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A knowledge base is a collection of documents that PromptJuggler chunks, embeds, and indexes for semantic search. Attach a KB Search tool to a prompt or use a KB Search node in a workflow, and your AI gains access to your own content at runtime. That’s RAG, without the infrastructure headaches.

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Supported file types

Upload any of these: .txt, .csv, .tsv, .md, .html, .pdf, .docx.

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How it works

Upload a document and PromptJuggler takes care of the rest: splitting it into chunks, generating embeddings, and indexing everything for fast semantic search. Each document shows a status badge that updates in real time – no need to refresh the page. Once a document’s status shows as ready, it’s searchable. Any prompt or workflow with a KB Search tool or node can query it immediately.

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Naming

Knowledge bases use naming (with the kb_ slug prefix) but not versioning. A knowledge base is a living collection – you add and remove documents over time rather than publishing frozen snapshots.

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Using knowledge bases

Two ways to connect a knowledge base to your AI:
  • As a tool – attach the KB Search tool to a prompt. The model decides when to search and what to search for. Good for conversational use cases where the model should judge when external context is needed.
  • As a workflow node – add a KB Search node to a workflow. The search happens at a fixed point in the pipeline with a query you define. Good for structured pipelines where you always want context injected at a specific step.