Apply suggestions from code review

Author: bene2k1

pages/generative-apis/troubleshooting/fixing-common-issues.mdx

@@ -124,22 +124,22 @@ Below are common issues that you may encounter when using Generative APIs, their
 ## Embeddings vectors cannot be stored in database or used with a third-party library
 
 ### Cause
-- The embedding model you are using generates vector representations of fixed dimensions number, which is too high for your database or third-party library.
+The embedding model you are using generates vector representations with a fixed dimension number, which is too high for your database or third-party library.
   - For example, the embedding model `bge-multilingual-gemma2` generates vector representations with `3584` dimensions. However, when storing vectors using PostgreSQL `pgvector` extensions, indexes (in `hnsw` or `ivvflat` formats) only support up to `2000` dimensions.
 
 ### Solution
 - Use a vector store supporting higher dimensions number, such as [Qdrant](https://www.scaleway.com/en/docs/tutorials/deploying-qdrant-vectordb-kubernetes/).
-- Do not use indexes for vectors or disable them from your third party library. This may limit performance in vector similarity search for significant volumes.  
-  - When using [Langchain PGVector method](https://python.langchain.com/docs/integrations/vectorstores/pgvector/), this method does not create index by default, and should not raise errors.
-  - When using [Mastra](https://mastra.ai/) library with `vectorStoreName: "pgvector"`,  specify indexConfig type as `flat` to avoid creating any index on vector dimensions.
+- Do not use indexes for vectors or disable them from your third-party library. This may limit performance in vector similarity search for significant volumes.  
+  - When using [Langchain PGVector method](https://python.langchain.com/docs/integrations/vectorstores/pgvector/), this method does not create an index by default and should not raise errors.
+  - When using the [Mastra](https://mastra.ai/) library with `vectorStoreName: "pgvector"`,  specify indexConfig type as `flat` to avoid creating any index on vector dimensions.
   ```typescript
   await vectorStore.createIndex({
     indexName: 'papers',
     dimension: 3584,
     indexConfig: {"type":"flat"},
   });
   ```
-- Use a model with a lower number of dimensions. Using [Managed Inference](https://console.scaleway.com/inference/deployments), you can deploy for instance `sentence-t5-xxl` model which represent vectors with `768` dimensions.
+- Use a model with a lower number of dimensions. Using [Managed Inference](https://console.scaleway.com/inference/deployments), you can deploy for instance  the`sentence-t5-xxl` model, which represents vectors with `768` dimensions.
 
 ## Best practices for optimizing model performance