examples/02_extract.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "sys.path.append(\"..\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aurelio_sdk import AurelioClient\n",
    "import os\n",
    "\n",
    "client = AurelioClient(debug=True)\n",
    "\n",
    "print(client.base_url)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extract text from PDFs and video MP4 files and urls\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From file\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aurelio_sdk import ExtractResponse\n",
    "\n",
    "# From a local file\n",
    "file_path = \"data/pdf/adaptive_semantic_search.pdf\"\n",
    "file_path = \"data/pdf/test_pdf.pdf\"\n",
    "\n",
    "response_pdf_file: ExtractResponse = client.extract_file(\n",
    "    file_path=file_path, quality=\"low\", chunk=False, wait=-1, polling_interval=5\n",
    ")\n",
    "response_pdf_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aurelio_sdk import ExtractResponse\n",
    "\n",
    "# From a local file\n",
    "file_path = \"data/video/how_to_overcome_our_mistakes.mp4\"\n",
    "\n",
    "response_video_file: ExtractResponse = client.extract_file(\n",
    "    file_path=file_path, quality=\"low\", chunk=False, wait=-1\n",
    ")\n",
    "\n",
    "response_video_file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From URL\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aurelio_sdk import ExtractResponse\n",
    "\n",
    "# From URL\n",
    "url = \"https://arxiv.org/pdf/2408.15291\"\n",
    "response_pdf_url: ExtractResponse = client.extract_url(\n",
    "    url=url, quality=\"low\", chunk=True, wait=-1, polling_interval=5\n",
    ")\n",
    "\n",
    "response_pdf_url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aurelio_sdk import ExtractResponse\n",
    "\n",
    "# From URL\n",
    "url = \"https://storage.googleapis.com/gtv-videos-bucket/sample/ForBiggerMeltdowns.mp4\"\n",
    "response_video_url: ExtractResponse = client.extract_url(\n",
    "    url=url, quality=\"low\", chunk=True, wait=-1, polling_interval=5\n",
    ")\n",
    "\n",
    "response_video_url"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get document status and handle timeouts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aurelio_sdk import ExtractResponse\n",
    "\n",
    "# From a local file\n",
    "file_path = \"data/pdf/adaptive_semantic_search.pdf\"\n",
    "\n",
    "with open(file_path, \"rb\") as file:\n",
    "    # Load file high quality and set wait time to 10 seconds\n",
    "    response_pdf_file: ExtractResponse = client.extract_file(\n",
    "        file=file, quality=\"high\", chunk=True, wait=10, polling_interval=5\n",
    "    )\n",
    "\n",
    "# Get document status, message and document id\n",
    "print(\"Status:\", response_pdf_file.status)\n",
    "print(\"Document ID:\", response_pdf_file.document.id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get document status and response\n",
    "document_response: ExtractResponse = client.get_document(\n",
    "    document_id=response_pdf_file.document.id\n",
    ")\n",
    "\n",
    "print(\"Status:\", document_response.status)\n",
    "print(\"Document ID:\", document_response.document.id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loop until the document is completed or wait time is reached\n",
    "document_response = client.wait_for(\n",
    "    document_id=response_pdf_file.document.id, wait=300, polling_interval=5\n",
    ")\n",
    "document_response\n"
   ]
  }
 ],
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