From 0cb379d6ce328d64a188f7bc4f14ada6da1b298b Mon Sep 17 00:00:00 2001 From: Diptanu Gon Choudhury Date: Sat, 20 Jul 2024 21:08:01 -0700 Subject: [PATCH] update docs --- examples/pdf/chunking/README.md | 30 ++++-------------- .../{pdf_chunking_graph.py => setup_graph.py} | 0 ...and_retreive.py => upload_and_retrieve.py} | 0 examples/pdf/image/README.md | 26 ++++----------- .../pdf/image/{setup.py => setup_graph.py} | 0 examples/pdf/indexing_and_rag/README.md | 23 ++++++-------- examples/pdf/pdf_to_markdown/README.md | 19 +---------- examples/pdf/sample_invoice.pdf | Bin 0 -> 542355 bytes examples/pdf/structured_extraction/README.md | 21 ++---------- examples/pdf/table_extraction/README.md | 21 +++--------- ...and_retreive.py => upload_and_retrieve.py} | 0 examples/video/transcript/README.md | 19 ++--------- 12 files changed, 33 insertions(+), 126 deletions(-) rename examples/pdf/chunking/{pdf_chunking_graph.py => setup_graph.py} (100%) rename examples/pdf/chunking/{upload_and_retreive.py => upload_and_retrieve.py} (100%) rename examples/pdf/image/{setup.py => setup_graph.py} (100%) create mode 100644 examples/pdf/sample_invoice.pdf rename examples/pdf/table_extraction/{upload_and_retreive.py => upload_and_retrieve.py} (100%) diff --git a/examples/pdf/chunking/README.md b/examples/pdf/chunking/README.md index dde4b4821..942e8f65b 100644 --- a/examples/pdf/chunking/README.md +++ b/examples/pdf/chunking/README.md @@ -1,25 +1,9 @@ # PDF Chunking with Indexify and RecursiveCharacterTextSplitter -In this cookbook, we'll explore how to create a PDF chunking pipeline using Indexify, the tensorlake/marker for PDF text extraction, and the tensorlake/chunk-extractor with RecursiveCharacterTextSplitter. By the end of this document, you should have a pipeline capable of ingesting PDF documents and chunking their content for further processing or analysis. +Pipeline to extract and chunk text from a PDF. The pipeline uses - -## Table of Contents - -1. [Introduction](#introduction) -2. [Prerequisites](#prerequisites) -3. [Setup](#setup) - - [Install Indexify](#install-indexify) - - [Install Required Extractors](#install-required-extractors) -4. [Creating the Extraction Graph](#creating-the-extraction-graph) -5. [Implementing the Chunking Pipeline](#implementing-the-chunking-pipeline) -6. [Running the Chunking Process](#running-the-chunking-process) -7. [Customization and Advanced Usage](#customization-and-advanced-usage) -8. [Conclusion](#conclusion) - -## Introduction - -The PDF chunking pipeline will be composed of two main steps: -1. PDF to Text extraction using the `tensorlake/marker` extractor. -2. Text chunking using the `tensorlake/chunk-extractor` with RecursiveCharacterTextSplitter. +1. `tensorlake/marker` for PDF text extraction +2. `tensorlake/chunk-extractor` chunks markdown from the previous step with Langchain's RecursiveCharacterTextSplitter. ## Prerequisites @@ -91,12 +75,12 @@ client.create_extraction_graph(extraction_graph) You can run this script to set up the pipeline: ```bash -python pdf_chunking_graph.py +python setup_graph.py ``` -## Ingestion and Retreival from the Pipeline +## Ingestion and Retrieval from the Pipeline -Now that we have our extraction graph set up, we can upload files and make the pipeline generate chunks. Create a file `upload_and_retreive.py`: +Now that we have our extraction graph set up, we can upload files and make the pipeline generate chunks. Create a file `upload_and_retrieve.py`: ```python import os @@ -146,7 +130,7 @@ if __name__ == "__main__": You can run the Python script to process a PDF and generate chunks: ```bash -python upload_and_retreive.py +python upload_and_retrieve.py ``` diff --git a/examples/pdf/chunking/pdf_chunking_graph.py b/examples/pdf/chunking/setup_graph.py similarity index 100% rename from examples/pdf/chunking/pdf_chunking_graph.py rename to examples/pdf/chunking/setup_graph.py diff --git a/examples/pdf/chunking/upload_and_retreive.py b/examples/pdf/chunking/upload_and_retrieve.py similarity index 100% rename from examples/pdf/chunking/upload_and_retreive.py rename to examples/pdf/chunking/upload_and_retrieve.py diff --git a/examples/pdf/image/README.md b/examples/pdf/image/README.md index e2b17673c..3fb908d2c 100644 --- a/examples/pdf/image/README.md +++ b/examples/pdf/image/README.md @@ -1,22 +1,10 @@ # PDF Image Extraction with Indexify -This project demonstrates how to extract images from PDF documents using Indexify. It includes two main components: setting up an extraction graph for image extraction and a script to process PDFs and retrieve the extracted images. +Pipeline to extract images from PDF. The pipeline uses - -## Table of Contents +1. `tensorlake/pdfextractor` to extract images from PDFs. -1. [Introduction](#introduction) -2. [Prerequisites](#prerequisites) -3. [Setup](#setup) -4. [File Descriptions](#file-descriptions) -5. [Usage](#usage) -6. [Customization](#customization) -7. [Conclusion](#conclusion) - -## Introduction - -This project showcases the use of Indexify to create a pipeline for extracting images from PDF documents. It consists of two main parts: -- An extraction graph that defines the process of converting PDFs to images. -- A script that downloads a PDF, uploads it to Indexify, and retrieves the extracted images. +We provide a script that downloads a PDF, uploads it to Indexify, and retrieves the extracted images. ## Prerequisites @@ -47,15 +35,15 @@ Before we begin, ensure you have the following: ## File Descriptions -1. `setup.py`: This script sets up the extraction graph for converting PDFs to images. +1. `setup_graph.py`: This script sets up the extraction graph for converting PDFs to images. 2. `upload_and_retreive.py`: This script downloads a PDF, uploads it to Indexify, and retrieves the extracted images. ## Usage -1. First, run the [`setup.py`](https://github.com/tensorlakeai/indexify/blob/main/examples/pdf/image/setup.py) script to set up the extraction graph: +1. First, run the [`setup_graph.py`](https://github.com/tensorlakeai/indexify/blob/main/examples/pdf/image/setup_graph.py) script to set up the extraction graph: ```bash - python setup.py + python setup_graph.py ``` 2. Then, run the [`upload_and_retrieve.py`](https://github.com/tensorlakeai/indexify/blob/main/examples/pdf/image/upload_and_retrieve.py) script to process a PDF and extract images: @@ -75,7 +63,7 @@ Before we begin, ensure you have the following: ## Customization -You can customize the image extraction process by modifying the `extraction_graph_spec` in `image_pipeline.py`. For example, you could add additional extraction steps or change the output format. +You can customize the image extraction process by modifying the `extraction_graph_spec` in `setup_graph.py`. For example, you could add additional extraction steps or change the output format. In `upload_and_retrieve.py`, you can modify the `pdf_url` variable to process different PDF documents. diff --git a/examples/pdf/image/setup.py b/examples/pdf/image/setup_graph.py similarity index 100% rename from examples/pdf/image/setup.py rename to examples/pdf/image/setup_graph.py diff --git a/examples/pdf/indexing_and_rag/README.md b/examples/pdf/indexing_and_rag/README.md index 909205539..a182e0207 100644 --- a/examples/pdf/indexing_and_rag/README.md +++ b/examples/pdf/indexing_and_rag/README.md @@ -1,14 +1,9 @@ -# Retrieval-Augmented Generation (RAG) with Indexify - -In this cookbook, we'll explore how to create a Retrieval-Augmented Generation (RAG) system using Indexify. We'll cover two approaches: a text-based RAG system and a multimodal RAG system. - -## Introduction - -We'll explore two RAG pipelines: +# Retrieval-Augmented Generation (RAG) on PDFs with Indexify +We show how to create a Retrieval-Augmented Generation (RAG) system using Indexify. We'll cover two approaches: a text-based RAG system and a multi-modal RAG system. 1. A text-based RAG system using `tensorlake/pdfextractor`, `tensorlake/chunk-extractor`, and `tensorlake/minilm-l6`. -2. A multimodal RAG system that includes image processing using `tensorlake/clip-extractor` and GPT-4o mini for answer generation. +2. A multi-modal RAG system that includes image processing using `tensorlake/clip-extractor` and GPT-4o mini for answer generation. ## Prerequisites @@ -166,9 +161,9 @@ python upload_and_retreive.py ``` -## Part 2: Multimodal RAG with GPT-4o mini +## Part 2: Multi-Modal RAG with GPT-4o mini -### Creating the Multimodal Extraction Graph +### Creating the Multi-Modal Extraction Graph Create a new Python file called `mm_extraction_graph.py` and add the following code: @@ -205,7 +200,7 @@ extraction_graph = ExtractionGraph.from_yaml(extraction_graph_spec) client.create_extraction_graph(extraction_graph) ``` -Run this script to set up the multimodal pipeline: +Run this script to set up the multi-modal pipeline: ```bash python mm_extraction_graph.py ``` @@ -315,7 +310,7 @@ if __name__ == "__main__": Replace `"YOUR_OPENAI_API_KEY"` with your actual OpenAI API key. -### Running the Multimodal RAG System +### Running the Multi-Modal RAG System Reference from PDF file from which answer should be generated: @@ -358,11 +353,11 @@ These RAG systems demonstrate the power of combining Indexify with large languag 1. **Scalability**: Indexify can process and index large numbers of PDFs efficiently, including both text and images. 2. **Flexibility**: You can easily swap out components or adjust parameters to suit your specific needs. 3. **Integration**: The systems seamlessly integrate PDF processing, embedding generation, and text generation. -4. **Multimodal Capabilities**: The second system shows how to incorporate both text and image data for more comprehensive question answering. +4. **Multi-Modal Capabilities**: The second system shows how to incorporate both text and image data for more comprehensive question answering. ## Next Steps - Learn more about Indexify on our docs - https://docs.getindexify.ai - Explore ways to evaluate and improve the quality of retrieved contexts and generated answers. - Consider implementing a user interface for easier interaction with your RAG systems. -- Experiment with different multimodal models and ways of combining text and image data for more sophisticated question answering. +- Experiment with different multi-modal models and ways of combining text and image data for more sophisticated question answering. diff --git a/examples/pdf/pdf_to_markdown/README.md b/examples/pdf/pdf_to_markdown/README.md index 58098238b..01988bd5f 100644 --- a/examples/pdf/pdf_to_markdown/README.md +++ b/examples/pdf/pdf_to_markdown/README.md @@ -1,23 +1,6 @@ # PDF Text Extraction with Indexify and Marker -This guide demonstrates how to create a PDF text extraction pipeline using Indexify and the tensorlake/marker extractor. By the end of this document, you'll have a pipeline capable of extracting text content from PDF documents for further processing or analysis. - -## Table of Contents - -1. [Introduction](#introduction) -2. [Prerequisites](#prerequisites) -3. [Setup](#setup) - - [Install Indexify](#install-indexify) - - [Install Required Extractor](#install-required-extractor) -4. [Creating the Extraction Graph](#creating-the-extraction-graph) -5. [Implementing the Text Extraction Pipeline](#implementing-the-text-extraction-pipeline) -6. [Running the Text Extraction Process](#running-the-text-extraction-process) -7. [Customization and Advanced Usage](#customization-and-advanced-usage) -8. [Conclusion](#conclusion) - -## Introduction - -The PDF text extraction pipeline will use the `tensorlake/marker` extractor to convert PDF documents into plain text. +We show how to create a pipeline capable of extracting text content from PDF documents It uses the `tensorlake/marker` extractor to convert PDF documents into markdown. ## Prerequisites diff --git a/examples/pdf/sample_invoice.pdf b/examples/pdf/sample_invoice.pdf new file mode 100644 index 0000000000000000000000000000000000000000..235a8edd747d5c2dfda6d2cc9797ef60d6340459 GIT binary patch literal 542355 zcmb5VbzD^4+6Fu{(%s!1!_Y`~w=@!i3@~&`NGssb-BQxh4JsuqCDI+zog#jN=RD7O zzwbTg{he?BvDtg=JFok?*UVb8Vb+#c>yN4$oP*8+d9qjzV)1FQM$S1_B3ID^Z@8=5U)v|Wb^Mp!D zdU(2nt)SQ#_REVV5En`%f?u4!CKT!Fj*p%o+IaOw)p#GIWG9WInNTeB>J~}E2!E~| z`+pOdj%Q#W;zM*oPPUspC~RwsefAAY20~l_Oc~N_t*m?Y`9(?{P)sZ(5y(QBGR~}6 zSZBaepY=ID_`TuG^MIP6w(wfbxz)EA_HikOUAjWXRo2=<3zgQoP2FjOZ10l{5VZ9C zQX1S1pK&NkXy{ICK*A6)$Hd;(G)$lpDh-pKP<+a`s@4sue5p>x(Sb~DFdcCEgNu7s 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