Update for redisvl 0.11.0

Author: redisdocsapp[bot]

content/develop/ai/redisvl/api/_index.md

@@ -15,7 +15,10 @@ Reference documentation for the RedisVL API.
 * [Schema](schema/)
   * [IndexSchema](schema/#indexschema)
   * [Defining Fields](schema/#defining-fields)
-  * [Supported Field Types and Attributes](schema/#supported-field-types-and-attributes)
+  * [Basic Field Types](schema/#basic-field-types)
+  * [Vector Field Types](schema/#vector-field-types)
+  * [SVS-VAMANA Configuration Utilities](schema/#svs-vamana-configuration-utilities)
+  * [Vector Algorithm Comparison](schema/#vector-algorithm-comparison)
 * [Search Index Classes](searchindex/)
   * [SearchIndex](searchindex/#searchindex)
   * [AsyncSearchIndex](searchindex/#asyncsearchindex)

content/develop/ai/redisvl/api/query.md

@@ -730,39 +730,18 @@ Return self as the query object.
 
 ## HybridQuery
 
-### `class HybridQuery(text, text_field_name, vector, vector_field_name, text_scorer='BM25STD', filter_expression=None, alpha=0.7, dtype='float32', num_results=10, return_fields=None, stopwords='english', dialect=2)`
+### `class HybridQuery(*args, **kwargs)`
 
-Bases: `AggregationQuery`
-
-HybridQuery combines text and vector search in Redis.
-It allows you to perform a hybrid search using both text and vector similarity.
-It scores documents based on a weighted combination of text and vector similarity.
-
-```python
-from redisvl.query import HybridQuery
-from redisvl.index import SearchIndex
+Bases: `AggregateHybridQuery`
 
-index = SearchIndex.from_yaml("path/to/index.yaml")
+Backward compatibility wrapper for AggregateHybridQuery.
 
-query = HybridQuery(
-    text="example text",
-    text_field_name="text_field",
-    vector=[0.1, 0.2, 0.3],
-    vector_field_name="vector_field",
-    text_scorer="BM25STD",
-    filter_expression=None,
-    alpha=0.7,
-    dtype="float32",
-    num_results=10,
-    return_fields=["field1", "field2"],
-    stopwords="english",
-    dialect=2,
-)
-
-results = index.query(query)
-```
+#### `Deprecated`
+Deprecated since version HybridQuery: is a backward compatibility wrapper around AggregateHybridQuery
+and will eventually be replaced with a new hybrid query implementation.
+To maintain current functionality please use AggregateHybridQuery directly.",
 
-Instantiates a HybridQuery object.
+Instantiates a AggregateHybridQuery object.
 
 * **Parameters:**
   * **text** (*str*) – The text to search for.
@@ -785,6 +764,9 @@ Instantiates a HybridQuery object.
     set, or tuple of strings is provided then those will be used as stopwords.
     Defaults to "english". if set to "None" then no stopwords will be removed.
   * **dialect** (*int* *,* *optional*) – The Redis dialect version. Defaults to 2.
+  * **text_weights** (*Optional* *[* *Dict* *[* *str* *,* *float* *]* *]*) – The importance weighting of individual words
+    within the query text. Defaults to None, as no modifications will be made to the
+    text_scorer score.
 * **Raises:**
   * **ValueError** – If the text string is empty, or if the text string becomes empty after
         stopwords are removed.
@@ -922,6 +904,14 @@ Default is TFIDF.
 * **Return type:**
   *AggregateRequest*
 
+#### `set_text_weights(weights)`
+
+Set or update the text weights for the query.
+
+* **Parameters:**
+  * **text_weights** – Dictionary of word:weight mappings
+  * **weights** (*Dict* *[* *str* *,* *float* *]*)
+
 #### `sort_by(*fields, **kwargs)`
 
 Indicate how the results should be sorted. This can also be used for
@@ -975,9 +965,18 @@ Return the stopwords used in the query.
 :returns: The stopwords used in the query.
 :rtype: Set[str]
 
+#### `property text_weights: Dict[str, float]`
+
+Get the text weights.
+
+* **Returns:**
+  weight mappings.
+* **Return type:**
+  Dictionary of word
+
 ## TextQuery
 
-### `class TextQuery(text, text_field_name, text_scorer='BM25STD', filter_expression=None, return_fields=None, num_results=10, return_score=True, dialect=2, sort_by=None, in_order=False, params=None, stopwords='english')`
+### `class TextQuery(text, text_field_name, text_scorer='BM25STD', filter_expression=None, return_fields=None, num_results=10, return_score=True, dialect=2, sort_by=None, in_order=False, params=None, stopwords='english', text_weights=None)`
 
 Bases: `BaseQuery`
 
@@ -1038,6 +1037,9 @@ A query for running a full text search, along with an optional filter expression
     a default set of stopwords for that language will be used. Users may specify
     their own stop words by providing a List or Set of words. if set to None,
     then no words will be removed. Defaults to ‘english’.
+  * **text_weights** (*Optional* *[* *Dict* *[* *str* *,* *float* *]* *]*) – The importance weighting of individual words
+    within the query text. Defaults to None, as no modifications will be made to the
+    text_scorer score.
 * **Raises:**
   * **ValueError** – if stopwords language string cannot be loaded.
   * **TypeError** – If stopwords is not a valid iterable set of strings.
@@ -1184,6 +1186,14 @@ Set the filter expression for the query.
 * **Raises:**
   **TypeError** – If filter_expression is not a valid FilterExpression or string.
 
+#### `set_text_weights(weights)`
+
+Set or update the text weights for the query.
+
+* **Parameters:**
+  * **text_weights** – Dictionary of word:weight mappings
+  * **weights** (*Dict* *[* *str* *,* *float* *]*)
+
 #### `slop(slop)`
 
 Allow a maximum of N intervening non matched terms between
@@ -1289,6 +1299,15 @@ Get the text field name(s) - for backward compatibility.
   Either a single field name string (if only one field with weight 1.0)
   or a dictionary of field:weight mappings.
 
+#### `property text_weights: Dict[str, float]`
+
+Get the text weights.
+
+* **Returns:**
+  weight mappings.
+* **Return type:**
+  Dictionary of word
+
 ## FilterQuery
 
 ### `class FilterQuery(filter_expression=None, return_fields=None, num_results=10, dialect=2, sort_by=None, in_order=False, params=None)`
@@ -1797,7 +1816,7 @@ Return self as the query object.
 
 Bases: `AggregationQuery`
 
-MultiVectorQuery allows for search over multiple vector fields in a document simulateously.
+MultiVectorQuery allows for search over multiple vector fields in a document simultaneously.
 The final score will be a weighted combination of the individual vector similarity scores
 following the formula: