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fixing colab links #611

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Feb 10, 2025
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fixing colab links #611

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78b595d
fixing colab links
jenperson Feb 10, 2025
3c0958a
nit
logan-markewich Feb 10, 2025
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2 changes: 1 addition & 1 deletion examples/parse/advanced_rag/dynamic_section_retrieval.ipynb
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"source": [
"# Dynamic Section Retrieval with LlamaParse\n",
"\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llamacloud-demo/blob/main/examples/advanced_rag/dynamic_section_retrieval.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services-demo/blob/main/examples/parse/advanced_rag/dynamic_section_retrieval.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"\n",
"This notebook showcases a concept called \"dynamic section retrieval\".\n",
"\n",
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2 changes: 1 addition & 1 deletion examples/parse/caltrain/caltrain_text_mode.ipynb
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"source": [
"# RAG over the Caltrain Weekend Schedule \n",
"\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/caltrain/caltrain_text_mode.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/parse/caltrain/caltrain_text_mode.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"\n",
"This example shows off LlamaParse parsing capabilities to build a functioning query pipeline over the Caltrain weekend schedule, a big timetable containing all trains northbound and southbound and their stops in various cities.\n",
"\n",
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2 changes: 1 addition & 1 deletion examples/parse/demo_advanced_weaviate.ipynb
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"source": [
"# Advanced RAG with LlamaParse + Weaviate\n",
"\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/demo_advanced_weaviate.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\\\" alt=\"Open In Colab\"/></a>\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/parse/demo_advanced_weaviate.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\\\" alt=\"Open In Colab\"/></a>\n",
"\n",
"This notebook shows you how to use `LlamaParse` for advancd RAG applications with `LlamaIndex` and [Weaviate](https://weaviate.io/).\n",
"\n",
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2 changes: 1 addition & 1 deletion examples/parse/demo_excel.ipynb
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"source": [
"# RAG with Excel Spreadsheet using LlamaPrase\n",
"\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/demo_excel.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/demo_excel.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"\n",
"This notebook shows you using LlamaParse with Excel Spreadsheet.\n",
"\n",
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2 changes: 1 addition & 1 deletion examples/parse/demo_get_charts.ipynb
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"source": [
"# Download Charts\n",
"\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/demo_get_charts.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/demo_get_charts.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"\n",
"This notebook demonstrates how to download charts from a document using the JSON mode in LlamaParse.\n",
"\n",
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2 changes: 1 addition & 1 deletion examples/parse/demo_insurance.ipynb
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"source": [
"# LlamaParse - Fast checking Insurance Contract for Coverage\n",
"\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/demo_insurance.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/demo_insurance.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"\n",
"In this notebook we will look at how LlamaParse can be used to extract structured coverage information from an insurance policy."
]
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2 changes: 1 addition & 1 deletion examples/parse/demo_json.ipynb
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"source": [
"# LlamaParse JSON Mode + Multimodal RAG\n",
"\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/demo_json.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/parse/demo_json.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"\n",
"This notebook shows you how to use LlamaParse JSON mode with LlamaIndex to build a simple multimodal RAG pipeline.\n",
"\n",
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2 changes: 1 addition & 1 deletion examples/parse/demo_json_parsing.ipynb
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"source": [
"# LlamaParse JSON Mode + Advanced RAG with `LlamaParseJsonNodeParser`\n",
"\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/demo_json_parsing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/demo_json_parsing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"\n",
"This notebook shows you how to use LlamaParse JSON mode with LlamaIndex to build a simple recursive retrieval RAG pipeline using `LlamaParseJsonNodeParser`.\n",
"\n",
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2 changes: 1 addition & 1 deletion examples/parse/demo_json_tour.ipynb
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"source": [
"# LlamaParse JSON Mode Tour\n",
"\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/demo_json.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/demo_json.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"\n",
"This notebook shows you how to use LlamaParse JSON mode with LlamaIndex and all the features it supports.\n",
"\n",
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2 changes: 1 addition & 1 deletion examples/parse/demo_languages.ipynb
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"\n",
"LlamaParse supports users to specify a `language` parameter before uploading documents, giving users better OCR capabilities over non-English PDFs, parsing images into more accurate representations.\n",
"\n",
"You can specify 80+ different languages: see this file for a full list of supported languages: https://github.com/run-llama/llama_parse/blob/main/llama_parse/base.py.\n",
"You can specify 80+ different languages: see this file for a full list of supported languages: https://github.com/run-llama/llama_cloud_services/blob/main/llama_parse/base.py.\n",
"\n",
"This notebook shows a demo of this in action. "
]
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2 changes: 1 addition & 1 deletion examples/parse/demo_mongodb.ipynb
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"source": [
"# LlamaParse With MongoDB\n",
"\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/demo_mongodb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/parse/demo_mongodb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"\n",
"In this notebook, we provide a straightforward example of using LlamaParse with MongoDB Atlas VectorSearch.\n",
"\n",
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2 changes: 1 addition & 1 deletion examples/parse/demo_starter_multimodal.ipynb
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"id": "97c79c38-38a3-40f3-ba2e-250649347d63",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/demo_starter_multimodal.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/parse/demo_starter_multimodal.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
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2 changes: 1 addition & 1 deletion examples/parse/demo_starter_parse_selected_pages.ipynb
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"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/demo_starter_parse_selected_pages.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/parse/demo_starter_parse_selected_pages.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
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2 changes: 1 addition & 1 deletion examples/parse/demo_table_comparisons.ipynb
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"source": [
"# RAG for Table Comparisons with LlamaParse + LlamaIndex\n",
"\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/demo_table_comparisons.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/parse/demo_table_comparisons.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"\n",
"This notebook shows you how to do comparisons across both tabular and text data across multiple PDF documents.\n",
"\n",
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2 changes: 1 addition & 1 deletion examples/parse/excel/dcf_rag.ipynb
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"source": [
"# RAG with Excel Spreadsheet using LlamaPrase\n",
"\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/demo_excel.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/parse/excel/dcf_rag.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"\n",
"This notebook constructs a RAG pipeline over a simple DCF template [here](https://eqvista.com/app/uploads/2020/09/Eqvista_DCF-Excel-Template.xlsx).\n",
"\n"
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2 changes: 1 addition & 1 deletion examples/parse/excel/o1_excel_rag.ipynb
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"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/excel/o1_excel_rag.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/excel/o1_excel_rag.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
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2 changes: 1 addition & 1 deletion examples/parse/knowledge_graphs/kg_agent.ipynb
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"source": [
"# Knowledge Graph Agent with LlamaParse\n",
"\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/knowledge_graphs/kg_agent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/parse/knowledge_graphs/kg_agent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"\n",
"Here we build a knowledge graph agent over the SF 2023 Budget Proposal. We use LlamaIndex abstractions to construct a knowledge graph, and we store the property graph in neo4j. We then build an agent that can interact with the knowledge graph as a tool."
]
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2 changes: 1 addition & 1 deletion examples/parse/multimodal/claude_parse.ipynb
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"source": [
"# Multimodal Parsing using Anthropic Claude (Sonnet 3.5)\n",
"\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/multimodal/claude_parse.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/parse/multimodal/claude_parse.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"\n",
"This cookbook shows you how to use LlamaParse to parse any document with the multimodal capabilities of Sonnet 3.5. \n",
"\n",
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2 changes: 1 addition & 1 deletion examples/parse/multimodal/gemini2_flash.ipynb
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"source": [
"# Multimodal Parsing with Gemini 2.0 Flash\n",
"\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/multimodal/gemini2_flash.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/parse/multimodal/gemini2_flash.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"\n",
"This cookbook shows you how to use LlamaParse to parse any document with the multimodal capabilities of Gemini 2.0 Flash.\n",
"\n",
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2 changes: 1 addition & 1 deletion examples/parse/multimodal/gpt4o_mini.ipynb
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"source": [
"# Multimodal Parsing using GPT4o-mini\n",
"\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/multimodal/gpt4o_mini.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/parse/multimodal/gpt4o_mini.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"\n",
"This cookbook shows you how to use LlamaParse to parse any document with the multimodal capabilities of GPT4o-mini.\n",
"\n",
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2 changes: 1 addition & 1 deletion examples/parse/multimodal/insurance_rag.ipynb
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"source": [
"# Building a Multimodal RAG Pipeline over an Auto Insurance Claim\n",
"\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/multimodal/insurance_rag.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/parse/multimodal/insurance_rag.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
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2 changes: 1 addition & 1 deletion examples/parse/multimodal/legal_rag.ipynb
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"source": [
"# Building a RAG Pipeline over Legal Documents\n",
"\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/multimodal/legal_rag.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/parse/multimodal/legal_rag.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"\n",
"This example shows how LlamaParse and LlamaIndex can be used to parse various types of legal documents, which may contain complex tabular data. The advantage of this is being able to quickly retrieve a specific answer to a legal question with comprehensive context — knowledge of precedents, statutes, and cases presented in the given documents. A user can quickly find the answer to or find out more details about a specific legal question without having to read through the often long documents by using LLMs.\n",
"\n",
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2 changes: 1 addition & 1 deletion examples/parse/multimodal/multimodal_contextual_retrieval_rag.ipynb
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"source": [
"# Contextual Retrieval for Multimodal RAG\n",
"\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/multimodal/multimodal_contextual_retrieval_rag.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/parse/multimodal/multimodal_contextual_retrieval_rag.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"\n",
"In this cookbook we show you how to build a multimodal RAG pipeline with **contextual retrieval**.\n",
"\n",
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2 changes: 1 addition & 1 deletion examples/parse/multimodal/multimodal_rag_slide_deck.ipynb
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"source": [
"# Building a Natively Multimodal RAG Pipeline (over a Slide Deck)\n",
"\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/multimodal/multimodal_rag_slide_deck.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/parse/multimodal/multimodal_rag_slide_deck.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"\n",
"In this cookbook we show you how to build a multimodal RAG pipeline over a slide deck, with text, tables, images, diagrams, and complex layouts.\n",
"\n",
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2 changes: 1 addition & 1 deletion examples/parse/multimodal/multimodal_report_generation.ipynb
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"source": [
"# Multimodal Report Generation (from a Slide Deck)\n",
"\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/multimodal/multimodal_report_generation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/parse/multimodal/multimodal_report_generation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"\n",
"In this cookbook we show you how to build a multimodal report generator. The pipeline parses a slide deck and stores both text and image chunks. It generates a detailed response that contains interleaving text and images.\n",
"\n",
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2 changes: 1 addition & 1 deletion examples/parse/multimodal/multimodal_report_generation_agent.ipynb
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"source": [
"# Multimodal Report Generation Agent \n",
"\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/multimodal/multimodal_report_generation_agent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"<a href=\"https://colab.research.google.com/github/run-llama/llama_cloud_services/blob/main/examples/parse/multimodal/multimodal_report_generation_agent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
"\n",
"In this cookbook we show you how to build a multimodal report generation agent from a bank of research reports. We use the a set of ICLR papers (which were also used as the dataset in our [DeepLearning.ai course](https://www.deeplearning.ai/short-courses/building-agentic-rag-with-llamaindex/?utm_campaign=llamaindexC2-launch&utm_medium=headband&utm_source=dlai-homepage).\n",
"\n",
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