From 51ce1e07a8addfe397af9c559f7c34a930fb4596 Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Thu, 4 Dec 2025 14:26:59 +0100 Subject: [PATCH 01/34] Abstract full-text query construction into helper class --- redisvl/query/aggregate.py | 120 +++--------------- redisvl/utils/full_text_query_helper.py | 154 ++++++++++++++++++++++++ 2 files changed, 170 insertions(+), 104 deletions(-) create mode 100644 redisvl/utils/full_text_query_helper.py diff --git a/redisvl/query/aggregate.py b/redisvl/query/aggregate.py index 09981ca1..b64706b4 100644 --- a/redisvl/query/aggregate.py +++ b/redisvl/query/aggregate.py @@ -1,5 +1,5 @@ import warnings -from typing import Any, Dict, List, Optional, Set, Tuple, Union +from typing import Any, Dict, List, Optional, Set, Union from pydantic import BaseModel, field_validator, model_validator from redis.commands.search.aggregation import AggregateRequest, Desc @@ -8,7 +8,7 @@ from redisvl.query.filter import FilterExpression from redisvl.redis.utils import array_to_buffer from redisvl.schema.fields import VectorDataType -from redisvl.utils.token_escaper import TokenEscaper +from redisvl.utils.full_text_query_helper import FullTextQueryHelper from redisvl.utils.utils import lazy_import nltk = lazy_import("nltk") @@ -159,8 +159,11 @@ def __init__( self._alpha = alpha self._dtype = dtype self._num_results = num_results - self._set_stopwords(stopwords) - self._text_weights = self._parse_text_weights(text_weights) + + self._ft_helper = FullTextQueryHelper( + stopwords=stopwords, + text_weights=text_weights, + ) query_string = self._build_query_string() super().__init__(query_string) @@ -198,115 +201,29 @@ def stopwords(self) -> Set[str]: Returns: Set[str]: The stopwords used in the query. """ - return self._stopwords.copy() if self._stopwords else set() - - def _set_stopwords(self, stopwords: Optional[Union[str, Set[str]]] = "english"): - """Set the stopwords to use in the query. - Args: - stopwords (Optional[Union[str, Set[str]]]): The stopwords to use. If a string - such as "english" "german" is provided then a default set of stopwords for that - language will be used. if a list, 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. - - Raises: - TypeError: If the stopwords are not a set, list, or tuple of strings. - """ - if not stopwords: - self._stopwords = set() - elif isinstance(stopwords, str): - try: - nltk.download("stopwords", quiet=True) - self._stopwords = set(nltk_stopwords.words(stopwords)) - except ImportError: - raise ValueError( - f"Loading stopwords for {stopwords} failed: nltk is not installed." - ) - except Exception as e: - raise ValueError(f"Error trying to load {stopwords} from nltk. {e}") - elif isinstance(stopwords, (Set, List, Tuple)) and all( # type: ignore - isinstance(word, str) for word in stopwords - ): - self._stopwords = set(stopwords) - else: - raise TypeError("stopwords must be a set, list, or tuple of strings") + return self._ft_helper.stopwords - def _tokenize_and_escape_query(self, user_query: str) -> str: - """Convert a raw user query to a redis full text query joined by ORs - Args: - user_query (str): The user query to tokenize and escape. + @property + def text_weights(self) -> Dict[str, float]: + """Get the text weights. Returns: - str: The tokenized and escaped query string. - - Raises: - ValueError: If the text string becomes empty after stopwords are removed. + Dictionary of word:weight mappings. """ - escaper = TokenEscaper() - - tokens = [ - escaper.escape( - token.strip().strip(",").replace("“", "").replace("”", "").lower() - ) - for token in user_query.split() - ] - - token_list = [ - token for token in tokens if token and token not in self._stopwords - ] - for i, token in enumerate(token_list): - if token in self._text_weights: - token_list[i] = f"{token}=>{{$weight:{self._text_weights[token]}}}" - - if not token_list: - raise ValueError("text string cannot be empty after removing stopwords") - return " | ".join(token_list) - - def _parse_text_weights( - self, weights: Optional[Dict[str, float]] - ) -> Dict[str, float]: - parsed_weights: Dict[str, float] = {} - if not weights: - return parsed_weights - for word, weight in weights.items(): - word = word.strip().lower() - if not word or " " in word: - raise ValueError( - f"Only individual words may be weighted. Got {{ {word}:{weight} }}" - ) - if ( - not (isinstance(weight, float) or isinstance(weight, int)) - or weight < 0.0 - ): - raise ValueError( - f"Weights must be positive number. Got {{ {word}:{weight} }}" - ) - parsed_weights[word] = weight - return parsed_weights + return self._ft_helper.text_weights def set_text_weights(self, weights: Dict[str, float]): """Set or update the text weights for the query. Args: - text_weights: Dictionary of word:weight mappings + weights: Dictionary of word:weight mappings """ - self._text_weights = self._parse_text_weights(weights) + self._ft_helper.set_text_weights(weights) self._query = self._build_query_string() - @property - def text_weights(self) -> Dict[str, float]: - """Get the text weights. - - Returns: - Dictionary of word:weight mappings. - """ - return self._text_weights - def _build_query_string(self) -> str: """Build the full query string for text search with optional filtering.""" - filter_expression = self._filter_expression - if isinstance(self._filter_expression, FilterExpression): - filter_expression = str(self._filter_expression) + text = self._ft_helper.build_query_string(self._text, self._text_field, self._filter_expression) # Build KNN query knn_query = ( @@ -316,11 +233,6 @@ def _build_query_string(self) -> str: # Add distance field alias knn_query += f" AS {self.DISTANCE_ID}" - text = f"(~@{self._text_field}:({self._tokenize_and_escape_query(self._text)})" - - if filter_expression and filter_expression != "*": - text += f" AND {filter_expression}" - return f"{text})=>[{knn_query}]" def __str__(self) -> str: diff --git a/redisvl/utils/full_text_query_helper.py b/redisvl/utils/full_text_query_helper.py new file mode 100644 index 00000000..c28093a4 --- /dev/null +++ b/redisvl/utils/full_text_query_helper.py @@ -0,0 +1,154 @@ +from typing import Optional, Union, Set, Dict, List, Tuple + +from redisvl.query.filter import FilterExpression +from redisvl.utils.token_escaper import TokenEscaper +from redisvl.utils.utils import lazy_import + +nltk = lazy_import("nltk") +nltk_stopwords = lazy_import("nltk.corpus.stopwords") + + +def _parse_text_weights( + weights: Optional[Dict[str, float]] +) -> Dict[str, float]: + parsed_weights: Dict[str, float] = {} + if not weights: + return parsed_weights + for word, weight in weights.items(): + word = word.strip().lower() + if not word or " " in word: + raise ValueError( + f"Only individual words may be weighted. Got {{ {word}:{weight} }}" + ) + if ( + not (isinstance(weight, float) or isinstance(weight, int)) + or weight < 0.0 + ): + raise ValueError( + f"Weights must be positive number. Got {{ {word}:{weight} }}" + ) + parsed_weights[word] = weight + return parsed_weights + + +class FullTextQueryHelper: + """Convert raw user queries into Redis full-text queries - tokenizes, escapes, and filters stopwords from the query. + """ + + def __init__( + self, + stopwords: Optional[Union[str, Set[str]]] = "english", + text_weights: Optional[Dict[str, float]] = None, + ): + self._stopwords = self._get_stopwords(stopwords) + self._text_weights = _parse_text_weights(text_weights) + + @property + def stopwords(self) -> Set[str]: + """Return the stopwords used in the query. + Returns: + Set[str]: The stopwords used in the query. + """ + return self._stopwords.copy() if self._stopwords else set() + + @property + def text_weights(self) -> Dict[str, float]: + """Get the text weights. + + Returns: + Dictionary of word:weight mappings. + """ + return self._text_weights + + def build_query_string( + self, + text: str, + text_field_name: str, + filter_expression: Optional[Union[str, FilterExpression]] = None, + ) -> str: + """Build the full-text query string for text search with optional filtering.""" + if isinstance(filter_expression, FilterExpression): + filter_expression = str(filter_expression) + + query = f"(~@{text_field_name}:({self._tokenize_and_escape_query(text)})" + + if filter_expression and filter_expression != "*": + query += f" AND {filter_expression}" + + return query + + def _get_stopwords(self, stopwords: Optional[Union[str, Set[str]]] = "english") -> Set[str]: + """Get the stopwords to use in the query. + + Args: + stopwords (Optional[Union[str, Set[str]]]): The stopwords to use. If a string + such as "english" "german" is provided then a default set of stopwords for that + language will be used. if a list, 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. + + Returns: + The set of stopwords to use. + + Raises: + TypeError: If the stopwords are not a set, list, or tuple of strings. + """ + if not stopwords: + return set() + elif isinstance(stopwords, str): + try: + nltk.download("stopwords", quiet=True) + return set(nltk_stopwords.words(stopwords)) + except ImportError: + raise ValueError( + f"Loading stopwords for {stopwords} failed: nltk is not installed." + ) + except Exception as e: + raise ValueError(f"Error trying to load {stopwords} from nltk. {e}") + elif isinstance(stopwords, (Set, List, Tuple)) and all( # type: ignore + isinstance(word, str) for word in stopwords + ): + return set(stopwords) + else: + raise TypeError("stopwords must be a set, list, or tuple of strings") + + def set_text_weights(self, weights: Dict[str, float]): + """Set or update the text weights for the query. + + Args: + weights: Dictionary of word:weight mappings + """ + self._text_weights = _parse_text_weights(weights) + + def _tokenize_and_escape_query(self, user_query: str) -> str: + """Convert a raw user query to a redis full text query joined by ORs + + Args: + user_query (str): The user query to tokenize and escape. + + Returns: + str: The tokenized and escaped query string. + + Raises: + ValueError: If the text string becomes empty after stopwords are removed. + """ + escaper = TokenEscaper() + + tokens = [ + escaper.escape( + token.strip().strip(",").replace("“", "").replace("”", "").lower() + ) + for token in user_query.split() + ] + + token_list = [ + token for token in tokens if token and token not in self._stopwords + ] + for i, token in enumerate(token_list): + if token in self._text_weights: + token_list[i] = f"{token}=>{{$weight:{self._text_weights[token]}}}" + + if not token_list: + raise ValueError("text string cannot be empty after removing stopwords") + return " | ".join(token_list) + From 9b7283ba9d66ffdc012e11177bebe1c1d2c95bb5 Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Thu, 4 Dec 2025 14:27:35 +0100 Subject: [PATCH 02/34] Remove unused imports --- tests/unit/test_aggregation_types.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/tests/unit/test_aggregation_types.py b/tests/unit/test_aggregation_types.py index 22e89975..f2995c64 100644 --- a/tests/unit/test_aggregation_types.py +++ b/tests/unit/test_aggregation_types.py @@ -3,10 +3,7 @@ import pytest from redis.commands.search.aggregation import AggregateRequest -from redis.commands.search.query import Query -from redis.commands.search.result import Result -from redisvl.index.index import process_results from redisvl.query.aggregate import ( AggregateHybridQuery, HybridQuery, From 99f9d99aa67d4da3356cfb5101fac1eb10ce17da Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Thu, 4 Dec 2025 14:35:32 +0100 Subject: [PATCH 03/34] Formatting --- redisvl/query/aggregate.py | 4 +++- redisvl/utils/full_text_query_helper.py | 21 ++++++++------------- 2 files changed, 11 insertions(+), 14 deletions(-) diff --git a/redisvl/query/aggregate.py b/redisvl/query/aggregate.py index b64706b4..52360084 100644 --- a/redisvl/query/aggregate.py +++ b/redisvl/query/aggregate.py @@ -223,7 +223,9 @@ def set_text_weights(self, weights: Dict[str, float]): def _build_query_string(self) -> str: """Build the full query string for text search with optional filtering.""" - text = self._ft_helper.build_query_string(self._text, self._text_field, self._filter_expression) + text = self._ft_helper.build_query_string( + self._text, self._text_field, self._filter_expression + ) # Build KNN query knn_query = ( diff --git a/redisvl/utils/full_text_query_helper.py b/redisvl/utils/full_text_query_helper.py index c28093a4..ad364f97 100644 --- a/redisvl/utils/full_text_query_helper.py +++ b/redisvl/utils/full_text_query_helper.py @@ -1,4 +1,4 @@ -from typing import Optional, Union, Set, Dict, List, Tuple +from typing import Dict, List, Optional, Set, Tuple, Union from redisvl.query.filter import FilterExpression from redisvl.utils.token_escaper import TokenEscaper @@ -8,9 +8,7 @@ nltk_stopwords = lazy_import("nltk.corpus.stopwords") -def _parse_text_weights( - weights: Optional[Dict[str, float]] -) -> Dict[str, float]: +def _parse_text_weights(weights: Optional[Dict[str, float]]) -> Dict[str, float]: parsed_weights: Dict[str, float] = {} if not weights: return parsed_weights @@ -20,10 +18,7 @@ def _parse_text_weights( raise ValueError( f"Only individual words may be weighted. Got {{ {word}:{weight} }}" ) - if ( - not (isinstance(weight, float) or isinstance(weight, int)) - or weight < 0.0 - ): + if not (isinstance(weight, float) or isinstance(weight, int)) or weight < 0.0: raise ValueError( f"Weights must be positive number. Got {{ {word}:{weight} }}" ) @@ -32,8 +27,7 @@ def _parse_text_weights( class FullTextQueryHelper: - """Convert raw user queries into Redis full-text queries - tokenizes, escapes, and filters stopwords from the query. - """ + """Convert raw user queries into Redis full-text queries - tokenizes, escapes, and filters stopwords from the query.""" def __init__( self, @@ -77,7 +71,9 @@ def build_query_string( return query - def _get_stopwords(self, stopwords: Optional[Union[str, Set[str]]] = "english") -> Set[str]: + def _get_stopwords( + self, stopwords: Optional[Union[str, Set[str]]] = "english" + ) -> Set[str]: """Get the stopwords to use in the query. Args: @@ -106,7 +102,7 @@ def _get_stopwords(self, stopwords: Optional[Union[str, Set[str]]] = "english") except Exception as e: raise ValueError(f"Error trying to load {stopwords} from nltk. {e}") elif isinstance(stopwords, (Set, List, Tuple)) and all( # type: ignore - isinstance(word, str) for word in stopwords + isinstance(word, str) for word in stopwords ): return set(stopwords) else: @@ -151,4 +147,3 @@ def _tokenize_and_escape_query(self, user_query: str) -> str: if not token_list: raise ValueError("text string cannot be empty after removing stopwords") return " | ".join(token_list) - From 527b02463e510c5912fd03e674edcffe2e5afc11 Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Thu, 4 Dec 2025 14:37:23 +0100 Subject: [PATCH 04/34] Implement `HybridQuery` with tests --- pyproject.toml | 2 +- redisvl/query/hybrid.py | 68 ++++ tests/unit/test_hybrid_search.py | 618 +++++++++++++++++++++++++++++++ 3 files changed, 687 insertions(+), 1 deletion(-) create mode 100644 redisvl/query/hybrid.py create mode 100644 tests/unit/test_hybrid_search.py diff --git a/pyproject.toml b/pyproject.toml index 5e07da47..359a3284 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,7 +24,7 @@ classifiers = [ dependencies = [ "numpy>=1.26.0,<3", "pyyaml>=5.4,<7.0", - "redis>=5.0,<7.0", + "redis>=5.0,<7.2", "pydantic>=2,<3", "tenacity>=8.2.2", "ml-dtypes>=0.4.0,<1.0.0", diff --git a/redisvl/query/hybrid.py b/redisvl/query/hybrid.py new file mode 100644 index 00000000..5d1aa2d4 --- /dev/null +++ b/redisvl/query/hybrid.py @@ -0,0 +1,68 @@ +from typing import Any, Dict, List, Literal, Optional, Set, Union + +from redisvl.utils.full_text_query_helper import FullTextQueryHelper + +try: + from redis.commands.search.hybrid_query import HybridQuery as _HybridQuery + from redis.commands.search.hybrid_query import ( + HybridSearchQuery, + HybridVsimQuery, + VectorSearchMethods, + ) +except ImportError: + raise ImportError("Hybrid queries require redis>=7.1.0") + +from redisvl.query.filter import FilterExpression + + +class HybridQuery(_HybridQuery): + """TBD""" + + def __init__( + self, + text: str, + text_field_name: str, + vector: Union[bytes, List[float]], + vector_field_name: str, + text_scorer: str = "BM25STD", + filter_expression: Optional[Union[str, FilterExpression]] = None, + vector_search_method: Optional[Literal["KNN", "RANGE"]] = None, + vector_search_method_params: Optional[Dict[str, Any]] = None, + stopwords: Optional[Union[str, Set[str]]] = "english", + text_weights: Optional[Dict[str, float]] = None, + ): + self._ft_helper = FullTextQueryHelper( + stopwords=stopwords, + text_weights=text_weights, + ) + + search_query = HybridSearchQuery( + query_string=self._ft_helper.build_query_string( + text=text, + text_field_name=text_field_name, + filter_expression=filter_expression, + ), + scorer=text_scorer, + ) + + if not isinstance(vector, bytes): + vector_data: Union[str, bytes] = str(vector) + else: + vector_data = vector + + vsim_search_method = None + if vector_search_method: + vsim_search_method = VectorSearchMethods(vector_search_method) + + vsim_query = HybridVsimQuery( + vector_field_name=vector_field_name, + vector_data=vector_data, + vsim_search_method=vsim_search_method, + vsim_search_method_params=vector_search_method_params, + # TODO: Implement filter + ) + + super().__init__( + search_query=search_query, + vector_similarity_query=vsim_query, + ) diff --git a/tests/unit/test_hybrid_search.py b/tests/unit/test_hybrid_search.py new file mode 100644 index 00000000..468f5354 --- /dev/null +++ b/tests/unit/test_hybrid_search.py @@ -0,0 +1,618 @@ +"""Unit tests for HybridQuery class from redisvl.query.hybrid module. + +This test module validates the functionality of the HybridQuery class which +combines full-text search with vector similarity search using Redis's hybrid +query capabilities (requires redis>=7.1.0). +""" + +import pytest + +try: + from redis.commands.search.hybrid_query import HybridQuery as _HybridQuery + from redis.commands.search.hybrid_query import ( + HybridSearchQuery, + HybridVsimQuery, + VectorSearchMethods, + ) + + from redisvl.query.hybrid import HybridQuery + + REDIS_HYBRID_AVAILABLE = True +except ImportError: + REDIS_HYBRID_AVAILABLE = False + # Create dummy classes to avoid import errors + _HybridQuery = None # type: ignore + HybridSearchQuery = None # type: ignore + HybridVsimQuery = None # type: ignore + VectorSearchMethods = None # type: ignore + HybridQuery = None # type: ignore + +from redisvl.query.filter import Num, Tag, Text + +# Test data +sample_vector = [0.1, 0.2, 0.3, 0.4] +sample_text = "the toon squad play basketball against a gang of aliens" + +sample_vector_2 = [0.1, 0.2, 0.3, 0.4] +sample_vector_3 = [0.5, 0.5] +sample_vector_4 = [0.1, 0.1, 0.1] + + +# Basic init tests + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_basic_initialization(): + """Test basic HybridQuery initialization with required parameters.""" + text_field_name = "description" + vector_field_name = "embedding" + + hybrid_query = HybridQuery( + text=sample_text, + text_field_name=text_field_name, + vector=sample_vector, + vector_field_name=vector_field_name, + ) + + # Verify it's an instance of the base Redis HybridQuery + assert isinstance(hybrid_query, _HybridQuery) + + # Verify the FullTextQueryHelper was created + assert hasattr(hybrid_query, "_ft_helper") + assert hybrid_query._ft_helper is not None + + # Verify internal components exist (note: _vector_similarity_query, not _vsim_query) + assert hasattr(hybrid_query, "_search_query") + assert hasattr(hybrid_query, "_vector_similarity_query") + + # Verify they are the correct types + assert isinstance(hybrid_query._search_query, HybridSearchQuery) + assert isinstance(hybrid_query._vector_similarity_query, HybridVsimQuery) + + # Verify get_args() returns empty list (HybridQuery uses params, not args) + assert hybrid_query.get_args() == [ + "SEARCH", + "(~@description:(toon | squad | play | basketball | gang | aliens)", + "SCORER", + "BM25STD", + "VSIM", + "embedding", + [0.1, 0.2, 0.3, 0.4], + ] + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_with_all_parameters(): + """Test HybridQuery initialization with all optional parameters.""" + text_field_name = "description" + vector_field_name = "embedding" + text_scorer = "TFIDF" + filter_expression = Tag("genre") == "comedy" + vector_search_method = "KNN" + vector_search_method_params = {"K": 10} + stopwords = None + text_weights = {"toon": 2.0, "squad": 1.5} + + hybrid_query = HybridQuery( + text=sample_text, + text_field_name=text_field_name, + vector=sample_vector, + vector_field_name=vector_field_name, + text_scorer=text_scorer, + filter_expression=filter_expression, + vector_search_method=vector_search_method, + vector_search_method_params=vector_search_method_params, + stopwords=stopwords, + text_weights=text_weights, + ) + + assert isinstance(hybrid_query, _HybridQuery) + assert hybrid_query._ft_helper is not None + assert hybrid_query._ft_helper.stopwords == set() + assert hybrid_query._ft_helper.text_weights == text_weights + + # Verify get_args() returns correct serialized query with all parameters + args = hybrid_query.get_args() + expected = [ + "SEARCH", + "(~@description:(the | toon=>{$weight:2.0} | squad=>{$weight:1.5} | play | basketball | against | a | gang | of | aliens) AND @genre:{comedy}", + "SCORER", + "TFIDF", + "VSIM", + "embedding", + [0.1, 0.2, 0.3, 0.4], + "KNN", + 2, + "K", + 10, + ] + assert args == expected + + +# Stopwords tests + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_stopwords_default(): + """Test that default stopwords (english) are applied.""" + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + ) + + # Default should be english stopwords + stopwords = hybrid_query._ft_helper.stopwords + assert isinstance(stopwords, set) + assert len(stopwords) > 0 + # Common english stopwords should be present + assert "the" in stopwords + assert "a" in stopwords + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_stopwords_none(): + """Test that stopwords can be disabled with None.""" + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + stopwords=None, + ) + + assert hybrid_query._ft_helper.stopwords == set() + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_stopwords_empty_set(): + """Test that stopwords can be set to empty set.""" + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + stopwords=set(), + ) + + assert hybrid_query._ft_helper.stopwords == set() + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_stopwords_custom(): + """Test that custom stopwords are applied.""" + custom_stopwords = {"the", "a", "of", "and"} + + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + stopwords=custom_stopwords, + ) + + assert hybrid_query._ft_helper.stopwords == set(custom_stopwords) + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_stopwords_language(): + """Test that language-specific stopwords can be loaded.""" + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + stopwords="german", + ) + + # German stopwords should be loaded + stopwords = hybrid_query._ft_helper.stopwords + assert isinstance(stopwords, set) + assert len(stopwords) > 0 + for word in ("der", "die", "und"): # Common expected words + assert word in stopwords + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_stopwords_invalid_language(): + """Test that invalid language raises ValueError.""" + with pytest.raises(ValueError): + HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + stopwords="gibberish_language", + ) + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_stopwords_invalid_type(): + """Test that invalid stopwords type raises TypeError.""" + with pytest.raises(TypeError): + HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + stopwords=[1, 2, 3], # Invalid: list of integers + ) + + +# Text weight tests + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_text_weights_basic(): + """Test that text weights are properly applied.""" + text_weights = {"toon": 2.0, "squad": 1.5, "basketball": 3.0} + + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + text_weights=text_weights, + ) + + assert hybrid_query._ft_helper.text_weights == text_weights + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_text_weights_none(): + """Test that text_weights can be None.""" + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + text_weights=None, + ) + + assert hybrid_query._ft_helper.text_weights == {} + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_text_weights_empty(): + """Test that text_weights can be empty dict.""" + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + text_weights={}, + ) + + assert hybrid_query._ft_helper.text_weights == {} + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_text_weights_negative_value(): + """Test that negative text weights raise ValueError.""" + with pytest.raises(ValueError): + HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + text_weights={"word": -0.5}, + ) + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_text_weights_invalid_type(): + """Test that non-numeric text weights raise ValueError.""" + with pytest.raises(ValueError): + HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + text_weights={"word": "invalid"}, # type: ignore + ) + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_text_weights_multi_word_key(): + """Test that multi-word keys in text_weights raise ValueError.""" + with pytest.raises(ValueError): + HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + text_weights={"multi word": 2.0}, + ) + + +# Filter expression tests + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_with_string_filter(): + """Test HybridQuery with string filter expression.""" + string_filter = "@category:{tech|science|engineering}" + + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + filter_expression=string_filter, + ) + + assert hybrid_query.get_args() == [ + "SEARCH", + "(~@description:(toon | squad | play | basketball | gang | aliens) AND " + "@category:{tech|science|engineering}", + "SCORER", + "BM25STD", + "VSIM", + "embedding", + [0.1, 0.2, 0.3, 0.4], + ] + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_with_tag_filter(): + """Test HybridQuery with Tag FilterExpression.""" + tag_filter = Tag("genre") == "comedy" + + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + filter_expression=tag_filter, + ) + + assert hybrid_query.get_args() == [ + "SEARCH", + "(~@description:(toon | squad | play | basketball | gang | aliens) AND " + "@genre:{comedy}", + "SCORER", + "BM25STD", + "VSIM", + "embedding", + [0.1, 0.2, 0.3, 0.4], + ] + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_with_numeric_filter(): + """Test HybridQuery with Numeric FilterExpression.""" + numeric_filter = Num("age") > 30 + + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + filter_expression=numeric_filter, + ) + + # Verify filter is included in serialized query + args = hybrid_query.get_args() + assert args[0] == "SEARCH" + assert "@age:[(30 +inf]" in args[1] + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_with_text_filter(): + """Test HybridQuery with Text FilterExpression.""" + text_filter = Text("job") == "engineer" + + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + filter_expression=text_filter, + ) + + # Verify filter is included in serialized query + args = hybrid_query.get_args() + assert args[0] == "SEARCH" + assert '@job:("engineer")' in args[1] + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_with_combined_filters(): + """Test HybridQuery with combined FilterExpressions.""" + combined_filter = (Tag("genre") == "comedy") & (Num("rating") > 7.0) + + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + filter_expression=combined_filter, + ) + + # Verify both filters are included in serialized query + args = hybrid_query.get_args() + assert args[0] == "SEARCH" + assert "@genre:{comedy}" in args[1] + assert "@rating:[(7/- +inf]" in args[1] + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_with_wildcard_filter(): + """Test HybridQuery with wildcard filter.""" + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + filter_expression="*", + ) + + # Verify query structure - wildcard may or may not be included depending on implementation + args = hybrid_query.get_args() + assert ( + args[1] == "(~@description:(toon | squad | play | basketball | gang | aliens)" + ) # Query without filtering + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_without_filter(): + """Test HybridQuery without any filter expression.""" + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + filter_expression=None, + ) + + # Verify no filter in serialized query (only text query) + args = hybrid_query.get_args() + assert ( + args[1] == "(~@description:(toon | squad | play | basketball | gang | aliens)" + ) # No filter in query + + +# Vector search method tests + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_vector_search_method_knn(): + """Test HybridQuery with KNN vector search method.""" + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + vector_search_method="KNN", + ) + + # Without KNN params, it should not be in the args + args = hybrid_query.get_args() + assert "KNN" not in args + + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + vector_search_method="KNN", + vector_search_method_params={"K": 5}, + ) + + # KNN with params should be in args + args = hybrid_query.get_args() + assert args[-4:] == ["KNN", 2, "K", 5] + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_vector_search_method_range(): + """Test HybridQuery with RANGE vector search method.""" + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + vector_search_method="RANGE", + ) + + # Without RANGE params, it should not be in the args + args = hybrid_query.get_args() + assert "RANGE" not in args + + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + vector_search_method="RANGE", + vector_search_method_params={"RADIUS": 10}, + ) + + # RANGE with params should be in args + args = hybrid_query.get_args() + assert args[-4:] == ["RANGE", 2, "RADIUS", 10] + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_vector_search_method_none(): + """Test HybridQuery without specifying vector search method.""" + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + vector_search_method=None, + ) + + # Verify basic VSIM structure without explicit method + args = hybrid_query.get_args() + assert "VSIM" in args + assert "embedding" in args + # When None, should not have KNN or RANGE explicitly + assert "KNN" not in args + assert "RANGE" not in args + + +# Edge cases + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_empty_text_after_stopwords(): + """Test HybridQuery behavior when text becomes empty after stopword removal.""" + # All words are stopwords + with pytest.raises(ValueError, match="text string cannot be empty"): + HybridQuery( + text="the a an", + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + stopwords="english", + ) + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_special_characters_in_text(): + """Test HybridQuery with special characters in text.""" + special_text = "search for @user #hashtag $price 50% off!" + + hybrid_query = HybridQuery( + text=special_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + ) + + assert hybrid_query.get_args() == [ + "SEARCH", + "(~@description:(search | \\@user | \\#hashtag | \\$price | 50\\% | off\\!)", + "SCORER", + "BM25STD", + "VSIM", + "embedding", + [0.1, 0.2, 0.3, 0.4], + ] + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_unicode_text(): + """Test HybridQuery with Unicode characters in text.""" + unicode_text = "café résumé naïve 日本語 中文" + + hybrid_query = HybridQuery( + text=unicode_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + stopwords=None, # Disable stopwords for Unicode test + ) + + assert hybrid_query.get_args() == [ + "SEARCH", + "(~@description:(café | résumé | naïve | 日本語 | 中文)", + "SCORER", + "BM25STD", + "VSIM", + "embedding", + [0.1, 0.2, 0.3, 0.4], + ] From 6c0edd765851710237e0321dc7c745bfd9e35d8e Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Thu, 4 Dec 2025 16:15:29 +0100 Subject: [PATCH 05/34] Implement vsim search method params and vsim filtering in `HybridQuery` --- redisvl/query/aggregate.py | 2 +- redisvl/query/hybrid.py | 93 ++++++++++- tests/unit/test_hybrid_search.py | 258 ++++++++++++++++++++++++++----- 3 files changed, 308 insertions(+), 45 deletions(-) diff --git a/redisvl/query/aggregate.py b/redisvl/query/aggregate.py index 52360084..ab310b96 100644 --- a/redisvl/query/aggregate.py +++ b/redisvl/query/aggregate.py @@ -124,7 +124,7 @@ def __init__( num_results (int, optional): The number of results to return. Defaults to 10. return_fields (Optional[List[str]], optional): The fields to return. Defaults to None. stopwords (Optional[Union[str, Set[str]]], optional): The stopwords to remove from the - provided text prior to searchuse. If a string such as "english" "german" is + provided text prior to search-use. If a string such as "english" "german" is provided then a default set of stopwords for that language will be used. if a list, 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. diff --git a/redisvl/query/hybrid.py b/redisvl/query/hybrid.py index 5d1aa2d4..71b9726b 100644 --- a/redisvl/query/hybrid.py +++ b/redisvl/query/hybrid.py @@ -1,5 +1,7 @@ from typing import Any, Dict, List, Literal, Optional, Set, Union +from redis.commands.search.query import Filter + from redisvl.utils.full_text_query_helper import FullTextQueryHelper try: @@ -16,7 +18,9 @@ class HybridQuery(_HybridQuery): - """TBD""" + """ + A hybrid search query that combines text search and vector similarity, with configurable fusion methods. + """ def __init__( self, @@ -25,43 +29,116 @@ def __init__( vector: Union[bytes, List[float]], vector_field_name: str, text_scorer: str = "BM25STD", - filter_expression: Optional[Union[str, FilterExpression]] = None, + text_filter_expression: Optional[Union[str, FilterExpression]] = None, + yield_text_score_as: Optional[str] = None, vector_search_method: Optional[Literal["KNN", "RANGE"]] = None, - vector_search_method_params: Optional[Dict[str, Any]] = None, + knn_k: Optional[int] = None, + knn_ef_runtime: Optional[int] = None, + range_radius: Optional[float] = None, + range_epsilon: Optional[float] = None, + yield_vsim_score_as: Optional[str] = None, + vector_filter_expression: Optional[Union[str, FilterExpression]] = None, stopwords: Optional[Union[str, Set[str]]] = "english", text_weights: Optional[Dict[str, float]] = None, ): + """ + Instantiates a HybridQuery object. + + Args: + text: The text to search for. + text_field_name: The text field name to search in. + vector: The vector to perform vector similarity search. + vector_field_name: The vector field name to search in. + text_scorer: The text scorer to use. Options are {TFIDF, TFIDF.DOCNORM, + BM25, DISMAX, DOCSCORE, BM25STD}. Defaults to "BM25STD". + text_filter_expression: The filter expression to use for the text search. Defaults to None. + yield_text_score_as: The name of the field to yield the text score as. + vector_search_method: The vector search method to use. Options are {KNN, RANGE}. Defaults to None. + knn_k: The number of nearest neighbors to return, required if `vector_search_method` is "KNN". + knn_ef_runtime: The exploration factor parameter for HNSW, optional if `vector_search_method` is "KNN". + range_radius: The search radius to use, required if `vector_search_method` is "RANGE". + range_epsilon: The epsilon value to use, optional if `vector_search_method` is "RANGE"; defines the + accuracy of the search. + yield_vsim_score_as: The name of the field to yield the vector similarity score as. + vector_filter_expression: The filter expression to use for the vector similarity search. Defaults to None. + stopwords (Optional[Union[str, Set[str]]], optional): The stopwords to remove from the + provided text prior to search-use. If a string such as "english" "german" is + provided then a default set of stopwords for that language will be used. if a list, + 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. + + Note: This parameter controls query-time stopword filtering (client-side). + For index-level stopwords configuration (server-side), see IndexInfo.stopwords. + Using query-time stopwords with index-level STOPWORDS 0 is counterproductive. + 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: + TypeError: If the stopwords are not a set, list, or tuple of strings. + ValueError: If the text string is empty, or if the text string becomes empty after + stopwords are removed. + ValueError: If `vector_search_method` is not one of {KNN, RANGE} (or None). + ValueError: If `vector_search_method` is "KNN" and `knn_k` is not provided. + ValueError: If `vector_search_method` is "RANGE" and `range_radius` is not provided. + """ self._ft_helper = FullTextQueryHelper( stopwords=stopwords, text_weights=text_weights, ) + # Serialize the full-text search query search_query = HybridSearchQuery( query_string=self._ft_helper.build_query_string( text=text, text_field_name=text_field_name, - filter_expression=filter_expression, + filter_expression=text_filter_expression, ), scorer=text_scorer, + yield_score_as=yield_text_score_as, ) + # If the vector isn't already bytes, it needs to be represented as a string if not isinstance(vector, bytes): vector_data: Union[str, bytes] = str(vector) else: vector_data = vector + # Serialize vector similarity search method and params, if specified vsim_search_method = None - if vector_search_method: - vsim_search_method = VectorSearchMethods(vector_search_method) + vsim_search_method_params = {} + if vector_search_method == "KNN": + vsim_search_method = VectorSearchMethods.KNN + if not knn_k: + raise ValueError("Must provide K if vector_search_method is KNN") + + vsim_search_method_params["K"] = knn_k + if knn_ef_runtime: + vsim_search_method_params["EF_RUNTIME"] = knn_ef_runtime + + elif vector_search_method == "RANGE": + vsim_search_method = VectorSearchMethods.RANGE + if not range_radius: + raise ValueError("Must provide RADIUS if vector_search_method is RANGE") + + vsim_search_method_params["RADIUS"] = range_radius + if range_epsilon: + vsim_search_method_params["EPSILON"] = range_epsilon + + elif vector_search_method is not None: + raise ValueError(f"Unknown vector search method: {vector_search_method}") + # Serialize the vector similarity query vsim_query = HybridVsimQuery( vector_field_name=vector_field_name, vector_data=vector_data, vsim_search_method=vsim_search_method, - vsim_search_method_params=vector_search_method_params, - # TODO: Implement filter + vsim_search_method_params=vsim_search_method_params, + filter=vector_filter_expression and Filter("FILTER", str(vector_filter_expression)), + yield_score_as=yield_vsim_score_as, ) + # Initialize the base HybridQuery super().__init__( search_query=search_query, vector_similarity_query=vsim_query, diff --git a/tests/unit/test_hybrid_search.py b/tests/unit/test_hybrid_search.py index 468f5354..97a13ef4 100644 --- a/tests/unit/test_hybrid_search.py +++ b/tests/unit/test_hybrid_search.py @@ -77,32 +77,27 @@ def test_hybrid_query_basic_initialization(): "BM25STD", "VSIM", "embedding", - [0.1, 0.2, 0.3, 0.4], + "[0.1, 0.2, 0.3, 0.4]", ] @pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") def test_hybrid_query_with_all_parameters(): """Test HybridQuery initialization with all optional parameters.""" - text_field_name = "description" - vector_field_name = "embedding" - text_scorer = "TFIDF" filter_expression = Tag("genre") == "comedy" - vector_search_method = "KNN" - vector_search_method_params = {"K": 10} - stopwords = None text_weights = {"toon": 2.0, "squad": 1.5} hybrid_query = HybridQuery( text=sample_text, - text_field_name=text_field_name, + text_field_name="description", vector=sample_vector, - vector_field_name=vector_field_name, - text_scorer=text_scorer, - filter_expression=filter_expression, - vector_search_method=vector_search_method, - vector_search_method_params=vector_search_method_params, - stopwords=stopwords, + vector_field_name="embedding", + text_scorer="TFIDF", + text_filter_expression=filter_expression, + vector_search_method="KNN", + knn_k=10, + knn_ef_runtime=100, + stopwords=None, text_weights=text_weights, ) @@ -120,11 +115,13 @@ def test_hybrid_query_with_all_parameters(): "TFIDF", "VSIM", "embedding", - [0.1, 0.2, 0.3, 0.4], + "[0.1, 0.2, 0.3, 0.4]", "KNN", - 2, + 4, "K", 10, + "EF_RUNTIME", + 100, ] assert args == expected @@ -339,7 +336,7 @@ def test_hybrid_query_with_string_filter(): text_field_name="description", vector=sample_vector, vector_field_name="embedding", - filter_expression=string_filter, + text_filter_expression=string_filter, ) assert hybrid_query.get_args() == [ @@ -350,7 +347,7 @@ def test_hybrid_query_with_string_filter(): "BM25STD", "VSIM", "embedding", - [0.1, 0.2, 0.3, 0.4], + "[0.1, 0.2, 0.3, 0.4]", ] @@ -364,7 +361,7 @@ def test_hybrid_query_with_tag_filter(): text_field_name="description", vector=sample_vector, vector_field_name="embedding", - filter_expression=tag_filter, + text_filter_expression=tag_filter, ) assert hybrid_query.get_args() == [ @@ -375,7 +372,7 @@ def test_hybrid_query_with_tag_filter(): "BM25STD", "VSIM", "embedding", - [0.1, 0.2, 0.3, 0.4], + "[0.1, 0.2, 0.3, 0.4]", ] @@ -389,7 +386,7 @@ def test_hybrid_query_with_numeric_filter(): text_field_name="description", vector=sample_vector, vector_field_name="embedding", - filter_expression=numeric_filter, + text_filter_expression=numeric_filter, ) # Verify filter is included in serialized query @@ -408,7 +405,7 @@ def test_hybrid_query_with_text_filter(): text_field_name="description", vector=sample_vector, vector_field_name="embedding", - filter_expression=text_filter, + text_filter_expression=text_filter, ) # Verify filter is included in serialized query @@ -427,14 +424,14 @@ def test_hybrid_query_with_combined_filters(): text_field_name="description", vector=sample_vector, vector_field_name="embedding", - filter_expression=combined_filter, + text_filter_expression=combined_filter, ) # Verify both filters are included in serialized query args = hybrid_query.get_args() assert args[0] == "SEARCH" assert "@genre:{comedy}" in args[1] - assert "@rating:[(7/- +inf]" in args[1] + assert "@rating:[(7.0 +inf]" in args[1] @pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") @@ -445,7 +442,7 @@ def test_hybrid_query_with_wildcard_filter(): text_field_name="description", vector=sample_vector, vector_field_name="embedding", - filter_expression="*", + text_filter_expression="*", ) # Verify query structure - wildcard may or may not be included depending on implementation @@ -463,7 +460,7 @@ def test_hybrid_query_without_filter(): text_field_name="description", vector=sample_vector, vector_field_name="embedding", - filter_expression=None, + text_filter_expression=None, ) # Verify no filter in serialized query (only text query) @@ -479,59 +476,85 @@ def test_hybrid_query_without_filter(): @pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") def test_hybrid_query_vector_search_method_knn(): """Test HybridQuery with KNN vector search method.""" + with pytest.raises(ValueError): + # KNN requires K + HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + vector_search_method="KNN", + ) + hybrid_query = HybridQuery( text=sample_text, text_field_name="description", vector=sample_vector, vector_field_name="embedding", vector_search_method="KNN", + knn_k=10, ) - # Without KNN params, it should not be in the args + # KNN with params should be in args args = hybrid_query.get_args() - assert "KNN" not in args + assert args[-4:] == ["KNN", 2, "K", 10] + # With optional EF_RUNTIME param hybrid_query = HybridQuery( text=sample_text, text_field_name="description", vector=sample_vector, vector_field_name="embedding", vector_search_method="KNN", - vector_search_method_params={"K": 5}, + knn_k=10, + knn_ef_runtime=100 ) # KNN with params should be in args args = hybrid_query.get_args() - assert args[-4:] == ["KNN", 2, "K", 5] + assert args[-6:] == ["KNN", 4, "K", 10, "EF_RUNTIME", 100] @pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") def test_hybrid_query_vector_search_method_range(): """Test HybridQuery with RANGE vector search method.""" + with pytest.raises(ValueError): + # RANGE requires RADIUS + HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + vector_search_method="RANGE", + ) + hybrid_query = HybridQuery( text=sample_text, text_field_name="description", vector=sample_vector, vector_field_name="embedding", vector_search_method="RANGE", + range_radius=10, ) - # Without RANGE params, it should not be in the args + # RANGE with params should be in args args = hybrid_query.get_args() - assert "RANGE" not in args + assert args[-4:] == ["RANGE", 2, "RADIUS", 10] + # With optional EPSILON param hybrid_query = HybridQuery( text=sample_text, text_field_name="description", vector=sample_vector, vector_field_name="embedding", vector_search_method="RANGE", - vector_search_method_params={"RADIUS": 10}, + range_radius=10, + range_epsilon=0.1, ) # RANGE with params should be in args args = hybrid_query.get_args() - assert args[-4:] == ["RANGE", 2, "RADIUS", 10] + assert args[-6:] == ["RANGE", 4, "RADIUS", 10, "EPSILON", 0.1] @pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") @@ -590,7 +613,7 @@ def test_hybrid_query_special_characters_in_text(): "BM25STD", "VSIM", "embedding", - [0.1, 0.2, 0.3, 0.4], + "[0.1, 0.2, 0.3, 0.4]", ] @@ -614,5 +637,168 @@ def test_hybrid_query_unicode_text(): "BM25STD", "VSIM", "embedding", - [0.1, 0.2, 0.3, 0.4], + "[0.1, 0.2, 0.3, 0.4]", + ] + + +# Vector filter expression tests + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_with_vector_filter_tag(): + """Test HybridQuery with Tag FilterExpression on vector search.""" + tag_filter = Tag("genre") == "comedy" + + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + vector_filter_expression=tag_filter, + ) + + # Verify filter is included in serialized query + args = hybrid_query.get_args() + assert args[-2:] == ["FILTER", "@genre:{comedy}"] + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_with_vector_filter_string(): + """Test HybridQuery with string filter expression on vector search.""" + string_filter = "@category:{tech|science|engineering}" + + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + vector_filter_expression=string_filter, + ) + + # Verify filter is included in serialized query + args = hybrid_query.get_args() + assert args[-2:] == ["FILTER", "@category:{tech|science|engineering}"] + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_with_vector_filter_numeric(): + """Test HybridQuery with Numeric FilterExpression on vector search.""" + numeric_filter = Num("rating") > 7.0 + + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + vector_filter_expression=numeric_filter, + ) + + # Verify filter is included in serialized query + args = hybrid_query.get_args() + assert args[-2:] == ["FILTER", "@rating:[(7.0 +inf]"] + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_with_vector_filter_text(): + """Test HybridQuery with Text FilterExpression on vector search.""" + text_filter = Text("job") == "engineer" + + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + vector_filter_expression=text_filter, + ) + + # Verify filter is included in serialized query + args = hybrid_query.get_args() + assert args[-2:] == ["FILTER", '@job:("engineer")'] + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_with_vector_filter_combined(): + """Test HybridQuery with combined FilterExpressions on vector search.""" + combined_filter = (Tag("genre") == "comedy") & (Num("rating") > 7.0) + + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + vector_filter_expression=combined_filter, + ) + + # Verify both filters are included in serialized query + args = hybrid_query.get_args() + assert args[0] == "SEARCH" + assert args[-2] == "FILTER" + assert "@genre:{comedy}" in args[-1] + assert "@rating:[(7.0 +inf]" in args[-1] + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_with_vector_filter_none(): + """Test HybridQuery without vector filter expression.""" + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + vector_filter_expression=None, + ) + + # Verify no FILTER in serialized query + args = hybrid_query.get_args() + assert "FILTER" not in args + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_with_vector_filter_and_method(): + """Test HybridQuery with vector filter and a search method.""" + tag_filter = Tag("genre") == "comedy" + + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + vector_search_method="KNN", + knn_k=10, + vector_filter_expression=tag_filter, + ) + + # Verify KNN params and filter are both in args + args = hybrid_query.get_args() + assert args[-6:] == ["KNN", 2, "K", 10, "FILTER", "@genre:{comedy}"] + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_with_both_text_and_vector_filters(): + """Test HybridQuery with both text_filter_expression and vector_filter_expression.""" + text_filter = Tag("category") == "movies" + vector_filter = Tag("genre") == "comedy" + + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + text_filter_expression=text_filter, + vector_filter_expression=vector_filter, + ) + + # Verify both filters are in the query + args = hybrid_query.get_args() + assert args == [ + "SEARCH", + "(~@description:(toon | squad | play | basketball | gang | aliens) AND " + "@category:{movies}", + "SCORER", + "BM25STD", + "VSIM", + "embedding", + "[0.1, 0.2, 0.3, 0.4]", + "FILTER", + "@genre:{comedy}", ] From da2283e5848d01aa3f17214483971c2568a4c546 Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Thu, 4 Dec 2025 16:23:09 +0100 Subject: [PATCH 06/34] Update `redisvl.query.aggregate.HybridQuery` deprecation message --- redisvl/query/aggregate.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/redisvl/query/aggregate.py b/redisvl/query/aggregate.py index ab310b96..96904d4b 100644 --- a/redisvl/query/aggregate.py +++ b/redisvl/query/aggregate.py @@ -246,16 +246,16 @@ class HybridQuery(AggregateHybridQuery): """Backward compatibility wrapper for AggregateHybridQuery. .. deprecated:: - 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.", + This class is deprecated and will be removed in a future version. + Please use the new HybridQuery from redisvl.query.hybrid instead. + For maintaining current AggregateHybridQuery functionality, use AggregateHybridQuery directly. """ def __init__(self, *args, **kwargs): warnings.warn( - "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.", + "This HybridQuery class is deprecated and will be removed in a future version. " + "Please use the new HybridQuery from redisvl.query.hybrid instead. " + "For maintaining current AggregateHybridQuery functionality, use AggregateHybridQuery directly.", DeprecationWarning, stacklevel=2, ) From 80f1927adb5f166ff713c01dcc043a4cec1aa02a Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Fri, 5 Dec 2025 11:32:38 +0100 Subject: [PATCH 07/34] Add support for combination methods and postprocessing --- redisvl/query/hybrid.py | 138 ++++++++++-- tests/unit/test_hybrid_search.py | 350 ++++++++++++++++++++++++++----- 2 files changed, 416 insertions(+), 72 deletions(-) diff --git a/redisvl/query/hybrid.py b/redisvl/query/hybrid.py index 71b9726b..c18a043a 100644 --- a/redisvl/query/hybrid.py +++ b/redisvl/query/hybrid.py @@ -5,8 +5,11 @@ from redisvl.utils.full_text_query_helper import FullTextQueryHelper try: - from redis.commands.search.hybrid_query import HybridQuery as _HybridQuery from redis.commands.search.hybrid_query import ( + CombinationMethods, + CombineResultsMethod, + HybridPostProcessingConfig, + HybridQuery as RedisHybridQuery, HybridSearchQuery, HybridVsimQuery, VectorSearchMethods, @@ -17,7 +20,7 @@ from redisvl.query.filter import FilterExpression -class HybridQuery(_HybridQuery): +class HybridQuery: """ A hybrid search query that combines text search and vector similarity, with configurable fusion methods. """ @@ -34,12 +37,18 @@ def __init__( vector_search_method: Optional[Literal["KNN", "RANGE"]] = None, knn_k: Optional[int] = None, knn_ef_runtime: Optional[int] = None, - range_radius: Optional[float] = None, + range_radius: Optional[int] = None, range_epsilon: Optional[float] = None, yield_vsim_score_as: Optional[str] = None, vector_filter_expression: Optional[Union[str, FilterExpression]] = None, stopwords: Optional[Union[str, Set[str]]] = "english", text_weights: Optional[Dict[str, float]] = None, + combination_method: Optional[Literal["RRF", "LINEAR"]] = None, + rrf_window: Optional[int] = None, + rrf_constant: Optional[float] = None, + linear_alpha: Optional[float] = None, + linear_beta: Optional[float] = None, + yield_combined_score_as: Optional[str] = None, ): """ Instantiates a HybridQuery object. @@ -50,7 +59,9 @@ def __init__( vector: The vector to perform vector similarity search. vector_field_name: The vector field name to search in. text_scorer: The text scorer to use. Options are {TFIDF, TFIDF.DOCNORM, - BM25, DISMAX, DOCSCORE, BM25STD}. Defaults to "BM25STD". + BM25STD, BM25STD.NORM, BM25STD.TANH, DISMAX, DOCSCORE, HAMMING}. Defaults to "BM25STD". For more + information about supported scroring algorithms, + see https://redis.io/docs/latest/develop/ai/search-and-query/advanced-concepts/scoring/ text_filter_expression: The filter expression to use for the text search. Defaults to None. yield_text_score_as: The name of the field to yield the text score as. vector_search_method: The vector search method to use. Options are {KNN, RANGE}. Defaults to None. @@ -73,6 +84,18 @@ def __init__( 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. + combination_method: The combination method to use. Options are {RRF, LINEAR}. Defaults to None. + rrf_window: The window size to use for the reciprocal rank fusion (RRF) combination method. Limits + fusion scope. + rrf_constant: The constant to use for the reciprocal rank fusion (RRF) combination method. Controls decay + of rank influence. + linear_alpha: The weight of the first query for the linear combination method (LINEAR). + linear_beta: The weight of the second query for the linear combination method (LINEAR). + yield_combined_score_as: The name of the field to yield the combined score as. + + Notes: + If RRF combination method is used, then at least one of `rrf_window` or `rrf_constant` must be provided. + If LINEAR combination method is used, then at least one of `linear_alpha` or `linear_beta` must be provided. Raises: TypeError: If the stopwords are not a set, list, or tuple of strings. @@ -82,18 +105,65 @@ def __init__( ValueError: If `vector_search_method` is "KNN" and `knn_k` is not provided. ValueError: If `vector_search_method` is "RANGE" and `range_radius` is not provided. """ + self.postprocessing_config = HybridPostProcessingConfig() self._ft_helper = FullTextQueryHelper( stopwords=stopwords, text_weights=text_weights, ) + query_string = self._ft_helper.build_query_string( + text, text_field_name, text_filter_expression + ) + + self.query = self.build_query( + text_query=query_string, + vector=vector, + vector_field_name=vector_field_name, + text_scorer=text_scorer, + yield_text_score_as=yield_text_score_as, + vector_search_method=vector_search_method, + knn_k=knn_k, + knn_ef_runtime=knn_ef_runtime, + range_radius=range_radius, + range_epsilon=range_epsilon, + yield_vsim_score_as=yield_vsim_score_as, + vector_filter_expression=vector_filter_expression, + ) + + if combination_method: + self.combination_method: Optional[CombineResultsMethod] = ( + self.build_combination_method( + combination_method=combination_method, + rrf_window=rrf_window, + rrf_constant=rrf_constant, + linear_alpha=linear_alpha, + linear_beta=linear_beta, + yield_score_as=yield_combined_score_as, + ) + ) + else: + self.combination_method = None + + @staticmethod + def build_query( + text_query: str, + vector: Union[bytes, List[float]], + vector_field_name: str, + text_scorer: str = "BM25STD", + yield_text_score_as: Optional[str] = None, + vector_search_method: Optional[Literal["KNN", "RANGE"]] = None, + knn_k: Optional[int] = None, + knn_ef_runtime: Optional[int] = None, + range_radius: Optional[int] = None, + range_epsilon: Optional[float] = None, + yield_vsim_score_as: Optional[str] = None, + vector_filter_expression: Optional[Union[str, FilterExpression]] = None, + ) -> RedisHybridQuery: + """Build a Redis HybridQuery for the hybrid search.""" + # Serialize the full-text search query search_query = HybridSearchQuery( - query_string=self._ft_helper.build_query_string( - text=text, - text_field_name=text_field_name, - filter_expression=text_filter_expression, - ), + query_string=text_query, scorer=text_scorer, yield_score_as=yield_text_score_as, ) @@ -105,8 +175,8 @@ def __init__( vector_data = vector # Serialize vector similarity search method and params, if specified - vsim_search_method = None - vsim_search_method_params = {} + vsim_search_method: Optional[VectorSearchMethods] = None + vsim_search_method_params: Dict[str, Any] = {} if vector_search_method == "KNN": vsim_search_method = VectorSearchMethods.KNN if not knn_k: @@ -128,18 +198,58 @@ def __init__( elif vector_search_method is not None: raise ValueError(f"Unknown vector search method: {vector_search_method}") + if vector_filter_expression: + vsim_filter = Filter("FILTER", str(vector_filter_expression)) + else: + vsim_filter = None + # Serialize the vector similarity query vsim_query = HybridVsimQuery( vector_field_name=vector_field_name, vector_data=vector_data, vsim_search_method=vsim_search_method, vsim_search_method_params=vsim_search_method_params, - filter=vector_filter_expression and Filter("FILTER", str(vector_filter_expression)), + filter=vsim_filter, yield_score_as=yield_vsim_score_as, ) - # Initialize the base HybridQuery - super().__init__( + return RedisHybridQuery( search_query=search_query, vector_similarity_query=vsim_query, ) + + @staticmethod + def build_combination_method( + combination_method: Literal["RRF", "LINEAR"], + rrf_window: Optional[int] = None, + rrf_constant: Optional[float] = None, + linear_alpha: Optional[float] = None, + linear_beta: Optional[float] = None, + yield_score_as: Optional[str] = None, + ) -> CombineResultsMethod: + """Build a configuration for combining hybrid search scores.""" + method_params: Dict[str, Any] = {} + if combination_method == "RRF": + method = CombinationMethods.RRF + if rrf_window: + method_params["WINDOW"] = rrf_window + if rrf_constant: + method_params["CONSTANT"] = rrf_constant + + elif combination_method == "LINEAR": + method = CombinationMethods.LINEAR + if linear_alpha: + method_params["ALPHA"] = linear_alpha + if linear_beta: + method_params["BETA"] = linear_beta + + else: + raise ValueError(f"Unknown combination method: {combination_method}") + + if yield_score_as: + method_params["YIELD_SCORE_AS"] = yield_score_as + + return CombineResultsMethod( + method=method, + **method_params, + ) diff --git a/tests/unit/test_hybrid_search.py b/tests/unit/test_hybrid_search.py index 97a13ef4..ed8e8f9e 100644 --- a/tests/unit/test_hybrid_search.py +++ b/tests/unit/test_hybrid_search.py @@ -5,10 +5,12 @@ query capabilities (requires redis>=7.1.0). """ +from typing import List + import pytest try: - from redis.commands.search.hybrid_query import HybridQuery as _HybridQuery + from redis.commands.search.hybrid_query import HybridQuery as RedisHybridQuery from redis.commands.search.hybrid_query import ( HybridSearchQuery, HybridVsimQuery, @@ -21,7 +23,7 @@ except ImportError: REDIS_HYBRID_AVAILABLE = False # Create dummy classes to avoid import errors - _HybridQuery = None # type: ignore + RedisHybridQuery = None # type: ignore HybridSearchQuery = None # type: ignore HybridVsimQuery = None # type: ignore VectorSearchMethods = None # type: ignore @@ -33,9 +35,16 @@ sample_vector = [0.1, 0.2, 0.3, 0.4] sample_text = "the toon squad play basketball against a gang of aliens" -sample_vector_2 = [0.1, 0.2, 0.3, 0.4] -sample_vector_3 = [0.5, 0.5] -sample_vector_4 = [0.1, 0.1, 0.1] + +def get_query_pieces(query: HybridQuery) -> List[str]: + """Get all the pieces of the complete hybrid query.""" + # NOTE: Modeled after logic in `redis.commands.search.commands.SearchCommands.hybrid_search` + pieces = query.query.get_args() + if query.combination_method: + pieces.extend(query.combination_method.get_args()) + if query.postprocessing_config.build_args(): + pieces.extend(query.postprocessing_config.build_args()) + return pieces # Basic init tests @@ -54,23 +63,8 @@ def test_hybrid_query_basic_initialization(): vector_field_name=vector_field_name, ) - # Verify it's an instance of the base Redis HybridQuery - assert isinstance(hybrid_query, _HybridQuery) - - # Verify the FullTextQueryHelper was created - assert hasattr(hybrid_query, "_ft_helper") - assert hybrid_query._ft_helper is not None - - # Verify internal components exist (note: _vector_similarity_query, not _vsim_query) - assert hasattr(hybrid_query, "_search_query") - assert hasattr(hybrid_query, "_vector_similarity_query") - - # Verify they are the correct types - assert isinstance(hybrid_query._search_query, HybridSearchQuery) - assert isinstance(hybrid_query._vector_similarity_query, HybridVsimQuery) - # Verify get_args() returns empty list (HybridQuery uses params, not args) - assert hybrid_query.get_args() == [ + assert get_query_pieces(hybrid_query) == [ "SEARCH", "(~@description:(toon | squad | play | basketball | gang | aliens)", "SCORER", @@ -80,6 +74,9 @@ def test_hybrid_query_basic_initialization(): "[0.1, 0.2, 0.3, 0.4]", ] + # Verify that no combination method is set + assert hybrid_query.combination_method is None + @pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") def test_hybrid_query_with_all_parameters(): @@ -94,25 +91,31 @@ def test_hybrid_query_with_all_parameters(): vector_field_name="embedding", text_scorer="TFIDF", text_filter_expression=filter_expression, + yield_text_score_as="text_score", vector_search_method="KNN", knn_k=10, knn_ef_runtime=100, + yield_vsim_score_as="vsim_score", stopwords=None, text_weights=text_weights, + combination_method="RRF", + rrf_window=10, + rrf_constant=0.5, + yield_combined_score_as="hybrid_score", ) - assert isinstance(hybrid_query, _HybridQuery) assert hybrid_query._ft_helper is not None assert hybrid_query._ft_helper.stopwords == set() assert hybrid_query._ft_helper.text_weights == text_weights - # Verify get_args() returns correct serialized query with all parameters - args = hybrid_query.get_args() - expected = [ + # Verify that the expected query pieces have been defined + assert get_query_pieces(hybrid_query) == [ "SEARCH", "(~@description:(the | toon=>{$weight:2.0} | squad=>{$weight:1.5} | play | basketball | against | a | gang | of | aliens) AND @genre:{comedy}", "SCORER", "TFIDF", + "YIELD_SCORE_AS", + "text_score", "VSIM", "embedding", "[0.1, 0.2, 0.3, 0.4]", @@ -122,8 +125,22 @@ def test_hybrid_query_with_all_parameters(): 10, "EF_RUNTIME", 100, + "YIELD_SCORE_AS", + "vsim_score", + "COMBINE", + "RRF", + 6, + "WINDOW", + 10, + "CONSTANT", + 0.5, + "YIELD_SCORE_AS", + "hybrid_score", ] - assert args == expected + + # Add post-processing and verify that it is reflected in the query + hybrid_query.postprocessing_config.limit(offset=10, num=20) + assert get_query_pieces(hybrid_query)[-3:] == ["LIMIT", "10", "20"] # Stopwords tests @@ -339,7 +356,7 @@ def test_hybrid_query_with_string_filter(): text_filter_expression=string_filter, ) - assert hybrid_query.get_args() == [ + assert get_query_pieces(hybrid_query) == [ "SEARCH", "(~@description:(toon | squad | play | basketball | gang | aliens) AND " "@category:{tech|science|engineering}", @@ -364,7 +381,7 @@ def test_hybrid_query_with_tag_filter(): text_filter_expression=tag_filter, ) - assert hybrid_query.get_args() == [ + assert get_query_pieces(hybrid_query) == [ "SEARCH", "(~@description:(toon | squad | play | basketball | gang | aliens) AND " "@genre:{comedy}", @@ -390,8 +407,7 @@ def test_hybrid_query_with_numeric_filter(): ) # Verify filter is included in serialized query - args = hybrid_query.get_args() - assert args[0] == "SEARCH" + args = get_query_pieces(hybrid_query) assert "@age:[(30 +inf]" in args[1] @@ -409,8 +425,7 @@ def test_hybrid_query_with_text_filter(): ) # Verify filter is included in serialized query - args = hybrid_query.get_args() - assert args[0] == "SEARCH" + args = get_query_pieces(hybrid_query) assert '@job:("engineer")' in args[1] @@ -428,10 +443,8 @@ def test_hybrid_query_with_combined_filters(): ) # Verify both filters are included in serialized query - args = hybrid_query.get_args() - assert args[0] == "SEARCH" - assert "@genre:{comedy}" in args[1] - assert "@rating:[(7.0 +inf]" in args[1] + args = get_query_pieces(hybrid_query) + assert args[1].endswith("AND (@genre:{comedy} @rating:[(7.0 +inf])") @pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") @@ -446,7 +459,7 @@ def test_hybrid_query_with_wildcard_filter(): ) # Verify query structure - wildcard may or may not be included depending on implementation - args = hybrid_query.get_args() + args = get_query_pieces(hybrid_query) assert ( args[1] == "(~@description:(toon | squad | play | basketball | gang | aliens)" ) # Query without filtering @@ -464,7 +477,7 @@ def test_hybrid_query_without_filter(): ) # Verify no filter in serialized query (only text query) - args = hybrid_query.get_args() + args = get_query_pieces(hybrid_query) assert ( args[1] == "(~@description:(toon | squad | play | basketball | gang | aliens)" ) # No filter in query @@ -496,7 +509,7 @@ def test_hybrid_query_vector_search_method_knn(): ) # KNN with params should be in args - args = hybrid_query.get_args() + args = get_query_pieces(hybrid_query) assert args[-4:] == ["KNN", 2, "K", 10] # With optional EF_RUNTIME param @@ -507,11 +520,11 @@ def test_hybrid_query_vector_search_method_knn(): vector_field_name="embedding", vector_search_method="KNN", knn_k=10, - knn_ef_runtime=100 + knn_ef_runtime=100, ) # KNN with params should be in args - args = hybrid_query.get_args() + args = get_query_pieces(hybrid_query) assert args[-6:] == ["KNN", 4, "K", 10, "EF_RUNTIME", 100] @@ -538,7 +551,7 @@ def test_hybrid_query_vector_search_method_range(): ) # RANGE with params should be in args - args = hybrid_query.get_args() + args = get_query_pieces(hybrid_query) assert args[-4:] == ["RANGE", 2, "RADIUS", 10] # With optional EPSILON param @@ -553,7 +566,7 @@ def test_hybrid_query_vector_search_method_range(): ) # RANGE with params should be in args - args = hybrid_query.get_args() + args = get_query_pieces(hybrid_query) assert args[-6:] == ["RANGE", 4, "RADIUS", 10, "EPSILON", 0.1] @@ -569,7 +582,7 @@ def test_hybrid_query_vector_search_method_none(): ) # Verify basic VSIM structure without explicit method - args = hybrid_query.get_args() + args = get_query_pieces(hybrid_query) assert "VSIM" in args assert "embedding" in args # When None, should not have KNN or RANGE explicitly @@ -606,7 +619,7 @@ def test_hybrid_query_special_characters_in_text(): vector_field_name="embedding", ) - assert hybrid_query.get_args() == [ + assert get_query_pieces(hybrid_query) == [ "SEARCH", "(~@description:(search | \\@user | \\#hashtag | \\$price | 50\\% | off\\!)", "SCORER", @@ -630,7 +643,7 @@ def test_hybrid_query_unicode_text(): stopwords=None, # Disable stopwords for Unicode test ) - assert hybrid_query.get_args() == [ + assert get_query_pieces(hybrid_query) == [ "SEARCH", "(~@description:(café | résumé | naïve | 日本語 | 中文)", "SCORER", @@ -658,7 +671,7 @@ def test_hybrid_query_with_vector_filter_tag(): ) # Verify filter is included in serialized query - args = hybrid_query.get_args() + args = get_query_pieces(hybrid_query) assert args[-2:] == ["FILTER", "@genre:{comedy}"] @@ -676,7 +689,7 @@ def test_hybrid_query_with_vector_filter_string(): ) # Verify filter is included in serialized query - args = hybrid_query.get_args() + args = get_query_pieces(hybrid_query) assert args[-2:] == ["FILTER", "@category:{tech|science|engineering}"] @@ -694,7 +707,7 @@ def test_hybrid_query_with_vector_filter_numeric(): ) # Verify filter is included in serialized query - args = hybrid_query.get_args() + args = get_query_pieces(hybrid_query) assert args[-2:] == ["FILTER", "@rating:[(7.0 +inf]"] @@ -712,7 +725,7 @@ def test_hybrid_query_with_vector_filter_text(): ) # Verify filter is included in serialized query - args = hybrid_query.get_args() + args = get_query_pieces(hybrid_query) assert args[-2:] == ["FILTER", '@job:("engineer")'] @@ -730,11 +743,8 @@ def test_hybrid_query_with_vector_filter_combined(): ) # Verify both filters are included in serialized query - args = hybrid_query.get_args() - assert args[0] == "SEARCH" - assert args[-2] == "FILTER" - assert "@genre:{comedy}" in args[-1] - assert "@rating:[(7.0 +inf]" in args[-1] + args = get_query_pieces(hybrid_query) + assert args[-2:] == ["FILTER", "(@genre:{comedy} @rating:[(7.0 +inf])"] @pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") @@ -749,7 +759,7 @@ def test_hybrid_query_with_vector_filter_none(): ) # Verify no FILTER in serialized query - args = hybrid_query.get_args() + args = get_query_pieces(hybrid_query) assert "FILTER" not in args @@ -769,7 +779,7 @@ def test_hybrid_query_with_vector_filter_and_method(): ) # Verify KNN params and filter are both in args - args = hybrid_query.get_args() + args = get_query_pieces(hybrid_query) assert args[-6:] == ["KNN", 2, "K", 10, "FILTER", "@genre:{comedy}"] @@ -789,7 +799,7 @@ def test_hybrid_query_with_both_text_and_vector_filters(): ) # Verify both filters are in the query - args = hybrid_query.get_args() + args = get_query_pieces(hybrid_query) assert args == [ "SEARCH", "(~@description:(toon | squad | play | basketball | gang | aliens) AND " @@ -802,3 +812,227 @@ def test_hybrid_query_with_both_text_and_vector_filters(): "FILTER", "@genre:{comedy}", ] + + +# Combination method tests + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_combination_method_rrf_basic(): + """Test HybridQuery with RRF combination method.""" + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + combination_method="RRF", + rrf_window=10, + ) + + # Verify RRF combination method is set + assert hybrid_query.combination_method is not None + + # Verify that combination method args are correct + assert hybrid_query.combination_method.get_args() == [ + "COMBINE", + "RRF", + 2, + "WINDOW", + 10, + ] + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_combination_method_rrf_with_constant(): + """Test HybridQuery with RRF combination method and constant parameter.""" + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + combination_method="RRF", + rrf_constant=0.5, + ) + + # Verify RRF combination method is set + assert hybrid_query.combination_method is not None + + # Verify that combination method args are correct + assert hybrid_query.combination_method.get_args() == [ + "COMBINE", + "RRF", + 2, + "CONSTANT", + 0.5, + ] + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_combination_method_rrf_with_both_params(): + """Test HybridQuery with RRF combination method with both window and constant.""" + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + combination_method="RRF", + rrf_window=20, + rrf_constant=1.0, + yield_combined_score_as="rrf_score", + ) + + # Verify RRF combination method is set + assert hybrid_query.combination_method is not None + + # Verify that combination method args are correct + assert hybrid_query.combination_method.get_args() == [ + "COMBINE", + "RRF", + 6, + "WINDOW", + 20, + "CONSTANT", + 1.0, + "YIELD_SCORE_AS", + "rrf_score", + ] + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_combination_method_linear_with_alpha(): + """Test HybridQuery with LINEAR combination method.""" + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + combination_method="LINEAR", + linear_alpha=0.7, + ) + + # Verify LINEAR combination method is set + assert hybrid_query.combination_method is not None + + # Verify that combination method args are correct + assert hybrid_query.combination_method.get_args() == [ + "COMBINE", + "LINEAR", + 2, + "ALPHA", + 0.7, + ] + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_combination_method_linear_with_beta(): + """Test HybridQuery with LINEAR combination method and beta parameter.""" + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + combination_method="LINEAR", + linear_beta=0.3, + ) + + # Verify LINEAR combination method is set + assert hybrid_query.combination_method is not None + + # Verify that combination method args are correct + assert hybrid_query.combination_method.get_args() == [ + "COMBINE", + "LINEAR", + 2, + "BETA", + 0.3, + ] + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_combination_method_linear_with_yield_score(): + """Test HybridQuery with LINEAR combination method and yield_combined_score_as.""" + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + combination_method="LINEAR", + linear_alpha=0.3, + linear_beta=0.5, + yield_combined_score_as="linear_score", + ) + + assert hybrid_query.combination_method is not None + assert hybrid_query.combination_method.get_args() == [ + "COMBINE", + "LINEAR", + 6, + "ALPHA", + 0.3, + "BETA", + 0.5, + "YIELD_SCORE_AS", + "linear_score", + ] + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_combination_method_none(): + """Test HybridQuery without combination method.""" + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + combination_method=None, + ) + + # Verify no combination method is set + assert hybrid_query.combination_method is None + + # Verify COMBINE does not appear in query args + args = get_query_pieces(hybrid_query) + assert "COMBINE" not in args + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_combination_method_rrf_without_params(): + """Test HybridQuery with RRF combination method but no parameters.""" + # RRF without any params should still work (server may have defaults) + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + combination_method="RRF", + ) + + assert hybrid_query.combination_method is not None + assert hybrid_query.combination_method.get_args() == ["COMBINE", "RRF"] + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_hybrid_query_combination_method_linear_without_params(): + """Test HybridQuery with LINEAR combination method but no parameters.""" + # LINEAR without any params should still work (server may have defaults) + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + combination_method="LINEAR", + ) + + assert hybrid_query.combination_method is not None + assert hybrid_query.combination_method.get_args() == ["COMBINE", "LINEAR"] + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +def test_build_combination_method_invalid_method(): + """Test build_combination_method static method with invalid combination method.""" + from redisvl.query.hybrid import HybridQuery + + with pytest.raises(ValueError, match="Unknown combination method"): + HybridQuery.build_combination_method( + combination_method="INVALID", # type: ignore + ) From 9832369f1b7d6446e33edd5ebc8a934eaadb3061 Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Fri, 5 Dec 2025 16:11:28 +0100 Subject: [PATCH 08/34] Update hybrid search usage based on in-practice constraints --- redisvl/index/index.py | 36 ++ redisvl/query/aggregate.py | 2 +- redisvl/query/hybrid.py | 66 +++- redisvl/utils/full_text_query_helper.py | 2 +- tests/integration/test_hybrid.py | 373 ++++++++++++++++++ ..._hybrid_search.py => test_hybrid_types.py} | 158 ++++---- 6 files changed, 539 insertions(+), 98 deletions(-) create mode 100644 tests/integration/test_hybrid.py rename tests/unit/{test_hybrid_search.py => test_hybrid_types.py} (93%) diff --git a/redisvl/index/index.py b/redisvl/index/index.py index dcfaaab6..6047724e 100644 --- a/redisvl/index/index.py +++ b/redisvl/index/index.py @@ -45,8 +45,20 @@ from redis.commands.search.aggregation import AggregateResult from redis.commands.search.document import Document from redis.commands.search.result import Result + from redisvl.query.query import BaseQuery +try: + from redis.commands.search.hybrid_result import HybridResult + + from redisvl.query.hybrid import HybridQuery + + REDIS_HYBRID_AVAILABLE = True +except ImportError: + REDIS_HYBRID_AVAILABLE = False + HybridResult = None # type: ignore + HybridQuery = None # type: ignore + from redis import __version__ as redis_version from redis.client import NEVER_DECODE @@ -215,6 +227,13 @@ def _process(row): return [_process(r) for r in results.rows] +if REDIS_HYBRID_AVAILABLE: + + def process_hybrid_results(results: HybridResult) -> List[Dict[str, Any]]: + """Convert a hybrid result object into a list of document dictionaries.""" + return [convert_bytes(r) for r in results.results] + + class BaseSearchIndex: """Base search engine class""" @@ -1003,6 +1022,23 @@ def search(self, *args, **kwargs) -> "Result": except Exception as e: raise RedisSearchError(f"Unexpected error while searching: {str(e)}") from e + if REDIS_HYBRID_AVAILABLE: + + def hybrid_search(self, query: HybridQuery, **kwargs) -> List[Dict[str, Any]]: + results: HybridResult = self._redis_client.ft( + self.schema.index.name + ).hybrid_search( + query=query.query, + combine_method=query.combination_method, + post_processing=( + query.postprocessing_config + if query.postprocessing_config.build_args() + else None + ), + **kwargs, + ) # type: ignore + return process_hybrid_results(results) + def batch_query( self, queries: Sequence[BaseQuery], batch_size: int = 10 ) -> List[List[Dict[str, Any]]]: diff --git a/redisvl/query/aggregate.py b/redisvl/query/aggregate.py index 96904d4b..7426580e 100644 --- a/redisvl/query/aggregate.py +++ b/redisvl/query/aggregate.py @@ -235,7 +235,7 @@ def _build_query_string(self) -> str: # Add distance field alias knn_query += f" AS {self.DISTANCE_ID}" - return f"{text})=>[{knn_query}]" + return f"{text}=>[{knn_query}]" def __str__(self) -> str: """Return the string representation of the query.""" diff --git a/redisvl/query/hybrid.py b/redisvl/query/hybrid.py index c18a043a..464eee11 100644 --- a/redisvl/query/hybrid.py +++ b/redisvl/query/hybrid.py @@ -2,6 +2,7 @@ from redis.commands.search.query import Filter +from redisvl.redis.utils import array_to_buffer from redisvl.utils.full_text_query_helper import FullTextQueryHelper try: @@ -9,7 +10,9 @@ CombinationMethods, CombineResultsMethod, HybridPostProcessingConfig, - HybridQuery as RedisHybridQuery, + ) + from redis.commands.search.hybrid_query import HybridQuery as RedisHybridQuery + from redis.commands.search.hybrid_query import ( HybridSearchQuery, HybridVsimQuery, VectorSearchMethods, @@ -41,14 +44,17 @@ def __init__( range_epsilon: Optional[float] = None, yield_vsim_score_as: Optional[str] = None, vector_filter_expression: Optional[Union[str, FilterExpression]] = None, - stopwords: Optional[Union[str, Set[str]]] = "english", - text_weights: Optional[Dict[str, float]] = None, combination_method: Optional[Literal["RRF", "LINEAR"]] = None, rrf_window: Optional[int] = None, rrf_constant: Optional[float] = None, linear_alpha: Optional[float] = None, linear_beta: Optional[float] = None, yield_combined_score_as: Optional[str] = None, + dtype: str = "float32", + num_results: Optional[int] = None, + return_fields: Optional[List[str]] = None, + stopwords: Optional[Union[str, Set[str]]] = "english", + text_weights: Optional[Dict[str, float]] = None, ): """ Instantiates a HybridQuery object. @@ -56,7 +62,8 @@ def __init__( Args: text: The text to search for. text_field_name: The text field name to search in. - vector: The vector to perform vector similarity search. + vector: The vector to perform vector similarity search, converted to bytes (e.g. + using `redisvl.redis.utils.array_to_buffer`). vector_field_name: The vector field name to search in. text_scorer: The text scorer to use. Options are {TFIDF, TFIDF.DOCNORM, BM25STD, BM25STD.NORM, BM25STD.TANH, DISMAX, DOCSCORE, HAMMING}. Defaults to "BM25STD". For more @@ -72,6 +79,17 @@ def __init__( accuracy of the search. yield_vsim_score_as: The name of the field to yield the vector similarity score as. vector_filter_expression: The filter expression to use for the vector similarity search. Defaults to None. + combination_method: The combination method to use. Options are {RRF, LINEAR}. Defaults to None. + rrf_window: The window size to use for the reciprocal rank fusion (RRF) combination method. Limits + fusion scope. + rrf_constant: The constant to use for the reciprocal rank fusion (RRF) combination method. Controls decay + of rank influence. + linear_alpha: The weight of the first query for the linear combination method (LINEAR). + linear_beta: The weight of the second query for the linear combination method (LINEAR). + yield_combined_score_as: The name of the field to yield the combined score as. + dtype: The data type of the vector. Defaults to "float32". + num_results: The number of results to return. If not specified, the server default will be used (10). + return_fields: The fields to return. Defaults to None. stopwords (Optional[Union[str, Set[str]]], optional): The stopwords to remove from the provided text prior to search-use. If a string such as "english" "german" is provided then a default set of stopwords for that language will be used. if a list, @@ -84,14 +102,6 @@ def __init__( 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. - combination_method: The combination method to use. Options are {RRF, LINEAR}. Defaults to None. - rrf_window: The window size to use for the reciprocal rank fusion (RRF) combination method. Limits - fusion scope. - rrf_constant: The constant to use for the reciprocal rank fusion (RRF) combination method. Controls decay - of rank influence. - linear_alpha: The weight of the first query for the linear combination method (LINEAR). - linear_beta: The weight of the second query for the linear combination method (LINEAR). - yield_combined_score_as: The name of the field to yield the combined score as. Notes: If RRF combination method is used, then at least one of `rrf_window` or `rrf_constant` must be provided. @@ -101,11 +111,16 @@ def __init__( TypeError: If the stopwords are not a set, list, or tuple of strings. ValueError: If the text string is empty, or if the text string becomes empty after stopwords are removed. - ValueError: If `vector_search_method` is not one of {KNN, RANGE} (or None). + ValueError: If `vector_search_method` is defined and isn't one of {KNN, RANGE}. ValueError: If `vector_search_method` is "KNN" and `knn_k` is not provided. ValueError: If `vector_search_method` is "RANGE" and `range_radius` is not provided. """ self.postprocessing_config = HybridPostProcessingConfig() + if num_results: + self.postprocessing_config.limit(offset=0, num=num_results) + if return_fields: + self.postprocessing_config.load(*(f"@{f}" for f in return_fields)) + self._ft_helper = FullTextQueryHelper( stopwords=stopwords, text_weights=text_weights, @@ -128,6 +143,7 @@ def __init__( range_epsilon=range_epsilon, yield_vsim_score_as=yield_vsim_score_as, vector_filter_expression=vector_filter_expression, + dtype=dtype, ) if combination_method: @@ -147,7 +163,7 @@ def __init__( @staticmethod def build_query( text_query: str, - vector: Union[bytes, List[float]], + vector: bytes | List[float], vector_field_name: str, text_scorer: str = "BM25STD", yield_text_score_as: Optional[str] = None, @@ -158,6 +174,7 @@ def build_query( range_epsilon: Optional[float] = None, yield_vsim_score_as: Optional[str] = None, vector_filter_expression: Optional[Union[str, FilterExpression]] = None, + dtype: str = "float32", ) -> RedisHybridQuery: """Build a Redis HybridQuery for the hybrid search.""" @@ -168,11 +185,10 @@ def build_query( yield_score_as=yield_text_score_as, ) - # If the vector isn't already bytes, it needs to be represented as a string - if not isinstance(vector, bytes): - vector_data: Union[str, bytes] = str(vector) - else: + if isinstance(vector, bytes): vector_data = vector + else: + vector_data = array_to_buffer(vector, dtype) # Serialize vector similarity search method and params, if specified vsim_search_method: Optional[VectorSearchMethods] = None @@ -205,7 +221,7 @@ def build_query( # Serialize the vector similarity query vsim_query = HybridVsimQuery( - vector_field_name=vector_field_name, + vector_field_name="@" + vector_field_name, vector_data=vector_data, vsim_search_method=vsim_search_method, vsim_search_method_params=vsim_search_method_params, @@ -240,15 +256,27 @@ def build_combination_method( method = CombinationMethods.LINEAR if linear_alpha: method_params["ALPHA"] = linear_alpha + if not linear_beta: + method_params["BETA"] = 1 - linear_alpha + if linear_beta: + if not linear_alpha: # Defined first to preserve consistent ordering + method_params["ALPHA"] = 1 - linear_beta method_params["BETA"] = linear_beta + # TODO: Warn if alpha + beta != 1 + else: raise ValueError(f"Unknown combination method: {combination_method}") if yield_score_as: method_params["YIELD_SCORE_AS"] = yield_score_as + if not method_params: + raise ValueError( + "No parameters provided for combination method - must provide at least one parameter." + ) + return CombineResultsMethod( method=method, **method_params, diff --git a/redisvl/utils/full_text_query_helper.py b/redisvl/utils/full_text_query_helper.py index ad364f97..ee5b8cb7 100644 --- a/redisvl/utils/full_text_query_helper.py +++ b/redisvl/utils/full_text_query_helper.py @@ -69,7 +69,7 @@ def build_query_string( if filter_expression and filter_expression != "*": query += f" AND {filter_expression}" - return query + return query + ")" def _get_stopwords( self, stopwords: Optional[Union[str, Set[str]]] = "english" diff --git a/tests/integration/test_hybrid.py b/tests/integration/test_hybrid.py new file mode 100644 index 00000000..6269897b --- /dev/null +++ b/tests/integration/test_hybrid.py @@ -0,0 +1,373 @@ +import pytest +from redis.commands.search.hybrid_result import HybridResult + +from redisvl.index import SearchIndex +from redisvl.query.filter import FilterExpression, Geo, GeoRadius, Num, Tag, Text +from redisvl.query.hybrid import HybridQuery +from redisvl.redis.utils import array_to_buffer +from tests.conftest import skip_if_redis_version_below + + +@pytest.fixture +def index(multi_vector_data, redis_url, worker_id): + + index = SearchIndex.from_dict( + { + "index": { + "name": f"user_index_{worker_id}", + "prefix": f"v1_{worker_id}", + "storage_type": "hash", + }, + "fields": [ + {"name": "credit_score", "type": "tag"}, + {"name": "job", "type": "text"}, + {"name": "description", "type": "text"}, + {"name": "age", "type": "numeric"}, + {"name": "last_updated", "type": "numeric"}, + {"name": "location", "type": "geo"}, + { + "name": "user_embedding", + "type": "vector", + "attrs": { + "dims": 3, + "distance_metric": "cosine", + "algorithm": "flat", + "datatype": "float32", + }, + }, + { + "name": "image_embedding", + "type": "vector", + "attrs": { + "dims": 5, + "distance_metric": "cosine", + "algorithm": "flat", + "datatype": "float32", + }, + }, + { + "name": "audio_embedding", + "type": "vector", + "attrs": { + "dims": 6, + "distance_metric": "cosine", + "algorithm": "flat", + "datatype": "float64", + }, + }, + ], + }, + redis_url=redis_url, + ) + + # create the index (no data yet) + index.create(overwrite=True) + + # prepare and load the data + def hash_preprocess(item: dict) -> dict: + return { + **item, + "user_embedding": array_to_buffer(item["user_embedding"], "float32"), + "image_embedding": array_to_buffer(item["image_embedding"], "float32"), + "audio_embedding": array_to_buffer(item["audio_embedding"], "float64"), + } + + index.load(multi_vector_data, preprocess=hash_preprocess) + + # run the test + yield index + + # clean up + index.delete(drop=True) + + +def test_hybrid_query(index): + skip_if_redis_version_below(index.client, "8.4.0") + + text = "a medical professional with expertise in lung cancer" + text_field = "description" + vector = [0.1, 0.1, 0.5] + vector_field = "user_embedding" + return_fields = ["user", "credit_score", "age", "job", "location", "description"] + + hybrid_query = HybridQuery( + text=text, + text_field_name=text_field, + yield_text_score_as="text_score", + vector=vector, + vector_field_name=vector_field, + yield_vsim_score_as="vsim_score", + combination_method="RRF", + yield_combined_score_as="hybrid_score", + return_fields=return_fields, + ) + + results = index.hybrid_search(hybrid_query) + assert isinstance(results, list) + assert len(results) == 7 + for doc in results: + assert doc["user"] in [ + "john", + "derrick", + "nancy", + "tyler", + "tim", + "taimur", + "joe", + "mary", + ] + assert int(doc["age"]) in [18, 14, 94, 100, 12, 15, 35] + assert doc["job"] in ["engineer", "doctor", "dermatologist", "CEO", "dentist"] + assert doc["credit_score"] in ["high", "low", "medium"] + + hybrid_query = HybridQuery( + text=text, + text_field_name=text_field, + vector=vector, + vector_field_name=vector_field, + num_results=3, + combination_method="RRF", + yield_combined_score_as="hybrid_score", + ) + + results = index.hybrid_search(hybrid_query) + assert len(results) == 3 + assert ( + results[0]["hybrid_score"] + >= results[1]["hybrid_score"] + >= results[2]["hybrid_score"] + ) + + +def test_hybrid_query_with_filter(index): + skip_if_redis_version_below(index.client, "8.4.0") + + text = "a medical professional with expertise in lung cancer" + text_field = "description" + vector = [0.1, 0.1, 0.5] + vector_field = "user_embedding" + return_fields = ["user", "credit_score", "age", "job", "location", "description"] + filter_expression = (Tag("credit_score") == ("high")) & (Num("age") > 30) + + hybrid_query = HybridQuery( + text=text, + text_field_name=text_field, + vector=vector, + vector_field_name=vector_field, + text_filter_expression=filter_expression, + vector_filter_expression=filter_expression, + return_fields=return_fields, + ) + + results = index.hybrid_search(hybrid_query) + assert len(results) == 2 + for result in results: + assert result["credit_score"] == "high" + assert int(result["age"]) > 30 + + +def test_hybrid_query_with_geo_filter(index): + skip_if_redis_version_below(index.client, "8.4.0") + + text = "a medical professional with expertise in lung cancer" + text_field = "description" + vector = [0.1, 0.1, 0.5] + vector_field = "user_embedding" + return_fields = ["user", "credit_score", "age", "job", "location", "description"] + filter_expression = Geo("location") == GeoRadius(-122.4194, 37.7749, 1000, "m") + + hybrid_query = HybridQuery( + text=text, + text_field_name=text_field, + vector=vector, + vector_field_name=vector_field, + text_filter_expression=filter_expression, + vector_filter_expression=filter_expression, + return_fields=return_fields, + ) + + results = index.hybrid_search(hybrid_query) + assert len(results) == 3 + for result in results: + assert result["location"] is not None + + +@pytest.mark.parametrize("alpha", [0.1, 0.5, 0.9]) +def test_hybrid_query_alpha(index, alpha): + skip_if_redis_version_below(index.client, "8.4.0") + + text = "a medical professional with expertise in lung cancer" + text_field = "description" + vector = [0.1, 0.1, 0.5] + vector_field = "user_embedding" + + hybrid_query = HybridQuery( + text=text, + text_field_name=text_field, + vector=vector, + vector_field_name=vector_field, + combination_method="LINEAR", + linear_alpha=alpha, + yield_text_score_as="text_score", + yield_vsim_score_as="vector_similarity", + yield_combined_score_as="hybrid_score", + ) + + results = index.hybrid_search(hybrid_query) + assert len(results) == 7 + for result in results: + score = alpha * float(result["text_score"]) + (1 - alpha) * float( + result["vector_similarity"] + ) + assert ( + float(result["hybrid_score"]) - score <= 0.0001 + ) # allow for small floating point error + + +def test_hybrid_query_stopwords(index): + skip_if_redis_version_below(index.client, "8.4.0") + + text = "a medical professional with expertise in lung cancer" + text_field = "description" + vector = [0.1, 0.1, 0.5] + vector_field = "user_embedding" + alpha = 0.5 + + hybrid_query = HybridQuery( + text=text, + text_field_name=text_field, + vector=vector, + vector_field_name=vector_field, + combination_method="LINEAR", + linear_alpha=alpha, + stopwords={"medical", "expertise"}, + yield_text_score_as="text_score", + yield_vsim_score_as="vector_similarity", + yield_combined_score_as="hybrid_score", + ) + + query_string = hybrid_query.query._search_query.query_string() + assert "medical" not in query_string + assert "expertise" not in query_string + + results = index.hybrid_search(hybrid_query) + assert len(results) == 7 + for result in results: + score = alpha * float(result["text_score"]) + (1 - alpha) * float( + result["vector_similarity"] + ) + assert ( + float(result["hybrid_score"]) - score <= 0.0001 + ) # allow for small floating point error + + +def test_hybrid_query_with_text_filter(index): + skip_if_redis_version_below(index.client, "8.4.0") + + text = "a medical professional with expertise in lung cancer" + text_field = "description" + vector = [0.1, 0.1, 0.5] + vector_field = "user_embedding" + filter_expression = Text(text_field) == ("medical") + + # make sure we can still apply filters to the same text field we are querying + hybrid_query = HybridQuery( + text=text, + text_field_name=text_field, + vector=vector, + vector_field_name=vector_field, + text_filter_expression=filter_expression, + vector_filter_expression=filter_expression, + combination_method="LINEAR", + yield_combined_score_as="hybrid_score", + return_fields=[text_field], + ) + + results = index.hybrid_search(hybrid_query) + assert len(results) == 2 + for result in results: + assert "medical" in result[text_field].lower() + + filter_expression = (Text(text_field) == ("medical")) & ( + (Text(text_field) != ("research")) + ) + hybrid_query = HybridQuery( + text=text, + text_field_name=text_field, + vector=vector, + vector_field_name=vector_field, + text_filter_expression=filter_expression, + vector_filter_expression=filter_expression, + combination_method="LINEAR", + yield_combined_score_as="hybrid_score", + return_fields=[text_field], + ) + + results = index.hybrid_search(hybrid_query) + assert len(results) == 2 + for result in results: + assert "medical" in result[text_field].lower() + assert "research" not in result[text_field].lower() + + +@pytest.mark.parametrize("scorer", ["BM25STD", "TFIDF", "TFIDF.DOCNORM"]) +def test_hybrid_query_word_weights(index, scorer): + skip_if_redis_version_below(index.client, "8.4.0") + + text = "a medical professional with expertise in lung cancers" + text_field = "description" + vector = [0.1, 0.1, 0.5] + vector_field = "user_embedding" + return_fields = ["description"] + + weights = {"medical": 3.4, "cancers": 5} + + # test we can run a query with text weights + weighted_query = HybridQuery( + text=text, + text_field_name=text_field, + vector=vector, + vector_field_name=vector_field, + return_fields=return_fields, + text_scorer=scorer, + text_weights=weights, + yield_text_score_as="text_score", + ) + + weighted_results = index.hybrid_search(weighted_query) + assert len(weighted_results) == 7 + + # test that weights do change the scores on results + unweighted_query = HybridQuery( + text=text, + text_field_name=text_field, + vector=vector, + vector_field_name=vector_field, + return_fields=return_fields, + text_scorer=scorer, + text_weights={}, + yield_text_score_as="text_score", + ) + + unweighted_results = index.hybrid_search(unweighted_query) + + for weighted, unweighted in zip(weighted_results, unweighted_results): + for word in weights: + if word in weighted["description"] or word in unweighted["description"]: + assert float(weighted["text_score"]) > float(unweighted["text_score"]) + + # test that weights do change the document score and order of results + weights = {"medical": 5, "cancers": 3.4} # switch the weights + weighted_query = HybridQuery( + text=text, + text_field_name=text_field, + vector=vector, + vector_field_name=vector_field, + return_fields=return_fields, + text_scorer=scorer, + text_weights=weights, + yield_text_score_as="text_score", + ) + + weighted_results = index.hybrid_search(weighted_query) + assert weighted_results != unweighted_results diff --git a/tests/unit/test_hybrid_search.py b/tests/unit/test_hybrid_types.py similarity index 93% rename from tests/unit/test_hybrid_search.py rename to tests/unit/test_hybrid_types.py index ed8e8f9e..fe5bc2a0 100644 --- a/tests/unit/test_hybrid_search.py +++ b/tests/unit/test_hybrid_types.py @@ -5,10 +5,12 @@ query capabilities (requires redis>=7.1.0). """ -from typing import List +from typing import List, Literal import pytest +from redisvl.redis.utils import array_to_buffer + try: from redis.commands.search.hybrid_query import HybridQuery as RedisHybridQuery from redis.commands.search.hybrid_query import ( @@ -33,6 +35,7 @@ # Test data sample_vector = [0.1, 0.2, 0.3, 0.4] +bytes_vector = array_to_buffer(sample_vector, "float32") sample_text = "the toon squad play basketball against a gang of aliens" @@ -66,12 +69,12 @@ def test_hybrid_query_basic_initialization(): # Verify get_args() returns empty list (HybridQuery uses params, not args) assert get_query_pieces(hybrid_query) == [ "SEARCH", - "(~@description:(toon | squad | play | basketball | gang | aliens)", + "(~@description:(toon | squad | play | basketball | gang | aliens))", "SCORER", "BM25STD", "VSIM", - "embedding", - "[0.1, 0.2, 0.3, 0.4]", + "@embedding", + bytes_vector, ] # Verify that no combination method is set @@ -111,14 +114,14 @@ def test_hybrid_query_with_all_parameters(): # Verify that the expected query pieces have been defined assert get_query_pieces(hybrid_query) == [ "SEARCH", - "(~@description:(the | toon=>{$weight:2.0} | squad=>{$weight:1.5} | play | basketball | against | a | gang | of | aliens) AND @genre:{comedy}", + "(~@description:(the | toon=>{$weight:2.0} | squad=>{$weight:1.5} | play | basketball | against | a | gang | of | aliens) AND @genre:{comedy})", "SCORER", "TFIDF", "YIELD_SCORE_AS", "text_score", "VSIM", - "embedding", - "[0.1, 0.2, 0.3, 0.4]", + "@embedding", + bytes_vector, "KNN", 4, "K", @@ -358,13 +361,12 @@ def test_hybrid_query_with_string_filter(): assert get_query_pieces(hybrid_query) == [ "SEARCH", - "(~@description:(toon | squad | play | basketball | gang | aliens) AND " - "@category:{tech|science|engineering}", + "(~@description:(toon | squad | play | basketball | gang | aliens) AND @category:{tech|science|engineering})", "SCORER", "BM25STD", "VSIM", - "embedding", - "[0.1, 0.2, 0.3, 0.4]", + "@embedding", + bytes_vector, ] @@ -383,13 +385,12 @@ def test_hybrid_query_with_tag_filter(): assert get_query_pieces(hybrid_query) == [ "SEARCH", - "(~@description:(toon | squad | play | basketball | gang | aliens) AND " - "@genre:{comedy}", + "(~@description:(toon | squad | play | basketball | gang | aliens) AND @genre:{comedy})", "SCORER", "BM25STD", "VSIM", - "embedding", - "[0.1, 0.2, 0.3, 0.4]", + "@embedding", + bytes_vector, ] @@ -408,7 +409,7 @@ def test_hybrid_query_with_numeric_filter(): # Verify filter is included in serialized query args = get_query_pieces(hybrid_query) - assert "@age:[(30 +inf]" in args[1] + assert args[1].endswith("AND @age:[(30 +inf])") @pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") @@ -426,7 +427,7 @@ def test_hybrid_query_with_text_filter(): # Verify filter is included in serialized query args = get_query_pieces(hybrid_query) - assert '@job:("engineer")' in args[1] + assert args[1].endswith('AND @job:("engineer"))') @pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") @@ -444,7 +445,7 @@ def test_hybrid_query_with_combined_filters(): # Verify both filters are included in serialized query args = get_query_pieces(hybrid_query) - assert args[1].endswith("AND (@genre:{comedy} @rating:[(7.0 +inf])") + assert args[1].endswith("AND (@genre:{comedy} @rating:[(7.0 +inf]))") @pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") @@ -461,7 +462,7 @@ def test_hybrid_query_with_wildcard_filter(): # Verify query structure - wildcard may or may not be included depending on implementation args = get_query_pieces(hybrid_query) assert ( - args[1] == "(~@description:(toon | squad | play | basketball | gang | aliens)" + args[1] == "(~@description:(toon | squad | play | basketball | gang | aliens))" ) # Query without filtering @@ -479,7 +480,7 @@ def test_hybrid_query_without_filter(): # Verify no filter in serialized query (only text query) args = get_query_pieces(hybrid_query) assert ( - args[1] == "(~@description:(toon | squad | play | basketball | gang | aliens)" + args[1] == "(~@description:(toon | squad | play | basketball | gang | aliens))" ) # No filter in query @@ -584,7 +585,7 @@ def test_hybrid_query_vector_search_method_none(): # Verify basic VSIM structure without explicit method args = get_query_pieces(hybrid_query) assert "VSIM" in args - assert "embedding" in args + assert "@embedding" in args # When None, should not have KNN or RANGE explicitly assert "KNN" not in args assert "RANGE" not in args @@ -621,12 +622,12 @@ def test_hybrid_query_special_characters_in_text(): assert get_query_pieces(hybrid_query) == [ "SEARCH", - "(~@description:(search | \\@user | \\#hashtag | \\$price | 50\\% | off\\!)", + "(~@description:(search | \\@user | \\#hashtag | \\$price | 50\\% | off\\!))", "SCORER", "BM25STD", "VSIM", - "embedding", - "[0.1, 0.2, 0.3, 0.4]", + "@embedding", + bytes_vector, ] @@ -645,12 +646,12 @@ def test_hybrid_query_unicode_text(): assert get_query_pieces(hybrid_query) == [ "SEARCH", - "(~@description:(café | résumé | naïve | 日本語 | 中文)", + "(~@description:(café | résumé | naïve | 日本語 | 中文))", "SCORER", "BM25STD", "VSIM", - "embedding", - "[0.1, 0.2, 0.3, 0.4]", + "@embedding", + bytes_vector, ] @@ -802,13 +803,12 @@ def test_hybrid_query_with_both_text_and_vector_filters(): args = get_query_pieces(hybrid_query) assert args == [ "SEARCH", - "(~@description:(toon | squad | play | basketball | gang | aliens) AND " - "@category:{movies}", + "(~@description:(toon | squad | play | basketball | gang | aliens) AND @category:{movies})", "SCORER", "BM25STD", "VSIM", - "embedding", - "[0.1, 0.2, 0.3, 0.4]", + "@embedding", + bytes_vector, "FILTER", "@genre:{comedy}", ] @@ -899,7 +899,8 @@ def test_hybrid_query_combination_method_rrf_with_both_params(): @pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") -def test_hybrid_query_combination_method_linear_with_alpha(): +@pytest.mark.parametrize("alpha", [0.1, 0.5, 0.9]) +def test_hybrid_query_combination_method_linear_with_alpha(alpha: float): """Test HybridQuery with LINEAR combination method.""" hybrid_query = HybridQuery( text=sample_text, @@ -907,7 +908,7 @@ def test_hybrid_query_combination_method_linear_with_alpha(): vector=sample_vector, vector_field_name="embedding", combination_method="LINEAR", - linear_alpha=0.7, + linear_alpha=alpha, ) # Verify LINEAR combination method is set @@ -917,22 +918,44 @@ def test_hybrid_query_combination_method_linear_with_alpha(): assert hybrid_query.combination_method.get_args() == [ "COMBINE", "LINEAR", - 2, + 4, "ALPHA", - 0.7, + alpha, + "BETA", + 1 - alpha, ] + hybrid_query = HybridQuery( + text=sample_text, + text_field_name="description", + vector=sample_vector, + vector_field_name="embedding", + combination_method="LINEAR", + linear_beta=alpha, + ) + + # Verify LINEAR combination method is set + assert hybrid_query.combination_method is not None + + # Verify that combination method args are correct + assert hybrid_query.combination_method.get_args() == [ + "COMBINE", + "LINEAR", + 4, + "ALPHA", + 1 - alpha, + "BETA", + alpha, + ] -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") -def test_hybrid_query_combination_method_linear_with_beta(): - """Test HybridQuery with LINEAR combination method and beta parameter.""" hybrid_query = HybridQuery( text=sample_text, text_field_name="description", vector=sample_vector, vector_field_name="embedding", combination_method="LINEAR", - linear_beta=0.3, + linear_alpha=alpha, + linear_beta=2 * alpha, ) # Verify LINEAR combination method is set @@ -942,9 +965,11 @@ def test_hybrid_query_combination_method_linear_with_beta(): assert hybrid_query.combination_method.get_args() == [ "COMBINE", "LINEAR", - 2, + 4, + "ALPHA", + alpha, "BETA", - 0.3, + 2 * alpha, ] @@ -958,7 +983,7 @@ def test_hybrid_query_combination_method_linear_with_yield_score(): vector_field_name="embedding", combination_method="LINEAR", linear_alpha=0.3, - linear_beta=0.5, + linear_beta=0.7, yield_combined_score_as="linear_score", ) @@ -970,7 +995,7 @@ def test_hybrid_query_combination_method_linear_with_yield_score(): "ALPHA", 0.3, "BETA", - 0.5, + 0.7, "YIELD_SCORE_AS", "linear_score", ] @@ -995,44 +1020,23 @@ def test_hybrid_query_combination_method_none(): assert "COMBINE" not in args -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") -def test_hybrid_query_combination_method_rrf_without_params(): - """Test HybridQuery with RRF combination method but no parameters.""" - # RRF without any params should still work (server may have defaults) - hybrid_query = HybridQuery( - text=sample_text, - text_field_name="description", - vector=sample_vector, - vector_field_name="embedding", - combination_method="RRF", - ) - - assert hybrid_query.combination_method is not None - assert hybrid_query.combination_method.get_args() == ["COMBINE", "RRF"] - - -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") -def test_hybrid_query_combination_method_linear_without_params(): - """Test HybridQuery with LINEAR combination method but no parameters.""" - # LINEAR without any params should still work (server may have defaults) - hybrid_query = HybridQuery( - text=sample_text, - text_field_name="description", - vector=sample_vector, - vector_field_name="embedding", - combination_method="LINEAR", - ) - - assert hybrid_query.combination_method is not None - assert hybrid_query.combination_method.get_args() == ["COMBINE", "LINEAR"] - - @pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") def test_build_combination_method_invalid_method(): """Test build_combination_method static method with invalid combination method.""" - from redisvl.query.hybrid import HybridQuery - with pytest.raises(ValueError, match="Unknown combination method"): HybridQuery.build_combination_method( combination_method="INVALID", # type: ignore ) + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.parametrize("method", ["RRF", "LINEAR"]) +def test_build_combination_method_no_parameters(method: Literal["RRF", "LINEAR"]): + """Test build_combination_method static method raises ValueError when no parameters provided.""" + with pytest.raises( + ValueError, + match="No parameters provided for combination method - must provide at least one parameter", + ): + HybridQuery.build_combination_method( + combination_method=method, + ) From b6912550ba5b3da8c5a8fb3ddb50d4fd5bb13f85 Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Fri, 5 Dec 2025 16:12:07 +0100 Subject: [PATCH 09/34] Update/fix existing tests --- tests/integration/test_aggregation.py | 2 +- tests/unit/test_query_types.py | 11 +++++++++-- 2 files changed, 10 insertions(+), 3 deletions(-) diff --git a/tests/integration/test_aggregation.py b/tests/integration/test_aggregation.py index d7dec532..7de10583 100644 --- a/tests/integration/test_aggregation.py +++ b/tests/integration/test_aggregation.py @@ -259,7 +259,7 @@ def test_hybrid_query_stopwords(index): query_string = hybrid_query._build_query_string() assert "medical" not in query_string - assert "expertize" not in query_string + assert "expertise" not in query_string results = index.query(hybrid_query) assert len(results) == 7 diff --git a/tests/unit/test_query_types.py b/tests/unit/test_query_types.py index 5f37f96c..fa7ad69a 100644 --- a/tests/unit/test_query_types.py +++ b/tests/unit/test_query_types.py @@ -1,4 +1,5 @@ import pytest +from redis import __version__ as redis_version from redis.commands.search.query import Query from redis.commands.search.result import Result @@ -6,6 +7,7 @@ from redisvl.query import CountQuery, FilterQuery, RangeQuery, TextQuery, VectorQuery from redisvl.query.filter import Tag from redisvl.query.query import VectorRangeQuery +from redisvl.redis.connection import is_version_gte # Sample data for testing sample_vector = [0.1, 0.2, 0.3, 0.4] @@ -402,6 +404,11 @@ def test_text_query_word_weights(): ], ) def test_query_modifiers(query): + if is_version_gte(redis_version, "7.0.0"): # Format changed in Redis 7.0+ + expected_fields = ["test"] + else: + expected_fields = ("test",) + query.paging(3, 5) assert query._offset == 3 assert query._num == 5 @@ -437,7 +444,7 @@ def test_query_modifiers(query): assert query._with_scores query.limit_fields("test") - assert query._fields == ("test",) + assert query._fields == expected_fields f = Tag("test") == "foo" query.set_filter(f) @@ -456,7 +463,7 @@ def test_query_modifiers(query): assert query._no_content assert query._no_stopwords assert query._with_scores - assert query._fields == ("test",) + assert query._fields == expected_fields @pytest.mark.parametrize( From 4b3a1febdeb4e7ef94d71fb7f1561ab3162eaa36 Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Fri, 5 Dec 2025 16:55:29 +0100 Subject: [PATCH 10/34] Implement async hybrid search --- redisvl/index/index.py | 22 ++++++- tests/integration/test_hybrid.py | 102 ++++++++++++++++++++++++++++--- 2 files changed, 113 insertions(+), 11 deletions(-) diff --git a/redisvl/index/index.py b/redisvl/index/index.py index 6047724e..596611e8 100644 --- a/redisvl/index/index.py +++ b/redisvl/index/index.py @@ -56,8 +56,6 @@ REDIS_HYBRID_AVAILABLE = True except ImportError: REDIS_HYBRID_AVAILABLE = False - HybridResult = None # type: ignore - HybridQuery = None # type: ignore from redis import __version__ as redis_version from redis.client import NEVER_DECODE @@ -1860,6 +1858,26 @@ async def search(self, *args, **kwargs) -> "Result": except Exception as e: raise RedisSearchError(f"Unexpected error while searching: {str(e)}") from e + if REDIS_HYBRID_AVAILABLE: + + async def hybrid_search( + self, query: HybridQuery, **kwargs + ) -> List[Dict[str, Any]]: + client = await self._get_client() + results: HybridResult = await client.ft( + self.schema.index.name + ).hybrid_search( + query=query.query, + combine_method=query.combination_method, + post_processing=( + query.postprocessing_config + if query.postprocessing_config.build_args() + else None + ), + **kwargs, + ) # type: ignore + return process_hybrid_results(results) + async def batch_query( self, queries: List[BaseQuery], batch_size: int = 10 ) -> List[List[Dict[str, Any]]]: diff --git a/tests/integration/test_hybrid.py b/tests/integration/test_hybrid.py index 6269897b..1fa84876 100644 --- a/tests/integration/test_hybrid.py +++ b/tests/integration/test_hybrid.py @@ -1,17 +1,20 @@ import pytest -from redis.commands.search.hybrid_result import HybridResult -from redisvl.index import SearchIndex -from redisvl.query.filter import FilterExpression, Geo, GeoRadius, Num, Tag, Text +from redisvl.index import AsyncSearchIndex, SearchIndex +from redisvl.query.filter import Geo, GeoRadius, Num, Tag, Text from redisvl.query.hybrid import HybridQuery from redisvl.redis.utils import array_to_buffer -from tests.conftest import skip_if_redis_version_below +from redisvl.schema import IndexSchema +from tests.conftest import ( + get_redis_version_async, + skip_if_redis_version_below, + skip_if_redis_version_below_async, +) @pytest.fixture -def index(multi_vector_data, redis_url, worker_id): - - index = SearchIndex.from_dict( +def index_schema(worker_id): + return IndexSchema.from_dict( { "index": { "name": f"user_index_{worker_id}", @@ -56,10 +59,14 @@ def index(multi_vector_data, redis_url, worker_id): }, }, ], - }, - redis_url=redis_url, + } ) + +@pytest.fixture +def index(index_schema, multi_vector_data, redis_url): + index = SearchIndex(schema=index_schema, redis_url=redis_url) + # create the index (no data yet) index.create(overwrite=True) @@ -81,6 +88,24 @@ def hash_preprocess(item: dict) -> dict: index.delete(drop=True) +@pytest.fixture +async def async_index(index_schema, multi_vector_data, async_client): + index = AsyncSearchIndex(schema=index_schema, redis_client=async_client) + await index.create(overwrite=True) + + def hash_preprocess(item: dict) -> dict: + return { + **item, + "user_embedding": array_to_buffer(item["user_embedding"], "float32"), + "image_embedding": array_to_buffer(item["image_embedding"], "float32"), + "audio_embedding": array_to_buffer(item["audio_embedding"], "float64"), + } + + await index.load(multi_vector_data, preprocess=hash_preprocess) + yield index + await index.delete(drop=True) + + def test_hybrid_query(index): skip_if_redis_version_below(index.client, "8.4.0") @@ -371,3 +396,62 @@ def test_hybrid_query_word_weights(index, scorer): weighted_results = index.hybrid_search(weighted_query) assert weighted_results != unweighted_results + + +@pytest.mark.asyncio +async def test_hybrid_query_async(async_index): + await skip_if_redis_version_below_async(async_index.client, "8.4.0") + + text = "a medical professional with expertise in lung cancer" + text_field = "description" + vector = [0.1, 0.1, 0.5] + vector_field = "user_embedding" + return_fields = ["user", "credit_score", "age", "job", "location", "description"] + + hybrid_query = HybridQuery( + text=text, + text_field_name=text_field, + yield_text_score_as="text_score", + vector=vector, + vector_field_name=vector_field, + yield_vsim_score_as="vsim_score", + combination_method="RRF", + yield_combined_score_as="hybrid_score", + return_fields=return_fields, + ) + + results = await async_index.hybrid_search(hybrid_query) + assert isinstance(results, list) + assert len(results) == 7 + for doc in results: + assert doc["user"] in [ + "john", + "derrick", + "nancy", + "tyler", + "tim", + "taimur", + "joe", + "mary", + ] + assert int(doc["age"]) in [18, 14, 94, 100, 12, 15, 35] + assert doc["job"] in ["engineer", "doctor", "dermatologist", "CEO", "dentist"] + assert doc["credit_score"] in ["high", "low", "medium"] + + hybrid_query = HybridQuery( + text=text, + text_field_name=text_field, + vector=vector, + vector_field_name=vector_field, + num_results=3, + combination_method="RRF", + yield_combined_score_as="hybrid_score", + ) + + results = await async_index.hybrid_search(hybrid_query) + assert len(results) == 3 + assert ( + results[0]["hybrid_score"] + >= results[1]["hybrid_score"] + >= results[2]["hybrid_score"] + ) From 4d3ba7064e65e0cade5ef3ea8cb62ea8b099f0cd Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Fri, 5 Dec 2025 17:25:41 +0100 Subject: [PATCH 11/34] Update docstrings --- redisvl/index/index.py | 54 ++++++++ redisvl/query/hybrid.py | 286 +++++++++++++++++++++++----------------- 2 files changed, 222 insertions(+), 118 deletions(-) diff --git a/redisvl/index/index.py b/redisvl/index/index.py index 596611e8..c297819f 100644 --- a/redisvl/index/index.py +++ b/redisvl/index/index.py @@ -1023,6 +1023,33 @@ def search(self, *args, **kwargs) -> "Result": if REDIS_HYBRID_AVAILABLE: def hybrid_search(self, query: HybridQuery, **kwargs) -> List[Dict[str, Any]]: + """Perform a hybrid search against the index, combining text and vector search. + + Args: + query: The text+vector search query to be performed, with configurable fusion methods and + post-processing. + kwargs: Additional arguments to pass to the redis-py hybrid_search method (e.g. timeout). + + Returns: + List[Dict[str, Any]]: The search results ordered by combined score unless otherwise specified. + + Notes: + Hybrid search is only available in Redis 8.4.0+, and requires redis-py >= 7.1.0. + + .. code-block:: python + + from redisvl.query.hybrid import HybridQuery + + hybrid_query = HybridQuery( + text="lorem ipsum dolor sit amet", + text_field_name="description", + vector=[0.1, 0.2, 0.3], + vector_field_name="embedding" + ) + + results = index.hybrid_search(hybrid_query) + + """ results: HybridResult = self._redis_client.ft( self.schema.index.name ).hybrid_search( @@ -1863,6 +1890,33 @@ async def search(self, *args, **kwargs) -> "Result": async def hybrid_search( self, query: HybridQuery, **kwargs ) -> List[Dict[str, Any]]: + """Perform a hybrid search against the index, combining text and vector search. + + Args: + query: The text+vector search query to be performed, with configurable fusion methods and + post-processing. + kwargs: Additional arguments to pass to the redis-py hybrid_search method (e.g. timeout). + + Returns: + List[Dict[str, Any]]: The search results ordered by combined score unless otherwise specified. + + Notes: + Hybrid search is only available in Redis 8.4.0+, and requires redis-py >= 7.1.0. + + .. code-block:: python + + from redisvl.query.hybrid import HybridQuery + + hybrid_query = HybridQuery( + text="lorem ipsum dolor sit amet", + text_field_name="description", + vector=[0.1, 0.2, 0.3], + vector_field_name="embedding" + ) + + results = index.hybrid_search(hybrid_query) + + """ client = await self._get_client() results: HybridResult = await client.ft( self.schema.index.name diff --git a/redisvl/query/hybrid.py b/redisvl/query/hybrid.py index 464eee11..8ac40297 100644 --- a/redisvl/query/hybrid.py +++ b/redisvl/query/hybrid.py @@ -62,8 +62,7 @@ def __init__( Args: text: The text to search for. text_field_name: The text field name to search in. - vector: The vector to perform vector similarity search, converted to bytes (e.g. - using `redisvl.redis.utils.array_to_buffer`). + vector: The vector to perform vector similarity search. vector_field_name: The vector field name to search in. text_scorer: The text scorer to use. Options are {TFIDF, TFIDF.DOCNORM, BM25STD, BM25STD.NORM, BM25STD.TANH, DISMAX, DOCSCORE, HAMMING}. Defaults to "BM25STD". For more @@ -130,7 +129,7 @@ def __init__( text, text_field_name, text_filter_expression ) - self.query = self.build_query( + self.query = build_base_query( text_query=query_string, vector=vector, vector_field_name=vector_field_name, @@ -148,7 +147,7 @@ def __init__( if combination_method: self.combination_method: Optional[CombineResultsMethod] = ( - self.build_combination_method( + build_combination_method( combination_method=combination_method, rrf_window=rrf_window, rrf_constant=rrf_constant, @@ -160,124 +159,175 @@ def __init__( else: self.combination_method = None - @staticmethod - def build_query( - text_query: str, - vector: bytes | List[float], - vector_field_name: str, - text_scorer: str = "BM25STD", - yield_text_score_as: Optional[str] = None, - vector_search_method: Optional[Literal["KNN", "RANGE"]] = None, - knn_k: Optional[int] = None, - knn_ef_runtime: Optional[int] = None, - range_radius: Optional[int] = None, - range_epsilon: Optional[float] = None, - yield_vsim_score_as: Optional[str] = None, - vector_filter_expression: Optional[Union[str, FilterExpression]] = None, - dtype: str = "float32", - ) -> RedisHybridQuery: - """Build a Redis HybridQuery for the hybrid search.""" - - # Serialize the full-text search query - search_query = HybridSearchQuery( - query_string=text_query, - scorer=text_scorer, - yield_score_as=yield_text_score_as, - ) - - if isinstance(vector, bytes): - vector_data = vector - else: - vector_data = array_to_buffer(vector, dtype) - - # Serialize vector similarity search method and params, if specified - vsim_search_method: Optional[VectorSearchMethods] = None - vsim_search_method_params: Dict[str, Any] = {} - if vector_search_method == "KNN": - vsim_search_method = VectorSearchMethods.KNN - if not knn_k: - raise ValueError("Must provide K if vector_search_method is KNN") - - vsim_search_method_params["K"] = knn_k - if knn_ef_runtime: - vsim_search_method_params["EF_RUNTIME"] = knn_ef_runtime - - elif vector_search_method == "RANGE": - vsim_search_method = VectorSearchMethods.RANGE - if not range_radius: - raise ValueError("Must provide RADIUS if vector_search_method is RANGE") - - vsim_search_method_params["RADIUS"] = range_radius - if range_epsilon: - vsim_search_method_params["EPSILON"] = range_epsilon - - elif vector_search_method is not None: - raise ValueError(f"Unknown vector search method: {vector_search_method}") - - if vector_filter_expression: - vsim_filter = Filter("FILTER", str(vector_filter_expression)) - else: - vsim_filter = None - - # Serialize the vector similarity query - vsim_query = HybridVsimQuery( - vector_field_name="@" + vector_field_name, - vector_data=vector_data, - vsim_search_method=vsim_search_method, - vsim_search_method_params=vsim_search_method_params, - filter=vsim_filter, - yield_score_as=yield_vsim_score_as, - ) - return RedisHybridQuery( - search_query=search_query, - vector_similarity_query=vsim_query, - ) +def build_base_query( + text_query: str, + vector: bytes | List[float], + vector_field_name: str, + text_scorer: str = "BM25STD", + yield_text_score_as: Optional[str] = None, + vector_search_method: Optional[Literal["KNN", "RANGE"]] = None, + knn_k: Optional[int] = None, + knn_ef_runtime: Optional[int] = None, + range_radius: Optional[int] = None, + range_epsilon: Optional[float] = None, + yield_vsim_score_as: Optional[str] = None, + vector_filter_expression: Optional[Union[str, FilterExpression]] = None, + dtype: str = "float32", +) -> RedisHybridQuery: + """Build a Redis HybridQuery for performing hybrid search. + + Args: + text_query: The query for the text search. + vector: The vector to perform vector similarity search. + vector_field_name: The vector field name to search in. + text_scorer: The text scorer to use. Options are {TFIDF, TFIDF.DOCNORM, + BM25STD, BM25STD.NORM, BM25STD.TANH, DISMAX, DOCSCORE, HAMMING}. Defaults to "BM25STD". For more + information about supported scroring algorithms, + see https://redis.io/docs/latest/develop/ai/search-and-query/advanced-concepts/scoring/ + yield_text_score_as: The name of the field to yield the text score as. + vector_search_method: The vector search method to use. Options are {KNN, RANGE}. Defaults to None. + knn_k: The number of nearest neighbors to return, required if `vector_search_method` is "KNN". + knn_ef_runtime: The exploration factor parameter for HNSW, optional if `vector_search_method` is "KNN". + range_radius: The search radius to use, required if `vector_search_method` is "RANGE". + range_epsilon: The epsilon value to use, optional if `vector_search_method` is "RANGE"; defines the + accuracy of the search. + yield_vsim_score_as: The name of the field to yield the vector similarity score as. + vector_filter_expression: The filter expression to use for the vector similarity search. Defaults to None. + dtype: The data type of the vector. Defaults to "float32". + + Notes: + If RRF combination method is used, then at least one of `rrf_window` or `rrf_constant` must be provided. + If LINEAR combination method is used, then at least one of `linear_alpha` or `linear_beta` must be provided. + + Raises: + ValueError: If `vector_search_method` is defined and isn't one of {KNN, RANGE}. + ValueError: If `vector_search_method` is "KNN" and `knn_k` is not provided. + ValueError: If `vector_search_method` is "RANGE" and `range_radius` is not provided. + + Returns: + A Redis HybridQuery object that defines the text and vector searches to be performed. + """ - @staticmethod - def build_combination_method( - combination_method: Literal["RRF", "LINEAR"], - rrf_window: Optional[int] = None, - rrf_constant: Optional[float] = None, - linear_alpha: Optional[float] = None, - linear_beta: Optional[float] = None, - yield_score_as: Optional[str] = None, - ) -> CombineResultsMethod: - """Build a configuration for combining hybrid search scores.""" - method_params: Dict[str, Any] = {} - if combination_method == "RRF": - method = CombinationMethods.RRF - if rrf_window: - method_params["WINDOW"] = rrf_window - if rrf_constant: - method_params["CONSTANT"] = rrf_constant - - elif combination_method == "LINEAR": - method = CombinationMethods.LINEAR - if linear_alpha: - method_params["ALPHA"] = linear_alpha - if not linear_beta: - method_params["BETA"] = 1 - linear_alpha - - if linear_beta: - if not linear_alpha: # Defined first to preserve consistent ordering - method_params["ALPHA"] = 1 - linear_beta - method_params["BETA"] = linear_beta - - # TODO: Warn if alpha + beta != 1 + # Serialize the full-text search query + search_query = HybridSearchQuery( + query_string=text_query, + scorer=text_scorer, + yield_score_as=yield_text_score_as, + ) - else: - raise ValueError(f"Unknown combination method: {combination_method}") + if isinstance(vector, bytes): + vector_data = vector + else: + vector_data = array_to_buffer(vector, dtype) + + # Serialize vector similarity search method and params, if specified + vsim_search_method: Optional[VectorSearchMethods] = None + vsim_search_method_params: Dict[str, Any] = {} + if vector_search_method == "KNN": + vsim_search_method = VectorSearchMethods.KNN + if not knn_k: + raise ValueError("Must provide K if vector_search_method is KNN") + + vsim_search_method_params["K"] = knn_k + if knn_ef_runtime: + vsim_search_method_params["EF_RUNTIME"] = knn_ef_runtime + + elif vector_search_method == "RANGE": + vsim_search_method = VectorSearchMethods.RANGE + if not range_radius: + raise ValueError("Must provide RADIUS if vector_search_method is RANGE") + + vsim_search_method_params["RADIUS"] = range_radius + if range_epsilon: + vsim_search_method_params["EPSILON"] = range_epsilon + + elif vector_search_method is not None: + raise ValueError(f"Unknown vector search method: {vector_search_method}") + + if vector_filter_expression: + vsim_filter = Filter("FILTER", str(vector_filter_expression)) + else: + vsim_filter = None + + # Serialize the vector similarity query + vsim_query = HybridVsimQuery( + vector_field_name="@" + vector_field_name, + vector_data=vector_data, + vsim_search_method=vsim_search_method, + vsim_search_method_params=vsim_search_method_params, + filter=vsim_filter, + yield_score_as=yield_vsim_score_as, + ) - if yield_score_as: - method_params["YIELD_SCORE_AS"] = yield_score_as + return RedisHybridQuery( + search_query=search_query, + vector_similarity_query=vsim_query, + ) - if not method_params: - raise ValueError( - "No parameters provided for combination method - must provide at least one parameter." - ) - return CombineResultsMethod( - method=method, - **method_params, +def build_combination_method( + combination_method: Literal["RRF", "LINEAR"], + rrf_window: Optional[int] = None, + rrf_constant: Optional[float] = None, + linear_alpha: Optional[float] = None, + linear_beta: Optional[float] = None, + yield_score_as: Optional[str] = None, +) -> CombineResultsMethod: + """Build a configuration for combining hybrid search scores. + + Args: + combination_method: The combination method to use. Options are {RRF, LINEAR}. + rrf_window: The window size to use for the reciprocal rank fusion (RRF) combination method. Limits + fusion scope. + rrf_constant: The constant to use for the reciprocal rank fusion (RRF) combination method. Controls decay + of rank influence. + linear_alpha: The weight of the first query for the linear combination method (LINEAR). + linear_beta: The weight of the second query for the linear combination method (LINEAR). + yield_score_as: The name of the field to yield the combined score as. + + Raises: + ValueError: If `combination_method` is defined and isn't one of {RRF, LINEAR}. + ValueError: If `combination_method` is "RRF" and neither `rrf_window` nor `rrf_constant` is provided. + ValueError: If `combination_method` is "LINEAR" and neither `linear_alpha` nor `linear_beta` is provided. + + Returns: + A CombineResultsMethod object that defines how the text and vector scores should be combined. + """ + method_params: Dict[str, Any] = {} + if combination_method == "RRF": + method = CombinationMethods.RRF + if rrf_window: + method_params["WINDOW"] = rrf_window + if rrf_constant: + method_params["CONSTANT"] = rrf_constant + + elif combination_method == "LINEAR": + method = CombinationMethods.LINEAR + if linear_alpha: + method_params["ALPHA"] = linear_alpha + if not linear_beta: + method_params["BETA"] = 1 - linear_alpha + + if linear_beta: + if not linear_alpha: # Defined first to preserve consistent ordering + method_params["ALPHA"] = 1 - linear_beta + method_params["BETA"] = linear_beta + + # TODO: Warn if alpha + beta != 1 + + else: + raise ValueError(f"Unknown combination method: {combination_method}") + + if yield_score_as: + method_params["YIELD_SCORE_AS"] = yield_score_as + + if not method_params: + raise ValueError( + "No parameters provided for combination method - must provide at least one parameter." ) + + return CombineResultsMethod( + method=method, + **method_params, + ) From 093fbde5fda4d9328d5d693246064b627c9eb9cf Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Fri, 5 Dec 2025 17:52:27 +0100 Subject: [PATCH 12/34] Update GH Actions test configuration to include Redis 8.4.0 and redis-py 7.x --- .github/workflows/test.yml | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index ded2f84f..53b22777 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -96,8 +96,8 @@ jobs: matrix: # 3.11 tests are run in the service-tests job python-version: ["3.9", "3.10", "3.12", "3.13"] - redis-py-version: ["5.x", "6.x"] - redis-version: ["6.2.6-v9", "latest", "8.0.2"] + redis-py-version: ["5.x", "6.x", "7.x"] + redis-version: ["6.2.6-v9", "latest", "8.0.2", "8.4.0"] steps: - name: Check out repository uses: actions/checkout@v4 @@ -130,13 +130,15 @@ jobs: # Install right redis version based on redis py if [[ "${{ matrix.redis-py-version }}" == "5.x" ]]; then uv pip install "redis>=5,<6" - else + elif [[ "${{ matrix.redis-py-version }}" == "6.x" ]]; then uv pip install "redis>=6,<7" + else + uv pip install "redis>=7,<8" fi - name: Set Redis image name run: | - if [[ "${{ matrix.redis-version }}" == "8.0.2" ]]; then + if [[ "${{ 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).hybrid_search( + index = self._redis_client.ft(self.schema.index.name) + if not hasattr(index, "hybrid_search"): + # TODO: Clarify correct message - it seems to not be available in Python 3.9 + raise NotImplementedError( + "Hybrid search is not available in this version of redis-py. " + "Please upgrade to redis-py >= 7.1.0." + ) + + results: HybridResult = index.hybrid_search( query=query.query, combine_method=query.combination_method, post_processing=( @@ -1918,9 +1924,15 @@ async def hybrid_search( """ client = await self._get_client() - results: HybridResult = await client.ft( - self.schema.index.name - ).hybrid_search( + index = client.ft(self.schema.index.name) + if not hasattr(index, "hybrid_search"): + # TODO: Clarify correct message - it seems to not be available in Python 3.9 + raise NotImplementedError( + "Hybrid search is not available in this version of redis-py. " + "Please upgrade to redis-py >= 7.1.0." + ) + + results: HybridResult = await index.hybrid_search( query=query.query, combine_method=query.combination_method, post_processing=( diff --git a/redisvl/query/hybrid.py b/redisvl/query/hybrid.py index 8ac40297..003ed70f 100644 --- a/redisvl/query/hybrid.py +++ b/redisvl/query/hybrid.py @@ -162,7 +162,7 @@ def __init__( def build_base_query( text_query: str, - vector: bytes | List[float], + vector: Union[bytes, List[float]], vector_field_name: str, text_scorer: str = "BM25STD", yield_text_score_as: Optional[str] = None, From 900073a4c7da49633ca37453ed45ae712b84ba20 Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Fri, 5 Dec 2025 18:23:34 +0100 Subject: [PATCH 15/34] Fix method reference --- tests/unit/test_hybrid_types.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/unit/test_hybrid_types.py b/tests/unit/test_hybrid_types.py index fe5bc2a0..a9f02374 100644 --- a/tests/unit/test_hybrid_types.py +++ b/tests/unit/test_hybrid_types.py @@ -19,7 +19,7 @@ VectorSearchMethods, ) - from redisvl.query.hybrid import HybridQuery + from redisvl.query.hybrid import HybridQuery, build_combination_method REDIS_HYBRID_AVAILABLE = True except ImportError: @@ -1024,7 +1024,7 @@ def test_hybrid_query_combination_method_none(): def test_build_combination_method_invalid_method(): """Test build_combination_method static method with invalid combination method.""" with pytest.raises(ValueError, match="Unknown combination method"): - HybridQuery.build_combination_method( + build_combination_method( combination_method="INVALID", # type: ignore ) @@ -1037,6 +1037,6 @@ def test_build_combination_method_no_parameters(method: Literal["RRF", "LINEAR"] ValueError, match="No parameters provided for combination method - must provide at least one parameter", ): - HybridQuery.build_combination_method( + build_combination_method( combination_method=method, ) From e1e261d21da1ef1f1e53bc10e628071f810e8066 Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Mon, 8 Dec 2025 14:08:28 +0100 Subject: [PATCH 16/34] Catch `ModuleNotFoundError` as well --- tests/unit/test_hybrid_types.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/unit/test_hybrid_types.py b/tests/unit/test_hybrid_types.py index a9f02374..40cfab4c 100644 --- a/tests/unit/test_hybrid_types.py +++ b/tests/unit/test_hybrid_types.py @@ -22,7 +22,7 @@ from redisvl.query.hybrid import HybridQuery, build_combination_method REDIS_HYBRID_AVAILABLE = True -except ImportError: +except (ImportError, ModuleNotFoundError): REDIS_HYBRID_AVAILABLE = False # Create dummy classes to avoid import errors RedisHybridQuery = None # type: ignore From cee1fba0a7b582541c5dd9cd99972980e1f586d4 Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Mon, 8 Dec 2025 14:11:51 +0100 Subject: [PATCH 17/34] Standardize test skip reason --- tests/unit/test_hybrid_types.py | 89 +++++++++++++++++---------------- 1 file changed, 45 insertions(+), 44 deletions(-) diff --git a/tests/unit/test_hybrid_types.py b/tests/unit/test_hybrid_types.py index 40cfab4c..94504be3 100644 --- a/tests/unit/test_hybrid_types.py +++ b/tests/unit/test_hybrid_types.py @@ -24,6 +24,7 @@ REDIS_HYBRID_AVAILABLE = True except (ImportError, ModuleNotFoundError): REDIS_HYBRID_AVAILABLE = False + SKIP_REASON = "Requires redis>=8.4.0 and redis-py>=7.1.0" # Create dummy classes to avoid import errors RedisHybridQuery = None # type: ignore HybridSearchQuery = None # type: ignore @@ -53,7 +54,7 @@ def get_query_pieces(query: HybridQuery) -> List[str]: # Basic init tests -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_basic_initialization(): """Test basic HybridQuery initialization with required parameters.""" text_field_name = "description" @@ -81,7 +82,7 @@ def test_hybrid_query_basic_initialization(): assert hybrid_query.combination_method is None -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_with_all_parameters(): """Test HybridQuery initialization with all optional parameters.""" filter_expression = Tag("genre") == "comedy" @@ -149,7 +150,7 @@ def test_hybrid_query_with_all_parameters(): # Stopwords tests -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_stopwords_default(): """Test that default stopwords (english) are applied.""" hybrid_query = HybridQuery( @@ -168,7 +169,7 @@ def test_hybrid_query_stopwords_default(): assert "a" in stopwords -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_stopwords_none(): """Test that stopwords can be disabled with None.""" hybrid_query = HybridQuery( @@ -182,7 +183,7 @@ def test_hybrid_query_stopwords_none(): assert hybrid_query._ft_helper.stopwords == set() -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_stopwords_empty_set(): """Test that stopwords can be set to empty set.""" hybrid_query = HybridQuery( @@ -196,7 +197,7 @@ def test_hybrid_query_stopwords_empty_set(): assert hybrid_query._ft_helper.stopwords == set() -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_stopwords_custom(): """Test that custom stopwords are applied.""" custom_stopwords = {"the", "a", "of", "and"} @@ -212,7 +213,7 @@ def test_hybrid_query_stopwords_custom(): assert hybrid_query._ft_helper.stopwords == set(custom_stopwords) -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_stopwords_language(): """Test that language-specific stopwords can be loaded.""" hybrid_query = HybridQuery( @@ -231,7 +232,7 @@ def test_hybrid_query_stopwords_language(): assert word in stopwords -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_stopwords_invalid_language(): """Test that invalid language raises ValueError.""" with pytest.raises(ValueError): @@ -244,7 +245,7 @@ def test_hybrid_query_stopwords_invalid_language(): ) -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_stopwords_invalid_type(): """Test that invalid stopwords type raises TypeError.""" with pytest.raises(TypeError): @@ -260,7 +261,7 @@ def test_hybrid_query_stopwords_invalid_type(): # Text weight tests -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_text_weights_basic(): """Test that text weights are properly applied.""" text_weights = {"toon": 2.0, "squad": 1.5, "basketball": 3.0} @@ -276,7 +277,7 @@ def test_hybrid_query_text_weights_basic(): assert hybrid_query._ft_helper.text_weights == text_weights -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_text_weights_none(): """Test that text_weights can be None.""" hybrid_query = HybridQuery( @@ -290,7 +291,7 @@ def test_hybrid_query_text_weights_none(): assert hybrid_query._ft_helper.text_weights == {} -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_text_weights_empty(): """Test that text_weights can be empty dict.""" hybrid_query = HybridQuery( @@ -304,7 +305,7 @@ def test_hybrid_query_text_weights_empty(): assert hybrid_query._ft_helper.text_weights == {} -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_text_weights_negative_value(): """Test that negative text weights raise ValueError.""" with pytest.raises(ValueError): @@ -317,7 +318,7 @@ def test_hybrid_query_text_weights_negative_value(): ) -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_text_weights_invalid_type(): """Test that non-numeric text weights raise ValueError.""" with pytest.raises(ValueError): @@ -330,7 +331,7 @@ def test_hybrid_query_text_weights_invalid_type(): ) -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_text_weights_multi_word_key(): """Test that multi-word keys in text_weights raise ValueError.""" with pytest.raises(ValueError): @@ -346,7 +347,7 @@ def test_hybrid_query_text_weights_multi_word_key(): # Filter expression tests -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_with_string_filter(): """Test HybridQuery with string filter expression.""" string_filter = "@category:{tech|science|engineering}" @@ -370,7 +371,7 @@ def test_hybrid_query_with_string_filter(): ] -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_with_tag_filter(): """Test HybridQuery with Tag FilterExpression.""" tag_filter = Tag("genre") == "comedy" @@ -394,7 +395,7 @@ def test_hybrid_query_with_tag_filter(): ] -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_with_numeric_filter(): """Test HybridQuery with Numeric FilterExpression.""" numeric_filter = Num("age") > 30 @@ -412,7 +413,7 @@ def test_hybrid_query_with_numeric_filter(): assert args[1].endswith("AND @age:[(30 +inf])") -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_with_text_filter(): """Test HybridQuery with Text FilterExpression.""" text_filter = Text("job") == "engineer" @@ -430,7 +431,7 @@ def test_hybrid_query_with_text_filter(): assert args[1].endswith('AND @job:("engineer"))') -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_with_combined_filters(): """Test HybridQuery with combined FilterExpressions.""" combined_filter = (Tag("genre") == "comedy") & (Num("rating") > 7.0) @@ -448,7 +449,7 @@ def test_hybrid_query_with_combined_filters(): assert args[1].endswith("AND (@genre:{comedy} @rating:[(7.0 +inf]))") -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_with_wildcard_filter(): """Test HybridQuery with wildcard filter.""" hybrid_query = HybridQuery( @@ -466,7 +467,7 @@ def test_hybrid_query_with_wildcard_filter(): ) # Query without filtering -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_without_filter(): """Test HybridQuery without any filter expression.""" hybrid_query = HybridQuery( @@ -487,7 +488,7 @@ def test_hybrid_query_without_filter(): # Vector search method tests -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_vector_search_method_knn(): """Test HybridQuery with KNN vector search method.""" with pytest.raises(ValueError): @@ -529,7 +530,7 @@ def test_hybrid_query_vector_search_method_knn(): assert args[-6:] == ["KNN", 4, "K", 10, "EF_RUNTIME", 100] -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_vector_search_method_range(): """Test HybridQuery with RANGE vector search method.""" with pytest.raises(ValueError): @@ -571,7 +572,7 @@ def test_hybrid_query_vector_search_method_range(): assert args[-6:] == ["RANGE", 4, "RADIUS", 10, "EPSILON", 0.1] -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_vector_search_method_none(): """Test HybridQuery without specifying vector search method.""" hybrid_query = HybridQuery( @@ -594,7 +595,7 @@ def test_hybrid_query_vector_search_method_none(): # Edge cases -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_empty_text_after_stopwords(): """Test HybridQuery behavior when text becomes empty after stopword removal.""" # All words are stopwords @@ -608,7 +609,7 @@ def test_hybrid_query_empty_text_after_stopwords(): ) -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_special_characters_in_text(): """Test HybridQuery with special characters in text.""" special_text = "search for @user #hashtag $price 50% off!" @@ -631,7 +632,7 @@ def test_hybrid_query_special_characters_in_text(): ] -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_unicode_text(): """Test HybridQuery with Unicode characters in text.""" unicode_text = "café résumé naïve 日本語 中文" @@ -658,7 +659,7 @@ def test_hybrid_query_unicode_text(): # Vector filter expression tests -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_with_vector_filter_tag(): """Test HybridQuery with Tag FilterExpression on vector search.""" tag_filter = Tag("genre") == "comedy" @@ -676,7 +677,7 @@ def test_hybrid_query_with_vector_filter_tag(): assert args[-2:] == ["FILTER", "@genre:{comedy}"] -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_with_vector_filter_string(): """Test HybridQuery with string filter expression on vector search.""" string_filter = "@category:{tech|science|engineering}" @@ -694,7 +695,7 @@ def test_hybrid_query_with_vector_filter_string(): assert args[-2:] == ["FILTER", "@category:{tech|science|engineering}"] -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_with_vector_filter_numeric(): """Test HybridQuery with Numeric FilterExpression on vector search.""" numeric_filter = Num("rating") > 7.0 @@ -712,7 +713,7 @@ def test_hybrid_query_with_vector_filter_numeric(): assert args[-2:] == ["FILTER", "@rating:[(7.0 +inf]"] -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_with_vector_filter_text(): """Test HybridQuery with Text FilterExpression on vector search.""" text_filter = Text("job") == "engineer" @@ -730,7 +731,7 @@ def test_hybrid_query_with_vector_filter_text(): assert args[-2:] == ["FILTER", '@job:("engineer")'] -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_with_vector_filter_combined(): """Test HybridQuery with combined FilterExpressions on vector search.""" combined_filter = (Tag("genre") == "comedy") & (Num("rating") > 7.0) @@ -748,7 +749,7 @@ def test_hybrid_query_with_vector_filter_combined(): assert args[-2:] == ["FILTER", "(@genre:{comedy} @rating:[(7.0 +inf])"] -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_with_vector_filter_none(): """Test HybridQuery without vector filter expression.""" hybrid_query = HybridQuery( @@ -764,7 +765,7 @@ def test_hybrid_query_with_vector_filter_none(): assert "FILTER" not in args -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_with_vector_filter_and_method(): """Test HybridQuery with vector filter and a search method.""" tag_filter = Tag("genre") == "comedy" @@ -784,7 +785,7 @@ def test_hybrid_query_with_vector_filter_and_method(): assert args[-6:] == ["KNN", 2, "K", 10, "FILTER", "@genre:{comedy}"] -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_with_both_text_and_vector_filters(): """Test HybridQuery with both text_filter_expression and vector_filter_expression.""" text_filter = Tag("category") == "movies" @@ -817,7 +818,7 @@ def test_hybrid_query_with_both_text_and_vector_filters(): # Combination method tests -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_combination_method_rrf_basic(): """Test HybridQuery with RRF combination method.""" hybrid_query = HybridQuery( @@ -842,7 +843,7 @@ def test_hybrid_query_combination_method_rrf_basic(): ] -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_combination_method_rrf_with_constant(): """Test HybridQuery with RRF combination method and constant parameter.""" hybrid_query = HybridQuery( @@ -867,7 +868,7 @@ def test_hybrid_query_combination_method_rrf_with_constant(): ] -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_combination_method_rrf_with_both_params(): """Test HybridQuery with RRF combination method with both window and constant.""" hybrid_query = HybridQuery( @@ -898,7 +899,7 @@ def test_hybrid_query_combination_method_rrf_with_both_params(): ] -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) @pytest.mark.parametrize("alpha", [0.1, 0.5, 0.9]) def test_hybrid_query_combination_method_linear_with_alpha(alpha: float): """Test HybridQuery with LINEAR combination method.""" @@ -973,7 +974,7 @@ def test_hybrid_query_combination_method_linear_with_alpha(alpha: float): ] -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_combination_method_linear_with_yield_score(): """Test HybridQuery with LINEAR combination method and yield_combined_score_as.""" hybrid_query = HybridQuery( @@ -1001,7 +1002,7 @@ def test_hybrid_query_combination_method_linear_with_yield_score(): ] -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_combination_method_none(): """Test HybridQuery without combination method.""" hybrid_query = HybridQuery( @@ -1020,7 +1021,7 @@ def test_hybrid_query_combination_method_none(): assert "COMBINE" not in args -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_build_combination_method_invalid_method(): """Test build_combination_method static method with invalid combination method.""" with pytest.raises(ValueError, match="Unknown combination method"): @@ -1029,7 +1030,7 @@ def test_build_combination_method_invalid_method(): ) -@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason="Requires redis>=7.1.0") +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) @pytest.mark.parametrize("method", ["RRF", "LINEAR"]) def test_build_combination_method_no_parameters(method: Literal["RRF", "LINEAR"]): """Test build_combination_method static method raises ValueError when no parameters provided.""" From cdba22f5971436bb4e808c735ae285e9c13648d1 Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Mon, 8 Dec 2025 14:17:36 +0100 Subject: [PATCH 18/34] Update expected number of results to hybrid search default --- tests/integration/test_hybrid.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/tests/integration/test_hybrid.py b/tests/integration/test_hybrid.py index 1fa84876..763ba800 100644 --- a/tests/integration/test_hybrid.py +++ b/tests/integration/test_hybrid.py @@ -2,15 +2,18 @@ from redisvl.index import AsyncSearchIndex, SearchIndex from redisvl.query.filter import Geo, GeoRadius, Num, Tag, Text -from redisvl.query.hybrid import HybridQuery from redisvl.redis.utils import array_to_buffer from redisvl.schema import IndexSchema from tests.conftest import ( - get_redis_version_async, skip_if_redis_version_below, skip_if_redis_version_below_async, ) +try: + from redisvl.query.hybrid import HybridQuery +except (ImportError, ModuleNotFoundError): + HybridQuery = None # type: ignore + @pytest.fixture def index_schema(worker_id): @@ -129,7 +132,7 @@ def test_hybrid_query(index): results = index.hybrid_search(hybrid_query) assert isinstance(results, list) - assert len(results) == 7 + assert len(results) == 10 # Server-side default for hybrid search for doc in results: assert doc["user"] in [ "john", From 3173502e20047280021d6cd1b4ac7fe8cca300e9 Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Mon, 8 Dec 2025 14:53:37 +0100 Subject: [PATCH 19/34] Remove ambiguous `redisvl.query` import for `HybridQuery` --- redisvl/query/__init__.py | 2 -- redisvl/query/hybrid.py | 8 +++----- tests/unit/test_hybrid_types.py | 1 + 3 files changed, 4 insertions(+), 7 deletions(-) diff --git a/redisvl/query/__init__.py b/redisvl/query/__init__.py index b561fe60..857b30b2 100644 --- a/redisvl/query/__init__.py +++ b/redisvl/query/__init__.py @@ -1,7 +1,6 @@ from redisvl.query.aggregate import ( AggregateHybridQuery, AggregationQuery, - HybridQuery, MultiVectorQuery, Vector, ) @@ -27,7 +26,6 @@ "TextQuery", "AggregationQuery", "AggregateHybridQuery", - "HybridQuery", "MultiVectorQuery", "Vector", ] diff --git a/redisvl/query/hybrid.py b/redisvl/query/hybrid.py index 003ed70f..91ee7396 100644 --- a/redisvl/query/hybrid.py +++ b/redisvl/query/hybrid.py @@ -78,7 +78,9 @@ def __init__( accuracy of the search. yield_vsim_score_as: The name of the field to yield the vector similarity score as. vector_filter_expression: The filter expression to use for the vector similarity search. Defaults to None. - combination_method: The combination method to use. Options are {RRF, LINEAR}. Defaults to None. + combination_method: The combination method to use. Options are {RRF, LINEAR}. Defaults to None. If "RRF" is + specified, then at least one of `rrf_window` or `rrf_constant` must be provided. If "LINEAR" is + specified, then at least one of `linear_alpha` or `linear_beta` must be provided. rrf_window: The window size to use for the reciprocal rank fusion (RRF) combination method. Limits fusion scope. rrf_constant: The constant to use for the reciprocal rank fusion (RRF) combination method. Controls decay @@ -102,10 +104,6 @@ def __init__( within the query text. Defaults to None, as no modifications will be made to the text_scorer score. - Notes: - If RRF combination method is used, then at least one of `rrf_window` or `rrf_constant` must be provided. - If LINEAR combination method is used, then at least one of `linear_alpha` or `linear_beta` must be provided. - Raises: TypeError: If the stopwords are not a set, list, or tuple of strings. ValueError: If the text string is empty, or if the text string becomes empty after diff --git a/tests/unit/test_hybrid_types.py b/tests/unit/test_hybrid_types.py index 94504be3..31b0ee8b 100644 --- a/tests/unit/test_hybrid_types.py +++ b/tests/unit/test_hybrid_types.py @@ -22,6 +22,7 @@ from redisvl.query.hybrid import HybridQuery, build_combination_method REDIS_HYBRID_AVAILABLE = True + SKIP_REASON = "" except (ImportError, ModuleNotFoundError): REDIS_HYBRID_AVAILABLE = False SKIP_REASON = "Requires redis>=8.4.0 and redis-py>=7.1.0" From ecd5c1b28a41c6aefae6f57b002958537f82612c Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Mon, 8 Dec 2025 15:35:57 +0100 Subject: [PATCH 20/34] Update docs --- docs/api/query.rst | 36 +- docs/user_guide/11_advanced_queries.ipynb | 713 +++++++++++++++++----- redisvl/query/hybrid.py | 26 + 3 files changed, 631 insertions(+), 144 deletions(-) diff --git a/docs/api/query.rst b/docs/api/query.rst index 1410bd3a..1f6daf14 100644 --- a/docs/api/query.rst +++ b/docs/api/query.rst @@ -105,21 +105,21 @@ VectorRangeQuery use_search_history='AUTO' # SVS-VAMANA only ) -HybridQuery +AggregateHybridQuery ================ .. currentmodule:: redisvl.query -.. autoclass:: HybridQuery +.. autoclass:: AggregateHybridQuery :members: :inherited-members: :show-inheritance: :exclude-members: add_filter,get_args,highlight,return_field,summarize .. note:: - The ``stopwords`` parameter in :class:`HybridQuery` (and :class:`AggregateHybridQuery`) controls query-time stopword filtering (client-side). + The ``stopwords`` parameter in :class:`AggregateHybridQuery` (and :class:`HybridQuery`) controls query-time stopword filtering (client-side). For index-level stopwords configuration (server-side), see :class:`redisvl.schema.IndexInfo.stopwords`. Using query-time stopwords with index-level ``STOPWORDS 0`` is counterproductive. @@ -130,22 +130,42 @@ HybridQuery Runtime parameters (``ef_runtime``, ``search_window_size``, ``use_search_history``, ``search_buffer_capacity``) are only supported with FT.SEARCH commands. - For runtime parameter support, use :class:`VectorQuery` or :class:`VectorRangeQuery` instead of AggregateHybridQuery. + For runtime parameter support, use :class:`HybridQuery`, :class:`VectorQuery`, or :class:`VectorRangeQuery` instead of AggregateHybridQuery. - Example with VectorQuery (supports runtime parameters): + Example with HybridQuery (supports runtime parameters): .. code-block:: python - from redisvl.query import VectorQuery + from redisvl.query.hybrid import HybridQuery - query = VectorQuery( + query = HybridQuery( + text="query string", + text_field_name="description", vector=[0.1, 0.2, 0.3], vector_field_name="embedding", + vector_search_method="KNN", + knn_k=10, + knn_ef_runtime=150, # Runtime parameters work with HybridQuery return_fields=["description"], num_results=10, - ef_runtime=150 # Runtime parameters work with VectorQuery ) +HybridQuery +================ + + +.. currentmodule:: redisvl.query.hybrid + + +.. autoclass:: HybridQuery + :members: + :inherited-members: + :show-inheritance: + +.. note:: + The ``stopwords`` parameter in :class:`HybridQuery` (and :class:`AggregateHybridQuery`) controls query-time stopword filtering (client-side). + For index-level stopwords configuration (server-side), see :class:`redisvl.schema.IndexInfo.stopwords`. + Using query-time stopwords with index-level ``STOPWORDS 0`` is counterproductive. TextQuery ================ diff --git a/docs/user_guide/11_advanced_queries.ipynb b/docs/user_guide/11_advanced_queries.ipynb index 3125d848..77d5125c 100644 --- a/docs/user_guide/11_advanced_queries.ipynb +++ b/docs/user_guide/11_advanced_queries.ipynb @@ -9,14 +9,15 @@ "In this notebook, we will explore advanced query types available in RedisVL:\n", "\n", "1. **`TextQuery`**: Full text search with advanced scoring\n", - "2. **`AggregateHybridQuery`**: Combines text and vector search for hybrid retrieval\n", + "2. **`AggregateHybridQuery` and `HybridQuery`**: Combines text and vector search for hybrid retrieval\n", "3. **`MultiVectorQuery`**: Search over multiple vector fields simultaneously\n", "\n", "These query types are powerful tools for building sophisticated search applications that go beyond simple vector similarity search.\n", "\n", "Prerequisites:\n", "- Ensure `redisvl` is installed in your Python environment.\n", - "- Have a running instance of [Redis Stack](https://redis.io/docs/install/install-stack/) or [Redis Cloud](https://redis.io/cloud).\n" + "- Have a running instance of [Redis Stack](https://redis.io/docs/install/install-stack/) or [Redis Cloud](https://redis.io/cloud).\n", + "- For `HybridQuery`, we will need Redis Open Source >= 8.4.0 and redis-py >= 7.1.0.\n" ] }, { @@ -38,8 +39,8 @@ "shell.execute_reply": "2025-11-21T00:42:12.301163Z" }, "ExecuteTime": { - "end_time": "2025-11-21T21:27:49.998123Z", - "start_time": "2025-11-21T21:27:49.993513Z" + "end_time": "2025-12-08T13:39:31.340482Z", + "start_time": "2025-12-08T13:39:31.227119Z" } }, "source": [ @@ -111,7 +112,7 @@ "]" ], "outputs": [], - "execution_count": 6 + "execution_count": 1 }, { "cell_type": "markdown", @@ -136,8 +137,8 @@ "shell.execute_reply": "2025-11-21T00:42:12.305407Z" }, "ExecuteTime": { - "end_time": "2025-11-21T21:27:50.362957Z", - "start_time": "2025-11-21T21:27:50.360561Z" + "end_time": "2025-12-08T13:39:50.714664Z", + "start_time": "2025-12-08T13:39:50.711756Z" } }, "source": [ @@ -178,7 +179,7 @@ "}" ], "outputs": [], - "execution_count": 7 + "execution_count": 2 }, { "cell_type": "markdown", @@ -197,8 +198,8 @@ "shell.execute_reply": "2025-11-21T00:42:12.415926Z" }, "ExecuteTime": { - "end_time": "2025-11-21T21:27:50.727271Z", - "start_time": "2025-11-21T21:27:50.715789Z" + "end_time": "2025-12-08T13:40:18.661710Z", + "start_time": "2025-12-08T13:40:18.538641Z" } }, "source": [ @@ -218,12 +219,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "16:27:50 redisvl.index.index INFO Index already exists, overwriting.\n", "Loaded 6 products into the index\n" ] } ], - "execution_count": 8 + "execution_count": 3 }, { "cell_type": "markdown", @@ -248,8 +248,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.708647Z" }, "ExecuteTime": { - "end_time": "2025-11-21T21:27:51.127508Z", - "start_time": "2025-11-21T21:27:51.123980Z" + "end_time": "2025-12-08T13:40:23.622092Z", + "start_time": "2025-12-08T13:40:22.813571Z" } }, "source": [ @@ -273,7 +273,7 @@ "" ], "text/html": [ - "
scoreproduct_idbrief_descriptioncategoryprice
4.080705480646511prod_1comfortable running shoes for athletesfootwear89.99
1.4504838715161907prod_5basketball shoes with excellent ankle supportfootwear139.99
1.431980178975859prod_2lightweight running jacket with water resistanceouterwear129.99
" + "
scoreproduct_idbrief_descriptioncategoryprice
5.953989333038773prod_1comfortable running shoes for athletesfootwear89.99
2.085315593627535prod_5basketball shoes with excellent ankle supportfootwear139.99
2.0410082774474088prod_2lightweight running jacket with water resistanceouterwear129.99
" ] }, "metadata": {}, @@ -283,7 +283,7 @@ } } ], - "execution_count": 9 + "execution_count": 4 }, { "cell_type": "markdown", @@ -304,8 +304,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.748398Z" }, "ExecuteTime": { - "end_time": "2025-11-21T21:27:51.537001Z", - "start_time": "2025-11-21T21:27:51.532996Z" + "end_time": "2025-12-08T13:40:26.303477Z", + "start_time": "2025-12-08T13:40:26.298327Z" } }, "source": [ @@ -336,7 +336,7 @@ "" ], "text/html": [ - "
scoreproduct_idbrief_descriptionprice
4.165936382048982prod_1comfortable running shoes for athletes89.99
1.769051138581863prod_4yoga mat with extra cushioning for comfort39.99
1.2306902673750557prod_5basketball shoes with excellent ankle support139.99
" + "
scoreproduct_idbrief_descriptionprice
6.031534703977659prod_1comfortable running shoes for athletes89.99
2.085315593627535prod_5basketball shoes with excellent ankle support139.99
1.5268074873573214prod_4yoga mat with extra cushioning for comfort39.99
" ] }, "metadata": {}, @@ -346,7 +346,7 @@ } } ], - "execution_count": 10 + "execution_count": 5 }, { "cell_type": "code", @@ -358,8 +358,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.754345Z" }, "ExecuteTime": { - "end_time": "2025-11-21T21:27:51.747761Z", - "start_time": "2025-11-21T21:27:51.742796Z" + "end_time": "2025-12-08T13:40:26.911718Z", + "start_time": "2025-12-08T13:40:26.906103Z" } }, "source": [ @@ -390,7 +390,7 @@ "" ], "text/html": [ - "
scoreproduct_idbrief_descriptionprice
1.3333333333333333prod_1comfortable running shoes for athletes89.99
1.3333333333333333prod_1comfortable running shoes for athletes89.99
1.0prod_5basketball shoes with excellent ankle support139.99
" + "
scoreproduct_idbrief_descriptionprice
2.3333333333333335prod_1comfortable running shoes for athletes89.99
2.0prod_5basketball shoes with excellent ankle support139.99
1.0prod_4yoga mat with extra cushioning for comfort39.99
" ] }, "metadata": {}, @@ -400,7 +400,7 @@ } } ], - "execution_count": 11 + "execution_count": 6 }, { "cell_type": "markdown", @@ -421,8 +421,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.759844Z" }, "ExecuteTime": { - "end_time": "2025-11-21T21:27:52.153660Z", - "start_time": "2025-11-21T21:27:52.150061Z" + "end_time": "2025-12-08T13:40:28.084810Z", + "start_time": "2025-12-08T13:40:28.079570Z" } }, "source": [ @@ -447,7 +447,7 @@ "" ], "text/html": [ - "
scoreproduct_idbrief_descriptioncategoryprice
2.385806908729779prod_1comfortable running shoes for athletesfootwear89.99
2.385806908729779prod_1comfortable running shoes for athletesfootwear89.99
1.9340948871093797prod_5basketball shoes with excellent ankle supportfootwear139.99
" + "
scoreproduct_idbrief_descriptioncategoryprice
3.9314935770863046prod_1comfortable running shoes for athletesfootwear89.99
3.1279733904413027prod_5basketball shoes with excellent ankle supportfootwear139.99
" ] }, "metadata": {}, @@ -457,7 +457,7 @@ } } ], - "execution_count": 12 + "execution_count": 7 }, { "cell_type": "code", @@ -469,8 +469,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.765316Z" }, "ExecuteTime": { - "end_time": "2025-11-21T21:27:52.357623Z", - "start_time": "2025-11-21T21:27:52.351735Z" + "end_time": "2025-12-08T13:40:28.525493Z", + "start_time": "2025-12-08T13:40:28.520152Z" } }, "source": [ @@ -493,7 +493,7 @@ "" ], "text/html": [ - "
scoreproduct_idbrief_descriptionprice
2.2775029612659465prod_1comfortable running shoes for athletes89.99
1.1387514806329733prod_1comfortable running shoes for athletes89.99
1.1190633543347508prod_4yoga mat with extra cushioning for comfort39.99
1.1190633543347508prod_4yoga mat with extra cushioning for comfort39.99
" + "
scoreproduct_idbrief_descriptionprice
3.1541404034996914prod_1comfortable running shoes for athletes89.99
1.5268074873573214prod_4yoga mat with extra cushioning for comfort39.99
" ] }, "metadata": {}, @@ -503,7 +503,7 @@ } } ], - "execution_count": 13 + "execution_count": 8 }, { "cell_type": "markdown", @@ -525,8 +525,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.770555Z" }, "ExecuteTime": { - "end_time": "2025-11-21T21:27:52.754720Z", - "start_time": "2025-11-21T21:27:52.751189Z" + "end_time": "2025-12-08T13:40:29.658650Z", + "start_time": "2025-12-08T13:40:29.654660Z" } }, "source": [ @@ -547,7 +547,7 @@ "" ], "text/html": [ - "
scoreproduct_idbrief_description
3.040323653363804prod_1comfortable running shoes for athletes
3.040323653363804prod_1comfortable running shoes for athletes
1.289396591406253prod_5basketball shoes with excellent ankle support
" + "
scoreproduct_idbrief_description
5.035440025836444prod_1comfortable running shoes for athletes
2.085315593627535prod_5basketball shoes with excellent ankle support
" ] }, "metadata": {}, @@ -557,7 +557,7 @@ } } ], - "execution_count": 14 + "execution_count": 9 }, { "cell_type": "markdown", @@ -578,8 +578,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.775861Z" }, "ExecuteTime": { - "end_time": "2025-11-21T21:27:53.171295Z", - "start_time": "2025-11-21T21:27:53.167127Z" + "end_time": "2025-12-08T13:40:32.312718Z", + "start_time": "2025-12-08T13:40:32.308620Z" } }, "source": [ @@ -602,7 +602,7 @@ "" ], "text/html": [ - "
scoreproduct_idbrief_description
4.1444591833267275prod_1comfortable running shoes for athletes
4.1444591833267275prod_1comfortable running shoes for athletes
1.4875097606385526prod_5basketball shoes with excellent ankle support
" + "
scoreproduct_idbrief_description
5.953989333038773prod_1comfortable running shoes for athletes
2.085315593627535prod_5basketball shoes with excellent ankle support
2.0410082774474088prod_2lightweight running jacket with water resistance
" ] }, "metadata": {}, @@ -612,7 +612,7 @@ } } ], - "execution_count": 15 + "execution_count": 10 }, { "cell_type": "code", @@ -624,8 +624,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.780713Z" }, "ExecuteTime": { - "end_time": "2025-11-21T21:27:53.528245Z", - "start_time": "2025-11-21T21:27:53.525116Z" + "end_time": "2025-12-08T13:40:32.930041Z", + "start_time": "2025-12-08T13:40:32.925758Z" } }, "source": [ @@ -648,7 +648,7 @@ "" ], "text/html": [ - "
scoreproduct_idbrief_description
2.5107799078325prod_1comfortable running shoes for athletes
2.5107799078325prod_1comfortable running shoes for athletes
2.482820220115406prod_3professional tennis racket for competitive players
" + "
scoreproduct_idbrief_description
3.1541404034996914prod_1comfortable running shoes for athletes
3.0864038416103prod_3professional tennis racket for competitive players
" ] }, "metadata": {}, @@ -658,7 +658,7 @@ } } ], - "execution_count": 16 + "execution_count": 11 }, { "cell_type": "code", @@ -670,8 +670,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.786617Z" }, "ExecuteTime": { - "end_time": "2025-11-21T21:27:53.892142Z", - "start_time": "2025-11-21T21:27:53.888038Z" + "end_time": "2025-12-08T13:40:33.691546Z", + "start_time": "2025-12-08T13:40:33.686402Z" } }, "source": [ @@ -694,7 +694,7 @@ "" ], "text/html": [ - "
scoreproduct_idbrief_description
3.69730364515632prod_1comfortable running shoes for athletes
3.69730364515632prod_1comfortable running shoes for athletes
1.5329921800414583prod_5basketball shoes with excellent ankle support
" + "
scoreproduct_idbrief_description
5.953989333038773prod_1comfortable running shoes for athletes
2.085315593627535prod_5basketball shoes with excellent ankle support
2.0410082774474088prod_2lightweight running jacket with water resistance
" ] }, "metadata": {}, @@ -704,25 +704,19 @@ } } ], - "execution_count": 17 + "execution_count": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 2. AggregateHybridQuery: Combining Text and Vector Search\n", + "## 2. Hybrid Queries: Combining Text and Vector Search\n", "\n", - "The `AggregateHybridQuery` combines text search and vector similarity to provide the best of both worlds:\n", + "Hybrid queries combine text search and vector similarity to provide the best of both worlds:\n", "- **Text search**: Finds exact keyword matches\n", "- **Vector search**: Captures semantic similarity\n", "\n", - "Results are scored using a weighted combination:\n", - "\n", - "```\n", - "hybrid_score = (alpha) * vector_score + (1 - alpha) * text_score\n", - "```\n", - "\n", - "Where `alpha` controls the balance between vector and text search (default: 0.7)." + "As of Redis Open Source 8.4.0, Redis natively supports a [`FT.HYBRID`](https://redis.io/docs/latest/commands/ft.hybrid) search command. RedisVL provides a `HybridQuery` class that makes it easy to construct and execute hybrid queries. For earlier versions of Redis, RedisVL provides an `AggregateHybridQuery` class that uses Redis aggregation to achieve similar results." ] }, { @@ -759,8 +753,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.794662Z" }, "ExecuteTime": { - "end_time": "2025-11-21T21:27:55.430188Z", - "start_time": "2025-11-21T21:27:55.420369Z" + "end_time": "2025-12-08T13:44:47.182730Z", + "start_time": "2025-12-08T13:44:47.173733Z" } }, "source": [ @@ -791,11 +785,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Index created with STOPWORDS 0: \n" + "Index created with STOPWORDS 0: \n" ] } ], - "execution_count": 18 + "execution_count": 13 }, { "cell_type": "code", @@ -807,8 +801,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.802098Z" }, "ExecuteTime": { - "end_time": "2025-11-21T21:27:55.640718Z", - "start_time": "2025-11-21T21:27:55.635077Z" + "end_time": "2025-12-08T13:44:48.226137Z", + "start_time": "2025-12-08T13:44:48.218807Z" } }, "source": [ @@ -824,18 +818,18 @@ "for i, company in enumerate(companies):\n", " company_index.load([company], keys=[f\"company:{i}\"])\n", "\n", - "print(f\"\u2713 Loaded {len(companies)} companies\")" + "print(f\"✓ Loaded {len(companies)} companies\")" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u2713 Loaded 5 companies\n" + "✓ Loaded 5 companies\n" ] } ], - "execution_count": 19 + "execution_count": 14 }, { "cell_type": "code", @@ -847,8 +841,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.806491Z" }, "ExecuteTime": { - "end_time": "2025-11-21T21:27:55.833033Z", - "start_time": "2025-11-21T21:27:55.829220Z" + "end_time": "2025-12-08T13:44:49.547154Z", + "start_time": "2025-12-08T13:44:49.542606Z" } }, "source": [ @@ -876,7 +870,7 @@ ] } ], - "execution_count": 20 + "execution_count": 15 }, { "cell_type": "markdown", @@ -886,9 +880,9 @@ "\n", "If we had used the default stopwords (not specifying `stopwords` in the schema), the word \"of\" would be filtered out during indexing. This means:\n", "\n", - "- \u274c Searching for `\"Bank of Glasberliner\"` might not find exact matches\n", - "- \u274c The phrase would be indexed as `\"Bank Berlin\"` (without \"of\")\n", - "- \u2705 With `STOPWORDS 0`, all words including \"of\" are indexed\n", + "- ❌ Searching for `\"Bank of Glasberliner\"` might not find exact matches\n", + "- ❌ The phrase would be indexed as `\"Bank Berlin\"` (without \"of\")\n", + "- ✅ With `STOPWORDS 0`, all words including \"of\" are indexed\n", "\n", "**Custom Stopwords Example:**\n", "\n", @@ -905,8 +899,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.810083Z" }, "ExecuteTime": { - "end_time": "2025-11-21T21:27:56.463470Z", - "start_time": "2025-11-21T21:27:56.461409Z" + "end_time": "2025-12-08T13:44:56.551634Z", + "start_time": "2025-12-08T13:44:56.549334Z" } }, "source": [ @@ -934,7 +928,7 @@ ] } ], - "execution_count": 21 + "execution_count": 16 }, { "cell_type": "markdown", @@ -975,48 +969,101 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-11-21T21:27:57.036397Z", - "start_time": "2025-11-21T21:27:57.030555Z" + "end_time": "2025-12-08T13:44:59.280976Z", + "start_time": "2025-12-08T13:44:59.276691Z" } }, "source": [ "# Cleanup\n", "company_index.delete(drop=True)\n", - "print(\"\u2713 Cleaned up company_index\")" + "print(\"✓ Cleaned up company_index\")" ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u2713 Cleaned up company_index\n" + "✓ Cleaned up company_index\n" ] } ], - "execution_count": 22 + "execution_count": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Basic Aggregate Hybrid Query\n", + "### Basic Hybrid Query\n", + "\n", + "> NOTE: `HybridQuery` requires Redis >= 8.4.0 and redis-py >= 7.1.0.\n", "\n", - "Let's search for \"running\" with both text and semantic search:" + "Let's search for \"running\" with both text and semantic search, combining the results' scores using a linear combination:" ] }, { "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-11-21T21:27:57.725041Z", - "start_time": "2025-11-21T21:27:57.719775Z" + "end_time": "2025-12-08T14:02:29.152907Z", + "start_time": "2025-12-08T14:02:29.146817Z" } }, "source": [ - "from redisvl.query import AggregateHybridQuery\n", + "from redisvl.query.hybrid import HybridQuery\n", "\n", "# Create a hybrid query\n", - "hybrid_query = AggregateHybridQuery(\n", + "hybrid_query = HybridQuery(\n", + " text=\"running shoes\",\n", + " text_field_name=\"brief_description\",\n", + " vector=[0.1, 0.2, 0.1], # Query vector\n", + " vector_field_name=\"text_embedding\",\n", + " return_fields=[\"product_id\", \"brief_description\", \"category\", \"price\"],\n", + " num_results=5,\n", + " yield_text_score_as=\"text_score\",\n", + " yield_vsim_score_as=\"vector_similarity\",\n", + " combination_method=\"LINEAR\",\n", + " yield_combined_score_as=\"hybrid_score\",\n", + ")\n", + "\n", + "results = index.hybrid_search(hybrid_query)\n", + "result_print(results)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
text_scoreproduct_idbrief_descriptioncategorypricevector_similarityhybrid_score
5.95398933304prod_1comfortable running shoes for athletesfootwear89.990.9999999701982.48619677905
2.08531559363prod_5basketball shoes with excellent ankle supportfootwear139.990.9950737357141.32214629309
2.04100827745prod_2lightweight running jacket with water resistanceouterwear129.990.9950737357141.30885409823
0prod_4yoga mat with extra cushioning for comfortaccessories39.990.9980582594870.698640781641
0prod_6swimming goggles with anti-fog coatingaccessories24.990.8818812966350.617316907644
" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 31 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "For earlier versions of Redis, you can use `AggregateHybridQuery` instead:" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-08T14:02:30.673828Z", + "start_time": "2025-12-08T14:02:30.663228Z" + } + }, + "cell_type": "code", + "source": [ + "from redisvl.query.aggregate import AggregateHybridQuery\n", + "\n", + "agg_hybrid_query = AggregateHybridQuery(\n", " text=\"running shoes\",\n", " text_field_name=\"brief_description\",\n", " vector=[0.1, 0.2, 0.1], # Query vector\n", @@ -1025,7 +1072,7 @@ " num_results=5\n", ")\n", "\n", - "results = index.query(hybrid_query)\n", + "results = index.query(agg_hybrid_query)\n", "result_print(results)" ], "outputs": [ @@ -1035,7 +1082,7 @@ "" ], "text/html": [ - "
vector_distanceproduct_idbrief_descriptioncategorypricevector_similaritytext_scorehybrid_score
5.96046447754e-08prod_1comfortable running shoes for athletesfootwear89.990.9999999701984.829774426092.14893230697
5.96046447754e-08prod_1comfortable running shoes for athletesfootwear89.990.9999999701984.829774426092.14893230697
5.96046447754e-08prod_1comfortable running shoes for athletesfootwear89.990.9999999701984.829774426092.14893230697
0.0038834810257prod_4yoga mat with extra cushioning for comfortaccessories39.990.99805825948700.698640781641
0.0038834810257prod_4yoga mat with extra cushioning for comfortaccessories39.990.99805825948700.698640781641
" + "
vector_distanceproduct_idbrief_descriptioncategorypricevector_similaritytext_scorehybrid_score
5.96046447754e-08prod_1comfortable running shoes for athletesfootwear89.990.9999999701985.953989333042.48619677905
0.00985252857208prod_5basketball shoes with excellent ankle supportfootwear139.990.9950737357142.085315593631.32214629309
0.00985252857208prod_2lightweight running jacket with water resistanceouterwear129.990.9950737357142.041008277451.30885409823
0.0038834810257prod_4yoga mat with extra cushioning for comfortaccessories39.990.99805825948700.698640781641
0.236237406731prod_6swimming goggles with anti-fog coatingaccessories24.990.88188129663500.617316907644
" ] }, "metadata": {}, @@ -1045,7 +1092,7 @@ } } ], - "execution_count": 23 + "execution_count": 32 }, { "cell_type": "markdown", @@ -1053,18 +1100,79 @@ "source": [ "### Adjusting the Alpha Parameter\n", "\n", - "The `alpha` parameter controls the weight between vector and text search:\n", - "- `alpha=1.0`: Pure vector search\n", - "- `alpha=0.0`: Pure text search\n", - "- `alpha=0.7` (default): 70% vector, 30% text" + "Results are scored using a weighted combination:\n", + "\n", + "```\n", + "hybrid_score = (alpha) * text_score + (1 - alpha) * vector_score\n", + "```\n", + "\n", + "Where `alpha` controls the balance between text and vector search (default: 0.3 for `HybridQuery` and 0.7 for `AggregateHybridQuery`). Note that `AggregateHybridQuery` reverses the definition of `alpha` to be the weight of the vector score.\n", + "\n", + "The `alpha` parameter controls the weight between text and vector search:\n", + "- `alpha=1.0`: Pure text search (or pure vector search for `AggregateHybridQuery`)\n", + "- `alpha=0.0`: Pure vector search (or pure text search for `AggregateHybridQuery`)\n", + "- `alpha=0.3` (default - `HybridQuery`): 30% text, 70% vector" ] }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-08T14:03:02.867269Z", + "start_time": "2025-12-08T14:03:02.860829Z" + } + }, + "cell_type": "code", + "source": [ + "vector_heavy_query = HybridQuery(\n", + " text=\"comfortable\",\n", + " text_field_name=\"brief_description\",\n", + " vector=[0.15, 0.25, 0.15],\n", + " vector_field_name=\"text_embedding\",\n", + " combination_method=\"LINEAR\",\n", + " linear_beta=0.9, # 90% vector, 10% text\n", + " return_fields=[\"product_id\", \"brief_description\"],\n", + " num_results=3,\n", + " yield_text_score_as=\"text_score\",\n", + " yield_vsim_score_as=\"vector_similarity\",\n", + " yield_combined_score_as=\"hybrid_score\",\n", + ")\n", + "\n", + "print(\"Results with alpha=0.9 (vector-heavy):\")\n", + "results = index.hybrid_search(vector_heavy_query)\n", + "result_print(results)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results with alpha=0.9 (vector-heavy):\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
text_scoreproduct_idbrief_descriptionvector_similarityhybrid_score
3.1541404035prod_1comfortable running shoes for athletes0.9980582594871.21366647389
1.52680748736prod_4yoga mat with extra cushioning for comfort1.00000005961.05268080238
0prod_2lightweight running jacket with water resistance0.9993155598640.899384003878
" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 36 + }, { "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-11-21T21:28:02.908824Z", - "start_time": "2025-11-21T21:28:02.902585Z" + "end_time": "2025-12-08T14:01:39.717367Z", + "start_time": "2025-12-08T14:01:39.711383Z" } }, "source": [ @@ -1097,7 +1205,71 @@ "" ], "text/html": [ - "
vector_distanceproduct_idbrief_descriptionvector_similaritytext_scorehybrid_score
-1.19209289551e-07prod_4yoga mat with extra cushioning for comfort1.00000005961.538380705411.05383812419
-1.19209289551e-07prod_4yoga mat with extra cushioning for comfort1.00000005961.538380705411.05383812419
-1.19209289551e-07prod_4yoga mat with extra cushioning for comfort1.00000005961.538380705411.05383812419
" + "
vector_distanceproduct_idbrief_descriptionvector_similaritytext_scorehybrid_score
-1.19209289551e-07prod_4yoga mat with extra cushioning for comfort1.00000005961.526807487361.05268080238
0.00136888027191prod_5basketball shoes with excellent ankle support0.99931555986400.899384003878
0.00136888027191prod_2lightweight running jacket with water resistance0.99931555986400.899384003878
" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 29 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "##" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### Reciprocal Rank Fusion (RRF)\n", + "\n", + "In addition to combining scores using a linear combination, `HybridQuery` also supports reciprocal rank fusion (RRF) for combining scores. This method is useful when you want to combine scores giving more weight to the top results from each query.\n", + "\n", + "`HybridQuery` allows for the following parameters to be specified for RRF:\n", + "- `rrf_window`: The window size to use for the RRF combination method. Limits the fusion scope.\n", + "- `rrf_constant`: The constant to use for the RRF combination method. Controls the decay of rank influence.\n", + "\n", + "`AggregateHybridQuery` does not support RRF, and only supports a linear combination of scores." + ] + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-08T14:08:21.449231Z", + "start_time": "2025-12-08T14:08:21.444211Z" + } + }, + "cell_type": "code", + "source": [ + "rrf_query = HybridQuery(\n", + " text=\"comfortable\",\n", + " text_field_name=\"brief_description\",\n", + " vector=[0.15, 0.25, 0.15],\n", + " vector_field_name=\"text_embedding\",\n", + " combination_method=\"RRF\",\n", + " return_fields=[\"product_id\", \"brief_description\"],\n", + " num_results=3,\n", + " yield_text_score_as=\"text_score\",\n", + " yield_vsim_score_as=\"vector_similarity\",\n", + " yield_combined_score_as=\"hybrid_score\",\n", + ")\n", + "\n", + "results = index.hybrid_search(rrf_query)\n", + "result_print(results)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
text_scoreproduct_idbrief_descriptionvector_similarityhybrid_score
1.52680748736prod_4yoga mat with extra cushioning for comfort1.00000005960.032522474881
3.1541404035prod_1comfortable running shoes for athletes0.9980582594870.032018442623
0prod_2lightweight running jacket with water resistance0.9993155598640.0320020481311
" ] }, "metadata": {}, @@ -1107,23 +1279,70 @@ } } ], - "execution_count": 24 + "execution_count": 41 }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Aggregate Hybrid Query with Filters\n", + "### Hybrid Query with Filters\n", "\n", "You can also combine hybrid search with filters:" ] }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-08T14:13:50.669478Z", + "start_time": "2025-12-08T14:13:50.663580Z" + } + }, + "cell_type": "code", + "source": [ + "# Hybrid search with a price filter\n", + "filtered_hybrid_query = HybridQuery(\n", + " text=\"professional equipment\",\n", + " text_field_name=\"brief_description\",\n", + " vector=[0.9, 0.1, 0.05],\n", + " vector_field_name=\"text_embedding\",\n", + " text_filter_expression=Num(\"price\") > 100,\n", + " vector_filter_expression=Num(\"price\") > 100,\n", + " return_fields=[\"product_id\", \"brief_description\", \"category\", \"price\"],\n", + " num_results=5,\n", + " combination_method=\"LINEAR\",\n", + " yield_text_score_as=\"text_score\",\n", + " yield_vsim_score_as=\"vector_similarity\",\n", + " yield_combined_score_as=\"hybrid_score\",\n", + ")\n", + "\n", + "results = index.hybrid_search(filtered_hybrid_query)\n", + "result_print(results)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
text_scoreproduct_idbrief_descriptioncategorypricevector_similarityhybrid_score
3.08640384161prod_3professional tennis racket for competitive playersequipment199.991.00000005961.62592119421
0prod_2lightweight running jacket with water resistanceouterwear129.990.7941712737080.555919891596
0prod_5basketball shoes with excellent ankle supportfootwear139.990.7941712737080.555919891596
" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 44 + }, { "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-11-21T21:28:04.309151Z", - "start_time": "2025-11-21T21:28:04.302860Z" + "end_time": "2025-12-08T14:05:32.261289Z", + "start_time": "2025-12-08T14:05:32.254487Z" } }, "source": [ @@ -1148,7 +1367,7 @@ "" ], "text/html": [ - "
vector_distanceproduct_idbrief_descriptioncategorypricevector_similaritytext_scorehybrid_score
-1.19209289551e-07prod_3professional tennis racket for competitive playersequipment199.991.00000005961.547237055061.16417115824
-1.19209289551e-07prod_3professional tennis racket for competitive playersequipment199.991.00000005961.547237055061.16417115824
-1.19209289551e-07prod_3professional tennis racket for competitive playersequipment199.991.00000005961.547237055061.16417115824
0.411657452583prod_2lightweight running jacket with water resistanceouterwear129.990.79417127370800.555919891596
0.411657452583prod_2lightweight running jacket with water resistanceouterwear129.990.79417127370800.555919891596
" + "
vector_distanceproduct_idbrief_descriptioncategorypricevector_similaritytext_scorehybrid_score
-1.19209289551e-07prod_3professional tennis racket for competitive playersequipment199.991.00000005963.086403841611.62592119421
0.411657452583prod_5basketball shoes with excellent ankle supportfootwear139.990.79417127370800.555919891596
0.411657452583prod_2lightweight running jacket with water resistanceouterwear129.990.79417127370800.555919891596
" ] }, "metadata": {}, @@ -1158,7 +1377,7 @@ } } ], - "execution_count": 25 + "execution_count": 40 }, { "cell_type": "markdown", @@ -1166,15 +1385,61 @@ "source": [ "### Using Different Text Scorers\n", "\n", - "AggregateHybridQuery supports the same text scoring algorithms as TextQuery:" + "Hybrid queries support the same text scoring algorithms as TextQuery:" ] }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-08T14:14:08.035537Z", + "start_time": "2025-12-08T14:14:08.028950Z" + } + }, + "cell_type": "code", + "source": [ + "# Aggregate Hybrid query with TFIDF scorer\n", + "hybrid_tfidf = HybridQuery(\n", + " text=\"shoes support\",\n", + " text_field_name=\"brief_description\",\n", + " vector=[0.12, 0.18, 0.12],\n", + " vector_field_name=\"text_embedding\",\n", + " text_scorer=\"TFIDF\",\n", + " return_fields=[\"product_id\", \"brief_description\"],\n", + " num_results=3,\n", + " combination_method=\"LINEAR\",\n", + " yield_text_score_as=\"text_score\",\n", + " yield_vsim_score_as=\"vector_similarity\",\n", + " yield_combined_score_as=\"hybrid_score\",\n", + ")\n", + "\n", + "results = index.hybrid_search(hybrid_tfidf)\n", + "result_print(results)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
text_scoreproduct_idbrief_descriptionvector_similarityhybrid_score
2.66666666667prod_1comfortable running shoes for athletes0.9950737357141.496551615
1.66666666667prod_5basketball shoes with excellent ankle support11.2
0prod_2lightweight running jacket with water resistance10.7
" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 45 + }, { "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-11-21T21:28:05.455328Z", - "start_time": "2025-11-21T21:28:05.450590Z" + "end_time": "2025-12-08T14:14:08.653797Z", + "start_time": "2025-12-08T14:14:08.645983Z" } }, "source": [ @@ -1199,7 +1464,7 @@ "" ], "text/html": [ - "
vector_distanceproduct_idbrief_descriptionvector_similaritytext_scorehybrid_score
0prod_5basketball shoes with excellent ankle support131.6
0prod_2lightweight running jacket with water resistance100.7
0prod_2lightweight running jacket with water resistance100.7
" + "
vector_distanceproduct_idbrief_descriptionvector_similaritytext_scorehybrid_score
0prod_5basketball shoes with excellent ankle support152.2
0prod_2lightweight running jacket with water resistance100.7
0.00136888027191prod_4yoga mat with extra cushioning for comfort0.99931555986400.699520891905
" ] }, "metadata": {}, @@ -1209,7 +1474,7 @@ } } ], - "execution_count": 26 + "execution_count": 46 }, { "metadata": {}, @@ -1219,10 +1484,11 @@ "\n", "**Important:** `AggregateHybridQuery` uses FT.AGGREGATE commands which do NOT support runtime parameters.\n", "\n", - "Runtime parameters (such as `ef_runtime` for HNSW indexes or `search_window_size` for SVS-VAMANA indexes) are only supported with FT.SEARCH commands.\n", + "Runtime parameters (such as `ef_runtime` for HNSW indexes or `search_window_size` for SVS-VAMANA indexes) are only supported with FT.SEARCH (and partially FT.HYBRID) commands.\n", "\n", - "**For runtime parameter support, use `VectorQuery` or `VectorRangeQuery` instead:**\n", + "**For runtime parameter support, use `HybridQuery`, `VectorQuery`, or `VectorRangeQuery` instead:**\n", "\n", + "- `HybridQuery`: Supports `ef_runtime` for HNSW indexes\n", "- `VectorQuery`: Supports all runtime parameters (HNSW and SVS-VAMANA)\n", "- `VectorRangeQuery`: Supports all runtime parameters (HNSW and SVS-VAMANA)\n", "- `AggregateHybridQuery`: Does NOT support runtime parameters (uses FT.AGGREGATE)\n", @@ -1256,7 +1522,12 @@ }, { "cell_type": "code", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-08T14:18:28.141238Z", + "start_time": "2025-12-08T14:18:28.134798Z" + } + }, "source": [ "from redisvl.query import MultiVectorQuery, Vector\n", "\n", @@ -1285,8 +1556,24 @@ "results = index.query(multi_vector_query)\n", "result_print(results)" ], - "outputs": [], - "execution_count": null + "outputs": [ + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
distance_0distance_1product_idbrief_descriptioncategoryscore_0score_1combined_score
5.96046447754e-085.96046447754e-08prod_1comfortable running shoes for athletesfootwear0.9999999701980.9999999701980.999999970198
0.009852528572080.00266629457474prod_5basketball shoes with excellent ankle supportfootwear0.9950737357140.9986668527130.996151670814
0.009852528572080.0118260979652prod_2lightweight running jacket with water resistanceouterwear0.9950737357140.9940869510170.994777700305
0.00388348102570.210647821426prod_4yoga mat with extra cushioning for comfortaccessories0.9980582594870.8946760892870.967043608427
0.2362374067310.639005899429prod_6swimming goggles with anti-fog coatingaccessories0.8818812966350.6804970502850.82146602273
" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 47 }, { "cell_type": "markdown", @@ -1299,7 +1586,12 @@ }, { "cell_type": "code", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-08T14:18:29.897949Z", + "start_time": "2025-12-08T14:18:29.890447Z" + } + }, "source": [ "# More emphasis on image similarity\n", "text_vec = Vector(\n", @@ -1326,8 +1618,31 @@ "results = index.query(image_heavy_query)\n", "result_print(results)" ], - "outputs": [], - "execution_count": null + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results with emphasis on image similarity:\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
distance_0distance_1product_idbrief_descriptioncategoryscore_0score_1combined_score
-1.19209289551e-070prod_3professional tennis racket for competitive playersequipment1.000000059611.00000001192
0.145393729210.00900757312775prod_6swimming goggles with anti-fog coatingaccessories0.9273031353950.9954962134360.981857597828
0.4366961717610.219131231308prod_4yoga mat with extra cushioning for comfortaccessories0.781651914120.8904343843460.868677890301
" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 48 }, { "cell_type": "markdown", @@ -1340,7 +1655,12 @@ }, { "cell_type": "code", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-08T14:18:31.186114Z", + "start_time": "2025-12-08T14:18:31.181620Z" + } + }, "source": [ "# Multi-vector search with category filter\n", "text_vec = Vector(\n", @@ -1367,8 +1687,24 @@ "results = index.query(filtered_multi_query)\n", "result_print(results)" ], - "outputs": [], - "execution_count": null + "outputs": [ + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
distance_0distance_1product_idbrief_descriptioncategorypricescore_0score_1combined_score
5.96046447754e-085.96046447754e-08prod_1comfortable running shoes for athletesfootwear89.990.9999999701980.9999999701980.999999970198
0.009852528572080.00266629457474prod_5basketball shoes with excellent ankle supportfootwear139.990.9950737357140.9986668527130.996510982513
" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 49 }, { "cell_type": "markdown", @@ -1381,7 +1717,12 @@ }, { "cell_type": "code", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-08T14:18:32.476420Z", + "start_time": "2025-12-08T14:18:32.468211Z" + } + }, "source": [ "# TextQuery - keyword-based search\n", "text_q = TextQuery(\n", @@ -1395,49 +1736,119 @@ "result_print(index.query(text_q))\n", "print()" ], - "outputs": [], - "execution_count": null + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TextQuery Results (keyword-based):\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
scoreproduct_idbrief_description
2.8773943004779676prod_1comfortable running shoes for athletes
2.085315593627535prod_5basketball shoes with excellent ankle support
" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "execution_count": 50 }, { "cell_type": "code", - "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-11-21T00:42:13.860414Z", "iopub.status.busy": "2025-11-21T00:42:13.860347Z", "iopub.status.idle": "2025-11-21T00:42:13.864887Z", "shell.execute_reply": "2025-11-21T00:42:13.864461Z" + }, + "ExecuteTime": { + "end_time": "2025-12-08T14:19:14.130046Z", + "start_time": "2025-12-08T14:19:14.123582Z" } }, - "outputs": [], "source": [ - "# AggregateHybridQuery - combines text and vector search\n", - "hybrid_q = AggregateHybridQuery(\n", + "# HybridQuery - combines text and vector search\n", + "hybrid_q = HybridQuery(\n", " text=\"shoes\",\n", " text_field_name=\"brief_description\",\n", " vector=[0.1, 0.2, 0.1],\n", " vector_field_name=\"text_embedding\",\n", " return_fields=[\"product_id\", \"brief_description\"],\n", - " num_results=3\n", + " num_results=3,\n", + " combination_method=\"LINEAR\",\n", + " yield_text_score_as=\"text_score\",\n", + " yield_vsim_score_as=\"vector_similarity\",\n", + " yield_combined_score_as=\"hybrid_score\",\n", ")\n", "\n", - "print(\"AggregateHybridQuery Results (text + vector):\")\n", - "result_print(index.query(hybrid_q))\n", + "print(\"HybridQuery Results (text + vector):\")\n", + "result_print(index.hybrid_search(hybrid_q))\n", "print()" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HybridQuery Results (text + vector):\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
text_scoreproduct_idbrief_descriptionvector_similarityhybrid_score
2.87739430048prod_1comfortable running shoes for athletes0.9999999701981.56321826928
2.08531559363prod_5basketball shoes with excellent ankle support0.9950737357141.32214629309
0prod_4yoga mat with extra cushioning for comfort0.9980582594870.698640781641
" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "execution_count": 51 }, { "cell_type": "code", - "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-11-21T00:42:13.865922Z", "iopub.status.busy": "2025-11-21T00:42:13.865857Z", "iopub.status.idle": "2025-11-21T00:42:13.869441Z", "shell.execute_reply": "2025-11-21T00:42:13.868990Z" + }, + "ExecuteTime": { + "end_time": "2025-12-08T14:19:32.109121Z", + "start_time": "2025-12-08T14:19:32.103443Z" } }, - "outputs": [], "source": [ "# MultiVectorQuery - searches multiple vector fields\n", "mv_text = Vector(\n", @@ -1462,7 +1873,32 @@ "\n", "print(\"MultiVectorQuery Results (multiple vectors):\")\n", "result_print(index.query(multi_q))" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MultiVectorQuery Results (multiple vectors):\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
distance_0distance_1product_idbrief_descriptionscore_0score_1combined_score
5.96046447754e-085.96046447754e-08prod_1comfortable running shoes for athletes0.9999999701980.9999999701980.999999970198
0.009852528572080.00266629457474prod_5basketball shoes with excellent ankle support0.9950737357140.9986668527130.996870294213
0.009852528572080.0118260979652prod_2lightweight running jacket with water resistance0.9950737357140.9940869510170.994580343366
" + ] + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 52 }, { "cell_type": "markdown", @@ -1478,7 +1914,7 @@ " - When text relevance scoring is important\n", " - Example: Product search, document retrieval\n", "\n", - "2. **`AggregateHybridQuery`**:\n", + "2. **`HybridQuery`**:\n", " - When you want to combine keyword and semantic search\n", " - For improved search quality over pure text or vector search\n", " - When you have both text and vector representations of your data\n", @@ -1493,13 +1929,18 @@ }, { "cell_type": "code", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-08T14:19:51.406116Z", + "start_time": "2025-12-08T14:19:51.402825Z" + } + }, "source": [ "# Cleanup\n", "index.delete()" ], "outputs": [], - "execution_count": null + "execution_count": 53 } ], "metadata": { @@ -1522,4 +1963,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/redisvl/query/hybrid.py b/redisvl/query/hybrid.py index 91ee7396..8416ade1 100644 --- a/redisvl/query/hybrid.py +++ b/redisvl/query/hybrid.py @@ -26,6 +26,32 @@ class HybridQuery: """ A hybrid search query that combines text search and vector similarity, with configurable fusion methods. + + .. code-block:: python + + from redisvl.query import HybridQuery + from redisvl.index import SearchIndex + + index = SearchIndex.from_yaml("path/to/index.yaml") + + 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", + yield_text_score_as="text_score", + yield_vsim_score_as="vector_similarity", + combination_method="LINEAR", + linear_alpha=0.3, + yield_combined_score_as="hybrid_score", + num_results=10, + return_fields=["field1", "field2"], + stopwords="english", + ) + + results = index.hybrid_search(query) + """ def __init__( From bf87c6a459b2fc35ce8ea0194616c1ac8aa9db15 Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Mon, 8 Dec 2025 15:47:19 +0100 Subject: [PATCH 21/34] Update imports --- redisvl/query/hybrid.py | 2 +- tests/integration/test_aggregation.py | 5 +++-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/redisvl/query/hybrid.py b/redisvl/query/hybrid.py index 8416ade1..8fbdbe04 100644 --- a/redisvl/query/hybrid.py +++ b/redisvl/query/hybrid.py @@ -29,7 +29,7 @@ class HybridQuery: .. code-block:: python - from redisvl.query import HybridQuery + from redisvl.query.hybrid import HybridQuery from redisvl.index import SearchIndex index = SearchIndex.from_yaml("path/to/index.yaml") diff --git a/tests/integration/test_aggregation.py b/tests/integration/test_aggregation.py index 7de10583..fed07665 100644 --- a/tests/integration/test_aggregation.py +++ b/tests/integration/test_aggregation.py @@ -1,8 +1,9 @@ import pytest from redisvl.index import SearchIndex -from redisvl.query import AggregateHybridQuery, HybridQuery, MultiVectorQuery, Vector -from redisvl.query.filter import FilterExpression, Geo, GeoRadius, Num, Tag, Text +from redisvl.query import AggregateHybridQuery, MultiVectorQuery, Vector +from redisvl.query.aggregate import HybridQuery +from redisvl.query.filter import Geo, GeoRadius, Num, Tag, Text from redisvl.redis.utils import array_to_buffer from tests.conftest import skip_if_redis_version_below From e44c439a530a33d6afdc40a1196738d9c2cb504d Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Mon, 8 Dec 2025 16:00:05 +0100 Subject: [PATCH 22/34] Fix test skipping logic (for Python 3.9 issues) --- tests/integration/test_hybrid.py | 29 +++++++++++++---------------- 1 file changed, 13 insertions(+), 16 deletions(-) diff --git a/tests/integration/test_hybrid.py b/tests/integration/test_hybrid.py index 763ba800..fad00f4b 100644 --- a/tests/integration/test_hybrid.py +++ b/tests/integration/test_hybrid.py @@ -11,7 +11,12 @@ try: from redisvl.query.hybrid import HybridQuery + + REDIS_HYBRID_AVAILABLE = True + SKIP_REASON = "" except (ImportError, ModuleNotFoundError): + REDIS_HYBRID_AVAILABLE = False + SKIP_REASON = "Requires redis>=8.4.0 and redis-py>=7.1.0" HybridQuery = None # type: ignore @@ -109,9 +114,8 @@ def hash_preprocess(item: dict) -> dict: await index.delete(drop=True) +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query(index): - skip_if_redis_version_below(index.client, "8.4.0") - text = "a medical professional with expertise in lung cancer" text_field = "description" vector = [0.1, 0.1, 0.5] @@ -167,9 +171,8 @@ def test_hybrid_query(index): ) +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_with_filter(index): - skip_if_redis_version_below(index.client, "8.4.0") - text = "a medical professional with expertise in lung cancer" text_field = "description" vector = [0.1, 0.1, 0.5] @@ -194,9 +197,8 @@ def test_hybrid_query_with_filter(index): assert int(result["age"]) > 30 +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_with_geo_filter(index): - skip_if_redis_version_below(index.client, "8.4.0") - text = "a medical professional with expertise in lung cancer" text_field = "description" vector = [0.1, 0.1, 0.5] @@ -220,10 +222,9 @@ def test_hybrid_query_with_geo_filter(index): assert result["location"] is not None +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) @pytest.mark.parametrize("alpha", [0.1, 0.5, 0.9]) def test_hybrid_query_alpha(index, alpha): - skip_if_redis_version_below(index.client, "8.4.0") - text = "a medical professional with expertise in lung cancer" text_field = "description" vector = [0.1, 0.1, 0.5] @@ -252,9 +253,8 @@ def test_hybrid_query_alpha(index, alpha): ) # allow for small floating point error +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_stopwords(index): - skip_if_redis_version_below(index.client, "8.4.0") - text = "a medical professional with expertise in lung cancer" text_field = "description" vector = [0.1, 0.1, 0.5] @@ -289,9 +289,8 @@ def test_hybrid_query_stopwords(index): ) # allow for small floating point error +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_with_text_filter(index): - skip_if_redis_version_below(index.client, "8.4.0") - text = "a medical professional with expertise in lung cancer" text_field = "description" vector = [0.1, 0.1, 0.5] @@ -338,10 +337,9 @@ def test_hybrid_query_with_text_filter(index): assert "research" not in result[text_field].lower() +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) @pytest.mark.parametrize("scorer", ["BM25STD", "TFIDF", "TFIDF.DOCNORM"]) def test_hybrid_query_word_weights(index, scorer): - skip_if_redis_version_below(index.client, "8.4.0") - text = "a medical professional with expertise in lung cancers" text_field = "description" vector = [0.1, 0.1, 0.5] @@ -401,10 +399,9 @@ def test_hybrid_query_word_weights(index, scorer): assert weighted_results != unweighted_results +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) @pytest.mark.asyncio async def test_hybrid_query_async(async_index): - await skip_if_redis_version_below_async(async_index.client, "8.4.0") - text = "a medical professional with expertise in lung cancer" text_field = "description" vector = [0.1, 0.1, 0.5] From 7210aaeb1566a8c65254f6ac90bf389adf8b5689 Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Mon, 8 Dec 2025 16:33:40 +0100 Subject: [PATCH 23/34] Re-add additional test skipping logic --- tests/integration/test_hybrid.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/tests/integration/test_hybrid.py b/tests/integration/test_hybrid.py index fad00f4b..69fc4653 100644 --- a/tests/integration/test_hybrid.py +++ b/tests/integration/test_hybrid.py @@ -173,6 +173,8 @@ def test_hybrid_query(index): @pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_with_filter(index): + skip_if_redis_version_below(index.client, "8.4.0") + text = "a medical professional with expertise in lung cancer" text_field = "description" vector = [0.1, 0.1, 0.5] @@ -199,6 +201,8 @@ def test_hybrid_query_with_filter(index): @pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_with_geo_filter(index): + skip_if_redis_version_below(index.client, "8.4.0") + text = "a medical professional with expertise in lung cancer" text_field = "description" vector = [0.1, 0.1, 0.5] @@ -225,6 +229,8 @@ def test_hybrid_query_with_geo_filter(index): @pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) @pytest.mark.parametrize("alpha", [0.1, 0.5, 0.9]) def test_hybrid_query_alpha(index, alpha): + skip_if_redis_version_below(index.client, "8.4.0") + text = "a medical professional with expertise in lung cancer" text_field = "description" vector = [0.1, 0.1, 0.5] @@ -255,6 +261,8 @@ def test_hybrid_query_alpha(index, alpha): @pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_stopwords(index): + skip_if_redis_version_below(index.client, "8.4.0") + text = "a medical professional with expertise in lung cancer" text_field = "description" vector = [0.1, 0.1, 0.5] @@ -291,6 +299,8 @@ def test_hybrid_query_stopwords(index): @pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query_with_text_filter(index): + skip_if_redis_version_below(index.client, "8.4.0") + text = "a medical professional with expertise in lung cancer" text_field = "description" vector = [0.1, 0.1, 0.5] @@ -340,6 +350,8 @@ def test_hybrid_query_with_text_filter(index): @pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) @pytest.mark.parametrize("scorer", ["BM25STD", "TFIDF", "TFIDF.DOCNORM"]) def test_hybrid_query_word_weights(index, scorer): + skip_if_redis_version_below(index.client, "8.4.0") + text = "a medical professional with expertise in lung cancers" text_field = "description" vector = [0.1, 0.1, 0.5] @@ -402,6 +414,8 @@ def test_hybrid_query_word_weights(index, scorer): @pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) @pytest.mark.asyncio async def test_hybrid_query_async(async_index): + await skip_if_redis_version_below_async(async_index.client, "8.4.0") + text = "a medical professional with expertise in lung cancer" text_field = "description" vector = [0.1, 0.1, 0.5] From 3b6ef3a0c73c3bdc5ac68d328e85397a9864274d Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Mon, 8 Dec 2025 16:41:15 +0100 Subject: [PATCH 24/34] Oops missed one --- tests/integration/test_hybrid.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tests/integration/test_hybrid.py b/tests/integration/test_hybrid.py index 69fc4653..b6c8b623 100644 --- a/tests/integration/test_hybrid.py +++ b/tests/integration/test_hybrid.py @@ -116,6 +116,8 @@ def hash_preprocess(item: dict) -> dict: @pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) def test_hybrid_query(index): + skip_if_redis_version_below(index.client, "8.4.0") + text = "a medical professional with expertise in lung cancer" text_field = "description" vector = [0.1, 0.1, 0.5] From 9c5fb4b8094dd51584115757ba45935b4f77fc2b Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Mon, 8 Dec 2025 16:50:14 +0100 Subject: [PATCH 25/34] Remove deprecated `HybridQuery` class --- redisvl/query/aggregate.py | 20 ------------- redisvl/query/hybrid.py | 4 ++- tests/integration/test_aggregation.py | 42 --------------------------- tests/unit/test_aggregation_types.py | 39 +------------------------ 4 files changed, 4 insertions(+), 101 deletions(-) diff --git a/redisvl/query/aggregate.py b/redisvl/query/aggregate.py index 7426580e..1f1da2ac 100644 --- a/redisvl/query/aggregate.py +++ b/redisvl/query/aggregate.py @@ -242,26 +242,6 @@ def __str__(self) -> str: return " ".join([str(x) for x in self.build_args()]) -class HybridQuery(AggregateHybridQuery): - """Backward compatibility wrapper for AggregateHybridQuery. - - .. deprecated:: - This class is deprecated and will be removed in a future version. - Please use the new HybridQuery from redisvl.query.hybrid instead. - For maintaining current AggregateHybridQuery functionality, use AggregateHybridQuery directly. - """ - - def __init__(self, *args, **kwargs): - warnings.warn( - "This HybridQuery class is deprecated and will be removed in a future version. " - "Please use the new HybridQuery from redisvl.query.hybrid instead. " - "For maintaining current AggregateHybridQuery functionality, use AggregateHybridQuery directly.", - DeprecationWarning, - stacklevel=2, - ) - super().__init__(*args, **kwargs) - - class MultiVectorQuery(AggregationQuery): """ MultiVectorQuery allows for search over multiple vector fields in a document simultaneously. diff --git a/redisvl/query/hybrid.py b/redisvl/query/hybrid.py index 8fbdbe04..96e09e1e 100644 --- a/redisvl/query/hybrid.py +++ b/redisvl/query/hybrid.py @@ -18,7 +18,9 @@ VectorSearchMethods, ) except ImportError: - raise ImportError("Hybrid queries require redis>=7.1.0") + raise ImportError( + "Hybrid queries require Redis Open Source >= 8.4.0 and redis-py>=7.1.0" + ) from redisvl.query.filter import FilterExpression diff --git a/tests/integration/test_aggregation.py b/tests/integration/test_aggregation.py index fed07665..1ee7a2a1 100644 --- a/tests/integration/test_aggregation.py +++ b/tests/integration/test_aggregation.py @@ -2,7 +2,6 @@ from redisvl.index import SearchIndex from redisvl.query import AggregateHybridQuery, MultiVectorQuery, Vector -from redisvl.query.aggregate import HybridQuery from redisvl.query.filter import Geo, GeoRadius, Num, Tag, Text from redisvl.redis.utils import array_to_buffer from tests.conftest import skip_if_redis_version_below @@ -744,44 +743,3 @@ def test_multivector_query_mixed_index(index): assert ( float(r["combined_score"]) - score <= 0.0001 ) # allow for small floating point error - - -def test_hybrid_query_backward_compatibility(index): - skip_if_redis_version_below(index.client, "7.2.0") - - text = "a medical professional with expertise in lung cancer" - text_field = "description" - vector = [0.1, 0.1, 0.5] - vector_field = "user_embedding" - return_fields = ["user", "credit_score", "age", "job", "location", "description"] - - hybrid_query = AggregateHybridQuery( - text=text, - text_field_name=text_field, - vector=vector, - vector_field_name=vector_field, - return_fields=return_fields, - ) - - results = index.query(hybrid_query) - assert len(results) == 7 - for result in results: - assert result["user"] in [ - "john", - "derrick", - "nancy", - "tyler", - "tim", - "taimur", - "joe", - "mary", - ] - - with pytest.warns(DeprecationWarning): - _ = HybridQuery( - text=text, - text_field_name=text_field, - vector=vector, - vector_field_name=vector_field, - return_fields=return_fields, - ) diff --git a/tests/unit/test_aggregation_types.py b/tests/unit/test_aggregation_types.py index f2995c64..503674f9 100644 --- a/tests/unit/test_aggregation_types.py +++ b/tests/unit/test_aggregation_types.py @@ -4,12 +4,7 @@ import pytest from redis.commands.search.aggregation import AggregateRequest -from redisvl.query.aggregate import ( - AggregateHybridQuery, - HybridQuery, - MultiVectorQuery, - Vector, -) +from redisvl.query.aggregate import AggregateHybridQuery, MultiVectorQuery, Vector from redisvl.query.filter import Tag from redisvl.redis.utils import array_to_buffer @@ -432,35 +427,3 @@ def test_vector_object_handles_byte_conversion(): byte_string = array_to_buffer(sample_vector, datatype) vec = Vector(vector=byte_string, field_name="field 1") assert vec.vector == byte_string - - -def test_hybrid_query_backward_compatibility(): - # test that HybridQuery is a backward compatibility wrapper for AggregateHybridQuery - with pytest.warns(DeprecationWarning): - hybrid_query = HybridQuery( - text="sample text query", - text_field_name="description", - vector=sample_vector, - vector_field_name="embedding", - ) - - # Verify HybridQuery is actually an instance of AggregateHybridQuery - assert isinstance(hybrid_query, AggregateHybridQuery) - - # Verify AggregateHybridQuery does not emit warnings - with assert_no_warnings(): - aggregate_query = AggregateHybridQuery( - text="sample text query", - text_field_name="description", - vector=sample_vector, - vector_field_name="embedding", - ) - - # Verify that creating another HybridQuery also warns - with pytest.warns(DeprecationWarning): - another_hybrid_query = HybridQuery( - text="sample text query", - text_field_name="description", - vector=sample_vector, - vector_field_name="embedding", - ) From 8d4e91de527d889c5e77964e912ef37f3912117d Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Mon, 8 Dec 2025 17:20:19 +0100 Subject: [PATCH 26/34] Manage dependency logic for user guide notebook --- docs/user_guide/11_advanced_queries.ipynb | 458 +++++++++++++--------- 1 file changed, 269 insertions(+), 189 deletions(-) diff --git a/docs/user_guide/11_advanced_queries.ipynb b/docs/user_guide/11_advanced_queries.ipynb index 77d5125c..0c4334de 100644 --- a/docs/user_guide/11_advanced_queries.ipynb +++ b/docs/user_guide/11_advanced_queries.ipynb @@ -39,8 +39,8 @@ "shell.execute_reply": "2025-11-21T00:42:12.301163Z" }, "ExecuteTime": { - "end_time": "2025-12-08T13:39:31.340482Z", - "start_time": "2025-12-08T13:39:31.227119Z" + "end_time": "2025-12-08T16:20:05.160169Z", + "start_time": "2025-12-08T16:20:05.069744Z" } }, "source": [ @@ -137,8 +137,8 @@ "shell.execute_reply": "2025-11-21T00:42:12.305407Z" }, "ExecuteTime": { - "end_time": "2025-12-08T13:39:50.714664Z", - "start_time": "2025-12-08T13:39:50.711756Z" + "end_time": "2025-12-08T16:20:05.166795Z", + "start_time": "2025-12-08T16:20:05.164829Z" } }, "source": [ @@ -198,8 +198,8 @@ "shell.execute_reply": "2025-11-21T00:42:12.415926Z" }, "ExecuteTime": { - "end_time": "2025-12-08T13:40:18.661710Z", - "start_time": "2025-12-08T13:40:18.538641Z" + "end_time": "2025-12-08T16:20:05.274761Z", + "start_time": "2025-12-08T16:20:05.174137Z" } }, "source": [ @@ -248,8 +248,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.708647Z" }, "ExecuteTime": { - "end_time": "2025-12-08T13:40:23.622092Z", - "start_time": "2025-12-08T13:40:22.813571Z" + "end_time": "2025-12-08T16:20:05.968210Z", + "start_time": "2025-12-08T16:20:05.279252Z" } }, "source": [ @@ -304,8 +304,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.748398Z" }, "ExecuteTime": { - "end_time": "2025-12-08T13:40:26.303477Z", - "start_time": "2025-12-08T13:40:26.298327Z" + "end_time": "2025-12-08T16:20:05.980224Z", + "start_time": "2025-12-08T16:20:05.975566Z" } }, "source": [ @@ -358,8 +358,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.754345Z" }, "ExecuteTime": { - "end_time": "2025-12-08T13:40:26.911718Z", - "start_time": "2025-12-08T13:40:26.906103Z" + "end_time": "2025-12-08T16:20:06.004296Z", + "start_time": "2025-12-08T16:20:05.999747Z" } }, "source": [ @@ -421,8 +421,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.759844Z" }, "ExecuteTime": { - "end_time": "2025-12-08T13:40:28.084810Z", - "start_time": "2025-12-08T13:40:28.079570Z" + "end_time": "2025-12-08T16:20:06.018570Z", + "start_time": "2025-12-08T16:20:06.014666Z" } }, "source": [ @@ -469,8 +469,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.765316Z" }, "ExecuteTime": { - "end_time": "2025-12-08T13:40:28.525493Z", - "start_time": "2025-12-08T13:40:28.520152Z" + "end_time": "2025-12-08T16:20:06.037811Z", + "start_time": "2025-12-08T16:20:06.034036Z" } }, "source": [ @@ -525,8 +525,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.770555Z" }, "ExecuteTime": { - "end_time": "2025-12-08T13:40:29.658650Z", - "start_time": "2025-12-08T13:40:29.654660Z" + "end_time": "2025-12-08T16:20:06.049944Z", + "start_time": "2025-12-08T16:20:06.046139Z" } }, "source": [ @@ -578,8 +578,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.775861Z" }, "ExecuteTime": { - "end_time": "2025-12-08T13:40:32.312718Z", - "start_time": "2025-12-08T13:40:32.308620Z" + "end_time": "2025-12-08T16:20:06.061674Z", + "start_time": "2025-12-08T16:20:06.058071Z" } }, "source": [ @@ -624,8 +624,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.780713Z" }, "ExecuteTime": { - "end_time": "2025-12-08T13:40:32.930041Z", - "start_time": "2025-12-08T13:40:32.925758Z" + "end_time": "2025-12-08T16:20:06.074400Z", + "start_time": "2025-12-08T16:20:06.071337Z" } }, "source": [ @@ -670,8 +670,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.786617Z" }, "ExecuteTime": { - "end_time": "2025-12-08T13:40:33.691546Z", - "start_time": "2025-12-08T13:40:33.686402Z" + "end_time": "2025-12-08T16:20:06.086886Z", + "start_time": "2025-12-08T16:20:06.083802Z" } }, "source": [ @@ -719,6 +719,36 @@ "As of Redis Open Source 8.4.0, Redis natively supports a [`FT.HYBRID`](https://redis.io/docs/latest/commands/ft.hybrid) search command. RedisVL provides a `HybridQuery` class that makes it easy to construct and execute hybrid queries. For earlier versions of Redis, RedisVL provides an `AggregateHybridQuery` class that uses Redis aggregation to achieve similar results." ] }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-08T16:20:06.114392Z", + "start_time": "2025-12-08T16:20:06.110771Z" + } + }, + "cell_type": "code", + "source": [ + "from packaging.version import Version\n", + "\n", + "from redis import __version__ as _redis_py_version\n", + "\n", + "redis_py_version = Version(_redis_py_version)\n", + "redis_version = Version(index.client.info()[\"redis_version\"])\n", + "\n", + "HYBRID_SEARCH_AVAILABLE = redis_version >= Version(\"8.4.0\") and redis_py_version >= Version(\"7.1.0\")\n", + "print(HYBRID_SEARCH_AVAILABLE)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "execution_count": 13 + }, { "cell_type": "markdown", "metadata": {}, @@ -753,8 +783,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.794662Z" }, "ExecuteTime": { - "end_time": "2025-12-08T13:44:47.182730Z", - "start_time": "2025-12-08T13:44:47.173733Z" + "end_time": "2025-12-08T16:20:06.140445Z", + "start_time": "2025-12-08T16:20:06.126737Z" } }, "source": [ @@ -785,11 +815,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Index created with STOPWORDS 0: \n" + "Index created with STOPWORDS 0: \n" ] } ], - "execution_count": 13 + "execution_count": 14 }, { "cell_type": "code", @@ -801,8 +831,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.802098Z" }, "ExecuteTime": { - "end_time": "2025-12-08T13:44:48.226137Z", - "start_time": "2025-12-08T13:44:48.218807Z" + "end_time": "2025-12-08T16:20:06.151693Z", + "start_time": "2025-12-08T16:20:06.145345Z" } }, "source": [ @@ -829,7 +859,7 @@ ] } ], - "execution_count": 14 + "execution_count": 15 }, { "cell_type": "code", @@ -841,8 +871,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.806491Z" }, "ExecuteTime": { - "end_time": "2025-12-08T13:44:49.547154Z", - "start_time": "2025-12-08T13:44:49.542606Z" + "end_time": "2025-12-08T16:20:06.160234Z", + "start_time": "2025-12-08T16:20:06.157533Z" } }, "source": [ @@ -870,7 +900,7 @@ ] } ], - "execution_count": 15 + "execution_count": 16 }, { "cell_type": "markdown", @@ -899,8 +929,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.810083Z" }, "ExecuteTime": { - "end_time": "2025-12-08T13:44:56.551634Z", - "start_time": "2025-12-08T13:44:56.549334Z" + "end_time": "2025-12-08T16:20:06.172514Z", + "start_time": "2025-12-08T16:20:06.170848Z" } }, "source": [ @@ -928,7 +958,7 @@ ] } ], - "execution_count": 16 + "execution_count": 17 }, { "cell_type": "markdown", @@ -969,8 +999,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-12-08T13:44:59.280976Z", - "start_time": "2025-12-08T13:44:59.276691Z" + "end_time": "2025-12-08T16:20:06.188091Z", + "start_time": "2025-12-08T16:20:06.185213Z" } }, "source": [ @@ -987,7 +1017,7 @@ ] } ], - "execution_count": 17 + "execution_count": 18 }, { "cell_type": "markdown", @@ -1004,31 +1034,49 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-12-08T14:02:29.152907Z", - "start_time": "2025-12-08T14:02:29.146817Z" + "end_time": "2025-12-08T16:20:06.200935Z", + "start_time": "2025-12-08T16:20:06.196023Z" } }, "source": [ - "from redisvl.query.hybrid import HybridQuery\n", - "\n", - "# Create a hybrid query\n", - "hybrid_query = HybridQuery(\n", - " text=\"running shoes\",\n", - " text_field_name=\"brief_description\",\n", - " vector=[0.1, 0.2, 0.1], # Query vector\n", - " vector_field_name=\"text_embedding\",\n", - " return_fields=[\"product_id\", \"brief_description\", \"category\", \"price\"],\n", - " num_results=5,\n", - " yield_text_score_as=\"text_score\",\n", - " yield_vsim_score_as=\"vector_similarity\",\n", - " combination_method=\"LINEAR\",\n", - " yield_combined_score_as=\"hybrid_score\",\n", - ")\n", - "\n", - "results = index.hybrid_search(hybrid_query)\n", - "result_print(results)" + "if HYBRID_SEARCH_AVAILABLE:\n", + " from redisvl.query.hybrid import HybridQuery\n", + "\n", + " # Create a hybrid query\n", + " hybrid_query = HybridQuery(\n", + " text=\"running shoes\",\n", + " text_field_name=\"brief_description\",\n", + " vector=[0.1, 0.2, 0.1], # Query vector\n", + " vector_field_name=\"text_embedding\",\n", + " return_fields=[\"product_id\", \"brief_description\", \"category\", \"price\"],\n", + " num_results=5,\n", + " yield_text_score_as=\"text_score\",\n", + " yield_vsim_score_as=\"vector_similarity\",\n", + " combination_method=\"LINEAR\",\n", + " yield_combined_score_as=\"hybrid_score\",\n", + " )\n", + "\n", + " results = index.hybrid_search(hybrid_query)\n", + " result_print(results)\n", + "\n", + "else:\n", + " print(\"Hybrid search is not available in this version of Redis/redis-py.\")" ], "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/vishal.bala/PycharmProjects/redis-vl-python/redisvl/query/hybrid.py:143: UserWarning: HybridPostProcessingConfig is an experimental and may change or be removed in future versions.\n", + " self.postprocessing_config = HybridPostProcessingConfig()\n", + "/Users/vishal.bala/PycharmProjects/redis-vl-python/redisvl/query/hybrid.py:239: UserWarning: HybridSearchQuery is an experimental and may change or be removed in future versions.\n", + " search_query = HybridSearchQuery(\n", + "/Users/vishal.bala/PycharmProjects/redis-vl-python/redisvl/query/hybrid.py:280: UserWarning: HybridVsimQuery is an experimental and may change or be removed in future versions.\n", + " vsim_query = HybridVsimQuery(\n", + "/Users/vishal.bala/PycharmProjects/redis-vl-python/redisvl/query/hybrid.py:356: UserWarning: CombineResultsMethod is an experimental and may change or be removed in future versions.\n", + " return CombineResultsMethod(\n" + ] + }, { "data": { "text/plain": [ @@ -1045,7 +1093,7 @@ } } ], - "execution_count": 31 + "execution_count": 19 }, { "metadata": {}, @@ -1055,8 +1103,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-12-08T14:02:30.673828Z", - "start_time": "2025-12-08T14:02:30.663228Z" + "end_time": "2025-12-08T16:20:06.215087Z", + "start_time": "2025-12-08T16:20:06.210947Z" } }, "cell_type": "code", @@ -1092,7 +1140,7 @@ } } ], - "execution_count": 32 + "execution_count": 20 }, { "cell_type": "markdown", @@ -1117,29 +1165,33 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-12-08T14:03:02.867269Z", - "start_time": "2025-12-08T14:03:02.860829Z" + "end_time": "2025-12-08T16:20:06.233961Z", + "start_time": "2025-12-08T16:20:06.228765Z" } }, "cell_type": "code", "source": [ - "vector_heavy_query = HybridQuery(\n", - " text=\"comfortable\",\n", - " text_field_name=\"brief_description\",\n", - " vector=[0.15, 0.25, 0.15],\n", - " vector_field_name=\"text_embedding\",\n", - " combination_method=\"LINEAR\",\n", - " linear_beta=0.9, # 90% vector, 10% text\n", - " return_fields=[\"product_id\", \"brief_description\"],\n", - " num_results=3,\n", - " yield_text_score_as=\"text_score\",\n", - " yield_vsim_score_as=\"vector_similarity\",\n", - " yield_combined_score_as=\"hybrid_score\",\n", - ")\n", - "\n", - "print(\"Results with alpha=0.9 (vector-heavy):\")\n", - "results = index.hybrid_search(vector_heavy_query)\n", - "result_print(results)" + "if HYBRID_SEARCH_AVAILABLE:\n", + " vector_heavy_query = HybridQuery(\n", + " text=\"comfortable\",\n", + " text_field_name=\"brief_description\",\n", + " vector=[0.15, 0.25, 0.15],\n", + " vector_field_name=\"text_embedding\",\n", + " combination_method=\"LINEAR\",\n", + " linear_beta=0.9, # 90% vector, 10% text\n", + " return_fields=[\"product_id\", \"brief_description\"],\n", + " num_results=3,\n", + " yield_text_score_as=\"text_score\",\n", + " yield_vsim_score_as=\"vector_similarity\",\n", + " yield_combined_score_as=\"hybrid_score\",\n", + " )\n", + "\n", + " print(\"Results with alpha=0.9 (vector-heavy):\")\n", + " results = index.hybrid_search(vector_heavy_query)\n", + " result_print(results)\n", + "\n", + "else:\n", + " print(\"Hybrid search is not available in this version of Redis/redis-py.\")" ], "outputs": [ { @@ -1165,14 +1217,14 @@ } } ], - "execution_count": 36 + "execution_count": 21 }, { "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-12-08T14:01:39.717367Z", - "start_time": "2025-12-08T14:01:39.711383Z" + "end_time": "2025-12-08T16:20:06.253251Z", + "start_time": "2025-12-08T16:20:06.248241Z" } }, "source": [ @@ -1215,7 +1267,7 @@ } } ], - "execution_count": 29 + "execution_count": 22 }, { "metadata": {}, @@ -1240,27 +1292,31 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-12-08T14:08:21.449231Z", - "start_time": "2025-12-08T14:08:21.444211Z" + "end_time": "2025-12-08T16:20:06.274755Z", + "start_time": "2025-12-08T16:20:06.271025Z" } }, "cell_type": "code", "source": [ - "rrf_query = HybridQuery(\n", - " text=\"comfortable\",\n", - " text_field_name=\"brief_description\",\n", - " vector=[0.15, 0.25, 0.15],\n", - " vector_field_name=\"text_embedding\",\n", - " combination_method=\"RRF\",\n", - " return_fields=[\"product_id\", \"brief_description\"],\n", - " num_results=3,\n", - " yield_text_score_as=\"text_score\",\n", - " yield_vsim_score_as=\"vector_similarity\",\n", - " yield_combined_score_as=\"hybrid_score\",\n", - ")\n", - "\n", - "results = index.hybrid_search(rrf_query)\n", - "result_print(results)" + "if HYBRID_SEARCH_AVAILABLE:\n", + " rrf_query = HybridQuery(\n", + " text=\"comfortable\",\n", + " text_field_name=\"brief_description\",\n", + " vector=[0.15, 0.25, 0.15],\n", + " vector_field_name=\"text_embedding\",\n", + " combination_method=\"RRF\",\n", + " return_fields=[\"product_id\", \"brief_description\"],\n", + " num_results=3,\n", + " yield_text_score_as=\"text_score\",\n", + " yield_vsim_score_as=\"vector_similarity\",\n", + " yield_combined_score_as=\"hybrid_score\",\n", + " )\n", + "\n", + " results = index.hybrid_search(rrf_query)\n", + " result_print(results)\n", + "\n", + "else:\n", + " print(\"Hybrid search is not available in this version of Redis/redis-py.\")" ], "outputs": [ { @@ -1279,7 +1335,7 @@ } } ], - "execution_count": 41 + "execution_count": 23 }, { "cell_type": "markdown", @@ -1293,30 +1349,34 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-12-08T14:13:50.669478Z", - "start_time": "2025-12-08T14:13:50.663580Z" + "end_time": "2025-12-08T16:20:06.289097Z", + "start_time": "2025-12-08T16:20:06.285319Z" } }, "cell_type": "code", "source": [ - "# Hybrid search with a price filter\n", - "filtered_hybrid_query = HybridQuery(\n", - " text=\"professional equipment\",\n", - " text_field_name=\"brief_description\",\n", - " vector=[0.9, 0.1, 0.05],\n", - " vector_field_name=\"text_embedding\",\n", - " text_filter_expression=Num(\"price\") > 100,\n", - " vector_filter_expression=Num(\"price\") > 100,\n", - " return_fields=[\"product_id\", \"brief_description\", \"category\", \"price\"],\n", - " num_results=5,\n", - " combination_method=\"LINEAR\",\n", - " yield_text_score_as=\"text_score\",\n", - " yield_vsim_score_as=\"vector_similarity\",\n", - " yield_combined_score_as=\"hybrid_score\",\n", - ")\n", - "\n", - "results = index.hybrid_search(filtered_hybrid_query)\n", - "result_print(results)" + "if HYBRID_SEARCH_AVAILABLE:\n", + " # Hybrid search with a price filter\n", + " filtered_hybrid_query = HybridQuery(\n", + " text=\"professional equipment\",\n", + " text_field_name=\"brief_description\",\n", + " vector=[0.9, 0.1, 0.05],\n", + " vector_field_name=\"text_embedding\",\n", + " text_filter_expression=Num(\"price\") > 100,\n", + " vector_filter_expression=Num(\"price\") > 100,\n", + " return_fields=[\"product_id\", \"brief_description\", \"category\", \"price\"],\n", + " num_results=5,\n", + " combination_method=\"LINEAR\",\n", + " yield_text_score_as=\"text_score\",\n", + " yield_vsim_score_as=\"vector_similarity\",\n", + " yield_combined_score_as=\"hybrid_score\",\n", + " )\n", + "\n", + " results = index.hybrid_search(filtered_hybrid_query)\n", + " result_print(results)\n", + "\n", + "else:\n", + " print(\"Hybrid search is not available in this version of Redis/redis-py.\")" ], "outputs": [ { @@ -1335,14 +1395,14 @@ } } ], - "execution_count": 44 + "execution_count": 24 }, { "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-12-08T14:05:32.261289Z", - "start_time": "2025-12-08T14:05:32.254487Z" + "end_time": "2025-12-08T16:20:06.301824Z", + "start_time": "2025-12-08T16:20:06.297464Z" } }, "source": [ @@ -1377,7 +1437,7 @@ } } ], - "execution_count": 40 + "execution_count": 25 }, { "cell_type": "markdown", @@ -1391,29 +1451,33 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-12-08T14:14:08.035537Z", - "start_time": "2025-12-08T14:14:08.028950Z" + "end_time": "2025-12-08T16:20:06.314989Z", + "start_time": "2025-12-08T16:20:06.309695Z" } }, "cell_type": "code", "source": [ - "# Aggregate Hybrid query with TFIDF scorer\n", - "hybrid_tfidf = HybridQuery(\n", - " text=\"shoes support\",\n", - " text_field_name=\"brief_description\",\n", - " vector=[0.12, 0.18, 0.12],\n", - " vector_field_name=\"text_embedding\",\n", - " text_scorer=\"TFIDF\",\n", - " return_fields=[\"product_id\", \"brief_description\"],\n", - " num_results=3,\n", - " combination_method=\"LINEAR\",\n", - " yield_text_score_as=\"text_score\",\n", - " yield_vsim_score_as=\"vector_similarity\",\n", - " yield_combined_score_as=\"hybrid_score\",\n", - ")\n", - "\n", - "results = index.hybrid_search(hybrid_tfidf)\n", - "result_print(results)" + "if HYBRID_SEARCH_AVAILABLE:\n", + " # Aggregate Hybrid query with TFIDF scorer\n", + " hybrid_tfidf = HybridQuery(\n", + " text=\"shoes support\",\n", + " text_field_name=\"brief_description\",\n", + " vector=[0.12, 0.18, 0.12],\n", + " vector_field_name=\"text_embedding\",\n", + " text_scorer=\"TFIDF\",\n", + " return_fields=[\"product_id\", \"brief_description\"],\n", + " num_results=3,\n", + " combination_method=\"LINEAR\",\n", + " yield_text_score_as=\"text_score\",\n", + " yield_vsim_score_as=\"vector_similarity\",\n", + " yield_combined_score_as=\"hybrid_score\",\n", + " )\n", + "\n", + " results = index.hybrid_search(hybrid_tfidf)\n", + " result_print(results)\n", + "\n", + "else:\n", + " print(\"Hybrid search is not available in this version of Redis/redis-py.\")" ], "outputs": [ { @@ -1432,14 +1496,14 @@ } } ], - "execution_count": 45 + "execution_count": 26 }, { "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-12-08T14:14:08.653797Z", - "start_time": "2025-12-08T14:14:08.645983Z" + "end_time": "2025-12-08T16:20:06.333575Z", + "start_time": "2025-12-08T16:20:06.328856Z" } }, "source": [ @@ -1474,7 +1538,7 @@ } } ], - "execution_count": 46 + "execution_count": 27 }, { "metadata": {}, @@ -1524,8 +1588,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-12-08T14:18:28.141238Z", - "start_time": "2025-12-08T14:18:28.134798Z" + "end_time": "2025-12-08T16:20:06.349718Z", + "start_time": "2025-12-08T16:20:06.345385Z" } }, "source": [ @@ -1573,7 +1637,7 @@ } } ], - "execution_count": 47 + "execution_count": 28 }, { "cell_type": "markdown", @@ -1588,8 +1652,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-12-08T14:18:29.897949Z", - "start_time": "2025-12-08T14:18:29.890447Z" + "end_time": "2025-12-08T16:20:06.369451Z", + "start_time": "2025-12-08T16:20:06.365674Z" } }, "source": [ @@ -1642,7 +1706,7 @@ } } ], - "execution_count": 48 + "execution_count": 29 }, { "cell_type": "markdown", @@ -1657,8 +1721,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-12-08T14:18:31.186114Z", - "start_time": "2025-12-08T14:18:31.181620Z" + "end_time": "2025-12-08T16:20:06.404023Z", + "start_time": "2025-12-08T16:20:06.399153Z" } }, "source": [ @@ -1704,7 +1768,7 @@ } } ], - "execution_count": 49 + "execution_count": 30 }, { "cell_type": "markdown", @@ -1719,8 +1783,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-12-08T14:18:32.476420Z", - "start_time": "2025-12-08T14:18:32.468211Z" + "end_time": "2025-12-08T16:20:06.416236Z", + "start_time": "2025-12-08T16:20:06.410716Z" } }, "source": [ @@ -1767,7 +1831,7 @@ ] } ], - "execution_count": 50 + "execution_count": 31 }, { "cell_type": "code", @@ -1779,27 +1843,43 @@ "shell.execute_reply": "2025-11-21T00:42:13.864461Z" }, "ExecuteTime": { - "end_time": "2025-12-08T14:19:14.130046Z", - "start_time": "2025-12-08T14:19:14.123582Z" + "end_time": "2025-12-08T16:20:06.439866Z", + "start_time": "2025-12-08T16:20:06.433866Z" } }, "source": [ - "# HybridQuery - combines text and vector search\n", - "hybrid_q = HybridQuery(\n", - " text=\"shoes\",\n", - " text_field_name=\"brief_description\",\n", - " vector=[0.1, 0.2, 0.1],\n", - " vector_field_name=\"text_embedding\",\n", - " return_fields=[\"product_id\", \"brief_description\"],\n", - " num_results=3,\n", - " combination_method=\"LINEAR\",\n", - " yield_text_score_as=\"text_score\",\n", - " yield_vsim_score_as=\"vector_similarity\",\n", - " yield_combined_score_as=\"hybrid_score\",\n", - ")\n", - "\n", - "print(\"HybridQuery Results (text + vector):\")\n", - "result_print(index.hybrid_search(hybrid_q))\n", + "if HYBRID_SEARCH_AVAILABLE:\n", + " # HybridQuery - combines text and vector search\n", + " hybrid_q = HybridQuery(\n", + " text=\"shoes\",\n", + " text_field_name=\"brief_description\",\n", + " vector=[0.1, 0.2, 0.1],\n", + " vector_field_name=\"text_embedding\",\n", + " return_fields=[\"product_id\", \"brief_description\"],\n", + " num_results=3,\n", + " combination_method=\"LINEAR\",\n", + " yield_text_score_as=\"text_score\",\n", + " yield_vsim_score_as=\"vector_similarity\",\n", + " yield_combined_score_as=\"hybrid_score\",\n", + " )\n", + "\n", + " results = index.hybrid_search(hybrid_q)\n", + "\n", + "else:\n", + " hybrid_q = AggregateHybridQuery(\n", + " text=\"shoes\",\n", + " text_field_name=\"brief_description\",\n", + " vector=[0.1, 0.2, 0.1],\n", + " vector_field_name=\"text_embedding\",\n", + " return_fields=[\"product_id\", \"brief_description\"],\n", + " num_results=3,\n", + " )\n", + "\n", + " results = index.query(hybrid_q)\n", + "\n", + "\n", + "print(f\"{hybrid_q.__class__.__name__} Results (text + vector):\")\n", + "result_print(results)\n", "print()" ], "outputs": [ @@ -1833,7 +1913,7 @@ ] } ], - "execution_count": 51 + "execution_count": 32 }, { "cell_type": "code", @@ -1845,8 +1925,8 @@ "shell.execute_reply": "2025-11-21T00:42:13.868990Z" }, "ExecuteTime": { - "end_time": "2025-12-08T14:19:32.109121Z", - "start_time": "2025-12-08T14:19:32.103443Z" + "end_time": "2025-12-08T16:20:06.455448Z", + "start_time": "2025-12-08T16:20:06.451747Z" } }, "source": [ @@ -1898,7 +1978,7 @@ } } ], - "execution_count": 52 + "execution_count": 33 }, { "cell_type": "markdown", @@ -1931,8 +2011,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2025-12-08T14:19:51.406116Z", - "start_time": "2025-12-08T14:19:51.402825Z" + "end_time": "2025-12-08T16:20:06.480814Z", + "start_time": "2025-12-08T16:20:06.477300Z" } }, "source": [ @@ -1940,7 +2020,7 @@ "index.delete()" ], "outputs": [], - "execution_count": 53 + "execution_count": 34 } ], "metadata": { From 362fbf51aa24f9cb4611d86e686701ec9ac6aa25 Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Tue, 9 Dec 2025 13:25:49 +0100 Subject: [PATCH 27/34] Make `hybrid_search` always available but validate and raise errors --- redisvl/index/index.py | 191 ++++++++++++++----------------- tests/integration/test_hybrid.py | 40 +++++++ 2 files changed, 129 insertions(+), 102 deletions(-) diff --git a/redisvl/index/index.py b/redisvl/index/index.py index 6a4cd8ee..6b672dbb 100644 --- a/redisvl/index/index.py +++ b/redisvl/index/index.py @@ -39,7 +39,12 @@ make_dict, ) from redisvl.types import AsyncRedisClient, SyncRedisClient -from redisvl.utils.utils import deprecated_argument, deprecated_function, sync_wrapper +from redisvl.utils.utils import ( + deprecated_argument, + deprecated_function, + lazy_import, + sync_wrapper, +) if TYPE_CHECKING: from redis.commands.search.aggregation import AggregateResult @@ -48,15 +53,6 @@ from redisvl.query.query import BaseQuery -try: - from redis.commands.search.hybrid_result import HybridResult - - from redisvl.query.hybrid import HybridQuery - - REDIS_HYBRID_AVAILABLE = True -except ImportError: - REDIS_HYBRID_AVAILABLE = False - from redis import __version__ as redis_version from redis.client import NEVER_DECODE @@ -111,6 +107,8 @@ logger = get_logger(__name__) +_HYBRID_SEARCH_ERROR_MESSAGE = "Hybrid search is not available in this version of redis-py. Please upgrade to redis-py >= 7.1.0." + REQUIRED_MODULES_FOR_INTROSPECTION = [ {"name": "search", "ver": 20810}, @@ -225,13 +223,6 @@ def _process(row): return [_process(r) for r in results.rows] -if REDIS_HYBRID_AVAILABLE: - - def process_hybrid_results(results: HybridResult) -> List[Dict[str, Any]]: - """Convert a hybrid result object into a list of document dictionaries.""" - return [convert_bytes(r) for r in results.results] - - class BaseSearchIndex: """Base search engine class""" @@ -295,6 +286,18 @@ def _validate_query(self, query: BaseQuery) -> None: stacklevel=3, ) + def _validate_hybrid_query(self, query: Any) -> None: + """Validate that a hybrid query can be executed.""" + try: + from redis.commands.search.hybrid_result import HybridResult + + from redisvl.query.hybrid import HybridQuery + except (ImportError, ModuleNotFoundError): + raise NotImplementedError(_HYBRID_SEARCH_ERROR_MESSAGE) + + if not isinstance(query, HybridQuery): + raise TypeError(f"query must be of type HybridQuery, got {type(query)}") + @property def name(self) -> str: """The name of the Redis search index.""" @@ -1020,55 +1023,48 @@ def search(self, *args, **kwargs) -> "Result": except Exception as e: raise RedisSearchError(f"Unexpected error while searching: {str(e)}") from e - if REDIS_HYBRID_AVAILABLE: - - def hybrid_search(self, query: HybridQuery, **kwargs) -> List[Dict[str, Any]]: - """Perform a hybrid search against the index, combining text and vector search. + def hybrid_search(self, query: Any, **kwargs) -> List[Dict[str, Any]]: + """Perform a hybrid search against the index, combining text and vector search. - Args: - query: The text+vector search query to be performed, with configurable fusion methods and - post-processing. - kwargs: Additional arguments to pass to the redis-py hybrid_search method (e.g. timeout). - - Returns: - List[Dict[str, Any]]: The search results ordered by combined score unless otherwise specified. + Args: + query (HybridQuery): The text+vector search query to be performed, with configurable fusion methods and + post-processing. + kwargs: Additional arguments to pass to the redis-py hybrid_search method (e.g. timeout). - Notes: - Hybrid search is only available in Redis 8.4.0+, and requires redis-py >= 7.1.0. + Returns: + List[Dict[str, Any]]: The search results ordered by combined score unless otherwise specified. - .. code-block:: python + Notes: + Hybrid search is only available in Redis 8.4.0+, and requires redis-py >= 7.1.0. - from redisvl.query.hybrid import HybridQuery + .. code-block:: python - hybrid_query = HybridQuery( - text="lorem ipsum dolor sit amet", - text_field_name="description", - vector=[0.1, 0.2, 0.3], - vector_field_name="embedding" - ) + from redisvl.query.hybrid import HybridQuery - results = index.hybrid_search(hybrid_query) + hybrid_query = HybridQuery( + text="lorem ipsum dolor sit amet", + text_field_name="description", + vector=[0.1, 0.2, 0.3], + vector_field_name="embedding" + ) - """ - index = self._redis_client.ft(self.schema.index.name) - if not hasattr(index, "hybrid_search"): - # TODO: Clarify correct message - it seems to not be available in Python 3.9 - raise NotImplementedError( - "Hybrid search is not available in this version of redis-py. " - "Please upgrade to redis-py >= 7.1.0." - ) + results = index.hybrid_search(hybrid_query) - results: HybridResult = index.hybrid_search( - query=query.query, - combine_method=query.combination_method, - post_processing=( - query.postprocessing_config - if query.postprocessing_config.build_args() - else None - ), - **kwargs, - ) # type: ignore - return process_hybrid_results(results) + """ + index = self._redis_client.ft(self.schema.index.name) + self._validate_hybrid_query(query) + + results = index.hybrid_search( + query=query.query, + combine_method=query.combination_method, + post_processing=( + query.postprocessing_config + if query.postprocessing_config.build_args() + else None + ), + **kwargs, + ) # type: ignore + return [convert_bytes(r) for r in results.results] # type: ignore[union-attr] def batch_query( self, queries: Sequence[BaseQuery], batch_size: int = 10 @@ -1891,58 +1887,49 @@ async def search(self, *args, **kwargs) -> "Result": except Exception as e: raise RedisSearchError(f"Unexpected error while searching: {str(e)}") from e - if REDIS_HYBRID_AVAILABLE: + async def hybrid_search(self, query: Any, **kwargs) -> List[Dict[str, Any]]: + """Perform a hybrid search against the index, combining text and vector search. - async def hybrid_search( - self, query: HybridQuery, **kwargs - ) -> List[Dict[str, Any]]: - """Perform a hybrid search against the index, combining text and vector search. - - Args: - query: The text+vector search query to be performed, with configurable fusion methods and - post-processing. - kwargs: Additional arguments to pass to the redis-py hybrid_search method (e.g. timeout). - - Returns: - List[Dict[str, Any]]: The search results ordered by combined score unless otherwise specified. + Args: + query (HybridQuery): The text+vector search query to be performed, with configurable fusion methods and + post-processing. + kwargs: Additional arguments to pass to the redis-py hybrid_search method (e.g. timeout). - Notes: - Hybrid search is only available in Redis 8.4.0+, and requires redis-py >= 7.1.0. + Returns: + List[Dict[str, Any]]: The search results ordered by combined score unless otherwise specified. - .. code-block:: python + Notes: + Hybrid search is only available in Redis 8.4.0+, and requires redis-py >= 7.1.0. - from redisvl.query.hybrid import HybridQuery + .. code-block:: python - hybrid_query = HybridQuery( - text="lorem ipsum dolor sit amet", - text_field_name="description", - vector=[0.1, 0.2, 0.3], - vector_field_name="embedding" - ) + from redisvl.query.hybrid import HybridQuery - results = index.hybrid_search(hybrid_query) + hybrid_query = HybridQuery( + text="lorem ipsum dolor sit amet", + text_field_name="description", + vector=[0.1, 0.2, 0.3], + vector_field_name="embedding" + ) - """ - client = await self._get_client() - index = client.ft(self.schema.index.name) - if not hasattr(index, "hybrid_search"): - # TODO: Clarify correct message - it seems to not be available in Python 3.9 - raise NotImplementedError( - "Hybrid search is not available in this version of redis-py. " - "Please upgrade to redis-py >= 7.1.0." - ) + results = await async_index.hybrid_search(hybrid_query) - results: HybridResult = await index.hybrid_search( - query=query.query, - combine_method=query.combination_method, - post_processing=( - query.postprocessing_config - if query.postprocessing_config.build_args() - else None - ), - **kwargs, - ) # type: ignore - return process_hybrid_results(results) + """ + client = await self._get_client() + index = client.ft(self.schema.index.name) + self._validate_hybrid_query(query) + + results = await index.hybrid_search( + query=query.query, + combine_method=query.combination_method, + post_processing=( + query.postprocessing_config + if query.postprocessing_config.build_args() + else None + ), + **kwargs, + ) # type: ignore + return [convert_bytes(r) for r in results.results] # type: ignore[union-attr] async def batch_query( self, queries: List[BaseQuery], batch_size: int = 10 diff --git a/tests/integration/test_hybrid.py b/tests/integration/test_hybrid.py index b6c8b623..79d31614 100644 --- a/tests/integration/test_hybrid.py +++ b/tests/integration/test_hybrid.py @@ -1,10 +1,13 @@ import pytest +from packaging.version import Version +from redis import ResponseError from redisvl.index import AsyncSearchIndex, SearchIndex from redisvl.query.filter import Geo, GeoRadius, Num, Tag, Text from redisvl.redis.utils import array_to_buffer from redisvl.schema import IndexSchema from tests.conftest import ( + get_redis_version, skip_if_redis_version_below, skip_if_redis_version_below_async, ) @@ -471,3 +474,40 @@ async def test_hybrid_query_async(async_index): >= results[1]["hybrid_score"] >= results[2]["hybrid_score"] ) + + +@pytest.mark.skipif(not REDIS_HYBRID_AVAILABLE, reason=SKIP_REASON) +def test_hybrid_search_not_available_in_server(index): + if Version(get_redis_version(index.client)) >= Version("8.4.0"): + pytest.skip("Hybrid search is available in this version of Redis") + + hybrid_query = HybridQuery( + text="a medical professional with expertise in lung cancer", + text_field_name="description", + yield_text_score_as="text_score", + vector=[0.1, 0.1, 0.5], + vector_field_name="user_embedding", + yield_vsim_score_as="vsim_score", + combination_method="RRF", + yield_combined_score_as="hybrid_score", + return_fields=["user", "credit_score", "age", "job", "location", "description"], + ) + + with pytest.raises(ResponseError, match="unknown command 'FT.HYBRID'"): + index.hybrid_search(hybrid_query) + + +@pytest.mark.skipif( + REDIS_HYBRID_AVAILABLE, reason="Requires hybrid search to NOT be available" +) +def test_hybrid_query_not_available(): + with pytest.raises(ImportError): + from redisvl.query.hybrid import HybridQuery # noqa: F401 + + +@pytest.mark.skipif( + REDIS_HYBRID_AVAILABLE, reason="Requires hybrid search to NOT be available" +) +def test_hybrid_search_method_missing(index): + with pytest.raises(NotImplementedError): + index.hybrid_search(None) From eb8e3aed41a591ac22d56aa1044cdcfa96c95b0d Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Tue, 9 Dec 2025 13:30:20 +0100 Subject: [PATCH 28/34] Add warning note about inconsistent linear combination definitions between `HybridQuery` and `AggregateHybridQuery` --- docs/api/query.rst | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/docs/api/query.rst b/docs/api/query.rst index 1f6daf14..ecb55df3 100644 --- a/docs/api/query.rst +++ b/docs/api/query.rst @@ -123,6 +123,9 @@ AggregateHybridQuery For index-level stopwords configuration (server-side), see :class:`redisvl.schema.IndexInfo.stopwords`. Using query-time stopwords with index-level ``STOPWORDS 0`` is counterproductive. +.. note:: + :class:`HybridQuery` and :class:`AggregateHybridQuery` apply linear combination inconsistently. :class:`HybridQuery` uses ``alpha`` to weight the text score and ``beta`` to weight the vector score. :class:`AggregateHybridQuery` uses ``alpha`` to weight the vector score and ``beta`` to weight the text score. + .. note:: **Runtime Parameters for Hybrid Queries** @@ -167,6 +170,9 @@ HybridQuery For index-level stopwords configuration (server-side), see :class:`redisvl.schema.IndexInfo.stopwords`. Using query-time stopwords with index-level ``STOPWORDS 0`` is counterproductive. +.. note:: + :class:`HybridQuery` and :class:`AggregateHybridQuery` apply linear combination inconsistently. :class:`HybridQuery` uses ``alpha`` to weight the text score and ``beta`` to weight the vector score. :class:`AggregateHybridQuery` uses ``alpha`` to weight the vector score and ``beta`` to weight the text score. + TextQuery ================ From 5109c7a4a12c4f85041e3b19f5626d996376d9d1 Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Tue, 9 Dec 2025 13:32:12 +0100 Subject: [PATCH 29/34] Validate that `hybrid_search` method exists --- redisvl/index/index.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/redisvl/index/index.py b/redisvl/index/index.py index 6b672dbb..552cc8a0 100644 --- a/redisvl/index/index.py +++ b/redisvl/index/index.py @@ -1054,6 +1054,9 @@ def hybrid_search(self, query: Any, **kwargs) -> List[Dict[str, Any]]: index = self._redis_client.ft(self.schema.index.name) self._validate_hybrid_query(query) + if not hasattr(index, "hybrid_search"): + raise NotImplementedError(_HYBRID_SEARCH_ERROR_MESSAGE) + results = index.hybrid_search( query=query.query, combine_method=query.combination_method, @@ -1919,6 +1922,9 @@ async def hybrid_search(self, query: Any, **kwargs) -> List[Dict[str, Any]]: index = client.ft(self.schema.index.name) self._validate_hybrid_query(query) + if not hasattr(index, "hybrid_search"): + raise NotImplementedError(_HYBRID_SEARCH_ERROR_MESSAGE) + results = await index.hybrid_search( query=query.query, combine_method=query.combination_method, From 1a71befdd6828e85d8457257c4485b998f1fed89 Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Tue, 9 Dec 2025 13:46:20 +0100 Subject: [PATCH 30/34] Fix error message check --- tests/integration/test_hybrid.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/integration/test_hybrid.py b/tests/integration/test_hybrid.py index 79d31614..5eff6f5a 100644 --- a/tests/integration/test_hybrid.py +++ b/tests/integration/test_hybrid.py @@ -493,7 +493,7 @@ def test_hybrid_search_not_available_in_server(index): return_fields=["user", "credit_score", "age", "job", "location", "description"], ) - with pytest.raises(ResponseError, match="unknown command 'FT.HYBRID'"): + with pytest.raises(ResponseError, match=r"unknown command .FT\.HYBRID"): index.hybrid_search(hybrid_query) From 9acdaab14856e537755e1a1bb0dda147dd57ac78 Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Tue, 9 Dec 2025 14:22:49 +0100 Subject: [PATCH 31/34] Add See Also references [skip ci] --- redisvl/index/index.py | 4 ++++ redisvl/query/hybrid.py | 3 +++ 2 files changed, 7 insertions(+) diff --git a/redisvl/index/index.py b/redisvl/index/index.py index 552cc8a0..17dbc13e 100644 --- a/redisvl/index/index.py +++ b/redisvl/index/index.py @@ -1037,6 +1037,10 @@ def hybrid_search(self, query: Any, **kwargs) -> List[Dict[str, Any]]: Notes: Hybrid search is only available in Redis 8.4.0+, and requires redis-py >= 7.1.0. + See Also: + - `FT.HYBRID command documentation `_ + - `redis-py hybrid_search documentation `_ + .. code-block:: python from redisvl.query.hybrid import HybridQuery diff --git a/redisvl/query/hybrid.py b/redisvl/query/hybrid.py index 96e09e1e..583b3a41 100644 --- a/redisvl/query/hybrid.py +++ b/redisvl/query/hybrid.py @@ -54,6 +54,9 @@ class HybridQuery: results = index.hybrid_search(query) + See Also: + - `FT.HYBRID command documentation `_ + - `redis-py hybrid_search documentation `_ """ def __init__( From eb49885bf6757f7ff638233b722c89c29d5ebe4e Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Wed, 10 Dec 2025 10:34:00 +0100 Subject: [PATCH 32/34] Ditch Redis 8.0.2 from testing matrix --- .github/workflows/test.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 53b22777..0b68e31e 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -97,7 +97,7 @@ jobs: # 3.11 tests are run in the service-tests job python-version: ["3.9", "3.10", "3.12", "3.13"] redis-py-version: ["5.x", "6.x", "7.x"] - redis-version: ["6.2.6-v9", "latest", "8.0.2", "8.4.0"] + redis-version: ["6.2.6-v9", "latest", "8.4.0"] steps: - name: Check out repository uses: actions/checkout@v4 @@ -138,7 +138,7 @@ jobs: - name: Set Redis image name run: | - if [[ "${{ matrix.redis-version }}" == "8.0.2" || "${{ matrix.redis-version }}" == "8.4.0" ]]; then + if [[ "${{ matrix.redis-version }}" == "8.4.0" ]]; then echo "REDIS_IMAGE=redis:${{ matrix.redis-version }}" >> $GITHUB_ENV else echo "REDIS_IMAGE=redis/redis-stack-server:${{ matrix.redis-version }}" >> $GITHUB_ENV From d8accf5a9f4f3d19009524953aca41eb13e63e7c Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Wed, 10 Dec 2025 10:43:04 +0100 Subject: [PATCH 33/34] Reflect query syntax in hybrid query docs note --- docs/api/query.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/api/query.rst b/docs/api/query.rst index ecb55df3..c4fb2d33 100644 --- a/docs/api/query.rst +++ b/docs/api/query.rst @@ -124,7 +124,7 @@ AggregateHybridQuery Using query-time stopwords with index-level ``STOPWORDS 0`` is counterproductive. .. note:: - :class:`HybridQuery` and :class:`AggregateHybridQuery` apply linear combination inconsistently. :class:`HybridQuery` uses ``alpha`` to weight the text score and ``beta`` to weight the vector score. :class:`AggregateHybridQuery` uses ``alpha`` to weight the vector score and ``beta`` to weight the text score. + :class:`HybridQuery` and :class:`AggregateHybridQuery` apply linear combination inconsistently. :class:`HybridQuery` uses ``linear_alpha`` to weight the text score and ``linear_beta`` to weight the vector score. :class:`AggregateHybridQuery` uses ``alpha`` to weight the vector score and ``beta`` to weight the text score. .. note:: **Runtime Parameters for Hybrid Queries** @@ -171,7 +171,7 @@ HybridQuery Using query-time stopwords with index-level ``STOPWORDS 0`` is counterproductive. .. note:: - :class:`HybridQuery` and :class:`AggregateHybridQuery` apply linear combination inconsistently. :class:`HybridQuery` uses ``alpha`` to weight the text score and ``beta`` to weight the vector score. :class:`AggregateHybridQuery` uses ``alpha`` to weight the vector score and ``beta`` to weight the text score. + :class:`HybridQuery` and :class:`AggregateHybridQuery` apply linear combination inconsistently. :class:`HybridQuery` uses ``linear_alpha`` to weight the text score and ``linear_beta`` to weight the vector score. :class:`AggregateHybridQuery` uses ``alpha`` to weight the vector score and ``beta`` to weight the text score. TextQuery ================ From 58e8da63526355ba762db6a84850a03ae3696719 Mon Sep 17 00:00:00 2001 From: Vishal Bala Date: Wed, 10 Dec 2025 16:37:15 +0100 Subject: [PATCH 34/34] Document that default combination method is RRF --- redisvl/query/hybrid.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/redisvl/query/hybrid.py b/redisvl/query/hybrid.py index 583b3a41..39a8b9d5 100644 --- a/redisvl/query/hybrid.py +++ b/redisvl/query/hybrid.py @@ -109,9 +109,10 @@ def __init__( accuracy of the search. yield_vsim_score_as: The name of the field to yield the vector similarity score as. vector_filter_expression: The filter expression to use for the vector similarity search. Defaults to None. - combination_method: The combination method to use. Options are {RRF, LINEAR}. Defaults to None. If "RRF" is - specified, then at least one of `rrf_window` or `rrf_constant` must be provided. If "LINEAR" is - specified, then at least one of `linear_alpha` or `linear_beta` must be provided. + combination_method: The combination method to use. Options are {RRF, LINEAR}. If not specified, the server + defaults to RRF. If "RRF" is specified, then at least one of `rrf_window` or `rrf_constant` must be + provided. If "LINEAR" is specified, then at least one of `linear_alpha` or `linear_beta` must be + provided. rrf_window: The window size to use for the reciprocal rank fusion (RRF) combination method. Limits fusion scope. rrf_constant: The constant to use for the reciprocal rank fusion (RRF) combination method. Controls decay