Skip to main content

AI Assistant (Bring Your Own Endpoint)

Chat with the open PDF — using your AI endpoint, your key, and no vendor backend.

The most important thing to understand about this feature is what it does not do:

  • The library never calls any AI service on its own. There is no bundled API key, no default endpoint, no telemetry. If you don't configure an endpoint, no AI-related network request is ever made.
  • You point it at any OpenAI-compatible /chat/completions endpoint: OpenAI, Azure OpenAI, Ollama, vLLM, LM Studio, or any gateway/proxy that speaks the same protocol.
  • Everything runs client-side. The component extracts the document's text from the PDF.js text layer, builds the prompt in the browser, and sends a single fetch to the endpoint you configured. Your PDF text goes to your endpoint and nowhere else.

This means you can run fully private setups (a local Ollama model — the PDF text never leaves the machine) or production setups behind your own server-side proxy.

Where the request runs: choose local or a proxy​

PdfAiAssistant sends its fetch from wherever you construct it — in an Angular app, that is the user's browser. Pointing endpoint directly at a hosted cloud LLM (OpenAI, Azure OpenAI, …) from the browser has two problems:

  • Your API key is exposed. Anything shipped to the browser is readable by whoever opens devtools. A long-lived cloud key in client code is a leak, not a secret.
  • CORS will usually block the call anyway. Hosted LLM APIs don't return the Access-Control-Allow-Origin headers a browser requires for cross-origin requests, so the fetch fails before it ever reaches the model. (Azure OpenAI and OpenAI both reject direct browser calls.)

So pick one of these — don't call a cloud provider straight from the browser:

Setupendpoint points atKey locationBest for
Local modela local runtime — Ollama, LM Studionone neededprivate/offline use; the PDF text never leaves the machine. Start the runtime with browser origins allowed (e.g. Ollama's OLLAMA_ORIGINS).
Your backend proxy (recommended for cloud models)a small route on your server that forwards to the providerserver-side only, never in the browserproduction with a hosted model. The proxy injects the secret key, adds CORS, and can authenticate/rate-limit per user.

A proxy is tiny — accept the request, attach your secret key, forward to the provider, return the response:

// POST /api/pdf-chat  — your server, key stays here
app.post('/api/pdf-chat', async (req, res) => {
  const r = await fetch('https://api.openai.com/v1/chat/completions', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json', Authorization: `Bearer ${process.env.OPENAI_KEY}` },
    body: JSON.stringify(req.body),
  });
  res.status(r.status).send(await r.text());
});
// browser — no key in sight
new PdfAiAssistant({ endpoint: '/api/pdf-chat' });

Built-in Chat Panel​

Bind [aiAssistantConfig] to render a floating chat button in the bottom-right corner of the viewer. Clicking it opens a chat panel where users can ask questions about the open document.

import { PdfAiPanelConfig } from 'ng2-pdfjs-viewer';

export class MyComponent {
  aiConfig: PdfAiPanelConfig = {
    endpoint: 'https://api.openai.com/v1/chat/completions',
    apiKey: 'sk-...',          // sent as 'Authorization: Bearer <apiKey>'
    model: 'gpt-4o-mini',
    title: 'Ask this report',
  };
}
<ng2-pdfjs-viewer
  pdfSrc="assets/report.pdf"
  [aiAssistantConfig]="aiConfig">
</ng2-pdfjs-viewer>

PdfAiPanelConfig​

OptionTypeDefaultDescription
endpointstring— (required)Full URL of an OpenAI-compatible /chat/completions endpoint
apiKeystring—Sent as Authorization: Bearer <apiKey> when provided
modelstring—Model name passed in the request body
headersRecord<string, string>—Extra request headers (e.g. Azure's api-key)
maxContextCharsnumber100000Cap on document characters included in the prompt
temperaturenumber0.2Sampling temperature
titlestring'Ask this document'Panel header text
placeholderstring'Ask the document…'Input placeholder

Clickable Page Citations​

The system prompt instructs the model to cite page numbers like [p.3] when referencing content. The panel parses those citations out of every answer and renders them as clickable chips — clicking p.3 jumps the viewer to page 3. This turns answers into navigation: "Where is the termination clause?" → click the citation, land on the page.

Asking Programmatically​

aiAsk(question) goes through the exact same path as the panel's input — the question and answer appear in the panel's conversation:

@ViewChild('viewer') viewer!: PdfJsViewerComponent;

summarize(): Promise<void> {
  return this.viewer.aiAsk('Summarize this document in three bullet points.');
}

aiAsk requires [aiAssistantConfig] to be set, and one question runs at a time (calls made while a request is in flight are ignored). Document text is extracted once per document and reused across questions.

Chat Lifecycle​

The conversation is tied to the document. When the document changes (new pdfSrc, a viewer reload), the chat clears automatically and any in-flight request is aborted — a slow answer about the old document can never land in the new document's chat.

Theming the Panel​

The panel and its floating button are styled with CSS custom properties:

ng2-pdfjs-viewer {
  --ng2-ai-accent: #0d9488;        /* button, header, citations, send button */
  --ng2-ai-bg: #ffffff;            /* panel background */
  --ng2-ai-text: #222222;          /* panel text color */
  --ng2-ai-question-bg: #e4f0fe;   /* user message bubbles */
  --ng2-ai-answer-bg: #f2f1f7;     /* assistant message bubbles */
  --ng2-ai-fab-color: #ffffff;     /* floating button icon color */
}

Headless: PdfAiAssistant + getDocumentText()​

If you want your own chat UI (or no chat at all — just a "Summarize" button), skip [aiAssistantConfig] entirely and use the two lower-level building blocks:

  • viewer.getDocumentText(from?, to?) — returns Promise<DocumentPageText[]> ({ page: number; text: string } per page), the plain text of the document or a 1-based page range, extracted from the PDF.js text layer.
  • PdfAiAssistant — a minimal OpenAI-compatible chat client, exported from the package:
    • constructor(config: PdfAiAssistantConfig) — same options as the panel config, minus title/placeholder; throws if endpoint is missing
    • ask(question, documentText, history?, signal?) — answers a question about the document; history carries prior PdfAiMessage turns for multi-turn chat, signal is an AbortSignal for cancellation
    • summarize(documentText) — one-shot document summary
    • complete(messages, signal?) — raw chat-completions call for fully custom prompting

Example: Local Ollama + a Custom Summarize Button​

A fully private setup — the model runs on localhost, so the document text never leaves the machine:

import { Component, ViewChild } from '@angular/core';
import { PdfJsViewerComponent, PdfAiAssistant } from 'ng2-pdfjs-viewer';

@Component({
  selector: 'app-report',
  templateUrl: './report.component.html',
})
export class ReportComponent {
  @ViewChild('viewer') viewer!: PdfJsViewerComponent;

  summary = '';
  busy = false;

  private ai = new PdfAiAssistant({
    endpoint: 'http://localhost:11434/v1/chat/completions', // Ollama
    model: 'llama3.2',
    // no apiKey needed for a local model
  });

  async summarize(): Promise<void> {
    this.busy = true;
    try {
      const text = await this.viewer.getDocumentText();
      this.summary = await this.ai.summarize(text);
    } finally {
      this.busy = false;
    }
  }
}
<button (click)="summarize()" [disabled]="busy">
  {{ busy ? 'Summarizing…' : 'Summarize' }}
</button>
<p *ngIf="summary">{{ summary }}</p>

<ng2-pdfjs-viewer #viewer pdfSrc="assets/report.pdf"></ng2-pdfjs-viewer>

The same PdfAiAssistant config shape works for vLLM, LM Studio, and OpenAI-compatible gateways — only the endpoint (and credentials) change.

Honest Caveats​

Context stuffing, not RAG. The assistant includes the document text directly in the prompt as [page N] blocks, capped at maxContextChars (default 100,000 characters). Very large documents are truncated at the cap — questions about content past the cutoff won't be answerable. There is no chunking, embedding, or retrieval; for very large corpora, build retrieval on top of getDocumentText() and use complete() for the final call.

Text extraction has limits. getDocumentText() reads the PDF.js text layer, so scanned/image-only PDFs yield little or no text — there is no OCR.

Key handling and CORS. The apiKey is sent only as an Authorization: Bearer header to the endpoint you configured — nothing else sees it. But this is client-side code: any key shipped to the browser is visible to that browser's user, and most hosted providers also block direct browser calls via CORS. Don't point endpoint straight at a cloud LLM from the browser — use a local model or a backend proxy. See Where the request runs.

Embedded mode only. The built-in panel is not rendered when the viewer runs in external-window mode.