Make versioning a little easier, and fix llama_parse version (#808 )

* Make versioning a little easier * fix up ci
feat: default to _public agent data (#803 )
2026-07-20 19:47:38 -04:00 · 2025-07-21 18:49:07 -04:00 · 2025-07-21 15:58:03 -04:00 · 2025-07-21 14:19:15 +09:00 · 2025-07-18 18:59:25 +09:00 · 2025-07-18 18:50:29 +09:00
20 changed files with 1041 additions and 105 deletions
@@ -0,0 +1,19 @@
+# Installation
+
+This project uses poetry. Create a virtual environment, and run `poetry install`
+
+# Versioning (Maintainers only)
+
+Before merging your changes, make sure to bump the versions.
+
+Make a version bump to `pyproject.toml`. If the underlying dependency on the llamacloud platform OpenAPI
+sdk needs bumping, make sure to bring that in as well. If updating dependencies, run `poetry lock`.
+
+The legacy `llama_parse` package re-exports some of `llama_cloud_services` in the old namespace. The
+versions need to be kept consistent to sidecar it with `llama_cloud_services`. Bump it's version in `llama_parse/pyproject.toml`, and also bump it's dependency version of `llama-cloud-services` to match.
+
+You can also do this with `./scripts/version-bump.py set 0.x.x` if you have `uv` installed.
+
+Once the change is merged, push a tag `git tag -a v0.x.x -m 0.x.x` and `git push origin 0.x.x`.
+
+This tagging step can be done with `./scripts/version-bump tag`.
@@ -0,0 +1,516 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Table Extraction with LlamaParse\n",
+    "\n",
+    "This notebook will show you how to extract tables and save them as CSV files thanks to LlamaParse advanced parsing capabilities."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**1. Install needed dependencies**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install llama-cloud-services pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**2. Set you LLAMA_CLOUD_API_KEY as env variable**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LLAMA_CLOUD_API_KEY: ··········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "os.environ[\"LLAMA_CLOUD_API_KEY\"] = getpass(\"LLAMA_CLOUD_API_KEY: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**3. Initialiaze the parser**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_cloud_services import LlamaParse\n",
+    "\n",
+    "parser = LlamaParse(result_type=\"markdown\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**4. Get data**\n",
+    "\n",
+    "This is a PDF with _lots_ of tables!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2025-07-16 16:20:41--  https://assets.accessible-digital-documents.com/uploads/2017/01/sample-tables.pdf\n",
+      "Resolving assets.accessible-digital-documents.com (assets.accessible-digital-documents.com)... 3.166.135.2, 3.166.135.62, 3.166.135.51, ...\n",
+      "Connecting to assets.accessible-digital-documents.com (assets.accessible-digital-documents.com)|3.166.135.2|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 145494 (142K) [application/pdf]\n",
+      "Saving to: ‘sample-tables.pdf’\n",
+      "\n",
+      "sample-tables.pdf   100%[===================>] 142.08K  --.-KB/s    in 0.04s   \n",
+      "\n",
+      "2025-07-16 16:20:41 (3.72 MB/s) - ‘sample-tables.pdf’ saved [145494/145494]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "! wget https://assets.accessible-digital-documents.com/uploads/2017/01/sample-tables.pdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**5. Parse document**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Started parsing the file under job_id b53949f7-9017-4b6a-b30c-be6227271ed2\n"
+     ]
+    }
+   ],
+   "source": [
+    "json_result = parser.get_json_result(\"sample-tables.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**6. Get tables!**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tables = parser.get_tables(json_result, \"tables/\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**7. Load tables**\n",
+    "\n",
+    "Let's show one example table!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "summary": "{\n  \"name\": \"display(df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Rainfall\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Average\",\n          \"\",\n          \"24 hour high\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Americas\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 908,\n        \"min\": 9,\n        \"max\": 2010,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          104,\n          133,\n          2010\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Asia\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"\",\n          201.0,\n          28.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Europe\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"\",\n          193.0,\n          29.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Africa\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"\",\n          144.0,\n          20.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+       "type": "dataframe"
+      },
+      "text/html": [
+       "\n",
+       "  <div id=\"df-94a74c8f-1062-4a80-8d3f-32f0fbadf7bb\" class=\"colab-df-container\">\n",
+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Rainfall</th>\n",
+       "      <th>Americas</th>\n",
+       "      <th>Asia</th>\n",
+       "      <th>Europe</th>\n",
+       "      <th>Africa</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>(inches)</td>\n",
+       "      <td>2010</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Average</td>\n",
+       "      <td>104</td>\n",
+       "      <td>201.0</td>\n",
+       "      <td>193.0</td>\n",
+       "      <td>144.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>24 hour high</td>\n",
+       "      <td>15</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>18.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>12 hour high</td>\n",
+       "      <td>9</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td></td>\n",
+       "      <td>2009</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Average</td>\n",
+       "      <td>133</td>\n",
+       "      <td>244.0</td>\n",
+       "      <td>155.0</td>\n",
+       "      <td>166.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>24 hour high</td>\n",
+       "      <td>27</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>12 hour high</td>\n",
+       "      <td>11</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
+       "  <div class=\"colab-df-container\">\n",
+       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-94a74c8f-1062-4a80-8d3f-32f0fbadf7bb')\"\n",
+       "            title=\"Convert this dataframe to an interactive table.\"\n",
+       "            style=\"display:none;\">\n",
+       "\n",
+       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+       "  </svg>\n",
+       "    </button>\n",
+       "\n",
+       "  <style>\n",
+       "    .colab-df-container {\n",
+       "      display:flex;\n",
+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-94a74c8f-1062-4a80-8d3f-32f0fbadf7bb button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-94a74c8f-1062-4a80-8d3f-32f0fbadf7bb');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "    <div id=\"df-54b2aa43-838b-47d3-9209-2fb18153cf87\">\n",
+       "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-54b2aa43-838b-47d3-9209-2fb18153cf87')\"\n",
+       "                title=\"Suggest charts\"\n",
+       "                style=\"display:none;\">\n",
+       "\n",
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+       "    </g>\n",
+       "</svg>\n",
+       "      </button>\n",
+       "\n",
+       "<style>\n",
+       "  .colab-df-quickchart {\n",
+       "      --bg-color: #E8F0FE;\n",
+       "      --fill-color: #1967D2;\n",
+       "      --hover-bg-color: #E2EBFA;\n",
+       "      --hover-fill-color: #174EA6;\n",
+       "      --disabled-fill-color: #AAA;\n",
+       "      --disabled-bg-color: #DDD;\n",
+       "  }\n",
+       "\n",
+       "  [theme=dark] .colab-df-quickchart {\n",
+       "      --bg-color: #3B4455;\n",
+       "      --fill-color: #D2E3FC;\n",
+       "      --hover-bg-color: #434B5C;\n",
+       "      --hover-fill-color: #FFFFFF;\n",
+       "      --disabled-bg-color: #3B4455;\n",
+       "      --disabled-fill-color: #666;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart {\n",
+       "    background-color: var(--bg-color);\n",
+       "    border: none;\n",
+       "    border-radius: 50%;\n",
+       "    cursor: pointer;\n",
+       "    display: none;\n",
+       "    fill: var(--fill-color);\n",
+       "    height: 32px;\n",
+       "    padding: 0;\n",
+       "    width: 32px;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart:hover {\n",
+       "    background-color: var(--hover-bg-color);\n",
+       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "    fill: var(--button-hover-fill-color);\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-quickchart-complete:disabled,\n",
+       "  .colab-df-quickchart-complete:disabled:hover {\n",
+       "    background-color: var(--disabled-bg-color);\n",
+       "    fill: var(--disabled-fill-color);\n",
+       "    box-shadow: none;\n",
+       "  }\n",
+       "\n",
+       "  .colab-df-spinner {\n",
+       "    border: 2px solid var(--fill-color);\n",
+       "    border-color: transparent;\n",
+       "    border-bottom-color: var(--fill-color);\n",
+       "    animation:\n",
+       "      spin 1s steps(1) infinite;\n",
+       "  }\n",
+       "\n",
+       "  @keyframes spin {\n",
+       "    0% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "      border-left-color: var(--fill-color);\n",
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+       "      border-left-color: var(--fill-color);\n",
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+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-top-color: var(--fill-color);\n",
+       "    }\n",
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+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
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+       "      border-color: transparent;\n",
+       "      border-right-color: var(--fill-color);\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "    90% {\n",
+       "      border-color: transparent;\n",
+       "      border-bottom-color: var(--fill-color);\n",
+       "    }\n",
+       "  }\n",
+       "</style>\n",
+       "\n",
+       "      <script>\n",
+       "        async function quickchart(key) {\n",
+       "          const quickchartButtonEl =\n",
+       "            document.querySelector('#' + key + ' button');\n",
+       "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+       "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+       "          try {\n",
+       "            const charts = await google.colab.kernel.invokeFunction(\n",
+       "                'suggestCharts', [key], {});\n",
+       "          } catch (error) {\n",
+       "            console.error('Error during call to suggestCharts:', error);\n",
+       "          }\n",
+       "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+       "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+       "        }\n",
+       "        (() => {\n",
+       "          let quickchartButtonEl =\n",
+       "            document.querySelector('#df-54b2aa43-838b-47d3-9209-2fb18153cf87 button');\n",
+       "          quickchartButtonEl.style.display =\n",
+       "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "        })();\n",
+       "      </script>\n",
+       "    </div>\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
+      ],
+      "text/plain": [
+       "       Rainfall  Americas   Asia Europe Africa\n",
+       "0      (inches)      2010                     \n",
+       "1       Average       104  201.0  193.0  144.0\n",
+       "2  24 hour high        15   26.0   27.0   18.0\n",
+       "3  12 hour high         9   10.0   11.0   12.0\n",
+       "4                    2009                     \n",
+       "5       Average       133  244.0  155.0  166.0\n",
+       "6  24 hour high        27   28.0   29.0   20.0\n",
+       "7  12 hour high        11   12.0   13.0   16.0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from IPython.display import display\n",
+    "\n",
+    "df = pd.read_csv(\n",
+    "    \"/content/tables/table_2025_16_07_16_30_01_569.csv\",\n",
+    ")\n",
+    "display(df.fillna(\"\"))"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
@@ -5,6 +5,7 @@ from .schema import (
    StatusType,
    ExtractedT,
    AgentDataT,
+    ComparisonOperator,
 )
 from .client import AsyncAgentDataClient

@@ -16,4 +17,5 @@ __all__ = [
    "StatusType",
    "ExtractedT",
    "AgentDataT",
+    "ComparisonOperator",
 ]
@@ -1,7 +1,6 @@
 import os
-from typing import Dict, Generic, List, Optional, Type
+from typing import Any, Dict, Generic, List, Optional, Type

-from llama_cloud import FilterOperation
 from llama_cloud.client import AsyncLlamaCloud
 from tenacity import (
    WrappedFn,
@@ -14,6 +13,7 @@ import httpx

 from .schema import (
    AgentDataT,
+    ComparisonOperator,
    TypedAgentData,
    TypedAgentDataItems,
    TypedAggregateGroup,
@@ -42,7 +42,7 @@ def agent_data_retry(func: WrappedFn) -> WrappedFn:
    )(func)


-def get_default_agent_id() -> Optional[str]:
+def get_default_agent_id() -> str:
    """
    Retrieve the default agent ID from environment variables.

@@ -55,7 +55,7 @@ def get_default_agent_id() -> Optional[str]:
        via environment variables instead of passing it explicitly
        to each client instance.
    """
-    return os.getenv("LLAMA_DEPLOY_DEPLOYMENT_NAME")
+    return os.getenv("LLAMA_DEPLOY_DEPLOYMENT_NAME") or "_public"


 class AsyncAgentDataClient(Generic[AgentDataT]):
@@ -85,7 +85,7 @@ class AsyncAgentDataClient(Generic[AgentDataT]):
        agent_client = AsyncAgentDataClient(
            client=llama_client,
            type=ExtractedPerson,
-            collection_name="extracted_people",
+            collection="extracted_people",
            agent_url_id="person-extraction-agent"
        )

@@ -94,8 +94,8 @@ class AsyncAgentDataClient(Generic[AgentDataT]):
        result = await agent_client.create_agent_data(person)

        # Search data
-        results = await agent_client.search_agent_data(
-            filter={"age": FilterOperation(gt=25)},
+        results = await agent_client.search(
+            filter={"age": {"gt": 25}},
            order_by="data.name",
            page_size=20
        )
@@ -107,19 +107,20 @@ class AsyncAgentDataClient(Generic[AgentDataT]):

    def __init__(
        self,
-        client: AsyncLlamaCloud,
        type: Type[AgentDataT],
-        collection_name: str = "default",
+        collection: str = "default",
        agent_url_id: Optional[str] = None,
+        client: Optional[AsyncLlamaCloud] = None,
+        token: Optional[str] = None,
+        base_url: Optional[str] = None,
    ):
        """
        Initialize the AsyncAgentDataClient.

        Args:
-            client: AsyncLlamaCloud client instance for API communication
            type: Pydantic BaseModel class that defines the data structure.
                All agent data will be validated against this type.
-            collection_name: Named collection within the agent for organizing data.
+            collection: Named collection within the agent for organizing data.
                Defaults to "default". Collections allow logical separation of
                different data types or workflows within the same agent.
            agent_url_id: Unique identifier for the agent. This normally appears in the
@@ -127,6 +128,11 @@ class AsyncAgentDataClient(Generic[AgentDataT]):
                will attempt to use the LLAMA_DEPLOY_DEPLOYMENT_NAME environment
                variable. Data can only be added to an already existing agent in the
                platform.
+            client: AsyncLlamaCloud client instance for API communication. If not provided, will
+                construct one from the provided api token and base url
+            token: Llama Cloud API token. Reads from LLAMA_CLOUD_API_KEY if not provided
+            base_url: Llama Cloud API token. Reads from LLAMA_CLOUD_BASE_URL if not provided, and
+                defaults to https://api.cloud.llamaindex.ai

        Raises:
            ValueError: If agent_url_id is not provided and the
@@ -138,33 +144,34 @@ class AsyncAgentDataClient(Generic[AgentDataT]):
        """

        self.agent_url_id = agent_url_id or get_default_agent_id()
-        if not self.agent_url_id:
-            raise ValueError(
-                "Agent ID is required, or set the LLAMA_DEPLOY_DEPLOYMENT_NAME environment variable"
-            )

-        self.collection_name = collection_name
+        self.collection = collection
+        if not client:
+            client = AsyncLlamaCloud(
+                token=token or os.getenv("LLAMA_CLOUD_API_KEY"),
+                base_url=base_url or os.getenv("LLAMA_CLOUD_BASE_URL"),
+            )
        self.client = client
        self.type = type

    @agent_data_retry
-    async def get_agent_data(self, item_id: str) -> TypedAgentData[AgentDataT]:
+    async def get_item(self, item_id: str) -> TypedAgentData[AgentDataT]:
        raw_data = await self.client.beta.get_agent_data(
            item_id=item_id,
        )
        return TypedAgentData.from_raw(raw_data, validator=self.type)

    @agent_data_retry
-    async def create_agent_data(self, data: AgentDataT) -> TypedAgentData[AgentDataT]:
+    async def create_item(self, data: AgentDataT) -> TypedAgentData[AgentDataT]:
        raw_data = await self.client.beta.create_agent_data(
            agent_slug=self.agent_url_id,
-            collection=self.collection_name,
+            collection=self.collection,
            data=data.model_dump(),
        )
        return TypedAgentData.from_raw(raw_data, validator=self.type)

    @agent_data_retry
-    async def update_agent_data(
+    async def update_item(
        self, item_id: str, data: AgentDataT
    ) -> TypedAgentData[AgentDataT]:
        raw_data = await self.client.beta.update_agent_data(
@@ -174,13 +181,13 @@ class AsyncAgentDataClient(Generic[AgentDataT]):
        return TypedAgentData.from_raw(raw_data, validator=self.type)

    @agent_data_retry
-    async def delete_agent_data(self, item_id: str) -> None:
+    async def delete_item(self, item_id: str) -> None:
        await self.client.beta.delete_agent_data(item_id=item_id)

    @agent_data_retry
-    async def search_agent_data(
+    async def search(
        self,
-        filter: Optional[Dict[str, Optional[FilterOperation]]] = None,
+        filter: Optional[Dict[str, Dict[ComparisonOperator, Any]]] = None,
        order_by: Optional[str] = None,
        offset: Optional[int] = None,
        page_size: Optional[int] = None,
@@ -191,11 +198,11 @@ class AsyncAgentDataClient(Generic[AgentDataT]):
        Args:
            filter: Filter conditions to apply to the search. Dict mapping field names to FilterOperation objects. Filters only by data fields
                Examples:
-                - {"age": FilterOperation(gt=18)} - age greater than 18
-                - {"status": FilterOperation(eq="active")} - status equals "active"
-                - {"tags": FilterOperation(includes=["python", "ml"])} - tags include "python" or "ml"
-                - {"created_at": FilterOperation(gte="2024-01-01")} - created after date
-                - {"score": FilterOperation(lt=100, gte=50)} - score between 50 and 100
+                - {"age": {"gt": 18}} - age greater than 18
+                - {"status": {"eq": "active"}} - status equals "active"
+                - {"tags": {"includes": ["python", "ml"]}} - tags include "python" or "ml"
+                - {"created_at": {"gte": "2024-01-01"}} - created after date
+                - {"score": {"lt": 100, "gte": 50}} - score between 50 and 100
            order_by: Comma delimited list of fields to sort results by. Can order by standard agent fields like created_at, or by data fields. Data fields must be prefixed with "data.". If ordering desceding, use a " desc" suffix.
                Examples:
                - "data.name desc, created_at" - sort by name in descending order, and then by creation date
@@ -205,7 +212,7 @@ class AsyncAgentDataClient(Generic[AgentDataT]):
        """
        raw = await self.client.beta.search_agent_data_api_v_1_beta_agent_data_search_post(
            agent_slug=self.agent_url_id,
-            collection=self.collection_name,
+            collection=self.collection,
            filter=filter,
            order_by=order_by,
            offset=offset,
@@ -221,9 +228,9 @@ class AsyncAgentDataClient(Generic[AgentDataT]):
        )

    @agent_data_retry
-    async def aggregate_agent_data(
+    async def aggregate(
        self,
-        filter: Optional[Dict[str, Optional[FilterOperation]]] = None,
+        filter: Optional[Dict[str, Dict[ComparisonOperator, Any]]] = None,
        group_by: Optional[List[str]] = None,
        count: Optional[bool] = None,
        first: Optional[bool] = None,
@@ -235,20 +242,20 @@ class AsyncAgentDataClient(Generic[AgentDataT]):
        Aggregate agent data into groups according to the group_by fields.
        Args:
            filter: Filter conditions to apply to the search. Dict mapping field names to FilterOperation objects. Filters only by data fields
-                See search_agent_data for more details on filtering.
+                See search for more details on filtering.
            group_by: List of fields to group by. Groups strictly by equality. Can only group by data fields.
                Examples:
                - ["name"] - group by name
                - ["name", "age"] - group by name and age
            count: Whether to include the count of items in each group.
            first: Whether to include the first item in each group.
-            order_by: Comma delimited list of fields to sort results by. See search_agent_data for more details on ordering.
+            order_by: Comma delimited list of fields to sort results by. See search for more details on ordering.
            offset: Number of groups to skip from the beginning. Defaults to 0.
            page_size: Maximum number of groups to return per page.
        """
        raw = await self.client.beta.aggregate_agent_data_api_v_1_beta_agent_data_aggregate_post(
            agent_slug=self.agent_url_id,
-            collection=self.collection_name,
+            collection=self.collection,
            page_size=page_size,
            filter=filter,
            order_by=order_by,
@@ -60,7 +60,11 @@ AgentDataT = TypeVar("AgentDataT", bound=BaseModel)
 ExtractedT = TypeVar("ExtractedT", bound=Union[BaseModel, dict])

 # Status types for extracted data workflow
-StatusType = Union[Literal["error", "accepted", "rejected", "in_review"], str]
+StatusType = Union[Literal["error", "accepted", "rejected", "pending_review"], str]
+
+ComparisonOperator = Dict[
+    str, Dict[Literal["gt", "gte", "lt", "lte", "eq", "includes"], Any]
+]


 class TypedAgentData(BaseModel, Generic[AgentDataT]):
@@ -142,7 +146,7 @@ class TypedAgentDataItems(BaseModel, Generic[AgentDataT]):
    Example:
        ```python
        # Search with pagination
-        results = await client.search_agent_data(
+        results = await client.search(
            page_size=10,
            include_total=True
        )
@@ -152,7 +156,7 @@ class TypedAgentDataItems(BaseModel, Generic[AgentDataT]):

        if results.has_more:
            # Load next page
-            next_page = await client.search_agent_data(
+            next_page = await client.search(
                page_size=10,
                offset=10
            )
@@ -183,9 +187,12 @@ class ExtractedData(BaseModel, Generic[ExtractedT]):
        data: The current state of the data (may differ from original after edits)
        status: Current workflow status (in_review, accepted, rejected, error)
        confidence: Confidence scores for individual fields (if available)
+        file_id: The llamacloud file ID of the file that was used to extract the data
+        file_name: The name of the file that was used to extract the data
+        file_hash: A content hash of the file that was used to extract the data, for de-duplication

    Status Workflow:
-        - "in_review": Initial state, awaiting human review
+        - "pending_review": Initial state, awaiting human review
        - "accepted": Data approved and ready for use
        - "rejected": Data rejected, needs re-extraction or manual fix
        - "error": Processing error occurred
@@ -194,8 +201,8 @@ class ExtractedData(BaseModel, Generic[ExtractedT]):
        ```python
        # Create extracted data for review
        extracted = ExtractedData.create(
-            extracted_data=person_data,
-            status="in_review",
+            data=person_data,
+            status="pending_review",
            confidence={"name": 0.95, "age": 0.87}
        )

@@ -212,20 +219,35 @@ class ExtractedData(BaseModel, Generic[ExtractedT]):
    data: ExtractedT = Field(
        description="The latest state of the data. Will differ if data has been updated"
    )
-    status: Union[Literal["error", "accepted", "rejected", "in_review"], str] = Field(
-        description="The status of the extracted data"
-    )
-    confidence: Dict[str, Union[float, Dict]] = Field(
+    status: StatusType = Field(description="The status of the extracted data")
+    confidence: Dict[str, Any] = Field(
        default_factory=dict,
        description="Confidence scores, if any, for each primitive field in the original_data data",
    )
+    file_id: Optional[str] = Field(
+        None, description="The ID of the file that was used to extract the data"
+    )
+    file_name: Optional[str] = Field(
+        None, description="The name of the file that was used to extract the data"
+    )
+    file_hash: Optional[str] = Field(
+        None, description="The hash of the file that was used to extract the data"
+    )
+    metadata: Optional[Dict[str, Any]] = Field(
+        default_factory=dict,
+        description="Additional metadata about the extracted data, such as errors, tokens, etc.",
+    )

    @classmethod
    def create(
        cls,
-        extracted_data: ExtractedT,
-        status: StatusType = "in_review",
-        confidence: Optional[Dict[str, Union[float, Dict]]] = None,
+        data: ExtractedT,
+        status: StatusType = "pending_review",
+        confidence: Optional[Dict[str, Any]] = None,
+        file_id: Optional[str] = None,
+        file_name: Optional[str] = None,
+        file_hash: Optional[str] = None,
+        metadata: Optional[Dict[str, Any]] = None,
    ) -> "ExtractedData[ExtractedT]":
        """
        Create a new ExtractedData instance with sensible defaults.
@@ -234,15 +256,22 @@ class ExtractedData(BaseModel, Generic[ExtractedT]):
            extracted_data: The extracted data payload
            status: Initial workflow status
            confidence: Optional confidence scores for fields
+            file_id: The llamacloud file ID of the file that was used to extract the data
+            file_name: The name of the file that was used to extract the data
+            file_hash: A content hash of the file that was used to extract the data, for de-duplication

        Returns:
            New ExtractedData instance ready for storage
        """
        return cls(
-            original_data=extracted_data,
-            data=extracted_data,
+            original_data=data,
+            data=data,
            status=status,
            confidence=confidence or {},
+            file_id=file_id,
+            file_name=file_name,
+            file_hash=file_hash,
+            metadata=metadata or {},
        )


@@ -137,15 +137,9 @@ def run_in_thread(


 def _extraction_config_warning(config: ExtractConfig) -> None:
-    if config.use_reasoning:
+    if config.cite_sources or config.confidence_scores:
        warnings.warn(
-            "`use_reasoning` is an experimental feature. Results will be available in "
-            "the `extraction_metadata` field for the extraction run.",
-            ExperimentalWarning,
-        )
-    if config.cite_sources:
-        warnings.warn(
-            "`cite_sources` is an experimental feature. This may greatly increase the "
+            "`cite_sources`/`confidence_scores` could 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.",
            ExperimentalWarning,
@@ -37,6 +37,7 @@ from llama_cloud_services.parse.utils import (
    nest_asyncio_msg,
    make_api_request,
    partition_pages,
+    extract_tables_from_json_results,
 )

 # can put in a path to the file or the file bytes itself
@@ -380,6 +381,10 @@ class LlamaParse(BasePydanticReader):
        default=False,
        description="Preserve grid alignment across page in text mode.",
    )
+    preserve_very_small_text: Optional[bool] = Field(
+        default=False,
+        description="If set, the parser will try to preserve very small text lines. This can be useful for documents containing vector graphics with very small text lines that may not be recognized by OCR or a vision model (such as in CAD drawings).",
+    )
    replace_failed_page_mode: Optional[FailedPageMode] = Field(
        default=None,
        description="The mode to use to replace the failed page, see FailedPageMode enum for possible value. If set, the parser will replace the failed page with the specified mode. If not set, the default mode (raw_text) will be used.",
@@ -911,6 +916,9 @@ class LlamaParse(BasePydanticReader):
                "preserve_layout_alignment_across_pages"
            ] = self.preserve_layout_alignment_across_pages

+        if self.preserve_very_small_text:
+            data["preserve_very_small_text"] = self.preserve_very_small_text
+
        if self.preset is not None:
            data["preset"] = self.preset

@@ -1620,6 +1628,16 @@ class LlamaParse(BasePydanticReader):
            else:
                raise e

+    def get_tables(self, json_results: List[dict], download_path: str) -> List[str]:
+        if not os.path.exists(download_path):
+            os.makedirs(download_path)
+        return extract_tables_from_json_results(json_results, download_path)
+
+    async def aget_tables(
+        self, json_result: List[dict], download_path: str
+    ) -> List[str]:
+        return await asyncio.to_thread(self.get_tables, json_result, download_path)
+
    async def aget_xlsx(
        self, json_result: List[dict], download_path: str
    ) -> List[dict]:
@@ -1686,3 +1704,79 @@ class LlamaParse(BasePydanticReader):
                sub_docs.append(sub_doc)

        return sub_docs
+
+    async def aget_result(
+        self, job_id: Union[str, List[str]]
+    ) -> Union[JobResult, List[JobResult]]:
+        """
+        Return JobResult object for previously parsed job(s).
+
+        If the job is still pending, the result will not be returned until it is completed.
+
+        Args:
+            job_id: Job ID or list of multiple Job IDs to be retrieved.
+
+        Returns:
+            JobResult object or list of JobResult objects if multiple job IDs were provided.
+        """
+        if isinstance(job_id, str):
+            result = await self._get_job_result(
+                job_id, ResultType.JSON.value, verbose=self.verbose
+            )
+            return JobResult(
+                job_id=job_id,
+                file_name="",
+                job_result=result,
+                api_key=self.api_key,
+                base_url=self.base_url,
+                client=self.aclient,
+                page_separator=self.page_separator or _DEFAULT_SEPARATOR,
+            )
+        elif isinstance(job_id, list):
+            results = []
+            jobs = [
+                self._get_job_result(id_, ResultType.JSON.value, verbose=self.verbose)
+                for id_ in job_id
+            ]
+            results = await run_jobs(
+                jobs,
+                workers=self.num_workers,
+                desc="Getting job results",
+                show_progress=self.show_progress,
+            )
+            return [
+                JobResult(
+                    job_id=job_id[i],
+                    file_name="",
+                    job_result=result,
+                    api_key=self.api_key,
+                    base_url=self.base_url,
+                    client=self.aclient,
+                    page_separator=self.page_separator or _DEFAULT_SEPARATOR,
+                )
+                for i, result in enumerate(results)
+            ]
+        else:
+            raise ValueError("The input job_id must be a string or a list of strings.")
+
+    def get_result(
+        self, job_id: Union[str, List[str]]
+    ) -> Union[JobResult, List[JobResult]]:
+        """
+        Return JobResult object for previously parsed job(s).
+
+        If the job is still pending, the result will not be returned until it is completed.
+
+        Args:
+            job_id: Job ID or list of multiple Job IDs to be retrieved.
+
+        Returns:
+            JobResult object or list of JobResult objects if multiple job IDs were provided.
+        """
+        try:
+            return asyncio_run(self.aget_result(job_id))
+        except RuntimeError as e:
+            if nest_asyncio_err in str(e):
+                raise RuntimeError(nest_asyncio_msg)
+            else:
+                raise e
@@ -295,13 +295,64 @@ class JobResult(BaseModel):
    async def aget_markdown_nodes(self, split_by_page: bool = False) -> List[TextNode]:
        """
        Get the markdown nodes from the job.
-
        Args:
            split_by_page: Whether to split the pages into separate documents
        """
        documents = await self.aget_markdown_documents(split_by_page)
        return [TextNode(text=doc.text, metadata=doc.metadata) for doc in documents]

+    def get_markdown(self) -> str:
+        """
+        Get the raw parsed markdown from the job, distinct from the markdown documents.
+        This does not include page separators, e.g. if merge_tables_across_pages_in_markdown is True
+        """
+        return asyncio_run(self.aget_markdown())
+
+    async def aget_markdown(self) -> str:
+        """
+        Get the raw parsed markdown from the job, distinct from the markdown documents.
+        This does not include page separators, e.g. if merge_tables_across_pages_in_markdown is True
+        """
+        url = f"{self._base_url}/api/v1/parsing/job/{self.job_id}/result/raw/markdown"
+        response = await make_api_request(self._client, "GET", url)
+        return response.content.decode("utf-8")
+
+    def get_text(self) -> str:
+        """
+        Get the raw parsed text from the job.
+        """
+        return asyncio_run(self.aget_text())
+
+    async def aget_text(self) -> str:
+        """
+        Get the raw parsed text from the job.
+        """
+        url = f"{self._base_url}/api/v1/parsing/job/{self.job_id}/result/raw/text"
+        response = await make_api_request(self._client, "GET", url)
+        return response.content.decode("utf-8")
+
+    def get_json(self) -> Dict[str, Any]:
+        """
+        Get the full parsed JSON result from the job.
+
+        Note:
+            This is not the same as JobResult.json(), which is a
+            JSON serialized version of the JobResult Page Documents.
+        """
+        return asyncio_run(self.aget_json())
+
+    async def aget_json(self) -> Dict[str, Any]:
+        """
+        Get the full parsed JSON result from the job.
+
+        Note:
+            This is not the same as JobResult.json(), which is a
+            JSON serialized version of the JobResult Page Documents.
+        """
+        url = f"{self._base_url}/api/v1/parsing/job/{self.job_id}/result/json"
+        response = await make_api_request(self._client, "GET", url)
+        return response.json()
+
    async def _get_image_document_with_bytes(
        self, image: ImageItem, page: Page
    ) -> ImageDocument:
@@ -1,7 +1,10 @@
 import httpx
 import itertools
 import logging
+import os
 from enum import Enum
+from pathlib import Path
+from datetime import datetime
 from tenacity import (
    retry,
    stop_after_attempt,
@@ -9,7 +12,7 @@ from tenacity import (
    retry_if_exception,
    before_sleep_log,
 )
-from typing import Any, Iterable, Iterator, Optional
+from typing import Any, Iterable, Iterator, Optional, List, cast

 logger = logging.getLogger(__name__)

@@ -351,3 +354,30 @@ def partition_pages(
            yield ",".join(targets)
        else:
            return
+
+
+def extract_tables_from_json_results(
+    json_results: List[dict], download_path: str
+) -> List[str]:
+    tables = []
+    for json_result in json_results:
+        pages = json_result["pages"]
+        for page in pages:
+            page = cast(dict, page)
+            items = page.get("items", [])
+            if items:
+                for i, item in enumerate(items):
+                    item = cast(dict, item)
+                    if item.get("type", "") == "table" and item.get("csv", ""):
+                        savepath = os.path.join(
+                            download_path,
+                            f"table_{datetime.now().strftime('%Y_%d_%m_%H_%M_%S_%f')[:-3]}.csv",
+                        )
+                        if Path(savepath).exists():
+                            savepath = (
+                                savepath.replace(".csv", "_")[0] + str(i) + ".csv"
+                            )
+                        with open(savepath, "w") as f:
+                            f.write(item["csv"])
+                        tables.append(savepath)
+    return tables
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand.

 [[package]]
 name = "aiohappyeyeballs"
@@ -257,7 +257,7 @@ version = "2025.1.31"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
-groups = ["main", "dev"]
+groups = ["main"]
 files = [
    {file = "certifi-2025.1.31-py3-none-any.whl", hash = "sha256:ca78db4565a652026a4db2bcdf68f2fb589ea80d0be70e03929ed730746b84fe"},
    {file = "certifi-2025.1.31.tar.gz", hash = "sha256:3d5da6925056f6f18f119200434a4780a94263f10d1c21d032a6f6b2baa20651"},
@@ -350,7 +350,7 @@ version = "3.4.1"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7"
-groups = ["main", "dev"]
+groups = ["main"]
 files = [
    {file = "charset_normalizer-3.4.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:91b36a978b5ae0ee86c394f5a54d6ef44db1de0815eb43de826d41d21e4af3de"},
    {file = "charset_normalizer-3.4.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7461baadb4dc00fd9e0acbe254e3d7d2112e7f92ced2adc96e54ef6501c5f176"},
@@ -593,7 +593,7 @@ description = "Like `typing._eval_type`, but lets older Python versions use newe
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
-markers = "python_version < \"3.10\""
+markers = "python_version == \"3.9\""
 files = [
    {file = "eval_type_backport-0.2.2-py3-none-any.whl", hash = "sha256:cb6ad7c393517f476f96d456d0412ea80f0a8cf96f6892834cd9340149111b0a"},
    {file = "eval_type_backport-0.2.2.tar.gz", hash = "sha256:f0576b4cf01ebb5bd358d02314d31846af5e07678387486e2c798af0e7d849c1"},
@@ -896,7 +896,7 @@ version = "0.16.0"
 description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
 optional = false
 python-versions = ">=3.8"
-groups = ["main", "dev"]
+groups = ["main"]
 files = [
    {file = "h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86"},
    {file = "h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1"},
@@ -955,7 +955,7 @@ version = "3.10"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.6"
-groups = ["main", "dev"]
+groups = ["main"]
 files = [
    {file = "idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"},
    {file = "idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9"},
@@ -971,7 +971,7 @@ description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.9"
 groups = ["dev"]
-markers = "python_version < \"3.10\""
+markers = "python_version == \"3.9\""
 files = [
    {file = "importlib_metadata-8.6.1-py3-none-any.whl", hash = "sha256:02a89390c1e15fdfdc0d7c6b25cb3e62650d0494005c97d6f148bf5b9787525e"},
    {file = "importlib_metadata-8.6.1.tar.gz", hash = "sha256:310b41d755445d74569f993ccfc22838295d9fe005425094fad953d7f15c8580"},
@@ -1170,14 +1170,14 @@ test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"

 [[package]]
 name = "llama-cloud"
-version = "0.1.23"
+version = "0.1.34"
 description = ""
 optional = false
 python-versions = "<4,>=3.8"
 groups = ["main"]
 files = [
-    {file = "llama_cloud-0.1.23-py3-none-any.whl", hash = "sha256:ce95b0705d85c99b3b27b0af0d16a17d9a81b14c96bf13c1063a1bd13d8d0446"},
-    {file = "llama_cloud-0.1.23.tar.gz", hash = "sha256:3d84a24a860f046d39a106c06742ec0ea39a574ac42bbf91706fe025f44e233e"},
+    {file = "llama_cloud-0.1.34-py3-none-any.whl", hash = "sha256:9b06fb109b1d9f652095a11732ae3dbe84e48cc00c580b2eeb19e71e901267be"},
+    {file = "llama_cloud-0.1.34.tar.gz", hash = "sha256:6866e4bab47d2c1840bdf169c13c06176931c1d30697ac1fa71bab7942a041e9"},
 ]

 [package.dependencies]
@@ -1187,24 +1187,25 @@ pydantic = ">=1.10"

 [[package]]
 name = "llama-cloud-services"
-version = "0.6.30"
+version = "0.6.48"
 description = "Tailored SDK clients for LlamaCloud services."
 optional = false
 python-versions = "<4.0,>=3.9"
 groups = ["main"]
 files = [
-    {file = "llama_cloud_services-0.6.30-py3-none-any.whl", hash = "sha256:4d5817a9841fc3ba3409865c52d082090f4ef827931f0e5e4a89f5818c0d4e36"},
-    {file = "llama_cloud_services-0.6.30.tar.gz", hash = "sha256:2cb5004d13127aac52888ae9b3d70f899d598633520b2a2542bb62682d08d776"},
+    {file = "llama_cloud_services-0.6.48-py3-none-any.whl", hash = "sha256:5faadcfa270f3be516dd7d3568cbb54b3014b2c8878c5bd7d3252ab1bd635869"},
+    {file = "llama_cloud_services-0.6.48.tar.gz", hash = "sha256:64fe8d8ce7434d6a481646c35be55973ae10765d20b6be0067a262e6be61f2df"},
 ]

 [package.dependencies]
 click = ">=8.1.7,<9.0.0"
 eval-type-backport = {version = ">=0.2.0,<0.3.0", markers = "python_version < \"3.10\""}
-llama-cloud = "0.1.23"
+llama-cloud = "0.1.34"
 llama-index-core = ">=0.12.0"
 platformdirs = ">=4.3.7,<5.0.0"
 pydantic = ">=2.8,<2.10 || >2.10"
 python-dotenv = ">=1.0.1,<2.0.0"
+tenacity = ">=8.5.0,<10.0"

 [[package]]
 name = "llama-index-core"
@@ -2473,7 +2474,7 @@ version = "2.32.4"
 description = "Python HTTP for Humans."
 optional = false
 python-versions = ">=3.8"
-groups = ["main", "dev"]
+groups = ["main"]
 files = [
    {file = "requests-2.32.4-py3-none-any.whl", hash = "sha256:27babd3cda2a6d50b30443204ee89830707d396671944c998b5975b031ac2b2c"},
    {file = "requests-2.32.4.tar.gz", hash = "sha256:27d0316682c8a29834d3264820024b62a36942083d52caf2f14c0591336d3422"},
@@ -2807,7 +2808,7 @@ files = [
    {file = "typing_extensions-4.13.2-py3-none-any.whl", hash = "sha256:a439e7c04b49fec3e5d3e2beaa21755cadbbdc391694e28ccdd36ca4a1408f8c"},
    {file = "typing_extensions-4.13.2.tar.gz", hash = "sha256:e6c81219bd689f51865d9e372991c540bda33a0379d5573cddb9a3a23f7caaef"},
 ]
-markers = {dev = "python_version < \"3.10\""}
+markers = {dev = "python_version == \"3.9\""}

 [[package]]
 name = "typing-inspect"
@@ -2846,7 +2847,7 @@ version = "2.4.0"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.9"
-groups = ["main", "dev"]
+groups = ["main"]
 files = [
    {file = "urllib3-2.4.0-py3-none-any.whl", hash = "sha256:4e16665048960a0900c702d4a66415956a584919c03361cac9f1df5c5dd7e813"},
    {file = "urllib3-2.4.0.tar.gz", hash = "sha256:414bc6535b787febd7567804cc015fee39daab8ad86268f1310a9250697de466"},
@@ -3068,7 +3069,7 @@ description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.9"
 groups = ["dev"]
-markers = "python_version < \"3.10\""
+markers = "python_version == \"3.9\""
 files = [
    {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"},
    {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"},
@@ -3085,4 +3086,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.9,<4.0"
-content-hash = "1be767f62c8a8e1c9acb76347055a837ea2c44130004703361d0303b049bede7"
+content-hash = "0b5ab34c44ce7802b02e01b6535b9bb0e08f99f26a43a76be88534dd8a417666"
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"

 [tool.poetry]
 name = "llama-parse"
-version = "0.6.45"
+version = "0.6.51"
 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 = ">=0.6.45"
+llama-cloud-services = ">=0.6.51"

 [tool.poetry.group.dev.dependencies]
 pytest = "^8.0.0"
@@ -1925,14 +1925,14 @@ rapidfuzz = ">=3.9.0,<4.0.0"

 [[package]]
 name = "llama-cloud"
-version = "0.1.33"
+version = "0.1.34"
 description = ""
 optional = false
 python-versions = "<4,>=3.8"
 groups = ["main"]
 files = [
-    {file = "llama_cloud-0.1.33-py3-none-any.whl", hash = "sha256:35b7d4a30b013f0a343f7e09126b531c697d65bffd4eb4d2d79bf7d65f256178"},
-    {file = "llama_cloud-0.1.33.tar.gz", hash = "sha256:a0bb900d5a6e86f8c767b48686c5253679ad7ca1b57612dc39b0767e57ad3d78"},
+    {file = "llama_cloud-0.1.34-py3-none-any.whl", hash = "sha256:9b06fb109b1d9f652095a11732ae3dbe84e48cc00c580b2eeb19e71e901267be"},
+    {file = "llama_cloud-0.1.34.tar.gz", hash = "sha256:6866e4bab47d2c1840bdf169c13c06176931c1d30697ac1fa71bab7942a041e9"},
 ]

 [package.dependencies]
@@ -4623,4 +4623,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.9,<4.0"
-content-hash = "487717a0bbe7ff67360e3e9f187e15a33c77e4942a7c909cb37b95425a81544c"
+content-hash = "62bbed6ef774d11a0e775524ea3fc86e840fc35823c199dbbe1980ce9c81d2a1"
@@ -8,7 +8,7 @@ python_version = "3.10"

 [tool.poetry]
 name = "llama-cloud-services"
-version = "0.6.45"
+version = "0.6.51"
 description = "Tailored SDK clients for LlamaCloud services."
 authors = ["Logan Markewich <logan@runllama.ai>"]
 license = "MIT"
@@ -18,7 +18,7 @@ packages = [{include = "llama_cloud_services"}]
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
 llama-index-core = ">=0.12.0"
-llama-cloud = "==0.1.33"
+llama-cloud = "==0.1.34"
 pydantic = ">=2.8,!=2.10"
 click = "^8.1.7"
 python-dotenv = "^1.0.1"
@@ -0,0 +1,147 @@
+#!/usr/bin/env -S uv run --script
+# /// script
+# dependencies = ["click", "tomlkit"]
+# ///
+
+import click
+import subprocess
+import sys
+import tomlkit
+from pathlib import Path
+
+
+def get_current_versions() -> tuple[str, str, str]:
+    """Get current versions from both pyproject.toml files."""
+    # Read main pyproject.toml
+    main_content = Path("pyproject.toml").read_text()
+    main_doc = tomlkit.parse(main_content)
+    main_version = main_doc["tool"]["poetry"]["version"]
+
+    # Read llama_parse/pyproject.toml
+    llama_parse_content = Path("llama_parse/pyproject.toml").read_text()
+    llama_parse_doc = tomlkit.parse(llama_parse_content)
+    llama_parse_version = llama_parse_doc["tool"]["poetry"]["version"]
+    dependency_version = llama_parse_doc["tool"]["poetry"]["dependencies"][
+        "llama-cloud-services"
+    ]
+
+    return str(main_version), str(llama_parse_version), str(dependency_version)
+
+
+def validate_versions(
+    main_version: str, llama_parse_version: str, dependency_version: str
+) -> list[str]:
+    """Validate that versions are consistent and return warnings."""
+    warnings = []
+
+    if main_version != llama_parse_version:
+        warnings.append(
+            f"Version mismatch: main={main_version}, llama_parse={llama_parse_version}"
+        )
+
+    # Extract version from dependency string (e.g., ">=0.6.51" -> "0.6.51")
+    if dependency_version and dependency_version.startswith(">="):
+        dep_ver = dependency_version[2:]
+        if dep_ver != main_version:
+            warnings.append(
+                f"Dependency version mismatch: dependency={dep_ver}, main={main_version}"
+            )
+
+    return warnings
+
+
+def set_version(version: str) -> None:
+    """Set version across all pyproject.toml files using tomlkit to preserve formatting."""
+    # Update main pyproject.toml
+    main_content = Path("pyproject.toml").read_text()
+    main_doc = tomlkit.parse(main_content)
+    main_doc["tool"]["poetry"]["version"] = version
+    Path("pyproject.toml").write_text(tomlkit.dumps(main_doc))
+
+    # Update llama_parse/pyproject.toml
+    llama_parse_content = Path("llama_parse/pyproject.toml").read_text()
+    llama_parse_doc = tomlkit.parse(llama_parse_content)
+    llama_parse_doc["tool"]["poetry"]["version"] = version
+    llama_parse_doc["tool"]["poetry"]["dependencies"][
+        "llama-cloud-services"
+    ] = f">={version}"
+    Path("llama_parse/pyproject.toml").write_text(tomlkit.dumps(llama_parse_doc))
+
+    click.echo(f"Updated all versions to {version}")
+
+
+def get_current_branch() -> str:
+    """Get the current git branch."""
+    result = subprocess.run(
+        ["git", "branch", "--show-current"], capture_output=True, text=True, check=True
+    )
+    return result.stdout.strip()
+
+
+def create_and_push_tag(version: str) -> None:
+    """Create a git tag and push it."""
+    current_branch = get_current_branch()
+    if current_branch != "main":
+        click.echo(
+            f"Error: Not on main branch (currently on {current_branch})", err=True
+        )
+        sys.exit(1)
+
+    tag_name = f"v{version}"
+
+    # Create tag
+    subprocess.run(["git", "tag", tag_name], check=True)
+    click.echo(f"Created tag {tag_name}")
+
+    # Push tag
+    subprocess.run(["git", "push", "origin", tag_name], check=True)
+    click.echo(f"Pushed tag {tag_name}")
+
+
+@click.group()
+def cli() -> None:
+    """Version management for llama-cloud-services."""
+    pass
+
+
+@cli.command()
+def get() -> None:
+    """Get current versions and show validation warnings."""
+    main_version, llama_parse_version, dependency_version = get_current_versions()
+
+    click.echo("Current versions:")
+    click.echo(f"  llama-cloud-services: {main_version}")
+    click.echo(f"  llama-parse: {llama_parse_version}")
+    click.echo(f"  dependency reference: {dependency_version}")
+
+    warnings = validate_versions(main_version, llama_parse_version, dependency_version)
+    if warnings:
+        click.echo("\nValidation warnings:")
+        for warning in warnings:
+            click.echo(f"  ⚠️  {warning}")
+    else:
+        click.echo("\n✅ All versions are consistent")
+
+
+@cli.command()
+@click.argument("version")
+def set(version: str) -> None:
+    """Set version across all pyproject.toml files."""
+    set_version(version)
+
+
+@cli.command()
+@click.option(
+    "--version", help="Version to tag (uses current version if not specified)"
+)
+def tag(version: str | None = None) -> None:
+    """Create and push a git tag for the current version."""
+    if not version:
+        main_version, _, _ = get_current_versions()
+        version = main_version
+
+    create_and_push_tag(version)
+
+
+if __name__ == "__main__":
+    cli()
@@ -48,8 +48,8 @@ class TestData(BaseModel):
 # Skip all tests if API key is not set
@pytest.mark.asyncio
@pytest.mark.skipif(
-    not LLAMA_CLOUD_API_KEY or not LLAMA_DEPLOY_DEPLOYMENT_NAME,
-    reason="LLAMA_CLOUD_API_KEY or LLAMA_DEPLOY_DEPLOYMENT_NAME not set",
+    not LLAMA_CLOUD_API_KEY,
+    reason="LLAMA_CLOUD_API_KEY not set",
 )
 async def test_agent_data_crud_operations():
    """Test basic CRUD operations for agent data with automatic cleanup"""
@@ -67,7 +67,7 @@ async def test_agent_data_crud_operations():
    agent_data_client = AsyncAgentDataClient(
        client=client,
        type=TestData,
-        collection_name=f"test-collection-{test_id[:8]}",
+        collection=f"test-collection-{test_id[:8]}",
        agent_url_id=LLAMA_DEPLOY_DEPLOYMENT_NAME,
    )

@@ -77,21 +77,21 @@ async def test_agent_data_crud_operations():
    created_item = None
    try:
        # Test CREATE
-        created_item = await agent_data_client.create_agent_data(test_data)
+        created_item = await agent_data_client.create_item(test_data)
        assert created_item.data.name == "test-item"
        assert created_item.data.test_id == test_id
        assert created_item.data.value == 42
        assert created_item.id is not None

        # Test READ
-        retrieved_item = await agent_data_client.get_agent_data(created_item.id)
+        retrieved_item = await agent_data_client.get_item(created_item.id)
        assert retrieved_item.id == created_item.id
        assert retrieved_item.data.name == "test-item"
        assert retrieved_item.data.test_id == test_id
        assert retrieved_item.data.value == 42

        # Test SEARCH
-        search_results = await agent_data_client.search_agent_data(
+        search_results = await agent_data_client.search(
            filter={"test_id": {"eq": test_id}}, page_size=10, include_total=True
        )
        assert len(search_results.items) == 1
@@ -99,7 +99,7 @@ async def test_agent_data_crud_operations():
        assert search_results.total == 1

        # Test AGGREGATE
-        aggregate_results = await agent_data_client.aggregate_agent_data(
+        aggregate_results = await agent_data_client.aggregate(
            group_by=["test_id"], count=True
        )
        assert len(aggregate_results.items) == 1
@@ -108,7 +108,7 @@ async def test_agent_data_crud_operations():

        # Test UPDATE
        updated_data = TestData(name="updated-item", test_id=test_id, value=84)
-        updated_item = await agent_data_client.update_agent_data(
+        updated_item = await agent_data_client.update_item(
            created_item.id, updated_data
        )
        assert updated_item.data.name == "updated-item"
@@ -116,7 +116,7 @@ async def test_agent_data_crud_operations():
        assert updated_item.id == created_item.id

        # Verify update persisted
-        verified_item = await agent_data_client.get_agent_data(created_item.id)
+        verified_item = await agent_data_client.get_item(created_item.id)
        assert verified_item.data.name == "updated-item"
        assert verified_item.data.value == 84

@@ -124,6 +124,6 @@ async def test_agent_data_crud_operations():
        # Clean up test data
        if created_item is not None:
            try:
-                await agent_data_client.delete_agent_data(created_item.id)
+                await agent_data_client.delete_item(created_item.id)
            except Exception as e:
                print(f"Warning: Failed to cleanup test data {created_item.id}: {e}")
@@ -70,7 +70,7 @@ def test_extracted_data_create_method():
    extracted = ExtractedData.create(person)
    assert extracted.original_data == person
    assert extracted.data == person
-    assert extracted.status == "in_review"
+    assert extracted.status == "pending_review"
    assert extracted.confidence == {}

    # Test with custom values
@@ -8,6 +8,7 @@ import uuid
 from llama_cloud.types import ExtractConfig, ExtractMode
 from deepdiff import DeepDiff
 from tests.extract.util import json_subset_match_score, load_test_dotenv
+from .conftest import register_agent_for_cleanup

 load_test_dotenv()

@@ -115,13 +116,11 @@ def extraction_agent(test_case: TestCase, extractor: LlamaExtract):

    # Create new agent
    agent = extractor.create_agent(agent_name, schema, config=test_case.config)
-    yield agent

-    # Cleanup after test
-    try:
-        extractor.delete_agent(agent.id)
-    except Exception as e:
-        print(f"Warning: Failed to delete agent {agent.id}: {str(e)}")
+    # Register agent for cleanup at the end of the test session
+    register_agent_for_cleanup(agent.id)
+
+    yield agent


@pytest.mark.skipif(
@@ -130,7 +129,7 @@ def extraction_agent(test_case: TestCase, extractor: LlamaExtract):
 )
@pytest.mark.parametrize("test_case", get_test_cases(), ids=lambda x: x.name)
 def test_extraction(test_case: TestCase, extraction_agent: ExtractionAgent) -> None:
-    result = extraction_agent.extract(test_case.input_file).data
+    result = extraction_agent.extract(test_case.input_file).data  # type: ignore
    with open(test_case.expected_output, "r") as f:
        expected = json.load(f)
    # TODO: fix the saas_slide test
@@ -193,3 +193,12 @@ async def test_multiple_page_markdown(
    result = await markdown_parser.aload_data(filepath)
    assert len(result) == expected
    assert all(len(doc.text) > 0 for doc in result)
+
+
+@pytest.mark.asyncio
+async def test_get_result(markdown_parser: LlamaParse) -> None:
+    filepath = "tests/test_files/attention_is_all_you_need.pdf"
+    expected = await markdown_parser.aparse(filepath)
+    result = await markdown_parser.aget_result(expected.job_id)
+    assert result.job_id == expected.job_id
+    assert len(result.pages) == len(expected.pages)
@@ -83,6 +83,12 @@ async def test_basic_parse_result(file_path: str, partition_pages: Optional[int]
    assert image_documents[0].image is not None
    assert len(image_documents[0].resolve_image().getvalue()) > 0

+    assert len(await result.aget_text()) > 0
+    assert len(await result.aget_markdown()) > 0
+    json = await result.aget_json()
+    assert json.get("job_metadata")
+    assert len(json.get("pages", [])) == len(result.pages)
+

@pytest.mark.asyncio
@pytest.mark.skip(
@@ -1,7 +1,30 @@
 import pytest

+from pathlib import Path
+from llama_cloud_services.parse.utils import (
+    expand_target_pages,
+    partition_pages,
+    extract_tables_from_json_results,
+)
+from typing import List

-from llama_cloud_services.parse.utils import expand_target_pages, partition_pages
+
+@pytest.fixture()
+def pseudo_json_results() -> List[dict]:
+    return [
+        {
+            "pages": [
+                {
+                    "items": [
+                        {
+                            "type": "table",
+                            "csv": "Name,Age,Height (cm)\nAnna,12,140\nBob,22,175\nClaire,33,173\nDenis,44,185\n",
+                        }
+                    ]
+                }
+            ]
+        }
+    ]


 def test_expand_target_pages() -> None:
@@ -28,3 +51,12 @@ def test_partion_pages() -> None:
    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"]
+
+
+def test_table_extraction(pseudo_json_results: List[dict], tmpdir: Path) -> None:
+    tables = extract_tables_from_json_results(pseudo_json_results, tmpdir)
+    assert len(tables) == 1
+    for table in tables:
+        assert Path(table).exists()
+        with open(table) as t:
+            assert t.read() == pseudo_json_results[0]["pages"][0]["items"][0]["csv"]
Author	SHA1	Message	Date
Adrian Lyjak	083d8109c2	Make versioning a little easier, and fix llama_parse version (#808 ) * Make versioning a little easier * fix up ci	2025-07-21 18:49:07 -04:00
Adrian Lyjak	89cfc8b25f	feat: default to _public agent data (#803 ) * feat: default to _public agent data * version bump	2025-07-21 15:58:03 -04:00
Peter Rowlands (변기호)	c46e157f92	parse: expose preserve_very_small_text option (#806 )	2025-07-21 14:19:15 +09:00
Peter Rowlands (변기호)	05d6026d37	bump version to v0.6.50 (#802 )	2025-07-18 18:59:25 +09:00
Peter Rowlands (변기호)	8e98d5c146	parse: expose functionality to get raw job results (#801 ) * add LlamaParse.get_result() * add JobResult.get_text/get_markdown/get_json * add tests	2025-07-18 18:50:29 +09:00
Adrian Lyjak	3f311c0669	Bump v0.6.49 (#797 )	2025-07-16 19:42:09 -04:00
Adrian Lyjak	b1a2f9d42b	Add new method to fetch the full, non-paginated markdown (#796 ) Add new method to fetch the full, non-paginated markdown for proper merge_tables_across_pages_in_markdown support	2025-07-16 19:29:57 -04:00
Neeraj Pradhan	142f55c94c	Update to version 0.6.48 (#795 ) * Update to version 0.6.48 * pin version * poetry lock * adjust warnings * collect all agents for cleanup	2025-07-16 13:24:44 -07:00
Clelia (Astra) Bertelli	230a110e52	chore: vbump to 0.6.47 and example notebook (#794 ) * chore: vbump to 0.6.47 and example notebook * chore: update llama-parse pyproject.toml	2025-07-16 19:08:44 +02:00
Clelia (Astra) Bertelli	83e2b031cd	feat: add table extraction for LlamaParse as CSV files (#793 ) * feat: add table extraction for LlamaParse as CSV files * chore: poetry lock * chore: add tests * fix: handle the case where no tables are present * chore: implement suggestions	2025-07-16 17:08:09 +02:00
Adrian Lyjak	4844e26e5c	Improve Agent Data interface, and add file related fields to extracted data for file tracking (#785 ) Add file related fields for file tracking. Simplify API	2025-07-09 14:27:24 -04:00