# PDF Processing *** ### Introduction FastRouter supports PDF understanding in chat completions by attaching PDFs to messages and enabling the **file-parser** plugin with the **mistral-ocr** engine. This allows you to ask questions about PDFs (including scanned/image PDFs) using your chosen chat model—FastRouter will OCR/parse the document and provide the extracted text to the model. **Pricing:** PDF OCR via `mistral-ocr` is billed at **$2 / 1,000 pages**, in addition to normal model token usage. **Note:** If you’re using a model with native document understanding (e.g., `openai/gpt-4.1`), you can leverage the model’s built-in document processing — no plugin may be needed. *** ### Endpoint **POST** `https://api.fastrouter.ai/api/v1/chat/completions` *** ### How PDF processing works When you include a PDF in `messages[].content[]` and enable the plugin: * FastRouter fetches/decodes the PDF (URL or base64) * Runs **mistral-ocr** to extract text (works well on scanned pages and embedded images) * Feeds the extracted content into the target model so it can answer your prompt *** ### Plugin configuration (required for OCR) Enable PDF processing with the `plugins` parameter: ```json { "plugins": [ { "id": "file-parser", "pdf": { "engine": "mistral-ocr" } } ] } ``` *** ### Sending PDFs in messages Attach PDFs inside a message’s `content` array using `type: "file"`: ```json { "type": "file", "file": { "filename": "document.pdf", "file_data": "https://example.com/document.pdf" } } ``` #### Supported `file_data` formats * **Public URL** (recommended): `https://.../file.pdf` * **Base64 data URL** (for local/private docs): `data:application/pdf;base64,JVBERi0xLjc...` *** ### Example: PDF via public URL {% tabs %} {% tab title="cURL" %} ```bash curl https://api.fastrouter.ai/api/v1/chat/completions \ -H "Authorization: Bearer $FASTROUTER_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "z-ai/glm-4.7", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Summarize this PDF in 5 bullets and list any key dates." }, { "type": "file", "file": { "filename": "document.pdf", "file_data": "https://example.com/document.pdf" } } ] } ], "plugins": [ { "id": "file-parser", "pdf": { "engine": "mistral-ocr" } } ] }' ``` {% endtab %} {% tab title="Python" %} ```python import requests url = "https://api.fastrouter.ai/api/v1/chat/completions" headers = { "Authorization": f"Bearer {FASTROUTER_API_KEY}", "Content-Type": "application/json", } payload = { "model": "anthropic/claude-4.5-sonnet", "messages": [ { "role": "user", "content": [ {"type": "text", "text": "What does this document say about termination clauses? Quote the relevant sections."}, { "type": "file", "file": { "filename": "contract.pdf", "file_data": "https://example.com/contract.pdf", }, }, ], } ], "plugins": [ { "id": "file-parser", "pdf": {"engine": "mistral-ocr"}, } ], } resp = requests.post(url, headers=headers, json=payload) print(resp.json()) ``` {% endtab %} {% tab title="JavaScript" %} ```javascript const response = await fetch("https://api.fastrouter.ai/api/v1/chat/completions", { method: "POST", headers: { Authorization: `Bearer ${process.env.FASTROUTER_API_KEY}`, "Content-Type": "application/json", }, body: JSON.stringify({ model: "z-ai/glm-4.7", messages: [ { role: "user", content: [ { type: "text", text: "Extract the invoice total, invoice date, and vendor name." }, { type: "file", file: { filename: "invoice.pdf", file_data: "https://example.com/invoice.pdf", }, }, ], }, ], plugins: [ { id: "file-parser", pdf: { engine: "mistral-ocr" }, }, ], }), }); const data = await response.json(); console.log(data); ``` {% endtab %} {% endtabs %} *** ### Example: PDF via base64 (data URL) Use base64 when the PDF is local or not publicly reachable. {% tabs %} {% tab title="cURL" %} ```bash curl https://api.fastrouter.ai/api/v1/chat/completions \ -H "Authorization: Bearer $FASTROUTER_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "z-ai/glm-4.7", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Summarize this PDF in 5 bullets and list any key dates." }, { "type": "file", "file": { "filename": "document.pdf", "file_data": "data:application/pdf;base64,..." } } ] } ], "plugins": [ { "id": "file-parser", "pdf": { "engine": "mistral-ocr" } } ] }' ``` {% endtab %} {% tab title="Python" %} ```python import base64 import requests from pathlib import Path def pdf_to_data_url(path: str) -> str: b = Path(path).read_bytes() return "data:application/pdf;base64," + base64.b64encode(b).decode("utf-8") data_url = pdf_to_data_url("path/to/document.pdf") payload = { "model": "anthropic/claude-haiku-4.5", "messages": [ { "role": "user", "content": [ {"type": "text", "text": "Give me a structured outline of this document with headings."}, { "type": "file", "file": { "filename": "document.pdf", "file_data": data_url, }, }, ], } ], "plugins": [ { "id": "file-parser", "pdf": {"engine": "mistral-ocr"}, } ], } resp = requests.post( "https://api.fastrouter.ai/api/v1/chat/completions", headers={"Authorization": f"Bearer {FASTROUTER_API_KEY}", "Content-Type": "application/json"}, json=payload, ) print(resp.json()) ``` {% endtab %} {% tab title="Node.js" %} ```javascript import fs from "node:fs/promises"; const buf = await fs.readFile("path/to/document.pdf"); const dataUrl = `data:application/pdf;base64,${buf.toString("base64")}`; const res = await fetch("https://api.fastrouter.ai/api/v1/chat/completions", { method: "POST", headers: { Authorization: `Bearer ${process.env.FASTROUTER_API_KEY}`, "Content-Type": "application/json", }, body: JSON.stringify({ model: "z-ai/glm-4.7", messages: [ { role: "user", content: [ { type: "text", text: "List the risks mentioned and the mitigation steps." }, { type: "file", file: { filename: "risk-report.pdf", file_data: dataUrl }, }, ], }, ], plugins: [ { id: "file-parser", pdf: { engine: "mistral-ocr" }, }, ], }), }); console.log(await res.json()); ``` {% endtab %} {% endtabs %} *** ### Pricing * **mistral-ocr:** **$2 / 1,000 pages** processed * **Model tokens:** billed normally for the model you select * Prefer **URLs** for large PDFs to avoid request size limits and base64 overhead. *** --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.fastrouter.ai/pdf-processing.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.