IBM has introduced Granite 4.0 3B Vision, a specialized vision-language model (VLM) engineered for high-fidelity enterprise document data extraction. Unlike monolithic multimodal models, this release uses a modular LoRA adapter architecture, adding approximately 0.5B parameters to the Granite 4.0 Micro base model. This design allows for efficient dual-mode deployment, activating vision capabilities only when multimodal processing is required. The model excels at converting complex visual elements, such as charts and tables, into structured machine-readable formats like JSON, HTML, and CSV. By utilizing a high-resolution tiling mechanism and a DeepStack architecture for improved spatial alignment, Granite 4.0 3B Vision achieves impressive accuracy in tasks like Key-Value Pair extraction and chart reasoning, ranking highly on industry benchmarks.
This tutorial demonstrates how to build a powerful document search engine using Hugging Face embeddings, Chroma DB, and Langchain for semantic search capabilities.
This article introduces the pyramid search approach using Agentic Knowledge Distillation to address the limitations of traditional RAG strategies in document ingestion.
The pyramid structure allows for multi-level retrieval, including atomic insights, concepts, abstracts, and recollections. This structure mimics a knowledge graph but uses natural language, making it more efficient for LLMs to interact with.
**Knowledge Distillation Process**:
- **Conversion to Markdown**: Documents are converted to Markdown for better token efficiency and processing.
- **Atomic Insights Extraction**: Each page is processed using a two-page sliding window to generate a list of insights in simple sentences.
- **Concept Distillation**: Higher-level concepts are identified from the insights to reduce noise and preserve essential information.
- **Abstract Creation**: An LLM writes a comprehensive abstract for each document, capturing dense information efficiently.
- **Recollections/Memories**: Critical information useful across all tasks is stored at the top of the pyramid.
The pyramid structure allows for multi-level retrieval, including atomic insights, concepts, abstracts, and recollections. This structure mimics a knowledge graph but uses natural language, making it more efficient for LLMs to interact with.
**Knowledge Distillation Process**:
- **Conversion to Markdown**: Documents are converted to Markdown for better token efficiency and processing.
- **Atomic Insights Extraction**: Each page is processed using a two-page sliding window to generate a list of insights in simple sentences.
- **Concept Distillation**: Higher-level concepts are identified from the insights to reduce noise and preserve essential information.
- **Abstract Creation**: An LLM writes a comprehensive abstract for each document, capturing dense information efficiently.
- **Recollections/Memories**: Critical information useful across all tasks is stored at the top of the pyramid.
MarkItDown is a utility for converting various files to Markdown, including PDF, PowerPoint, Word, Excel, Images, Audio, HTML, text-based formats, and ZIP files.
Docling is a tool that parses documents and exports them to desired formats like Markdown and JSON. It supports various document formats including PDF, DOCX, PPTX, Images, HTML, AsciiDoc, and Markdown.
We introduce LayoutLM, one of the renowned models for extracting information from documents, developed by Microsoft. To tailor a solution for our specific needs, we label our documents using Label Studio, an open-source labeling tool, connected to our remote storage AWS S3.
train models for processing documents based on specific needs and requirements. It offers capabilities such as entity recognition, key information extraction, and data validation,
pip install 'ragna builtin » ' # Install ragna with all extensions
ragna config # Initialize configuration
ragna ui # Launch the web app
ragna config # Initialize configuration
ragna ui # Launch the web app
