TotalApp Docs

Knowledge Base

Upload PDFs and text documents, convert them to vector embeddings, and make them available to any AI agent.

Overview

The Knowledge Base is TotalApp's Retrieval-Augmented Generation (RAG) system. You upload documents — PDFs, plain text, markdown — and TotalApp chunks them, converts each chunk to a vector embedding, and stores them in a searchable vector index. When an agent is configured to use a knowledge base, it queries this index at runtime and injects the most relevant chunks into its context window before generating a response.

Coming Soon

Knowledge Base ingestion and querying are currently in development. The UI is visible in the AI Orchestration sidebar but document upload and vector search are not yet active.

Planned Features

  • Document upload — drag-and-drop interface for PDF, TXT, MD, and DOCX files. Batch upload up to 50 files at a time.
  • Chunking strategy — choose between fixed-size chunks (e.g. 512 tokens) or semantic chunking that splits on paragraph or section boundaries.
  • Collections — organise documents into named collections (e.g. "Product Manuals", "Legal Contracts"). Agents can query a specific collection or search across all of them.
  • Query testing — enter a test query and see which chunks are retrieved before attaching the collection to an agent. Lets you validate that the retrieval is surfacing relevant content.
  • Re-index — update a document in place without rebuilding the entire collection. Useful for frequently-updated source documents.
  • Access control — mark collections as shared or private. Shared collections are available to all users' agents; private collections are only available to the creating user.

RAG vs. Full Context

For small documents (under ~5 pages), you can paste the full text into an agent's system prompt and skip the Knowledge Base entirely. Use the Knowledge Base when you have more text than fits in a context window, or when you want multiple agents to share the same document store without duplicating the content in every prompt.