+            "hnsw",
+            "int4_flat",
+            "int4_hnsw",
+            "int8_flat",
+            "int8_hnsw",
+        ],
         DefaultType,
     ]
     confidence_interval: Union[float, DefaultType]
     ef_construction: Union[int, DefaultType]
     m: Union[int, DefaultType]
+    rescore_vector: Union[
+        "DenseVectorIndexOptionsRescoreVector", Dict[str, Any], DefaultType
+    ]
 
     def __init__(
         self,
         *,
         type: Union[
-            Literal["flat", "hnsw", "int4_flat", "int4_hnsw", "int8_flat", "int8_hnsw"],
+            Literal[
+                "bbq_flat",
+                "bbq_hnsw",
+                "flat",
+                "hnsw",
+                "int4_flat",
+                "int4_hnsw",
+                "int8_flat",
+                "int8_hnsw",
+            ],
             DefaultType,
         ] = DEFAULT,
         confidence_interval: Union[float, DefaultType] = DEFAULT,
         ef_construction: Union[int, DefaultType] = DEFAULT,
         m: Union[int, DefaultType] = DEFAULT,
+        rescore_vector: Union[
+            "DenseVectorIndexOptionsRescoreVector", Dict[str, Any], DefaultType
+        ] = DEFAULT,
         **kwargs: Any,
     ):
         if type is not DEFAULT:
@@ -359,6 +386,29 @@ def __init__(
             kwargs["ef_construction"] = ef_construction
         if m is not DEFAULT:
             kwargs["m"] = m
+        if rescore_vector is not DEFAULT:
+            kwargs["rescore_vector"] = rescore_vector
+        super().__init__(kwargs)
+
+
+class DenseVectorIndexOptionsRescoreVector(AttrDict[Any]):
+    """
+    :arg oversample: (required) The oversampling factor to use when
+        searching for the nearest neighbor. This is only applicable to the
+        quantized formats: `bbq_*`, `int4_*`, and `int8_*`. When provided,
+        `oversample * k` vectors will be gathered and then their scores
+        will be re-computed with the original vectors.  valid values are
+        between `1.0` and `10.0` (inclusive), or `0` exactly to disable
+        oversampling.
+    """
+
+    oversample: Union[float, DefaultType]
+
+    def __init__(
+        self, *, oversample: Union[float, DefaultType] = DEFAULT, **kwargs: Any
+    ):
+        if oversample is not DEFAULT:
+            kwargs["oversample"] = oversample
         super().__init__(kwargs)