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NVIDIA-Ingest

NVIDIA-Ingest is a microservice for document content and metadata extraction.

#Document processing
#Data extraction
#microservices
NVIDIA-Ingest

Product Details

NVIDIA-Ingest is a scalable, high-performance document content and metadata extraction microservice. It supports parsing PDF, Word and PowerPoint documents and uses NVIDIA NIM microservices to find, contextualize and extract text, tables, charts and images for use in downstream generative applications. Its main advantages include high performance, strong scalability, support for multiple document types and extraction methods, etc. Currently in early access, the code base is updated frequently.

Main Features

1
Accepts JSON job description containing document load and ingest tasks
2
Allows retrieval of job results as a JSON dictionary containing extracted object metadata and processing annotations
3
Supports multiple document types such as PDF, Docx, pptx and images
4
Supports multiple extraction methods for each document type, e.g. PDF supports pdfium, Unstructured.io and Adobe Content Extraction Services
5
Supports pre- and post-processing operations, including text segmentation, conversion, filtering, embedding generation, etc.

How to Use

1
1. Start supporting NIM microservices
2
2. Install NVIDIA Ingest client dependencies in Python environment
3
3. Submit the ingestion job
4
4. Check and use the results
5
5. Optional: Deploy the library directly

Target Users

The target audience includes organizations and individuals who need to process large amounts of complex unstructured PDFs and other enterprise documents and convert them into metadata and text that can be used in retrieval systems, such as enterprise data analysts, researchers, etc. Because it can efficiently and accurately extract useful information from a variety of documents to meet their data processing and analysis needs.

Examples

Used by enterprises to extract key information from a large number of business documents and build knowledge graphs

Research institutions extract data from academic literature to assist scientific research work

Data analysts use the extracted text data for subsequent data analysis and mining

Quick Access

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Categories

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› data analysis
› Development and Tools

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