AI chatbot for PDF Embedder: explain embedded documents in chat
SleekAI maps PDF Embedder shortcode targets, the attached PDF text, and the surrounding page into chatbot variables. Bring your own OpenAI, Anthropic, Google, or OpenRouter key and stay in control of usage.
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Talk to your embedded PDFs
PDF Embedder by WP Hive embeds a PDF using the [pdf-embedder] shortcode that points at an attachment URL on the WP Media Library. SleekAI reads the attachment ID and grabs the attachment's stored text, either from the WordPress attachment meta or from an OpenAI Files vector store you sync the PDF into. The chatbot can then answer questions about the embedded document content, not just describe that there is a PDF on the page.
The page context variable feeds the post or page slug into the prompt, so a chatbot scoped to documentation pages sees both the page text and the embedded PDF text together. Display conditions restrict the bot to pages where PDF embeds appear, and multibot can scope a separate bot per document type, like manuals on one and policy documents on another.
Every conversation is logged inside WordPress with model, tokens, and origin page, so editorial can see which documents generate the most questions and which sections need rewording. The JS API can launch the bot from a button placed inside the PDF Embedder shell, no template rebuild required.
Workflow
How SleekAI plugs into PDF Embedder
Sync the PDFs
[pdf-embedder] shortcodes. Upload them into an OpenAI Files vector store of up to one gigabyte per file, so the model can retrieve relevant chunks at chat time.
Map page context
Scope per document type
Iterate from logs
Try it now
A typical embedded-PDF chat
Comparison
Generic chatbot vs SleekAI for PDF Embedder
Generic chatbot
- Has no view of the PDF embedded on the current page
- Cannot quote page numbers or sections inside a manual
- Forces visitors to download, search, and read PDFs themselves
- Misses which page the visitor is currently on for context
- Cannot point at specific compliance or warranty sections
SleekAI chatbot
-
Reads
[pdf-embedder]shortcode attachment IDs - Quotes content from the embedded PDF's stored text
- Cites real page numbers and section headings
- Vector store handles long manuals up to 1GB per file
- Scoped per document type via display conditions
Features
What SleekAI gives you for PDF Embedder
Real PDF citations
The chatbot quotes from the embedded PDF's text, not from a generic web search. Page numbers and section headings come from the document itself, so a question about a warranty clause lands on the exact paragraph the visitor needs.
Page-context aware
The bot reads both the surrounding page text and the embedded PDF, so questions like 'what does the manual on this page say about X' work without the visitor specifying which document or section to look at.
Multi-document scoping
Multibot lets a manuals bot run on product documentation pages and a policy bot run on legal pages, each scoped to its own PDF library with its own tone, with no overlap or accidental document leakage.
Use cases
Where doc-heavy sites use SleekAI
Product manuals
Manufacturers embed manuals via PDF Embedder. Visitors ask warranty or assembly questions and get a quoted answer with the exact page reference.
Compliance documents
Public sector and regulated industries publish compliance PDFs. The bot surfaces specific clauses without forcing visitors to read a 60-page document end to end.
Education resources
Schools and trainers embed lesson PDFs. Students can ask a question about an exercise and the bot quotes the relevant page directly from the embedded file.
The bigger picture
Why embedded PDFs need a chat layer
PDF Embedder is everywhere a WordPress site has a manual, a brochure, a policy, or a compliance document. The plugin renders the file inline so visitors do not need to download it just to read a page. But viewing is not the same as understanding.
A visitor with a single question often has to scroll through 40 pages to find the section they care about, and a meaningful share of them either guess or abandon. A SleekAI chatbot with the embedded PDF in its vector store turns a passive viewer into an interactive one. The same question that used to require a download and a Ctrl-F search becomes a sentence in the chat bubble, and the answer comes back with the section and page number quoted from the original document.
Logging every conversation builds a record of which documents generate the most questions, which usually points at writing problems or missing summaries in the PDFs themselves. Scoping per document type lets you keep policy and product documentation cleanly separated, so the manuals bot never accidentally cites a refund clause and the policy bot never quotes a torque spec. The PDFs were already on the site.
SleekAI makes them readable in seconds.
Questions
Common questions about SleekAI for PDF Embedder
PDF Embedder points the [pdf-embedder] shortcode at a WordPress attachment. SleekAI reads the attachment ID and either extracts the stored text or syncs the file into an OpenAI Files vector store, so the model has the actual document text available at chat time.
OCR the PDF before uploading, or use a service that produces a text layer. Once the text exists, SleekAI's vector store can ingest it and the chatbot can answer questions across the now-searchable content, with page references where the OCR preserved them.
 Yes. When the vector store ingestion keeps page boundaries, the bot can cite the page number it pulled the answer from, which is much more useful than a generic 'check the manual' reply for visitors trying to find a specific section.
 Multibot supports several chatbots on one site with their own data sources. Run a product manuals bot on product pages and a separate policies bot on legal pages, each pointed at its own document set with its own tone of voice.
 OpenAI Files vector stores accept up to one gigabyte per file. SleekAI handles the upload pipeline and the retrieval, so the model pulls only the chunks relevant to the question and the context window stays manageable even on hundred-page manuals.
 Yes. Each chatbot can be pointed at a folder or set of attachments, so dozens of manuals across a product catalog can all live in one bot, with the page-context variable still helping the bot prioritise the document on the current page.
 Display conditions and login-based scoping restrict which chatbot runs on which pages. A logged-in customer bot can read protected PDFs while a public bot stays restricted to marketing documents, so private documents never end up in front of guests.
 Re-upload the new file to the same attachment and resync the vector store. The next chat reflects the updated text, so a corrected warranty period or a revised compliance clause shows up in answers without manual prompt edits.
 Pricing
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