I’m having trouble with Google Vertex AI Agent Builder and need some help. I set up an agent with multiple data stores including one for FAQ content. The website data store works fine and returns proper search results when I test queries. But the FAQ data store is giving me problems.
When I search for questions that I know exist in the FAQ documents, the agent returns empty results most of the time. This happens even when I type the exact question that’s in the FAQ. I’ve tried several fixes like disconnecting and reconnecting the data store, deleting all FAQ files and uploading them again, but nothing seems to work consistently. A few FAQ items show up in searches but most don’t.
Has anyone else run into this problem? I’m not sure how to troubleshoot this issue further. Any suggestions would be helpful.
I’ve hit this before - it’s usually a metadata config issue. Check your data store settings and make sure the FAQ docs are getting processed with the right content type. The agent builder handles FAQ content differently than regular docs when it parses them. What fixed it for me was tweaking the retrieval config in agent settings, specifically the search parameters for structured content. Also check if your FAQ files are saved in UTF-8 format. Special characters or weird formatting from Word can mess up indexing. Try making a simple test FAQ with just plain text to see if it’s the content or the config that’s broken.
also, make sure your document types are correctly categorized. i had issues too, but changing the search settings to reflect the right structure made a big difference. don’t forget about testing with variations, sometimes that helps it find the answers.
Had the same issue with my FAQ data store last month. Turns out it was document formatting and indexing timing causing problems. FAQ docs need different structure than regular website content - the parser handles them differently. Make sure your FAQ entries have clear question-answer pairs with consistent formatting across all files. Double-check your FAQ files are in supported formats and under size limits. What really helped me was waiting longer between uploads. The indexing takes several hours to finish properly, especially for FAQ content. Upload just one FAQ doc first and test it thoroughly before adding more. Also try different variations of questions instead of exact matches - the search works better that way.
Had this exact issue six months ago - drove me nuts for weeks. Finally figured out the FAQ data store is way pickier about document preprocessing than regular content stores. Check your FAQ docs for wonky spacing, bullet points, or numbered lists. They’ll completely break the parsing. What fixed it for me was making a standard template where each FAQ starts on a new line with consistent formatting. No fancy styling - just plain text with clear question/answer separation. Also had to reduce the chunk size in the data store config. Default settings work fine for websites but FAQ content needs smaller chunks to keep question-answer pairs together. After these changes, my search results jumped from maybe 10% accuracy to nearly perfect matches.
The FAQ data store search is super finicky. I’ve hit this same issue on multiple projects - it usually comes down to how Vertex AI handles semantic search vs exact matching for FAQ content.
What worked for me was switching to hybrid search instead of pure semantic search. FAQs need both semantic understanding and keyword matching. Check your agent settings for search method options.
Also look at your FAQ document structure. Each FAQ entry needs to be a separate chunk with clear boundaries. If your FAQs are lumped together in big documents, the chunking algorithm might split them weird and lose context.
Another trick - add context around your FAQ answers. Instead of “Q: How do I reset password? A: Click forgot password”, try “To reset your password, click the forgot password link. This sends you a reset email.”
Google has solid examples for building QA systems that might help you debug:
If nothing works, create a simple test FAQ with 3-4 entries and see if those index properly. That’ll tell you if it’s a config or content issue.
