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Documentation Index

Fetch the complete documentation index at: https://docs.blinkpdf.io/llms.txt

Use this file to discover all available pages before exploring further.

Large language models like OpenAI’s GPT-4 and Anthropic’s Claude produce Markdown natively, which means you can pipe their output straight into Blink PDF without any intermediate transformation step. This makes Blink PDF a natural fit for AI-powered document generation: your model writes the content, and you get a polished, shareable PDF in under 100ms. LLMs are trained on Markdown-heavy corpora and default to it whenever they generate structured content — reports, summaries, analyses, and proposals all arrive pre-formatted with headings, bullet lists, bold text, and tables. Blink PDF’s /v1/render endpoint accepts that Markdown directly as a JSON string, so the integration is essentially zero-glue:
  1. Prompt your model to produce a Markdown document.
  2. Take response.choices[0].message.content (or the Claude equivalent).
  3. POST it to /v1/render.
  4. Receive a ready-to-use PDF binary.
Blink PDF operates with zero data retention — your Markdown and generated PDFs are never stored on our servers. This makes the pipeline safe for sensitive AI outputs such as legal summaries, financial analyses, and medical drafts.

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Basic Pattern

LLM API → Markdown string → POST /v1/render → PDF binary → save / send
Every example below follows this pattern. Swap in any LLM provider; only the first step changes.

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Code Examples

import openai
import requests

client = openai.OpenAI(api_key="sk-...")

# Step 1: Ask the model to generate a Markdown report
completion = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {
            "role": "system",
            "content": (
                "You are a professional analyst. "
                "Respond with a well-structured Markdown report only — "
                "no commentary outside the document."
            ),
        },
        {
            "role": "user",
            "content": "Write a Q3 2024 performance summary for an e-commerce business.",
        },
    ],
)

markdown_content = completion.choices[0].message.content

# Step 2: Render the Markdown to PDF
response = requests.post(
    "https://api.blinkpdf.io/v1/render",
    headers={
        "Authorization": "Bearer sk_live_...",
        "Content-Type": "application/json",
    },
    json={
        "markdown": markdown_content,
        "metadata": {"title": "Q3 2024 Performance Summary"},
    },
)

response.raise_for_status()

# Step 3: Save the PDF
with open("q3_summary.pdf", "wb") as f:
    f.write(response.content)

render_ms = response.headers.get("X-Render-Ms")
request_id = response.headers.get("X-Request-Id")
print(f"PDF saved — rendered in {render_ms}ms (request ID: {request_id})")

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Agentic Workflows

When you’re building autonomous agents that produce multiple documents across a workflow run, a few patterns help you stay efficient and within your rate limits.

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Async Batch Generation

If your agent produces several reports in one run, fire the render requests concurrently rather than sequentially. At Pro plan (120 req/min) you can sustain roughly two renders per second without hitting limits. See the Batch Processing guide for a full async example.

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Prompt Engineering for Clean PDFs

Guide the model to produce render-ready Markdown by adding explicit instructions in your system prompt: 
Respond with a complete Markdown document only.
- Start with a single H1 title.
- Use H2 and H3 for sections.
- Use tables for structured comparisons.
- Do not include code fences, commentary, or apologies outside the document.
This prevents preamble text like “Sure, here’s your report:” from appearing in the rendered PDF.

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Streaming + Buffering

If you use OpenAI’s streaming API to display content progressively in a UI, buffer the full completion before sending it to /v1/render. Blink PDF requires the complete Markdown document in a single request.
Store the X-Request-Id header returned by each render call alongside your document record. It makes debugging and support requests significantly faster — you can reference the exact render that produced a given PDF.

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Connecting to Notion, Obsidian, and Tiptap

Blink PDF integrates well with editor-based AI workflows:
  • Notion AI — Export Notion page content as Markdown via the Notion API, then render with Blink PDF to produce a shareable PDF snapshot.
  • Obsidian — Use the Obsidian Local REST API plugin or a community plugin to pipe vault notes to /v1/render on demand.
  • Tiptap — Convert Tiptap’s JSON document model to Markdown using @tiptap/extension-markdown, then pass the string directly to Blink PDF.

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Plan Considerations

PlanRate LimitBest For
Free5/minPrototyping LLM-to-PDF pipelines
Starter ($12/mo)30/minSide projects, low-volume agents
Pro ($39/mo)120/minProduction AI apps, daily reporting
Business ($149/mo)600/minHigh-frequency agentic workflows
Scale ($999/mo)~5,000/minEnterprise AI document platforms
All plans include zero-retention processing. No Markdown content or rendered PDFs are stored on Blink PDF infrastructure at any tier.