chore: version packages (#1035)

* create notebook to parse a batch of documents

* remove local dev code

* tidy

* don't git track the sample pdfs

* update notebook to use client

* add logic to fetch parse results using job id from batch item

* generate example for fetching results via parse job id

* fix linting

* convert notebook to use httpx rather than client for now

* fix linting

.github/workflows/test_e2e_py.yml

@@ -12,6 +12,7 @@ env:
 jobs:
   test_e2e:
     runs-on: ubuntu-latest
+    timeout-minutes: 30
     strategy:
       # You can use PyPy versions in python-version.
       # For example, pypy-2.7 and pypy-3.8

.pre-commit-config.yaml

@@ -34,7 +34,7 @@ repos:
     rev: v1.0.1
     hooks:
       - id: mypy
-        exclude: ^py/tests|^py/unit_tests
+        exclude: ^py/tests|^py/unit_tests|^examples
         additional_dependencies:
           [
             "types-requests",

examples/batch/parse/.gitignore

@@ -0,0 +1 @@
+sample_files/

examples/batch/parse/batch_parse_directory.ipynb

@@ -0,0 +1,807 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cell-0",
+   "metadata": {},
+   "source": [
+    "# Batch Parse with LlamaCloud Directories\n",
+    "\n",
+    "This notebook demonstrates how to use LlamaCloud's batch processing API to parse multiple files in a directory. The workflow includes:\n",
+    "\n",
+    "1. **Creating a Directory** - Set up a directory to organize your files\n",
+    "2. **Uploading Files** - Upload multiple files to the directory\n",
+    "3. **Starting a Batch Parse Job** - Kick off batch processing on all files\n",
+    "4. **Monitoring Progress** - Check the status and view results\n",
+    "\n",
+    "This is useful when you need to parse many documents at once, as the batch API handles the orchestration and provides progress tracking."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-1",
+   "metadata": {},
+   "source": [
+    "## Setup and Installation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cell-2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install llama-cloud python-dotenv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cell-3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "import httpx\n",
+    "\n",
+    "# Load environment variables\n",
+    "load_dotenv()\n",
+    "\n",
+    "# Set your API key\n",
+    "LLAMA_CLOUD_API_KEY = os.environ.get(\"LLAMA_CLOUD_API_KEY\", \"llx-...\")\n",
+    "\n",
+    "# Optional: Set base URL (defaults to https://api.cloud.llamaindex.ai if not set)\n",
+    "LLAMA_CLOUD_BASE_URL = os.environ.get(\n",
+    "    \"LLAMA_CLOUD_BASE_URL\", \"https://api.cloud.llamaindex.ai\"\n",
+    ")\n",
+    "\n",
+    "# Optional: Set project_id if you have one, otherwise it will use your default project\n",
+    "PROJECT_ID = os.environ.get(\"LLAMA_CLOUD_PROJECT_ID\", None)\n",
+    "\n",
+    "print(\"✅ API key configured\")\n",
+    "print(f\"   Base URL: {LLAMA_CLOUD_BASE_URL}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-4",
+   "metadata": {},
+   "source": [
+    "## Setup HTTP Client\n",
+    "\n",
+    "Since the current version of the llama-cloud SDK has some issues with the beta endpoints, we'll use direct HTTP requests with httpx for reliability."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cell-5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create HTTP client with authentication\n",
+    "headers = {\n",
+    "    \"Authorization\": f\"Bearer {LLAMA_CLOUD_API_KEY}\",\n",
+    "}\n",
+    "\n",
+    "print(\"✅ HTTP client configured\")\n",
+    "print(f\"   Using base URL: {LLAMA_CLOUD_BASE_URL}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-6",
+   "metadata": {},
+   "source": [
+    "## Step 1: Create a Directory\n",
+    "\n",
+    "First, we'll create a directory to organize our files. Directories help you group related files together for batch processing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cell-7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "\n",
+    "# Create a directory with a timestamp in the name\n",
+    "timestamp = datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
+    "directory_name = f\"batch-parse-demo-{timestamp}\"\n",
+    "\n",
+    "# Create directory using HTTP request\n",
+    "response = httpx.post(\n",
+    "    f\"{LLAMA_CLOUD_BASE_URL}/api/v1/beta/directories\",\n",
+    "    headers=headers,\n",
+    "    params={\"project_id\": PROJECT_ID},\n",
+    "    json={\n",
+    "        \"name\": directory_name,\n",
+    "        \"description\": \"Demo directory for batch parse example\",\n",
+    "    },\n",
+    "    timeout=60.0,\n",
+    ")\n",
+    "\n",
+    "if response.status_code in [200, 201]:\n",
+    "    directory = response.json()\n",
+    "    directory_id = directory[\"id\"]\n",
+    "    project_id = directory[\"project_id\"]\n",
+    "\n",
+    "    print(f\"✅ Created directory: {directory['name']}\")\n",
+    "    print(f\"   Directory ID: {directory_id}\")\n",
+    "    print(f\"   Project ID: {project_id}\")\n",
+    "else:\n",
+    "    raise Exception(\n",
+    "        f\"Failed to create directory: {response.status_code} - {response.text}\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-8",
+   "metadata": {},
+   "source": [
+    "## Step 2: Upload Files to the Directory\n",
+    "\n",
+    "Now we'll upload some files to our directory. For this demo, we'll download some sample PDFs and upload them.\n",
+    "\n",
+    "You can replace these with your own files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cell-9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a directory for sample files\n",
+    "import requests\n",
+    "\n",
+    "os.makedirs(\"sample_files\", exist_ok=True)\n",
+    "\n",
+    "# Sample documents to download\n",
+    "sample_docs = {\n",
+    "    \"attention.pdf\": \"https://arxiv.org/pdf/1706.03762.pdf\",\n",
+    "    \"bert.pdf\": \"https://arxiv.org/pdf/1810.04805.pdf\",\n",
+    "}\n",
+    "\n",
+    "# Download sample documents\n",
+    "for filename, url in sample_docs.items():\n",
+    "    filepath = f\"sample_files/{filename}\"\n",
+    "    if not os.path.exists(filepath):\n",
+    "        print(f\"📥 Downloading {filename}...\")\n",
+    "        response = requests.get(url)\n",
+    "        if response.status_code == 200:\n",
+    "            with open(filepath, \"wb\") as f:\n",
+    "                f.write(response.content)\n",
+    "            print(f\"   ✅ Downloaded {filename}\")\n",
+    "        else:\n",
+    "            print(f\"   ❌ Failed to download {filename}\")\n",
+    "    else:\n",
+    "        print(f\"📁 {filename} already exists\")\n",
+    "\n",
+    "print(\"\\n✅ Sample files ready!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-10",
+   "metadata": {},
+   "source": [
+    "### Upload Files to Directory\n",
+    "\n",
+    "Now let's upload the files to our directory using the `upload_file_to_directory` endpoint."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cell-11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uploaded_files = []\n",
+    "\n",
+    "# Workaround: Use direct HTTP requests instead of SDK due to SDK bug\n",
+    "import httpx\n",
+    "\n",
+    "for filename in os.listdir(\"sample_files\"):\n",
+    "    if filename.endswith(\".pdf\"):\n",
+    "        filepath = f\"sample_files/{filename}\"\n",
+    "\n",
+    "        print(f\"📤 Uploading {filename}...\")\n",
+    "\n",
+    "        # Upload file using direct HTTP request (SDK has a bug with file uploads)\n",
+    "        with open(filepath, \"rb\") as f:\n",
+    "            # Prepare the multipart form data correctly\n",
+    "            files = {\"upload_file\": (filename, f, \"application/pdf\")}\n",
+    "\n",
+    "            # Make the request directly\n",
+    "            response = httpx.post(\n",
+    "                f\"{LLAMA_CLOUD_BASE_URL}/api/v1/beta/directories/{directory_id}/files/upload\",\n",
+    "                params={\"project_id\": project_id},\n",
+    "                files=files,\n",
+    "                headers={\"Authorization\": f\"Bearer {LLAMA_CLOUD_API_KEY}\"},\n",
+    "                timeout=60.0,\n",
+    "            )\n",
+    "\n",
+    "            if response.status_code in [200, 201]:\n",
+    "                directory_file = response.json()\n",
+    "                uploaded_files.append(directory_file)\n",
+    "                print(f\"   ✅ Uploaded: {directory_file.get('display_name')}\")\n",
+    "                print(f\"      File ID: {directory_file.get('id')}\")\n",
+    "            else:\n",
+    "                print(f\"   ❌ Upload failed: {response.status_code}\")\n",
+    "                print(f\"      Error: {response.text[:200]}\")\n",
+    "\n",
+    "print(f\"\\n✅ Uploaded {len(uploaded_files)} files to directory\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-12",
+   "metadata": {},
+   "source": [
+    "## Step 3: Create a Batch Parse Job\n",
+    "\n",
+    "Now that we have files in our directory, let's create a batch parse job to process them all at once.\n",
+    "\n",
+    "The batch processing API uses the same configuration as LlamaParse."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cell-13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure the parse job\n",
+    "# This configuration will apply to all files in the directory\n",
+    "job_config = {\n",
+    "    \"job_name\": \"parse_raw_file_job\",  # Must match the JobNames enum value\n",
+    "    \"partitions\": {},\n",
+    "    \"parameters\": {\n",
+    "        \"type\": \"parse\",\n",
+    "        \"lang\": \"en\",\n",
+    "        \"fast_mode\": True,\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "print(\"✅ Job configuration created\")\n",
+    "print(f\"   Language: {job_config['parameters']['lang']}\")\n",
+    "print(f\"   Fast mode: {job_config['parameters']['fast_mode']}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-14",
+   "metadata": {},
+   "source": [
+    "### Submit the Batch Job\n",
+    "\n",
+    "Now let's submit the batch job to process all files in the directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cell-15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(f\"🚀 Submitting batch parse job for directory: {directory_id}\")\n",
+    "print(f\"   Processing {len(uploaded_files)} files...\\n\")\n",
+    "\n",
+    "# Submit batch job using HTTP request\n",
+    "response = httpx.post(\n",
+    "    f\"{LLAMA_CLOUD_BASE_URL}/api/v1/beta/batch-processing\",\n",
+    "    headers=headers,\n",
+    "    params={\"project_id\": project_id},\n",
+    "    json={\n",
+    "        \"directory_id\": directory_id,\n",
+    "        \"job_config\": job_config,\n",
+    "        \"page_size\": 100,  # Number of files to fetch per batch\n",
+    "        \"continue_as_new_threshold\": 10,  # Workflow continuation threshold\n",
+    "    },\n",
+    "    timeout=60.0,\n",
+    ")\n",
+    "\n",
+    "if response.status_code in [200, 201]:\n",
+    "    batch_job = response.json()\n",
+    "    batch_job_id = batch_job[\"id\"]\n",
+    "\n",
+    "    print(\"✅ Batch job submitted successfully!\")\n",
+    "    print(f\"   Batch Job ID: {batch_job_id}\")\n",
+    "    print(f\"   Workflow ID: {batch_job.get('workflow_id')}\")\n",
+    "    print(f\"   Status: {batch_job.get('status')}\")\n",
+    "    print(f\"   Total Items: {batch_job.get('total_items')}\")\n",
+    "else:\n",
+    "    raise Exception(\n",
+    "        f\"Failed to create batch job: {response.status_code} - {response.text}\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-16",
+   "metadata": {},
+   "source": [
+    "## Step 4: Monitor Job Progress\n",
+    "\n",
+    "Now let's monitor the batch job progress. We'll poll the status endpoint to see how the job is progressing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cell-17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "\n",
+    "\n",
+    "def print_job_status(status_data):\n",
+    "    \"\"\"Helper function to print job status in a readable format.\"\"\"\n",
+    "    job = status_data[\"job\"]\n",
+    "    progress_pct = status_data[\"progress_percentage\"]\n",
+    "\n",
+    "    print(f\"\\n{'='*60}\")\n",
+    "    print(f\"Job Status: {job['status']}\")\n",
+    "    print(f\"{'='*60}\")\n",
+    "    print(f\"Total Items: {job['total_items']}\")\n",
+    "    print(f\"Completed: {job['processed_items']}\")\n",
+    "    print(f\"Failed: {job['failed_items']}\")\n",
+    "    print(f\"Skipped: {job['skipped_items']}\")\n",
+    "    print(f\"Progress: {progress_pct:.1f}%\")\n",
+    "\n",
+    "    if job.get(\"completed_at\"):\n",
+    "        print(f\"Completed At: {job['completed_at']}\")\n",
+    "    elif job.get(\"started_at\"):\n",
+    "        print(f\"Started At: {job['started_at']}\")\n",
+    "\n",
+    "    print(f\"{'='*60}\")\n",
+    "\n",
+    "\n",
+    "# Poll for status updates\n",
+    "print(\"🔄 Monitoring batch job progress...\")\n",
+    "print(\n",
+    "    \"Note: It may take a few seconds for the workflow to initialize and count files.\\n\"\n",
+    ")\n",
+    "\n",
+    "max_polls = 60  # Maximum number of status checks (increased for longer jobs)\n",
+    "poll_interval = 10  # Seconds between checks\n",
+    "\n",
+    "for i in range(max_polls):\n",
+    "    response = httpx.get(\n",
+    "        f\"{LLAMA_CLOUD_BASE_URL}/api/v1/beta/batch-processing/{batch_job_id}\",\n",
+    "        headers=headers,\n",
+    "        params={\"project_id\": project_id},\n",
+    "        timeout=60.0,\n",
+    "    )\n",
+    "\n",
+    "    if response.status_code == 200:\n",
+    "        status_data = response.json()\n",
+    "        print_job_status(status_data)\n",
+    "\n",
+    "        # Check if job is complete\n",
+    "        job_status = status_data[\"job\"][\"status\"]\n",
+    "        if job_status in [\"completed\", \"failed\", \"cancelled\"]:\n",
+    "            print(f\"\\n✅ Job finished with status: {job_status}\")\n",
+    "            break\n",
+    "\n",
+    "        if i < max_polls - 1:\n",
+    "            print(f\"\\n⏳ Waiting {poll_interval} seconds before next check...\")\n",
+    "            time.sleep(poll_interval)\n",
+    "    else:\n",
+    "        print(f\"Error getting status: {response.status_code} - {response.text}\")\n",
+    "        break\n",
+    "else:\n",
+    "    print(f\"\\n⚠️  Reached maximum polling attempts. Job may still be running.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-18",
+   "metadata": {},
+   "source": [
+    "## Step 5: View Job Items\n",
+    "\n",
+    "Let's look at the individual items in the batch job to see which files were processed successfully."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cell-19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get all items in the batch job\n",
+    "response = httpx.get(\n",
+    "    f\"{LLAMA_CLOUD_BASE_URL}/api/v1/beta/batch-processing/{batch_job_id}/items\",\n",
+    "    headers=headers,\n",
+    "    params={\"project_id\": project_id, \"limit\": 100},\n",
+    "    timeout=60.0,\n",
+    ")\n",
+    "\n",
+    "if response.status_code == 200:\n",
+    "    items_response = response.json()\n",
+    "\n",
+    "    print(f\"\\n📋 Batch Job Items ({items_response['total_size']} total)\")\n",
+    "    print(f\"{'='*80}\\n\")\n",
+    "\n",
+    "    for item in items_response[\"items\"]:\n",
+    "        status_emoji = (\n",
+    "            \"✅\"\n",
+    "            if item[\"status\"] == \"completed\"\n",
+    "            else \"❌\"\n",
+    "            if item[\"status\"] == \"failed\"\n",
+    "            else \"⏳\"\n",
+    "        )\n",
+    "        print(f\"{status_emoji} {item['item_name']}\")\n",
+    "        print(f\"   Status: {item['status']}\")\n",
+    "        print(f\"   Item ID: {item['item_id']}\")\n",
+    "\n",
+    "        if item.get(\"error_message\"):\n",
+    "            print(f\"   Error: {item['error_message']}\")\n",
+    "\n",
+    "        print()\n",
+    "else:\n",
+    "    print(f\"Error listing items: {response.status_code} - {response.text}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-20",
+   "metadata": {},
+   "source": [
+    "## Step 6: Retrieve Processing Results\n",
+    "\n",
+    "For each completed file, we can retrieve the processing results to see where the parsed output is stored."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cell-21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get processing results for a specific item\n",
+    "if items_response[\"items\"]:\n",
+    "    first_item = items_response[\"items\"][0]\n",
+    "\n",
+    "    print(f\"\\n🔍 Processing results for: {first_item['item_name']}\")\n",
+    "    print(f\"{'='*80}\\n\")\n",
+    "\n",
+    "    response = httpx.get(\n",
+    "        f\"{LLAMA_CLOUD_BASE_URL}/api/v1/beta/batch-processing/items/{first_item['item_id']}/processing-results\",\n",
+    "        headers=headers,\n",
+    "        params={\"project_id\": project_id},\n",
+    "        timeout=60.0,\n",
+    "    )\n",
+    "\n",
+    "    if response.status_code == 200:\n",
+    "        results = response.json()\n",
+    "\n",
+    "        print(f\"Item: {results['item_name']}\")\n",
+    "        print(f\"Total processing runs: {len(results['processing_results'])}\\n\")\n",
+    "\n",
+    "        for i, result in enumerate(results[\"processing_results\"], 1):\n",
+    "            print(f\"Run {i}:\")\n",
+    "            print(f\"  Job Type: {result['job_type']}\")\n",
+    "            print(f\"  Processed At: {result['processed_at']}\")\n",
+    "            print(f\"  Parameters Hash: {result['parameters_hash']}\")\n",
+    "\n",
+    "            if result.get(\"output_s3_path\"):\n",
+    "                print(f\"  Output S3 Path: {result['output_s3_path']}\")\n",
+    "\n",
+    "            if result.get(\"output_metadata\"):\n",
+    "                print(f\"  Output Metadata: {result['output_metadata']}\")\n",
+    "\n",
+    "            print()\n",
+    "    else:\n",
+    "        print(f\"Error getting results: {response.status_code} - {response.text}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-22",
+   "metadata": {},
+   "source": [
+    "## Optional: List All Batch Jobs\n",
+    "\n",
+    "You can also list all batch jobs in your project to see the history of batch processing operations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cell-23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# List all parse jobs in the project\n",
+    "response = httpx.get(\n",
+    "    f\"{LLAMA_CLOUD_BASE_URL}/api/v1/beta/batch-processing\",\n",
+    "    headers=headers,\n",
+    "    params={\"project_id\": project_id, \"job_type\": \"parse\", \"limit\": 10},\n",
+    "    timeout=60.0,\n",
+    ")\n",
+    "\n",
+    "if response.status_code == 200:\n",
+    "    jobs_response = response.json()\n",
+    "\n",
+    "    print(f\"\\n📊 Recent Batch Parse Jobs ({jobs_response['total_size']} total)\")\n",
+    "    print(f\"{'='*80}\\n\")\n",
+    "\n",
+    "    for job in jobs_response[\"items\"]:\n",
+    "        status_emoji = (\n",
+    "            \"✅\"\n",
+    "            if job[\"status\"] == \"completed\"\n",
+    "            else \"❌\"\n",
+    "            if job[\"status\"] == \"failed\"\n",
+    "            else \"⏳\"\n",
+    "        )\n",
+    "        print(f\"{status_emoji} Job ID: {job['id']}\")\n",
+    "        print(f\"   Status: {job['status']}\")\n",
+    "        print(f\"   Directory: {job['directory_id']}\")\n",
+    "        print(f\"   Total Items: {job['total_items']}\")\n",
+    "        print(f\"   Completed: {job['processed_items']}\")\n",
+    "        print(f\"   Created: {job['created_at']}\")\n",
+    "        print()\n",
+    "else:\n",
+    "    print(f\"Error listing jobs: {response.status_code} - {response.text}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "uug7591rkq",
+   "metadata": {},
+   "source": [
+    "## Step 7: Retrieve Parsed Text Results\n",
+    "\n",
+    "Once the batch job is complete, each BatchJobItem will have a `job_id` field that maps to a parse job ID. We can use this ID with the standard parse client methods to fetch the actual parsed text results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "vpp0vxtc0y",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get all completed items and their job IDs\n",
+    "completed_items = [\n",
+    "    item for item in items_response[\"items\"] if item[\"status\"] == \"completed\"\n",
+    "]\n",
+    "\n",
+    "print(f\"📄 Found {len(completed_items)} completed items\\n\")\n",
+    "print(f\"{'='*80}\\n\")\n",
+    "\n",
+    "# Display the job_id for each completed item\n",
+    "for item in completed_items:\n",
+    "    print(f\"📝 {item['item_name']}\")\n",
+    "    print(f\"   Item ID: {item['item_id']}\")\n",
+    "    print(f\"   Parse Job ID: {item['job_id']}\")\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4gck6hwpnl6",
+   "metadata": {},
+   "source": [
+    "### Fetch Parsed Text for a Specific Document\n",
+    "\n",
+    "Now let's use the `job_id` to retrieve the actual parsed text content using the parse client methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "g191kvgxxvk",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the parsed text for the first completed item\n",
+    "if completed_items:\n",
+    "    first_completed = completed_items[0]\n",
+    "\n",
+    "    print(f\"📖 Retrieving parsed text for: {first_completed['item_name']}\")\n",
+    "    print(f\"   Using Parse Job ID: {first_completed['job_id']}\\n\")\n",
+    "    print(f\"{'='*80}\\n\")\n",
+    "\n",
+    "    # Use the job_id to fetch the parse result\n",
+    "    response = httpx.get(\n",
+    "        f\"{LLAMA_CLOUD_BASE_URL}/api/v1/parsing/job/{first_completed['job_id']}/result/text\",\n",
+    "        headers=headers,\n",
+    "        params={\"project_id\": project_id},\n",
+    "        timeout=60.0,\n",
+    "    )\n",
+    "\n",
+    "    if response.status_code == 200:\n",
+    "        parse_result = response.text\n",
+    "\n",
+    "        print(f\"✅ Retrieved parsed text ({len(parse_result)} characters)\\n\")\n",
+    "\n",
+    "        # Display first 1000 characters as a preview\n",
+    "        print(\"Preview (first 1000 characters):\")\n",
+    "        print(\"-\" * 80)\n",
+    "        print(parse_result[:1000])\n",
+    "        print(\"-\" * 80)\n",
+    "\n",
+    "        if len(parse_result) > 1000:\n",
+    "            print(f\"\\n... and {len(parse_result) - 1000} more characters\")\n",
+    "    else:\n",
+    "        print(\n",
+    "            f\"Error retrieving parse result: {response.status_code} - {response.text}\"\n",
+    "        )\n",
+    "else:\n",
+    "    print(\"⚠️  No completed items found to retrieve results from\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2olccb4l8fj",
+   "metadata": {},
+   "source": [
+    "### Retrieve Parsed Results in Other Formats\n",
+    "\n",
+    "You can also retrieve the parsed results in JSON or Markdown format using different client methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "lcqsfxiw0sr",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if completed_items:\n",
+    "    first_completed = completed_items[0]\n",
+    "\n",
+    "    print(\n",
+    "        f\"📋 Retrieving parse results in different formats for: {first_completed['item_name']}\\n\"\n",
+    "    )\n",
+    "\n",
+    "    # Get as JSON (includes structured data with pages, images, etc.)\n",
+    "    print(\"1️⃣ Retrieving as JSON...\")\n",
+    "    response = httpx.get(\n",
+    "        f\"{LLAMA_CLOUD_BASE_URL}/api/v1/parsing/job/{first_completed['job_id']}/result/json\",\n",
+    "        headers=headers,\n",
+    "        params={\"project_id\": project_id},\n",
+    "        timeout=60.0,\n",
+    "    )\n",
+    "\n",
+    "    if response.status_code == 200:\n",
+    "        json_result = response.json()\n",
+    "        print(f\"   ✅ JSON result with {len(json_result['pages'])} pages\")\n",
+    "        print(f\"      Keys: {list(json_result.keys())}\\n\")\n",
+    "    else:\n",
+    "        print(f\"   Error: {response.status_code}\\n\")\n",
+    "\n",
+    "    # Get as Markdown\n",
+    "    print(\"2️⃣ Retrieving as Markdown...\")\n",
+    "    response = httpx.get(\n",
+    "        f\"{LLAMA_CLOUD_BASE_URL}/api/v1/parsing/job/{first_completed['job_id']}/result/markdown\",\n",
+    "        headers=headers,\n",
+    "        params={\"project_id\": project_id},\n",
+    "        timeout=60.0,\n",
+    "    )\n",
+    "\n",
+    "    if response.status_code == 200:\n",
+    "        markdown_result = response.text\n",
+    "        print(f\"   ✅ Markdown result ({len(markdown_result)} characters)\\n\")\n",
+    "\n",
+    "        # Display markdown preview\n",
+    "        print(\"Markdown Preview (first 500 characters):\")\n",
+    "        print(\"-\" * 80)\n",
+    "        print(markdown_result[:500])\n",
+    "        print(\"-\" * 80)\n",
+    "\n",
+    "        if len(markdown_result) > 500:\n",
+    "            print(f\"\\n... and {len(markdown_result) - 500} more characters\")\n",
+    "    else:\n",
+    "        print(f\"   Error: {response.status_code}\")\n",
+    "else:\n",
+    "    print(\"⚠️  No completed items found to retrieve results from\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "lr61wqkfq3",
+   "metadata": {},
+   "source": [
+    "### Batch Process All Parsed Results\n",
+    "\n",
+    "You can also loop through all completed items to retrieve and process all the parsed results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "kltydf9xzkl",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Process all completed items\n",
+    "print(f\"🔄 Processing all {len(completed_items)} completed items...\\n\")\n",
+    "print(f\"{'='*80}\\n\")\n",
+    "\n",
+    "all_results = {}\n",
+    "\n",
+    "for item in completed_items:\n",
+    "    print(f\"📄 Processing: {item['item_name']}\")\n",
+    "    print(f\"   Parse Job ID: {item['job_id']}\")\n",
+    "\n",
+    "    try:\n",
+    "        # Retrieve the parsed text for this item\n",
+    "        response = httpx.get(\n",
+    "            f\"{LLAMA_CLOUD_BASE_URL}/api/v1/parsing/job/{item['job_id']}/result/text\",\n",
+    "            headers=headers,\n",
+    "            params={\"project_id\": project_id},\n",
+    "            timeout=60.0,\n",
+    "        )\n",
+    "\n",
+    "        if response.status_code == 200:\n",
+    "            parsed_text = response.text\n",
+    "\n",
+    "            all_results[item[\"item_name\"]] = {\n",
+    "                \"job_id\": item[\"job_id\"],\n",
+    "                \"text\": parsed_text,\n",
+    "                \"length\": len(parsed_text),\n",
+    "            }\n",
+    "\n",
+    "            print(f\"   ✅ Retrieved {len(parsed_text)} characters\")\n",
+    "        else:\n",
+    "            all_results[item[\"item_name\"]] = {\n",
+    "                \"job_id\": item[\"job_id\"],\n",
+    "                \"error\": f\"HTTP {response.status_code}\",\n",
+    "            }\n",
+    "            print(f\"   ❌ Error: HTTP {response.status_code}\")\n",
+    "\n",
+    "    except Exception as e:\n",
+    "        print(f\"   ❌ Error: {str(e)}\")\n",
+    "        all_results[item[\"item_name\"]] = {\"job_id\": item[\"job_id\"], \"error\": str(e)}\n",
+    "\n",
+    "    print()\n",
+    "\n",
+    "print(f\"{'='*80}\")\n",
+    "print(f\"\\n✅ Processed {len(all_results)} items\")\n",
+    "print(f\"\\nSummary:\")\n",
+    "for name, result in all_results.items():\n",
+    "    if \"error\" in result:\n",
+    "        print(f\"  ❌ {name}: Error - {result['error']}\")\n",
+    "    else:\n",
+    "        print(f\"  ✅ {name}: {result['length']:,} characters\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/extract/tabular_extraction.ipynb

@@ -0,0 +1,508 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a7oq3cfnync",
+   "metadata": {},
+   "source": [
+    "# Extracting Repeating Entities from Documents\n",
+    "\n",
+    "This notebook demonstrates how to use the `PER_TABLE_ROW` extraction target to extract structured data from documents containing repeating entities like tables, lists, or catalogs.\n",
+    "\n",
+    "## Why Use the Tabular Extraction Target?\n",
+    "\n",
+    "`PER_DOC` (refer to the table below for a quick overview of the different extraction targets) is the default extraction target in LlamaExtract, which looks at the entire document's context when doing an extraction. When extracting lists of entities, LLM-based extraction has a critical failure mode — it often **only extracts the first few tens of entries** from a long list. This happens because LLMs have limited attention spans for repetitive data. Document-level extraction doesn't guarantee exhaustive coverage, and long lists lead to incomplete extractions.\n",
+    "\n",
+    "**The Solution**: `PER_TABLE_ROW` solves this by processing each entity individually or in smaller batches, ensuring **exhaustive extraction** of all entries regardless of list length.\n",
+    "\n",
+    "### Entity-Level Extraction\n",
+    "\n",
+    "When using `extraction_target=ExtractTarget.PER_TABLE_ROW`, you define a schema for a **single entity** (e.g., one hospital, one product, one invoice line item), not the full document. LlamaExtract automatically:\n",
+    "- Detects the formatting patterns that distinguish individual entities (table rows, list items, section headers, etc.)\n",
+    "- Applies your schema to each identified entity\n",
+    "- Returns a `list[YourSchema]` with one object per entity\n",
+    "\n",
+    "This approach is ideal when each entity locally contains all the information needed for your schema.\n",
+    "\n",
+    "### Choosing the Right Extraction Target\n",
+    "\n",
+    "| Extraction Target | Best For | Returns |\n",
+    "|-------------------|----------|---------|\n",
+    "| `PER_DOC` | Single-entity documents, summaries, or short lists | One JSON object for entire document |\n",
+    "| `PER_PAGE` | Multi-page documents where each page is independent | One JSON object per page |\n",
+    "| `PER_TABLE_ROW` | **Long lists, tables, catalogs with repeating entities** | List of JSON objects (one per entity) |\n",
+    "\n",
+    "📖 For more details, see the [Extraction Target documentation](https://developers.llamaindex.ai/python/cloud/llamaextract/features/concepts/#extraction-target)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9427d1de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dotenv import load_dotenv\n",
+    "from llama_cloud_services import LlamaExtract\n",
+    "\n",
+    "\n",
+    "# Load environment variables (put LLAMA_CLOUD_API_KEY in your .env file)\n",
+    "load_dotenv(override=True)\n",
+    "\n",
+    "# Optionally, add your project id/organization id\n",
+    "llama_extract = LlamaExtract()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4426b360",
+   "metadata": {},
+   "source": [
+    "## Table of Hospitals by County and Insurance Plans\n",
+    "\n",
+    "We have a PDF document with a list of hospitals by county and different insurance plans offered by Blue Shield of California. \n",
+    "\n",
+    "\n",
+    "![First few entries from the PDF](./data/tables/bsc_page1.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c86sjymhn1r",
+   "metadata": {},
+   "source": [
+    "We want to extract each hospital from this table along with a list of applicable insurance plans. \n",
+    "\n",
+    "### Example 1: Structured Table\n",
+    "\n",
+    "This is an ideal use case for `PER_TABLE_ROW` extraction:\n",
+    "- **Clear structure**: The document has explicit table formatting with rows and columns\n",
+    "- **Repeating entities**: Each row represents one hospital with consistent attributes\n",
+    "- **Local information**: All data for each hospital (county, name, plans) is contained within its row\n",
+    "\n",
+    "Notice that our `Hospital` schema describes a **single hospital**, not the full document. LlamaExtract will return a `list[Hospital]` with one entry per table row."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c61a802",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pydantic import BaseModel, Field\n",
+    "\n",
+    "\n",
+    "class Hospital(BaseModel):\n",
+    "    \"\"\"List of hospitals by county available for different BSC plans\"\"\"\n",
+    "\n",
+    "    county: str = Field(description=\"County name\")\n",
+    "    hospital_name: str = Field(description=\"Name of the hospital\")\n",
+    "    plan_names: list[str] = Field(\n",
+    "        description=\"List of plans available at the hospital. One of: Trio HMO, SaveNet, Access+ HMO, BlueHPN PPO, Tandem PPO, PPO\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b8a69b7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_cloud_services.extract import ExtractConfig, ExtractMode, ExtractTarget\n",
+    "\n",
+    "\n",
+    "result = await llama_extract.aextract(\n",
+    "    data_schema=Hospital,\n",
+    "    files=\"./data/tables/BSC-Hospital-List-by-County.pdf\",\n",
+    "    config=ExtractConfig(\n",
+    "        extraction_mode=ExtractMode.PREMIUM,\n",
+    "        extraction_target=ExtractTarget.PER_TABLE_ROW,\n",
+    "        parse_model=\"anthropic-sonnet-4.5\",\n",
+    "    ),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43722cda",
+   "metadata": {},
+   "source": [
+    "### Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95b5aca6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "380"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(result.data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1e355770",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'county': 'Alameda',\n",
+       "  'hospital_name': 'Alameda Hospital',\n",
+       "  'plan_names': ['Trio HMO',\n",
+       "   'SaveNet',\n",
+       "   'Access+ HMO',\n",
+       "   'BlueHPN PPO',\n",
+       "   'Tandem PPO',\n",
+       "   'PPO']},\n",
+       " {'county': 'Alameda',\n",
+       "  'hospital_name': 'Alta Bates Med Ctr Herrick Campus',\n",
+       "  'plan_names': ['Trio HMO',\n",
+       "   'Access+ HMO',\n",
+       "   'BlueHPN PPO',\n",
+       "   'Tandem PPO',\n",
+       "   'PPO']},\n",
+       " {'county': 'Alameda',\n",
+       "  'hospital_name': 'Alta Bates Summit Med Ctr Alta Bates Campus',\n",
+       "  'plan_names': ['Trio HMO',\n",
+       "   'Access+ HMO',\n",
+       "   'BlueHPN PPO',\n",
+       "   'Tandem PPO',\n",
+       "   'PPO']},\n",
+       " {'county': 'Alameda',\n",
+       "  'hospital_name': 'Alta Bates Summit Med Ctr Summit Campus',\n",
+       "  'plan_names': ['Trio HMO',\n",
+       "   'Access+ HMO',\n",
+       "   'BlueHPN PPO',\n",
+       "   'Tandem PPO',\n",
+       "   'PPO']},\n",
+       " {'county': 'Alameda',\n",
+       "  'hospital_name': 'Alta Bates Summit Medical Center',\n",
+       "  'plan_names': ['Trio HMO',\n",
+       "   'Access+ HMO',\n",
+       "   'BlueHPN PPO',\n",
+       "   'Tandem PPO',\n",
+       "   'PPO']},\n",
+       " {'county': 'Alameda',\n",
+       "  'hospital_name': 'BHC Fremont Hospital',\n",
+       "  'plan_names': ['Trio HMO',\n",
+       "   'SaveNet',\n",
+       "   'Access+ HMO',\n",
+       "   'BlueHPN PPO',\n",
+       "   'Tandem PPO',\n",
+       "   'PPO']},\n",
+       " {'county': 'Alameda',\n",
+       "  'hospital_name': 'Centre For Neuro Skills San Francisco',\n",
+       "  'plan_names': ['Trio HMO',\n",
+       "   'SaveNet',\n",
+       "   'Access+ HMO',\n",
+       "   'BlueHPN PPO',\n",
+       "   'Tandem PPO',\n",
+       "   'PPO']},\n",
+       " {'county': 'Alameda',\n",
+       "  'hospital_name': 'Eden Medical Center',\n",
+       "  'plan_names': ['Trio HMO', 'Access+ HMO', 'PPO']},\n",
+       " {'county': 'Alameda',\n",
+       "  'hospital_name': 'Fairmont Hospital',\n",
+       "  'plan_names': ['Trio HMO',\n",
+       "   'SaveNet',\n",
+       "   'Access+ HMO',\n",
+       "   'BlueHPN PPO',\n",
+       "   'Tandem PPO',\n",
+       "   'PPO']},\n",
+       " {'county': 'Alameda',\n",
+       "  'hospital_name': 'Highland Hospital',\n",
+       "  'plan_names': ['Trio HMO',\n",
+       "   'SaveNet',\n",
+       "   'Access+ HMO',\n",
+       "   'BlueHPN PPO',\n",
+       "   'Tandem PPO',\n",
+       "   'PPO']}]"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result.data[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e28f0de8",
+   "metadata": {},
+   "source": [
+    "![](./data/tables/bsc_results.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "di156pb7s6j",
+   "metadata": {},
+   "source": [
+    "**Success!** We extracted all **380 hospitals** from the multi-page PDF. Each entity was correctly parsed with its county, hospital name, and applicable insurance plans. With `PER_DOC`, we would likely have only gotten the first 20-30 entries."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "gelvl6db268",
+   "metadata": {},
+   "source": [
+    "## Extracting from a Toy Catalog\n",
+    "\n",
+    "### Example 2: Semi-Structured List\n",
+    "\n",
+    "The `PER_TABLE_ROW` extraction target also works well for documents that aren't explicit tables but have similar properties:\n",
+    "- **Ordered listing**: The toys are listed sequentially with visual separation (section headers, spacing)\n",
+    "- **Repeating pattern**: Each toy entry has a consistent structure (code, name, specs, description)\n",
+    "- **Local information**: All attributes for each toy are grouped together in its entry\n",
+    "\n",
+    "Even though this isn't a traditional table format, each toy entity locally contains all the information needed for our schema. LlamaExtract detects the formatting patterns that distinguish each toy and extracts them as separate entities.\n",
+    "\n",
+    "![](./data/tables/toy_catalog_page.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8cf0b2db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pydantic import BaseModel, Field\n",
+    "\n",
+    "\n",
+    "class ToyCatalog(BaseModel):\n",
+    "    \"\"\"Product information from a toy catalog.\"\"\"\n",
+    "\n",
+    "    section_name: str = Field(\n",
+    "        description=\"The name of the toy section (e.g. Table Toys, Active Toys).\"\n",
+    "    )\n",
+    "    product_code: str = Field(\n",
+    "        description=\"The unique product code for the toy (e.g., GA457).\"\n",
+    "    )\n",
+    "    toy_name: str = Field(description=\"The name of the toy.\")\n",
+    "    age_range: str = Field(\n",
+    "        description=\"The recommended age range for the toy (e.g., 6 +, 4 +).\",\n",
+    "    )\n",
+    "    player_range: str = Field(\n",
+    "        description=\"The number of players the toy is designed for (e.g., 2, 2-4, 1-6).\",\n",
+    "    )\n",
+    "    material: str = Field(\n",
+    "        description=\"The primary material(s) the toy is made of (e.g., wood, cardboard).\",\n",
+    "    )\n",
+    "    description: str = Field(\n",
+    "        description=\"A brief description of the toy and its components and dimensions.\",\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "mysu1i2qo9e",
+   "metadata": {},
+   "source": [
+    "### Results\n",
+    "\n",
+    "Again, our schema represents a **single toy product**, not the entire catalog. The system will return a `list[ToyCatalog]` with one entry per toy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b38b806",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = await llama_extract.aextract(\n",
+    "    data_schema=ToyCatalog,\n",
+    "    files=\"./data/tables/Click-BS-Toys-Catalogue-2024.pdf\",\n",
+    "    config=ExtractConfig(\n",
+    "        extraction_mode=ExtractMode.PREMIUM,\n",
+    "        extraction_target=ExtractTarget.PER_TABLE_ROW,\n",
+    "        parse_model=\"anthropic-sonnet-4.5\",\n",
+    "    ),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91aface0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "153"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(result.data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51278736",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'section_name': 'Table Toys',\n",
+       "  'product_code': 'GA457',\n",
+       "  'toy_name': 'Dots and Boxes',\n",
+       "  'age_range': '6+',\n",
+       "  'player_range': '2',\n",
+       "  'material': 'wood',\n",
+       "  'description': 'base 17x17 cm\\n50 border pieces 4x1,2x0,3 cm\\n34 trees 2,6x1,4 cm'},\n",
+       " {'section_name': 'Table Toys',\n",
+       "  'product_code': 'GA456',\n",
+       "  'toy_name': '3 In a Row',\n",
+       "  'age_range': '8+',\n",
+       "  'player_range': '2',\n",
+       "  'material': 'wood, pine, cardboard',\n",
+       "  'description': 'base 24x22,5x2,5 cm\\n30 cards 5,5x5 cm\\n6 chips'},\n",
+       " {'section_name': 'Table Toys',\n",
+       "  'product_code': 'GA467',\n",
+       "  'toy_name': 'Which Cow am i?',\n",
+       "  'age_range': '6+',\n",
+       "  'player_range': '2',\n",
+       "  'material': 'wood, beech',\n",
+       "  'description': '2 cow bases 56x4x4,5 cm\\n16 cards 4x5 cm'},\n",
+       " {'section_name': 'Table Toys',\n",
+       "  'product_code': 'GA460',\n",
+       "  'toy_name': 'Balance Bunnies',\n",
+       "  'age_range': '4+',\n",
+       "  'player_range': '2',\n",
+       "  'material': 'wood',\n",
+       "  'description': '1 base 35x12x25 cm\\n7 bunnies 7 foxes\\n1 dice 3 cm'},\n",
+       " {'section_name': 'Table Toys',\n",
+       "  'product_code': 'GA462',\n",
+       "  'toy_name': 'Color Combination Race',\n",
+       "  'age_range': '4+',\n",
+       "  'player_range': '2-4',\n",
+       "  'material': 'wood, cardboard',\n",
+       "  'description': 'base 6,5x6,5x15 cm, rings 5,5x5,5x0,5 mm\\ncardholder 6x6x2 cm, cards 5,5x5,5 cm\\ncolor cards Ø 15,5 cm - Ø 7 cm'},\n",
+       " {'section_name': 'Table Toys',\n",
+       "  'product_code': 'GA465',\n",
+       "  'toy_name': 'Plop It',\n",
+       "  'age_range': '6+',\n",
+       "  'player_range': '2-4',\n",
+       "  'material': 'wood, elastic, cardboard',\n",
+       "  'description': 'Catch the right balls and plop them in the net!\\n* 2 ploppers 8x5 cm\\n* 2 net holders Ø 5cm, length 55 cm\\n* 6 cards 1,5x2,5 cm, 30 balls Ø 2,5 cm\\n* 1 rope 120 cm'},\n",
+       " {'section_name': 'Table Toys',\n",
+       "  'product_code': 'GA466',\n",
+       "  'toy_name': 'Whack a Shape',\n",
+       "  'age_range': '4+',\n",
+       "  'player_range': '2-4',\n",
+       "  'material': 'wood',\n",
+       "  'description': '* base 38,5x15,5 cm\\n* 2 stands 36 half balls, 4 hammers\\n* 1 dice 2,5 cm\\n* 4 cards'},\n",
+       " {'section_name': 'Table Toys',\n",
+       "  'product_code': 'GA458',\n",
+       "  'toy_name': 'Sling Puck | Table Hockey',\n",
+       "  'age_range': '6+',\n",
+       "  'player_range': '2',\n",
+       "  'material': 'wood',\n",
+       "  'description': '* double sides base 39x21x3 cm\\n* 10 chips Ø 2,5 cm\\n* 2 pushers 4x4x3 cm'},\n",
+       " {'section_name': 'Table Toys',\n",
+       "  'product_code': 'GA039',\n",
+       "  'toy_name': 'DIY Birdhouse',\n",
+       "  'age_range': '3+',\n",
+       "  'player_range': '1',\n",
+       "  'material': 'wood',\n",
+       "  'description': '* house 9x9x13 cm'},\n",
+       " {'section_name': 'Table Toys',\n",
+       "  'product_code': 'GA319',\n",
+       "  'toy_name': 'Triangle Domino',\n",
+       "  'age_range': '6+',\n",
+       "  'player_range': '2-4',\n",
+       "  'material': 'wood',\n",
+       "  'description': '* 35 triangles 10x10 x10 cm'}]"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result.data[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1810c0a",
+   "metadata": {},
+   "source": [
+    "![](./data/tables/toy_catalog_results.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ezur9gnhmsb",
+   "metadata": {},
+   "source": [
+    "**Success!** Despite the semi-structured format, we extracted all **152 toy products** from the catalog (there's an extra repeated extracted toy from the Appendix section). LlamaExtract automatically detected the visual patterns separating each toy entry and applied our schema to each one."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aeyr3io29u",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "The `PER_TABLE_ROW` extraction target is powerful for extracting repeating structured entities from documents. Key takeaways:\n",
+    "\n",
+    "1. **Schema design**: Define your schema for a single entity, not the full document. The system returns `list[YourSchema]`.\n",
+    "\n",
+    "2. **Works with various formats**: Not just traditional tables—any document with distinguishable repeating entities (bullets, numbering, headers, visual separation, etc.). The common requirement is that each entity should contain all the necessary data for your schema within its local context.\n",
+    "\n",
+    "3. **Automatic pattern detection**: LlamaExtract identifies the formatting patterns that distinguish entities and applies your schema to each one."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/misc/parse_classify_extract_workflow.ipynb

@@ -280,7 +280,7 @@
    "source": [
     "## Phase 2: Document Classification\n",
     "\n",
-    "Next, let's classify our documents based on their content using the ClassifyClient."
+    "Next, let's classify our documents based on their content using `LlamaClassify`."
    ]
   },
   {
@@ -298,14 +298,14 @@
     }
    ],
    "source": [
-    "from llama_cloud_services.beta.classifier.client import ClassifyClient\n",
+    "from llama_cloud_services.beta.classifier.client import LlamaClassify\n",
     "from llama_cloud.types import ClassifierRule\n",
     "from llama_cloud_services.files.client import FileClient\n",
     "from llama_cloud.client import AsyncLlamaCloud\n",
     "\n",
     "# Initialize the classify client\n",
     "api_key = os.environ[\"LLAMA_CLOUD_API_KEY\"]\n",
-    "classify_client = ClassifyClient.from_api_key(api_key)\n",
+    "classify_client = LlamaClassify.from_api_key(api_key)\n",
     "\n",
     "print(\"🏷️  Setting up document classification...\")\n",
     "\n",
@@ -1097,7 +1097,7 @@
     "   - Preserves document structure and formatting\n",
     "   - Handles various file types (PDF, DOCX, etc.)\n",
     "\n",
-    "2. **ClassifyClient** (`llama_cloud_services.beta.classifier.client.ClassifyClient`):\n",
+    "2. **LlamaClassify** (`llama_cloud_services.beta.classifier.client.LlamaClassify`):\n",
     "   - Automatically categorizes documents based on content\n",
     "   - Uses customizable rules for classification\n",
     "   - Provides confidence scores for classifications\n",

examples/sheets/coding-agent-analysis/CLAUDE.md

@@ -0,0 +1,73 @@
+This project uses LlamaSheets to extract data from spreadsheets for analysis.
+
+## Current Project Structure
+
+- `data/` - Contains extracted parquet files from LlamaSheets
+  - `{name}_region_{N}.parquet` - Table data files
+  - `{name}_metadata_{N}.parquet` - Cell metadata files
+  - `{name}_job_metadata.json` - Extraction job information
+- `scripts/` - Analysis and helper scripts
+- `reports/` - Your generated reports and outputs
+
+## Working with LlamaSheets Data
+
+### Understanding the Files
+
+When a spreadsheet is extracted, you'll find:
+
+1. **Table parquet files** (`region_*.parquet`): The actual table data
+   - Columns correspond to spreadsheet columns
+   - Data types are preserved (dates, numbers, strings, booleans)
+
+2. **Metadata parquet files** (`metadata_*.parquet`): Rich cell-level metadata
+   - Formatting: `font_bold`, `font_italic`, `font_size`, `background_color_rgb`
+   - Position: `row_number`, `column_number`, `coordinate` (e.g., "A1")
+   - Type detection: `data_type`, `is_date_like`, `is_percentage`, `is_currency`
+   - Layout: `is_in_first_row`, `is_merged_cell`, `horizontal_alignment`
+   - Content: `cell_value`, `raw_cell_value`
+
+3. **Job metadata JSON** (`job_metadata.json`): Overall extraction results
+   - `regions[]`: List of extracted regions with IDs, locations, and titles/descriptions
+   - `worksheet_metadata[]`: Generated titles and descriptions
+   - `status`: Success/failure status
+
+### Key Principles
+
+1. **Use metadata to understand structure**: Bold cells often indicate headers, colors indicate groupings
+2. **Validate before analysis**: Check data types, look for missing values
+3. **Preserve formatting context**: The metadata tells you what the spreadsheet author emphasized
+4. **Save intermediate results**: Store cleaned data as new parquet files
+
+### Common Patterns
+
+**Loading data:**
+```python
+import pandas as pd
+
+df = pd.read_parquet("data/region_1_Sheet1.parquet")
+meta_df = pd.read_parquet("data/metadata_1_Sheet1.parquet")
+```
+
+**Finding headers:**
+```python
+headers = meta_df[meta_df["font_bold"] == True]["cell_value"].tolist()
+```
+
+**Finding date columns:**
+```python
+date_cols = meta_df[meta_df["is_date_like"] == True]["column_number"].unique()
+```
+
+## Tools Available
+
+- **Python 3.11+**: For data analysis
+- **pandas**: DataFrame manipulation
+- **pyarrow**: Parquet file reading
+- **matplotlib**: Visualization (optional)
+
+## Guidelines
+
+- Always read the job_metadata.json first to understand what was extracted
+- Check both table data and metadata before making assumptions
+- Write reusable functions for common operations
+- Document any data quality issues discovered

examples/sheets/coding-agent-analysis/generate_sample_data.py

@@ -0,0 +1,278 @@
+"""
+Generate sample spreadsheets for LlamaSheets + Claude workflows.
+
+This script creates example Excel files that demonstrate different use cases:
+1. Simple data table (for Workflow 1)
+2. Regional sales data (for Workflow 2)
+3. Complex budget with formatting (for Workflow 3)
+4. Weekly sales report (for Workflow 4)
+
+Usage:
+    python generate_sample_data.py
+"""
+
+import random
+from datetime import datetime, timedelta
+from pathlib import Path
+
+import pandas as pd
+from openpyxl import Workbook
+from openpyxl.styles import Font, PatternFill, Alignment
+
+
+def generate_workflow_1_data(output_dir: Path) -> None:
+    """Generate simple financial report for Workflow 1."""
+    print("📊 Generating Workflow 1: financial_report_q1.xlsx")
+
+    # Create sample quarterly data
+    months = ["January", "February", "March"]
+    categories = ["Revenue", "Cost of Goods Sold", "Operating Expenses", "Net Income"]
+
+    data = []
+    for category in categories:
+        row: dict[str, str | int] = {"Category": category}
+        for month in months:
+            if category == "Revenue":
+                value = random.randint(80000, 120000)
+            elif category == "Cost of Goods Sold":
+                value = random.randint(30000, 50000)
+            elif category == "Operating Expenses":
+                value = random.randint(20000, 35000)
+            else:  # Net Income
+                value = int(
+                    int(row.get("January", 0))
+                    + int(row.get("February", 0))
+                    + int(row.get("March", 0))
+                )
+                value = random.randint(15000, 40000)
+            row[month] = value
+        data.append(row)
+
+    df = pd.DataFrame(data)
+
+    # Write to Excel
+    output_file = output_dir / "financial_report_q1.xlsx"
+    with pd.ExcelWriter(output_file, engine="openpyxl") as writer:
+        df.to_excel(writer, sheet_name="Q1 Summary", index=False)
+
+        # Format it nicely
+        worksheet = writer.sheets["Q1 Summary"]
+        for cell in worksheet[1]:  # Header row
+            cell.font = Font(bold=True)
+            cell.fill = PatternFill(
+                start_color="4F81BD", end_color="4F81BD", fill_type="solid"
+            )
+            cell.font = Font(color="FFFFFF", bold=True)
+
+    print(f"  ✅ Created {output_file}")
+
+
+def generate_workflow_2_data(output_dir: Path) -> None:
+    """Generate regional sales data for Workflow 2."""
+    print("\n📊 Generating Workflow 2: Regional sales data")
+
+    regions = ["northeast", "southeast", "west"]
+    products = ["Widget A", "Widget B", "Widget C", "Gadget X", "Gadget Y"]
+
+    for region in regions:
+        data = []
+        start_date = datetime(2024, 1, 1)
+
+        # Generate 90 days of sales data
+        for day in range(90):
+            date = start_date + timedelta(days=day)
+            # Random number of sales per day (3-8)
+            for _ in range(random.randint(3, 8)):
+                product = random.choice(products)
+                units_sold = random.randint(1, 20)
+                price_per_unit = random.randint(50, 200)
+                revenue = units_sold * price_per_unit
+
+                data.append(
+                    {
+                        "Date": date.strftime("%Y-%m-%d"),
+                        "Product": product,
+                        "Units_Sold": units_sold,
+                        "Revenue": revenue,
+                    }
+                )
+
+        df = pd.DataFrame(data)
+
+        # Write to Excel
+        output_file = output_dir / f"sales_{region}.xlsx"
+        df.to_excel(output_file, sheet_name="Sales", index=False)
+        print(f"  ✅ Created {output_file} ({len(df)} rows)")
+
+
+def generate_workflow_3_data(output_dir: Path) -> None:
+    """Generate complex budget spreadsheet with formatting for Workflow 3."""
+    print("\n📊 Generating Workflow 3: company_budget_2024.xlsx")
+
+    wb = Workbook()
+    ws = wb.active
+    ws.title = "Budget"
+
+    # Define departments with colors
+    departments = {
+        "Engineering": "C6E0B4",
+        "Marketing": "FFD966",
+        "Sales": "F4B084",
+        "Operations": "B4C7E7",
+    }
+
+    # Define categories
+    categories = {
+        "Personnel": ["Salaries", "Benefits", "Training"],
+        "Infrastructure": ["Office Rent", "Equipment", "Software Licenses"],
+        "Operations": ["Travel", "Supplies", "Miscellaneous"],
+    }
+
+    # Styles
+    header_font = Font(bold=True, size=12)
+    category_font = Font(bold=True, size=11)
+
+    row = 1
+
+    # Title
+    ws.merge_cells(f"A{row}:E{row}")
+    ws[f"A{row}"] = "2024 Annual Budget"
+    ws[f"A{row}"].font = Font(bold=True, size=14)
+    ws[f"A{row}"].alignment = Alignment(horizontal="center")
+    row += 2
+
+    # Headers
+    ws[f"A{row}"] = "Category"
+    ws[f"B{row}"] = "Item"
+    for i, dept in enumerate(departments.keys()):
+        ws.cell(row, 3 + i, dept)
+        ws.cell(row, 3 + i).font = header_font
+
+    for cell in ws[row]:
+        cell.font = header_font
+    row += 1
+
+    # Data
+    for category, items in categories.items():
+        # Category header (bold)
+        ws[f"A{row}"] = category
+        ws[f"A{row}"].font = category_font
+        row += 1
+
+        # Items with department budgets
+        for item in items:
+            ws[f"A{row}"] = ""
+            ws[f"B{row}"] = item
+
+            # Add budget amounts for each department (with color)
+            for i, (dept, color) in enumerate(departments.items()):
+                amount = random.randint(5000, 50000)
+                cell = ws.cell(row, 3 + i, amount)
+                cell.fill = PatternFill(
+                    start_color=color, end_color=color, fill_type="solid"
+                )
+                cell.number_format = "$#,##0"
+
+            row += 1
+
+        row += 1  # Blank row between categories
+
+    # Adjust column widths
+    ws.column_dimensions["A"].width = 20
+    ws.column_dimensions["B"].width = 25
+    for i in range(len(departments)):
+        ws.column_dimensions[chr(67 + i)].width = 15  # C, D, E, F
+
+    output_file = output_dir / "company_budget_2024.xlsx"
+    wb.save(output_file)
+    print(f"  ✅ Created {output_file}")
+    print("     • Bold categories, colored departments, merged title cell")
+
+
+def generate_workflow_4_data(output_dir: Path) -> None:
+    """Generate weekly sales report for Workflow 4."""
+    print("\n📊 Generating Workflow 4: sales_weekly.xlsx")
+
+    products = [
+        "Product A",
+        "Product B",
+        "Product C",
+        "Product D",
+        "Product E",
+        "Product F",
+        "Product G",
+        "Product H",
+    ]
+
+    # Generate one week of data
+    data = []
+    start_date = datetime(2024, 11, 4)  # Monday
+
+    for day in range(7):
+        date = start_date + timedelta(days=day)
+        # Each product has 3-10 transactions per day
+        for product in products:
+            for _ in range(random.randint(3, 10)):
+                units = random.randint(1, 15)
+                price = random.randint(20, 150)
+                revenue = units * price
+
+                data.append(
+                    {
+                        "Date": date.strftime("%Y-%m-%d"),
+                        "Product": product,
+                        "Units": units,
+                        "Revenue": revenue,
+                    }
+                )
+
+    df = pd.DataFrame(data)
+
+    # Write to Excel with some formatting
+    output_file = output_dir / "sales_weekly.xlsx"
+    with pd.ExcelWriter(output_file, engine="openpyxl") as writer:
+        df.to_excel(writer, sheet_name="Weekly Sales", index=False)
+
+        # Format header
+        worksheet = writer.sheets["Weekly Sales"]
+        for cell in worksheet[1]:
+            cell.font = Font(bold=True)
+
+    print(f"  ✅ Created {output_file} ({len(df)} rows)")
+
+
+def main() -> None:
+    """Generate all sample data files."""
+    print("=" * 60)
+    print("Generating Sample Data for LlamaSheets + Coding Agent Workflows")
+    print("=" * 60)
+
+    # Create output directory
+    output_dir = Path("input_data")
+    output_dir.mkdir(exist_ok=True)
+
+    # Generate data for each workflow
+    generate_workflow_1_data(output_dir)
+    generate_workflow_2_data(output_dir)
+    generate_workflow_3_data(output_dir)
+    generate_workflow_4_data(output_dir)
+
+    print("\n" + "=" * 60)
+    print("✅ All sample data generated!")
+    print("=" * 60)
+    print(f"\nFiles created in {output_dir.absolute()}:")
+    print("\nWorkflow 1 (Understanding a New Spreadsheet):")
+    print("  • financial_report_q1.xlsx")
+    print("\nWorkflow 2 (Generating Analysis Scripts):")
+    print("  • sales_northeast.xlsx")
+    print("  • sales_southeast.xlsx")
+    print("  • sales_west.xlsx")
+    print("\nWorkflow 3 (Using Cell Metadata):")
+    print("  • company_budget_2024.xlsx")
+    print("\nWorkflow 4 (Complete Automation):")
+    print("  • sales_weekly.xlsx")
+    print("\nYou can now use these files with the workflows in the documentation!")
+
+
+if __name__ == "__main__":
+    main()

examples/sheets/coding-agent-analysis/requirements.txt

@@ -0,0 +1,5 @@
+llama-cloud-services  # LlamaSheets SDK
+pandas>=2.0.0
+pyarrow>=12.0.0
+openpyxl>=3.0.0       # For Excel file support
+matplotlib>=3.7.0     # For visualizations (optional)

examples/sheets/coding-agent-analysis/scripts/extract.py

@@ -0,0 +1,100 @@
+"""Helper script to extract spreadsheets using LlamaSheets."""
+
+import asyncio
+import json
+import os
+import dotenv
+from pathlib import Path
+
+from llama_cloud_services.beta.sheets import LlamaSheets
+from llama_cloud_services.beta.sheets.types import (
+    SpreadsheetParsingConfig,
+    SpreadsheetResultType,
+)
+
+dotenv.load_dotenv()
+
+
+async def extract_spreadsheet(
+    file_path: str, output_dir: str = "data", generate_metadata: bool = True
+) -> dict:
+    """Extract a spreadsheet using LlamaSheets."""
+
+    client = LlamaSheets(
+        base_url="https://api.cloud.llamaindex.ai",
+        api_key=os.getenv("LLAMA_CLOUD_API_KEY"),
+    )
+
+    print(f"Extracting {file_path}...")
+
+    # Extract regions
+    config = SpreadsheetParsingConfig(
+        sheet_names=None,  # Extract all sheets
+        generate_additional_metadata=generate_metadata,
+    )
+
+    job_result = await client.aextract_regions(file_path, config=config)
+
+    print(f"Extracted {len(job_result.regions)} region(s)")
+
+    # Create output directory
+    output_path = Path(output_dir)
+    output_path.mkdir(parents=True, exist_ok=True)
+
+    # Get base name for files
+    base_name = Path(file_path).stem
+
+    # Save job metadata
+    job_metadata_path = output_path / f"{base_name}_job_metadata.json"
+    with open(job_metadata_path, "w") as f:
+        json.dump(job_result.model_dump(mode="json"), f, indent=2)
+    print(f"Saved job metadata to {job_metadata_path}")
+
+    # Download each region
+    for idx, region in enumerate(job_result.regions, 1):
+        sheet_name = region.sheet_name.replace(" ", "_")
+
+        # Download region data
+        region_bytes = await client.adownload_region_result(
+            job_id=job_result.id,
+            region_id=region.region_id,
+            result_type=region.region_type,
+        )
+
+        region_path = output_path / f"{base_name}_region_{idx}_{sheet_name}.parquet"
+        with open(region_path, "wb") as f:
+            f.write(region_bytes)
+        print(f"  Table {idx}: {region_path}")
+
+        # Download metadata
+        metadata_bytes = await client.adownload_region_result(
+            job_id=job_result.id,
+            region_id=region.region_id,
+            result_type=SpreadsheetResultType.CELL_METADATA,
+        )
+
+        metadata_path = output_path / f"{base_name}_metadata_{idx}_{sheet_name}.parquet"
+        with open(metadata_path, "wb") as f:
+            f.write(metadata_bytes)
+        print(f"  Metadata {idx}: {metadata_path}")
+
+    print(f"\nAll files saved to {output_path}/")
+
+    return job_result.model_dump(mode="json")
+
+
+if __name__ == "__main__":
+    import sys
+
+    if len(sys.argv) < 2:
+        print("Usage: python scripts/extract.py <spreadsheet_file>")
+        sys.exit(1)
+
+    file_path = sys.argv[1]
+
+    if not Path(file_path).exists():
+        print(f"❌ File not found: {file_path}")
+        sys.exit(1)
+
+    result = asyncio.run(extract_spreadsheet(file_path))
+    print(f"\n✅ Extraction complete! Job ID: {result['id']}")

examples/sheets/llama-index-agent/generate_sample_data.py

@@ -0,0 +1,278 @@
+"""
+Generate sample spreadsheets for LlamaSheets + LlamaIndex Agent workflows.
+
+This script creates example Excel files that demonstrate different use cases:
+1. Simple data table (for Workflow 1)
+2. Regional sales data (for Workflow 2)
+3. Complex budget with formatting (for Workflow 3)
+4. Weekly sales report (for Workflow 4)
+
+Usage:
+    python generate_sample_data.py
+"""
+
+import random
+from datetime import datetime, timedelta
+from pathlib import Path
+
+import pandas as pd
+from openpyxl import Workbook
+from openpyxl.styles import Font, PatternFill, Alignment
+
+
+def generate_workflow_1_data(output_dir: Path) -> None:
+    """Generate simple financial report for Workflow 1."""
+    print("📊 Generating Workflow 1: financial_report_q1.xlsx")
+
+    # Create sample quarterly data
+    months = ["January", "February", "March"]
+    categories = ["Revenue", "Cost of Goods Sold", "Operating Expenses", "Net Income"]
+
+    data = []
+    for category in categories:
+        row: dict[str, str | int] = {"Category": category}
+        for month in months:
+            if category == "Revenue":
+                value = random.randint(80000, 120000)
+            elif category == "Cost of Goods Sold":
+                value = random.randint(30000, 50000)
+            elif category == "Operating Expenses":
+                value = random.randint(20000, 35000)
+            else:  # Net Income
+                value = int(
+                    int(row.get("January", 0))
+                    + int(row.get("February", 0))
+                    + int(row.get("March", 0))
+                )
+                value = random.randint(15000, 40000)
+            row[month] = value
+        data.append(row)
+
+    df = pd.DataFrame(data)
+
+    # Write to Excel
+    output_file = output_dir / "financial_report_q1.xlsx"
+    with pd.ExcelWriter(output_file, engine="openpyxl") as writer:
+        df.to_excel(writer, sheet_name="Q1 Summary", index=False)
+
+        # Format it nicely
+        worksheet = writer.sheets["Q1 Summary"]
+        for cell in worksheet[1]:  # Header row
+            cell.font = Font(bold=True)
+            cell.fill = PatternFill(
+                start_color="4F81BD", end_color="4F81BD", fill_type="solid"
+            )
+            cell.font = Font(color="FFFFFF", bold=True)
+
+    print(f"  ✅ Created {output_file}")
+
+
+def generate_workflow_2_data(output_dir: Path) -> None:
+    """Generate regional sales data for Workflow 2."""
+    print("\n📊 Generating Workflow 2: Regional sales data")
+
+    regions = ["northeast", "southeast", "west"]
+    products = ["Widget A", "Widget B", "Widget C", "Gadget X", "Gadget Y"]
+
+    for region in regions:
+        data = []
+        start_date = datetime(2024, 1, 1)
+
+        # Generate 90 days of sales data
+        for day in range(90):
+            date = start_date + timedelta(days=day)
+            # Random number of sales per day (3-8)
+            for _ in range(random.randint(3, 8)):
+                product = random.choice(products)
+                units_sold = random.randint(1, 20)
+                price_per_unit = random.randint(50, 200)
+                revenue = units_sold * price_per_unit
+
+                data.append(
+                    {
+                        "Date": date.strftime("%Y-%m-%d"),
+                        "Product": product,
+                        "Units_Sold": units_sold,
+                        "Revenue": revenue,
+                    }
+                )
+
+        df = pd.DataFrame(data)
+
+        # Write to Excel
+        output_file = output_dir / f"sales_{region}.xlsx"
+        df.to_excel(output_file, sheet_name="Sales", index=False)
+        print(f"  ✅ Created {output_file} ({len(df)} rows)")
+
+
+def generate_workflow_3_data(output_dir: Path) -> None:
+    """Generate complex budget spreadsheet with formatting for Workflow 3."""
+    print("\n📊 Generating Workflow 3: company_budget_2024.xlsx")
+
+    wb = Workbook()
+    ws = wb.active
+    ws.title = "Budget"
+
+    # Define departments with colors
+    departments = {
+        "Engineering": "C6E0B4",
+        "Marketing": "FFD966",
+        "Sales": "F4B084",
+        "Operations": "B4C7E7",
+    }
+
+    # Define categories
+    categories = {
+        "Personnel": ["Salaries", "Benefits", "Training"],
+        "Infrastructure": ["Office Rent", "Equipment", "Software Licenses"],
+        "Operations": ["Travel", "Supplies", "Miscellaneous"],
+    }
+
+    # Styles
+    header_font = Font(bold=True, size=12)
+    category_font = Font(bold=True, size=11)
+
+    row = 1
+
+    # Title
+    ws.merge_cells(f"A{row}:E{row}")
+    ws[f"A{row}"] = "2024 Annual Budget"
+    ws[f"A{row}"].font = Font(bold=True, size=14)
+    ws[f"A{row}"].alignment = Alignment(horizontal="center")
+    row += 2
+
+    # Headers
+    ws[f"A{row}"] = "Category"
+    ws[f"B{row}"] = "Item"
+    for i, dept in enumerate(departments.keys()):
+        ws.cell(row, 3 + i, dept)
+        ws.cell(row, 3 + i).font = header_font
+
+    for cell in ws[row]:
+        cell.font = header_font
+    row += 1
+
+    # Data
+    for category, items in categories.items():
+        # Category header (bold)
+        ws[f"A{row}"] = category
+        ws[f"A{row}"].font = category_font
+        row += 1
+
+        # Items with department budgets
+        for item in items:
+            ws[f"A{row}"] = ""
+            ws[f"B{row}"] = item
+
+            # Add budget amounts for each department (with color)
+            for i, (dept, color) in enumerate(departments.items()):
+                amount = random.randint(5000, 50000)
+                cell = ws.cell(row, 3 + i, amount)
+                cell.fill = PatternFill(
+                    start_color=color, end_color=color, fill_type="solid"
+                )
+                cell.number_format = "$#,##0"
+
+            row += 1
+
+        row += 1  # Blank row between categories
+
+    # Adjust column widths
+    ws.column_dimensions["A"].width = 20
+    ws.column_dimensions["B"].width = 25
+    for i in range(len(departments)):
+        ws.column_dimensions[chr(67 + i)].width = 15  # C, D, E, F
+
+    output_file = output_dir / "company_budget_2024.xlsx"
+    wb.save(output_file)
+    print(f"  ✅ Created {output_file}")
+    print("     • Bold categories, colored departments, merged title cell")
+
+
+def generate_workflow_4_data(output_dir: Path) -> None:
+    """Generate weekly sales report for Workflow 4."""
+    print("\n📊 Generating Workflow 4: sales_weekly.xlsx")
+
+    products = [
+        "Product A",
+        "Product B",
+        "Product C",
+        "Product D",
+        "Product E",
+        "Product F",
+        "Product G",
+        "Product H",
+    ]
+
+    # Generate one week of data
+    data = []
+    start_date = datetime(2024, 11, 4)  # Monday
+
+    for day in range(7):
+        date = start_date + timedelta(days=day)
+        # Each product has 3-10 transactions per day
+        for product in products:
+            for _ in range(random.randint(3, 10)):
+                units = random.randint(1, 15)
+                price = random.randint(20, 150)
+                revenue = units * price
+
+                data.append(
+                    {
+                        "Date": date.strftime("%Y-%m-%d"),
+                        "Product": product,
+                        "Units": units,
+                        "Revenue": revenue,
+                    }
+                )
+
+    df = pd.DataFrame(data)
+
+    # Write to Excel with some formatting
+    output_file = output_dir / "sales_weekly.xlsx"
+    with pd.ExcelWriter(output_file, engine="openpyxl") as writer:
+        df.to_excel(writer, sheet_name="Weekly Sales", index=False)
+
+        # Format header
+        worksheet = writer.sheets["Weekly Sales"]
+        for cell in worksheet[1]:
+            cell.font = Font(bold=True)
+
+    print(f"  ✅ Created {output_file} ({len(df)} rows)")
+
+
+def main() -> None:
+    """Generate all sample data files."""
+    print("=" * 60)
+    print("Generating Sample Data for LlamaSheets + Coding Agent Workflows")
+    print("=" * 60)
+
+    # Create output directory
+    output_dir = Path("input_data")
+    output_dir.mkdir(exist_ok=True)
+
+    # Generate data for each workflow
+    generate_workflow_1_data(output_dir)
+    generate_workflow_2_data(output_dir)
+    generate_workflow_3_data(output_dir)
+    generate_workflow_4_data(output_dir)
+
+    print("\n" + "=" * 60)
+    print("✅ All sample data generated!")
+    print("=" * 60)
+    print(f"\nFiles created in {output_dir.absolute()}:")
+    print("\nWorkflow 1 (Understanding a New Spreadsheet):")
+    print("  • financial_report_q1.xlsx")
+    print("\nWorkflow 2 (Generating Analysis Scripts):")
+    print("  • sales_northeast.xlsx")
+    print("  • sales_southeast.xlsx")
+    print("  • sales_west.xlsx")
+    print("\nWorkflow 3 (Using Cell Metadata):")
+    print("  • company_budget_2024.xlsx")
+    print("\nWorkflow 4 (Complete Automation):")
+    print("  • sales_weekly.xlsx")
+    print("\nYou can now use these files with the workflows in the documentation!")
+
+
+if __name__ == "__main__":
+    main()

examples/sheets/llama-index-agent/llama_index_example.py

@@ -0,0 +1,292 @@
+"""
+LlamaSheets Agent with LlamaIndex
+
+This example shows how to build an agent that can work with spreadsheet data
+extracted by LlamaSheets using Python code execution.
+
+The agent has minimal tools but maximum flexibility - it can execute arbitrary
+pandas code against the extracted data, similar to a coding agent.
+
+NOTE: Code execution should be handled safely in a sandboxed environment for security.
+"""
+
+import io
+import json
+import sys
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+import dotenv
+import pandas as pd
+from llama_index.core.agent import FunctionAgent, ToolCall, ToolCallResult, AgentStream
+from llama_index.llms.openai import OpenAI
+from workflows import Context
+
+dotenv.load_dotenv()
+
+# Global context for executed code
+_code_context: Dict[str, Any] = {}
+
+
+# Helper function for initial agent context
+def list_extracted_data(data_dir: str = "data") -> str:
+    """
+    List all regions and metadata files extracted by LlamaSheets.
+
+    This helps discover what data is available to work with.
+
+    Args:
+        data_dir: Directory containing extracted parquet files (default: "data")
+
+    Returns:
+        JSON string with information about available files
+    """
+    data_path = Path(data_dir)
+
+    if not data_path.exists():
+        return json.dumps({"error": f"Data directory '{data_dir}' not found"})
+
+    # Find all parquet and metadata files
+    region_files = list(data_path.glob("*_region_*.parquet"))
+    job_metadata_files = list(data_path.glob("*_job_metadata.json"))
+
+    regions = []
+    for region_file in region_files:
+        # Quick peek at dimensions
+        df = pd.read_parquet(region_file)
+
+        # Find corresponding metadata file
+        base_name = region_file.stem.replace("_region_", "_metadata_")
+        metadata_path = region_file.parent / f"{base_name}.parquet"
+
+        regions.append(
+            {
+                "region_file": str(region_file),
+                "metadata_file": str(metadata_path) if metadata_path.exists() else None,
+                "shape": {"rows": len(df), "columns": len(df.columns)},
+                "columns": list(df.columns),
+            }
+        )
+
+    result = {
+        "data_directory": str(data_path.absolute()),
+        "num_regions": len(regions),
+        "regions": regions,
+        "job_metadata_files": [str(f) for f in job_metadata_files],
+    }
+
+    return json.dumps(result, indent=2)
+
+
+# Agent tool for code execution against dataframes
+def execute_code(code: str) -> str:
+    """
+    Execute Python pandas code against LlamaSheets extracted data.
+
+    This tool allows flexible data analysis by executing arbitrary pandas code.
+    You can load parquet files, manipulate dataframes, and return results.
+
+    The code executes in a context where:
+    - pandas is available as 'pd'
+    - json is available for formatting output
+
+    Args:
+        code: Python code to execute. Any print() statements or stdout/stderr
+                will be captured and returned. Optionally set a 'result' variable
+                for structured output.
+
+    Returns:
+        String containing:
+        - Any stdout/stderr output from the code execution
+        - The 'result' variable if it was set (formatted appropriately)
+        - Error message if execution failed
+
+    Example usage:
+        code = '''
+    # Load and inspect data
+    df = pd.read_parquet("data/sales_region_1.parquet")
+    print(f"Loaded {len(df)} rows")
+
+    result = {
+        "shape": df.shape,
+        "columns": list(df.columns),
+        "sample": df.head(3).to_dict(orient="records")
+    }
+        '''
+    """
+    global _code_context
+
+    # Capture stdout and stderr
+    stdout_capture = io.StringIO()
+    stderr_capture = io.StringIO()
+    old_stdout = sys.stdout
+    old_stderr = sys.stderr
+
+    try:
+        # Redirect stdout/stderr
+        sys.stdout = stdout_capture
+        sys.stderr = stderr_capture
+
+        # Create execution context with pandas, json, and previously loaded dfs
+        exec_context = {
+            "pd": pd,
+            "json": json,
+            "Path": Path,
+            **_code_context,  # Include previously loaded dataframes
+        }
+
+        # Execute the code
+        exec(code, exec_context)
+
+        # Update global context with any new variables (excluding built-ins and modules)
+        for key, value in exec_context.items():
+            if not key.startswith("_") and key not in ["pd", "json", "Path"]:
+                _code_context[key] = value
+
+        # Restore stdout/stderr
+        sys.stdout = old_stdout
+        sys.stderr = old_stderr
+
+        # Collect output
+        stdout_output = stdout_capture.getvalue()
+        stderr_output = stderr_capture.getvalue()
+
+        output_parts = []
+
+        # Add stdout if any
+        if stdout_output:
+            output_parts.append(f"<stdout>{stdout_output}</stdout>")
+
+        # Add stderr if any
+        if stderr_output:
+            output_parts.append(f"<stderr>{stderr_output}</stderr>")
+
+        # Try to get a result (if code set a 'result' variable)
+        if "result" in exec_context:
+            result = exec_context["result"]
+            result_str = None
+
+            if isinstance(result, pd.DataFrame):
+                # Convert DataFrame to readable format
+                result_str = result.to_string()
+            elif isinstance(result, (dict, list)):
+                result_str = json.dumps(result, indent=2, default=str)
+            else:
+                result_str = str(result)
+
+            if result_str:
+                output_parts.append(f"<result_var>{result_str}</result_var>")
+
+        # Return combined output or success message
+        if output_parts:
+            return "\n\n".join(output_parts)
+        else:
+            return "Code executed successfully (no output or result)"
+
+    except Exception as e:
+        # Restore stdout/stderr in case of error
+        sys.stdout = old_stdout
+        sys.stderr = old_stderr
+
+        # Get any partial output
+        stdout_output = stdout_capture.getvalue()
+        stderr_output = stderr_capture.getvalue()
+
+        error_parts = []
+        if stdout_output:
+            error_parts.append(f"=== STDOUT (before error) ===\n{stdout_output}")
+        if stderr_output:
+            error_parts.append(f"=== STDERR (before error) ===\n{stderr_output}")
+
+        error_parts.append(f"=== ERROR ===\n{str(e)}")
+        error_parts.append(f"\n=== CODE ===\n{code}")
+
+        return "\n\n".join(error_parts)
+
+
+def create_llamasheets_agent(
+    llm_model: str = "gpt-4.1", api_key: Optional[str] = None
+) -> FunctionAgent:
+    # Initialize LLM
+    llm = OpenAI(model=llm_model, api_key=api_key)
+
+    # Create tools list
+    tools = [execute_code]
+
+    # System prompt to guide the agent
+    available_regions = list_extracted_data()
+    system_prompt = f"""You are an AI assistant that helps analyze spreadsheet data extracted by LlamaSheets.
+
+LlamaSheets extracts messy spreadsheets into clean parquet files with two types of outputs:
+1. Region files (*_region_*.parquet) - The actual data with columns and rows
+2. Metadata files (*_metadata_*.parquet) - Rich cell-level metadata including:
+   - Formatting: font_bold, font_italic, font_size, background_color_rgb
+   - Position: row_number, column_number, coordinate
+   - Type detection: data_type, is_date_like, is_percentage, is_currency
+   - Layout: is_in_first_row, is_merged_cell, horizontal_alignment
+
+You have access to tools that allow you to execute Python pandas code against these files.
+Use these tools to load the parquet files, analyze the data, and return results.
+
+Key tips:
+- Bold cells in metadata often indicate headers
+- Background colors often indicate groupings or departments
+- Load both region and metadata files for complete analysis
+- Write clear pandas code - you have full pandas functionality available
+- Store results in variables for reuse across multiple code executions
+
+Existing Processed Regions:
+{available_regions}
+"""
+
+    # Configure agent
+    return FunctionAgent(tools=tools, llm=llm, system_prompt=system_prompt)
+
+
+async def main():
+    """Example of using the LlamaSheets agent."""
+
+    # Create the agent
+    agent = create_llamasheets_agent()
+    ctx = Context(agent)
+
+    # Example queries the agent can handle:
+    queries = [
+        # Discovery
+        "What spreadsheet data is available?",
+        # Simple analysis
+        "Load the sales data and show me the first few rows with column info",
+        # Using metadata
+        "Find all bold cells in the metadata - these are likely headers",
+    ]
+
+    # Example: Run a query
+    for query in queries:
+        print(f"\n=== Query: {query} ===")
+        handler = agent.run(query, ctx=ctx)
+        async for ev in handler.stream_events():
+            if isinstance(ev, ToolCall):
+                tool_kwargs_str = (
+                    str(ev.tool_kwargs)[:500] + " ..."
+                    if len(str(ev.tool_kwargs)) > 500
+                    else str(ev.tool_kwargs)
+                )
+                print(f"\n[Tool Call] {ev.tool_name} with args:\n{tool_kwargs_str}\n\n")
+            elif isinstance(ev, ToolCallResult):
+                result_str = (
+                    str(ev.tool_output)[:500] + " ..."
+                    if len(str(ev.tool_output)) > 500
+                    else str(ev.tool_output)
+                )
+                print(f"\n[Tool Result] {ev.tool_name}:\n{result_str}\n\n")
+            elif isinstance(ev, AgentStream):
+                print(ev.delta, end="", flush=True)
+
+        _ = await handler
+        print("\n=== End Query ===\n")
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    asyncio.run(main())

examples/sheets/llama-index-agent/requirements.txt

@@ -0,0 +1,7 @@
+llama-cloud-services  # LlamaSheets SDK
+llama-index-core
+llama-index-llms-openai
+pandas>=2.0.0
+pyarrow>=12.0.0
+openpyxl>=3.0.0       # For Excel file support
+matplotlib>=3.7.0     # For visualizations (optional)

examples/sheets/llama-index-agent/scripts/extract.py

@@ -0,0 +1,100 @@
+"""Helper script to extract spreadsheets using LlamaSheets."""
+
+import asyncio
+import json
+import os
+import dotenv
+from pathlib import Path
+
+from llama_cloud_services.beta.sheets import LlamaSheets
+from llama_cloud_services.beta.sheets.types import (
+    SpreadsheetParsingConfig,
+    SpreadsheetResultType,
+)
+
+dotenv.load_dotenv()
+
+
+async def extract_spreadsheet(
+    file_path: str, output_dir: str = "data", generate_metadata: bool = True
+) -> dict:
+    """Extract a spreadsheet using LlamaSheets."""
+
+    client = LlamaSheets(
+        base_url="https://api.cloud.llamaindex.ai",
+        api_key=os.getenv("LLAMA_CLOUD_API_KEY"),
+    )
+
+    print(f"Extracting {file_path}...")
+
+    # Extract regions
+    config = SpreadsheetParsingConfig(
+        sheet_names=None,  # Extract all sheets
+        generate_additional_metadata=generate_metadata,
+    )
+
+    job_result = await client.aextract_regions(file_path, config=config)
+
+    print(f"Extracted {len(job_result.regions)} region(s)")
+
+    # Create output directory
+    output_path = Path(output_dir)
+    output_path.mkdir(parents=True, exist_ok=True)
+
+    # Get base name for files
+    base_name = Path(file_path).stem
+
+    # Save job metadata
+    job_metadata_path = output_path / f"{base_name}_job_metadata.json"
+    with open(job_metadata_path, "w") as f:
+        json.dump(job_result.model_dump(mode="json"), f, indent=2)
+    print(f"Saved job metadata to {job_metadata_path}")
+
+    # Download each region
+    for idx, region in enumerate(job_result.regions, 1):
+        sheet_name = region.sheet_name.replace(" ", "_")
+
+        # Download region data
+        region_bytes = await client.adownload_region_result(
+            job_id=job_result.id,
+            region_id=region.region_id,
+            result_type=region.region_type,
+        )
+
+        region_path = output_path / f"{base_name}_region_{idx}_{sheet_name}.parquet"
+        with open(region_path, "wb") as f:
+            f.write(region_bytes)
+        print(f"  Table {idx}: {region_path}")
+
+        # Download metadata
+        metadata_bytes = await client.adownload_region_result(
+            job_id=job_result.id,
+            region_id=region.region_id,
+            result_type=SpreadsheetResultType.CELL_METADATA,
+        )
+
+        metadata_path = output_path / f"{base_name}_metadata_{idx}_{sheet_name}.parquet"
+        with open(metadata_path, "wb") as f:
+            f.write(metadata_bytes)
+        print(f"  Metadata {idx}: {metadata_path}")
+
+    print(f"\nAll files saved to {output_path}/")
+
+    return job_result.model_dump(mode="json")
+
+
+if __name__ == "__main__":
+    import sys
+
+    if len(sys.argv) < 2:
+        print("Usage: python scripts/extract.py <spreadsheet_file>")
+        sys.exit(1)
+
+    file_path = sys.argv[1]
+
+    if not Path(file_path).exists():
+        print(f"❌ File not found: {file_path}")
+        sys.exit(1)
+
+    result = asyncio.run(extract_spreadsheet(file_path))
+    print(f"\n✅ Extraction complete! Job ID: {result['id']}")

py/CHANGELOG.md

@@ -1,5 +1,29 @@
 # llama-cloud-services-py
 
+## 0.6.83
+
+### Patch Changes
+
+- ca78113: Do not use presigned URLs by default in files client
+
+## 0.6.82
+
+### Patch Changes
+
+- bfaec79: Update for new page number params
+
+## 0.6.81
+
+### Patch Changes
+
+- f3233de: Propagate retrieval metadata to retriever nodes
+
+## 0.6.80
+
+### Patch Changes
+
+- 0506c88: Moved ClassifyClient to LlamaClassify (backward compatible)
+
 ## 0.6.79
 
 ### Patch Changes

py/Makefile

@@ -15,4 +15,4 @@ test:	## Run unit tests via pytest
 
 .PHONY: e2e
 e2e:	## Run all tests. Run with high parallelism using xdist since tests are bottlenecked bound by the slow backend parsing
-	uv run pytest -v -n 32 tests/
+	uv run pytest -v -n 32 --timeout=300 --session-timeout=1740 tests/

py/llama_cloud_services/beta/classifier/__init__.py

@@ -1,8 +1,9 @@
-from llama_cloud_services.beta.classifier.client import ClassifyClient
+from llama_cloud_services.beta.classifier.client import LlamaClassify, ClassifyClient
 from llama_cloud_services.beta.classifier.types import ClassifyJobResultsWithFiles
 from llama_cloud_services.utils import SourceText, FileInput
 
 __all__ = [
+    "LlamaClassify",
     "ClassifyClient",
     "ClassifyJobResultsWithFiles",
     "SourceText",

py/llama_cloud_services/beta/classifier/client.py

@@ -31,7 +31,7 @@ class ClassificationOutput(BaseModel):
     classification: str
 
 
-class ClassifyClient:
+class LlamaClassify:
     """
     Experimental - Client for interacting with the LlamaCloud Classifier API.
     The Classification API is currently in beta and may change in the future without notice.
@@ -366,3 +366,6 @@ class ClassifyClient:
                 job_id, project_id=self.project_id
             )
         return job
+
+
+ClassifyClient = LlamaClassify

py/llama_cloud_services/beta/sheets/__init__.py

@@ -0,0 +1,43 @@
+"""LlamaCloud Spreadsheet API SDK
+
+This module provides a Python SDK for the LlamaCloud Spreadsheet API.
+"""
+
+from llama_cloud_services.beta.sheets.client import (
+    LlamaSheets,
+    SpreadsheetAPIError,
+    SpreadsheetJobError,
+    SpreadsheetTimeoutError,
+)
+from llama_cloud_services.beta.sheets.types import (
+    ExtractedRegionSummary,
+    FileUploadResponse,
+    JobStatus,
+    PresignedUrlResponse,
+    SpreadsheetJob,
+    SpreadsheetJobResult,
+    SpreadsheetParseResult,
+    SpreadsheetParsingConfig,
+    SpreadsheetResultType,
+    WorksheetMetadata,
+)
+
+__all__ = [
+    # Client
+    "LlamaSheets",
+    # Exceptions
+    "SpreadsheetAPIError",
+    "SpreadsheetJobError",
+    "SpreadsheetTimeoutError",
+    # Types
+    "ExtractedRegionSummary",
+    "FileUploadResponse",
+    "JobStatus",
+    "PresignedUrlResponse",
+    "SpreadsheetJob",
+    "SpreadsheetJobResult",
+    "SpreadsheetParseResult",
+    "SpreadsheetParsingConfig",
+    "SpreadsheetResultType",
+    "WorksheetMetadata",
+]

py/llama_cloud_services/beta/sheets/client.py

@@ -0,0 +1,518 @@
+import asyncio
+import io
+import os
+import time
+from typing import TYPE_CHECKING
+
+import httpx
+from llama_cloud.client import AsyncLlamaCloud
+from tenacity import (
+    AsyncRetrying,
+    retry_if_exception,
+    stop_after_attempt,
+    wait_exponential,
+)
+
+from llama_cloud_services.beta.sheets.types import (
+    FileUploadResponse,
+    JobStatus,
+    PresignedUrlResponse,
+    SpreadsheetJob,
+    SpreadsheetJobResult,
+    SpreadsheetParsingConfig,
+    SpreadsheetResultType,
+)
+from llama_cloud_services.constants import BASE_URL
+from llama_cloud_services.files.client import FileClient
+from llama_cloud_services.utils import (
+    augment_async_errors,
+    FileInput,
+)
+
+if TYPE_CHECKING:
+    import pandas as pd
+
+
+def _should_retry_exception(exception: BaseException) -> bool:
+    """Determine if an exception should be retried."""
+    if isinstance(exception, httpx.HTTPStatusError):
+        return exception.response.status_code in (429, 500, 502, 503, 504)
+    return False
+
+
+class SpreadsheetAPIError(Exception):
+    """Base exception for spreadsheet API errors"""
+
+    pass
+
+
+class SpreadsheetJobError(SpreadsheetAPIError):
+    """Exception raised when a spreadsheet job fails"""
+
+    pass
+
+
+class SpreadsheetTimeoutError(SpreadsheetAPIError):
+    """Exception raised when a job times out"""
+
+    pass
+
+
+class LlamaSheets:
+    """Client for the LlamaCloud Spreadsheet API"""
+
+    def __init__(
+        self,
+        api_key: str | None = None,
+        base_url: str | None = None,
+        max_timeout: int = 300,
+        poll_interval: int = 5,
+        max_retries: int = 3,
+        async_httpx_client: httpx.AsyncClient | None = None,
+    ) -> None:
+        """Initialize the LlamaSheets client.
+
+        Args:
+            api_key: API key for authentication. If not provided, will use LLAMA_CLOUD_API_KEY env var
+            base_url: Base URL for the API
+            max_timeout: Maximum time to wait for job completion in seconds
+            poll_interval: Interval between status checks in seconds
+            max_retries: Maximum number of retries for failed requests
+            async_httpx_client: Optional custom async httpx client
+        """
+        self.api_key = api_key or os.environ.get("LLAMA_CLOUD_API_KEY")
+        if not self.api_key:
+            raise ValueError(
+                "An API key must be provided either as an argument or via the LLAMA_CLOUD_API_KEY environment variable."
+            )
+
+        base_url = base_url or os.environ.get("LLAMA_CLOUD_BASE_URL", BASE_URL)
+        self.base_url = str(base_url).rstrip("/")
+
+        self.max_timeout = max_timeout
+        self.poll_interval = poll_interval
+        self.max_retries = max_retries
+
+        self._async_client: httpx.AsyncClient | None = async_httpx_client
+        self._files_client = FileClient(
+            AsyncLlamaCloud(
+                token=self.api_key,
+                base_url=self.base_url,
+                httpx_client=async_httpx_client,
+            )
+        )
+
+    def _get_async_client(self) -> httpx.AsyncClient:
+        """Get or create the async httpx client"""
+        if self._async_client is None:
+            self._async_client = httpx.AsyncClient(
+                timeout=httpx.Timeout(60.0),
+                follow_redirects=True,
+            )
+        return self._async_client
+
+    def _get_headers(self) -> dict[str, str]:
+        """Get common headers for API requests"""
+        return {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json",
+        }
+
+    # Sync methods
+
+    def upload_file(
+        self, file_obj: FileInput, file_name: str | None = None
+    ) -> FileUploadResponse:
+        """Upload a file to the Files API.
+
+        Args:
+            file_obj: File to upload (path, bytes, or file-like object)
+            file_name: Optional name for the uploaded filename
+
+        Returns:
+            FileUploadResponse with the uploaded file ID
+        """
+        with augment_async_errors():
+            return asyncio.run(self.aupload_file(file_obj))
+
+    def create_job(
+        self,
+        file_id: str,
+        config: dict | SpreadsheetParsingConfig | None = None,
+    ) -> SpreadsheetJob:
+        """Create a new spreadsheet parsing job.
+
+        Args:
+            file_id: ID of the uploaded file
+            config: Parsing configuration
+
+        Returns:
+            SpreadsheetJob with job details
+        """
+        with augment_async_errors():
+            return asyncio.run(self.acreate_job(file_id, config))
+
+    def get_job(
+        self, job_id: str, include_results_metadata: bool = True
+    ) -> SpreadsheetJobResult:
+        """Get the status of a spreadsheet parsing job.
+
+        Args:
+            job_id: ID of the job
+            include_results_metadata: Whether to include results metadata in the response
+
+        Returns:
+            SpreadsheetJobResult with job status and optionally results
+        """
+        with augment_async_errors():
+            return asyncio.run(self.aget_job(job_id, include_results_metadata))
+
+    def wait_for_completion(self, job_id: str) -> SpreadsheetJobResult:
+        """Wait for a job to complete by polling.
+
+        Args:
+            job_id: ID of the job to wait for
+
+        Returns:
+            SpreadsheetJobResult when job is complete
+
+        Raises:
+            SpreadsheetTimeoutError: If job doesn't complete within max_timeout
+            SpreadsheetJobError: If job fails
+        """
+        with augment_async_errors():
+            return asyncio.run(self.await_for_completion(job_id))
+
+    def download_region_result(
+        self,
+        job_id: str,
+        region_id: str,
+        result_type: SpreadsheetResultType = SpreadsheetResultType.TABLE,
+    ) -> bytes:
+        """Download a region result (either region data or cell metadata).
+
+        Args:
+            job_id: ID of the job
+            region_id: ID of the region
+            result_type: Type of result to download (region or cell_metadata)
+
+        Returns:
+            Raw bytes of the parquet file
+        """
+        with augment_async_errors():
+            return asyncio.run(
+                self.adownload_region_result(job_id, region_id, result_type)
+            )
+
+    def download_region_as_dataframe(
+        self,
+        job_id: str,
+        region_id: str,
+        result_type: SpreadsheetResultType = SpreadsheetResultType.TABLE,
+    ) -> "pd.DataFrame":
+        """Download a region result as a pandas DataFrame.
+
+        Args:
+            job_id: ID of the job
+            region_id: ID of the region
+            result_type: Type of result to download (region or cell_metadata)
+
+        Returns:
+            pandas DataFrame
+        """
+        with augment_async_errors():
+            return asyncio.run(
+                self.adownload_region_as_dataframe(job_id, region_id, result_type)
+            )
+
+    def extract_regions(
+        self,
+        file_obj: FileInput,
+        config: dict | SpreadsheetParsingConfig | None = None,
+    ) -> SpreadsheetJobResult:
+        """High-level method to parse a spreadsheet file.
+
+        This method handles the entire workflow:
+        1. Upload the file
+        2. Create a parsing job
+        3. Wait for completion
+        4. Return results
+
+        Args:
+            file_obj: File to parse (path, bytes, or file-like object)
+            config: Parsing configuration
+
+        Returns:
+            SpreadsheetJobResult with parsing results
+        """
+        with augment_async_errors():
+            return asyncio.run(self.aextract_regions(file_obj, config))
+
+    # Async methods
+
+    async def aupload_file(
+        self, file_obj: FileInput, file_name: str | None = None
+    ) -> FileUploadResponse:
+        """Upload a file to the Files API.
+
+        Args:
+            file_obj: File to upload (path, bytes, or file-like object)
+            file_name: Optional name for the uploaded filename
+
+        Returns:
+            FileUploadResponse with the uploaded file ID
+        """
+        try:
+            async for attempt in AsyncRetrying(
+                stop=stop_after_attempt(self.max_retries),
+                wait=wait_exponential(multiplier=1, min=1, max=32),
+                retry=retry_if_exception(_should_retry_exception),
+                reraise=True,
+            ):
+                with attempt:
+                    return await self._files_client.upload_content(
+                        file_obj, external_file_id=file_name
+                    )
+        except Exception as e:
+            raise SpreadsheetAPIError(f"Failed to upload file: {e}") from e
+        raise RuntimeError("Tenacity did not execute")
+
+    async def acreate_job(
+        self,
+        file_id: str,
+        config: dict | SpreadsheetParsingConfig | None = None,
+    ) -> SpreadsheetJob:
+        """Create a new spreadsheet parsing job.
+
+        Args:
+            file_id: ID of the uploaded file
+            config: Parsing configuration
+
+        Returns:
+            SpreadsheetJob with job details
+        """
+        if config is None:
+            config = SpreadsheetParsingConfig()
+        elif isinstance(config, dict):
+            config = SpreadsheetParsingConfig.model_validate(config)
+
+        if not isinstance(config, SpreadsheetParsingConfig):
+            raise ValueError(
+                "config must be a dict or SpreadsheetParsingConfig instance"
+            )
+
+        payload = {
+            "file_id": file_id,
+            "config": config.model_dump(mode="json", exclude_none=True),
+        }
+
+        try:
+            async for attempt in AsyncRetrying(
+                stop=stop_after_attempt(self.max_retries),
+                wait=wait_exponential(multiplier=1, min=1, max=32),
+                retry=retry_if_exception(_should_retry_exception),
+                reraise=True,
+            ):
+                with attempt:
+                    client = self._get_async_client()
+                    response = await client.post(
+                        f"{self.base_url}/api/v1/beta/sheets/jobs",
+                        headers=self._get_headers(),
+                        json=payload,
+                    )
+                    response.raise_for_status()
+                    return SpreadsheetJob.model_validate(response.json())
+        except Exception as e:
+            raise SpreadsheetAPIError(f"Failed to create job: {e}") from e
+        raise RuntimeError("Tenacity did not execute")
+
+    async def aget_job(
+        self, job_id: str, include_results_metadata: bool = True
+    ) -> SpreadsheetJobResult:
+        """Get the status of a spreadsheet parsing job.
+
+        Args:
+            job_id: ID of the job
+            include_results_metadata: Whether to include results in the response
+
+        Returns:
+            SpreadsheetJobResult with job status and optionally results
+        """
+        try:
+            async for attempt in AsyncRetrying(
+                stop=stop_after_attempt(self.max_retries),
+                wait=wait_exponential(multiplier=1, min=1, max=32),
+                retry=retry_if_exception(_should_retry_exception),
+                reraise=True,
+            ):
+                with attempt:
+                    client = self._get_async_client()
+                    response = await client.get(
+                        f"{self.base_url}/api/v1/beta/sheets/jobs/{job_id}",
+                        headers=self._get_headers(),
+                        params={"include_results": include_results_metadata},
+                    )
+                    response.raise_for_status()
+                    return SpreadsheetJobResult.model_validate(response.json())
+        except Exception as e:
+            raise SpreadsheetAPIError(f"Failed to get job status: {e}") from e
+        raise RuntimeError("Tenacity did not execute")
+
+    async def await_for_completion(self, job_id: str) -> SpreadsheetJobResult:
+        """Wait for a job to complete by polling.
+
+        Args:
+            job_id: ID of the job to wait for
+
+        Returns:
+            SpreadsheetJobResult when job is complete
+
+        Raises:
+            SpreadsheetTimeoutError: If job doesn't complete within max_timeout
+            SpreadsheetJobError: If job fails
+        """
+        start_time = time.time()
+
+        while (time.time() - start_time) < self.max_timeout:
+            job_result = await self.aget_job(job_id, include_results_metadata=True)
+
+            if job_result.status in (
+                JobStatus.SUCCESS,
+                JobStatus.PARTIAL_SUCCESS,
+                JobStatus.ERROR,
+                JobStatus.FAILURE,
+            ):
+                if job_result.status in (JobStatus.SUCCESS, JobStatus.PARTIAL_SUCCESS):
+                    return job_result
+                else:
+                    error_msg = f"Job failed with status: {job_result.status}"
+                    if job_result.errors:
+                        error_msg += f"\nErrors: {', '.join(job_result.errors)}"
+                    raise SpreadsheetJobError(error_msg)
+
+            await asyncio.sleep(self.poll_interval)
+
+        raise SpreadsheetTimeoutError(
+            f"Job did not complete within {self.max_timeout} seconds"
+        )
+
+    async def adownload_region_result(
+        self,
+        job_id: str,
+        region_id: str,
+        result_type: SpreadsheetResultType = SpreadsheetResultType.TABLE,
+    ) -> bytes:
+        """Download a region result (either region data or cell metadata).
+
+        Args:
+            job_id: ID of the job
+            region_id: ID of the region
+            result_type: Type of result to download (region or cell_metadata)
+
+        Returns:
+            Raw bytes of the parquet file
+        """
+        # Get presigned URL
+        presigned_response = None
+        result_type_str = str(result_type)
+        try:
+            async for attempt in AsyncRetrying(
+                stop=stop_after_attempt(self.max_retries),
+                wait=wait_exponential(multiplier=1, min=1, max=32),
+                retry=retry_if_exception(_should_retry_exception),
+                reraise=True,
+            ):
+                with attempt:
+                    client = self._get_async_client()
+                    response = await client.get(
+                        f"{self.base_url}/api/v1/beta/sheets/jobs/{job_id}/regions/{region_id}/result/{result_type_str}",
+                        headers=self._get_headers(),
+                    )
+                    response.raise_for_status()
+                    presigned_response = PresignedUrlResponse.model_validate(
+                        response.json()
+                    )
+        except Exception as e:
+            raise SpreadsheetAPIError(f"Failed to get presigned URL: {e}") from e
+
+        # Download using presigned URL
+        if presigned_response is None:
+            raise SpreadsheetAPIError("Failed to obtain presigned URL.")
+
+        try:
+            async for attempt in AsyncRetrying(
+                stop=stop_after_attempt(self.max_retries),
+                wait=wait_exponential(multiplier=1, min=1, max=32),
+                retry=retry_if_exception(_should_retry_exception),
+                reraise=True,
+            ):
+                with attempt:
+                    download_response = await client.get(presigned_response.url)
+                    download_response.raise_for_status()
+                    return download_response.content
+        except Exception as e:
+            raise SpreadsheetAPIError(f"Failed to download result: {e}") from e
+        raise RuntimeError("Tenacity did not execute")
+
+    async def adownload_region_as_dataframe(
+        self,
+        job_id: str,
+        region_id: str,
+        result_type: SpreadsheetResultType = SpreadsheetResultType.TABLE,
+    ) -> "pd.DataFrame":
+        """Download a region result as a pandas DataFrame.
+
+        Args:
+            job_id: ID of the job
+            region_id: ID of the region
+            result_type: Type of result to download (region or cell_metadata)
+
+        Returns:
+            pandas DataFrame
+        """
+        import pandas as pd
+
+        parquet_bytes = await self.adownload_region_result(
+            job_id, region_id, result_type
+        )
+        return pd.read_parquet(io.BytesIO(parquet_bytes))
+
+    async def aextract_regions(
+        self,
+        file_obj: FileInput,
+        config: dict | SpreadsheetParsingConfig | None = None,
+    ) -> SpreadsheetJobResult:
+        """High-level method to parse a spreadsheet file.
+
+        This method handles the entire workflow:
+        1. Upload the file
+        2. Create a parsing job
+        3. Wait for completion
+        4. Return results
+
+        Args:
+            file_obj: File to parse (path, bytes, or file-like object)
+            config: Parsing configuration
+
+        Returns:
+            SpreadsheetJobResult with parsing results
+        """
+        # Upload file
+        file_response = await self.aupload_file(file_obj)
+
+        # Create job
+        job = await self.acreate_job(file_response.id, config)
+
+        # Wait for completion
+        return await self.await_for_completion(job.id)
+
+    async def aclose(self) -> None:
+        """Close all HTTP clients (async)"""
+        if self._async_client:
+            await self._async_client.aclose()
+
+    async def __aenter__(self) -> "LlamaSheets":
+        return self
+
+    async def __aexit__(self, _exc_type, _exc_val, _exc_tb) -> None:  # type: ignore
+        await self.aclose()

py/llama_cloud_services/beta/sheets/types.py

@@ -0,0 +1,156 @@
+from datetime import datetime
+from enum import Enum
+
+from pydantic import BaseModel, ConfigDict, Field, field_validator
+
+
+class SpreadsheetResultType(str, Enum):
+    TABLE = "table"
+    EXTRA = "extra"
+    CELL_METADATA = "cell_metadata"
+
+    def __str__(self) -> str:
+        return self.value
+
+
+class ExtractedRegionSummary(BaseModel):
+    """A summary of a single extracted region from a spreadsheet"""
+
+    region_id: str = Field(
+        ...,
+        description="Unique identifier for this region within the file",
+    )
+    sheet_name: str = Field(..., description="Worksheet name where region was found")
+    location: str = Field(..., description="Location of the region in the spreadsheet")
+    title: str | None = Field(None, description="Generated title for the region")
+    description: str | None = Field(
+        None, description="Generated description of the region"
+    )
+    region_type: SpreadsheetResultType = Field(
+        ..., description="Type of the extracted region"
+    )
+
+
+class WorksheetMetadata(BaseModel):
+    """Metadata about a worksheet in a spreadsheet"""
+
+    sheet_name: str = Field(..., description="Name of the worksheet")
+    title: str | None = Field(None, description="Generated title for the worksheet")
+    description: str | None = Field(
+        None, description="Generated description of the worksheet"
+    )
+
+
+class SpreadsheetParseResult(BaseModel):
+    """Result of parsing a single spreadsheet file"""
+
+    success: bool = Field(..., description="Whether parsing was successful")
+    file_name: str = Field(..., description="Original filename")
+
+    regions: list[ExtractedRegionSummary] = Field(
+        default_factory=list, description="All successfully extracted regions"
+    )
+    worksheet_metadata: list[WorksheetMetadata] = Field(
+        default_factory=list, description="Metadata for each processed worksheet"
+    )
+
+    # Error information
+    errors: list[str] = Field(
+        default_factory=list, description="Any errors encountered during parsing"
+    )
+
+
+class SpreadsheetParsingConfig(BaseModel):
+    """Configuration for spreadsheet parsing and region extraction"""
+
+    model_config = ConfigDict(extra="forbid")
+
+    sheet_names: list[str] | None = Field(
+        default=None,
+        description="The names of the sheets to extract regions from. If empty, the default sheet is extracted.",
+    )
+    include_hidden_cells: bool = Field(
+        default=True,
+        description="Whether to include hidden cells when extracting regions from the spreadsheet.",
+    )
+    extraction_range: str | None = Field(
+        default=None,
+        description="A1 notation of the range to extract a single region from. If None, the entire sheet is used.",
+    )
+    generate_additional_metadata: bool = Field(
+        default=True,
+        description="Whether to generate additional metadata (title, description) for each extracted region.",
+    )
+    use_experimental_processing: bool = Field(
+        default=False,
+        description="Enables experimental processing. Accuracy may be impacted.",
+    )
+
+
+class SpreadsheetJob(BaseModel):
+    """A spreadsheet parsing job"""
+
+    id: str = Field(..., description="The ID of the job")
+    user_id: str = Field(..., description="The ID of the user")
+    project_id: str = Field(..., description="The ID of the project")
+    file: dict = Field(..., description="The file object being parsed")
+    config: SpreadsheetParsingConfig = Field(
+        ..., description="Configuration for the parsing job"
+    )
+    status: str = Field(..., description="The status of the parsing job")
+    created_at: str = Field(..., description="When the job was created")
+    updated_at: str = Field(..., description="When the job was last updated")
+
+    @field_validator("created_at", "updated_at", mode="before")
+    def validate_dates(cls, v: str) -> str:
+        """Validate that the dates are in the correct format"""
+        if isinstance(v, datetime):
+            return v.isoformat()
+        else:
+            return v
+
+
+class SpreadsheetJobResult(SpreadsheetJob):
+    """A spreadsheet parsing job result."""
+
+    # Results are included when the job is complete
+    success: bool | None = Field(
+        None, description="Whether the job completed successfully"
+    )
+    regions: list[ExtractedRegionSummary] = Field(
+        default_factory=list,
+        description="All extracted regions (populated when job is complete)",
+    )
+    worksheet_metadata: list[WorksheetMetadata] = Field(
+        default_factory=list,
+        description="Metadata for each processed worksheet (populated when job is complete)",
+    )
+    errors: list[str] = Field(
+        default_factory=list, description="Any errors encountered"
+    )
+
+
+class JobStatus(str, Enum):
+    """Status of a spreadsheet parsing job"""
+
+    PENDING = "PENDING"
+    IN_PROGRESS = "IN_PROGRESS"
+    SUCCESS = "SUCCESS"
+    PARTIAL_SUCCESS = "PARTIAL_SUCCESS"
+    ERROR = "ERROR"
+    FAILURE = "FAILURE"
+
+
+class PresignedUrlResponse(BaseModel):
+    """Response containing a presigned URL for downloading results"""
+
+    url: str = Field(..., description="The presigned URL for downloading")
+
+
+class FileUploadResponse(BaseModel):
+    """Response from uploading a file"""
+
+    id: str = Field(..., description="The ID of the uploaded file")
+    name: str = Field(..., description="The name of the file")
+    project_id: str = Field(..., description="The project ID")
+    user_id: str = Field(..., description="The user ID")

py/llama_cloud_services/constants.py

@@ -1,2 +1,3 @@
+BASE_URL = "https://api.cloud.llamaindex.ai"
 EU_BASE_URL = "https://api.cloud.eu.llamaindex.ai"
 POLLING_TIMEOUT_SECONDS = 300.0

py/llama_cloud_services/files/client.py

@@ -11,7 +11,7 @@ from llama_cloud_services.utils import SourceText, FileInput
 class FileClient:
     """
     Higher-level client for interacting with the LlamaCloud Files API.
-    Uses presigned URLs for uploads by default.
+    Optionally uses presigned URLs for uploads.
 
     Args:
         client: The LlamaCloud client to use.
@@ -25,7 +25,7 @@ class FileClient:
         client: AsyncLlamaCloud,
         project_id: Optional[str] = None,
         organization_id: Optional[str] = None,
-        use_presigned_url: bool = True,
+        use_presigned_url: bool = False,
     ):
         self.client = client
         self.project_id = project_id

py/llama_cloud_services/index/api_utils.py

@@ -258,6 +258,7 @@ def page_screenshot_nodes_to_node_with_score(
     client: LlamaCloud,
     raw_image_nodes: Optional[List[PageScreenshotNodeWithScore]],
     project_id: str,
+    metadata: Optional[dict] = None,
 ) -> List[NodeWithScore]:
     if not raw_image_nodes:
         return []
@@ -273,6 +274,7 @@ def page_screenshot_nodes_to_node_with_score(
         image_base64 = base64.b64encode(image_bytes).decode("utf-8")
         image_node_metadata: Dict[str, Any] = {
             **(raw_image_node.node.metadata or {}),
+            **(metadata or {}),
             "file_id": raw_image_node.node.file_id,
             "page_index": raw_image_node.node.page_index,
         }
@@ -289,6 +291,7 @@ def image_nodes_to_node_with_score(
     client: LlamaCloud,
     raw_image_nodes: Optional[List[PageScreenshotNodeWithScore]],
     project_id: str,
+    metadata: Optional[dict] = None,
 ) -> List[NodeWithScore]:
     """
     Legacy method to alias page_screenshot_nodes_to_node_with_score.
@@ -297,7 +300,10 @@ def image_nodes_to_node_with_score(
         return []
 
     return page_screenshot_nodes_to_node_with_score(
-        client=client, raw_image_nodes=raw_image_nodes, project_id=project_id
+        client=client,
+        raw_image_nodes=raw_image_nodes,
+        project_id=project_id,
+        metadata=metadata,
     )
 
 
@@ -305,6 +311,7 @@ def page_figure_nodes_to_node_with_score(
     client: LlamaCloud,
     raw_figure_nodes: Optional[List[PageFigureNodeWithScore]],
     project_id: str,
+    metadata: Optional[dict] = None,
 ) -> List[NodeWithScore]:
     if not raw_figure_nodes:
         return []
@@ -321,6 +328,7 @@ def page_figure_nodes_to_node_with_score(
         figure_base64 = base64.b64encode(figure_bytes).decode("utf-8")
         figure_node_metadata: Dict[str, Any] = {
             **(raw_figure_node.node.metadata or {}),
+            **(metadata or {}),
             "file_id": raw_figure_node.node.file_id,
             "page_index": raw_figure_node.node.page_index,
             "figure_name": raw_figure_node.node.figure_name,
@@ -337,6 +345,7 @@ async def apage_screenshot_nodes_to_node_with_score(
     client: AsyncLlamaCloud,
     raw_image_nodes: Optional[List[PageScreenshotNodeWithScore]],
     project_id: str,
+    metadata: Optional[dict] = None,
 ) -> List[NodeWithScore]:
     if not raw_image_nodes:
         return []
@@ -357,6 +366,7 @@ async def apage_screenshot_nodes_to_node_with_score(
         image_base64 = base64.b64encode(image_bytes).decode("utf-8")
         image_node_metadata: Dict[str, Any] = {
             **(raw_image_node.node.metadata or {}),
+            **(metadata or {}),
             "file_id": raw_image_node.node.file_id,
             "page_index": raw_image_node.node.page_index,
         }
@@ -372,6 +382,7 @@ async def aimage_nodes_to_node_with_score(
     client: AsyncLlamaCloud,
     raw_image_nodes: Optional[List[PageScreenshotNodeWithScore]],
     project_id: str,
+    metadata: Optional[dict] = None,
 ) -> List[NodeWithScore]:
     """
     Legacy method to alias apage_screenshot_nodes_to_node_with_score.
@@ -380,7 +391,10 @@ async def aimage_nodes_to_node_with_score(
         return []
 
     return await apage_screenshot_nodes_to_node_with_score(
-        client=client, raw_image_nodes=raw_image_nodes, project_id=project_id
+        client=client,
+        raw_image_nodes=raw_image_nodes,
+        project_id=project_id,
+        metadata=metadata,
     )
 
 
@@ -388,6 +402,7 @@ async def apage_figure_nodes_to_node_with_score(
     client: AsyncLlamaCloud,
     raw_figure_nodes: Optional[List[PageFigureNodeWithScore]],
     project_id: str,
+    metadata: Optional[dict] = None,
 ) -> List[NodeWithScore]:
     if not raw_figure_nodes:
         return []
@@ -409,6 +424,7 @@ async def apage_figure_nodes_to_node_with_score(
         figure_base64 = base64.b64encode(figure_bytes).decode("utf-8")
         figure_node_metadata: Dict[str, Any] = {
             **(raw_figure_node.node.metadata or {}),
+            **(metadata or {}),
             "file_id": raw_figure_node.node.file_id,
             "page_index": raw_figure_node.node.page_index,
             "figure_name": raw_figure_node.node.figure_name,

py/llama_cloud_services/index/base.py

@@ -19,6 +19,7 @@ from llama_cloud import (
     PipelineCreate,
     PipelineCreateEmbeddingConfig,
     PipelineCreateTransformConfig,
+    PipelineFileCreateCustomMetadataValue,
     PipelineType,
     ProjectCreate,
     ManagedIngestionStatus,
@@ -905,6 +906,9 @@ class LlamaCloudIndex(BaseManagedIndex):
     def upload_file(
         self,
         file_path: str,
+        custom_metadata: Optional[
+            dict[str, Optional[PipelineFileCreateCustomMetadataValue]]
+        ] = None,
         verbose: bool = False,
         wait_for_ingestion: bool = True,
         raise_on_error: bool = False,
@@ -918,7 +922,9 @@ class LlamaCloudIndex(BaseManagedIndex):
                 print(f"Uploaded file {file.id} with name {file.name}")
 
         # Add file to pipeline
-        pipeline_file_create = PipelineFileCreate(file_id=file.id)
+        pipeline_file_create = PipelineFileCreate(
+            file_id=file.id, custom_metadata=custom_metadata
+        )
         self._client.pipeline_files.add_files_to_pipeline_api(
             pipeline_id=self.pipeline.id, request=[pipeline_file_create]
         )
@@ -932,6 +938,9 @@ class LlamaCloudIndex(BaseManagedIndex):
     async def aupload_file(
         self,
         file_path: str,
+        custom_metadata: Optional[
+            dict[str, Optional[PipelineFileCreateCustomMetadataValue]]
+        ] = None,
         verbose: bool = False,
         wait_for_ingestion: bool = True,
         raise_on_error: bool = False,
@@ -945,7 +954,9 @@ class LlamaCloudIndex(BaseManagedIndex):
                 print(f"Uploaded file {file.id} with name {file.name}")
 
         # Add file to pipeline
-        pipeline_file_create = PipelineFileCreate(file_id=file.id)
+        pipeline_file_create = PipelineFileCreate(
+            file_id=file.id, custom_metadata=custom_metadata
+        )
         await self._aclient.pipeline_files.add_files_to_pipeline_api(
             pipeline_id=self.pipeline.id, request=[pipeline_file_create]
         )
@@ -961,6 +972,9 @@ class LlamaCloudIndex(BaseManagedIndex):
         self,
         file_name: str,
         url: str,
+        custom_metadata: Optional[
+            dict[str, Optional[PipelineFileCreateCustomMetadataValue]]
+        ] = None,
         proxy_url: Optional[str] = None,
         request_headers: Optional[Dict[str, str]] = None,
         verify_ssl: bool = True,
@@ -983,7 +997,9 @@ class LlamaCloudIndex(BaseManagedIndex):
             print(f"Uploaded file {file.id} with ID {file.id}")
 
         # Add file to pipeline
-        pipeline_file_create = PipelineFileCreate(file_id=file.id)
+        pipeline_file_create = PipelineFileCreate(
+            file_id=file.id, custom_metadata=custom_metadata
+        )
         self._client.pipeline_files.add_files_to_pipeline_api(
             pipeline_id=self.pipeline.id, request=[pipeline_file_create]
         )
@@ -998,6 +1014,9 @@ class LlamaCloudIndex(BaseManagedIndex):
         self,
         file_name: str,
         url: str,
+        custom_metadata: Optional[
+            dict[str, Optional[PipelineFileCreateCustomMetadataValue]]
+        ] = None,
         proxy_url: Optional[str] = None,
         request_headers: Optional[Dict[str, str]] = None,
         verify_ssl: bool = True,
@@ -1020,7 +1039,9 @@ class LlamaCloudIndex(BaseManagedIndex):
             print(f"Uploaded file {file.id} with ID {file.id}")
 
         # Add file to pipeline
-        pipeline_file_create = PipelineFileCreate(file_id=file.id)
+        pipeline_file_create = PipelineFileCreate(
+            file_id=file.id, custom_metadata=custom_metadata
+        )
         await self._aclient.pipeline_files.add_files_to_pipeline_api(
             pipeline_id=self.pipeline.id, request=[pipeline_file_create]
         )

py/llama_cloud_services/index/retriever.py

@@ -129,11 +129,12 @@ class LlamaCloudRetriever(BaseRetriever):
         )
 
     def _result_nodes_to_node_with_score(
-        self, result_nodes: List[TextNodeWithScore]
+        self, result_nodes: List[TextNodeWithScore], metadata: Optional[dict] = None
     ) -> List[NodeWithScore]:
         nodes = []
         for res in result_nodes:
-            text_node = TextNode.parse_obj(res.node.dict())
+            text_node = TextNode.model_validate(res.node.dict())
+            text_node.metadata.update(metadata or {})
             nodes.append(NodeWithScore(node=text_node, score=res.score))
 
         return nodes
@@ -161,17 +162,25 @@ class LlamaCloudRetriever(BaseRetriever):
             search_filters_inference_schema=search_filters_inference_schema,
         )
 
-        result_nodes = self._result_nodes_to_node_with_score(results.retrieval_nodes)
+        result_nodes = self._result_nodes_to_node_with_score(
+            results.retrieval_nodes, metadata=results.metadata
+        )
         if self._retrieve_page_screenshot_nodes:
             result_nodes.extend(
                 page_screenshot_nodes_to_node_with_score(
-                    self._client, results.image_nodes, self.project.id
+                    self._client,
+                    results.image_nodes,
+                    self.project.id,
+                    metadata=results.metadata,
                 )
             )
         if self._retrieve_page_figure_nodes:
             result_nodes.extend(
                 page_figure_nodes_to_node_with_score(
-                    self._client, results.page_figure_nodes, self.project.id
+                    self._client,
+                    results.page_figure_nodes,
+                    self.project.id,
+                    metadata=results.metadata,
                 )
             )
 
@@ -200,17 +209,25 @@ class LlamaCloudRetriever(BaseRetriever):
             search_filters_inference_schema=search_filters_inference_schema,
         )
 
-        result_nodes = self._result_nodes_to_node_with_score(results.retrieval_nodes)
+        result_nodes = self._result_nodes_to_node_with_score(
+            results.retrieval_nodes, metadata=results.metadata
+        )
         if self._retrieve_page_screenshot_nodes:
             result_nodes.extend(
                 await apage_screenshot_nodes_to_node_with_score(
-                    self._aclient, results.image_nodes, self.project.id
+                    self._aclient,
+                    results.image_nodes,
+                    self.project.id,
+                    metadata=results.metadata,
                 )
             )
         if self._retrieve_page_figure_nodes:
             result_nodes.extend(
                 await apage_figure_nodes_to_node_with_score(
-                    self._aclient, results.page_figure_nodes, self.project.id
+                    self._aclient,
+                    results.page_figure_nodes,
+                    self.project.id,
+                    metadata=results.metadata,
                 )
             )
 

py/llama_cloud_services/parse/base.py

@@ -285,7 +285,7 @@ class LlamaParse(BasePydanticReader):
         description="Note: Non compatible with gpt-4o. If set to true, the parser will use a faster mode to extract text from documents. This mode will skip OCR of images, and table/heading reconstruction.",
     )
 
-    guess_xlsx_sheet_names: Optional[bool] = Field(
+    guess_xlsx_sheet_name: Optional[bool] = Field(
         default=False,
         description="Whether to guess the sheet names of the xlsx file.",
     )
@@ -313,6 +313,10 @@ class LlamaParse(BasePydanticReader):
         default=False,
         description="If set to true, the parser will ignore document elements for layout detection and only rely on a vision model.",
     )
+    inline_images_in_markdown: Optional[bool] = Field(
+        default=False,
+        description="If set to true, the parser will inline images in the markdown output.",
+    )
     input_s3_region: Optional[str] = Field(
         default=None,
         description="The region of the input S3 bucket if input_s3_path is specified.",
@@ -329,6 +333,10 @@ class LlamaParse(BasePydanticReader):
         default=None,
         description="The maximum timeout in seconds to wait for the parsing to finish. Override default timeout of 30 minutes. Minimum is 120 seconds.",
     )
+    keep_page_separator_when_merging_tables: Optional[bool] = Field(
+        default=False,
+        description="If set to true, the parser will keep the page separator when merging tables across pages.",
+    )
     language: Optional[str] = Field(
         default="en", description="The language of the text to parse."
     )
@@ -400,6 +408,10 @@ class LlamaParse(BasePydanticReader):
         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).",
     )
+    presentation_out_of_bounds_content: Optional[bool] = Field(
+        default=False,
+        description="If set to true, the parser will include out-of-bounds content in presentation files.",
+    )
     precise_bounding_box: Optional[bool] = Field(
         default=False,
         description="If set to true, the parser will use a more precise bounding box to extract text from documents. This will increase the accuracy of the parsing job, but reduce the speed.",
@@ -416,6 +428,14 @@ class LlamaParse(BasePydanticReader):
         default=None,
         description="A suffix to add after error message in failed pages. If not set, no suffix will be used.",
     )
+    remove_hidden_text: Optional[bool] = Field(
+        default=False,
+        description="If set to true, the parser will remove hidden text from the document.",
+    )
+    save_images: Optional[bool] = Field(
+        default=True,
+        description="If set to true, the parser will save images extracted from the document.",
+    )
     skip_diagonal_text: Optional[bool] = Field(
         default=False,
         description="If set to true, the parser will ignore diagonal text (when the text rotation in degrees modulo 90 is not 0).",
@@ -440,6 +460,10 @@ class LlamaParse(BasePydanticReader):
         default=False,
         description="If set to true, the parser will use a specialized one-shot chart parsing model to extract data from charts. This model is able to understand the chart type and extract the data accordingly. It is more accurate than the efficient model, but also more expensive.",
     )
+    specialized_image_parsing: Optional[bool] = Field(
+        default=False,
+        description="If set to true, the parser will use a specialized image parsing model to extract data from images.",
+    )
     strict_mode_buggy_font: Optional[bool] = Field(
         default=False,
         description="If set to true, the parser will fail if it can't extract text from a document because of a buggy font.",
@@ -536,6 +560,10 @@ class LlamaParse(BasePydanticReader):
         default=None,
         description="A prefix to add to the page footer in the output markdown.",
     )
+    extract_printed_page_number: Optional[bool] = Field(
+        default=None,
+        description="Whether to extract the printed page numbers from pages in the document.",
+    )
 
     # Deprecated
     bounding_box: Optional[str] = Field(
@@ -580,6 +608,23 @@ class LlamaParse(BasePydanticReader):
         description="Automatically check for Python SDK updates.",
     )
 
+    @model_validator(mode="before")
+    @classmethod
+    def handle_deprecated_params(cls, data: Dict[str, Any]) -> Dict[str, Any]:
+        # Handle deprecated guess_xlsx_sheet_names -> guess_xlsx_sheet_name
+        if "guess_xlsx_sheet_names" in data:
+            warnings.warn(
+                "The parameter 'guess_xlsx_sheet_names' is deprecated and will be removed in a future release. "
+                "Use 'guess_xlsx_sheet_name' instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            # Only set the new parameter if it's not already explicitly set
+            if "guess_xlsx_sheet_name" not in data:
+                data["guess_xlsx_sheet_name"] = data["guess_xlsx_sheet_names"]
+            del data["guess_xlsx_sheet_names"]
+        return data
+
     @model_validator(mode="before")
     @classmethod
     def warn_extra_params(cls, data: Dict[str, Any]) -> Dict[str, Any]:
@@ -820,8 +865,8 @@ class LlamaParse(BasePydanticReader):
             )
             data["formatting_instruction"] = self.formatting_instruction
 
-        if self.guess_xlsx_sheet_names:
-            data["guess_xlsx_sheet_names"] = self.guess_xlsx_sheet_names
+        if self.guess_xlsx_sheet_name:
+            data["guess_xlsx_sheet_name"] = self.guess_xlsx_sheet_name
 
         if self.html_make_all_elements_visible:
             data["html_make_all_elements_visible"] = self.html_make_all_elements_visible
@@ -845,6 +890,9 @@ class LlamaParse(BasePydanticReader):
                 "ignore_document_elements_for_layout_detection"
             ] = self.ignore_document_elements_for_layout_detection
 
+        if self.inline_images_in_markdown:
+            data["inline_images_in_markdown"] = self.inline_images_in_markdown
+
         if input_url is not None:
             files = None
             data["input_url"] = str(input_url)
@@ -873,6 +921,11 @@ class LlamaParse(BasePydanticReader):
         if self.job_timeout_in_seconds is not None:
             data["job_timeout_in_seconds"] = self.job_timeout_in_seconds
 
+        if self.keep_page_separator_when_merging_tables:
+            data[
+                "keep_page_separator_when_merging_tables"
+            ] = self.keep_page_separator_when_merging_tables
+
         if self.language:
             data["language"] = self.language
 
@@ -951,6 +1004,11 @@ class LlamaParse(BasePydanticReader):
         if self.preserve_very_small_text:
             data["preserve_very_small_text"] = self.preserve_very_small_text
 
+        if self.presentation_out_of_bounds_content:
+            data[
+                "presentation_out_of_bounds_content"
+            ] = self.presentation_out_of_bounds_content
+
         if self.preset is not None:
             data["preset"] = self.preset
 
@@ -970,6 +1028,11 @@ class LlamaParse(BasePydanticReader):
                 "replace_failed_page_with_error_message_suffix"
             ] = self.replace_failed_page_with_error_message_suffix
 
+        if self.remove_hidden_text:
+            data["remove_hidden_text"] = self.remove_hidden_text
+
+        data["save_images"] = self.save_images
+
         if self.skip_diagonal_text:
             data["skip_diagonal_text"] = self.skip_diagonal_text
 
@@ -994,6 +1057,9 @@ class LlamaParse(BasePydanticReader):
         if self.specialized_chart_parsing_plus:
             data["specialized_chart_parsing_plus"] = self.specialized_chart_parsing_plus
 
+        if self.specialized_image_parsing:
+            data["specialized_image_parsing"] = self.specialized_image_parsing
+
         if self.strict_mode_buggy_font:
             data["strict_mode_buggy_font"] = self.strict_mode_buggy_font
 
@@ -1049,6 +1115,9 @@ class LlamaParse(BasePydanticReader):
                 "markdown_table_multiline_header_separator"
             ] = self.markdown_table_multiline_header_separator
 
+        if self.extract_printed_page_number is not None:
+            data["extract_printed_page_number"] = self.extract_printed_page_number
+
         # Deprecated
         if self.bounding_box is not None:
             data["bounding_box"] = self.bounding_box

py/llama_cloud_services/parse/types.py

@@ -250,6 +250,19 @@ class Page(SafeBaseModel):
     slideSpeakerNotes: Optional[str] = Field(
         default=None, description="The speaker notes for the slide."
     )
+    confidence: Optional[float] = Field(
+        default=None, description="The confidence of the page parsing."
+    )
+    printedPageNumber: Optional[str] = Field(
+        default=None,
+        description="The printed page number on the page, if found and extractPrintedPageNumber is set to true.",
+    )
+    pageHeaderMarkdown: Optional[str] = Field(
+        default=None, description="The page header in markdown format."
+    )
+    pageFooterMarkdown: Optional[str] = Field(
+        default=None, description="The page footer in markdown format."
+    )
 
 
 class JobResult(SafeBaseModel):

py/llama_parse/CHANGELOG.md

@@ -1,5 +1,33 @@
 # llama_parse
 
+## 0.6.83
+
+### Patch Changes
+
+- Updated dependencies [ca78113]
+  - llama-cloud-services-py@0.6.83
+
+## 0.6.82
+
+### Patch Changes
+
+- Updated dependencies [bfaec79]
+  - llama-cloud-services-py@0.6.82
+
+## 0.6.81
+
+### Patch Changes
+
+- Updated dependencies [f3233de]
+  - llama-cloud-services-py@0.6.81
+
+## 0.6.80
+
+### Patch Changes
+
+- Updated dependencies [0506c88]
+  - llama-cloud-services-py@0.6.80
+
 ## 0.6.79
 
 ### Patch Changes

py/llama_parse/package.json

@@ -1,6 +1,6 @@
 {
   "name": "llama_parse",
-  "version": "0.6.79",
+  "version": "0.6.83",
   "description": "",
   "main": "index.js",
   "private": false,

py/llama_parse/pyproject.toml

@@ -11,13 +11,13 @@ dev = [
 
 [project]
 name = "llama-parse"
-version = "0.6.79"
+version = "0.6.83"
 description = "Parse files into RAG-Optimized formats."
 authors = [{name = "Logan Markewich", email = "logan@llamaindex.ai"}]
 requires-python = ">=3.9,<4.0"
 readme = "README.md"
 license = "MIT"
-dependencies = ["llama-cloud-services>=0.6.79"]
+dependencies = ["llama-cloud-services>=0.6.83"]
 
 [project.scripts]
 llama-parse = "llama_parse.cli.main:parse"

py/package.json

@@ -1,6 +1,6 @@
 {
   "name": "llama-cloud-services-py",
-  "version": "0.6.79",
+  "version": "0.6.83",
   "private": false,
   "license": "MIT",
   "scripts": {},

py/pyproject.toml

@@ -7,6 +7,7 @@ dev = [
   "pytest>=8.0.0,<9",
   "pytest-xdist>=3.6.1,<4",
   "pytest-asyncio",
+  "pytest-timeout>=2.3.1",
   "ipykernel>=6.29.0,<7",
   "pre-commit==3.2.0",
   "autoevals>=0.0.114,<0.0.115",
@@ -14,12 +15,15 @@ dev = [
   "ipython>=8.12.3,<9",
   "jupyter>=1.1.1,<2",
   "mypy>=1.14.1,<2",
-  "pydantic-settings>=2.10.1"
+  "pydantic-settings>=2.10.1",
+  "pandas",
+  "openpyxl",
+  "pyarrow"
 ]
 
 [project]
 name = "llama-cloud-services"
-version = "0.6.79"
+version = "0.6.83"
 description = "Tailored SDK clients for LlamaCloud services."
 authors = [{name = "Logan Markewich", email = "logan@runllama.ai"}]
 requires-python = ">=3.9,<4.0"

py/tests/beta/sheets/test_sheets.py

@@ -0,0 +1,171 @@
+import os
+import tempfile
+import pytest
+import pandas as pd
+
+from llama_cloud_services.beta.sheets import LlamaSheets
+from llama_cloud_services.beta.sheets.types import SpreadsheetParsingConfig
+
+
+@pytest.fixture
+def sheets_client():
+    """Create a LlamaSheets client for testing."""
+    api_key = os.getenv(
+        "LLAMA_CLOUD_API_KEY", "llx-3AEorIw5v0lnJPzEOI9xSl0N8yFx3fguw0Zn8QJHzGWmwg5r"
+    )
+    base_url = os.getenv("LLAMA_CLOUD_BASE_URL", "https://api.staging.llamaindex.ai")
+
+    client = LlamaSheets(
+        api_key=api_key,
+        base_url=base_url,
+        max_timeout=300,
+        poll_interval=2,
+    )
+    return client
+
+
+@pytest.fixture
+def sample_excel_file():
+    """Create a temporary Excel file with sample data."""
+    # Create a simple dataframe with various data types
+    data = {
+        "Name": ["Alice", "Bob", "Charlie", "David", "Eve"],
+        "Age": [25, 30, 35, 40, 45],
+        "City": ["New York", "Los Angeles", "Chicago", "Houston", "Phoenix"],
+        "Salary": [50000.50, 75000.75, 100000.00, 125000.25, 150000.50],
+    }
+    df = pd.DataFrame(data)
+
+    # Create a temporary file
+    with tempfile.NamedTemporaryFile(suffix=".xlsx", delete=False) as tmp:
+        tmp_path = tmp.name
+        df.to_excel(tmp_path, index=False, sheet_name="TestSheet")
+
+    yield tmp_path
+
+    # Cleanup
+    try:
+        os.unlink(tmp_path)
+    except Exception:
+        pass
+
+
+@pytest.mark.skipif(
+    os.environ.get(
+        "LLAMA_CLOUD_API_KEY", "llx-3AEorIw5v0lnJPzEOI9xSl0N8yFx3fguw0Zn8QJHzGWmwg5r"
+    )
+    == "",
+    reason="LLAMA_CLOUD_API_KEY not set",
+)
+@pytest.mark.asyncio
+async def test_spreadsheet_extraction_e2e(
+    sheets_client: LlamaSheets, sample_excel_file: str
+):
+    """End-to-end test for spreadsheet extraction.
+
+    This test:
+    1. Creates a temporary Excel file with sample data
+    2. Uploads and extracts tables from the file
+    3. Downloads the extracted table as a DataFrame
+    4. Verifies the extracted data matches the original data
+    """
+    # Extract tables from the spreadsheet
+    result = await sheets_client.aextract_regions(sample_excel_file)
+
+    # Verify job completed successfully
+    assert result.status in ("SUCCESS", "PARTIAL_SUCCESS")
+    assert result.success is True
+
+    # Verify we extracted at least one table
+    assert len(result.regions) > 0, "Expected at least one table to be extracted"
+
+    # Get the first table
+    first_table = result.regions[0]
+    assert first_table.sheet_name == "TestSheet"
+
+    # Download the table as a DataFrame
+    extracted_df = await sheets_client.adownload_region_as_dataframe(
+        job_id=result.id,
+        region_id=first_table.region_id,
+        result_type=first_table.region_type,
+    )
+
+    # Load the original dataframe for comparison
+    original_df = pd.read_excel(sample_excel_file)
+
+    # Verify the extracted DataFrame has the expected shape
+    assert extracted_df.shape[0] == original_df.shape[0], (
+        f"Row count mismatch: extracted {extracted_df.shape[0]}, "
+        f"original {original_df.shape[0]}"
+    )
+    assert extracted_df.shape[1] == original_df.shape[1], (
+        f"Column count mismatch: extracted {extracted_df.shape[1]}, "
+        f"original {original_df.shape[1]}"
+    )
+
+    # Verify column names match
+    assert list(extracted_df.columns) == list(original_df.columns), (
+        f"Column names mismatch: extracted {list(extracted_df.columns)}, "
+        f"original {list(original_df.columns)}"
+    )
+
+    # Verify data types are preserved (at least numerically)
+    for col in original_df.columns:
+        if original_df[col].dtype in ["int64", "float64"]:
+            assert extracted_df[col].dtype in ["int64", "float64"], (
+                f"Column {col} type mismatch: extracted {extracted_df[col].dtype}, "
+                f"original {original_df[col].dtype}"
+            )
+
+    # Verify the data values match (allowing for minor type conversions)
+    for col in original_df.columns:
+        original_values = original_df[col].tolist()
+        extracted_values = extracted_df[col].tolist()
+
+        # Convert both to strings for comparison to handle type differences
+        original_str = [str(v) for v in original_values]
+        extracted_str = [str(v) for v in extracted_values]
+
+        assert original_str == extracted_str, (
+            f"Column {col} values mismatch:\n"
+            f"Original: {original_str}\n"
+            f"Extracted: {extracted_str}"
+        )
+
+
+@pytest.mark.skipif(
+    os.environ.get(
+        "LLAMA_CLOUD_API_KEY", "llx-3AEorIw5v0lnJPzEOI9xSl0N8yFx3fguw0Zn8QJHzGWmwg5r"
+    )
+    == "",
+    reason="LLAMA_CLOUD_API_KEY not set",
+)
+@pytest.mark.asyncio
+async def test_spreadsheet_extraction_with_config(
+    sheets_client: LlamaSheets, sample_excel_file: str
+):
+    """Test spreadsheet extraction with custom configuration."""
+    # Create a config with specific settings
+    config = SpreadsheetParsingConfig(
+        sheet_names=["TestSheet"],
+        include_hidden_cells=True,
+        generate_additional_metadata=True,
+    )
+
+    # Extract tables with the config
+    result = await sheets_client.aextract_regions(sample_excel_file, config=config)
+
+    # Verify job completed successfully
+    assert result.status in ("SUCCESS", "PARTIAL_SUCCESS")
+    assert result.success is True
+
+    # Verify that additional metadata was generated
+    assert len(result.worksheet_metadata) > 0
+    assert result.worksheet_metadata[0].title is not None
+    assert result.worksheet_metadata[0].description is not None
+
+    # Verify we extracted at least one table
+    assert len(result.regions) > 0
+
+    # Verify the sheet name matches
+    assert result.regions[0].sheet_name == "TestSheet"

py/tests/index/test_index.py

@@ -170,6 +170,43 @@ def test_upload_file(index_name: str):
         os.remove(temp_file_path)
 
 
+@pytest.mark.skipif(
+    not base_url or not api_key, reason="No platform base url or api key set"
+)
+def test_upload_file_with_custom_metadata(index_name: str):
+    index = LlamaCloudIndex.create_index(
+        name=index_name,
+        project_name=project_name,
+        organization_id=organization_id,
+        api_key=api_key,
+        base_url=base_url,
+    )
+
+    # Create a temporary file to upload
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".txt") as temp_file:
+        temp_file.write(b"Sample content for testing upload.")
+        temp_file_path = temp_file.name
+    custom_metadata = {"foo": "bar"}
+
+    try:
+        # Upload the file
+        file_id = index.upload_file(
+            temp_file_path, custom_metadata=custom_metadata, verbose=True
+        )
+        assert file_id is not None
+
+        # Verify the file is part of the index
+        docs = index.ref_doc_info
+        temp_file_name = os.path.basename(temp_file_path)
+        assert any(
+            temp_file_name == doc.metadata.get("file_name") for doc in docs.values()
+        )
+
+    finally:
+        # Clean up the temporary file
+        os.remove(temp_file_path)
+
+
 @pytest.mark.skipif(
     not base_url or not api_key, reason="No platform base url or api key set"
 )
@@ -196,6 +233,38 @@ def test_upload_file_from_url(remote_file: Tuple[str, str], index_name: str):
     assert any(test_file_name == doc.metadata.get("file_name") for doc in docs.values())
 
 
+@pytest.mark.skipif(
+    not base_url or not api_key, reason="No platform base url or api key set"
+)
+def test_upload_file_from_url_with_custom_metadata(
+    remote_file: Tuple[str, str], index_name: str
+):
+    index = LlamaCloudIndex.create_index(
+        name=index_name,
+        project_name=project_name,
+        organization_id=organization_id,
+        api_key=api_key,
+        base_url=base_url,
+    )
+
+    # Define a URL to a file for testing
+    custom_metadata = {"foo": "bar"}
+    test_file_url, test_file_name = remote_file
+
+    # Upload the file from the URL
+    file_id = index.upload_file_from_url(
+        file_name=test_file_name,
+        url=test_file_url,
+        custom_metadata=custom_metadata,
+        verbose=True,
+    )
+    assert file_id is not None
+
+    # Verify the file is part of the index
+    docs = index.ref_doc_info
+    assert any(test_file_name == doc.metadata.get("file_name") for doc in docs.values())
+
+
 @pytest.mark.skipif(
     not base_url or not api_key, reason="No platform base url or api key set"
 )
@@ -507,6 +576,33 @@ async def test_async_upload_file_from_url(
     await index.await_for_completion()
 
 
+@pytest.mark.skipif(
+    not base_url or not api_key, reason="No platform base url or api key set"
+)
+@pytest.mark.asyncio
+async def test_async_upload_file_from_url_with_custom_metadata(
+    remote_file: Tuple[str, str], index_name: str
+):
+    index = await LlamaCloudIndex.acreate_index(
+        name=index_name,
+        project_name=project_name,
+        api_key=api_key,
+        base_url=base_url,
+    )
+
+    custom_metadata = {"foo": "bar"}
+    test_file_url, test_file_name = remote_file
+    file_id = await index.aupload_file_from_url(
+        file_name=test_file_name,
+        url=test_file_url,
+        custom_metadata=custom_metadata,
+        verbose=True,
+    )
+    assert file_id is not None
+
+    await index.await_for_completion()
+
+
 @pytest.mark.skipif(
     not base_url or not api_key, reason="No platform base url or api key set"
 )
@@ -525,6 +621,29 @@ async def test_async_index_from_file(index_name: str, local_file: str):
     await index.await_for_completion()
 
 
+@pytest.mark.skipif(
+    not base_url or not api_key, reason="No platform base url or api key set"
+)
+@pytest.mark.asyncio
+async def test_async_index_from_file_with_custom_metadata(
+    index_name: str, local_file: str
+):
+    index = await LlamaCloudIndex.acreate_index(
+        name=index_name,
+        project_name=project_name,
+        api_key=api_key,
+        base_url=base_url,
+    )
+
+    custom_metadata = {"foo": "bar"}
+    file_id = await index.aupload_file(
+        file_path=local_file, custom_metadata=custom_metadata, verbose=True
+    )
+    assert file_id is not None
+
+    await index.await_for_completion()
+
+
 class DummySchema(BaseModel):
     source: str
 

py/uv.lock

@@ -1596,7 +1596,7 @@ wheels = [
 
 [[package]]
 name = "llama-cloud-services"
-version = "0.6.77"
+version = "0.6.79"
 source = { editable = "." }
 dependencies = [
     { name = "click", version = "8.1.8", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },

ts/llama_cloud_services/CHANGELOG.md

@@ -1,5 +1,17 @@
 # llama-cloud-services
 
+## 0.4.2
+
+### Patch Changes
+
+- bfaec79: Update for new page number params
+
+## 0.4.1
+
+### Patch Changes
+
+- f3233de: Propagate retrieval metadata to retriever nodes
+
 ## 0.4.0
 
 ### Minor Changes

ts/llama_cloud_services/package.json

@@ -1,6 +1,6 @@
 {
   "name": "llama-cloud-services",
-  "version": "0.4.0",
+  "version": "0.4.2",
   "type": "module",
   "license": "MIT",
   "scripts": {

ts/llama_cloud_services/src/LlamaCloudRetriever.ts

@@ -34,12 +34,15 @@ export class LlamaCloudRetriever extends BaseRetriever {
 
   private resultNodesToNodeWithScore(
     nodes: TextNodeWithScore[],
+    metadata: Record<string, string> | undefined,
   ): NodeWithScore[] {
     return nodes.map((node: TextNodeWithScore) => {
       const textNode = jsonToNode(node.node, ObjectType.TEXT);
+      const extra_metadata = metadata || {};
       textNode.metadata = {
         ...textNode.metadata,
         ...node.node.extra_info, // append LlamaCloud extra_info to node metadata (file_name, pipeline_id, etc.)
+        ...extra_metadata, // append retrieval-level metadata
       };
       return {
         // Currently LlamaCloud only supports text nodes
@@ -63,6 +66,7 @@ export class LlamaCloudRetriever extends BaseRetriever {
   private async pageScreenshotNodesToNodeWithScore(
     nodes: PageScreenshotNodeWithScore[] | undefined,
     projectId: string,
+    metadata: Record<string, string> | undefined,
   ): Promise<NodeWithScore[]> {
     if (!nodes || nodes.length === 0) return [];
 
@@ -87,6 +91,7 @@ export class LlamaCloudRetriever extends BaseRetriever {
           image: base64,
           metadata: {
             ...(n.node.metadata ?? {}),
+            ...(metadata || {}),
             file_id: n.node.file_id,
             page_index: n.node.page_index,
           },
@@ -101,6 +106,7 @@ export class LlamaCloudRetriever extends BaseRetriever {
   private async pageFigureNodesToNodeWithScore(
     nodes: PageFigureNodeWithScore[] | undefined,
     projectId: string,
+    metadata: Record<string, string> | undefined,
   ): Promise<NodeWithScore[]> {
     if (!nodes || nodes.length === 0) return [];
 
@@ -126,6 +132,7 @@ export class LlamaCloudRetriever extends BaseRetriever {
           image: base64,
           metadata: {
             ...(n.node.metadata ?? {}),
+            ...(metadata || {}),
             file_id: n.node.file_id,
             page_index: n.node.page_index,
             figure_name: n.node.figure_name,
@@ -222,7 +229,10 @@ export class LlamaCloudRetriever extends BaseRetriever {
         },
       });
 
-    const textNodes = this.resultNodesToNodeWithScore(results.retrieval_nodes);
+    const textNodes = this.resultNodesToNodeWithScore(
+      results.retrieval_nodes,
+      results.metadata,
+    );
 
     const needScreenshots = (this.retrieveParams as RetrievalParams)
       .retrieve_page_screenshot_nodes;
@@ -240,12 +250,14 @@ export class LlamaCloudRetriever extends BaseRetriever {
         ? this.pageScreenshotNodesToNodeWithScore(
             results.image_nodes,
             projectId,
+            results.metadata,
           )
         : Promise.resolve([] as NodeWithScore[]),
       needFigures
         ? this.pageFigureNodesToNodeWithScore(
             results.page_figure_nodes,
             projectId,
+            results.metadata,
           )
         : Promise.resolve([] as NodeWithScore[]),
     ]);

ts/llama_cloud_services/src/reader.ts

@@ -185,6 +185,7 @@ export class LlamaParseReader extends FileReader {
   page_footer_prefix?: string | undefined;
   page_footer_suffix?: string | undefined;
   merge_tables_across_pages_in_markdown?: boolean | undefined;
+  extract_printed_page_number?: boolean | undefined;
 
   constructor(
     params: Partial<Omit<LlamaParseReader, "language" | "apiKey">> & {
@@ -381,6 +382,7 @@ export class LlamaParseReader extends FileReader {
       page_footer_suffix: this.page_footer_suffix,
       merge_tables_across_pages_in_markdown:
         this.merge_tables_across_pages_in_markdown,
+      extract_printed_page_number: this.extract_printed_page_number,
     } satisfies {
       [Key in keyof BodyUploadFileApiParsingUploadPost]-?:
         | BodyUploadFileApiParsingUploadPost[Key]