DOC-5491: add new CASE function and also augment the SVS_VAMANA chang…

content/commands/ft.aggregate.md

@@ -489,6 +489,28 @@ Next, count GitHub events by user (actor), to produce the most active users.
 
 </details>
 
+<details open>
+<summary><b>Use the case function for conditional logic</b></summary>
+{{< highlight bash >}}
+//Simple mapping
+FT.AGGREGATE products "*"
+APPLY case(@price > 100, "premium", "standard") AS category
+
+//Nested conditions where an error should be returned
+FT.AGGREGATE orders "*"
+APPLY case(@status == "pending", 
+           case(@priority == "high", 1, 2), 
+           case(@status == "completed", 3, 4)) AS status_code
+
+//Mapped approach
+FT.AGGREGATE orders "*"
+APPLY case(@status == "pending", 1, 0) AS is_pending
+APPLY case(@is_pending == 1 && @priority == "high", 1,2) AS status_high
+APPLY case(@is_pending == 0 && @priority == "high", 3,4) AS status_completed
+{{< / highlight >}}
+
+</details>
+
 ## See also
 
 [`FT.CONFIG SET`]({{< relref "commands/ft.config-set/" >}}) | [`FT.SEARCH`]({{< relref "commands/ft.search/" >}}) 

content/develop/ai/search-and-query/advanced-concepts/aggregations.md

@@ -379,6 +379,7 @@ Note that these operators apply only to numeric values and numeric sub-expressio
 | Function | Description                                                  | Example            |
 | -------- | ------------------------------------------------------------ | ------------------ |
 | exists(s)| Checks whether a field exists in a document.                 | `exists(@field)`   |
+| case(condition, if_true, if_false) | If condition is non-zero, return if_true, otherwise return if_false. | `case(exists(@foo), @foo, "no foo")` |
 
 ### List of numeric APPLY functions
 

content/develop/ai/search-and-query/vectors.md

@@ -152,18 +152,39 @@ Choose the `SVS-VAMANA` index type when all of the following requirements apply:
 
 | Attribute                  | Description                              | Default value |
 |:---------------------------|:-----------------------------------------|:-------------:|
-| `COMPRESSION`              | Compression algorithm (`LVQ8`, `LVQ4`, `LVQ4x4`, `LVQ4x8`, `LeanVec4x8`, or `LeanVec8x8`). Vectors will be compressed during indexing. See these Intel pages for best practices on using these algorithms: [`COMPRESSION` settings](https://intel.github.io/ScalableVectorSearch/howtos.html#compression-setting) and [`LeanVec`](https://intel.github.io/ScalableVectorSearch/python/experimental/leanvec.html). | None |
+| `COMPRESSION`              | Compression algorithm; one of `LVQ8`, `LVQ4`, `LVQ4x4`, `LVQ4x8`, `LeanVec4x8`, or `LeanVec8x8`. Vectors will be compressed during indexing. See below for descriptions of each algorithm. Also, see these Intel pages for best practices on using these algorithms: [`COMPRESSION` settings](https://intel.github.io/ScalableVectorSearch/howtos.html#compression-setting) and [`LeanVec`](https://intel.github.io/ScalableVectorSearch/python/experimental/leanvec.html). | `LVQ4x4` |
 | `CONSTRUCTION_WINDOW_SIZE` | The search window size to use during graph construction. A higher search window size will yield a higher quality graph since more overall vertexes are considered, but will increase construction time. | 200 |
-| `GRAPH_MAX_DEGREE`         | The maximum node degree in the graph. A higher max degree may yield a higher quality graph in terms of recall for performance, but the memory footprint of the graph is directly proportional to the maximum degree. | 32 |
-| `SEARCH_WINDOW_SIZE`       | The size of the search window. Increasing the search window size and capacity generally yields more accurate but slower search results. | 10 |
-| `EPSILON`                  | The range search approximation factor. | 0.01 |
-| `TRAINING_THRESHOLD`       | The number of vectors after which training is triggered. Applicable only when used with `COMPRESSION`. If a value is provided, it be less than `100 * DEFAULT_BLOCK_SIZE`, where `DEFAULT_BLOCK_SIZE` is 1024. | `10 * DEFAULT_BLOCK_SIZE` |
-| `LEANVEC_DIM`              | The dimension used when using `LeanVec4x8` or `LeanVec8x8` compression for dimensionality reduction. If a value is provided, it should be less than `DIM`. | `DIM / 2` |
+| `GRAPH_MAX_DEGREE`         | Sets the maximum number of edges per node; equivalent to `HNSW’s M*2`. A higher max degree may yield a higher quality graph in terms of recall for performance, but the memory footprint of the graph is directly proportional to the maximum degree. | 32 |
+| `SEARCH_WINDOW_SIZE`       | The size of the search window; the same as `HSNW's EF_RUNTIME`. Increasing the search window size and capacity generally yields more accurate but slower search results. | 10 |
+| `EPSILON`                  | The range search approximation factor; the same as `HSNW's EPSILON`. | 0.01 |
+| `TRAINING_THRESHOLD`       | Number of vectors needed to learn compression parameters. Applicable only when used with `COMPRESSION`. Increase if recall is low. Note: setting this too high may slow down search.If a value is provided, it must be less than `100 * DEFAULT_BLOCK_SIZE`, where `DEFAULT_BLOCK_SIZE` is 1024. | `10 * DEFAULT_BLOCK_SIZE` |
+| `LEANVEC_DIM`              | The dimension used when using `LeanVec4x8` or `LeanVec8x8` compression for dimensionality reduction. If a value is provided, it should be less than `DIM`. Lowering it can speed up search and reduce memory use. | `DIM / 2` |
 
 {{< warning >}}
 On non-Intel platforms, `SVS-VAMANA` with `COMPRESSION` will fall back to Intel’s basic scalar quantization implementation.
 {{< /warning >}}
 
+**SVS_VAMANA vector compression algorithms**
+
+LVQ is a scalar quantization method that applies scaling constants for each vector. LeanVec builds on this by combining query-aware dimensionality reduction with LVQ-based scalar quantization for efficient vector compression. 
+
+`LVQ4x4` (the default): Fast search with 4x vector compression relative to float32-encoded vectors (8 bits per dimension) and high accuracy.
+
+`LeanVec4x8`: Recommended for high-dimensional datasets. It offers the fastest search and ingestion. It's not the default because in rare cases it may reduce recall if the data does not compress well.
+
+`LeanVec` dimensional: For faster search and lower memory use, reduce the dimension further (default is input `dim / 2`; try `dim / 4` or even higher reduction).
+
+`LVQ8`: Faster ingestion than the default, but with slower search.
+
+| Compression algorithm | Best for |
+|-----------------------|----------|
+| `LVQ4x4` (default)    | Fast search in most cases with low memory use. |
+| `LeanVec4x8`          | Fastest search and ingestion. |
+| `LVQ4`                | Maximum memory savings. |
+| `LVQ8`                | Faster ingestion than the default. |
+| `LeanVec8x8`          | Improved recall in cases where `LeanVec4x8` is not sufficient. |
+| `LVQ4x8`              | Improved recall in cases where the default is not sufficient. |
+
 **Example**
 
 ```