Start/Search: More copy-editing

- Add links where some were missing
- Less bold
- Native notes instead of blockquotes
- Fix bogus SQL queries
- More cross-linking
- Muted teaser texts at top of pages
- Less mixed case

Author: amotl

docs/start/query/search/fulltext.md

@@ -1,11 +1,15 @@
 (start-fulltext)=
 # Full-text search
 
-Unlike exact-match filters, full-text search allows **fuzzy, linguistic matching** on human language text. It tokenizes input, analyzes language, and searches for **tokens, stems, synonyms**, etc.
+:::{div} sd-text-muted
+CrateDB enables real-time full-text search at scale.
+:::
 
-CrateDB supports powerful **full-text search** capabilities directly via the `FULLTEXT` index and the `MATCH()` SQL predicate. This allows you to **combine unstructured search with structured filtering and aggregations**—all in one query, with no need for external search systems like Elasticsearch.
+Unlike exact-match filters, **full-text search** allows **fuzzy, linguistic matching** on human language text. It tokenizes input, analyzes language, and searches for **tokens, stems, synonyms**, etc.
 
-Whether you're working with log messages, customer feedback, machine-generated data, or IoT event streams, CrateDB enables **real-time full-text search at scale**.
+CrateDB supports powerful full-text search capabilities directly via the `FULLTEXT` index and the `MATCH()` SQL predicate. This allows you to **combine unstructured search with structured filtering and aggregations**—all in one query, with no need for external search systems like Elasticsearch.
+
+CrateDB supports you whether you are working with log messages, customer feedback, machine-generated data, or IoT event streams.
 
 ## Why CrateDB for Full-text Search?
 

docs/start/query/search/geo.md

@@ -1,15 +1,17 @@
 (start-geospatial)=
 # Geospatial search
 
-CrateDB enables geospatial search using **Lucene’s Prefix Tree** and **BKD Tree** indexing structures. With CrateDB, you can:
+:::{div} sd-text-muted
+Query geospatial data through SQL, combining ease of use with advanced capabilities.
+:::
+
+CrateDB enables geospatial search using **Lucene’s prefix tree** and **BKD tree** indexing structures. With CrateDB, you can:
 
 * Store and index geographic **points** and **shapes**
 * Perform spatial queries using **bounding boxes**, **circles**, **donut shapes**, and more
 * Filter, sort, or boost results by **distance**, **area**, or **spatial relationship**
 
-You interact with geospatial data through SQL, combining ease of use with advanced capabilities.
-
-See the Data Modelling (!!! add link) section for details of Data Types and how to insert data.
+See the {ref}`data-modelling` section for details of data types and how to insert data.
 
 ## Querying Geospatial Data
 
@@ -70,7 +72,7 @@ You can choose and configure the indexing method when defining your table schema
 
 While CrateDB can perform **exact computations** on complex geometries (e.g. large polygons, geometry collections), these can be computationally expensive. Choose your index strategy carefully based on your query patterns.
 
-For full details, refer to the Geo Shape Column Definition section (!!! add link) in the reference.
+For full details, refer to the Geo Shape column definition section in the reference documentation.
 
 ## Further reading
 

docs/start/query/search/hybrid.md

@@ -1,22 +1,27 @@
 (start-hybrid)=
 # Hybrid search
 
+:::{div} sd-text-muted
+Combine vector similarity (kNN) and term-based full-text (BM25)
+searches in a single SQL query.
+:::
+
 While **vector search** provides powerful semantic retrieval based on machine learning models, it's not always optimal, especially when models are not fine-tuned for a specific domain. On the other hand, **traditional full-text search** (e.g., BM25 scoring) offers high precision on exact or keyword-based queries, with strong performance out of the box. **Hybrid search** blends these approaches, combining semantic understanding with keyword relevance to deliver more accurate, robust, and context-aware search results.
 
-Hybrid search is particularly effective for **Knowledge bases, Product or document search, Multilingual content search, FAQ bots and semantic assistants**, and **AI-powered search experiences.** It allows applications to go beyond keyword matching, incorporating vector similarity while still respecting domain-specific terms.
+Hybrid search is particularly effective for **knowledge bases, product or document search, multilingual content search, FAQ bots and semantic assistants**, and **AI-powered search experiences.** It allows applications to go beyond keyword matching, incorporating vector similarity while still respecting domain-specific terms.
 
-CrateDB supports **hybrid search** by combining **vector similarity search** (kNN) and **term-based full-text search** (BM25) in a single SQL query. CrateDB lets you implement hybrid search natively in SQL using **Common Table Expressions (CTEs)** and **scoring fusion techniques**, such as:
+CrateDB supports **hybrid search** by combining **vector similarity search** (kNN) and **term-based full-text search** (BM25) in a single SQL query. CrateDB lets you implement hybrid search natively in SQL using **common table expressions (CTEs)** and **scoring fusion techniques**, such as:
 
 * **Convex combination** (weighted sum of scores)
-* **Reciprocal Rank Fusion (RRF)**
+* **Reciprocal rank fusion (RRF)**
 
 ## Supported Search Capabilities in CrateDB
 
 | Search Type           | Function      | Description                                    |
-| --------------------- | ------------- | ---------------------------------------------- |
+| --------------------- | ------------- |------------------------------------------------|
 | **Vector search**     | `KNN_MATCH()` | Finds vectors closest to a given vector        |
 | **Full-text search**  | `MATCH()`     | Uses Lucene's BM25 scoring                     |
-| **Geospatial search** | `MATCH()`     | For shapes and points (see: Geospatial Search) |
+| **Geospatial search** | `MATCH()`     | For shapes and points (see: Geospatial search) |
 
 CrateDB enables all three through **pure SQL**, allowing flexible combinations and advanced analytics.
 
@@ -74,7 +79,11 @@ You can adjust the weighting (`0.5`) depending on your desired balance between k
 | 0.03105     | 7          | 2            | Searching On Multiple Columns                 |
 | 0.03057     | 8          | 3            | Usage                                         |
 
-> RRF rewards documents that rank highly across multiple methods, regardless of exact score values.
+:::{note}
+RRF rewards documents that rank highly across multiple methods,
+regardless of exact score values.
+:::
+
 ## Further reading
 
 :::::{grid} 1 3 3 3
@@ -101,7 +110,11 @@ You can adjust the weighting (`0.5`) depending on your desired balance between k
 
 ::::{grid-item-card} {material-outlined}`read_more;1.5em` Read more
 :columns: 6
+- [Doing Hybrid Search in CrateDB]
 - {ref}`Hybrid search feature details <hybrid-search>`
 ::::
 
 :::::
+
+
+[Doing Hybrid Search in CrateDB]: https://cratedb.com/blog/hybrid-search-explained

docs/start/query/search/vector.md

@@ -1,18 +1,23 @@
 (start-vector)=
 # Vector search
 
-Vector search retrieves the most semantically similar items to a query vector using **Approximate Nearest Neighbor (ANN)** algorithms (e.g., HNSW via Lucene). 
+:::{div} sd-text-muted
+Store, manage, and search vector embeddings at scale.
+:::
+
+Vector search retrieves the most semantically similar items to a query vector using **approximate nearest neighbor (ANN)** algorithms (e.g., HNSW via Lucene). 
 
 CrateDB supports **native vector search**, enabling you to perform **similarity-based retrieval** directly in SQL, without needing a separate vector database or search engine.
 
-Whether you're powering **semantic search**, **recommendation engines**, **anomaly detection**, or **AI-enhanced applications**, CrateDB lets you store, manage, and search vector embeddings at scale **right alongside your structured, JSON, and full-text data.**
+Whether you're powering **semantic search**, **recommendation engines**, **anomaly detection**, or **AI-enhanced applications**, CrateDB lets you manage vector data **right alongside your structured, JSON, and full-text data.**
 
 ## Why CrateDB for Vector Search?
 
-| FLOAT\_VECTOR      | Store embeddings up to 2048 dimensions                       |
-| ------------------ | ------------------------------------------------------------ |
-| KNN\_MATCH         | SQL-native k-nearest neighbor function with `_score` support |
-| VECTOR\_SIMILARITY | Compute similarity scores between vectors in queries         |
+| Feature            | Benefit                                                      |
+|--------------------|--------------------------------------------------------------|
+| FLOAT_VECTOR       | Store embeddings up to 2048 dimensions                       |
+| KNN_MATCH          | SQL-native k-nearest neighbor function with `_score` support |
+| VECTOR_SIMILARITY  | Compute similarity scores between vectors in queries         |
 | Real-time indexing | Fresh vectors are immediately searchable                     |
 | Hybrid queries     | Combine vector search with filters, full-text, and JSON      |
 
@@ -35,7 +40,7 @@ Returns top 3 most similar embeddings.
 SELECT product_name, _score
 FROM products
 WHERE category = 'shoes'
-  AND KNN_MATCH(features, [0.2, 0.1, …], 5)
+  AND KNN_MATCH(features, [0.2, 0.1, 0.3], 5)
 ORDER BY _score DESC;
 ```