Update schema docs with SVS

Author: Nitin Kanukolanu

docs/api/schema.rst

@@ -97,16 +97,64 @@ Each field type supports specific attributes that customize its behavior. Below
 **Common Vector Field Attributes**:
 
 - `dims`: Dimensionality of the vector.
-- `algorithm`: Indexing algorithm (`flat` or `hnsw`).
-- `datatype`: Float datatype of the vector (`bfloat16`, `float16`, `float32`, `float64`).
+- `algorithm`: Indexing algorithm (`flat`, `hnsw`, or `svs-vamana`).
+- `datatype`: Float datatype of the vector (`bfloat16`, `float16`, `float32`, `float64`). Note: SVS-VAMANA only supports `float16` and `float32`.
 - `distance_metric`: Metric for measuring query relevance (`COSINE`, `L2`, `IP`).
+- `initial_cap`: Initial capacity for the index (optional).
+- `index_missing`: When True, allows searching for documents missing this field (optional).
+
+**FLAT Vector Field Specific Attributes**:
+
+- `block_size`: Block size for the FLAT index (optional).
 
 **HNSW Vector Field Specific Attributes**:
 
-- `m`: Max outgoing edges per node in each layer.
-- `ef_construction`: Max edge candidates during build time.
-- `ef_runtime`: Max top candidates during search.
-- `epsilon`: Range search boundary factor.
+- `m`: Max outgoing edges per node in each layer (default: 16).
+- `ef_construction`: Max edge candidates during build time (default: 200).
+- `ef_runtime`: Max top candidates during search (default: 10).
+- `epsilon`: Range search boundary factor (default: 0.01).
+
+**SVS-VAMANA Vector Field Specific Attributes**:
+
+SVS-VAMANA (Scalable Vector Search with VAMANA graph algorithm) provides fast approximate nearest neighbor search with optional compression support. This algorithm is optimized for Intel hardware and offers reduced memory usage through vector compression.
+
+- `graph_max_degree`: Maximum degree of the Vamana graph, i.e., the number of edges per node (default: 40).
+- `construction_window_size`: Size of the candidate list during graph construction. Higher values yield better quality graphs but increase construction time (default: 250).
+- `search_window_size`: Size of the candidate list during search. Higher values increase accuracy but also increase search latency (default: 20).
+- `epsilon`: Relative factor for range query boundaries (default: 0.01).
+- `compression`: Optional vector compression algorithm. Supported types:
+
+  - `LVQ4`: 4-bit Learned Vector Quantization
+  - `LVQ4x4`: 4-bit LVQ with 4x compression
+  - `LVQ4x8`: 4-bit LVQ with 8x compression
+  - `LVQ8`: 8-bit Learned Vector Quantization
+  - `LeanVec4x8`: 4-bit LeanVec with 8x compression and dimensionality reduction
+  - `LeanVec8x8`: 8-bit LeanVec with 8x compression and dimensionality reduction
+
+- `reduce`: Reduced dimensionality for LeanVec compression. Must be less than `dims`. Only valid with `LeanVec4x8` or `LeanVec8x8` compression types. Lowering this value can speed up search and reduce memory usage (optional).
+- `training_threshold`: Minimum number of vectors required before compression training begins. Must be less than 100 * 1024 (default: 10 * 1024).
+
+**SVS-VAMANA Example**:
+
+.. code-block:: yaml
+
+    - name: embedding
+      type: vector
+      attrs:
+        algorithm: svs-vamana
+        dims: 768
+        distance_metric: cosine
+        datatype: float32
+        graph_max_degree: 64
+        construction_window_size: 500
+        search_window_size: 40
+        compression: LeanVec4x8
+        reduce: 384
+        training_threshold: 1000
 
 Note:
-    See fully documented Redis-supported fields and options here: https://redis.io/commands/ft.create/
+    - SVS-VAMANA requires Redis >= 8.2 with RediSearch >= 2.8.10.
+    - SVS-VAMANA only supports `float16` and `float32` datatypes.
+    - The `reduce` parameter is only valid with LeanVec compression types (`LeanVec4x8` or `LeanVec8x8`).
+    - 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.
+    - See fully documented Redis-supported fields and options here: https://redis.io/commands/ft.create/