Knowledge Base

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Pt1: KB - Simple RAG

This one is fun .. Building a Pentaho Knowledge Base ..

Each product should have its own Knowledge Base - Pentaho Data Integration

Pt1: Knowledge Base

You should be familiar with FlowiseAI before tackling this flow, as we're going to focus on just a few key areas and concepts.

Let's start with loading the PDF document into our Qdrant vector database.

Obviously we're going to require:

  • a pdf loader: upload pdf

  • a splitter: to chunk the text - recursive character.

  • an embedding model: to create the vectors - nomic-embed-text

  • vector database: Qdrant

Document loaders allow you to load documents from different sources like PDF, TXT, CSV, Notion, Confluence etc. They are often used together with Vector Stores to be upserted as embeddings, which can then retrieved upon query.

  1. Drag & drop the PDF file.

  2. Click Upload File and navigate to the PDF.

Load Pdf
  1. Select the option: One document per file

One document per page: each page of your original PDF will be extracted and saved as its own standalone PDF file

One document per file: each file will be treated as a separate, complete document rather than combining multiple files into a single document.

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