feat: Add runtime parameter support for HNSW and SVS-VAMANA vector indexes

README.md

@@ -190,7 +190,9 @@ Define queries and perform advanced searches over your indices, including the co
     query = VectorQuery(
       vector=[0.16, -0.34, 0.98, 0.23],
       vector_field_name="embedding",
-      num_results=3
+      num_results=3,
+      # Optional: tune search performance with runtime parameters
+      ef_runtime=100  # HNSW: higher for better recall
     )
     # run the vector search query against the embedding field
     results = index.query(query)

docs/api/query.rst

@@ -20,6 +20,47 @@ VectorQuery
    :show-inheritance:
    :exclude-members: add_filter,get_args,highlight,return_field,summarize
 
+.. note::
+   **Runtime Parameters for Performance Tuning**
+
+   VectorQuery supports runtime parameters for HNSW and SVS-VAMANA indexes that can be adjusted at query time without rebuilding the index:
+
+   **HNSW Parameters:**
+
+   - ``ef_runtime``: Controls search accuracy (higher = better recall, slower search)
+
+   **SVS-VAMANA Parameters:**
+
+   - ``search_window_size``: Size of search window for KNN searches
+   - ``use_search_history``: Whether to use search buffer (OFF/ON/AUTO)
+   - ``search_buffer_capacity``: Tuning parameter for 2-level compression
+
+   Example with HNSW runtime parameters:
+
+   .. code-block:: python
+
+      from redisvl.query import VectorQuery
+
+      query = VectorQuery(
+          vector=[0.1, 0.2, 0.3],
+          vector_field_name="embedding",
+          num_results=10,
+          ef_runtime=150  # Higher for better recall
+      )
+
+   Example with SVS-VAMANA runtime parameters:
+
+   .. code-block:: python
+
+      query = VectorQuery(
+          vector=[0.1, 0.2, 0.3],
+          vector_field_name="embedding",
+          num_results=10,
+          search_window_size=20,
+          use_search_history='ON',
+          search_buffer_capacity=30
+      )
+
 
 VectorRangeQuery
 ================
@@ -34,6 +75,36 @@ VectorRangeQuery
    :show-inheritance:
    :exclude-members: add_filter,get_args,highlight,return_field,summarize
 
+.. note::
+   **Runtime Parameters for Range Queries**
+
+   VectorRangeQuery supports runtime parameters for controlling range search behavior:
+
+   **HNSW & SVS-VAMANA Parameters:**
+
+   - ``epsilon``: Range search approximation factor (default: 0.01)
+
+   **SVS-VAMANA Parameters:**
+
+   - ``search_window_size``: Size of search window
+   - ``use_search_history``: Whether to use search buffer (OFF/ON/AUTO)
+   - ``search_buffer_capacity``: Tuning parameter for 2-level compression
+
+   Example:
+
+   .. code-block:: python
+
+      from redisvl.query import VectorRangeQuery
+
+      query = VectorRangeQuery(
+          vector=[0.1, 0.2, 0.3],
+          vector_field_name="embedding",
+          distance_threshold=0.3,
+          epsilon=0.05,              # Approximation factor
+          search_window_size=20,     # SVS-VAMANA only
+          use_search_history='AUTO'  # SVS-VAMANA only
+      )
+
 HybridQuery
 ================
 
@@ -52,6 +123,29 @@ 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::
+   **Runtime Parameters for Hybrid Queries**
+
+   **Important:** AggregateHybridQuery uses FT.AGGREGATE commands which do NOT support runtime parameters.
+   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.
+
+   Example with VectorQuery (supports runtime parameters):
+
+   .. code-block:: python
+
+      from redisvl.query import VectorQuery
+
+      query = VectorQuery(
+          vector=[0.1, 0.2, 0.3],
+          vector_field_name="embedding",
+          return_fields=["description"],
+          num_results=10,
+          ef_runtime=150  # Runtime parameters work with VectorQuery
+      )
+
 
 TextQuery
 ================

docs/api/schema.rst

@@ -208,11 +208,16 @@ HNSW (Hierarchical Navigable Small World) - Graph-based approximate search with
 
    **Performance characteristics:**
 
-    - **Search speed**: Very fast approximate search with tunable accuracy
+    - **Search speed**: Very fast approximate search with tunable accuracy (via ``ef_runtime`` at query time)
     - **Memory usage**: Higher than compressed SVS-VAMANA but reasonable for most applications
-    - **Recall quality**: Excellent recall rates (95-99%), often better than other approximate methods
+    - **Recall quality**: Excellent recall rates (95-99%), tunable via ``ef_runtime`` parameter
     - **Build time**: Moderate construction time, faster than SVS-VAMANA for smaller datasets
 
+   **Runtime parameters** (adjustable at query time without rebuilding index):
+
+    - ``ef_runtime``: Controls search accuracy (higher = better recall, slower search). Default: 10
+    - ``epsilon``: Range search approximation factor for VectorRangeQuery. Default: 0.01
+
 .. autoclass:: HNSWVectorField
    :members:
    :show-inheritance:
@@ -234,10 +239,10 @@ HNSW (Hierarchical Navigable Small World) - Graph-based approximate search with
         dims: 768
         distance_metric: cosine
         datatype: float32
-        # Balanced settings for good recall and performance
-        m: 16
-        ef_construction: 200
-        ef_runtime: 10
+        # Index-time parameters (set during index creation)
+        m: 16                    # Graph connectivity
+        ef_construction: 200     # Build-time accuracy
+        # Note: ef_runtime can be set at query time via VectorQuery
 
 **High-recall configuration:**
 
@@ -250,10 +255,10 @@ HNSW (Hierarchical Navigable Small World) - Graph-based approximate search with
         dims: 768
         distance_metric: cosine
         datatype: float32
-        # Tuned for maximum accuracy
+        # Index-time parameters tuned for maximum accuracy
         m: 32
         ef_construction: 400
-        ef_runtime: 50
+        # Note: ef_runtime=50 can be set at query time for higher recall
 
 SVS-VAMANA Vector Fields
 ------------------------
@@ -278,6 +283,13 @@ SVS-VAMANA (Scalable Vector Search with VAMANA graph algorithm) provides fast ap
 
     - **vs HNSW**: Better memory efficiency with compression, similar or better recall, Intel-optimized
 
+   **Runtime parameters** (adjustable at query time without rebuilding index):
+
+    - ``epsilon``: Range search approximation factor. Default: 0.01
+    - ``search_window_size``: Size of search window for KNN searches. Higher = better recall, slower search
+    - ``use_search_history``: Whether to use search buffer (OFF/ON/AUTO). Default: AUTO
+    - ``search_buffer_capacity``: Tuning parameter for 2-level compression. Default: search_window_size
+
    **Compression selection guide:**
 
     - **No compression**: Best performance, standard memory usage
@@ -314,10 +326,10 @@ SVS-VAMANA (Scalable Vector Search with VAMANA graph algorithm) provides fast ap
         dims: 768
         distance_metric: cosine
         datatype: float32
-        # Standard settings for balanced performance
+        # Index-time parameters (set during index creation)
         graph_max_degree: 40
         construction_window_size: 250
-        search_window_size: 20
+        # Note: search_window_size and other runtime params can be set at query time
 
 **High-performance configuration with compression:**
 
@@ -330,22 +342,22 @@ SVS-VAMANA (Scalable Vector Search with VAMANA graph algorithm) provides fast ap
         dims: 768
         distance_metric: cosine
         datatype: float32
-        # Tuned for better recall
+        # Index-time parameters tuned for better recall
         graph_max_degree: 64
         construction_window_size: 500
-        search_window_size: 40
         # Maximum compression with dimensionality reduction
         compression: LeanVec4x8
         reduce: 384  # 50% dimensionality reduction
         training_threshold: 1000
+        # Note: search_window_size=40 can be set at query time for higher recall
 
 **Important Notes:**
 
 - **Requirements**: SVS-VAMANA requires Redis >= 8.2 with RediSearch >= 2.8.10.
 - **Datatype limitations**: SVS-VAMANA only supports `float16` and `float32` datatypes (not `bfloat16` or `float64`).
 - **Compression compatibility**: The `reduce` parameter is only valid with LeanVec compression types (`LeanVec4x8` or `LeanVec8x8`).
 - **Platform considerations**: Intel's proprietary LVQ and LeanVec optimizations are not available in Redis Open Source. On non-Intel platforms and Redis Open Source, SVS-VAMANA with compression falls back to basic 8-bit scalar quantization.
-- **Performance tip**: Start with default parameters and tune `search_window_size` first for your speed vs accuracy requirements.
+- **Performance tip**: Runtime parameters like ``search_window_size``, ``epsilon``, and ``use_search_history`` can be adjusted at query time without rebuilding the index. Start with defaults and tune ``search_window_size`` first for your speed vs accuracy requirements.
 
 FLAT Vector Fields
 ------------------
@@ -487,16 +499,16 @@ Performance Characteristics
 
 **Recall Quality:**
  - FLAT: 100% (exact search)
- - HNSW: 95-99% (tunable via ef_runtime)
- - SVS-VAMANA: 90-95% (depends on compression)
+ - HNSW: 95-99% (tunable via ``ef_runtime`` at query time)
+ - SVS-VAMANA: 90-95% (tunable via ``search_window_size`` at query time, also depends on compression)
 
 Migration Considerations
 ------------------------
 
 **From FLAT to HNSW:**
  - Straightforward migration
  - Expect slight recall reduction but major speed improvement
- - Tune ef_runtime to balance speed vs accuracy
+ - Tune ``ef_runtime`` at query time to balance speed vs accuracy (no index rebuild needed)
 
 **From HNSW to SVS-VAMANA:**
  - Requires Redis >= 8.2 with RediSearch >= 2.8.10