Extract relations and perform NER from text
Identify and extract key entities from text
RAG with multiple types of loaders like text, pdf and web
A demo app which retrives information from multiple PDF docu
Analyze documents to extract and structure text
Multimodal retrieval using llamaindex/vdr-2b-multi-v1
Extract text from images using OCR
Visual RAG Tool
OCR that extract text from image of hindi and english
Extract text from PDF files
Query PDF documents using natural language
Extract text from images using OCR
Find relevant text chunks from documents based on queries
GLiREL is an AI-powered tool designed to extract text from scanned documents and analyze it for Named Entity Recognition (NER) and relation extraction. It helps users unlock insights from unstructured text data by identifying key entities and their relationships within the content.
• Text Extraction: Accurately extracts text from scanned documents, including PDFs and images. • Named Entity Recognition (NER): Identifies and categorizes key entities such as names, locations, organizations, and dates. • Relation Extraction: Analyzes text to identify relationships between entities. • Multi-Format Support: Processes text from various file formats, including PDF, DOCX, and JPG. • High Accuracy: Leverages advanced AI models to ensure precise extraction and analysis. • Customizable: Allows users to define specific entities or relationships to focus on. • Batch Processing: Enables processing of multiple documents simultaneously. • API Integration: Can be integrated into workflows via a robust API.
What formats does GLiREL support?
GLiREL supports PDF, DOCX, JPG, PNG, and other common document formats.
How accurate is GLiREL?
GLiREL uses advanced AI models to ensure high accuracy, but results may vary based on document quality and complexity.
Can GLiREL handle large volumes of data?
Yes, GLiREL supports batch processing, allowing users to process multiple documents simultaneously.