Docs/Quick Start/Add Knowledge Sources

Add Knowledge Sources

The Knowledge Base is what makes your agent useful. Without it, the agent can only reply using generic AI knowledge — with it, the agent answers specifically from your products, policies, and content. The more accurate and complete your knowledge base, the better every conversation.

How the agent uses your knowledge

AIChatVault uses Retrieval-Augmented Generation (RAG). When a visitor asks a question:

1

Q&A pairs are checked first

The system looks for a question-answer pair that closely matches. If found, that exact answer is used — highest precision.
2

Semantic search runs across all sources

Your uploaded files, crawled website pages, and Notion content are all searched together using vector similarity. The most relevant passages are retrieved.
3

A grounded prompt is built

The retrieved passages are injected into the prompt alongside the visitor's question and the conversation history.
4

The AI generates a grounded answer

The model produces a response based on your content — not on generic internet knowledge. If the answer isn't in your knowledge base, the agent says so.
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The agent will not make up information that isn't in your knowledge base. This is intentional — it prevents hallucinations and keeps answers accurate.

Source types at a glance

SourceBest used forUpdates
Files (PDF, DOCX, etc.)Product manuals, policy documents, price lists, SOPsManual re-upload
Websites & URLsHelp centres, product pages, blog posts, documentationManual or scheduled auto-sync
Manual Q&AFAQs, pricing, return policies, definitive one-answer questionsEdit any time, instant effect
NotionInternal wikis, onboarding docs, knowledge bases already in NotionManual or scheduled sync

Uploading files

Go to Knowledge Base → Files. You can upload one or many files at a time by dragging them in or clicking to browse.

Supported formats and limits

FormatSize limitNotes
PDF50 MBText-based PDFs process best. Scanned/image-only PDFs have lower accuracy — use a PDF with selectable text where possible.
DOCX / DOC50 MBMicrosoft Word. All body text, tables, and headers are indexed.
TXT10 MBPlain text. Useful for exporting content from other tools.
CSV10 MBEach row becomes a searchable chunk. Column headers are included for context.
XLSX / XLS20 MBAll sheets are indexed. Formulas are not evaluated — only their display values.

What happens when you upload

1

Upload

File is sent to the server. Status shows Uploading.
2

Processing

The file is split into chunks (typically 300–500 tokens each). Each chunk is converted into a vector embedding. Status shows Processing.
3

Ready

The green Ready badge appears. The agent can now answer from this file immediately.
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If you update a document (e.g. new price list), delete the old version and re-upload the new one. The agent will only use the latest version once the old file is removed.

Tips for better file quality

  • Use text-based PDFs, not scanned images. If you only have a scan, run it through OCR software first.
  • Structure documents with clear headings — they help the chunking algorithm group content logically.
  • Remove duplicate or outdated content from files before uploading to avoid conflicting answers.
  • For large files (>10 MB), consider splitting into topic-specific documents — targeted retrieval is more accurate than retrieving from one massive file.
  • CSV files work well for product catalogues — ensure each row has enough context (e.g. include the product name in every row, not just the header).

Adding websites and URLs

Go to Knowledge Base → Websites & URLs. The crawler visits the URL and indexes the readable text content from each page.

1

Enter the starting URL

Paste the full URL including https:// (e.g. https://help.yoursite.com).
2

Choose the crawl scope

  • Single page — Only the URL you entered is indexed. Use this for a specific policy page or FAQ.
  • All pages on domain — The crawler follows internal links across the entire domain. Use this for a complete help centre or product site.
  • Custom depth — The crawler follows links up to N levels deep from the starting URL. Useful for crawling one section of a large site.
3

Add the URL

Click Add. Crawling begins immediately. The status updates in real time showing pages found and indexed.
4

Enable auto-resync (optional)

Toggle Auto Resync and choose a frequency (daily, weekly, monthly). The crawler will automatically re-index the URL on the chosen schedule so the agent stays current with your site changes.

What the crawler can and cannot access

Can crawlCannot crawl
Public HTML pagesPages behind login / authentication
Server-rendered contentJavaScript-only single-page apps where content loads asynchronously
Linked PDF filesPages blocked by robots.txt
Sitemap.xml URLsCAPTCHA-protected pages
Sub-pages linked from the starting URLContent inside iframes from external domains
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Very large sites (1,000+ pages) can take 10–30 minutes to fully crawl. The agent can answer from pages that are already indexed while the crawl continues.

Manual Q&A pairs

Q&A pairs are the most precise knowledge source. Unlike RAG retrieval which finds the best-matching passage from a document, Q&A pairs match on the question itself — so the answer is returned with near-perfect reliability when a visitor asks something similar.

When to use Q&A pairs

  • Your most frequently asked questions — the top 20 questions you get from customers.
  • Pricing information that needs to be 100% accurate.
  • Policies (return policy, shipping times, refund procedure) where paraphrasing is risky.
  • Questions where the document-based answer might be buried or retrieved inconsistently.
  • Any question where the agent has previously given a wrong or incomplete answer.

Adding Q&A pairs

1

Go to Knowledge Base → Q&A

Click the Q&A tab inside the Knowledge Base panel.
2

Click Add Q&A

A form opens with Question and Answer fields.
3

Write the question naturally

Phrase it the way a real visitor would type it. "What is your return policy?" not "Returns".
4

Write a complete, accurate answer

Include all relevant details. If there are conditions, state them. If there's a link, include it.
5

Save

The pair is active immediately — no indexing delay. The agent can use it on the very next message.
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For the same answer, add multiple question variations as separate Q&A pairs. "How do I return an item?", "Can I get a refund?", "I'd like to return my order" — each phrasing improves recall.

Notion sync

If your business already maintains documentation in Notion, you can sync those pages directly into your agent's knowledge base instead of exporting and re-uploading them.

1

Go to Knowledge Base → Notion

Click the Notion tab.
2

Click Connect Notion

An authorisation popup opens. Log in to Notion and grant AIChatVault access to the pages you want to share.
3

Select pages to sync

Browse your Notion workspace and check the pages or databases you want to include.
4

Click Sync Now

Selected pages are fetched and indexed. A count shows how many pages were imported successfully.
5

Enable auto-sync (optional)

Toggle auto-sync to keep the knowledge base current as you update your Notion pages.
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Only pages you explicitly share with the AIChatVault integration in Notion are imported. Your other Notion pages remain private.

Managing your knowledge base

Checking what's indexed

Each source tab shows all uploaded files, URLs, and Q&A pairs with their status (Ready, Processing, Error). You can see exactly what the agent has access to at any time.

Removing outdated content

Delete sources that are no longer accurate. Stale information is worse than no information — if the agent retrieves an old price or discontinued policy, it will give wrong answers. Delete first, then upload the updated version.

How many sources should I add?

  • There is no strict limit on the number of sources, but quality matters more than quantity.
  • Start with your top 10–20 Q&A pairs and your most important document or page.
  • Add sources gradually and test after each addition.
  • Too many low-quality or duplicate sources can dilute retrieval quality — the model may pull an irrelevant chunk from an unrelated document.

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