Knowledge Base Nodes

A knowledge base node performs a search or retrieval operation on a connected knowledge base, returning relevant document chunks or information in response to a query. The files uploaded to the Knowledge Base node can be reused across multiple flows. All created knowledge bases are automatically synced to the Knowledge Base Dashboard.


Node Settings & Search Parameters

Click on the node to change its settings.

At the top of the window, you will find a drop-down menu to select a Knowledge Base or choose documents to form a new Knowledge Base.

Below that you will see the configurations for Settings and Search Parameters

  • Output Format: Choose between chunks, pages, and docs.

  • Metadata Filter Strategy: Choose between Strict Filter, Loose Filter, and No Filter.

  • Query Strategy: Choose between Semantic, Keyword, and Hybrid.

  • Top Results: Number of search results ranked by relevance.

  • Max Characters: Limits the number of characters sent to the LLM.

  • Answer Multiple Questions: Get the answers from multiple questions in parallel.

  • Advanced Q&A: Handle questions to compare or summarize documents.

  • Rerank: Get more precise information retrieval

  • Query Transformation: Get more precise information retrieval

File Status

You will see a label for each document that you upload with the following icons:

  • Pending: the document is being processed and indexed.

  • ✅: the document was successfully indexed.

  • Error: the document could not be indexed (e.g., due to a formatting issue).

Typical Workflow Structure

A common pattern is:

  1. User Input (Input Node): The user provides a question or prompt.

  2. Knowledge Base Node: Receives the user’s query (directly or via an LLM node) and retrieves relevant information from the knowledge base.

  3. LLM Node: Uses both the user’s input and the retrieved knowledge base content to generate a final, context-rich answer.


How the Interface Works

A. Data Flow

  • The Knowledge Base node typically takes the user’s input as its query, searches the knowledge base, and outputs relevant text chunks.

  • The LLM node can reference the output of the Knowledge Base node in its prompt using the node’s ID.

B. Connections (Edges)

  • The Input node is connected to the Knowledge Base node (for the query).

  • The Knowledge Base node is connected to the LLM node (providing retrieved content).

  • The LLM node is connected to the Output node (displaying the answer).

C. Execution Order

  1. The user submits a question.

  2. The Knowledge Base node receives the question and retrieves relevant information.

  3. The LLM node receives both the user’s question and the retrieved information, then generates a response.

  4. The Output node displays the LLM’s answer.


Why Use This Pattern?

  • Retrieval-Augmented Generation (RAG): This approach allows the LLM to ground its answers in specific, up-to-date, or proprietary knowledge, improving accuracy and relevance.

  • Separation of Concerns: The Knowledge Base node handles retrieval, while the LLM node handles synthesis and reasoning.


Key Points

  • The LLM node does not “search” the knowledge base directly; it relies on the Knowledge Base node to do the retrieval.

  • The LLM node’s prompt must reference the Knowledge Base node’s output to use the retrieved information.

  • All node references must match actual node IDs in the workflow.


Available Knowledge Bases

  • Documents

  • Websites

  • Tables

  • Data

  • Sharepoint

  • Google Drive

  • OneDrive

  • Dropbox

  • Azure Blob Storage

  • AWS S3

  • Notion

  • Confluence

  • Veeva

  • ServiceNow

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