I set up a RAG system on Google Cloud Platform and everything seemed to work fine during setup. I uploaded a CSV file with structured data to create the data store, and that part completed without any issues. Then I connected this data store to my tool and made sure to reference it properly in my agent configuration, just like the official documentation shows. But now when I try to ask questions about specific entries from my dataset, I keep getting error responses instead of the actual data. Has anyone run into similar problems with GCP RAG setups? What could be causing these query errors?
Been there. This happens when your CSV structure doesn’t match what the RAG system expects.
Check your error logs in Cloud Logging first. The error message will show you exactly what’s broken - usually it’s a schema mismatch or the chunking strategy doesn’t work with structured data.
I hit this same issue last year with a product catalog CSV. The RAG was chunking rows in weird ways that broke data relationships. Had to preprocess the CSV into a text-friendly format before uploading.
Double check your query format too. Are you asking questions that match how the data’s actually stored? If your CSV has “product_id” and “price” columns but you’re asking “what does item X cost”, the semantic matching won’t work.
Try querying with specific column names first to test basic retrieval. Then make your queries more natural gradually.
Your problem’s probably in the data store config, not the CSV. I had the same issue with financial data last month - GCP’s document AI wasn’t reading my CSV headers right, so the whole indexing process failed silently. The data store looked fine but queries kept erroring because nothing got parsed properly. Check your data store settings and verify the document parsing config. You’ll probably need to set the content type and structure parameters manually. Also check if your CSV has weird encoding or delimiter issues that slipped through upload. Sometimes you just need to recreate the data store with different parsing options and it fixes everything.
I’ve hit this exact problem multiple times. It’s not your setup - it’s how GCP processes your CSV data.
GCP’s RAG system treats each cell as a separate chunk. It doesn’t understand that cells belong to the same row. So when you search for something, it finds pieces but can’t connect related data from the same row.
Had a client project with order data spread across columns. The system would find customer names but couldn’t link them to order totals from the same row. Super frustrating.
Don’t fight GCP’s limitations. Automate this differently instead. Set up workflows that transform your CSV into proper chunks first, then handle preprocessing and queries while keeping data relationships intact.
You control exactly how your structured data gets processed and queried. Plus you can automate error handling and validation before it hits your RAG system.
Used this pattern for several enterprise clients - completely eliminates these query errors. Check out the automation capabilities at https://latenode.com
i had the same problem. it’s often those pesky hidden characters in the csv. try cleaning up your data before uploading it. also, testing with a smaller dataset can help u locate where things go wrong way faster. good luck!
Sounds like an indexing config issue, not the CSV. I had the same problem - turns out the default embedding model doesn’t perform well with structured data. Google’s RAG excels in semantic search for text but struggles with precise table lookups. I eventually implemented a hybrid setup. Keep your structured queries separate from RAG; for exact matches such as product IDs or numbers, query the original dataset directly. Use RAG only for fuzzy searches or when context is needed. Additionally, check your agent’s grounding settings, as it may ignore your datastore if confidence scores are too low.