Adding RAG to LangGraph Agents

The video demonstrates integrating the RAG model into the LangGraph system to create a custom email reply system for customer queries about the Westworld theme park using a dataset of 148 question-answer pairs. The presenter outlines the process of setting up the RAG model, generating personalized email responses, testing for improvements, and suggests potential enhancements for the system’s performance and capabilities.

In the video, the presenter demonstrates how to integrate the RAG (Retrieval-Augmented Generative) model into the LangGraph system to build an email reply system. The goal is to create a system that can reply to customer queries about the Westworld theme park using custom data instead of relying on internet searches. They provide a dataset of 148 question-answer pairs related to the park, characters, and activities. The presenter explains the process of splitting the data into separate documents for the RAG to use as source material for generating replies.

The integration involves creating a vector store using the BGE embedding library and setting up a retriever to fetch information from the vector store. The presenter uses a RAG prompt to formulate questions based on the email content and category, which are then sent to the RAG model. The RAG model retrieves answers based on the questions, which are utilized to draft personalized email responses to customer inquiries about Westworld. The presenter emphasizes the importance of testing the RAG to identify gaps in the dataset and refine the prompts for better responses.

The workflow includes nodes for categorizing emails, generating RAG questions, drafting replies, and analyzing drafts for potential rewrites. The presenter outlines the steps for creating the agents and compiling the workflow for testing with sample emails. The system successfully processes an email inquiry about meeting a character in the park, generates relevant questions for the RAG, retrieves answers, and formulates a detailed response tailored to the customer’s query. The presenter highlights the adaptability of the RAG model in extracting core information and repurposing it into coherent email responses.

The presenter discusses potential enhancements for the RAG system, such as using different RAG strategies like HyDE, incorporating text splitting techniques, and implementing query rewriting for improved performance. They suggest exploring advanced RAG features like RAG fusion and self-checking mechanisms to ensure consistent outputs aligned with brand standards. The video concludes with an invitation for viewers to experiment with the presented code, provide feedback on challenges faced, and express interest in future in-depth tutorials or courses on enhancing RAG integration. Viewers are encouraged to engage by asking questions and subscribing for future content updates.