Retrieval Augmented Generation (RAG) overview and demo of a simple RAG prototype & chat interface

RAG (Retrieval-Augmented Generation) is an advanced approach in natural language processing (NLP) that combines a retrieval module and a generation module to improve the quality and relevance of generated responses.
I explain briefly what RAG is and some technologies that can be used. I also show a very simple prototype of a RAG Chat interface on top of a PDF.
This demo (cuparfife.com ) contains:
Retrieval Module: This component retrieves relevant external information (e.g., documents, knowledge base entries) based on the user's query. It helps provide up-to-date and accurate context for the generation process.
Generation Module: Typically based on a language model (like GPT), this component takes the retrieved information and generates coherent and contextually appropriate responses or content.
Key Features:
Context-Enriched Responses: By using external data, RAG can answer questions more accurately, even for specialized or less common topics.
Scalability: It leverages vast external datasets or knowledge bases without requiring the generation model itself to store all the knowledge internally.
Flexibility: It can be fine-tuned or adapted for specific domains (e.g., large pdf's).