LLM Based Chatbot using RAG

Index

• Introduction
• Paradigms for Inserting Information
• RAG Stack
• End-to-End Application
• Conclusion

Introduction

Large Language Models (LLMs) operate as powerful sequence-to-sequence models, trained on extensive datasets to understand and generate human-like text. However, their sheer size makes frequent fine-tuning challenging. What if we need information beyond the LLM’s knowledge cutoff date? To address this, we employ two techniques: Retrieval Augmentation and Fine-tuning.

Paradigms for Inserting Information

1. Retrieval Augmentation (RAG): This involves fixing the LLM model and injecting context directly into the prompt.
2. Fine-tuning: Fine-tuning LLM weights with new information, but this method is comparatively complex.

In this discussion, we’ll focus on the first method due to its simplicity and efficiency.

RAG Stack for Building QA System

The RAG architecture comprises two primary components:

a. Data Ingestion (Indexing)

1. Load: Begin by loading your data using DocumentLoaders. This initial step is crucial for preparing the dataset for further processing.
2. Split: Break down large documents into smaller chunks using Text Splitters. This facilitates both data indexing and passing it into a model, ensuring compatibility with a model’s finite context window.
3. Store: Establish a system for storing and indexing splits. This is commonly achieved using a VectorStore and an Embeddings model, providing a searchable repository for future reference.

b. Retrieval and Generation

1. Retrieve: When a user submits an input, relevant splits are retrieved from storage using a Retriever. This step is fundamental for pulling contextually significant information from the indexed dataset.
2. Generate: Utilize a ChatModel or LLM to produce an answer. This is accomplished by creating a prompt that includes the user’s question and the retrieved data. The seamless integration of retrieval and generation ensures that the system generates accurate and contextually relevant responses.

End-to-End Application

Deploy the end-to-end application, which can be accessed by following RAG Application.

Conclusion

By adopting the RAG-based approach, we can build a robust Question-Answer System that efficiently handles information retrieval beyond the LLM’s original training data. This method empowers developers to create dynamic and up-to-date conversational AI systems.

References:

• Building Production-Ready RAG Applications: Jerry Liu https://www.youtube.com/watch?v=TRjq7t2Ms5I
• https://python.langchain.com/docs/use_cases/question_answering/