Knowledge Base & Retrieval
Every project gets its own knowledge base: a searchable library of that business's content that the assistant looks things up in at answer time. New here? Start with the Q-AI Bot overview.
How retrieval works, briefly
On each turn the assistant uses retrieval-augmented generation (RAG): it searches the project's knowledge base, pulls back the most relevant passages, and grounds its answer in them rather than in what the model learned in training. The content lives in a single vector store per project (its own search index, so businesses never see each other's content), created automatically at onboarding. Retrieval happens via a built-in tool, file_search: as long as the right content is in the store, the bot finds it.
For how content becomes searchable (chunking, embeddings, vector search), why we recommend splitting content into separate files, and the model settings that shape answers, see RAG & OpenAI Concepts.
Two ways content gets into the knowledge base
A project's vector store is filled from two independent pipelines. Both end up in the same store, and the bot searches across everything at answer time.
| Source | What it is | Best for | Set up in |
|---|---|---|---|
| Uploaded files | Documents you place in the project's Google Drive folders (PDFs, docs, text, etc.) | Stable reference material you control directly: price lists, policies, FAQs, brochures | Knowledge Files (Drive Sync) |
| Curated websites | A hand-picked list of web pages, fetched as markdown | Content that already lives on the public site and changes over time | Website Scraping |
Most projects use both. For a fuller decision guide, see When to use website content vs uploaded files.
Keeping the index fresh
Both pipelines keep the vector store in step with the source material:
- Files: a Drive-watcher notices when a file is added, changed, or removed and syncs the store to match. See Knowledge Files (Drive Sync).
- Websites: a scheduled run re-fetches the curated pages, detects which ones changed, and updates only those. See Website Scraping.
If the bot gives an outdated answer, check whether the source file or page was updated and whether the sync has run since. A correct knowledge base that is out of date will confidently return the old facts.
Where to go next
- Knowledge Files (Drive Sync): manage uploaded documents through the project's Google Drive folders.
- Website Scraping: keep curated web pages in sync with the vector store.
- Onboard AI Bot: provision a new project, including its vector store.