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An AI assistant only knows what you tell it. A knowledge base is your own material (a price list, a treatment FAQ, a returns policy, your website) grouped into collections that the assistant can search while it writes a reply. Add the price list once, and every AI Reply step pointed at that collection quotes the current prices. It lives at Settings → AI Knowledge, and it is workspace-wide: build a collection once and any automation in the workspace can search it. Prefer to watch first? This walkthrough covers uploading your business data for the chatbot to use.

The two knowledge screens

DMLY has two places that show the same underlying collections, and it is worth knowing which one you are on. Use Settings → AI Knowledge. The Vector stores tab is the older per-provider panel; it is still there, and when your workspace replies on that provider’s key the two screens list the same collections. Everything below describes the Settings screen unless it says otherwise; the provider tab is covered in Vector stores on a provider panel at the end.

Before you start

  • The AI Agent is a plan feature. On a plan without it the AI Knowledge tab is still in the Settings strip, but it won’t open: Your plan does not include the AI Agent. Upgrade to use AI in automations, connect an AI provider, or use AI replies. See Plans.
  • It works before you connect anything. Knowledge runs on whichever AI answers for your workspace: your own connected OpenAI or Google Gemini key if your plan allows one and you have connected it, otherwise DMLY’s built-in AI. A line at the top of the page names the account it is backed by, and confirms the knowledge is private to your workspace.
  • Claude and DeepSeek cannot search documents. If an AI Reply step replies on one of those, it gets no knowledge whatever you have built. Use OpenAI or Gemini for a step that needs to read your material.
  • Teammates need the right permission. Viewing the page needs access to the Setup & Integrations module. Creating or deleting a collection, adding, importing or removing a source and running the test box all need Manage bot setup. See Roles and permissions.

Build a collection

1

Open AI Knowledge

Go to Settings → AI Knowledge.
2

Create a collection

In the New collection panel, type a Collection name that says what’s in it (Prices, Policies, Support) and select Add collection. You pick collections by name later on the AI Reply step, so one collection per subject beats one collection for everything.
3

Add your material

Select Add source on the collection and pick a Source type. Four kinds are offered, and they are covered in the next section.
4

Point an AI Reply step at it

Open the AI Reply step in your automation, go to the Built-in Tools tab, and set the AI Knowledge selector to something other than No knowledge. This is the step people miss: a new step ships with No knowledge selected and never searches anything. See Point an automation at it below.
5

Test it

Use the Test knowledge panel at the bottom of the page and ask a question your material answers. Nothing is sent to a contact and nothing lands in the Inbox.
Whatever you add can be quoted back to a contact. Don’t put staff notes, internal margins, supplier costs or anything with someone else’s personal details in a collection.

The four source types

Add source offers exactly four kinds, listed under Source type: The list shows each row’s type as File, URL, FAQ or Text, with the page address underneath for anything ingested from the web.
Prefer Web page URL and Import pages over uploading a PDF of the same page. Only a source with an address can be linked back to a contact: when the assistant answers from one, it appends the page address to its reply as a plain link (at most two links per reply). A file, an FAQ or pasted text has no address, so the assistant can name it but never link it.
There is no in-place editing in the sources list. To change an FAQ or a pasted note, select Remove on the row and add it again.

Import a whole site at once

Adding a documentation site one page at a time is a job people abandon halfway, which leaves the assistant confidently half-informed. Import pages on a collection does it in bulk.
1

Choose how you'll supply the pages

Source offers Sitemap (imports the pages it lists) or Paste a list of page addresses.
2

Give the sitemap, or the list

For a sitemap, put the address in Sitemap address: Most documentation sites publish one at /sitemap.xml. Sitemap-index files are followed one level. For a list, paste the addresses into Page addresses: One per line. Pages already in this collection are skipped.
3

Select Import and wait

Each page is fetched and indexed during the request, so a large import is not instant. Up to 100 pages per import.
4

Run it again for a bigger site

When more pages are outstanding, the confirmation says how many are left and that running Import again continues from where it stopped. Re-run it with the same address until nothing is left.
The confirmation reports every address’s fate, not just the wins: how many pages were imported, how many were already in the collection, and how many failed (with the first failing address named). A page already in the collection is skipped rather than added twice, so re-running an import is safe.
A sitemap is read up to the first 2,000 addresses it lists, which is the ceiling on what an import can ever consider from one sitemap. Only public web addresses are fetched.

Find a source in a big collection

Each collection’s header shows how many sources it holds and how many automations use it, and the list itself is paged:
  • 20 sources per page, newest first, with Previous and Next and a page count.
  • A Search sources by name or address… box appears once a collection holds more than 20 sources. It searches the whole collection, not just the rows on screen, and matches both the source name and its web address.
  • Remove on a row deletes that source from the collection and from the AI account behind it.
Delete collection removes the collection and every source in it, and cannot be undone.

Indexing status

Adding a source hands it to the AI account, which indexes it in the background, so a new row starts at processing and settles by itself:
  • processing: still indexing. Nothing to press.
  • ready: searchable.
  • error: indexing failed, and the reason is shown on the row.
The page updates the statuses on its own while it has something indexing; otherwise reload it to see where a source got to. Indexing is also reconciled in the background every few minutes, so a source settles whether or not anyone is watching the page.
There is no training step and no “publish” step. Once a source reads ready it is searchable on the next reply.

Test what it will find

The Test knowledge panel appears once you have at least one collection: Ask a question to check what the assistant would retrieve.
  1. Leave the scope on All collections, or pick one collection to test it alone.
  2. Type the question and select Test.
  3. You get the answer, plus Retrieved snippets naming the source each snippet came from.
The test is deliberately strict: it answers only from the documents it retrieved and says it couldn’t find the answer otherwise. So “couldn’t find it” is a real result, and it means the material isn’t there (or isn’t phrased the way the question is). With no sources yet you get Add some sources first, then test. Nothing here reaches a contact and nothing is written to the Inbox.

Point an automation at it

Building a collection doesn’t make an assistant use it. On the AI Reply step’s Built-in Tools tab, the AI Knowledge block (with a Manage link back to this page) reads Let the assistant answer from your uploaded knowledge (files, URLs, FAQs). and offers four choices: All workspace knowledge is the right answer for most support assistants: a collection added next month is searched without editing the flow. Pick Selected collections when a sales bot and a support bot should read different material. Each collection’s line on the Settings page counts the automations that reference it, so you can spot a collection nothing reads from. Only published automations count, so a collection wired only into a draft still reads zero.
Collections belong to the AI account that created them. If you later connect your own OpenAI or Gemini key, the page shows that new account’s collections and the ones built on DMLY’s built-in AI stop being listed or searched; you would build them again on the new account. The same applies in reverse if a plan change stops your own key being used. An AI Reply step that finds no usable knowledge is told not to guess business facts and to hand over to a human instead, so the symptom is an assistant that suddenly refuses to answer what it used to know.

What makes the answers good

The assistant searches your collections and writes an answer from what it finds. So the quality of the answer is mostly the quality of what you put in. This helps:
  • Write for the question, not for the page. A document that reads like an FAQ (a heading that is the question, a short answer under it) is far easier to search than a brochure. If contacts ask “do you take walk-ins?”, have a line that says exactly that. The FAQ (question & answer) source type exists for precisely this.
  • Keep one subject per collection. Prices and Policies as two collections lets you point a sales bot at one and a support bot at the other.
  • Say the specifics. Prices, opening hours, addresses, service names, how long something takes. These are what people actually ask for, and they are exactly what the assistant can’t invent.
  • Keep one current version. Adding a new price list adds to the collection; it doesn’t overwrite the old one. Two versions in one collection means the assistant can find and quote either.
This hurts:
  • A scanned or image-only PDF. If you can’t select the text with your cursor, neither can the search. Nothing errors; the source just contributes nothing.
  • Marketing copy. Pages of tone with no facts give the assistant nothing to answer with.
  • Contradictions. Two documents with different prices produce confidently wrong answers, and they’re the hardest kind to notice.
  • Importing everything. A whole site imported wholesale, blog posts and press releases included, buries the price list among things nobody asked about. Import the pages that answer questions.
Treat it like onboarding a new receptionist. If a document wouldn’t get a new hire to the right answer on their first day, it won’t get the assistant there either.

When it doesn’t answer from your material

Four causes, in the order worth checking. The step’s AI Knowledge selector is still on No knowledge, which is where a new step starts. Or the step replies on Claude or DeepSeek, which can’t search documents at all; switch it to OpenAI or Gemini. Or the collection is empty, or everything in it is still processing. Or the collections were built on a different AI account from the one now answering (see the warning above).
The old file is still in the collection. Adding a new one adds to the collection, it doesn’t replace what was there, so both versions are searchable. Remove the old row, or create a fresh collection with only the new file and tick that one on the step instead.
Open the PDF and try to select the text. If you can’t, it’s a scan (an image of words, with no words in it). Re-export it from the original document, or retype the key facts as a Pasted text source.
One import adds at most 100 pages. The confirmation says how many are still outstanding: run Import again with the same address and it carries on from where it stopped. If a sitemap returned nothing at all, check the address, or paste the page addresses instead.
That’s expected, not a fault. Knowledge search runs inside the AI provider rather than in DMLY, so it never shows in the trace. An assistant answering perfectly from your collections still shows an empty trace.
AI Knowledge needs a knowledge-capable AI (OpenAI or Gemini). Connect your own OpenAI or Google Gemini key under Integrations, and the page comes back with that account behind it. See Connect OpenAI.

Vector stores on a provider panel

The older per-provider panel is still available, and it is the only knowledge screen scoped to a single connected key. Go to Integrations, open OpenAI or Google Gemini, and select the Vector stores tab. It appears only for providers with native file search, and only once the provider is connected: until then it reads Connect the provider first to manage vector stores.
  • Create a vector store takes a Store name and creates a collection on that provider’s account: Group documents the AI can search at reply time. Add a store, then upload PDFs / text files or ingest a web URL.
  • Each store lists its files with an indexing dot, and offers + Upload file and an address box with Add URL. The note under the list reads PDF, TXT or MD up to 20 MB. URLs are fetched server-side and indexed.
  • Refresh status re-checks indexing at the provider. Indexing… and Indexing failed show per file, with the reason under a failure.
  • There is no way to remove a single file here: Delete applies to the whole store. Remove an individual source from Settings → AI Knowledge instead.
Stores created here are the same collections you manage in Settings → AI Knowledge, as long as that provider is the account your workspace replies on. What this tab cannot do is FAQs, pasted text, bulk import, search, the test box or the usage counts, which is why the Settings screen is the one to build in.

Reply with AI

Write the prompt the assistant answers with.

AI tools and functions

Choose what a step searches, and what else it may do.

Connect OpenAI

Add your own key for un-metered replies and your own knowledge account.

Plans

Check whether your plan includes the AI Agent.