Improve Sparse Vectors Readability (#448)

* * docs(indexing.md): update section title from "Sparse vector index" to "Sparse Vector Index"
* docs(indexing.md): add key features of sparse vector index
* docs(indexing.md): add search mechanism details for sparse vector index
* docs(indexing.md): add optimizations details for sparse

* * docs(search.md): update comparison table between sparse and dense vector search

* * docs(indexing.md): update availability information for Sparse Vector Index feature

qdrant-landing/content/documentation/concepts/indexing.md

@@ -228,32 +228,31 @@ The HNSW parameters can also be configured on a collection and named vector
 level by setting [`hnsw_config`](../indexing/#vector-index) to fine-tune search
 performance.
 
-## Sparse vector index
+## Sparse Vector Index
 
 *Available as of v1.7.0*
 
-Qdrant supports sparse vectors, which are vectors with a large number of zeroes.
+### Key Features of Sparse Vector Index
+- **Support for Sparse Vectors:** Qdrant supports sparse vectors, characterized by a high proportion of zeroes.
+- **Efficient Indexing:** Utilizes an inverted index structure to store vectors for each non-zero dimension, optimizing memory and search speed.
 
-We can take advantage of this property to index the vectors in a specialized way, which allows to save space and speed up search.
+### Search Mechanism
+- **Index Usage:** The index identifies vectors with non-zero values in query dimensions during a search.
+- **Scoring Method:** Vectors are scored using the dot product.
 
-The underlying structure is an inverted index, which stores the list of vectors for each non-zero dimension.
+### Optimizations
+- **Reducing Vectors to Score:** Implementations are in place to minimize the number of vectors scored, especially for dimensions with numerous vectors.
 
-Upon search, the index is used to find the list of vectors that have non-zero values in the query dimensions.
-Then, the vectors are scored using the dot product.
+### Filtering and Configuration
+- **Filtering Support:** Similar to dense vectors, supports filtering by payload fields.
+- **`full_scan_threshold` Configuration:** Allows control over when to switch search from the payload index to minimize scoring vectors.
+- **Threshold for Sparse Vectors:** Specifies the threshold in terms of the number of matching vectors found by the query planner.
 
-There are optimizations in place to reduce the number of vectors to score for dimensions with a large number of vectors.
+### Index Storage and Management
+- **Memory-Based Index:** The index resides in memory for appendable segments, ensuring fast search and update operations.
+- **Handling Immutable Segments:** For immutable segments, the sparse index can either stay in memory or be mapped to disk with the `on_disk` flag.
 
-Similar to dense vectors, the sparse vector index supports filtering by payload fields, which allows to use it in combination with indexed payload fields.
-
-It is possible configure `full_scan_threshold` to control when to drive the search from the payload index to decrease the number of vectors to score.
-
-In the case of sparse vectors, the threshold is specified in the number of matching vectors found by the query planner.
-
-The index always resides in memory for appendable segments providing fast search and update operations by default.
-
-When the segment becomes immutable, the sparse index can either be kept in memory or mmaped to disk by setting the `on_disk` flag on the index.
-
-For instance, to enable on-disk storage for immutable segments and full scan for queries inspecting less than 5000 vectors:
+**Example Configuration:** To enable on-disk storage for immutable segments and full scan for queries inspecting less than 5000 vectors:
 
 ```http
 PUT /collections/{collection_name}

qdrant-landing/content/documentation/concepts/search.md

@@ -258,9 +258,12 @@ If the collection was created with sparse vectors, the name of the sparse vector
 You can still use payload filtering and other features of the search API with sparse vectors.
 
 There are however important differences between dense and sparse vector search:
-- only `Dot` metric is supported for sparse vectors (no need to specify it in the request)
-- the sparse search is not approximate, it is always returning the exact match
-- the spearse search returns only the vectors which have non-zero values in the same indices as the query vector, for this reason, you can can receive less than `limit` results.
+
+| Index| Sparse Query | Dense Query |
+| --- | --- | --- | 
+| Scoring Metric | Default is `Dot product`, no need to specify it | `Distance` has supported metrics e.g. Dot, Cosine |
+| Search Type | Always exact in Qdrant | HNSW is an approximate NN |
+| Return Behaviour | Returns only vectors with non-zero values in the same indices as the query vector | Returns `limit` vectors |
 
 In general, the speed of the search is proportional to the number of non-zero values in the query vector.