Machine learning: Implement suggestions by CodeRabbit

Author: amotl

docs/feature/query/index.md

@@ -167,6 +167,7 @@ search, all based on vanilla SQL.
 - {ref}`hybrid-search`
 
 ## Text-to-SQL
+Natural language to SQL conversions using adapters and frameworks.
 - {ref}`text-to-sql`
 
 ## Time Bucketing

docs/feature/search/vector/index.md

@@ -23,9 +23,9 @@ CrateDB can be used as a [vector database] (VDBMS) for storing and retrieving
 vector embeddings.
 
 CrateDB's FLOAT_VECTOR data type implements a vector store and the k-nearest
-neighbour (kNN) search algorithm to find vectors that are similar to a query
+neighbor (kNN) search algorithm to find vectors that are similar to a query
 vector. This works by using its accompanying KNN_MATCH and VECTOR_SIMILARITY
-functions, effectively conducting HNSW semantic similarity searches on them,
+functions to perform HNSW-based semantic similarity search,
 also known as vector search.
 
 :::{rubric} About
@@ -39,9 +39,8 @@ search finds similar data using approximate nearest neighbor (ANN) algorithms.
 Compared to traditional keyword search, vector search yields more relevant
 results and executes faster.
 
-Feature vectors may be computed from raw data using machine learning
-methods such as feature extraction algorithms, word embeddings, or deep
-learning networks.
+Feature vectors are computed from raw data via ML methods such as feature
+extraction, word embeddings, or deep neural networks.
 
 
 :::{rubric} Details

docs/solution/machine-learning/index.md

@@ -31,7 +31,7 @@ often private or real-time.
 :link: vector-search
 :link-type: ref
 CrateDB's FLOAT_VECTOR data type implements a vector store and the k-nearest
-neighbour (kNN) search algorithm to find vectors that are similar to a query
+neighbor (kNN) search algorithm to find vectors that are similar to a query
 vector.
 +++
 Vector search on machine learning embeddings: CrateDB is all you need.
@@ -55,9 +55,8 @@ LangChain is a framework for developing applications powered by language models,
 written in Python, and with a strong focus on composability.
 It supports retrieval-augmented generation (RAG).
 +++
-The LangChain adapter for CrateDB provides support to use CrateDB as a vector
-store database, to load documents using LangChain’s DocumentLoader, and also
-supports LangChain’s conversational memory subsystem.
+The LangChain adapter lets you use CrateDB as a vector store database, load
+documents via DocumentLoader, and use LangChain’s conversational memory.
 :::
 
 ::::