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How RAG Works

Estimated Time: 4 minutes

The Knowledge Base tool, also known as File Search or Retrieval-Augmented Generation (RAG), lets a skill find and reference information from uploaded documents. This allows the chatbot to provide accurate, context-specific answers based on your own content.

How Knowledge Base & RAG Works​

  1. Document Processing - Your files are broken into smaller chunks and converted to vector embeddings
  2. Vector Storage - These embeddings are stored in a searchable vector database
  3. Semantic Search - When users ask questions, the system finds relevant chunks using semantic similarity
  4. Response Generation - The AI incorporates the retrieved information into its response

Key Concepts​

Chunk size​

Documents are split into smaller pieces (chunks) to improve search relevance.

Chunk Size Considerations:

  • Smaller chunks (100-400 tokens) - More precise, specific information
  • Larger chunks (400-800 tokens) - Better context and relationships

Defaults to 100

Chunk Overlap​

Overlapping text between chunks ensures important context isn't lost at chunk boundaries.

Defaults to 20

Max chunks per answer​

Adjust how many chunks to use per answer.

Defaults to 3