Add support for html_remove_navigation_elements.

Add demo videos notebooks (#529 )
Add more parameters (#525 )
2026-07-21 12:05:23 -04:00 · 2024-12-06 11:03:16 +01:00 · 2024-12-05 08:38:34 -08:00 · 2024-12-04 15:39:00 +01:00 · 2024-12-04 10:20:07 +01:00 · 2024-12-04 10:17:08 +01:00
12 changed files with 3292 additions and 1134 deletions
@@ -0,0 +1,415 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "97c79c38-38a3-40f3-ba2e-250649347d63",
+      "metadata": {
+        "id": "97c79c38-38a3-40f3-ba2e-250649347d63"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/demo_starter_multimodal.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4e081457",
+      "metadata": {},
+      "source": [
+        "# Multimodal Parsing using LlamaParse\n",
+        "\n",
+        "This cookbook shows you how to use LlamaParse to parse any document with the multimodal capabilities of Multi-Modal LLMs from Anthropic/ OpenAI.\n",
+        "\n",
+        "LlamaParse allows you to plug in external, multimodal model vendors for parsing - we handle the error correction, validation, and scalability/reliability for you.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "qOdqBxCS51Ow",
+      "metadata": {
+        "id": "qOdqBxCS51Ow"
+      },
+      "source": [
+        "### Installation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "H_Vqcylb50vm",
+      "metadata": {
+        "id": "H_Vqcylb50vm"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install llama-parse"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "15e60ecf-519c-41fc-911b-765adaf8bad4",
+      "metadata": {
+        "id": "15e60ecf-519c-41fc-911b-765adaf8bad4"
+      },
+      "source": [
+        "### Setup\n",
+        "\n",
+        "Here we setup `LLAMA_CLOUD_API_KEY` for using `LlamaParse`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "91a9e532-1454-40e0-bbf0-fd442c350121",
+      "metadata": {
+        "id": "91a9e532-1454-40e0-bbf0-fd442c350121"
+      },
+      "outputs": [],
+      "source": [
+        "import nest_asyncio\n",
+        "\n",
+        "nest_asyncio.apply()\n",
+        "\n",
+        "import os\n",
+        "\n",
+        "# API access to llama-cloud\n",
+        "os.environ[\"LLAMA_CLOUD_API_KEY\"] = \"<YOUR LLAMACLOUD API KEY>\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "LGwBNPNotZRQ",
+      "metadata": {
+        "id": "LGwBNPNotZRQ"
+      },
+      "source": [
+        "## Download Data\n",
+        "\n",
+        "For this demonstration, we will use OpenAI's recent paper `Evaluation of OpenAI o1: Opportunities and Challenges of AGI`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "IjtKDQRLrylI",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IjtKDQRLrylI",
+        "outputId": "31df0fac-51f2-4697-f78b-0b7c0b8cd145"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "--2024-12-05 18:54:24--  https://arxiv.org/pdf/2409.18486\n",
+            "Resolving arxiv.org (arxiv.org)... 151.101.67.42, 151.101.131.42, 151.101.3.42, ...\n",
+            "Connecting to arxiv.org (arxiv.org)|151.101.67.42|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 13986265 (13M) [application/pdf]\n",
+            "Saving to: ‘o1.pdf’\n",
+            "\n",
+            "o1.pdf              100%[===================>]  13.34M  11.8MB/s    in 1.1s    \n",
+            "\n",
+            "2024-12-05 18:54:26 (11.8 MB/s) - ‘o1.pdf’ saved [13986265/13986265]\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "!wget \"https://arxiv.org/pdf/2409.18486\" -O \"o1.pdf\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4e29a9d7-5bd9-4fb8-8ec1-4c128a748662",
+      "metadata": {
+        "id": "4e29a9d7-5bd9-4fb8-8ec1-4c128a748662"
+      },
+      "source": [
+        "## Initialize LlamaParse\n",
+        "\n",
+        "Initialize LlamaParse in multimodal mode, and specify the vendor.\n",
+        "\n",
+        "**NOTE**: optionally you can specify the Anthropic/ OpenAI API key. If you choose to do so LlamaParse will only charge you 1 credit (0.3c) per page. \n",
+        "\n",
+        "\n",
+        "Using your own API key may incur additional costs from your model provider and could result in failed pages or documents if you do not have sufficient usage limits."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "dc921729-3446-42ca-8e1b-a6fd26195ed9",
+      "metadata": {
+        "id": "dc921729-3446-42ca-8e1b-a6fd26195ed9"
+      },
+      "outputs": [],
+      "source": [
+        "from llama_index.core.schema import TextNode\n",
+        "from typing import List\n",
+        "\n",
+        "def get_text_nodes(json_list: List[dict]):\n",
+        "    text_nodes = []\n",
+        "    for idx, page in enumerate(json_list):\n",
+        "        text_node = TextNode(text=page[\"md\"], metadata={\"page\": page[\"page\"]})\n",
+        "        text_nodes.append(text_node)\n",
+        "    return text_nodes"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1b5d6da6",
+      "metadata": {},
+      "source": [
+        "### With anthropic-sonnet-3.5"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "f2e9d9cf-8189-4fcb-b34f-cde6cc0b59c8",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "f2e9d9cf-8189-4fcb-b34f-cde6cc0b59c8",
+        "outputId": "a337cbdd-60db-4a73-b66b-2bd6159e81f2"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Started parsing the file under job_id dd9d5e0f-160e-486a-89a2-6005e5a1c2ac\n"
+          ]
+        }
+      ],
+      "source": [
+        "from llama_parse import LlamaParse\n",
+        "\n",
+        "parser = LlamaParse(\n",
+        "    result_type=\"markdown\",\n",
+        "    use_vendor_multimodal_model=True,\n",
+        "    vendor_multimodal_model_name=\"anthropic-sonnet-3.5\",\n",
+        "    target_pages=\"24\"\n",
+        "    # invalidate_cache=True\n",
+        ")\n",
+        "json_objs = parser.get_json_result(\"o1.pdf\")\n",
+        "json_list = json_objs[0][\"pages\"]\n",
+        "docs = get_text_nodes(json_list)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4f3c51b0-7878-48d7-9bc3-02b516500128",
+      "metadata": {
+        "id": "4f3c51b0-7878-48d7-9bc3-02b516500128"
+      },
+      "source": [
+        "### With GPT-4o\n",
+        "\n",
+        "For comparison, we will also parse the document using GPT-4o."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "6fc3f258-50ae-4988-b904-c105463a498f",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6fc3f258-50ae-4988-b904-c105463a498f",
+        "outputId": "89c525c4-2b93-4909-9657-55646e034637"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Started parsing the file under job_id 6a4dea44-4f90-406b-b290-9e98620b1232\n"
+          ]
+        }
+      ],
+      "source": [
+        "from llama_parse import LlamaParse\n",
+        "\n",
+        "parser_gpt4o = LlamaParse(\n",
+        "    result_type=\"markdown\",\n",
+        "    use_vendor_multimodal_model=True,\n",
+        "    vendor_multimodal_model=\"openai-gpt4o\",\n",
+        "    target_pages=\"24\",\n",
+        "    # invalidate_cache=True\n",
+        ")\n",
+        "json_objs_gpt4o = parser_gpt4o.get_json_result(\"o1.pdf\")\n",
+        "json_list_gpt4o = json_objs_gpt4o[0][\"pages\"]\n",
+        "docs_gpt4o = get_text_nodes(json_list_gpt4o)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "44c20f7a-2901-4dd0-b635-a4b33c5664c1",
+      "metadata": {
+        "id": "44c20f7a-2901-4dd0-b635-a4b33c5664c1"
+      },
+      "source": [
+        "### View Results\n",
+        "\n",
+        "Let's visualize the results along with the original document page."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "778698aa-da7e-4081-b3b5-0372f228536f",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "778698aa-da7e-4081-b3b5-0372f228536f",
+        "outputId": "bb89e323-7041-4fc3-d835-95e373189d02"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "page: 25\n",
+            "\n",
+            "| Participant_ID | clinical Description Reference |\n",
+            "|-----------------|----------------------------------|\n",
+            "| Attribute | Value | Basic Personal Information: Subject 098_S_0896 is a 72.0-year-old Female who has completed 15 years of education. The ethnicity is Not Hisp/Latino and race is White. Marital status is Married. Initially diagnosed as AD, as of the date 2007-10-24, the final diagnosis was Dementia. |\n",
+            "| Age | 72.0 |\n",
+            "| Sex | Female |\n",
+            "| Education | 15 |\n",
+            "| Race | White | Biomarker Measurements: The subject's genetic profile includes an ApoE4 status of 0.0... |\n",
+            "| DX_bl | AD |\n",
+            "| DX | Dementia |\n",
+            "| ... | ... | Cognitive and Neurofunctional Assessments: The Mini-Mental State Examination score stands at 29.0. The Clinical Dementia Rating, sum of boxes, is 1.0. ADAS 11 and 13 scores are 4.67 and 4.67 respectively, with a score of 1.0 in delayed word recall... |\n",
+            "| APOE4 | 1.0 |\n",
+            "| TAU | 212.5 |\n",
+            "| ... | ... |\n",
+            "| MMSE | 29.0 | Volumetric Data: Under MRI conditions at a field strength of 1.5 Tesla MRI Tesla, using Cross Sectional FreeSurfer (FreeSurfer Version 4.3), the imaging data recorded includes ventricles volume at 54422.0, hippocampus volume at 6677.0, whole brain volume at 1147980.0, entorhinal cortex volume at 2782.0, fusiform gyrus volume at 19432.0, and middle temporal area volume at 24951.0. The intracranial volume measured is 1799580.0.... |\n",
+            "| CDRSB | 0.0 |\n",
+            "| ... | ... |\n",
+            "| FLDSTRENG | 1.5 Tesla MRI |\n",
+            "| Ventricles | 84599 |\n",
+            "| Hippocampus | 5319 |\n",
+            "| ... | ... |\n",
+            "\n",
+            "Figure 2: An example of a patient table and its corresponding clinical description.\n",
+            "\n",
+            "skills. Mathematics, as a highly structured and logic-driven discipline, provides an ideal testing ground for evaluating this reasoning ability. To investigate o1-preview's performance, we designed a series of tests covering various difficulty levels. We begin with high school-level math competition problems in this section, followed by college-level mathematics problems in the next section, allowing us to observe the model's logical reasoning across varying levels of complexity.\n",
+            "\n",
+            "In this section, we selected two primary areas of mathematics: algebra and counting and probability in this section. We chose these two topics because of their heavy reliance on problem-solving skills and their frequent use in assessing logical and abstract thinking [46]. The dataset used in testing is from the MATH dataset [46]. The problems in the dataset cover a wide range of subjects, including Prealgebra, Intermediate Algebra, Algebra, Geometry, Counting and Probability, Number Theory, and Precalculus. Each problem is categorized based on difficulty, ranked from level 1 to 5, according to the Art of Problem Solving (AoPS). The dataset mainly comprises problems from various high school math competitions, including the American Mathematics Competitions (AMC) 10 and 12, as well as the American Invitational Mathematics Examination (AIME), and other similar contests. Each problem comes with detailed reference solutions, allowing for a comprehensive comparison of o1-preview's solutions.\n",
+            "\n",
+            "In addition to evaluating the final answers produced by o1-preview, our analysis delves into the step-by-step reasoning process of the o1-preview's solutions. By comparing o1-preview's solutions with the dataset's solutions, we assess its ability to engage in logical reasoning, handle abstract problem-solving tasks, and apply structured approaches to reach correct answers. This deeper analysis offers insights into o1-preview's overall reasoning capabilities, using mathematics as a reliable indicator for logical and structured thought processes.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# using Sonnet-3.5\n",
+        "print(docs[0].get_content(metadata_mode=\"all\"))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "1511a30f-3efc-4142-9668-7dc056a24d0c",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1511a30f-3efc-4142-9668-7dc056a24d0c",
+        "outputId": "2e5e8e20-2b41-4183-f21f-dff503a03089"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "page: 25\n",
+            "\n",
+            "\n",
+            "| Participant_ID | clinical Description Reference |\n",
+            "|----------------|--------------------------------|\n",
+            "| **Attribute**  | **Value**                      |\n",
+            "| Age            | 72.0                           |\n",
+            "| Sex            | Female                         |\n",
+            "| Education      | 15                             |\n",
+            "| Race           | White                          |\n",
+            "| DX_bl          | AD                             |\n",
+            "| DX             | Dementia                       |\n",
+            "| ...            | ...                            |\n",
+            "| APOE4          | 1.0                            |\n",
+            "| TAU            | 212.5                          |\n",
+            "| ...            | ...                            |\n",
+            "| MMSE           | 29.0                           |\n",
+            "| CDRSB          | 0.0                            |\n",
+            "| ...            | ...                            |\n",
+            "| FLDSTRENG      | 1.5 Tesla MRI                  |\n",
+            "| Ventricles     | 84599                          |\n",
+            "| Hippocampus    | 5319                           |\n",
+            "| ...            | ...                            |\n",
+            "\n",
+            "**Basic Personal Information:** Subject 098_S_0896 is a 72.0-year-old Female who has completed 15 years of education. The ethnicity is Not Hisp/Latino and race is White. Marital status is Married. Initially diagnosed as AD, as of the date 2007-10-24, the final diagnosis was Dementia.\n",
+            "\n",
+            "**Biomarker Measurements:** The subject's genetic profile includes an ApoE4 status of 0.0...\n",
+            "\n",
+            "**Cognitive and Neurofunctional Assessments:** The Mini-Mental State Examination score stands at 29.0. The Clinical Dementia Rating, sum of boxes, is 1.0. ADAS 11 and 13 scores are 4.67 and 4.67 respectively, with a score of 1.0 in delayed word recall...\n",
+            "\n",
+            "**Volumetric Data:** Under MRI conditions at a field strength of 1.5 Tesla MRI Tesla, using Cross-Sectional FreeSurfer (FreeSurfer Version 4.3), the imaging data recorded includes ventricles volume at 84422.0, hippocampus volume at 6677.0, whole brain volume at 1147980.0, entorhinal cortex volume at 27820.0, fusiform gyrus volume at 19432.0, and middle temporal area volume at 24951.0. The intracranial volume measured is 1799580.0...\n",
+            "\n",
+            "Figure 2: An example of a patient table and its corresponding clinical description.\n",
+            "\n",
+            "----\n",
+            "\n",
+            "Skills. Mathematics, as a highly structured and logic-driven discipline, provides an ideal testing ground for evaluating this reasoning ability. To investigate o1-preview’s performance, we designed a series of tests covering various difficulty levels. We begin with high school-level math competition problems in this section, followed by college-level mathematics problems in the next section, allowing us to observe the model’s logical reasoning across varying levels of complexity.\n",
+            "\n",
+            "In this section, we selected two primary areas of mathematics: algebra and counting and probability in this section. We chose these two topics because of their heavy reliance on problem-solving skills and their frequent use in assessing logical and abstract thinking [46]. The dataset used in testing is from the MATH dataset [46]. The problems in the dataset cover a wide range of subjects, including Prealgebra, Intermediate Algebra, Algebra, Geometry, Counting and Probability, Number Theory, and Precalculus. Each problem is categorized based on difficulty, ranked from level 1 to 5, according to the Art of Problem Solving (AoPS). The dataset mainly comprises problems from various high school math competitions, including the American Mathematics Competitions (AMC) 10 and 12, as well as the American Invitational Mathematics Examination (AIME), and other similar contests. Each problem comes with detailed reference solutions, allowing for a comprehensive comparison of o1-preview’s solutions.\n",
+            "\n",
+            "In addition to evaluating the final answers produced by o1-preview, our analysis delves into the step-by-step reasoning process of the o1-preview’s solutions. By comparing o1-preview’s solutions with the dataset’s solutions, we assess its ability to engage in logical reasoning, handle abstract problem-solving tasks, and apply structured approaches to reach correct answers. This deeper analysis offers insights into o1-preview’s overall reasoning capabilities, using mathematics as a reliable indicator for logical and structured thought processes.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# using GPT-4o\n",
+        "print(docs_gpt4o[0].get_content(metadata_mode=\"all\"))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1c75bb85",
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "llamacloud",
+      "language": "python",
+      "name": "llamacloud"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
@@ -0,0 +1,181 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/demo_starter_parse_selected_pages.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Parse Selected Pages \n",
+    "\n",
+    "In this notebook we will demonstrate how to parse selected pages in a document using LlamaParse."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "Here we install `llama-parse` used for parsing the document"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install llama-parse"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set API Key"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# llama-parse is async-first, running the async code in a notebook requires the use of nest_asyncio\n",
+    "import nest_asyncio\n",
+    "\n",
+    "nest_asyncio.apply()\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "# API access to llama-cloud\n",
+    "os.environ[\"LLAMA_CLOUD_API_KEY\"] = \"<YOUR LLAMACLOUD API KEY>\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Download Data\n",
+    "\n",
+    "Here we download Uber 2021 10K SEC filings data for the demonstration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2024-12-05 11:40:59--  https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/uber_2021.pdf\n",
+      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 2606:50c0:8000::154, 2606:50c0:8002::154, 2606:50c0:8003::154, ...\n",
+      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|2606:50c0:8000::154|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 1880483 (1.8M) [application/octet-stream]\n",
+      "Saving to: ‘./uber_2021.pdf’\n",
+      "\n",
+      "./uber_2021.pdf     100%[===================>]   1.79M  --.-KB/s    in 0.1s    \n",
+      "\n",
+      "2024-12-05 11:40:59 (14.2 MB/s) - ‘./uber_2021.pdf’ saved [1880483/1880483]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/uber_2021.pdf' -O './uber_2021.pdf'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Parse the PDF file in selected pages\n",
+    "\n",
+    "Here we will parse the PDF file in selected pages and get the text in `markdown` format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started parsing the file under job_id ad1087c1-b085-4dc7-9aa8-d13cdd440f2b\n"
+     ]
+    }
+   ],
+   "source": [
+    "from llama_parse import LlamaParse\n",
+    "\n",
+    "parser = LlamaParse(\n",
+    "  target_pages=\"0,1,2\",\n",
+    "  result_type=\"markdown\"\n",
+    ")\n",
+    "\n",
+    "documents = parser.load_data('./uber_2021.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(id_='d0b34f4a-27ef-48e2-a92a-386e5e265f4c', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, metadata_template='{key}: {value}', metadata_separator='\\n', text='# UNITED STATES SECURITIES AND EXCHANGE COMMISSION\\n\\n# Washington, D.C. 20549\\n\\n# FORM 10-K\\n\\n(Mark One)\\n\\n☒ ANNUAL REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934\\n\\nFor the fiscal year ended December 31, 2021\\n\\nOR\\n\\n☐ TRANSITION REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934\\n\\nFor the transition period from _____ to _____\\n\\nCommission File Number: 001-38902\\n\\n# UBER TECHNOLOGIES, INC.\\n\\n(Exact name of registrant as specified in its charter)\\n\\nDelaware\\n\\n45-2647441\\n\\n(State or other jurisdiction of incorporation or organization) (I.R.S. Employer Identification No.)\\n\\n1515 3rd Street\\n\\nSan Francisco, California 94158\\n\\n(Address of principal executive offices, including zip code)\\n\\n(415) 612-8582\\n\\n(Registrant’s telephone number, including area code)\\n\\n# Securities registered pursuant to Section 12(b) of the Act:\\n\\n|Title of each class|Trading Symbol(s)|Name of each exchange on which registered|\\n|---|---|---|\\n|Common Stock, par value $0.00001 per share|UBER|New York Stock Exchange|\\n\\nSecurities registered pursuant to Section 12(g) of the Act: None\\n\\nIndicate by check mark whether the registrant is a well-known seasoned issuer, as defined in Rule 405 of the Securities Act. Yes ☒ No ☐\\n\\nIndicate by check mark whether the registrant is not required to file reports pursuant to Section 13 or Section 15(d) of the Act. Yes ☐ No ☒\\n\\nIndicate by check mark whether the registrant (1) has filed all reports required to be filed by Section 13 or 15(d) of the Securities Exchange Act of 1934 during the preceding 12 months (or for such shorter period that the registrant was required to file such reports), and (2) has been subject to such filing requirements for the past 90 days. Yes ☒ No ☐\\n\\nIndicate by check mark whether the registrant has submitted electronically every Interactive Data File required to be submitted pursuant to Rule 405 of Regulation S-T (§232.405 of this chapter) during the preceding 12 months (or for such shorter period that the registrant was required to submit such files). Yes ☒ No ☐\\n\\nIndicate by check mark whether the registrant is a large accelerated filer, an accelerated filer, a non-accelerated filer, a smaller reporting company, or an emerging growth company. See the definitions of “large accelerated filer,” “accelerated filer,” “smaller reporting company,” and “emerging growth company” in Rule 12b-2 of the Exchange Act.', mimetype='text/plain', start_char_idx=None, end_char_idx=None, metadata_seperator='\\n', text_template='{metadata_str}\\n\\n{content}'),\n",
+       " Document(id_='253b1141-a260-466e-b164-b39df67ef799', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, metadata_template='{key}: {value}', metadata_separator='\\n', text=\"# Large accelerated filer\\n\\n☒\\n\\n# Accelerated filer\\n\\n☐\\n\\n# Non-accelerated filer\\n\\n☐\\n\\n# Smaller reporting company\\n\\n☐\\n\\n# Emerging growth company\\n\\n☐\\n\\nIf an emerging growth company, indicate by check mark if the registrant has elected not to use the extended transition period for complying with any new or revised financial accounting standards provided pursuant to Section 13(a) of the Exchange Act.\\n\\n☐\\n\\nIndicate by check mark whether the registrant has filed a report on and attestation to its management’s assessment of the effectiveness of its internal control over financial reporting under Section 404(b) of the Sarbanes-Oxley Act (15 U.S.C. 7262(b)) by the registered public accounting firm that prepared or issued\\n\\n☒\\n\\nIndicate by check mark whether the registrant is a shell company (as defined in Rule 12b-2 of the Exchange Act). Yes\\n\\n☐\\n\\nNo\\n\\n☒\\n\\nThe aggregate market value of the voting and non-voting common equity held by non-affiliates of the registrant as of June 30, 2021, the last business day of the registrant's most recently completed second fiscal quarter, was approximately $90.5 billion based upon the closing price reported for such date on the New York Stock Exchange.\\n\\nThe number of shares of the registrant's common stock outstanding as of February 22, 2022 was 1,954,464,088.\\n\\n# DOCUMENTS INCORPORATED BY REFERENCE\\n\\nPortions of the registrant’s Definitive Proxy Statement relating to the Annual Meeting of Stockholders are incorporated by reference into Part III of this Annual Report on Form 10-K where indicated. Such Definitive Proxy Statement will be filed with the Securities and Exchange Commission within 120 days after the end of the registrant’s fiscal year ended December 31, 2021.\", mimetype='text/plain', start_char_idx=None, end_char_idx=None, metadata_seperator='\\n', text_template='{metadata_str}\\n\\n{content}'),\n",
+       " Document(id_='ad988239-3ab5-498d-85ba-a29241db24d4', embedding=None, metadata={}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, metadata_template='{key}: {value}', metadata_separator='\\n', text='# UBER TECHNOLOGIES, INC.\\n\\n# TABLE OF CONTENTS\\n\\n|Special Note Regarding Forward-Looking Statements|2|\\n|---|---|\\n|PART I|PART I|\\n|Item 1. Business|4|\\n|Item 1A. Risk Factors|11|\\n|Item 1B. Unresolved Staff Comments|46|\\n|Item 2. Properties|46|\\n|Item 3. Legal Proceedings|46|\\n|Item 4. Mine Safety Disclosures|47|\\n|PART II|PART II|\\n|Item 5. Market for Registrant’s Common Equity, Related Stockholder Matters and Issuer Purchases of Equity Securities|47|\\n|Item 6. [Reserved]|48|\\n|Item 7. Management’s Discussion and Analysis of Financial Condition and Results of Operations|48|\\n|Item 7A. Quantitative and Qualitative Disclosures About Market Risk|69|\\n|Item 8. Financial Statements and Supplementary Data|70|\\n|Item 9. Changes in and Disagreements with Accountants on Accounting and Financial Disclosure|146|\\n|Item 9A. Controls and Procedures|147|\\n|Item 9B. Other Information|147|\\n|Item 9C. Disclosure Regarding Foreign Jurisdictions that Prevent Inspections|147|\\n|PART III|PART III|\\n|Item 10. Directors, Executive Officers and Corporate Governance|147|\\n|Item 11. Executive Compensation|147|\\n|Item 12. Security Ownership of Certain Beneficial Owners and Management and Related Stockholder Matters|148|\\n|Item 13. Certain Relationships and Related Transactions, and Director Independence|148|\\n|Item 14. Principal Accounting Fees and Services|148|\\n|PART IV|PART IV|\\n|Item 15. Exhibits, Financial Statement Schedules|148|\\n|Item 16. Form 10-K Summary|148|\\n|Exhibit Index|149|\\n|Signatures|152|', mimetype='text/plain', start_char_idx=None, end_char_idx=None, metadata_seperator='\\n', text_template='{metadata_str}\\n\\n{content}')]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "documents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "llamacloud",
+   "language": "python",
+   "name": "llamacloud"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
@@ -11,6 +11,8 @@
    "\n",
    "In this cookbook we show you how to build a multimodal report generation agent from a bank of research reports. We use the a set of ICLR papers (which were also used as the dataset in our [DeepLearning.ai course](https://www.deeplearning.ai/short-courses/building-agentic-rag-with-llamaindex/?utm_campaign=llamaindexC2-launch&utm_medium=headband&utm_source=dlai-homepage).\n",
    "\n",
+    "![](multimodal_report_generation_agent_img.png)\n",
+    "\n",
    "We use our workflow abstraction to define an agentic system that contains two main phases: a research phase that pulls in relevant files through chunk-level or file-level retrieval, and then a blog generation phase that synthesizes the final report."
   ]
  },
@@ -1,6 +1,6 @@
 import os
 import asyncio
-from io import TextIOWrapper
+from urllib.parse import urlparse

 import httpx
 import mimetypes
@@ -11,8 +11,7 @@ from contextlib import asynccontextmanager
 from io import BufferedIOBase

 from fsspec import AbstractFileSystem
-from fsspec.spec import AbstractBufferedFile
-from llama_index.core.async_utils import run_jobs
+from llama_index.core.async_utils import asyncio_run, run_jobs
 from llama_index.core.bridge.pydantic import Field, field_validator
 from llama_index.core.constants import DEFAULT_BASE_URL
 from llama_index.core.readers.base import BasePydanticReader
@@ -22,7 +21,6 @@ from llama_parse.utils import (
    nest_asyncio_err,
    nest_asyncio_msg,
    ResultType,
-    Language,
    SUPPORTED_FILE_TYPES,
 )
 from copy import deepcopy
@@ -37,6 +35,7 @@ _DEFAULT_SEPARATOR = "\n---\n"
 class LlamaParse(BasePydanticReader):
    """A smart-parser for files."""

+    # Library / access specific configurations
    api_key: str = Field(
        default="",
        description="The API key for the LlamaParse API.",
@@ -46,8 +45,20 @@ class LlamaParse(BasePydanticReader):
        default=DEFAULT_BASE_URL,
        description="The base URL of the Llama Parsing API.",
    )
-    result_type: ResultType = Field(
-        default=ResultType.TXT, description="The result type for the parser."
+    check_interval: int = Field(
+        default=1,
+        description="The interval in seconds to check if the parsing is done.",
+    )
+    custom_client: Optional[httpx.AsyncClient] = Field(
+        default=None, description="A custom HTTPX client to use for sending requests."
+    )
+    ignore_errors: bool = Field(
+        default=True,
+        description="Whether or not to ignore and skip errors raised during parsing.",
+    )
+    max_timeout: int = Field(
+        default=2000,
+        description="The maximum timeout in seconds to wait for the parsing to finish.",
    )
    num_workers: int = Field(
        default=4,
@@ -55,124 +66,47 @@ class LlamaParse(BasePydanticReader):
        lt=10,
        description="The number of workers to use sending API requests for parsing.",
    )
-    check_interval: int = Field(
-        default=1,
-        description="The interval in seconds to check if the parsing is done.",
-    )
-    max_timeout: int = Field(
-        default=2000,
-        description="The maximum timeout in seconds to wait for the parsing to finish.",
-    )
-    verbose: bool = Field(
-        default=True, description="Whether to print the progress of the parsing."
+    result_type: ResultType = Field(
+        default=ResultType.TXT, description="The result type for the parser."
    )
    show_progress: bool = Field(
        default=True, description="Show progress when parsing multiple files."
    )
-    language: Language = Field(
-        default=Language.ENGLISH, description="The language of the text to parse."
-    )
-    parsing_instruction: Optional[str] = Field(
-        default="", description="The parsing instruction for the parser."
-    )
-    skip_diagonal_text: Optional[bool] = Field(
-        default=False,
-        description="If set to true, the parser will ignore diagonal text (when the text rotation in degrees modulo 90 is not 0).",
-    )
-    invalidate_cache: Optional[bool] = Field(
-        default=False,
-        description="If set to true, the cache will be ignored and the document re-processes. All document are kept in cache for 48hours after the job was completed to avoid processing the same document twice.",
-    )
-    do_not_cache: Optional[bool] = Field(
-        default=False,
-        description="If set to true, the document will not be cached. This mean that you will be re-charged it you reprocess them as they will not be cached.",
-    )
-    fast_mode: Optional[bool] = Field(
-        default=False,
-        description="Note: Non compatible with gpt-4o. If set to true, the parser will use a faster mode to extract text from documents. This mode will skip OCR of images, and table/heading reconstruction.",
-    )
-    premium_mode: bool = Field(
-        default=False,
-        description="Use our best parser mode if set to True.",
-    )
-    continuous_mode: bool = Field(
-        default=False,
-        description="Parse documents continuously, leading to better results on documents where tables span across two pages.",
-    )
-    do_not_unroll_columns: Optional[bool] = Field(
-        default=False,
-        description="If set to true, the parser will keep column in the text according to document layout. Reduce reconstruction accuracy, and LLM's/embedings performances in most case.",
-    )
-    page_separator: Optional[str] = Field(
-        default=None,
-        description="A templated  page separator to use to split the text.  If it contain `{page_number}`,it will be replaced by the next page number. If not set will the default separator '\\n---\\n' will be used.",
-    )
-    page_prefix: Optional[str] = Field(
-        default=None,
-        description="A templated prefix to add to the beginning of each page. If it contain `{page_number}`, it will be replaced by the page number.",
-    )
-    page_suffix: Optional[str] = Field(
-        default=None,
-        description="A templated suffix to add to the beginning of each page. If it contain `{page_number}`, it will be replaced by the page number.",
-    )
-    gpt4o_mode: bool = Field(
-        default=False,
-        description="Whether to use gpt-4o extract text from documents.",
-    )
-    gpt4o_api_key: Optional[str] = Field(
-        default=None,
-        description="The API key for the GPT-4o API. Lowers the cost of parsing.",
-    )
-    bounding_box: Optional[str] = Field(
-        default=None,
-        description="The bounding box to use to extract text from documents describe as a string containing the bounding box margins",
-    )
-    target_pages: Optional[str] = Field(
-        default=None,
-        description="The target pages to extract text from documents. Describe as a comma separated list of page numbers. The first page of the document is page 0",
-    )
-    ignore_errors: bool = Field(
-        default=True,
-        description="Whether or not to ignore and skip errors raised during parsing.",
-    )
    split_by_page: bool = Field(
        default=True,
        description="Whether to split by page using the page separator",
    )
-    vendor_multimodal_api_key: Optional[str] = Field(
-        default=None,
-        description="The API key for the multimodal API.",
+    verbose: bool = Field(
+        default=True, description="Whether to print the progress of the parsing."
    )
-    use_vendor_multimodal_model: bool = Field(
-        default=False,
-        description="Whether to use the vendor multimodal API.",
-    )
-    vendor_multimodal_model_name: Optional[str] = Field(
-        default=None,
-        description="The model name for the vendor multimodal API.",
-    )
-    take_screenshot: bool = Field(
-        default=False,
-        description="Whether to take screenshot of each page of the document.",
-    )
-    custom_client: Optional[httpx.AsyncClient] = Field(
-        default=None, description="A custom HTTPX client to use for sending requests."
-    )
-    disable_ocr: bool = Field(
-        default=False,
-        description="Disable the OCR on the document. LlamaParse will only extract the copyable text from the document.",
-    )
-    is_formatting_instruction: bool = Field(
-        default=True,
-        description="Allow the parsing instruction to also format the output. Disable to have a cleaner markdown output.",
-    )
-    annotate_links: bool = Field(
+
+    # Parsing specific configurations (Alphabetical order)
+    annotate_links: Optional[bool] = Field(
        default=False,
        description="Annotate links found in the document to extract their URL.",
    )
-    webhook_url: Optional[str] = Field(
+    auto_mode: Optional[bool] = Field(
+        default=False,
+        description="If set to true, the parser will automatically select the best mode to extract text from documents based on the rules provide. Will use the 'accurate' default mode by default and will upgrade page that match the rule to Premium mode.",
+    )
+    auto_mode_trigger_on_image_in_page: Optional[bool] = Field(
+        default=False,
+        description="If auto_mode is set to true, the parser will upgrade the page that contain an image to Premium mode.",
+    )
+    auto_mode_trigger_on_table_in_page: Optional[bool] = Field(
+        default=False,
+        description="If auto_mode is set to true, the parser will upgrade the page that contain a table to Premium mode.",
+    )
+    auto_mode_trigger_on_text_in_page: Optional[str] = Field(
        default=None,
-        description="A URL that needs to be called at the end of the parsing job.",
+        description="If auto_mode is set to true, the parser will upgrade the page that contain the text to Premium mode.",
+    )
+    auto_mode_trigger_on_regexp_in_page: Optional[str] = Field(
+        default=None,
+        description="If auto_mode is set to true, the parser will upgrade the page that match the regexp to Premium mode.",
+    )
+    azure_openai_api_version: Optional[str] = Field(
+        default=None, description="Azure Openai API Version"
    )
    azure_openai_deployment_name: Optional[str] = Field(
        default=None, description="Azure Openai Deployment Name"
@@ -180,12 +114,169 @@ class LlamaParse(BasePydanticReader):
    azure_openai_endpoint: Optional[str] = Field(
        default=None, description="Azure Openai Endpoint"
    )
-    azure_openai_api_version: Optional[str] = Field(
-        default=None, description="Azure Openai API Version"
-    )
    azure_openai_key: Optional[str] = Field(
        default=None, description="Azure Openai Key"
    )
+    bbox_bottom: Optional[float] = Field(
+        default=None,
+        description="The bottom margin of the bounding box to use to extract text from documents expressed as a float between 0 and 1 representing the percentage of the page height.",
+    )
+    bbox_left: Optional[float] = Field(
+        default=None,
+        description="The left margin of the bounding box to use to extract text from documents expressed as a float between 0 and 1 representing the percentage of the page width.",
+    )
+    bbox_right: Optional[float] = Field(
+        default=None,
+        description="The right margin of the bounding box to use to extract text from documents expressed as a float between 0 and 1 representing the percentage of the page width.",
+    )
+    bbox_top: Optional[float] = Field(
+        default=None,
+        description="The top margin of the bounding box to use to extract text from documents expressed as a float between 0 and 1 representing the percentage of the page height.",
+    )
+    continuous_mode: Optional[bool] = Field(
+        default=False,
+        description="Parse documents continuously, leading to better results on documents where tables span across two pages.",
+    )
+    disable_ocr: Optional[bool] = Field(
+        default=False,
+        description="Disable the OCR on the document. LlamaParse will only extract the copyable text from the document.",
+    )
+    disable_image_extraction: Optional[bool] = Field(
+        default=False,
+        description="If set to true, the parser will not extract images from the document. Make the parser faster.",
+    )
+    do_not_cache: Optional[bool] = Field(
+        default=False,
+        description="If set to true, the document will not be cached. This mean that you will be re-charged it you reprocess them as they will not be cached.",
+    )
+    do_not_unroll_columns: Optional[bool] = Field(
+        default=False,
+        description="If set to true, the parser will keep column in the text according to document layout. Reduce reconstruction accuracy, and LLM's/embedings performances in most case.",
+    )
+    extract_charts: Optional[bool] = Field(
+        default=False,
+        description="If set to true, the parser will extract/tag charts from the document.",
+    )
+    fast_mode: Optional[bool] = Field(
+        default=False,
+        description="Note: Non compatible with gpt-4o. If set to true, the parser will use a faster mode to extract text from documents. This mode will skip OCR of images, and table/heading reconstruction.",
+    )
+    guess_xlsx_sheet_names: Optional[bool] = Field(
+        default=False,
+        description="Whether to guess the sheet names of the xlsx file.",
+    )
+    html_make_all_elements_visible: Optional[bool] = Field(
+        default=False,
+        description="If set to true, when parsing HTML the parser will consider all elements display not element as display block.",
+    )
+    html_remove_fixed_elements: Optional[bool] = Field(
+        default=False,
+        description="If set to true, when parsing HTML the parser will remove fixed elements. Useful to hide cookie banners.",
+    )
+    html_remove_navigation_elements: Optional[bool] = Field(
+        default=False,
+        description="If set to true, when parsing HTML the parser will remove navigation elements. Useful to hide menus, header, footer.",
+    )
+    http_proxy: Optional[str] = Field(
+        default=None,
+        description="(optional) If set with input_url will use the specified http proxy to download the file.",
+    )
+    invalidate_cache: Optional[bool] = Field(
+        default=False,
+        description="If set to true, the cache will be ignored and the document re-processes. All document are kept in cache for 48hours after the job was completed to avoid processing the same document twice.",
+    )
+    is_formatting_instruction: Optional[bool] = Field(
+        default=False,
+        description="Allow the parsing instruction to also format the output. Disable to have a cleaner markdown output.",
+    )
+    language: Optional[str] = Field(
+        default="en", description="The language of the text to parse."
+    )
+    max_pages: Optional[int] = Field(
+        default=None,
+        description="The maximum number of pages to extract text from documents. If set to 0 or not set, all pages will be that should be extracted will be extracted (can work in combination with targetPages).",
+    )
+    output_pdf_of_document: Optional[bool] = Field(
+        default=False,
+        description="If set to true, the parser will also output a PDF of the document. (except for spreadsheets)",
+    )
+    output_s3_path_prefix: Optional[str] = Field(
+        default=None,
+        description="An S3 path prefix to store the output of the parsing job. If set, the parser will upload the output to S3. The bucket need to be accessible from the LlamaIndex organization.",
+    )
+    page_prefix: Optional[str] = Field(
+        default=None,
+        description="A templated prefix to add to the beginning of each page. If it contain `{page_number}`, it will be replaced by the page number.",
+    )
+    page_separator: Optional[str] = Field(
+        default=None,
+        description="A templated  page separator to use to split the text.  If it contain `{page_number}`,it will be replaced by the next page number. If not set will the default separator '\\n---\\n' will be used.",
+    )
+    page_suffix: Optional[str] = Field(
+        default=None,
+        description="A templated suffix to add to the beginning of each page. If it contain `{page_number}`, it will be replaced by the page number.",
+    )
+    parsing_instruction: Optional[str] = Field(
+        default="", description="The parsing instruction for the parser."
+    )
+    premium_mode: Optional[bool] = Field(
+        default=False,
+        description="Use our best parser mode if set to True.",
+    )
+    skip_diagonal_text: Optional[bool] = Field(
+        default=False,
+        description="If set to true, the parser will ignore diagonal text (when the text rotation in degrees modulo 90 is not 0).",
+    )
+    structured_output: Optional[bool] = Field(
+        default=False,
+        description="If set to true, the parser will output structured data based on the provided JSON Schema.",
+    )
+    structured_output_json_schema: Optional[str] = Field(
+        default=None,
+        description="A JSON Schema to use to structure the output of the parsing job. If set, the parser will output structured data based on the provided JSON Schema.",
+    )
+    structured_output_json_schema_name: Optional[str] = Field(
+        default=None,
+        description="The named JSON Schema to use to structure the output of the parsing job. For convenience / testing, LlamaParse provides a few named JSON Schema that can be used directly. Use 'imFeelingLucky' to let llamaParse dream the schema.",
+    )
+    take_screenshot: Optional[bool] = Field(
+        default=False,
+        description="Whether to take screenshot of each page of the document.",
+    )
+    target_pages: Optional[str] = Field(
+        default=None,
+        description="The target pages to extract text from documents. Describe as a comma separated list of page numbers. The first page of the document is page 0",
+    )
+    use_vendor_multimodal_model: Optional[bool] = Field(
+        default=False,
+        description="Whether to use the vendor multimodal API.",
+    )
+    vendor_multimodal_api_key: Optional[str] = Field(
+        default=None,
+        description="The API key for the multimodal API.",
+    )
+    vendor_multimodal_model_name: Optional[str] = Field(
+        default=None,
+        description="The model name for the vendor multimodal API.",
+    )
+    webhook_url: Optional[str] = Field(
+        default=None,
+        description="A URL that needs to be called at the end of the parsing job.",
+    )
+
+    # Deprecated
+    bounding_box: Optional[str] = Field(
+        default=None,
+        description="The bounding box to use to extract text from documents describe as a string containing the bounding box margins",
+    )
+    gpt4o_mode: Optional[bool] = Field(
+        default=False,
+        description="Whether to use gpt-4o extract text from documents.",
+    )
+    gpt4o_api_key: Optional[str] = Field(
+        default=None,
+        description="The API key for the GPT-4o API. Lowers the cost of parsing.",
+    )

    @field_validator("api_key", mode="before", check_fields=True)
    @classmethod
@@ -217,6 +308,38 @@ class LlamaParse(BasePydanticReader):
            async with httpx.AsyncClient(timeout=self.max_timeout) as client:
                yield client

+    def _is_input_url(self, file_path: FileInput) -> bool:
+        """Check if the input is a valid URL.
+
+        This method checks for:
+        - Proper URL scheme (http/https)
+        - Valid URL structure
+        - Network location (domain)
+        """
+        if not isinstance(file_path, str):
+            return False
+        try:
+            result = urlparse(file_path)
+            return all(
+                [
+                    result.scheme in ("http", "https"),
+                    result.netloc,  # Has domain
+                    result.scheme,  # Has scheme
+                ]
+            )
+        except Exception:
+            return False
+
+    def _is_s3_url(self, file_path: FileInput) -> bool:
+        """Check if the input is a valid URL.
+
+        This method checks for:
+        - Proper S3 scheme (s3://)
+        """
+        if isinstance(file_path, str):
+            return file_path.startswith("s3://")
+        return False
+
    # upload a document and get back a job_id
    async def _create_job(
        self,
@@ -228,6 +351,8 @@ class LlamaParse(BasePydanticReader):
        url = f"{self.base_url}/api/parsing/upload"
        files = None
        file_handle = None
+        input_url = file_input if self._is_input_url(file_input) else None
+        input_s3_path = file_input if self._is_s3_url(file_input) else None

        if isinstance(file_input, (bytes, BufferedIOBase)):
            if not extra_info or "file_name" not in extra_info:
@@ -237,6 +362,10 @@ class LlamaParse(BasePydanticReader):
            file_name = extra_info["file_name"]
            mime_type = mimetypes.guess_type(file_name)[0]
            files = {"file": (file_name, file_input, mime_type)}
+        elif input_url is not None:
+            files = None
+        elif input_s3_path is not None:
+            files = None
        elif isinstance(file_input, (str, Path, PurePosixPath, PurePath)):
            file_path = str(file_input)
            file_ext = os.path.splitext(file_path)[1].lower()
@@ -256,60 +385,183 @@ class LlamaParse(BasePydanticReader):
                "file_input must be either a file path string, file bytes, or buffer object"
            )

-        data = {
-            "language": self.language.value,
-            "parsing_instruction": self.parsing_instruction,
-            "invalidate_cache": self.invalidate_cache,
-            "skip_diagonal_text": self.skip_diagonal_text,
-            "do_not_cache": self.do_not_cache,
-            "fast_mode": self.fast_mode,
-            "premium_mode": self.premium_mode,
-            "continuous_mode": self.continuous_mode,
-            "do_not_unroll_columns": self.do_not_unroll_columns,
-            "gpt4o_mode": self.gpt4o_mode,
-            "gpt4o_api_key": self.gpt4o_api_key,
-            "vendor_multimodal_api_key": self.vendor_multimodal_api_key,
-            "use_vendor_multimodal_model": self.use_vendor_multimodal_model,
-            "vendor_multimodal_model_name": self.vendor_multimodal_model_name,
-            "take_screenshot": self.take_screenshot,
-            "disable_ocr": self.disable_ocr,
-            "is_formatting_instruction": self.is_formatting_instruction,
-            "annotate_links": self.annotate_links,
-        }
+        data: Dict[str, Any] = {}

-        # only send page separator to server if it is not None
-        # as if a null, "" string is sent the server will then ignore the page separator instead of using the default
-        if self.page_separator is not None:
-            data["page_separator"] = self.page_separator
+        data["from_python_package"] = True

-        if self.page_prefix is not None:
-            data["page_prefix"] = self.page_prefix
+        if self.annotate_links:
+            data["annotate_links"] = self.annotate_links

-        if self.page_suffix is not None:
-            data["page_suffix"] = self.page_suffix
+        if self.auto_mode:
+            data["auto_mode"] = self.auto_mode

-        if self.bounding_box is not None:
-            data["bounding_box"] = self.bounding_box
+        if self.auto_mode_trigger_on_image_in_page:
+            data[
+                "auto_mode_trigger_on_image_in_page"
+            ] = self.auto_mode_trigger_on_image_in_page

-        if self.target_pages is not None:
-            data["target_pages"] = self.target_pages
+        if self.auto_mode_trigger_on_table_in_page:
+            data[
+                "auto_mode_trigger_on_table_in_page"
+            ] = self.auto_mode_trigger_on_table_in_page

-        if self.webhook_url is not None:
-            data["webhook_url"] = self.webhook_url
+        if self.auto_mode_trigger_on_text_in_page is not None:
+            data[
+                "auto_mode_trigger_on_text_in_page"
+            ] = self.auto_mode_trigger_on_text_in_page
+
+        if self.auto_mode_trigger_on_regexp_in_page is not None:
+            data[
+                "auto_mode_trigger_on_regexp_in_page"
+            ] = self.auto_mode_trigger_on_regexp_in_page
+
+        if self.azure_openai_api_version is not None:
+            data["azure_openai_api_version"] = self.azure_openai_api_version

-        # Azure OpenAI
        if self.azure_openai_deployment_name is not None:
            data["azure_openai_deployment_name"] = self.azure_openai_deployment_name

        if self.azure_openai_endpoint is not None:
            data["azure_openai_endpoint"] = self.azure_openai_endpoint

-        if self.azure_openai_api_version is not None:
-            data["azure_openai_api_version"] = self.azure_openai_api_version
-
        if self.azure_openai_key is not None:
            data["azure_openai_key"] = self.azure_openai_key

+        if self.bbox_bottom is not None:
+            data["bbox_bottom"] = self.bbox_bottom
+
+        if self.bbox_left is not None:
+            data["bbox_left"] = self.bbox_left
+
+        if self.bbox_right is not None:
+            data["bbox_right"] = self.bbox_right
+
+        if self.bbox_top is not None:
+            data["bbox_top"] = self.bbox_top
+
+        if self.continuous_mode:
+            data["continuous_mode"] = self.continuous_mode
+
+        if self.disable_ocr:
+            data["disable_ocr"] = self.disable_ocr
+
+        if self.disable_image_extraction:
+            data["disable_image_extraction"] = self.disable_image_extraction
+
+        if self.do_not_cache:
+            data["do_not_cache"] = self.do_not_cache
+
+        if self.do_not_unroll_columns:
+            data["do_not_unroll_columns"] = self.do_not_unroll_columns
+
+        if self.extract_charts:
+            data["extract_charts"] = self.extract_charts
+
+        if self.fast_mode:
+            data["fast_mode"] = self.fast_mode
+
+        if self.guess_xlsx_sheet_names:
+            data["guess_xlsx_sheet_names"] = self.guess_xlsx_sheet_names
+
+        if self.html_make_all_elements_visible:
+            data["html_make_all_elements_visible"] = self.html_make_all_elements_visible
+
+        if self.html_remove_fixed_elements:
+            data["html_remove_fixed_elements"] = self.html_remove_fixed_elements
+
+        if self.html_remove_navigation_elements:
+            data[
+                "html_remove_navigation_elements"
+            ] = self.html_remove_navigation_elements
+
+        if self.http_proxy is not None:
+            data["http_proxy"] = self.http_proxy
+
+        if input_url is not None:
+            files = None
+            data["input_url"] = str(input_url)
+
+        if input_s3_path is not None:
+            files = None
+            data["input_s3_path"] = str(input_s3_path)
+
+        if self.invalidate_cache:
+            data["invalidate_cache"] = self.invalidate_cache
+
+        if self.is_formatting_instruction:
+            data["is_formatting_instruction"] = self.is_formatting_instruction
+
+        if self.language:
+            data["language"] = self.language
+
+        if self.max_pages is not None:
+            data["max_pages"] = self.max_pages
+
+        if self.output_pdf_of_document:
+            data["output_pdf_of_document"] = self.output_pdf_of_document
+
+        if self.output_s3_path_prefix is not None:
+            data["output_s3_path_prefix"] = self.output_s3_path_prefix
+
+        if self.page_prefix is not None:
+            data["page_prefix"] = self.page_prefix
+
+        # only send page separator to server if it is not None
+        # as if a null, "" string is sent the server will then ignore the page separator instead of using the default
+        if self.page_separator is not None:
+            data["page_separator"] = self.page_separator
+
+        if self.page_suffix is not None:
+            data["page_suffix"] = self.page_suffix
+
+        if self.parsing_instruction is not None:
+            data["parsing_instruction"] = self.parsing_instruction
+
+        if self.premium_mode:
+            data["premium_mode"] = self.premium_mode
+
+        if self.skip_diagonal_text:
+            data["skip_diagonal_text"] = self.skip_diagonal_text
+
+        if self.structured_output:
+            data["structured_output"] = self.structured_output
+
+        if self.structured_output_json_schema is not None:
+            data["structured_output_json_schema"] = self.structured_output_json_schema
+
+        if self.structured_output_json_schema_name is not None:
+            data[
+                "structured_output_json_schema_name"
+            ] = self.structured_output_json_schema_name
+
+        if self.take_screenshot:
+            data["take_screenshot"] = self.take_screenshot
+
+        if self.target_pages is not None:
+            data["target_pages"] = self.target_pages
+
+        if self.use_vendor_multimodal_model:
+            data["use_vendor_multimodal_model"] = self.use_vendor_multimodal_model
+
+        if self.vendor_multimodal_api_key is not None:
+            data["vendor_multimodal_api_key"] = self.vendor_multimodal_api_key
+
+        if self.vendor_multimodal_model_name is not None:
+            data["vendor_multimodal_model_name"] = self.vendor_multimodal_model_name
+
+        if self.webhook_url is not None:
+            data["webhook_url"] = self.webhook_url
+
+        # Deprecated
+        if self.bounding_box is not None:
+            data["bounding_box"] = self.bounding_box
+
+        if self.gpt4o_mode:
+            data["gpt4o_mode"] = self.gpt4o_mode
+
+        if self.gpt4o_api_key is not None:
+            data["gpt4o_api_key"] = self.gpt4o_api_key
+
        try:
            async with self.client_context() as client:
                response = await client.post(
@@ -326,12 +578,6 @@ class LlamaParse(BasePydanticReader):
            if file_handle is not None:
                file_handle.close()

-    @staticmethod
-    def __get_filename(f: Union[TextIOWrapper, AbstractBufferedFile]) -> str:
-        if isinstance(f, TextIOWrapper):
-            return f.name
-        return f.full_name
-
    async def _get_job_result(
        self, job_id: str, result_type: str, verbose: bool = False
    ) -> Dict[str, Any]:
@@ -467,7 +713,7 @@ class LlamaParse(BasePydanticReader):
    ) -> List[Document]:
        """Load data from the input path."""
        try:
-            return asyncio.run(self.aload_data(file_path, extra_info, fs=fs))
+            return asyncio_run(self.aload_data(file_path, extra_info, fs=fs))
        except RuntimeError as e:
            if nest_asyncio_err in str(e):
                raise RuntimeError(nest_asyncio_msg)
@@ -534,7 +780,7 @@ class LlamaParse(BasePydanticReader):
    ) -> List[dict]:
        """Parse the input path."""
        try:
-            return asyncio.run(self.aget_json(file_path, extra_info))
+            return asyncio_run(self.aget_json(file_path, extra_info))
        except RuntimeError as e:
            if nest_asyncio_err in str(e):
                raise RuntimeError(nest_asyncio_msg)
@@ -597,7 +843,61 @@ class LlamaParse(BasePydanticReader):
    def get_images(self, json_result: List[dict], download_path: str) -> List[dict]:
        """Download images from the parsed result."""
        try:
-            return asyncio.run(self.aget_images(json_result, download_path))
+            return asyncio_run(self.aget_images(json_result, download_path))
+        except RuntimeError as e:
+            if nest_asyncio_err in str(e):
+                raise RuntimeError(nest_asyncio_msg)
+            else:
+                raise e
+
+    async def aget_xlsx(
+        self, json_result: List[dict], download_path: str
+    ) -> List[dict]:
+        """Download images from the parsed result."""
+        headers = {"Authorization": f"Bearer {self.api_key}"}
+
+        # make the download path
+        if not os.path.exists(download_path):
+            os.makedirs(download_path)
+        try:
+            xlsx_list = []
+            for result in json_result:
+                job_id = result["job_id"]
+                if self.verbose:
+                    print("> XLSX")
+
+                xlsx_path = os.path.join(download_path, f"{job_id}.xlsx")
+
+                xlsx = {}
+
+                xlsx["path"] = xlsx_path
+                xlsx["job_id"] = job_id
+                xlsx["original_file_path"] = result.get("file_path", None)
+
+                with open(xlsx_path, "wb") as f:
+                    xlsx_url = (
+                        f"{self.base_url}/api/parsing/job/{job_id}/result/raw/xlsx"
+                    )
+                    async with self.client_context() as client:
+                        res = await client.get(
+                            xlsx_url, headers=headers, timeout=self.max_timeout
+                        )
+                        res.raise_for_status()
+                        f.write(res.content)
+                xlsx_list.append(xlsx)
+            return xlsx_list
+
+        except Exception as e:
+            print("Error while downloading xlsx:", e)
+            if self.ignore_errors:
+                return []
+            else:
+                raise e
+
+    def get_xlsx(self, json_result: List[dict], download_path: str) -> List[dict]:
+        """Download xlsx from the parsed result."""
+        try:
+            return asyncio_run(self.aget_xlsx(json_result, download_path))
        except RuntimeError as e:
            if nest_asyncio_err in str(e):
                raise RuntimeError(nest_asyncio_msg)
@@ -10,6 +10,8 @@ class ResultType(str, Enum):

    TXT = "text"
    MD = "markdown"
+    JSON = "json"
+    STRUCTURED = "structured"


 class Language(str, Enum):
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"

 [tool.poetry]
 name = "llama-parse"
-version = "0.5.12"
+version = "0.5.17"
 description = "Parse files into RAG-Optimized formats."
 authors = ["Logan Markewich <logan@llamaindex.ai>"]
 license = "MIT"
@@ -12,12 +12,14 @@ readme = "README.md"
 packages = [{include = "llama_parse"}]

 [tool.poetry.dependencies]
-python = ">=3.8.1,<4.0"
+python = ">=3.9,<4.0"
 llama-index-core = ">=0.11.0"
+pydantic = "!=2.10"
 click = "^8.1.7"

 [tool.poetry.group.dev.dependencies]
 pytest = "^8.0.0"
+pytest-asyncio = "*"
 ipykernel = "^6.29.0"

 [tool.poetry.scripts]
@@ -1,5 +1,6 @@
 import os
 import pytest
+import shutil
 from fsspec.implementations.local import LocalFileSystem
 from httpx import AsyncClient

@@ -76,13 +77,14 @@ def test_simple_page_markdown_buffer(markdown_parser: LlamaParse) -> None:
    os.environ.get("LLAMA_CLOUD_API_KEY", "") == "",
    reason="LLAMA_CLOUD_API_KEY not set",
 )
-def test_simple_page_with_custom_fs() -> None:
+@pytest.mark.asyncio
+async def test_simple_page_with_custom_fs() -> None:
    parser = LlamaParse(result_type="markdown")
    fs = LocalFileSystem()
    filepath = os.path.join(
        os.path.dirname(__file__), "test_files/attention_is_all_you_need.pdf"
    )
-    result = parser.load_data(filepath, fs=fs)
+    result = await parser.aload_data(filepath, fs=fs)
    assert len(result) == 1


@@ -90,13 +92,14 @@ def test_simple_page_with_custom_fs() -> None:
    os.environ.get("LLAMA_CLOUD_API_KEY", "") == "",
    reason="LLAMA_CLOUD_API_KEY not set",
 )
-def test_simple_page_progress_workers() -> None:
+@pytest.mark.asyncio
+async def test_simple_page_progress_workers() -> None:
    parser = LlamaParse(result_type="markdown", show_progress=True, verbose=True)

    filepath = os.path.join(
        os.path.dirname(__file__), "test_files/attention_is_all_you_need.pdf"
    )
-    result = parser.load_data([filepath, filepath])
+    result = await parser.aload_data([filepath, filepath])
    assert len(result) == 2
    assert len(result[0].text) > 0

@@ -107,7 +110,7 @@ def test_simple_page_progress_workers() -> None:
    filepath = os.path.join(
        os.path.dirname(__file__), "test_files/attention_is_all_you_need.pdf"
    )
-    result = parser.load_data([filepath, filepath])
+    result = await parser.aload_data([filepath, filepath])
    assert len(result) == 2
    assert len(result[0].text) > 0

@@ -116,12 +119,79 @@ def test_simple_page_progress_workers() -> None:
    os.environ.get("LLAMA_CLOUD_API_KEY", "") == "",
    reason="LLAMA_CLOUD_API_KEY not set",
 )
-def test_custom_client() -> None:
+@pytest.mark.asyncio
+async def test_custom_client() -> None:
    custom_client = AsyncClient(verify=False, timeout=10)
    parser = LlamaParse(result_type="markdown", custom_client=custom_client)
    filepath = os.path.join(
        os.path.dirname(__file__), "test_files/attention_is_all_you_need.pdf"
    )
-    result = parser.load_data(filepath)
+    result = await parser.aload_data(filepath)
    assert len(result) == 1
    assert len(result[0].text) > 0
+
+
+@pytest.mark.skipif(
+    os.environ.get("LLAMA_CLOUD_API_KEY", "") == "",
+    reason="LLAMA_CLOUD_API_KEY not set",
+)
+@pytest.mark.asyncio
+async def test_input_url() -> None:
+    parser = LlamaParse(result_type="markdown")
+
+    # links to a resume example
+    input_url = "https://cdn-blog.novoresume.com/articles/google-docs-resume-templates/basic-google-docs-resume.png"
+    result = await parser.aload_data(input_url)
+    assert len(result) == 1
+    assert "your name" in result[0].text.lower()
+
+
+@pytest.mark.skipif(
+    os.environ.get("LLAMA_CLOUD_API_KEY", "") == "",
+    reason="LLAMA_CLOUD_API_KEY not set",
+)
+@pytest.mark.asyncio
+async def test_input_url_with_website_input() -> None:
+    parser = LlamaParse(result_type="markdown")
+    input_url = "https://www.google.com"
+    result = await parser.aload_data(input_url)
+    assert len(result) == 1
+    assert "google" in result[0].text.lower()
+
+
+@pytest.mark.skipif(
+    os.environ.get("LLAMA_CLOUD_API_KEY", "") == "",
+    reason="LLAMA_CLOUD_API_KEY not set",
+)
+@pytest.mark.asyncio
+async def test_mixing_input_types() -> None:
+    parser = LlamaParse(result_type="markdown")
+    filepath = os.path.join(
+        os.path.dirname(__file__), "test_files/attention_is_all_you_need.pdf"
+    )
+    input_url = "https://cdn-blog.novoresume.com/articles/google-docs-resume-templates/basic-google-docs-resume.png"
+    result = await parser.aload_data([filepath, input_url])
+
+    assert len(result) == 2
+
+
+@pytest.mark.skipif(
+    os.environ.get("LLAMA_CLOUD_API_KEY", "") == "",
+    reason="LLAMA_CLOUD_API_KEY not set",
+)
+@pytest.mark.asyncio
+async def test_download_images() -> None:
+    parser = LlamaParse(result_type="markdown", take_screenshot=True)
+    filepath = os.path.join(
+        os.path.dirname(__file__), "test_files/attention_is_all_you_need.pdf"
+    )
+    json_result = await parser.aget_json([filepath])
+
+    assert len(json_result) == 1
+    assert len(json_result[0]["pages"][0]["images"]) > 0
+
+    download_path = os.path.join(os.path.dirname(__file__), "test_files/images")
+    shutil.rmtree(download_path, ignore_errors=True)
+
+    await parser.aget_images(json_result, download_path)
+    assert len(os.listdir(download_path)) == len(json_result[0]["pages"][0]["images"])
Author	SHA1	Message	Date
Pierre-Loic Doulcet	963bd2dd2f	Add support for html_remove_navigation_elements.	2024-12-06 11:03:16 +01:00
Ravi Theja	cdbddef86d	Add demo videos notebooks (#529 )	2024-12-05 08:38:34 -08:00
Pierre-Loic Doulcet	3690109abf	Add more parameters (#525 ) * add after revert * 3.8 so numpy work * change defaults * change requested * change requested	2024-12-04 15:39:00 +01:00
Pierre-Loic Doulcet	2e322b4fc8	Revert "Add more paramerters" This reverts commit `735e5f3ddc`.	2024-12-04 10:20:07 +01:00
Pierre-Loic Doulcet	735e5f3ddc	Add more paramerters	2024-12-04 10:17:08 +01:00
Logan	e4cb4c75e5	add test for downloading images (#506 )	2024-11-21 13:08:29 -06:00
Jerry Liu	1693deff72	dynamic section retrieval nb (#484 )	2024-11-13 13:29:30 +01:00
Jerry Liu	3270f1228d	multimodal report generation image (#461 ) * cr * cr	2024-11-13 13:28:07 +01:00
Pierre-Loic Doulcet	eeabf48d29	add input url and http_proxy (#475 )	2024-11-12 12:56:58 -06:00
Pierre-Loic Doulcet	89348aa8e5	add xlsx support (#472 )	2024-11-01 10:09:17 -06:00