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Video Tutorial


Check out this Github Repository for a quick overview on how to Implement RAG Capabilities.

Understanding RAG Implementation

Retrieval-Augmented Generation (RAG) enhances your flows with specific domain knowledge. The implementation process involves three main stages:
  1. 🗂️ Create a Dataset: Establish a knowledge base that contains specialized information your flow will reference during execution.
  2. Add Data Sources: Populate your dataset with relevant information from various supported sources.
  3. 🔗 Link Dataset to Flow: Connect the dataset to your flow, enabling it to leverage this information during processing.

Creating Your Dataset

Begin by establishing your knowledge base:
Python

Adding Data Sources

Populate your dataset with information from various supported formats:
Python

Linking Dataset to Flow

Connect your dataset to an existing flow by modifying its configuration:
.yaml

Example

Implement RAG capabilities

Complete example showcasing how to set up and configure datasets for Retrieval Augmented Generation (RAG) capabilities.