#atom

Subtitle:

Granular semantic representations of note subsections

Core Idea:

Block-level embeddings apply vector representation at the subsection level (paragraphs, headings, or logical blocks) rather than entire notes, enabling more precise semantic search and connections at a granular level within knowledge management systems.

Key Principles:

1. Granular Representation:
   • Creates separate embeddings for distinct sections within notes
   • Treats logical blocks as individual semantic units
2. Contextual Boundaries:
   • Uses document structure (headings, paragraphs) to define meaningful blocks
   • Respects the natural organization of information
3. Hierarchical Relationships:
   • Maintains connection between blocks and their parent documents
   • Enables both block-specific and note-level retrieval

Why It Matters:

• Precision Improvement:
  • Finds specific relevant sections rather than entire documents
  • Reduces noise from irrelevant sections within otherwise relevant notes
• Content Reusability:
  • Enables using specific blocks as modular units of knowledge
  • Facilitates transclusion and reference of precise content chunks
• Concept Isolation:
  • Separates mixed topics within a single note
  • Allows multi-topic notes to be matched appropriately to different queries

How to Implement:

1. Define Block Boundaries:
   • Use structural elements like headings to identify logical sections
   • Consider paragraph breaks or semantic shifts as block delimiters
2. Generate Block Embeddings:
   • Process each block separately through embedding models
   • Store block identifiers with position information and parent document
3. Implement Retrieval Logic:
   • Create search functions that can target blocks or whole documents
   • Develop display methods for showing block context within parent documents

Example:

• Scenario:

  • A researcher has lengthy notes covering multiple related topics

• Application:

  // Example processing of a note into block-level embeddings
  function processNoteIntoBlocks(note) {
    // Split note by headings (## Heading format)
    const blocks = note.content.split(/(?=## )/);
    
    // Generate embeddings for each block
    return blocks.map((blockContent, index) => {
      const embedding = embedModel.encode(blockContent);
      return {
        noteId: note.id,
        blockId: `${note.id}-block-${index}`,
        content: blockContent,
        embedding: embedding,
        position: index
      };
    });
  }
  

• Result:

  • Search results point to specific sections rather than entire notes
  • User can jump directly to relevant paragraphs within lengthy documents
  • Multi-topic notes appear in different search results based on relevant sections

Connections:

• Related Concepts:
  • Note Embeddings: Broader concept of vector representation for notes
  • Semantic Search: Application enhanced by block-level granularity
• Broader Concepts:
  • Content Chunking: Methods for breaking content into meaningful units
  • Knowledge Representation: Techniques for modeling information structure

References:

1. Primary Source:
   • Smart Connections Plugin documentation on block-level embeddings
2. Additional Resources:
   • "Passage Retrieval in Question Answering Systems" research
   • "Chunking Strategies for LLM Applications" (LangChain documentation)

Tags:

#embeddings #blocks #granularity #knowledge-management #semantic-search #note-structure

Connections:

Sources:

• From: Obsidian Plugin Smart Connections