Lyzr Studio’s Knowledge Base feature lets you create, populate, and query a persistent repository of documents and data for Retrieval-Augmented Generation (RAG).

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1. Create a Knowledge Base

First, define your new KB by providing basic metadata and selecting a vector store backend.

  • Name: A unique identifier for your KB (e.g., “Product Docs”).
  • Description: Brief summary of the KB’s contents or purpose.
  • Vector Database: Choose where embeddings will be stored (e.g., Qdrant or Weaviate).

Click Create to provision your Knowledge Base.

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2. Add Content & Parsing

Next, ingest data by uploading files or pointing to external sources. LyZR will auto-select the appropriate parser based on file type.

  • Upload: Drag-and-drop or select file(s) (PDF, DOCX, TXT, CSV, JSON, etc.).
  • Parser Selection: LyZR auto-detects format (e.g., text, table, JSON) and chooses the right parser.
  • Indexing: Content is broken into chunks, embedded, and stored in the vector DB.

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3. Knowledge Base Retrieval

Once populated, you can query the KB directly within Studio to retrieve relevant chunks.

  • Search Bar: Enter a query and press Enter.
  • Number of Chunks: How many results to return (default: 10).
  • Retrieval Type: Choose between Basic (standard similarity search) or advanced modes.
  • Score Threshold: Filter out low-relevance results.

By default, this setup creates a turnkey RAG pipeline. To customize retrieval strategies or integrate with your own agents, scroll down to the Custom RAG section.

With these steps, you can rapidly spin up a knowledge-backed RAG system in minutes. For API-based KB management, see the Knowledge Base API Reference.