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.
   
    
 
 
  
   
   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
   
    
 
 
  
   
   Image Similarity Search
Reverse Image Search
Object Similarity Search
Robust OCR Document Search
Semantic Search
Cross-modal Retrieval
Probing Perceptual Similarity
Comparing Model Representations
Concept Interpolation
Concept Space Traversal
Image Similarity Search