Auto-generated API code

Author: elasticmachine

elasticsearch/dsl/field.py

@@ -3866,9 +3866,6 @@ class SemanticText(Field):
         by using the Update mapping API. Use the Create inference API to
         create the endpoint. If not specified, the inference endpoint
         defined by inference_id will be used at both index and query time.
-    :arg index_options: Settings for index_options that override any
-        defaults used by semantic_text, for example specific quantization
-        settings.
     :arg chunking_settings: Settings for chunking text into smaller
         passages. If specified, these will override the chunking settings
         sent in the inference endpoint associated with inference_id. If
@@ -3888,11 +3885,8 @@ def __init__(
         meta: Union[Mapping[str, str], "DefaultType"] = DEFAULT,
         inference_id: Union[str, "DefaultType"] = DEFAULT,
         search_inference_id: Union[str, "DefaultType"] = DEFAULT,
-        index_options: Union[
-            "types.SemanticTextIndexOptions", Dict[str, Any], "DefaultType"
-        ] = DEFAULT,
         chunking_settings: Union[
-            "types.ChunkingSettings", None, Dict[str, Any], "DefaultType"
+            "types.ChunkingSettings", Dict[str, Any], "DefaultType"
         ] = DEFAULT,
         fields: Union[Mapping[str, Field], "DefaultType"] = DEFAULT,
         **kwargs: Any,
@@ -3903,8 +3897,6 @@ def __init__(
             kwargs["inference_id"] = inference_id
         if search_inference_id is not DEFAULT:
             kwargs["search_inference_id"] = search_inference_id
-        if index_options is not DEFAULT:
-            kwargs["index_options"] = index_options
         if chunking_settings is not DEFAULT:
             kwargs["chunking_settings"] = chunking_settings
         if fields is not DEFAULT:

elasticsearch/dsl/query.py

@@ -1079,8 +1079,6 @@ class Knn(Query):
         a query_vector_builder or query_vector, but not both.
     :arg num_candidates: The number of nearest neighbor candidates to
         consider per shard
-    :arg visit_percentage: The percentage of vectors to explore per shard
-        while doing knn search with bbq_disk
     :arg k: The final number of nearest neighbors to return as top hits
     :arg filter: Filters for the kNN search query
     :arg similarity: The minimum similarity for a vector to be considered
@@ -1109,7 +1107,6 @@ def __init__(
             "types.QueryVectorBuilder", Dict[str, Any], "DefaultType"
         ] = DEFAULT,
         num_candidates: Union[int, "DefaultType"] = DEFAULT,
-        visit_percentage: Union[float, "DefaultType"] = DEFAULT,
         k: Union[int, "DefaultType"] = DEFAULT,
         filter: Union[Query, Sequence[Query], "DefaultType"] = DEFAULT,
         similarity: Union[float, "DefaultType"] = DEFAULT,
@@ -1125,7 +1122,6 @@ def __init__(
             query_vector=query_vector,
             query_vector_builder=query_vector_builder,
             num_candidates=num_candidates,
-            visit_percentage=visit_percentage,
             k=k,
             filter=filter,
             similarity=similarity,
@@ -1437,7 +1433,7 @@ def __init__(
         ] = DEFAULT,
         version: Union[int, "DefaultType"] = DEFAULT,
         version_type: Union[
-            Literal["internal", "external", "external_gte"], "DefaultType"
+            Literal["internal", "external", "external_gte", "force"], "DefaultType"
         ] = DEFAULT,
         boost: Union[float, "DefaultType"] = DEFAULT,
         _name: Union[str, "DefaultType"] = DEFAULT,

elasticsearch/dsl/types.py

@@ -151,10 +151,9 @@ class ChunkingSettings(AttrDict[Any]):
         strategies in the linked documentation. Defaults to `sentence` if
         omitted.
     :arg max_chunk_size: (required) The maximum size of a chunk in words.
-        This value cannot be lower than `20` (for `sentence` strategy) or
-        `10` (for `word` strategy). This value should not exceed the
-        window size for the associated model. Defaults to `250` if
-        omitted.
+        This value cannot be higher than `300` or lower than `20` (for
+        `sentence` strategy) or `10` (for `word` strategy). Defaults to
+        `250` if omitted.
     :arg separator_group: Only applicable to the `recursive` strategy and
         required when using it.  Sets a predefined list of separators in
         the saved chunking settings based on the selected text type.
@@ -398,17 +397,14 @@ class DenseVectorIndexOptions(AttrDict[Any]):
         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_disk`, `bbq_hnsw`, `int4_hnsw`, `int8_hnsw`,
-        `bbq_flat`, `int4_flat`, and `int8_flat` index types.
-    :arg on_disk_rescore: `true` if vector rescoring should be done on-
-        disk  Only applicable to `bbq_hnsw`
+        applicable to `bbq_hnsw`, `int4_hnsw`, `int8_hnsw`, `bbq_flat`,
+        `int4_flat`, and `int8_flat` index types.
     """
 
     type: Union[
         Literal[
             "bbq_flat",
             "bbq_hnsw",
-            "bbq_disk",
             "flat",
             "hnsw",
             "int4_flat",
@@ -424,7 +420,6 @@ class DenseVectorIndexOptions(AttrDict[Any]):
     rescore_vector: Union[
         "DenseVectorIndexOptionsRescoreVector", Dict[str, Any], DefaultType
     ]
-    on_disk_rescore: Union[bool, DefaultType]
 
     def __init__(
         self,
@@ -433,7 +428,6 @@ def __init__(
             Literal[
                 "bbq_flat",
                 "bbq_hnsw",
-                "bbq_disk",
                 "flat",
                 "hnsw",
                 "int4_flat",
@@ -449,7 +443,6 @@ def __init__(
         rescore_vector: Union[
             "DenseVectorIndexOptionsRescoreVector", Dict[str, Any], DefaultType
         ] = DEFAULT,
-        on_disk_rescore: Union[bool, DefaultType] = DEFAULT,
         **kwargs: Any,
     ):
         if type is not DEFAULT:
@@ -462,8 +455,6 @@ def __init__(
             kwargs["m"] = m
         if rescore_vector is not DEFAULT:
             kwargs["rescore_vector"] = rescore_vector
-        if on_disk_rescore is not DEFAULT:
-            kwargs["on_disk_rescore"] = on_disk_rescore
         super().__init__(kwargs)
 
 
@@ -2335,7 +2326,9 @@ class LikeDocument(AttrDict[Any]):
     per_field_analyzer: Union[Mapping[Union[str, InstrumentedField], str], DefaultType]
     routing: Union[str, DefaultType]
     version: Union[int, DefaultType]
-    version_type: Union[Literal["internal", "external", "external_gte"], DefaultType]
+    version_type: Union[
+        Literal["internal", "external", "external_gte", "force"], DefaultType
+    ]
 
     def __init__(
         self,
@@ -2350,7 +2343,7 @@ def __init__(
         routing: Union[str, DefaultType] = DEFAULT,
         version: Union[int, DefaultType] = DEFAULT,
         version_type: Union[
-            Literal["internal", "external", "external_gte"], DefaultType
+            Literal["internal", "external", "external_gte", "force"], DefaultType
         ] = DEFAULT,
         **kwargs: Any,
     ):
@@ -3196,33 +3189,6 @@ def __init__(
         super().__init__(kwargs)
 
 
-class SemanticTextIndexOptions(AttrDict[Any]):
-    """
-    :arg dense_vector:
-    :arg sparse_vector:
-    """
-
-    dense_vector: Union["DenseVectorIndexOptions", Dict[str, Any], DefaultType]
-    sparse_vector: Union["SparseVectorIndexOptions", Dict[str, Any], DefaultType]
-
-    def __init__(
-        self,
-        *,
-        dense_vector: Union[
-            "DenseVectorIndexOptions", Dict[str, Any], DefaultType
-        ] = DEFAULT,
-        sparse_vector: Union[
-            "SparseVectorIndexOptions", Dict[str, Any], DefaultType
-        ] = DEFAULT,
-        **kwargs: Any,
-    ):
-        if dense_vector is not DEFAULT:
-            kwargs["dense_vector"] = dense_vector
-        if sparse_vector is not DEFAULT:
-            kwargs["sparse_vector"] = sparse_vector
-        super().__init__(kwargs)
-
-
 class ShapeFieldQuery(AttrDict[Any]):
     """
     :arg indexed_shape: Queries using a pre-indexed shape.