# Advanced Query Types ```json metadata { "title": "Advanced Query Types", "description": "", "categories": null, "tableOfContents": {"sections":[{"id":"setup-and-data-preparation","title":"Setup and Data Preparation"},{"id":"define-the-schema","title":"Define the Schema"},{"id":"create-index-and-load-data","title":"Create Index and Load Data"},{"children":[{"id":"basic-text-search","title":"Basic Text Search"},{"id":"text-search-with-different-scoring-algorithms","title":"Text Search with Different Scoring Algorithms"},{"id":"text-search-with-filters","title":"Text Search with Filters"},{"id":"text-search-with-multiple-fields-and-weights","title":"Text Search with Multiple Fields and Weights"},{"id":"text-search-with-custom-stopwords","title":"Text Search with Custom Stopwords"}],"id":"1-textquery-full-text-search","title":"1. TextQuery: Full Text Search"},{"children":[{"id":"index-level-stopwords-configuration","title":"Index-Level Stopwords Configuration"},{"id":"basic-hybrid-query","title":"Basic Hybrid Query"},{"id":"adjusting-the-alpha-parameter","title":"Adjusting the Alpha Parameter"},{"id":"reciprocal-rank-fusion-rrf","title":"Reciprocal Rank Fusion (RRF)"},{"id":"hybrid-query-with-filters","title":"Hybrid Query with Filters"},{"id":"using-different-text-scorers","title":"Using Different Text Scorers"},{"id":"runtime-parameters-for-vector-search-tuning","title":"Runtime Parameters for Vector Search Tuning"}],"id":"2-hybrid-queries-combining-text-and-vector-search","title":"2. Hybrid Queries: Combining Text and Vector Search"},{"children":[{"id":"basic-multi-vector-query","title":"Basic Multi-Vector Query"},{"id":"adjusting-vector-weights","title":"Adjusting Vector Weights"},{"id":"multi-vector-query-with-filters","title":"Multi-Vector Query with Filters"}],"id":"3-multivectorquery-multi-vector-search","title":"3. MultiVectorQuery: Multi-Vector Search"},{"id":"comparing-query-types","title":"Comparing Query Types"},{"children":[{"id":"when-to-use-each-query-type","title":"When to Use Each Query Type:"}],"id":"best-practices","title":"Best Practices"}]} , "codeExamples": [] } ``` In this notebook, we will explore advanced query types available in RedisVL: 1. **`TextQuery`**: Full text search with advanced scoring 2. **`AggregateHybridQuery` and `HybridQuery`**: Combines text and vector search for hybrid retrieval 3. **`MultiVectorQuery`**: Search over multiple vector fields simultaneously These query types are powerful tools for building sophisticated search applications that go beyond simple vector similarity search. Prerequisites: - Ensure `redisvl` is installed in your Python environment. - Have a running instance of [Redis Stack](https://redis.io/docs/install/install-stack/) or [Redis Cloud](https://redis.io/cloud). - For `HybridQuery`, we will need Redis >= 8.4.0 and redis-py >= 7.1.0. ## Setup and Data Preparation First, let's create a schema and prepare sample data that includes text fields, numeric fields, and vector fields. ```python import numpy as np from jupyterutils import result_print # Sample data with text descriptions, categories, and vectors data = [ { 'product_id': 'prod_1', 'brief_description': 'comfortable running shoes for athletes', 'full_description': 'Engineered with a dual-layer EVA foam midsole and FlexWeave breathable mesh upper, these running shoes deliver responsive cushioning for long-distance runs. The anatomical footbed adapts to your stride while the carbon rubber outsole provides superior traction on varied terrain.', 'category': 'footwear', 'price': 89.99, 'rating': 4.5, 'text_embedding': np.array([0.1, 0.2, 0.1], dtype=np.float32).tobytes(), 'image_embedding': np.array([0.8, 0.1], dtype=np.float32).tobytes(), }, { 'product_id': 'prod_2', 'brief_description': 'lightweight running jacket with water resistance', 'full_description': 'Stay protected with this ultralight 2.5-layer DWR-coated shell featuring laser-cut ventilation zones and reflective piping for low-light visibility. Packs into its own chest pocket and weighs just 4.2 oz, making it ideal for unpredictable weather conditions.', 'category': 'outerwear', 'price': 129.99, 'rating': 4.8, 'text_embedding': np.array([0.2, 0.3, 0.2], dtype=np.float32).tobytes(), 'image_embedding': np.array([0.7, 0.2], dtype=np.float32).tobytes(), }, { 'product_id': 'prod_3', 'brief_description': 'professional tennis racket for competitive players', 'full_description': 'Competition-grade racket featuring a 98 sq in head size, 16x19 string pattern, and aerospace-grade graphite frame that delivers explosive power with pinpoint control. Tournament-approved specs include 315g weight and 68 RA stiffness rating for advanced baseline play.', 'category': 'equipment', 'price': 199.99, 'rating': 4.9, 'text_embedding': np.array([0.9, 0.1, 0.05], dtype=np.float32).tobytes(), 'image_embedding': np.array([0.1, 0.9], dtype=np.float32).tobytes(), }, { 'product_id': 'prod_4', 'brief_description': 'yoga mat with extra cushioning for comfort', 'full_description': 'Premium 8mm thick TPE yoga mat with dual-texture surface - smooth side for hot yoga flow and textured side for maximum grip during balancing poses. Closed-cell technology prevents moisture absorption while alignment markers guide proper positioning in asanas.', 'category': 'accessories', 'price': 39.99, 'rating': 4.3, 'text_embedding': np.array([0.15, 0.25, 0.15], dtype=np.float32).tobytes(), 'image_embedding': np.array([0.5, 0.5], dtype=np.float32).tobytes(), }, { 'product_id': 'prod_5', 'brief_description': 'basketball shoes with excellent ankle support', 'full_description': 'High-top basketball sneakers with Zoom Air units in forefoot and heel, reinforced lateral sidewalls for explosive cuts, and herringbone traction pattern optimized for hardwood courts. The internal bootie construction and extended ankle collar provide lockdown support during aggressive drives.', 'category': 'footwear', 'price': 139.99, 'rating': 4.7, 'text_embedding': np.array([0.12, 0.18, 0.12], dtype=np.float32).tobytes(), 'image_embedding': np.array([0.75, 0.15], dtype=np.float32).tobytes(), }, { 'product_id': 'prod_6', 'brief_description': 'swimming goggles with anti-fog coating', 'full_description': 'Low-profile competition goggles with curved polycarbonate lenses offering 180-degree peripheral vision and UV protection. Hydrophobic anti-fog coating lasts 10x longer than standard treatments, while the split silicone strap and interchangeable nose bridges ensure a watertight, custom fit.', 'category': 'accessories', 'price': 24.99, 'rating': 4.4, 'text_embedding': np.array([0.3, 0.1, 0.2], dtype=np.float32).tobytes(), 'image_embedding': np.array([0.2, 0.8], dtype=np.float32).tobytes(), }, ] ``` ## Define the Schema Our schema includes: - **Tag fields**: `product_id`, `category` - **Text fields**: `brief_description` and `full_description` for full-text search - **Numeric fields**: `price`, `rating` - **Vector fields**: `text_embedding` (3 dimensions) and `image_embedding` (2 dimensions) for semantic search ```python schema = { "index": { "name": "advanced_queries", "prefix": "products", "storage_type": "hash", }, "fields": [ {"name": "product_id", "type": "tag"}, {"name": "category", "type": "tag"}, {"name": "brief_description", "type": "text"}, {"name": "full_description", "type": "text"}, {"name": "price", "type": "numeric"}, {"name": "rating", "type": "numeric"}, { "name": "text_embedding", "type": "vector", "attrs": { "dims": 3, "distance_metric": "cosine", "algorithm": "flat", "datatype": "float32" } }, { "name": "image_embedding", "type": "vector", "attrs": { "dims": 2, "distance_metric": "cosine", "algorithm": "flat", "datatype": "float32" } } ], } ``` ## Create Index and Load Data ```python from redisvl.index import SearchIndex # Create the search index index = SearchIndex.from_dict(schema, redis_url="redis://localhost:6379") # Create the index and load data index.create(overwrite=True) keys = index.load(data) print(f"Loaded {len(keys)} products into the index") ``` Loaded 6 products into the index ## 1. TextQuery: Full Text Search The `TextQuery` class enables full text search with advanced scoring algorithms. It's ideal for keyword-based search with relevance ranking. ### Basic Text Search Let's search for products related to "running shoes": ```python from redisvl.query import TextQuery # Create a text query text_query = TextQuery( text="running shoes", text_field_name="brief_description", return_fields=["product_id", "brief_description", "category", "price"], num_results=5 ) results = index.query(text_query) result_print(results) ```
scoreproduct_idbrief_descriptioncategoryprice
6.134713688880119prod_1comfortable running shoes for athletesfootwear89.99
6.134713688880119prod_1comfortable running shoes for athletesfootwear89.99
2.148612199701887prod_5basketball shoes with excellent ankle supportfootwear139.99
2.148612199701887prod_5basketball shoes with excellent ankle supportfootwear139.99
2.102960001841964prod_2lightweight running jacket with water resistanceouterwear129.99
### Text Search with Different Scoring Algorithms RedisVL supports multiple text scoring algorithms. Let's compare `BM25STD` and `TFIDF`: ```python # BM25 standard scoring (default) bm25_query = TextQuery( text="comfortable shoes", text_field_name="brief_description", text_scorer="BM25STD", return_fields=["product_id", "brief_description", "price"], num_results=3 ) print("Results with BM25 scoring:") results = index.query(bm25_query) result_print(results) ``` Results with BM25 scoring:
scoreproduct_idbrief_descriptionprice
6.340446315760713prod_1comfortable running shoes for athletes89.99
6.340446315760713prod_1comfortable running shoes for athletes89.99
2.148612199701887prod_5basketball shoes with excellent ankle support139.99
```python # TFIDF scoring tfidf_query = TextQuery( text="comfortable shoes", text_field_name="brief_description", text_scorer="TFIDF", return_fields=["product_id", "brief_description", "price"], num_results=3 ) print("Results with TFIDF scoring:") results = index.query(tfidf_query) result_print(results) ``` Results with TFIDF scoring:
scoreproduct_idbrief_descriptionprice
2.0prod_1comfortable running shoes for athletes89.99
2.0prod_5basketball shoes with excellent ankle support139.99
2.0prod_1comfortable running shoes for athletes89.99
### Text Search with Filters Combine text search with filters to narrow results: ```python from redisvl.query.filter import Tag, Num # Search for "shoes" only in the footwear category filtered_text_query = TextQuery( text="shoes", text_field_name="brief_description", filter_expression=Tag("category") == "footwear", return_fields=["product_id", "brief_description", "category", "price"], num_results=5 ) results = index.query(filtered_text_query) result_print(results) ```
scoreproduct_idbrief_descriptioncategoryprice
4.050828128169667prod_1comfortable running shoes for athletesfootwear89.99
4.050828128169667prod_1comfortable running shoes for athletesfootwear89.99
3.2229182995528305prod_5basketball shoes with excellent ankle supportfootwear139.99
3.2229182995528305prod_5basketball shoes with excellent ankle supportfootwear139.99
```python # Search for products under $100 price_filtered_query = TextQuery( text="comfortable", text_field_name="brief_description", filter_expression=Num("price") < 100, return_fields=["product_id", "brief_description", "price"], num_results=5 ) results = index.query(price_filtered_query) result_print(results) ```
scoreproduct_idbrief_descriptionprice
3.3757130560793973prod_1comfortable running shoes for athletes89.99
3.3757130560793973prod_1comfortable running shoes for athletes89.99
1.6340629489648504prod_4yoga mat with extra cushioning for comfort39.99
1.6340629489648504prod_4yoga mat with extra cushioning for comfort39.99
### Text Search with Multiple Fields and Weights You can search across multiple text fields with different weights to prioritize certain fields. Here we'll prioritize the `brief_description` field and make text similarity in that field twice as important as text similarity in `full_description`: ```python weighted_query = TextQuery( text="shoes", text_field_name={"brief_description": 1.0, "full_description": 0.5}, return_fields=["product_id", "brief_description"], num_results=3 ) results = index.query(weighted_query) result_print(results) ```
scoreproduct_idbrief_description
5.1882832044423015prod_1comfortable running shoes for athletes
5.1882832044423015prod_1comfortable running shoes for athletes
2.148612199701887prod_5basketball shoes with excellent ankle support
### Text Search with Custom Stopwords Stopwords are common words that are filtered out before processing the query. You can specify which language's default stopwords should be filtered out, like `english`, `french`, or `german`. You can also define your own list of stopwords: ```python # Use English stopwords (default) query_with_stopwords = TextQuery( text="the best shoes for running", text_field_name="brief_description", stopwords="english", # Common words like "the", "for" will be removed return_fields=["product_id", "brief_description"], num_results=3 ) results = index.query(query_with_stopwords) result_print(results) ```
scoreproduct_idbrief_description
6.134713688880119prod_1comfortable running shoes for athletes
6.134713688880119prod_1comfortable running shoes for athletes
2.148612199701887prod_5basketball shoes with excellent ankle support
```python # Use custom stopwords custom_stopwords_query = TextQuery( text="professional equipment for athletes", text_field_name="brief_description", stopwords=["for", "with"], # Only these words will be filtered return_fields=["product_id", "brief_description"], num_results=3 ) results = index.query(custom_stopwords_query) result_print(results) ```
scoreproduct_idbrief_description
3.3757130560793973prod_1comfortable running shoes for athletes
3.3757130560793973prod_1comfortable running shoes for athletes
3.303218123358508prod_3professional tennis racket for competitive players
```python # No stopwords no_stopwords_query = TextQuery( text="the best shoes for running", text_field_name="brief_description", stopwords=None, # All words will be included return_fields=["product_id", "brief_description"], num_results=3 ) results = index.query(no_stopwords_query) result_print(results) ```
scoreproduct_idbrief_description
6.134713688880119prod_1comfortable running shoes for athletes
6.134713688880119prod_1comfortable running shoes for athletes
2.148612199701887prod_5basketball shoes with excellent ankle support
## 2. Hybrid Queries: Combining Text and Vector Search Hybrid queries combine text search and vector similarity to provide the best of both worlds: - **Text search**: Finds exact keyword matches - **Vector search**: Captures semantic similarity As of Redis 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. ```python from packaging.version import Version from redis import __version__ as _redis_py_version redis_py_version = Version(_redis_py_version) redis_version = Version(index.client.info()["redis_version"]) HYBRID_SEARCH_AVAILABLE = redis_version >= Version("8.4.0") and redis_py_version >= Version("7.1.0") print(HYBRID_SEARCH_AVAILABLE) ``` True ### Index-Level Stopwords Configuration The previous example showed **query-time stopwords** using `TextQuery.stopwords`, which filters words from the query before searching. RedisVL also supports **index-level stopwords** configuration, which determines which words are indexed in the first place. **Key Difference:** - **Query-time stopwords** (`TextQuery.stopwords`): Filters words from your search query (client-side) - **Index-level stopwords** (`IndexInfo.stopwords`): Controls which words get indexed in Redis (server-side) **Three Configuration Modes:** 1. **`None` (default)**: Use Redis's default stopwords list 2. **`[]` (empty list)**: Disable stopwords completely (`STOPWORDS 0` in FT.CREATE) 3. **`["the", "a", "an"]`**: Use a custom stopwords list **When to use `STOPWORDS 0`:** - When you need to search for common words like "of", "at", "the" - For entity names containing stopwords (e.g., "Bank of Glasberliner", "University of Glasberliner") - When working with structured data where every word matters ```python # Create a schema with index-level stopwords disabled from redisvl.index import SearchIndex stopwords_schema = { "index": { "name": "company_index", "prefix": "company:", "storage_type": "hash", "stopwords": [] # STOPWORDS 0 - disable stopwords completely }, "fields": [ {"name": "company_name", "type": "text"}, {"name": "description", "type": "text"} ] } # Create index using from_dict (handles schema creation internally) company_index = SearchIndex.from_dict(stopwords_schema, redis_url="redis://localhost:6379") company_index.create(overwrite=True, drop=True) print(f"Index created with STOPWORDS 0: {company_index}") ``` Index created with STOPWORDS 0: ```python # Load sample data with company names containing common stopwords companies = [ {"company_name": "Bank of Glasberliner", "description": "Major financial institution"}, {"company_name": "University of Glasberliner", "description": "Public university system"}, {"company_name": "Department of Glasberliner Affairs", "description": "A government agency"}, {"company_name": "Glasberliner FC", "description": "Football Club"}, {"company_name": "The Home Market", "description": "Home improvement retailer"}, ] for i, company in enumerate(companies): company_index.load([company], keys=[f"company:{i}"]) print(f"✓ Loaded {len(companies)} companies") ``` ✓ Loaded 5 companies ```python # Search for "Bank of Glasberliner" - with STOPWORDS 0, "of" is indexed and searchable from redisvl.query import FilterQuery query = FilterQuery( filter_expression='@company_name:(Bank of Glasberliner)', return_fields=["company_name", "description"], ) results = company_index.search(query.query, query_params=query.params) print(f"Found {len(results.docs)} results for 'Bank of Glasberliner':") for doc in results.docs: print(f" - {doc.company_name}: {doc.description}") ``` Found 1 results for 'Bank of Glasberliner': - Bank of Glasberliner: Major financial institution **Comparison: With vs Without Stopwords** If we had used the default stopwords (not specifying `stopwords` in the schema), the word "of" would be filtered out during indexing. This means: - ❌ Searching for `"Bank of Glasberliner"` might not find exact matches - ❌ The phrase would be indexed as `"Bank Berlin"` (without "of") - ✅ With `STOPWORDS 0`, all words including "of" are indexed **Custom Stopwords Example:** You can also provide a custom list of stopwords: ```python # Example: Create index with custom stopwords custom_stopwords_schema = { "index": { "name": "custom_stopwords_index", "prefix": "custom:", "stopwords": ["inc", "llc", "corp"] # Filter out legal entity suffixes }, "fields": [ {"name": "name", "type": "text"} ] } # This would create an index where "inc", "llc", "corp" are not indexed print("Custom stopwords:", custom_stopwords_schema["index"]["stopwords"]) ``` Custom stopwords: ['inc', 'llc', 'corp'] **YAML Format:** You can also define stopwords in YAML schema files: ```yaml version: '0.1.0' index: name: company_index prefix: company: storage_type: hash stopwords: [] # Disable stopwords (STOPWORDS 0) fields: - name: company_name type: text - name: description type: text ``` Or with custom stopwords: ```yaml index: stopwords: - the - a - an ``` ```python # Cleanup company_index.delete(drop=True) print("✓ Cleaned up company_index") ``` ✓ Cleaned up company_index ### Basic Hybrid Query NOTE: `HybridQuery` requires Redis >= 8.4.0 and redis-py >= 7.1.0. Let's search for "running" with both text and semantic search, combining the results' scores using a linear combination: ```python if HYBRID_SEARCH_AVAILABLE: from redisvl.query import HybridQuery # Create a hybrid query hybrid_query = HybridQuery( text="running shoes", text_field_name="brief_description", vector=[0.1, 0.2, 0.1], # Query vector vector_field_name="text_embedding", return_fields=["product_id", "brief_description", "category", "price"], num_results=5, yield_text_score_as="text_score", yield_vsim_score_as="vector_similarity", combination_method="LINEAR", yield_combined_score_as="hybrid_score", ) results = index.query(hybrid_query) result_print(results) else: print("Hybrid search is not available in this version of Redis/redis-py.") ``` /Users/vishal.bala/PycharmProjects/redis-vl-python/redisvl/query/hybrid.py:133: UserWarning: HybridPostProcessingConfig is an experimental and may change or be removed in future versions. self.postprocessing_config = HybridPostProcessingConfig() /Users/vishal.bala/PycharmProjects/redis-vl-python/redisvl/query/hybrid.py:237: UserWarning: HybridSearchQuery is an experimental and may change or be removed in future versions. search_query = HybridSearchQuery( /Users/vishal.bala/PycharmProjects/redis-vl-python/redisvl/query/hybrid.py:278: UserWarning: HybridVsimQuery is an experimental and may change or be removed in future versions. vsim_query = HybridVsimQuery( /Users/vishal.bala/PycharmProjects/redis-vl-python/redisvl/query/hybrid.py:352: UserWarning: CombineResultsMethod is an experimental and may change or be removed in future versions. return CombineResultsMethod(
text_scoreproduct_idbrief_descriptioncategorypricevector_similarityhybrid_score
6.13471368888prod_1comfortable running shoes for athletesfootwear89.990.9999999701982.5404140858
6.13471368888prod_1comfortable running shoes for athletesfootwear89.990.9999999701982.5404140858
2.1486121997prod_5basketball shoes with excellent ankle supportfootwear139.990.9950737357141.34113527491
2.1486121997prod_5basketball shoes with excellent ankle supportfootwear139.990.9950737357141.34113527491
2.10296000184prod_2lightweight running jacket with water resistanceouterwear129.990.9950737357141.32743961555
For earlier versions of Redis, you can use `AggregateHybridQuery` instead: ```python from redisvl.query import AggregateHybridQuery agg_hybrid_query = AggregateHybridQuery( text="running shoes", text_field_name="brief_description", vector=[0.1, 0.2, 0.1], # Query vector vector_field_name="text_embedding", return_fields=["product_id", "brief_description", "category", "price"], num_results=5 ) results = index.query(agg_hybrid_query) result_print(results) ```
vector_distanceproduct_idbrief_descriptioncategorypricevector_similaritytext_scorehybrid_score
5.96046447754e-08prod_1comfortable running shoes for athletesfootwear89.990.9999999701986.134713688882.5404140858
5.96046447754e-08prod_1comfortable running shoes for athletesfootwear89.990.9999999701986.134713688882.5404140858
0.00985252857208prod_5basketball shoes with excellent ankle supportfootwear139.990.9950737357142.14861219971.34113527491
0.0038834810257prod_4yoga mat with extra cushioning for comfortaccessories39.990.99805825948700.698640781641
0.0038834810257prod_4yoga mat with extra cushioning for comfortaccessories39.990.99805825948700.698640781641
### Adjusting the Alpha Parameter Results are scored using a weighted combination: ``` hybrid_score = (alpha) * text_score + (1 - alpha) * vector_score ``` 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. The `alpha` parameter controls the weight between text and vector search: - `alpha=1.0`: Pure text search (or pure vector search for `AggregateHybridQuery`) - `alpha=0.0`: Pure vector search (or pure text search for `AggregateHybridQuery`) - `alpha=0.3` (default - `HybridQuery`): 30% text, 70% vector ```python if HYBRID_SEARCH_AVAILABLE: vector_heavy_query = HybridQuery( text="comfortable", text_field_name="brief_description", vector=[0.15, 0.25, 0.15], vector_field_name="text_embedding", combination_method="LINEAR", linear_alpha=0.1, # 10% text, 90% vector return_fields=["product_id", "brief_description"], num_results=3, yield_text_score_as="text_score", yield_vsim_score_as="vector_similarity", yield_combined_score_as="hybrid_score", ) print("Results with alpha=0.1 (vector-heavy):") results = index.query(vector_heavy_query) result_print(results) else: print("Hybrid search is not available in this version of Redis/redis-py.") ``` Results with alpha=0.1 (vector-heavy):
text_scoreproduct_idbrief_descriptionvector_similarityhybrid_score
3.37571305608prod_1comfortable running shoes for athletes0.9980582594871.23582373915
3.37571305608prod_1comfortable running shoes for athletes0.9980582594871.23582373915
1.63406294896prod_4yoga mat with extra cushioning for comfort1.00000005961.06340634854
```python # More emphasis on vector search (alpha=0.9) vector_heavy_query = AggregateHybridQuery( text="comfortable", text_field_name="brief_description", vector=[0.15, 0.25, 0.15], vector_field_name="text_embedding", alpha=0.9, # 90% vector, 10% text return_fields=["product_id", "brief_description"], num_results=3 ) print("Results with alpha=0.9 (vector-heavy):") results = index.query(vector_heavy_query) result_print(results) ``` Results with alpha=0.9 (vector-heavy):
vector_distanceproduct_idbrief_descriptionvector_similaritytext_scorehybrid_score
-1.19209289551e-07prod_4yoga mat with extra cushioning for comfort1.00000005961.634062948961.06340634854
-1.19209289551e-07prod_4yoga mat with extra cushioning for comfort1.00000005961.634062948961.06340634854
0.00136888027191prod_5basketball shoes with excellent ankle support0.99931555986400.899384003878
### Reciprocal Rank Fusion (RRF) 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. `HybridQuery` allows for the following parameters to be specified for RRF: - `rrf_window`: The window size to use for the RRF combination method. Limits the fusion scope. - `rrf_constant`: The constant to use for the RRF combination method. Controls the decay of rank influence. `AggregateHybridQuery` does not support RRF, and only supports a linear combination of scores. ```python if HYBRID_SEARCH_AVAILABLE: rrf_query = HybridQuery( text="comfortable", text_field_name="brief_description", vector=[0.15, 0.25, 0.15], vector_field_name="text_embedding", combination_method="RRF", return_fields=["product_id", "brief_description"], num_results=3, yield_text_score_as="text_score", yield_vsim_score_as="vector_similarity", yield_combined_score_as="hybrid_score", ) results = index.query(rrf_query) result_print(results) else: print("Hybrid search is not available in this version of Redis/redis-py.") ```
text_scoreproduct_idbrief_descriptionvector_similarityhybrid_score
1.63406294896prod_4yoga mat with extra cushioning for comfort1.00000005960.032266458496
1.63406294896prod_4yoga mat with extra cushioning for comfort1.00000005960.0317540322581
3.37571305608prod_1comfortable running shoes for athletes0.9980582594870.0313188157573
### Hybrid Query with Filters You can also combine hybrid search with filters: ```python if HYBRID_SEARCH_AVAILABLE: # Hybrid search with a price filter filtered_hybrid_query = HybridQuery( text="professional equipment", text_field_name="brief_description", vector=[0.9, 0.1, 0.05], vector_field_name="text_embedding", filter_expression=Num("price") > 100, return_fields=["product_id", "brief_description", "category", "price"], num_results=5, combination_method="LINEAR", yield_text_score_as="text_score", yield_vsim_score_as="vector_similarity", yield_combined_score_as="hybrid_score", ) results = index.query(filtered_hybrid_query) result_print(results) else: print("Hybrid search is not available in this version of Redis/redis-py.") ```
text_scoreproduct_idbrief_descriptioncategorypricevector_similarityhybrid_score
3.30321812336prod_3professional tennis racket for competitive playersequipment199.991.00000005961.69096547873
3.30321812336prod_3professional tennis racket for competitive playersequipment199.991.00000005961.69096547873
0prod_2lightweight running jacket with water resistanceouterwear129.990.7941712737080.555919891596
0prod_5basketball shoes with excellent ankle supportfootwear139.990.7941712737080.555919891596
0prod_2lightweight running jacket with water resistanceouterwear129.990.7941712737080.555919891596
```python # Hybrid search with a price filter filtered_hybrid_query = AggregateHybridQuery( text="professional equipment", text_field_name="brief_description", vector=[0.9, 0.1, 0.05], vector_field_name="text_embedding", filter_expression=Num("price") > 100, return_fields=["product_id", "brief_description", "category", "price"], num_results=5 ) results = index.query(filtered_hybrid_query) result_print(results) ```
vector_distanceproduct_idbrief_descriptioncategorypricevector_similaritytext_scorehybrid_score
-1.19209289551e-07prod_3professional tennis racket for competitive playersequipment199.991.00000005963.303218123361.69096547873
-1.19209289551e-07prod_3professional tennis racket for competitive playersequipment199.991.00000005963.303218123361.69096547873
0.411657452583prod_2lightweight running jacket with water resistanceouterwear129.990.79417127370800.555919891596
0.411657452583prod_5basketball shoes with excellent ankle supportfootwear139.990.79417127370800.555919891596
0.411657452583prod_2lightweight running jacket with water resistanceouterwear129.990.79417127370800.555919891596
### Using Different Text Scorers Hybrid queries support the same text scoring algorithms as TextQuery: ```python if HYBRID_SEARCH_AVAILABLE: # Aggregate Hybrid query with TFIDF scorer hybrid_tfidf = HybridQuery( text="shoes support", text_field_name="brief_description", vector=[0.12, 0.18, 0.12], vector_field_name="text_embedding", text_scorer="TFIDF", return_fields=["product_id", "brief_description"], num_results=3, combination_method="LINEAR", yield_text_score_as="text_score", yield_vsim_score_as="vector_similarity", yield_combined_score_as="hybrid_score", ) results = index.query(hybrid_tfidf) result_print(results) else: print("Hybrid search is not available in this version of Redis/redis-py.") ```
text_scoreproduct_idbrief_descriptionvector_similarityhybrid_score
2.66666666667prod_1comfortable running shoes for athletes0.9950737357141.496551615
2.66666666667prod_1comfortable running shoes for athletes0.9950737357141.496551615
1.33333333333prod_5basketball shoes with excellent ankle support11.1
```python # Aggregate Hybrid query with TFIDF scorer hybrid_tfidf = AggregateHybridQuery( text="shoes support", text_field_name="brief_description", vector=[0.12, 0.18, 0.12], vector_field_name="text_embedding", text_scorer="TFIDF", return_fields=["product_id", "brief_description"], num_results=3 ) results = index.query(hybrid_tfidf) result_print(results) ```
vector_distanceproduct_idbrief_descriptionvector_similaritytext_scorehybrid_score
0prod_5basketball shoes with excellent ankle support141.9
0prod_2lightweight running jacket with water resistance100.7
0prod_2lightweight running jacket with water resistance100.7
### Runtime Parameters for Vector Search Tuning **Important:** `AggregateHybridQuery` uses FT.AGGREGATE commands which do NOT support runtime parameters. 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. **For runtime parameter support, use `HybridQuery`, `VectorQuery`, or `VectorRangeQuery` instead:** - `HybridQuery`: Supports `ef_runtime` for HNSW indexes - `VectorQuery`: Supports all runtime parameters (HNSW and SVS-VAMANA) - `VectorRangeQuery`: Supports all runtime parameters (HNSW and SVS-VAMANA) - `AggregateHybridQuery`: Does NOT support runtime parameters (uses FT.AGGREGATE) See the **Runtime Parameters** section earlier in this notebook for examples of using runtime parameters with `VectorQuery`. ## 3. MultiVectorQuery: Multi-Vector Search The `MultiVectorQuery` allows you to search over multiple vector fields simultaneously. This is useful when you have different types of embeddings (e.g., text and image embeddings) and want to find results that match across multiple modalities. The final score is calculated as a weighted combination: ``` combined_score = w_1 * score_1 + w_2 * score_2 + w_3 * score_3 + ... ``` ### Basic Multi-Vector Query First, we need to import the `Vector` class to define our query vectors: ```python from redisvl.query import MultiVectorQuery, Vector # Define multiple vectors for the query text_vector = Vector( vector=[0.1, 0.2, 0.1], field_name="text_embedding", dtype="float32", weight=0.7 # 70% weight for text embedding ) image_vector = Vector( vector=[0.8, 0.1], field_name="image_embedding", dtype="float32", weight=0.3 # 30% weight for image embedding ) # Create a multi-vector query multi_vector_query = MultiVectorQuery( vectors=[text_vector, image_vector], return_fields=["product_id", "brief_description", "category"], num_results=5 ) results = index.query(multi_vector_query) result_print(results) ```
distance_0distance_1product_idbrief_descriptioncategoryscore_0score_1combined_score
5.96046447754e-085.96046447754e-08prod_1comfortable running shoes for athletesfootwear0.9999999701980.9999999701980.999999970198
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
0.009852528572080.00266629457474prod_5basketball shoes with excellent ankle supportfootwear0.9950737357140.9986668527130.996151670814
0.009852528572080.0118260979652prod_2lightweight running jacket with water resistanceouterwear0.9950737357140.9940869510170.994777700305
### Adjusting Vector Weights You can adjust the weights to prioritize different vector fields: ```python # More emphasis on image similarity text_vec = Vector( vector=[0.9, 0.1, 0.05], field_name="text_embedding", dtype="float32", weight=0.2 # 20% weight ) image_vec = Vector( vector=[0.1, 0.9], field_name="image_embedding", dtype="float32", weight=0.8 # 80% weight ) image_heavy_query = MultiVectorQuery( vectors=[text_vec, image_vec], return_fields=["product_id", "brief_description", "category"], num_results=3 ) print("Results with emphasis on image similarity:") results = index.query(image_heavy_query) result_print(results) ``` Results with emphasis on image similarity:
distance_0distance_1product_idbrief_descriptioncategoryscore_0score_1combined_score
-1.19209289551e-070prod_3professional tennis racket for competitive playersequipment1.000000059611.00000001192
-1.19209289551e-070prod_3professional tennis racket for competitive playersequipment1.000000059611.00000001192
0.145393729210.00900757312775prod_6swimming goggles with anti-fog coatingaccessories0.9273031353950.9954962134360.981857597828
### Multi-Vector Query with Filters Combine multi-vector search with filters to narrow results: ```python # Multi-vector search with category filter text_vec = Vector( vector=[0.1, 0.2, 0.1], field_name="text_embedding", dtype="float32", weight=0.6 ) image_vec = Vector( vector=[0.8, 0.1], field_name="image_embedding", dtype="float32", weight=0.4 ) filtered_multi_query = MultiVectorQuery( vectors=[text_vec, image_vec], filter_expression=Tag("category") == "footwear", return_fields=["product_id", "brief_description", "category", "price"], num_results=5 ) results = index.query(filtered_multi_query) result_print(results) ```
distance_0distance_1product_idbrief_descriptioncategorypricescore_0score_1combined_score
5.96046447754e-085.96046447754e-08prod_1comfortable running shoes for athletesfootwear89.990.9999999701980.9999999701980.999999970198
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
0.009852528572080.00266629457474prod_5basketball shoes with excellent ankle supportfootwear139.990.9950737357140.9986668527130.996510982513
## Comparing Query Types Let's compare the three query types side by side: ```python # TextQuery - keyword-based search text_q = TextQuery( text="shoes", text_field_name="brief_description", return_fields=["product_id", "brief_description"], num_results=3 ) print("TextQuery Results (keyword-based):") result_print(index.query(text_q)) print() ``` TextQuery Results (keyword-based):
scoreproduct_idbrief_description
2.9647332596813154prod_1comfortable running shoes for athletes
2.9647332596813154prod_1comfortable running shoes for athletes
2.148612199701887prod_5basketball shoes with excellent ankle support
```python if HYBRID_SEARCH_AVAILABLE: # HybridQuery - combines text and vector search hybrid_q = HybridQuery( text="shoes", text_field_name="brief_description", vector=[0.1, 0.2, 0.1], vector_field_name="text_embedding", return_fields=["product_id", "brief_description"], num_results=3, combination_method="LINEAR", yield_text_score_as="text_score", yield_vsim_score_as="vector_similarity", yield_combined_score_as="hybrid_score", ) results = index.query(hybrid_q) else: hybrid_q = AggregateHybridQuery( text="shoes", text_field_name="brief_description", vector=[0.1, 0.2, 0.1], vector_field_name="text_embedding", return_fields=["product_id", "brief_description"], num_results=3, ) results = index.query(hybrid_q) print(f"{hybrid_q.__class__.__name__} Results (text + vector):") result_print(results) print() ``` HybridQuery Results (text + vector):
text_scoreproduct_idbrief_descriptionvector_similarityhybrid_score
2.96473325968prod_1comfortable running shoes for athletes0.9999999701981.58941995704
2.96473325968prod_1comfortable running shoes for athletes0.9999999701981.58941995704
2.1486121997prod_5basketball shoes with excellent ankle support0.9950737357141.34113527491
```python # MultiVectorQuery - searches multiple vector fields mv_text = Vector( vector=[0.1, 0.2, 0.1], field_name="text_embedding", dtype="float32", weight=0.5 ) mv_image = Vector( vector=[0.8, 0.1], field_name="image_embedding", dtype="float32", weight=0.5 ) multi_q = MultiVectorQuery( vectors=[mv_text, mv_image], return_fields=["product_id", "brief_description"], num_results=3 ) print("MultiVectorQuery Results (multiple vectors):") result_print(index.query(multi_q)) ``` MultiVectorQuery Results (multiple vectors):
distance_0distance_1product_idbrief_descriptionscore_0score_1combined_score
5.96046447754e-085.96046447754e-08prod_1comfortable running shoes for athletes0.9999999701980.9999999701980.999999970198
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
## Best Practices ### When to Use Each Query Type: 1. **`TextQuery`**: - When you need precise keyword matching - For traditional search engine functionality - When text relevance scoring is important - Example: Product search, document retrieval 2. **`HybridQuery`**: - When you want to combine keyword and semantic search - For improved search quality over pure text or vector search - When you have both text and vector representations of your data - Example: E-commerce search, content recommendation 3. **`MultiVectorQuery`**: - When you have multiple types of embeddings (text, image, audio, etc.) - For multi-modal search applications - When you want to balance multiple semantic signals - Example: Image-text search, cross-modal retrieval ```python # Cleanup index.delete() ```