read project_id from env

* parse: fix job result format

* bump to 0.6.28

.github/workflows/publish_release.yml

@@ -39,10 +39,18 @@ jobs:
           pypi_token: ${{ secrets.LLAMA_PARSE_PYPI_TOKEN }}
           poetry_install_options: "--without dev"
 
+      - name: Wait for PyPI to update
+        run: |
+          sleep 60
+
+      - name: Update llama-parse lock file
+        run: |
+          cd llama_parse && poetry lock
+
       - name: Build and publish llama-parse
         uses: JRubics/poetry-publish@v2.1
         with:
-          working_directory: "llama_parse"
+          package_directory: "./llama_parse"
           pypi_token: ${{ secrets.LLAMA_PARSE_PYPI_TOKEN }}
           poetry_install_options: "--without dev"
 

examples/extract/data/insider_transactions/.gitignore

@@ -0,0 +1 @@
+sec_form_4_dump.json

examples/extract/extract_data_with_citations.ipynb

@@ -0,0 +1,440 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extract Data from Financial Reports - with Citations and Reasoning\n",
+    "\n",
+    "Given complex files like financial reports, contracts, invoices etc, Llama Extract allows you to make use of an LLM to extract the information relevant to you, in a structured format.\n",
+    "\n",
+    "In this example, we'll be using [LlamaExtract](https://docs.cloud.llamaindex.ai/llamaextract/getting_started?utm_campaign=extract&utm_medium=recipe) to extract structured data from an SEC filing (specifically, the filing by Nvidia for fiscal year 2025).\n",
+    "\n",
+    "On top of simple data extraction, we'll ask our extraction agent to provide citations and reasoning for each extracted field. This allows us to:\n",
+    "- Confirm  the accuracy of the extracted field\n",
+    "- Understand the reasoning behind why the LLM extracted a given piece of information\n",
+    "- This last point allows us an opportunity to adjust the system prompt or field descriptions and improve on results where needed.\n",
+    "\n",
+    "\n",
+    "The example we go through below is also replicable within Llama Cloud as well, where you will also be able to pick between a number of pre-defined schemas, instead of building your own."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install llama-cloud-services"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Connect to Llama Cloud\n",
+    "\n",
+    "To get started, make sure you provide your [Llama Cloud](https://cloud.llamaindex.ai?utm_campaign=extract&utm_medium=recipe) API key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Enter your Llama Cloud API Key: ··········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "if \"LLAMA_CLOUD_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"LLAMA_CLOUD_API_KEY\"] = getpass(\"Enter your Llama Cloud API Key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extract Data with Llama Extract Agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No project_id provided, fetching default project.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from llama_cloud_services import LlamaExtract\n",
+    "\n",
+    "# Optionally, provide your project id, if not, it will use the 'Default' project\n",
+    "llama_extract = LlamaExtract()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Provide Your Custom Schema\n",
+    "\n",
+    "When using LlamaExtract via the API, you provide your own schema that describes what you want extracted from files and data provided to your agent. Here, we are essentially building an SEC filings extraction agent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pydantic import BaseModel, Field\n",
+    "from enum import Enum\n",
+    "\n",
+    "\n",
+    "class FilingType(str, Enum):\n",
+    "    ten_k = \"10 K\"\n",
+    "    ten_q = \"10-Q\"\n",
+    "    ten_ka = \"10-K/A\"\n",
+    "    ten_qa = \"10-Q/A\"\n",
+    "\n",
+    "\n",
+    "class FinancialReport(BaseModel):\n",
+    "    company_name: str = Field(description=\"The name of the company\")\n",
+    "    description: str = Field(\n",
+    "        description=\"Short description of the filing and what it contains\"\n",
+    "    )\n",
+    "    filing_type: FilingType = Field(description=\"Type of SEC filing\")\n",
+    "    filing_date: str = Field(description=\"Date when filing was submitted to SEC\")\n",
+    "    fiscal_year: int = Field(description=\"Fiscal year\")\n",
+    "    unit: str = Field(\n",
+    "        description=\"Unit of financial figures (thousands, millions, etc.)\"\n",
+    "    )\n",
+    "    revenue: int = Field(description=\"Total revenue for period\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set Up Citations and Reasoning\n",
+    "\n",
+    "Optionally, we can set the `ExtractConfig` to extract citations for each field the agent extracts. These cications will cite the specific pages and sections of the file from which a given field was extractedd.\n",
+    "\n",
+    "By setting `use_reasoning` to True, we als ask the agent to do an additional reasoning step, explaining why a given field was extracted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_cloud.types import ExtractConfig, ExtractMode\n",
+    "\n",
+    "config = ExtractConfig(\n",
+    "    use_reasoning=True, cite_sources=True, extraction_mode=ExtractMode.MULTIMODAL\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.11/dist-packages/llama_cloud_services/extract/extract.py:127: ExperimentalWarning: `use_reasoning` is an experimental feature. Results will be available in the `extraction_metadata` field for the extraction run.\n",
+      "  warnings.warn(\n",
+      "/usr/local/lib/python3.11/dist-packages/llama_cloud_services/extract/extract.py:133: ExperimentalWarning: `cite_sources` is an experimental feature. This may greatly increase the size of the response, and slow down the extraction. Results will be available in the `extraction_metadata` field for the extraction run.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "agent = llama_extract.create_agent(\n",
+    "    name=\"filing-parser\", data_schema=FinancialReport, config=config\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Demo Time - Download a PDF and Extract Data with Citations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PDF downloaded successfully.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "\n",
+    "url = \"https://raw.githubusercontent.com/run-llama/llama_cloud_services/refs/heads/main/examples/extract/data/sec_filings/nvda_10k.pdf\"\n",
+    "\n",
+    "response = requests.get(url)\n",
+    "\n",
+    "if response.status_code == 200:\n",
+    "    with open(\"/content/nvda_10k.pdf\", \"wb\") as f:\n",
+    "        f.write(response.content)\n",
+    "    print(\"PDF downloaded successfully.\")\n",
+    "else:\n",
+    "    print(f\"Failed to download. Status code: {response.status_code}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Uploading files: 100%|██████████| 1/1 [00:00<00:00,  1.83it/s]\n",
+      "Creating extraction jobs: 100%|██████████| 1/1 [00:00<00:00,  4.38it/s]\n",
+      "Extracting files: 100%|██████████| 1/1 [02:03<00:00, 123.40s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "filing_info = agent.extract(\"/content/nvda_10k.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'company_name': 'NVIDIA Corporation',\n",
+       " 'description': \"The filing provides a detailed overview of NVIDIA's business as a full-stack computing infrastructure company, discusses various technologies including digital avatars and autonomous vehicles, outlines numerous risk factors affecting operations such as supply chain issues and geopolitical tensions, and describes employee stock purchase plans and related compliance requirements.\",\n",
+       " 'filing_type': '10 K',\n",
+       " 'filing_date': 'February 26, 2025',\n",
+       " 'fiscal_year': 2025,\n",
+       " 'unit': 'millions',\n",
+       " 'revenue': 130497}"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filing_info.data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Inspect Citations and Reasoning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'field_metadata': {'company_name': {'reasoning': 'VERBATIM EXTRACTION',\n",
+       "   'citation': [{'page': 1, 'matching_text': 'NVIDIA CORPORATION'},\n",
+       "    {'page': 2, 'matching_text': 'NVIDIA Corporation'},\n",
+       "    {'page': 3,\n",
+       "     'matching_text': 'All references to \"NVIDIA,\" \"we,\" \"us,\" \"our,\" or the \"Company\" mean NVIDIA Corporation and its subsidiaries.'},\n",
+       "    {'page': 35,\n",
+       "     'matching_text': 'Comparison of 5 Year Cumulative Total Return* Among NVIDIA Corporation'},\n",
+       "    {'page': 49,\n",
+       "     'matching_text': 'To the Board of Directors and Shareholders of NVIDIA Corporation'},\n",
+       "    {'page': 90, 'matching_text': 'NVIDIA Corporation'},\n",
+       "    {'page': 119,\n",
+       "     'matching_text': '*\"Company\"* means NVIDIA Corporation, a Delaware corporation.'},\n",
+       "    {'page': 126,\n",
+       "     'matching_text': 'Annual Report on Form 10-K of NVIDIA Corporation'}]},\n",
+       "  'filing_type': {'reasoning': \"VERBATIM EXTRACTION from multiple sources confirming the filing type as '10 K'.\",\n",
+       "   'citation': [{'page': 1, 'matching_text': 'FORM 10-K'},\n",
+       "    {'page': 2, 'matching_text': 'Item 16. | Form 10-K Summary'},\n",
+       "    {'page': 3,\n",
+       "     'matching_text': 'This Annual Report on Form 10-K contains forward-looking statements...'},\n",
+       "    {'page': 13, 'matching_text': 'this Annual Report on Form 10-K'},\n",
+       "    {'page': 15, 'matching_text': 'this Annual Report on Form 10-K'},\n",
+       "    {'page': 32,\n",
+       "     'matching_text': 'Annual Report on Form 10-K, which information is hereby incorporated by reference.'},\n",
+       "    {'page': 36, 'matching_text': 'this Annual Report on Form 10-K'},\n",
+       "    {'page': 43,\n",
+       "     'matching_text': 'Annual Report on Form 10-K for additional information'},\n",
+       "    {'page': 45, 'matching_text': 'Annual Report on Form 10-K'},\n",
+       "    {'page': 46, 'matching_text': 'this Annual Report on Form 10-K'},\n",
+       "    {'page': 62, 'matching_text': 'Annual Report on Form 10-K'},\n",
+       "    {'page': 83,\n",
+       "     'matching_text': 'Restated Certificate of Incorporation | 10-K'},\n",
+       "    {'page': 84, 'matching_text': 'Item 16. Form 10-K Summary'},\n",
+       "    {'page': 126, 'matching_text': 'which appears in this Form 10-K'},\n",
+       "    {'page': 127, 'matching_text': 'Annual Report on Form 10-K'},\n",
+       "    {'page': 128, 'matching_text': 'Annual Report on Form 10-K'},\n",
+       "    {'page': 129, 'matching_text': \"The Company's Annual Report on Form 10-K\"},\n",
+       "    {'page': 130,\n",
+       "     'matching_text': \"The Company's Annual Report on Form 10-K for the year ended January 26, 2025\"}]},\n",
+       "  'fiscal_year': {'reasoning': 'The fiscal year ended January 26, 2025, indicates the fiscal year is 2025. Additionally, multiple references throughout the text confirm the fiscal year 2025 in various contexts.',\n",
+       "   'citation': [{'page': 1,\n",
+       "     'matching_text': 'For the fiscal year ended January 26, 2025'},\n",
+       "    {'page': 6,\n",
+       "     'matching_text': 'In fiscal year 2025, we launched the NVIDIA Blackwell architecture'},\n",
+       "    {'page': 12, 'matching_text': 'fiscal year 2025'},\n",
+       "    {'page': 17,\n",
+       "     'matching_text': 'our gross margins in the second quarter of fiscal year 2025 were negatively impacted'},\n",
+       "    {'page': 20,\n",
+       "     'matching_text': 'we generated 53% of our revenue in fiscal year 2025 from sales outside the United States.'},\n",
+       "    {'page': 23,\n",
+       "     'matching_text': 'For fiscal year 2025, an indirect customer which primarily purchases our products through system integrators...'},\n",
+       "    {'page': 33,\n",
+       "     'matching_text': 'In fiscal year 2025, we repurchased 310 million shares of our common stock for $34.0 billion.'},\n",
+       "    {'page': 37,\n",
+       "     'matching_text': 'Our Data Center revenue in China grew in fiscal year 2025.'},\n",
+       "    {'page': 44,\n",
+       "     'matching_text': 'Cash provided by operating activities increased in fiscal year 2025 compared to fiscal year 2024'},\n",
+       "    {'page': 57,\n",
+       "     'matching_text': 'Fiscal years 2025, 2024 and 2023 were all 52-week years.'},\n",
+       "    {'page': 65,\n",
+       "     'matching_text': 'Beginning in the second quarter of fiscal year 2025'},\n",
+       "    {'page': 69, 'matching_text': 'In the fourth quarter of fiscal year 2025'},\n",
+       "    {'page': 78,\n",
+       "     'matching_text': 'Depreciation and amortization expense attributable to our Compute and Networking segment for fiscal years 2025'},\n",
+       "    {'page': 129, 'matching_text': 'for the year ended January 26, 2025'}]},\n",
+       "  'description': {'reasoning': 'The extracted data combines multiple descriptions from the source text, ensuring no duplication while maintaining the order and context of the information. Each section of the filing is summarized to reflect the key points without losing the essence of the original text.',\n",
+       "   'citation': [{'page': 4,\n",
+       "     'matching_text': 'NVIDIA is now a full-stack computing infrastructure company with data-center-scale offerings that are reshaping industry.'},\n",
+       "    {'page': 8,\n",
+       "     'matching_text': 'a suite of technologies that help developers bring digital avatars to life with generative Al...autonomous vehicles, or AV, and electric vehicles, or EV, is revolutionizing the transportation industry...Our worldwide sales and marketing strategy is key to achieving our objective of providing markets with our high-performance and efficient computing platforms and software.'},\n",
+       "    {'page': 14, 'matching_text': 'Risk Factors Summary'},\n",
+       "    {'page': 16,\n",
+       "     'matching_text': 'Risks Related to Demand, Supply, and Manufacturing\\n\\nLong manufacturing lead times and uncertain supply and component availability...'},\n",
+       "    {'page': 18,\n",
+       "     'matching_text': 'cryptocurrency mining, on demand for our products. Volatility in the cryptocurrency market, including new compute technologies...'},\n",
+       "    {'page': 21,\n",
+       "     'matching_text': 'supply-chain attacks or other business disruptions. We cannot guarantee that third parties and infrastructure in our supply chain...'},\n",
+       "    {'page': 22,\n",
+       "     'matching_text': 'We are monitoring the impact of the geopolitical conflict in and around Israel on our operations... Climate change may have a long-term impact on our business.'},\n",
+       "    {'page': 25,\n",
+       "     'matching_text': 'We are subject to complex laws, rules, regulations, and political and other actions, including restrictions on the export of our products, which may adversely impact our business.'},\n",
+       "    {'page': 28,\n",
+       "     'matching_text': 'Our competitive position has been harmed by the existing export controls, and our competitive position and future results may be further harmed'},\n",
+       "    {'page': 29,\n",
+       "     'matching_text': 'restrictions imposed by the Chinese government on the duration of gaming activities and access to games may adversely affect our Gaming revenue'},\n",
+       "    {'page': 29,\n",
+       "     'matching_text': 'our business depends on our ability to receive consistent and reliable supply from our overseas partners, especially in Taiwan and South Korea'},\n",
+       "    {'page': 29,\n",
+       "     'matching_text': 'Increased scrutiny from shareholders, regulators and others regarding our corporate sustainability practices could result in additional costs'},\n",
+       "    {'page': 29,\n",
+       "     'matching_text': 'Concerns relating to the responsible use of new and evolving technologies, such as Al, in our products and services may result in reputational or financial harm'},\n",
+       "    {'page': 31,\n",
+       "     'matching_text': 'Data protection laws around the world are quickly changing and may be interpreted and applied in an increasingly stringent fashion...'}]},\n",
+       "  'filing_date': {'reasoning': 'The filing date is consistently mentioned as February 26, 2025 across multiple entries, making it the most reliable date for the filing.',\n",
+       "   'citation': [{'page': 51, 'matching_text': 'February 26, 2025'},\n",
+       "    {'page': 86, 'matching_text': 'on February 26, 2025.'},\n",
+       "    {'page': 87, 'matching_text': 'February 26, 2025'},\n",
+       "    {'page': 126, 'matching_text': 'our report dated February 26, 2025'},\n",
+       "    {'page': 127, 'matching_text': 'Date: February 26, 2025'},\n",
+       "    {'page': 128, 'matching_text': 'Date: February 26, 2025'},\n",
+       "    {'page': 129, 'matching_text': 'Date: February 26, 2025'},\n",
+       "    {'page': 130, 'matching_text': 'Date: February 26, 2025'}]},\n",
+       "  'unit': {'reasoning': \"The unit of financial figures is explicitly mentioned multiple times in the text as 'millions', including in table headers and notes. This is confirmed by various citations from pages 38, 42, 43, 52, 53, 54, 56, 65, 71, 72, 73, 75, 77, 79, 80, and 82.\",\n",
+       "   'citation': [{'page': 38,\n",
+       "     'matching_text': '($ in millions, except per share data)'},\n",
+       "    {'page': 42, 'matching_text': '($ in millions)'},\n",
+       "    {'page': 43, 'matching_text': '($ in millions)'},\n",
+       "    {'page': 52, 'matching_text': '(In millions, except per share data)'},\n",
+       "    {'page': 53,\n",
+       "     'matching_text': 'Consolidated Statements of Comprehensive Income (In millions)'},\n",
+       "    {'page': 54,\n",
+       "     'matching_text': 'Consolidated Balance Sheets (In millions, except par value)'},\n",
+       "    {'page': 55, 'matching_text': '(In millions, except per share data)'},\n",
+       "    {'page': 56,\n",
+       "     'matching_text': 'Consolidated Statements of Cash Flows (In millions)'},\n",
+       "    {'page': 65,\n",
+       "     'matching_text': 'Year Ended<br/>Jan 26, 2025<br/>(In millions, except per share data)'},\n",
+       "    {'page': 71, 'matching_text': '(In millions) | (In millions)'},\n",
+       "    {'page': 72, 'matching_text': '(In millions)'}]},\n",
+       "  'revenue': {'reasoning': 'The total revenue for fiscal year 2025 is extracted from multiple sources within the text, all confirming the same figure of $130,497 million. The revenue recognized for fiscal year 2025 is also noted as $4,607 million, which is a separate figure. However, the primary focus is on the total revenue figure, which is consistently cited.',\n",
+       "   'citation': [{'page': 38,\n",
+       "     'matching_text': 'Revenue for fiscal year 2025 was $130.5 billion'},\n",
+       "    {'page': 41,\n",
+       "     'matching_text': 'Total                | $ 130,497    | $ | 60,922'},\n",
+       "    {'page': 52, 'matching_text': 'Revenue | $ 130,497'},\n",
+       "    {'page': 78,\n",
+       "     'matching_text': 'Revenue | $ 116,193 | $ 14,304 | $ - | $ 130,497'},\n",
+       "    {'page': 79, 'matching_text': 'Total revenue | $ 130,497'},\n",
+       "    {'page': 80, 'matching_text': 'Total revenue              | $ 130,497'}]}},\n",
+       " 'usage': {'num_pages_extracted': 130,\n",
+       "  'num_document_tokens': 105932,\n",
+       "  'num_output_tokens': 31306}}"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filing_info.extraction_metadata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What's Next?\n",
+    "\n",
+    "In this example, we built an Extraction Agent that is capable of citing it's sources from the document it's extracting data from, and reasoning about its reponse. To further customize and improve on the results, you can also try to customize the `system_prompt` in the `ExtractConfig`.\n",
+    "\n",
+    "#### Learn More\n",
+    "\n",
+    "- [LlamaExtract Documentation](https://docs.cloud.llamaindex.ai/llamaextract/getting_started)\n",
+    "- [Example Notebooks](https://github.com/run-llama/llama_cloud_services/tree/main/examples/extract)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

llama_cloud_services/extract/extract.py

@@ -17,7 +17,6 @@ from llama_cloud import (
     File,
     ExtractMode,
     StatusEnum,
-    Project,
     ExtractTarget,
     LlamaExtractSettings,
     PaginatedExtractRunsResponse,
@@ -633,21 +632,8 @@ class LlamaExtract(BaseComponent):
         self._thread_pool = ThreadPoolExecutor(
             max_workers=min(10, (os.cpu_count() or 1) + 4)
         )
-        # Fetch default project id if not provided
         if not project_id:
             project_id = os.getenv("LLAMA_CLOUD_PROJECT_ID", None)
-            if not project_id:
-                print("No project_id provided, fetching default project.")
-                projects: List[Project] = self._run_in_thread(
-                    self._async_client.projects.list_projects()
-                )
-                default_project = [p for p in projects if p.is_default]
-                if not default_project:
-                    raise ValueError(
-                        "No default project found. Please provide a project_id."
-                    )
-                project_id = default_project[0].id
-
         self._project_id = project_id
         self._organization_id = organization_id
 
@@ -711,6 +697,8 @@ class LlamaExtract(BaseComponent):
             num_workers=self.num_workers,
             show_progress=self.show_progress,
             verbose=self.verbose,
+            verify=self.verify,
+            httpx_timeout=self.httpx_timeout,
         )
 
     def get_agent(

llama_cloud_services/parse/base.py

@@ -7,7 +7,7 @@ from copy import deepcopy
 from enum import Enum
 from io import BufferedIOBase
 from pathlib import Path, PurePath, PurePosixPath
-from typing import Any, AsyncGenerator, Dict, List, Optional, Union
+from typing import Any, AsyncGenerator, Dict, List, Optional, Tuple, Union
 from urllib.parse import urlparse
 
 import httpx
@@ -25,9 +25,11 @@ from llama_cloud_services.parse.utils import (
     ResultType,
     ParsingMode,
     FailedPageMode,
+    expand_target_pages,
     nest_asyncio_err,
     nest_asyncio_msg,
     make_api_request,
+    partition_pages,
 )
 
 # can put in a path to the file or the file bytes itself
@@ -57,6 +59,36 @@ def build_url(
     return base_url
 
 
+class JobFailedException(Exception):
+    """Parse job failed exception."""
+
+    def __init__(
+        self,
+        job_id: str,
+        status: str,
+        error_code: Optional[str] = None,
+        error_message: Optional[str] = None,
+    ):
+        exception_str = (
+            f"Job ID: {job_id} failed with status: {status}, "
+            f'Error code: {error_code or "No error code found"}, '
+            f'Error message: {error_message or "No error message found"}'
+        )
+        super().__init__(exception_str)
+        self.job_id = job_id
+        self.status = status
+        self.error_code = error_code
+        self.error_message = error_message
+
+    @classmethod
+    def from_result(cls, result_json: Dict[str, Any]) -> "JobFailedException":
+        job_id = result_json["id"]
+        status = result_json["status"]
+        error_code = result_json.get("error_code")
+        error_message = result_json.get("error_message")
+        return cls(job_id, status, error_code=error_code, error_message=error_message)
+
+
 class BackoffPattern(str, Enum):
     """Backoff pattern for polling."""
 
@@ -115,7 +147,7 @@ class LlamaParse(BasePydanticReader):
     num_workers: int = Field(
         default=4,
         gt=0,
-        lt=10,
+        lt=20,
         description="The number of workers to use sending API requests for parsing.",
     )
     result_type: ResultType = Field(
@@ -145,6 +177,10 @@ class LlamaParse(BasePydanticReader):
         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_configuration_json: Optional[str] = Field(
+        default=None,
+        description="A JSON string containing the configuration for the auto mode. If set, the parser will use the provided configuration for the auto 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.",
@@ -293,6 +329,10 @@ class LlamaParse(BasePydanticReader):
         default=False,
         description="If set to true, the parser will output tables as HTML in the markdown.",
     )
+    outlined_table_extraction: Optional[bool] = Field(
+        default=False,
+        description="If set to true, the parser will use a dedicated approach to extract tables with outlined cells. This is useful for documents with spreadsheet-like tables where cells are outlined with borders. This could lead to false positives, so use with caution.",
+    )
     page_error_tolerance: Optional[float] = Field(
         default=None,
         description="The error tolerance for the number of pages with error in a doc (percentage express as 0-1). If we fail to parse a greater percentage of pages than the tolerance value we fail the job.",
@@ -406,6 +446,10 @@ class LlamaParse(BasePydanticReader):
         default=None,
         description="The model name for the vendor multimodal API.",
     )
+    model: Optional[str] = Field(
+        default=None,
+        description="The document model name to be used with `parse_with_agent`.",
+    )
     webhook_url: Optional[str] = Field(
         default=None,
         description="A URL that needs to be called at the end of the parsing job.",
@@ -449,6 +493,11 @@ class LlamaParse(BasePydanticReader):
         description="Whether to use the vendor multimodal API.",
     )
 
+    partition_pages: Optional[int] = Field(
+        default=None,
+        description="If set, documents will automatically be partitioned into segments containing the specified number of pages at most. Parsing will be split into separate jobs for each partition segment. Can be used in combination with targetPages and maxPages.",
+    )
+
     @field_validator("api_key", mode="before", check_fields=True)
     @classmethod
     def validate_api_key(cls, v: str) -> str:
@@ -536,6 +585,7 @@ class LlamaParse(BasePydanticReader):
         file_input: FileInput,
         extra_info: Optional[dict] = None,
         fs: Optional[AbstractFileSystem] = None,
+        partition_target_pages: Optional[str] = None,
     ) -> str:
         files = None
         file_handle = None
@@ -586,6 +636,9 @@ class LlamaParse(BasePydanticReader):
         if self.auto_mode:
             data["auto_mode"] = self.auto_mode
 
+        if self.auto_mode_configuration_json is not None:
+            data["auto_mode_configuration_json"] = self.auto_mode_configuration_json
+
         if self.auto_mode_trigger_on_image_in_page:
             data[
                 "auto_mode_trigger_on_image_in_page"
@@ -745,6 +798,9 @@ class LlamaParse(BasePydanticReader):
         if self.output_tables_as_HTML:
             data["output_tables_as_HTML"] = self.output_tables_as_HTML
 
+        if self.outlined_table_extraction:
+            data["outlined_table_extraction"] = self.outlined_table_extraction
+
         if self.page_error_tolerance is not None:
             data["page_error_tolerance"] = self.page_error_tolerance
 
@@ -827,7 +883,9 @@ class LlamaParse(BasePydanticReader):
         if self.take_screenshot:
             data["take_screenshot"] = self.take_screenshot
 
-        if self.target_pages is not None:
+        if partition_target_pages is not None:
+            data["target_pages"] = partition_target_pages
+        elif self.target_pages is not None:
             data["target_pages"] = self.target_pages
         if self.user_prompt is not None:
             data["user_prompt"] = self.user_prompt
@@ -840,6 +898,9 @@ class LlamaParse(BasePydanticReader):
         if self.vendor_multimodal_model_name is not None:
             data["vendor_multimodal_model_name"] = self.vendor_multimodal_model_name
 
+        if self.model is not None:
+            data["model"] = self.model
+
         if self.webhook_url is not None:
             data["webhook_url"] = self.webhook_url
 
@@ -921,15 +982,7 @@ class LlamaParse(BasePydanticReader):
                         print(".", end="", flush=True)
                     current_interval = self._calculate_backoff(current_interval)
                 else:
-                    error_code = result_json.get("error_code", "No error code found")
-                    error_message = result_json.get(
-                        "error_message", "No error message found"
-                    )
-                    exception_str = (
-                        f"Job ID: {job_id} failed with status: {status}, "
-                        f"Error code: {error_code}, Error message: {error_message}"
-                    )
-                    raise Exception(exception_str)
+                    raise JobFailedException.from_result(result_json)
             except (
                 httpx.ConnectError,
                 httpx.ReadError,
@@ -952,26 +1005,151 @@ class LlamaParse(BasePydanticReader):
                     )
                 current_interval = self._calculate_backoff(current_interval)
 
+    async def _parse_one(
+        self,
+        file_path: FileInput,
+        extra_info: Optional[dict] = None,
+        fs: Optional[AbstractFileSystem] = None,
+        result_type: Optional[str] = None,
+        num_workers: Optional[int] = None,
+    ) -> List[Tuple[str, Dict[str, Any]]]:
+        if self.partition_pages is None:
+            job_results = [
+                await self._parse_one_unpartitioned(
+                    file_path,
+                    extra_info=extra_info,
+                    fs=fs,
+                    result_type=result_type,
+                )
+            ]
+        else:
+            job_results = await self._parse_one_partitioned(
+                file_path,
+                extra_info,
+                fs=fs,
+                result_type=result_type,
+                num_workers=num_workers,
+            )
+        return job_results
+
+    async def _parse_one_unpartitioned(
+        self,
+        file_path: FileInput,
+        extra_info: Optional[dict] = None,
+        fs: Optional[AbstractFileSystem] = None,
+        result_type: Optional[str] = None,
+        **create_kwargs: Any,
+    ) -> Tuple[str, Dict[str, Any]]:
+        """Create one parse job and wait for the result."""
+        job_id = await self._create_job(
+            file_path, extra_info=extra_info, fs=fs, **create_kwargs
+        )
+        if self.verbose:
+            print("Started parsing the file under job_id %s" % job_id)
+        result = await self._get_job_result(
+            job_id, result_type or self.result_type.value, verbose=self.verbose
+        )
+        return job_id, result
+
+    async def _parse_one_partitioned(
+        self,
+        file_path: FileInput,
+        extra_info: Optional[dict] = None,
+        fs: Optional[AbstractFileSystem] = None,
+        result_type: Optional[str] = None,
+        num_workers: Optional[int] = None,
+    ) -> List[Tuple[str, Dict[str, Any]]]:
+        """Partition a file and run separate parse jobs per partition segment."""
+        assert self.partition_pages is not None
+
+        num_workers = num_workers or self.num_workers
+        if num_workers < 1:
+            raise ValueError("Invalid number of workers")
+        if self.target_pages is not None:
+            jobs = [
+                self._parse_one_unpartitioned(
+                    file_path,
+                    extra_info=extra_info,
+                    fs=fs,
+                    result_type=result_type,
+                    partition_target_pages=target_pages,
+                )
+                for target_pages in partition_pages(
+                    expand_target_pages(self.target_pages),
+                    self.partition_pages,
+                    max_pages=self.max_pages,
+                )
+            ]
+            return await run_jobs(
+                jobs,
+                workers=num_workers,
+                desc="Getting job results",
+                show_progress=self.show_progress,
+            )
+
+        total = 0
+        results: List[Tuple[str, Dict[str, Any]]] = []
+        while self.max_pages is None or total < self.max_pages:
+            if (
+                self.max_pages is not None
+                and total + self.partition_pages >= self.max_pages
+            ):
+                size = self.max_pages - total
+            else:
+                size = self.partition_pages
+            if not size:
+                break
+            try:
+                # Fetch JSON result type first to get accurate pagination data
+                # and then fetch the user's desired result type if needed
+                job_id, json_result = await self._parse_one_unpartitioned(
+                    file_path,
+                    extra_info=extra_info,
+                    fs=fs,
+                    result_type=ResultType.JSON.value,
+                    partition_target_pages=f"{total}-{total + size - 1}",
+                )
+                result_type = result_type or self.result_type.value
+                if result_type == ResultType.JSON.value:
+                    job_result = json_result
+                else:
+                    job_result = await self._get_job_result(
+                        job_id, result_type, verbose=self.verbose
+                    )
+            except JobFailedException as e:
+                if results and e.error_code == "NO_DATA_FOUND_IN_FILE":
+                    # Expected when we try to read past the end of the file
+                    return results
+                raise
+            results.append((job_id, job_result))
+            if len(json_result["pages"]) < size:
+                break
+            total += size
+        return results
+
     async def _aload_data(
         self,
         file_path: FileInput,
         extra_info: Optional[dict] = None,
         fs: Optional[AbstractFileSystem] = None,
         verbose: bool = False,
+        num_workers: Optional[int] = None,
     ) -> List[Document]:
         """Load data from the input path."""
         try:
-            job_id = await self._create_job(file_path, extra_info=extra_info, fs=fs)
-            if verbose:
-                print("Started parsing the file under job_id %s" % job_id)
-
-            result = await self._get_job_result(
-                job_id, self.result_type.value, verbose=verbose
-            )
-
+            results = [
+                job_result
+                for _, job_result in await self._parse_one(
+                    file_path, extra_info, fs=fs, num_workers=num_workers
+                )
+            ]
+            # Flatten the resulting doc if it was partitioned
+            separator = self.page_separator or _DEFAULT_SEPARATOR
             docs = [
                 Document(
-                    text=result[self.result_type.value],
+                    text=separator.join(
+                        result[self.result_type.value] for result in results
+                    ),
                     metadata=extra_info or {},
                 )
             ]
@@ -994,7 +1172,11 @@ class LlamaParse(BasePydanticReader):
         extra_info: Optional[dict] = None,
         fs: Optional[AbstractFileSystem] = None,
     ) -> List[Document]:
-        """Load data from the input path."""
+        """Load data from the input path.
+
+        File(s) which were partitioned before parsing will be loaded as a single
+        re-assembled Document.
+        """
         if isinstance(file_path, (str, PurePosixPath, Path, bytes, BufferedIOBase)):
             return await self._aload_data(
                 file_path, extra_info=extra_info, fs=fs, verbose=self.verbose
@@ -1006,6 +1188,7 @@ class LlamaParse(BasePydanticReader):
                     extra_info=extra_info,
                     fs=fs,
                     verbose=self.verbose and not self.show_progress,
+                    num_workers=1,
                 )
                 for f in file_path
             ]
@@ -1044,6 +1227,34 @@ class LlamaParse(BasePydanticReader):
             else:
                 raise e
 
+    async def _aparse_one(
+        self,
+        file_path: FileInput,
+        file_name: str,
+        extra_info: Optional[dict] = None,
+        fs: Optional[AbstractFileSystem] = None,
+        num_workers: Optional[int] = None,
+    ) -> List[JobResult]:
+        job_results = await self._parse_one(
+            file_path,
+            extra_info,
+            fs=fs,
+            result_type=ResultType.JSON.value,
+            num_workers=num_workers,
+        )
+        return [
+            JobResult(
+                job_id=job_id,
+                file_name=file_name,
+                job_result=job_result,
+                api_key=self.api_key,
+                base_url=self.base_url,
+                client=self.aclient,
+                page_separator=self.page_separator or _DEFAULT_SEPARATOR,
+            )
+            for job_id, job_result in job_results
+        ]
+
     async def aparse(
         self,
         file_path: Union[List[FileInput], FileInput],
@@ -1062,14 +1273,10 @@ class LlamaParse(BasePydanticReader):
             fs: Optional filesystem to use for reading files.
 
         Returns:
-            JobResult object or list of JobResult objects if multiple files were provided
+            JobResult object or list of JobResult objects if either multiple files were provided or file(s) were partitioned before parsing.
         """
 
         if isinstance(file_path, (str, PurePosixPath, Path, bytes, BufferedIOBase)):
-            job_id = await self._create_job(file_path, extra_info=extra_info, fs=fs)
-            if self.verbose:
-                print("Started parsing the file under job_id %s" % job_id)
-
             if isinstance(file_path, (bytes, BufferedIOBase)):
                 if not extra_info or "file_name" not in extra_info:
                     raise ValueError(
@@ -1078,29 +1285,12 @@ class LlamaParse(BasePydanticReader):
                 file_name = extra_info["file_name"]
             else:
                 file_name = str(file_path)
-
-            job_result = await self._get_job_result(
-                job_id, ResultType.JSON.value, verbose=self.verbose
-            )
-            return JobResult(
-                job_id=job_id,
-                file_name=file_name,
-                job_result=job_result,
-                api_key=self.api_key,
-                base_url=self.base_url,
-                client=self.aclient,
-                page_separator=self.page_separator or _DEFAULT_SEPARATOR,
+            result = await self._aparse_one(
+                file_path, file_name, extra_info=extra_info, fs=fs
             )
+            return result[0] if len(result) == 1 else result
 
         elif isinstance(file_path, list):
-            jobs = [
-                self._create_job(
-                    f,
-                    extra_info=extra_info,
-                    fs=fs,
-                )
-                for f in file_path
-            ]
             file_names = []
             for f in file_path:
                 if isinstance(f, (bytes, BufferedIOBase)):
@@ -1112,40 +1302,24 @@ class LlamaParse(BasePydanticReader):
                 else:
                     file_names.append(str(f))
 
+            job_results = []
             try:
-                job_ids = await run_jobs(
-                    jobs,
-                    workers=self.num_workers,
-                    desc="Creating parsing jobs",
-                    show_progress=self.show_progress,
-                )
-
-                job_results = await run_jobs(
+                for result in await run_jobs(
                     [
-                        self._get_job_result(
-                            job_id, ResultType.JSON.value, verbose=self.verbose
+                        self._aparse_one(
+                            f,
+                            file_names[i],
+                            extra_info=extra_info,
+                            fs=fs,
+                            num_workers=1,
                         )
-                        for job_id in job_ids
+                        for i, f in enumerate(file_path)
                     ],
                     workers=self.num_workers,
                     desc="Getting job results",
                     show_progress=self.show_progress,
-                )
-
-                # Create JobResults just using the job_ids and job_results
-                job_results = [
-                    JobResult(
-                        job_id=job_id,
-                        file_name=file_names[i],
-                        job_result=job_results[i],
-                        api_key=self.api_key,
-                        base_url=self.base_url,
-                        client=self.aclient,
-                        page_separator=self.page_separator or _DEFAULT_SEPARATOR,
-                    )
-                    for i, job_id in enumerate(job_ids)
-                ]
-
+                ):
+                    job_results.extend(result)
                 return job_results
 
             except RuntimeError as e:
@@ -1187,20 +1361,27 @@ class LlamaParse(BasePydanticReader):
                 raise e
 
     async def _aget_json(
-        self, file_path: FileInput, extra_info: Optional[dict] = None
+        self,
+        file_path: FileInput,
+        extra_info: Optional[dict] = None,
+        num_workers: Optional[int] = None,
     ) -> List[dict]:
         """Load data from the input path."""
         try:
-            job_id = await self._create_job(file_path, extra_info=extra_info)
-            if self.verbose:
-                print("Started parsing the file under job_id %s" % job_id)
-            result = await self._get_job_result(job_id, "json")
-            result["job_id"] = job_id
+            job_results = await self._parse_one(
+                file_path,
+                extra_info=extra_info,
+                result_type=ResultType.JSON.value,
+                num_workers=num_workers,
+            )
 
-            if not isinstance(file_path, (bytes, BufferedIOBase)):
-                result["file_path"] = str(file_path)
-
-            return [result]
+            results = []
+            for job_id, job_result in job_results:
+                job_result["job_id"] = job_id
+                if not isinstance(file_path, (bytes, BufferedIOBase)):
+                    job_result["file_path"] = str(file_path)
+                results.append(job_result)
+            return results
         except Exception as e:
             file_repr = file_path if isinstance(file_path, str) else "<bytes/buffer>"
             print(f"Error while parsing the file '{file_repr}':", e)

llama_cloud_services/parse/types.py

@@ -14,9 +14,6 @@ PAGE_REGEX = r"page[-_](\d+)\.jpg$"
 class JobMetadata(BaseModel):
     """Metadata about the job."""
 
-    job_credits_usage: int = Field(
-        default_factory=dict, description="The credits usage for the job."
-    )
     job_pages: int = Field(description="The number of pages in the job.")
     job_auto_mode_triggered_pages: int = Field(
         description="The number of pages that triggered auto mode (thus increasing the cost)."
@@ -49,17 +46,25 @@ class PageItem(BaseModel):
     rows: Optional[List[List[str]]] = Field(
         default=None, description="The rows of the item."
     )
-    bBox: BBox = Field(description="The bounding box of the item.")
+    bBox: Optional[BBox] = Field(
+        default=None, description="The bounding box of the item."
+    )
 
 
 class ImageItem(BaseModel):
     """An image in a page."""
 
     name: str = Field(description="The name of the image.")
-    height: float = Field(description="The height of the image.")
-    width: float = Field(description="The width of the image.")
-    x: float = Field(description="The x-coordinate of the image.")
-    y: float = Field(description="The y-coordinate of the image.")
+    height: Optional[float] = Field(
+        default=None, description="The height of the image."
+    )
+    width: Optional[float] = Field(default=None, description="The width of the image.")
+    x: Optional[float] = Field(
+        default=None, description="The x-coordinate of the image."
+    )
+    y: Optional[float] = Field(
+        default=None, description="The y-coordinate of the image."
+    )
     original_width: Optional[int] = Field(
         default=None, description="The original width of the image."
     )
@@ -75,7 +80,9 @@ class LayoutItem(BaseModel):
     image: str = Field(description="The name of the image containing the layout item")
     confidence: float = Field(description="The confidence of the layout item.")
     label: str = Field(description="The label of the layout item.")
-    bbox: BBox = Field(description="The bounding box of the layout item.")
+    bbox: Optional[BBox] = Field(
+        default=None, description="The bounding box of the layout item."
+    )
     isLikelyNoise: bool = Field(description="Whether the layout item is likely noise.")
 
 
@@ -83,18 +90,24 @@ class ChartItem(BaseModel):
     """A chart in a page."""
 
     name: str = Field(description="The name of the chart.")
-    x: float = Field(description="The x-coordinate of the chart.")
-    y: float = Field(description="The y-coordinate of the chart.")
-    width: float = Field(description="The width of the chart.")
-    height: float = Field(description="The height of the chart.")
+    x: Optional[float] = Field(
+        default=None, description="The x-coordinate of the chart."
+    )
+    y: Optional[float] = Field(
+        default=None, description="The y-coordinate of the chart."
+    )
+    width: Optional[float] = Field(default=None, description="The width of the chart.")
+    height: Optional[float] = Field(
+        default=None, description="The height of the chart."
+    )
 
 
 class Page(BaseModel):
     """A page of the document."""
 
     page: int = Field(description="The page number.")
-    text: str = Field(description="The text of the page.")
-    md: str = Field(description="The markdown of the page.")
+    text: Optional[str] = Field(default=None, description="The text of the page.")
+    md: Optional[str] = Field(default=None, description="The markdown of the page.")
     images: List[ImageItem] = Field(
         default_factory=list,
         description="The names of the image IDs in the page, including both objects and page screenshots.",
@@ -115,8 +128,8 @@ class Page(BaseModel):
     links: List[SerializeAsAny[Any]] = Field(
         default_factory=list, description="The links in the page."
     )
-    width: float = Field(description="The width of the page.")
-    height: float = Field(description="The height of the page.")
+    width: Optional[float] = Field(default=None, description="The width of the page.")
+    height: Optional[float] = Field(default=None, description="The height of the page.")
     triggeredAutoMode: bool = Field(
         description="Whether the page triggered auto mode (thus increasing the cost)."
     )
@@ -189,7 +202,9 @@ class JobResult(BaseModel):
                 for page in self.pages
             ]
         else:
-            text = self._page_separator.join([page.text for page in self.pages])
+            text = self._page_separator.join(
+                [page.text if page.text is not None else "" for page in self.pages]
+            )
             return [Document(text=text, metadata={"file_name": self.file_name})]
 
     async def aget_text_documents(self, split_by_page: bool = False) -> List[Document]:
@@ -234,7 +249,9 @@ class JobResult(BaseModel):
         else:
             return [
                 Document(
-                    text=self._page_separator.join([page.md for page in self.pages]),
+                    text=self._page_separator.join(
+                        [page.md if page.md is not None else "" for page in self.pages]
+                    ),
                     metadata={"file_name": self.file_name},
                 )
             ]

llama_cloud_services/parse/utils.py

@@ -1,4 +1,5 @@
 import httpx
+import itertools
 import logging
 from enum import Enum
 from tenacity import (
@@ -8,7 +9,7 @@ from tenacity import (
     retry_if_exception,
     before_sleep_log,
 )
-from typing import Any
+from typing import Any, Iterable, Iterator, Optional
 
 logger = logging.getLogger(__name__)
 
@@ -297,3 +298,56 @@ async def make_api_request(
         return response
 
     return await _make_request(url, **httpx_kwargs)
+
+
+def expand_target_pages(target_pages: str) -> Iterator[int]:
+    """Yield all values in target_pages."""
+    for target in target_pages.strip().split(","):
+        if "-" in target:
+            try:
+                start, end = map(int, target.strip().split("-"))
+                if start > end:
+                    raise ValueError
+                yield from range(start, end + 1)
+            except ValueError as e:
+                raise ValueError(f"Invalid page range: {target}") from e
+        else:
+            try:
+                yield int(target)
+            except ValueError as e:
+                raise ValueError(f"Invalid page number: {target}") from e
+
+
+def partition_pages(
+    pages: Iterable[int], size: int, max_pages: Optional[int] = None
+) -> Iterator[str]:
+    """Yield partitioned target_pages segments."""
+    if size < 1:
+        raise ValueError(f"Invalid partition segment size: {size}")
+    if max_pages is not None and max_pages < 1:
+        raise ValueError("Max pages must be > 0")
+    it = iter(pages)
+    total = 0
+    while max_pages is None or total < max_pages:
+        segment = tuple(itertools.islice(it, size))
+        if segment:
+            targets = []
+            for _k, g in itertools.groupby(enumerate(segment), lambda x: x[0] - x[1]):
+                group = [item[1] for item in g]
+                if len(group) > 1:
+                    start, end = group
+                    group_size = end - start + 1
+                    if max_pages is not None and total + group_size > max_pages:
+                        end -= total + group_size - max_pages
+                        group_size = end - start + 1
+                    if group_size > 1:
+                        targets.append(f"{start}-{end}")
+                    else:
+                        targets.append(str(start))
+                    total += group_size
+                else:
+                    targets.append(str(group[0]))
+                    total += 1
+            yield ",".join(targets)
+        else:
+            return

llama_parse/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "llama-parse"
-version = "0.6.18"
+version = "0.6.28"
 description = "Parse files into RAG-Optimized formats."
 authors = ["Logan Markewich <logan@llamaindex.ai>"]
 license = "MIT"
@@ -13,7 +13,7 @@ packages = [{include = "llama_parse"}]
 
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
-llama-cloud-services = ">=0.6.17"
+llama-cloud-services = ">=0.6.28"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^8.0.0"

pyproject.toml

@@ -8,7 +8,7 @@ python_version = "3.10"
 
 [tool.poetry]
 name = "llama-cloud-services"
-version = "0.6.18"
+version = "0.6.28"
 description = "Tailored SDK clients for LlamaCloud services."
 authors = ["Logan Markewich <logan@runllama.ai>"]
 license = "MIT"
@@ -17,9 +17,9 @@ packages = [{include = "llama_cloud_services"}]
 
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
-llama-index-core = ">=0.11.0"
-llama-cloud = "==0.1.19"
-pydantic = "!=2.10"
+llama-index-core = ">=0.12.0"
+llama-cloud = "==0.1.23"
+pydantic = ">=2.8,!=2.10"
 click = "^8.1.7"
 python-dotenv = "^1.0.1"
 eval-type-backport = {python = "<3.10", version = "^0.2.0"}

tests/parse/test_llama_parse.py

@@ -1,6 +1,7 @@
 import os
 import pytest
 import shutil
+from typing import Optional, cast
 from fsspec.implementations.local import LocalFileSystem
 from httpx import AsyncClient
 
@@ -20,11 +21,15 @@ def test_simple_page_text() -> None:
     assert len(result[0].text) > 0
 
 
-@pytest.fixture
-def markdown_parser() -> LlamaParse:
+@pytest.fixture(params=[None, 2])
+def markdown_parser(request: pytest.FixtureRequest) -> LlamaParse:
     if os.environ.get("LLAMA_CLOUD_API_KEY", "") == "":
         pytest.skip("LLAMA_CLOUD_API_KEY not set")
-    return LlamaParse(result_type="markdown", ignore_errors=False)
+    return LlamaParse(
+        result_type="markdown",
+        ignore_errors=False,
+        partition_pages=cast(Optional[int], request.param),
+    )
 
 
 def test_simple_page_markdown(markdown_parser: LlamaParse) -> None:
@@ -35,8 +40,6 @@ def test_simple_page_markdown(markdown_parser: LlamaParse) -> None:
 
 
 def test_simple_page_markdown_bytes(markdown_parser: LlamaParse) -> None:
-    markdown_parser = LlamaParse(result_type="markdown", ignore_errors=False)
-
     filepath = "tests/test_files/attention_is_all_you_need.pdf"
     with open(filepath, "rb") as f:
         file_bytes = f.read()
@@ -51,8 +54,6 @@ def test_simple_page_markdown_bytes(markdown_parser: LlamaParse) -> None:
 
 
 def test_simple_page_markdown_buffer(markdown_parser: LlamaParse) -> None:
-    markdown_parser = LlamaParse(result_type="markdown", ignore_errors=False)
-
     filepath = "tests/test_files/attention_is_all_you_need.pdf"
     with open(filepath, "rb") as f:
         # client must provide extra_info with file_name
@@ -161,9 +162,12 @@ async def test_mixing_input_types() -> None:
     os.environ.get("LLAMA_CLOUD_API_KEY", "") == "",
     reason="LLAMA_CLOUD_API_KEY not set",
 )
+@pytest.mark.parametrize("partition_pages", [None, 2])
 @pytest.mark.asyncio
-async def test_download_images() -> None:
-    parser = LlamaParse(result_type="markdown", take_screenshot=True)
+async def test_download_images(partition_pages: Optional[int]) -> None:
+    parser = LlamaParse(
+        result_type="markdown", take_screenshot=True, partition_pages=partition_pages
+    )
     filepath = "tests/test_files/attention_is_all_you_need.pdf"
     json_result = await parser.aget_json([filepath])
 
@@ -175,3 +179,17 @@ async def test_download_images() -> None:
 
     await parser.aget_images(json_result, download_path)
     assert len(os.listdir(download_path)) == len(json_result[0]["pages"][0]["images"])
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("split_by_page,expected", [(True, 4), (False, 1)])
+async def test_multiple_page_markdown(
+    markdown_parser: LlamaParse,
+    split_by_page: bool,
+    expected: int,
+) -> None:
+    markdown_parser.split_by_page = split_by_page
+    filepath = "tests/test_files/TOS.pdf"
+    result = await markdown_parser.aload_data(filepath)
+    assert len(result) == expected
+    assert all(len(doc.text) > 0 for doc in result)

tests/parse/test_llama_parse_result.py

@@ -1,6 +1,7 @@
 import tempfile
 import os
 import pytest
+from typing import Optional
 from llama_cloud_services import LlamaParse
 from llama_cloud_services.parse.types import JobResult
 
@@ -15,16 +16,23 @@ def chart_file_path() -> str:
     return "tests/test_files/attention_is_all_you_need_chart.pdf"
 
 
+@pytest.fixture
+def multiple_page_path() -> str:
+    return "tests/test_files/TOS.pdf"
+
+
 @pytest.mark.asyncio
 @pytest.mark.skipif(
     os.environ.get("LLAMA_CLOUD_API_KEY", "") == "",
     reason="LLAMA_CLOUD_API_KEY not set",
 )
-async def test_basic_parse_result(file_path: str):
+@pytest.mark.parametrize("partition_pages", [None, 2])
+async def test_basic_parse_result(file_path: str, partition_pages: Optional[int]):
     parser = LlamaParse(
         take_screenshot=True,
         auto_mode=True,
         fast_mode=False,
+        partition_pages=partition_pages,
     )
     result = await parser.aparse(file_path)
 
@@ -142,8 +150,11 @@ async def test_parse_layout(file_path: str):
     os.environ.get("LLAMA_CLOUD_API_KEY", "") == "",
     reason="LLAMA_CLOUD_API_KEY not set",
 )
-def test_parse_multiple_files(file_path: str, chart_file_path: str):
-    parser = LlamaParse()
+@pytest.mark.parametrize("partition_pages", [None, 2])
+def test_parse_multiple_files(
+    file_path: str, chart_file_path: str, partition_pages: Optional[int]
+):
+    parser = LlamaParse(partition_pages=partition_pages)
     result = parser.parse([file_path, chart_file_path])
 
     assert isinstance(result, list)
@@ -152,3 +163,40 @@ def test_parse_multiple_files(file_path: str, chart_file_path: str):
     assert isinstance(result[1], JobResult)
     assert result[0].file_name == file_path
     assert result[1].file_name == chart_file_path
+
+
+@pytest.mark.asyncio
+@pytest.mark.skipif(
+    os.environ.get("LLAMA_CLOUD_API_KEY", "") == "",
+    reason="LLAMA_CLOUD_API_KEY not set",
+)
+@pytest.mark.parametrize("partition_pages", [None, 2])
+async def test_multiple_page_parse_result(
+    multiple_page_path: str, partition_pages: Optional[int]
+):
+    parser = LlamaParse(
+        take_screenshot=True,
+        auto_mode=True,
+        fast_mode=False,
+        partition_pages=partition_pages,
+    )
+    results = await parser.aparse(multiple_page_path)
+    if partition_pages is None:
+        assert isinstance(results, JobResult)
+        results = [results]
+    else:
+        assert isinstance(results, list)
+
+    for result in results:
+        assert isinstance(result, JobResult)
+        assert result.job_id is not None
+        assert result.file_name == multiple_page_path
+        assert len(result.pages) > 0
+
+        assert result.pages[0].text is not None
+        assert len(result.pages[0].text) > 0
+
+        assert result.pages[0].md is not None
+        assert len(result.pages[0].md) > 0
+
+        assert result.pages[0].md != result.pages[0].text

tests/parse/test_utils.py

@@ -0,0 +1,30 @@
+import pytest
+
+
+from llama_cloud_services.parse.utils import expand_target_pages, partition_pages
+
+
+def test_expand_target_pages() -> None:
+    with pytest.raises(ValueError):
+        list(expand_target_pages("x"))
+    with pytest.raises(ValueError):
+        list(expand_target_pages("1-2-3"))
+    with pytest.raises(ValueError):
+        list(expand_target_pages("2-1"))
+    result = list(expand_target_pages("0,2-3,5,8-10"))
+    assert result == [0, 2, 3, 5, 8, 9, 10]
+
+
+def test_partion_pages() -> None:
+    pages = [0, 2, 3, 5, 8, 9, 10]
+    with pytest.raises(ValueError):
+        list(partition_pages(pages, 0))
+    result = list(partition_pages(pages, 3))
+    assert result == ["0,2-3", "5,8-9", "10"]
+
+    with pytest.raises(ValueError):
+        list(partition_pages(pages, 3, 0))
+    result = list(partition_pages(pages, 3, max_pages=5))
+    assert result == ["0,2-3", "5,8"]
+    result = list(partition_pages(pages, 3, max_pages=10))
+    assert result == ["0,2-3", "5,8-9", "10"]