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6 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Logan Markewich
|
92380987b6 | ||
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Logan Markewich
|
44bf80346c | ||
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Logan Markewich
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4273e27c48 | ||
|
Logan Markewich
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3b9d63e019 | ||
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Logan Markewich
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51b4c80515 | ||
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George He
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6f96fab48e |
@@ -1 +0,0 @@
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sample_files/
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@@ -1,807 +0,0 @@
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||||
{
|
||||
"cells": [
|
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{
|
||||
"cell_type": "markdown",
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||||
"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",
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||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%pip install llama-cloud python-dotenv"
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||||
]
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||||
},
|
||||
{
|
||||
"cell_type": "code",
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||||
"execution_count": null,
|
||||
"id": "cell-3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"from dotenv import load_dotenv\n",
|
||||
"import httpx\n",
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"\n",
|
||||
"# Load environment variables\n",
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"load_dotenv()\n",
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||||
"\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",
|
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"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",
|
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"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",
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||||
"response = httpx.post(\n",
|
||||
" f\"{LLAMA_CLOUD_BASE_URL}/api/v1/beta/directories\",\n",
|
||||
" headers=headers,\n",
|
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" 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
|
||||
}
|
||||
@@ -1,29 +1,5 @@
|
||||
# llama-cloud-services-py
|
||||
|
||||
## 0.6.86
|
||||
|
||||
### Patch Changes
|
||||
|
||||
- 1b7198d: Update extract to have confidence scores available in all modes
|
||||
|
||||
## 0.6.85
|
||||
|
||||
### Patch Changes
|
||||
|
||||
- ae30990: Add line-level bbox support
|
||||
|
||||
## 0.6.84
|
||||
|
||||
### Patch Changes
|
||||
|
||||
- 0a110de: Release to re-align versions
|
||||
|
||||
## 0.6.83
|
||||
|
||||
### Patch Changes
|
||||
|
||||
- ca78113: Do not use presigned URLs by default in files client
|
||||
|
||||
## 0.6.82
|
||||
|
||||
### Patch Changes
|
||||
|
||||
@@ -2,7 +2,7 @@ import asyncio
|
||||
import io
|
||||
import os
|
||||
import time
|
||||
from typing import TYPE_CHECKING
|
||||
from typing import Any, Dict, TYPE_CHECKING
|
||||
|
||||
import httpx
|
||||
from llama_cloud.client import AsyncLlamaCloud
|
||||
@@ -68,6 +68,8 @@ class LlamaSheets:
|
||||
max_timeout: int = 300,
|
||||
poll_interval: int = 5,
|
||||
max_retries: int = 3,
|
||||
project_id: str | None = None,
|
||||
organization_id: str | None = None,
|
||||
async_httpx_client: httpx.AsyncClient | None = None,
|
||||
) -> None:
|
||||
"""Initialize the LlamaSheets client.
|
||||
@@ -78,6 +80,8 @@ class LlamaSheets:
|
||||
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
|
||||
project_id: Project ID for file operations. If not provided, will use LLAMA_CLOUD_PROJECT_ID env var
|
||||
organization_id: Organization ID for file operations. If not provided, will use LLAMA_CLOUD_ORGANIZATION_ID env var
|
||||
async_httpx_client: Optional custom async httpx client
|
||||
"""
|
||||
self.api_key = api_key or os.environ.get("LLAMA_CLOUD_API_KEY")
|
||||
@@ -93,15 +97,32 @@ class LlamaSheets:
|
||||
self.poll_interval = poll_interval
|
||||
self.max_retries = max_retries
|
||||
|
||||
self.project_id = project_id or os.environ.get("LLAMA_CLOUD_PROJECT_ID")
|
||||
self.organization_id = organization_id or os.environ.get(
|
||||
"LLAMA_CLOUD_ORGANIZATION_ID"
|
||||
)
|
||||
|
||||
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,
|
||||
)
|
||||
),
|
||||
project_id=self.project_id,
|
||||
organization_id=self.organization_id,
|
||||
)
|
||||
|
||||
def _get_default_params(self) -> dict[str, str]:
|
||||
"""Get default query parameters for API requests"""
|
||||
params = {}
|
||||
if self.project_id is not None:
|
||||
params["project_id"] = self.project_id
|
||||
if self.organization_id is not None:
|
||||
params["organization_id"] = self.organization_id
|
||||
|
||||
return params
|
||||
|
||||
def _get_async_client(self) -> httpx.AsyncClient:
|
||||
"""Get or create the async httpx client"""
|
||||
if self._async_client is None:
|
||||
@@ -306,6 +327,8 @@ class LlamaSheets:
|
||||
"config": config.model_dump(mode="json", exclude_none=True),
|
||||
}
|
||||
|
||||
params = self._get_default_params()
|
||||
|
||||
try:
|
||||
async for attempt in AsyncRetrying(
|
||||
stop=stop_after_attempt(self.max_retries),
|
||||
@@ -318,6 +341,7 @@ class LlamaSheets:
|
||||
response = await client.post(
|
||||
f"{self.base_url}/api/v1/beta/sheets/jobs",
|
||||
headers=self._get_headers(),
|
||||
params=params,
|
||||
json=payload,
|
||||
)
|
||||
response.raise_for_status()
|
||||
@@ -347,12 +371,17 @@ class LlamaSheets:
|
||||
):
|
||||
with attempt:
|
||||
client = self._get_async_client()
|
||||
params: Dict[str, Any] = {
|
||||
"include_results": include_results_metadata,
|
||||
**self._get_default_params(),
|
||||
}
|
||||
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},
|
||||
params=params,
|
||||
)
|
||||
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
|
||||
@@ -415,6 +444,8 @@ class LlamaSheets:
|
||||
# Get presigned URL
|
||||
presigned_response = None
|
||||
result_type_str = str(result_type)
|
||||
params = self._get_default_params()
|
||||
|
||||
try:
|
||||
async for attempt in AsyncRetrying(
|
||||
stop=stop_after_attempt(self.max_retries),
|
||||
@@ -427,6 +458,7 @@ class LlamaSheets:
|
||||
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(),
|
||||
params=params,
|
||||
)
|
||||
response.raise_for_status()
|
||||
presigned_response = PresignedUrlResponse.model_validate(
|
||||
|
||||
@@ -240,6 +240,11 @@ def _extraction_config_warning(config: ExtractConfig) -> None:
|
||||
raise ValueError(
|
||||
"`cite_sources` is only supported with MULTIMODAL or PREMIUM extraction modes."
|
||||
)
|
||||
if config.confidence_scores:
|
||||
if config.extraction_mode in (ExtractMode.FAST, ExtractMode.BALANCED):
|
||||
raise ValueError(
|
||||
"`confidence_scores` is only supported with MULTIMODAL or PREMIUM extraction modes."
|
||||
)
|
||||
|
||||
|
||||
class ExtractionAgent:
|
||||
|
||||
@@ -11,7 +11,7 @@ from llama_cloud_services.utils import SourceText, FileInput
|
||||
class FileClient:
|
||||
"""
|
||||
Higher-level client for interacting with the LlamaCloud Files API.
|
||||
Optionally uses presigned URLs for uploads.
|
||||
Uses presigned URLs for uploads by default.
|
||||
|
||||
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 = False,
|
||||
use_presigned_url: bool = True,
|
||||
):
|
||||
self.client = client
|
||||
self.project_id = project_id
|
||||
@@ -91,10 +91,6 @@ class FileClient:
|
||||
organization_id=self.organization_id,
|
||||
)
|
||||
else:
|
||||
# Set buffer.name if not already set, so the upload uses external_file_id
|
||||
# for file type detection
|
||||
if not getattr(buffer, "name", None):
|
||||
setattr(buffer, "name", external_file_id)
|
||||
return await self.client.files.upload_file(
|
||||
upload_file=buffer,
|
||||
external_file_id=external_file_id,
|
||||
|
||||
@@ -654,6 +654,9 @@ class LlamaCloudIndex(BaseManagedIndex):
|
||||
],
|
||||
)
|
||||
|
||||
# Trigger a sync
|
||||
client.pipelines.sync_pipeline(pipeline_id=index.pipeline.id)
|
||||
|
||||
doc_ids = [doc.id for doc in upserted_documents]
|
||||
index.wait_for_completion(
|
||||
doc_ids=doc_ids, verbose=verbose, raise_on_error=raise_on_error
|
||||
@@ -738,6 +741,10 @@ class LlamaCloudIndex(BaseManagedIndex):
|
||||
)
|
||||
],
|
||||
)
|
||||
|
||||
# Trigger a sync
|
||||
self._client.pipelines.sync_pipeline(pipeline_id=self.pipeline.id)
|
||||
|
||||
upserted_document = upserted_documents[0]
|
||||
self.wait_for_completion(
|
||||
doc_ids=[upserted_document.id], verbose=verbose, raise_on_error=True
|
||||
@@ -760,6 +767,9 @@ class LlamaCloudIndex(BaseManagedIndex):
|
||||
)
|
||||
],
|
||||
)
|
||||
# Trigger a sync
|
||||
await self._aclient.pipelines.sync_pipeline(pipeline_id=self.pipeline.id)
|
||||
|
||||
upserted_document = upserted_documents[0]
|
||||
await self.await_for_completion(
|
||||
doc_ids=[upserted_document.id], verbose=verbose, raise_on_error=True
|
||||
@@ -782,6 +792,9 @@ class LlamaCloudIndex(BaseManagedIndex):
|
||||
)
|
||||
],
|
||||
)
|
||||
# Trigger a sync
|
||||
self._client.pipelines.sync_pipeline(pipeline_id=self.pipeline.id)
|
||||
|
||||
upserted_document = upserted_documents[0]
|
||||
self.wait_for_completion(
|
||||
doc_ids=[upserted_document.id], verbose=verbose, raise_on_error=True
|
||||
@@ -804,6 +817,9 @@ class LlamaCloudIndex(BaseManagedIndex):
|
||||
)
|
||||
],
|
||||
)
|
||||
# Trigger a sync
|
||||
await self._aclient.pipelines.sync_pipeline(pipeline_id=self.pipeline.id)
|
||||
|
||||
upserted_document = upserted_documents[0]
|
||||
await self.await_for_completion(
|
||||
doc_ids=[upserted_document.id], verbose=verbose, raise_on_error=True
|
||||
@@ -827,6 +843,9 @@ class LlamaCloudIndex(BaseManagedIndex):
|
||||
for doc in documents
|
||||
],
|
||||
)
|
||||
# Trigger a sync
|
||||
self._client.pipelines.sync_pipeline(pipeline_id=self.pipeline.id)
|
||||
|
||||
doc_ids = [doc.id for doc in upserted_documents]
|
||||
self.wait_for_completion(doc_ids=doc_ids, verbose=True, raise_on_error=True)
|
||||
return [True] * len(doc_ids)
|
||||
@@ -849,6 +868,9 @@ class LlamaCloudIndex(BaseManagedIndex):
|
||||
for doc in documents
|
||||
],
|
||||
)
|
||||
# Trigger a sync
|
||||
await self._aclient.pipelines.sync_pipeline(pipeline_id=self.pipeline.id)
|
||||
|
||||
doc_ids = [doc.id for doc in upserted_documents]
|
||||
await self.await_for_completion(
|
||||
doc_ids=doc_ids, verbose=True, raise_on_error=True
|
||||
|
||||
@@ -564,10 +564,6 @@ class LlamaParse(BasePydanticReader):
|
||||
default=None,
|
||||
description="Whether to extract the printed page numbers from pages in the document.",
|
||||
)
|
||||
line_level_bounding_box: Optional[bool] = Field(
|
||||
default=False,
|
||||
description="If set to true, the parser will include line-level bounding boxes in the result.",
|
||||
)
|
||||
|
||||
# Deprecated
|
||||
bounding_box: Optional[str] = Field(
|
||||
@@ -1122,9 +1118,6 @@ class LlamaParse(BasePydanticReader):
|
||||
if self.extract_printed_page_number is not None:
|
||||
data["extract_printed_page_number"] = self.extract_printed_page_number
|
||||
|
||||
if self.line_level_bounding_box is not None:
|
||||
data["line_level_bounding_box"] = self.line_level_bounding_box
|
||||
|
||||
# Deprecated
|
||||
if self.bounding_box is not None:
|
||||
data["bounding_box"] = self.bounding_box
|
||||
|
||||
@@ -115,26 +115,6 @@ class BBox(SafeBaseModel):
|
||||
)
|
||||
|
||||
|
||||
class LineLevelBboxItem(SafeBaseModel):
|
||||
"""A line-level bounding box item."""
|
||||
|
||||
md: Optional[str] = Field(
|
||||
default=None, description="The markdown-formatted content of the line."
|
||||
)
|
||||
text: Optional[str] = Field(
|
||||
default=None, description="The text content of the line."
|
||||
)
|
||||
bBox: Optional[BBox] = Field(
|
||||
default=None, description="The bounding box of the line."
|
||||
)
|
||||
startIndex: Optional[int] = Field(
|
||||
default=None, description="The start index of the line in the page text."
|
||||
)
|
||||
endIndex: Optional[int] = Field(
|
||||
default=None, description="The end index of the line in the page text."
|
||||
)
|
||||
|
||||
|
||||
class PageItem(SafeBaseModel):
|
||||
"""An item in a page."""
|
||||
|
||||
@@ -158,9 +138,6 @@ class PageItem(SafeBaseModel):
|
||||
default=None,
|
||||
description="The HTML-formatted content of the item. Only applicable for table items when output_tables_as_HTML=True.",
|
||||
)
|
||||
lines: Optional[List[LineLevelBboxItem]] = Field(
|
||||
default=None, description="The line-level bounding box items of the item."
|
||||
)
|
||||
|
||||
|
||||
class ImageItem(SafeBaseModel):
|
||||
|
||||
@@ -1,34 +1,5 @@
|
||||
# llama_parse
|
||||
|
||||
## 0.6.86
|
||||
|
||||
### Patch Changes
|
||||
|
||||
- 1b7198d: Update extract to have confidence scores available in all modes
|
||||
- Updated dependencies [1b7198d]
|
||||
- llama-cloud-services-py@0.6.86
|
||||
|
||||
## 0.6.85
|
||||
|
||||
### Patch Changes
|
||||
|
||||
- Updated dependencies [ae30990]
|
||||
- llama-cloud-services-py@0.6.85
|
||||
|
||||
## 0.6.84
|
||||
|
||||
### Patch Changes
|
||||
|
||||
- Updated dependencies [0a110de]
|
||||
- llama-cloud-services-py@0.6.84
|
||||
|
||||
## 0.6.83
|
||||
|
||||
### Patch Changes
|
||||
|
||||
- Updated dependencies [ca78113]
|
||||
- llama-cloud-services-py@0.6.83
|
||||
|
||||
## 0.6.82
|
||||
|
||||
### Patch Changes
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "llama_parse",
|
||||
"version": "0.6.86",
|
||||
"version": "0.6.82",
|
||||
"description": "",
|
||||
"main": "index.js",
|
||||
"private": false,
|
||||
|
||||
@@ -11,13 +11,13 @@ dev = [
|
||||
|
||||
[project]
|
||||
name = "llama-parse"
|
||||
version = "0.6.86"
|
||||
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>=0.6.86"]
|
||||
dependencies = ["llama-cloud-services>=0.6.82"]
|
||||
|
||||
[project.scripts]
|
||||
llama-parse = "llama_parse.cli.main:parse"
|
||||
|
||||
+1
-1
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "llama-cloud-services-py",
|
||||
"version": "0.6.86",
|
||||
"version": "0.6.82",
|
||||
"private": false,
|
||||
"license": "MIT",
|
||||
"scripts": {},
|
||||
|
||||
+2
-2
@@ -23,7 +23,7 @@ dev = [
|
||||
|
||||
[project]
|
||||
name = "llama-cloud-services"
|
||||
version = "0.6.86"
|
||||
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"
|
||||
@@ -31,7 +31,7 @@ readme = "README.md"
|
||||
license = "MIT"
|
||||
dependencies = [
|
||||
"llama-index-core>=0.12.0",
|
||||
"llama-cloud==0.1.45",
|
||||
"llama-cloud==0.1.44",
|
||||
"pydantic>=2.8,!=2.10",
|
||||
"click>=8.1.7,<9",
|
||||
"python-dotenv>=1.0.1,<2",
|
||||
|
||||
@@ -10,16 +10,16 @@ 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")
|
||||
api_key = os.getenv("LLAMA_CLOUD_API_KEY")
|
||||
base_url = os.getenv("LLAMA_CLOUD_BASE_URL", "https://api.cloud.llamaindex.ai")
|
||||
project_id = os.getenv("LLAMA_CLOUD_PROJECT_ID")
|
||||
|
||||
client = LlamaSheets(
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
max_timeout=300,
|
||||
poll_interval=2,
|
||||
project_id=project_id,
|
||||
)
|
||||
return client
|
||||
|
||||
@@ -51,10 +51,7 @@ def sample_excel_file():
|
||||
|
||||
|
||||
@pytest.mark.skipif(
|
||||
os.environ.get(
|
||||
"LLAMA_CLOUD_API_KEY", "llx-3AEorIw5v0lnJPzEOI9xSl0N8yFx3fguw0Zn8QJHzGWmwg5r"
|
||||
)
|
||||
== "",
|
||||
os.environ.get("LLAMA_CLOUD_API_KEY", "") == "",
|
||||
reason="LLAMA_CLOUD_API_KEY not set",
|
||||
)
|
||||
@pytest.mark.asyncio
|
||||
@@ -134,10 +131,7 @@ async def test_spreadsheet_extraction_e2e(
|
||||
|
||||
|
||||
@pytest.mark.skipif(
|
||||
os.environ.get(
|
||||
"LLAMA_CLOUD_API_KEY", "llx-3AEorIw5v0lnJPzEOI9xSl0N8yFx3fguw0Zn8QJHzGWmwg5r"
|
||||
)
|
||||
== "",
|
||||
os.environ.get("LLAMA_CLOUD_API_KEY", "") == "",
|
||||
reason="LLAMA_CLOUD_API_KEY not set",
|
||||
)
|
||||
@pytest.mark.asyncio
|
||||
|
||||
@@ -78,7 +78,7 @@ async def test_upload_bytes(
|
||||
uploaded_file = await file_client.upload_bytes(file_bytes, external_file_id)
|
||||
|
||||
assert isinstance(uploaded_file, File)
|
||||
expected_name = external_file_id if use_presigned_url else "upload"
|
||||
expected_name = external_file_id
|
||||
assert uploaded_file.name == expected_name
|
||||
assert uploaded_file.external_file_id == external_file_id
|
||||
|
||||
@@ -100,7 +100,7 @@ async def test_upload_buffer(
|
||||
uploaded_file = await file_client.upload_buffer(buffer, external_file_id, file_size)
|
||||
|
||||
assert isinstance(uploaded_file, File)
|
||||
expected_name = external_file_id if use_presigned_url else "upload"
|
||||
expected_name = external_file_id
|
||||
assert uploaded_file.name == expected_name
|
||||
assert uploaded_file.external_file_id == external_file_id
|
||||
|
||||
|
||||
Generated
+12
-264
@@ -1,10 +1,9 @@
|
||||
version = 1
|
||||
revision = 2
|
||||
revision = 3
|
||||
requires-python = ">=3.9, <4.0"
|
||||
resolution-markers = [
|
||||
"python_full_version >= '3.14'",
|
||||
"python_full_version >= '3.12' and python_full_version < '3.14'",
|
||||
"python_full_version == '3.11.*'",
|
||||
"python_full_version >= '3.11' and python_full_version < '3.14'",
|
||||
"python_full_version == '3.10.*'",
|
||||
"python_full_version < '3.10'",
|
||||
]
|
||||
@@ -221,8 +220,7 @@ name = "argon2-cffi-bindings"
|
||||
version = "25.1.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
resolution-markers = [
|
||||
"python_full_version >= '3.12' and python_full_version < '3.14'",
|
||||
"python_full_version == '3.11.*'",
|
||||
"python_full_version >= '3.11' and python_full_version < '3.14'",
|
||||
"python_full_version == '3.10.*'",
|
||||
"python_full_version < '3.10'",
|
||||
]
|
||||
@@ -591,8 +589,7 @@ version = "8.2.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
resolution-markers = [
|
||||
"python_full_version >= '3.14'",
|
||||
"python_full_version >= '3.12' and python_full_version < '3.14'",
|
||||
"python_full_version == '3.11.*'",
|
||||
"python_full_version >= '3.11' and python_full_version < '3.14'",
|
||||
"python_full_version == '3.10.*'",
|
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{ url = "https://files.pythonhosted.org/packages/17/69/cd203477f944c353c31bade965f880aa1061fd6bf05ded0726ca845b6ff7/typing_inspection-0.4.1-py3-none-any.whl", hash = "sha256:389055682238f53b04f7badcb49b989835495a96700ced5dab2d8feae4b26f51", size = 14552, upload-time = "2025-05-21T18:55:22.152Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tzdata"
|
||||
version = "2025.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/95/32/1a225d6164441be760d75c2c42e2780dc0873fe382da3e98a2e1e48361e5/tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9", size = 196380, upload-time = "2025-03-23T13:54:43.652Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8", size = 347839, upload-time = "2025-03-23T13:54:41.845Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "uri-template"
|
||||
version = "1.3.0"
|
||||
|
||||
Reference in New Issue
Block a user