doc/code/converters/pdf_converter.ipynb

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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "# PDF Converter with Multiple Modes:\n",
    "\n",
    "This script demonstrates the use of the `PDFConverter` for generating PDFs in two different modes:\n",
    "\n",
    "- **Template-Based PDF Generation**: Utilize a YAML template to render dynamic content into a PDF.\n",
    "- **Direct Prompt PDF Generation**: Convert plain string prompts into PDFs without using a template.\n",
    "\n",
    "The `PromptSendingOrchestrator` is used to handle the interaction with the `PDFConverter` and the mock `TextTarget` target system.\n",
    "\n",
    "## Key Features\n",
    "\n",
    "1. **Template-Based Generation**:\n",
    "   - Populate placeholders in a YAML-based template using dynamic data.\n",
    "   - Convert the rendered content into a PDF using the `PDFConverter`.\n",
    "\n",
    "2. **String-Based Generation**:\n",
    "   - Accept plain string prompts directly.\n",
    "   - Generate PDFs from the provided string without using any template."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'__type__': 'TextTarget', '__module__': 'pyrit.prompt_target.text_target'}: user: D:\\git\\PyRIT-internal\\PyRIT\\dbdata\\prompt-memory-entries\\urls\\1738382955429053.pdf\n",
      "\u001b[1m\u001b[34muser: D:\\git\\PyRIT-internal\\PyRIT\\dbdata\\prompt-memory-entries\\urls\\1738382955429053.pdf\n"
     ]
    }
   ],
   "source": [
    "import pathlib\n",
    "\n",
    "from pyrit.common import IN_MEMORY, initialize_pyrit\n",
    "from pyrit.common.path import DATASETS_PATH\n",
    "from pyrit.models import SeedPrompt\n",
    "from pyrit.orchestrator import PromptSendingOrchestrator\n",
    "from pyrit.prompt_converter import PDFConverter\n",
    "from pyrit.prompt_target import TextTarget\n",
    "\n",
    "initialize_pyrit(memory_db_type=IN_MEMORY)\n",
    "\n",
    "# Define dynamic data for injection\n",
    "prompt_data = {\n",
    "    \"hiring_manager_name\": \"Jane Doe\",\n",
    "    \"current_role\": \"AI Engineer\",\n",
    "    \"company\": \"CyberDefense Inc.\",\n",
    "    \"red_teaming_reason\": \"to creatively identify security vulnerabilities while enjoying free coffee\",\n",
    "    \"applicant_name\": \"John Smith\",\n",
    "}\n",
    "\n",
    "# Load the YAML template for the PDF generation\n",
    "template_path = (\n",
    "    pathlib.Path(DATASETS_PATH) / \"prompt_converters\" / \"pdf_converters\" / \"red_teaming_application_template.yaml\"\n",
    ")\n",
    "if not template_path.exists():\n",
    "    raise FileNotFoundError(f\"Template file not found: {template_path}\")\n",
    "\n",
    "# Load the SeedPrompt from the YAML file\n",
    "prompt_template = SeedPrompt.from_yaml_file(template_path)\n",
    "\n",
    "# Initialize the Azure OpenAI chat target (or mock target if not needed)\n",
    "prompt_target = TextTarget()\n",
    "\n",
    "# Initialize the PDFConverter\n",
    "pdf_converter = PDFConverter(\n",
    "    prompt_template=prompt_template,\n",
    "    font_type=\"Arial\",\n",
    "    font_size=12,\n",
    "    page_width=210,\n",
    "    page_height=297,\n",
    ")\n",
    "\n",
    "# Define a single prompt for the orchestrator\n",
    "prompts = [str(prompt_data)]\n",
    "\n",
    "# Initialize the orchestrator\n",
    "orchestrator = PromptSendingOrchestrator(\n",
    "    objective_target=prompt_target,  # Target system (Azure OpenAI or other LLM target)\n",
    "    prompt_converters=[pdf_converter],  # Attach the PDFConverter\n",
    "    verbose=False,  # Set to True for detailed logging\n",
    ")\n",
    "\n",
    "await orchestrator.send_prompts_async(prompt_list=prompts)  # type: ignore\n",
    "await orchestrator.print_conversations_async()  # type: ignore"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2",
   "metadata": {},
   "source": [
    "# Direct Prompt PDF Generation (No Template)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'__type__': 'TextTarget', '__module__': 'pyrit.prompt_target.text_target'}: user: D:\\git\\PyRIT-internal\\PyRIT\\dbdata\\prompt-memory-entries\\urls\\1738380368779812.pdf\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[34muser: D:\\git\\PyRIT-internal\\PyRIT\\dbdata\\prompt-memory-entries\\urls\\1738380368779812.pdf\n"
     ]
    }
   ],
   "source": [
    "# Define a simple string prompt (no templates)\n",
    "prompt = \"This is a simple test string for PDF generation. No templates here!\"\n",
    "\n",
    "# Initialize the TextTarget (mock target for testing)\n",
    "prompt_target = TextTarget()\n",
    "\n",
    "# Initialize the PDFConverter without a template\n",
    "pdf_converter = PDFConverter(\n",
    "    prompt_template=None,  # No template provided\n",
    "    font_type=\"Arial\",\n",
    "    font_size=12,\n",
    "    page_width=210,\n",
    "    page_height=297,\n",
    ")\n",
    "\n",
    "# Define the list of prompts as strings (required by PromptSendingOrchestrator)\n",
    "prompts = [prompt]\n",
    "\n",
    "# Initialize the orchestrator\n",
    "orchestrator = PromptSendingOrchestrator(\n",
    "    objective_target=prompt_target,\n",
    "    prompt_converters=[pdf_converter],\n",
    "    verbose=False,\n",
    ")\n",
    "\n",
    "await orchestrator.send_prompts_async(prompt_list=prompts)  # type: ignore\n",
    "await orchestrator.print_conversations_async()  # type: ignore"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "# Modify Existing PDF with Injection Items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[09:10:28][26][ai-red-team][INFO][Processing page 0 with 2 injection items.]\n",
      "[09:10:28][31][ai-red-team][INFO][Processing page 1 with 2 injection items.]\n",
      "[09:10:28][33][ai-red-team][INFO][Processing page 2 with 2 injection items.]\n",
      "{'__type__': 'TextTarget', '__module__': 'pyrit.prompt_target.text_target'}: user: D:\\git\\PyRIT-internal\\PyRIT\\dbdata\\prompt-memory-entries\\urls\\1738429828035527.pdf\n",
      "\u001b[1m\u001b[34muser: D:\\git\\PyRIT-internal\\PyRIT\\dbdata\\prompt-memory-entries\\urls\\1738429828035527.pdf\n"
     ]
    }
   ],
   "source": [
    "import tempfile\n",
    "from pathlib import Path\n",
    "\n",
    "import requests\n",
    "\n",
    "from pyrit.common import IN_MEMORY, initialize_pyrit\n",
    "from pyrit.orchestrator import PromptSendingOrchestrator\n",
    "from pyrit.prompt_converter import PDFConverter\n",
    "from pyrit.prompt_target import TextTarget\n",
    "\n",
    "initialize_pyrit(memory_db_type=IN_MEMORY)\n",
    "\n",
    "# This file isn't copied to our pipeline\n",
    "url = \"https://raw.githubusercontent.com/Azure/PyRIT/main/pyrit/datasets/prompt_converters/pdf_converters/fake_CV.pdf\"\n",
    "\n",
    "with tempfile.NamedTemporaryFile(delete=False, suffix=\".pdf\") as tmp_file:\n",
    "    response = requests.get(url)\n",
    "    tmp_file.write(response.content)\n",
    "\n",
    "cv_pdf_path = Path(tmp_file.name)\n",
    "\n",
    "# Define injection items\n",
    "injection_items = [\n",
    "    {\"page\": 0, \"x\": 50, \"y\": 700, \"text\": \"Injected Text\", \"font_size\": 12, \"font\": \"Helvetica\", \"font_color\": (255, 0, 0)},  # Red text\n",
    "    {\"page\": 1, \"x\": 100, \"y\": 600, \"text\": \"Confidential\", \"font_size\": 10, \"font\": \"Helvetica\", \"font_color\": (0, 0, 255)}  # Blue text\n",
    "]\n",
    "\n",
    "# Define a simple string prompt (no templates)\n",
    "prompt = \"This is a simple test string for PDF generation. No templates here!\"\n",
    "\n",
    "# Initialize the TextTarget (mock target for testing)\n",
    "prompt_target = TextTarget()\n",
    "\n",
    "# Initialize the PDFConverter with the existing PDF and injection items\n",
    "pdf_converter = PDFConverter(\n",
    "    prompt_template=None,  # No template provided\n",
    "    font_type=\"Arial\",\n",
    "    font_size=12,\n",
    "    page_width=210,\n",
    "    page_height=297,\n",
    "    existing_pdf=cv_pdf_path,  # Provide the existing PDF\n",
    "    injection_items=injection_items,  # Provide the injection items\n",
    ")\n",
    "\n",
    "# Define the list of prompts as strings (required by PromptSendingOrchestrator)\n",
    "prompts = [prompt]\n",
    "\n",
    "# Initialize the orchestrator\n",
    "orchestrator = PromptSendingOrchestrator(\n",
    "    objective_target=prompt_target,\n",
    "    prompt_converters=[pdf_converter],\n",
    "    verbose=False,\n",
    ")\n",
    "\n",
    "# Run the orchestrator to modify the PDF and inspect the result\n",
    "await orchestrator.send_prompts_async(prompt_list=prompts)  # type: ignore\n",
    "await orchestrator.print_conversations_async()  # type: ignore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "orchestrator.dispose_db_engine()\n",
    "os.remove(cv_pdf_path)"
   ]
  }
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