prorag-developer

To build and customize a RAG application pipeline, you can scaffold a new project and modify the ingestion and retrieval processes to handle specific data sources and strategies. This enables efficient creation of AI-powered knowledge systems.

The best way to initialize a new RAG project is by scaffolding it with predefined structures. This sets up the foundational pipeline composition, data models, and LLM protocols for your application.

Yes, you can modify the ingestion pipeline to handle various document types for RAG. The customization features allow you to adjust data models and retrieval mechanisms for specific documentation sources.

You can configure LLM providers and manage the RAG lifecycle through CLI operations. This includes managing application settings, operational commands, and deployment configurations for your retrieval augmented generation system.

Yes, this RAG development approach supports configuring retrieval to use OpenSearch. You can customize the retrieval mechanisms within the pipeline to integrate with your chosen search providers and data sources.