wehub-resource-sync
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185 lines
16 KiB
Plaintext
185 lines
16 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# LlamaCloud Page Figure Retrieval\n",
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"\n",
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"This notebook shows an example of retrieving images embedded within a PDF document.\n",
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"More docs on using this feature can be found on the [LlamaCloud docs page](https://docs.cloud.llamaindex.ai/llamacloud/retrieval/images)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install llama-index llama-index-llms-openai llama-cloud-services"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Create an Index and upload the figures PDF to it"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get the LlamaCloud API Key\n",
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"import os\n",
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"\n",
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"api_key = os.environ[\"LLAMA_CLOUD_API_KEY\"]\n",
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"org_id = os.environ.get(\"LLAMA_CLOUD_ORGANIZATION_ID\")\n",
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"openai_api_key = os.environ[\"OPENAI_API_KEY\"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"/Users/sourabhdesai/workspace/llama_index/docs/examples/llama_cloud\n"
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]
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}
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],
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"source": [
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"# print cwd to see where to load PDF file from\n",
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"import os\n",
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"\n",
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"print(os.getcwd())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'26a39046-f8d5-46e1-9259-66fa58776f2f'"
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]
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},
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"execution_count": null,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from llama_cloud.types import LlamaParseParameters\n",
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"from llama_cloud_services import LlamaCloudIndex\n",
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"\n",
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"embedding_config = {\n",
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" \"type\": \"OPENAI_EMBEDDING\",\n",
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" \"component\": {\n",
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" \"api_key\": openai_api_key,\n",
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" \"model_name\": \"text-embedding-ada-002\", # You can choose any OpenAI Embedding model\n",
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" },\n",
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"}\n",
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"\n",
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"index = LlamaCloudIndex.create_index(\n",
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" name=\"my_index\",\n",
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" organization_id=org_id,\n",
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" api_key=api_key,\n",
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" embedding_config=embedding_config,\n",
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" llama_parse_parameters=LlamaParseParameters(\n",
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" take_screenshot=True,\n",
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" extract_layout=True,\n",
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" ),\n",
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")\n",
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"\n",
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"\n",
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"image_figure_slides_path = \"../data/figures/image_figure_slides.pdf\"\n",
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"index.upload_file(\n",
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" image_figure_slides_path, wait_for_ingestion=True, raise_on_error=True\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Start Retrieving Page Figures\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'file_size': 267370, 'last_modified_at': '2025-06-17T20:52:08', 'file_path': 'image_figure_slides.pdf', 'file_name': 'image_figure_slides.pdf', 'external_file_id': 'image_figure_slides.pdf', 'file_id': '26a39046-f8d5-46e1-9259-66fa58776f2f', 'pipeline_file_id': 'b005a2d5-fb2b-425d-a550-7297793c7410', 'pipeline_id': '75613bd7-5690-4405-9b19-0b0f06e28a04', 'page_label': 2, 'start_page_index': 1, 'start_page_label': 2, 'end_page_index': 1, 'end_page_label': 2, 'document_id': 'e8d76745946110aa1a7190c2a507499cbcf1dc9c19112e1f16', 'start_char_idx': 40, 'end_char_idx': 41, 'page_index': 1, 'figure_name': 'page_1_picture_1.jpg'}\n",
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"Image saved to /var/folders/lw/jz_6fgds7yx0n7_5w0741f_00000gn/T/tmp3l_uiwtb.jpg\n"
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]
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},
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{
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"data": {
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"image/jpeg": 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",
|
|
"text/plain": [
|
|
"<IPython.core.display.Image object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"from llama_index.core.schema import ImageNode\n",
|
|
"import base64\n",
|
|
"import tempfile\n",
|
|
"from IPython.display import Image, display\n",
|
|
"\n",
|
|
"retriever = index.as_retriever(\n",
|
|
" retrieve_page_figure_nodes=True, dense_similarity_top_k=1\n",
|
|
")\n",
|
|
"\n",
|
|
"nodes = retriever.retrieve(\"Sample query\")\n",
|
|
"\n",
|
|
"image_nodes = [n.node for n in nodes if isinstance(n.node, ImageNode)]\n",
|
|
"\n",
|
|
"for img_node in image_nodes:\n",
|
|
" print(img_node.metadata)\n",
|
|
" with tempfile.NamedTemporaryFile(suffix=\".jpg\") as temp_file:\n",
|
|
" temp_file.write(base64.b64decode(img_node.image))\n",
|
|
" print(f\"Image saved to {temp_file.name}\")\n",
|
|
" # Display the image in Jupyter Notebook\n",
|
|
" display(Image(filename=temp_file.name))"
|
|
]
|
|
}
|
|
],
|
|
"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": 2
|
|
}
|