feat(genapi): document embedding dimensions size issues

Author: fpagny

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

@@ -120,7 +120,26 @@ Below are common issues that you may encounter when using Generative APIs, their
 - When displaying the Cockpit of a specific Project, but waiting for average token consumption to display:
   - Counter for **Tokens Processed** or **API Requests** should display a correct value (different from 0)
   - Graph across time should be empty
-```
+
+## 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.
+  - 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.
+  ```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.
 
 ## Best practices for optimizing model performance
 
@@ -135,4 +154,4 @@ Below are common issues that you may encounter when using Generative APIs, their
 ### Debugging silent errors
 - For cases where no explicit error is returned:
   - Verify all fields in the API request are correctly named and formatted.
-  - Test the request with smaller and simpler inputs to isolate potential issues.
+  - Test the request with smaller and simpler inputs to isolate potential issues.