I’m using the Agent Builder feature in Google Vertex AI, and I’m having trouble with the FAQ data store. I have three data stores set up. My website data store works well and returns accurate results when I query it. However, I’m facing significant issues with the FAQ data store.
When I attempt to query it with specific questions that are definitely included in my FAQ documents, it returns no results at all, which is quite frustrating. Interestingly, there are a few FAQ documents that are searchable, but the majority aren’t showing up in the results.
I’ve tried troubleshooting actions, such as detaching and reattaching the data store and even deleting all FAQ documents before reimporting them, but the issue persists.
Has anyone encountered similar problems with the FAQ data store on the Agent Builder? Any advice or insights on how I can resolve this would be greatly appreciated.
processing delays might be the culprit here. vertex ai sometimes takes ages to fully index faq content even after upload shows complete. i waited like 48hrs before my faqs started showing up properly in search results, super annoying but worth trying if you havent given it enough time yet.
Sounds like a data preparation issue to me. I’ve seen this happen when the FAQ content isn’t structured properly for the AI to understand the context.
In my experience, the problem usually comes down to how you’re formatting the question-answer pairs. Vertex AI needs clear boundaries between questions and answers. If your FAQ documents are just plain text without proper delimiters or if questions and answers are mixed together, the indexing gets confused.
Try this: extract your FAQ content and restructure it so each question-answer pair is clearly separated. Use consistent formatting like “Q:” and “A:” prefixes or put each pair in its own section. Make sure there’s no extra whitespace or hidden characters that could mess with parsing.
Also check if your FAQ documents have any tables or complex layouts. These often break the extraction process even though they look fine to us.
This video covers exactly how to prep your documents for AI systems like this:
Once you get the formatting right, delete the data store completely and recreate it from scratch. Don’t just reimport - I’ve found that corrupted indexes stick around even after document replacement.
had this exact same problem last month! turns out my faq documents had weird formatting that was messing with the indexing. try checking if your docs have special characters or inconsistent structure - that fixed it for me after weeks of frustration.
Check your indexing configuration and document chunking settings. I experienced similar behavior where Vertex AI was splitting FAQ documents into chunks that separated questions from their corresponding answers, making retrieval nearly impossible. The default chunking parameters don’t work well for FAQ format since they treat each chunk independently during search. What worked for me was adjusting the chunk size and overlap settings in the data store configuration to ensure question-answer pairs stayed together. You might also want to verify the search mode settings - sometimes switching between semantic and keyword search modes reveals different retrieval patterns that can help diagnose where the disconnect is happening. Another thing to check is whether your FAQ documents have consistent language patterns. I noticed that documents with varied question phrasing or informal language performed worse than those with standardized question formats.
I ran into something similar and discovered the issue was with my document metadata configuration. The FAQ documents need proper schema mapping to be indexed correctly by Vertex AI. Check your data store settings and make sure the question-answer pairs are properly tagged in the metadata fields. Also verify that your FAQ documents are in the supported format and size limits. In my case, some documents were too large and getting partially indexed, which explained why only certain FAQs were searchable while others completely disappeared from results. Re-uploading with proper metadata structure solved most of my retrieval issues.
