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data_extraction

README.md

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SambaNova AI Starter Kits

Data Extraction Examples

This kit include a series of Notebooks that demonstrates various methods for extracting text from documents in different input formats. including Markdown, PDF, CSV, RTF, DOCX, XLS, HTML

Deploy the starter kit

Option 1: Run through local virtual environment

Important: With this option you have to install some packages directly in your system:

  1. Clone the repo.
git clone https://github.sambanovasystems.com/SambaNova/ai-starter-kit.git
  1. (Recommended) Set up a venv or conda environment for installation.
cd ai-starter-kit
python3 -m venv data_extract_env
source data_extract_env/bin/activate
cd data_extraction
pip install -r requirements.txt
  1. Install files required for the paddle utility: We recommend that you use virtualenv or conda environment for installation.

Use this in case you want to use Paddle OCR recipe for PDF OCR and table extraction you should use the requirementsPaddle file instead.

cd ai-starter-kit
python3 -m venv data_extract_env
source data_extract_env/bin/activate
cd data_extraction
pip install -r requirementsPaddle.txt
  1. Some text extraction examples use the Unstructured library. Register at Unstructured.io to get a free API key and create an enviroment file to store the API key and URL:
echo 'UNSTRUCTURED_API_KEY="your_API_key_here"\nUNSTRUCTURED_API_KEY="your_API_url_here"' > .env

Option 2: Run via Docker

With this option, all functionality and Jupyter notebooks are ready to use.

  1. Ensure that you have the Docker engine installed Docker installation.

  2. Clone the repo.

git clone https://github.sambanovasystems.com/SambaNova/ai-starter-kit.git
  1. Some text extraction examples use the Unstructured library. Register at Unstructured.io to get a free API key and create an enviroment file to store the API key and URL:
echo 'UNSTRUCTURED_API_KEY="your_API_key_here"\nUNSTRUCTURED_API_KEY="your_API_url_here"' > .env
  1. Run the data extraction Docker container:
sudo docker-compose up data_extraction_service
  1. Run data extraction docker container for Paddle utility.
  2. Run data extraction docker container.
sudo docker-compose up data_extraction_service
  1. Run data extraction docker container for Paddle utility.

Use this in case you want to use Paddle OCR recipe for PDF OCR and table extraction, use the startPaddle script instead

sudo docker-compose up data_extraction_paddle_service

File loaders

The notebooks folder has several data extraction recipes and pipelines:

CSV Documents

  • csv_extraction.ipynb: Examples of text extraction from CSV files using different packages. Depending on your use case, some packages may perform better than others.

XLS/XLSX Documents

  • xls_extraction.ipynb: Examples of text extraction from files in different input formats using the Unstructured library. Section 2 includes two examples, one using the Unstructured API and the other using the local unstructured loader.

DOC/DOCX Documents

  • docx_extraction.ipynb: Examples of text extraction from files in different input formats using the Unstructured library. Section 3 includes two examples, one using the Unstructured API and the other using the local unstructured loader.

RTF Documents

  • rtf_extraction.ipynb: Examples of text extraction from files in different input formats using the Unstructured library. Section 4 includes two examples, one using the Unstructured API and the other using the local unstructured loader.

Markdown Documents

  • markdown_extraction.ipynb: Examples of text extraction from files in different input formats using the Unstructured library. Section 5 includes two examples, one using the Unstructured API and the other using the local unstructured loader.

HTML Documents

  • web_extraction.ipynb: Examples of text extraction from files in different input format using the Unstructured library. Section 6 includes two loading examples, one using the Unstructured API and the other using the local unstructured loader.

PDF Documents

  • pdf_extraction.ipynb: Examples of text extraction from PDF documents using different packages including different OCR and non-OCR packages. Depending on your specific use case, some packages may perform better than others.

  • retrieval_from_pdf_tables.ipynb: Example of a simple RAG retiever and an example of a multivector RAG retriever for PDF with tables retrieval. For SambaNova model endpoint usage, refer to the top-level ai-starter-kit README

  • qa_qc_util.ipynb: Simple utility for visualizing text boxes extracted using the Fitz package. This visualization can be particularly helpful when dealing with complex multi-column PDF documents, and in the debugging process.

Included files

  • data: Sample data for running the notebooks. Used as storage for intermediate steps.

  • src: Source code for some functionalities used in the notebooks.

  • docker: Docker file for the data extraction starter kit.