2 posts tagged with "RAG"

Previously, we had developed a simple chatbot by creating an Information Tool. This Information Tool would dynamically generate a Cypher Query and fetch information from the Neo4j Database.

We are using a technique called Retrieval-Augmented Generation (RAG) to enhance responses by integrating data from Neo4j. The chatbot utilizes LangChain to manage language models and workflow orchestration.

We hit a roadblock in terms of cost and performance as our schema became more complex. In order to overcome this challenge, we've made significant changes to our approach. Now, we are using two tools instead of one Information Tool to retrieve the data. We will discuss these changes in more detail in the below post.

A chatbot is a software application that uses AI to have conversations with users, helping them find information or answer questions. We built this chatbot using Retrieval-Augmented Generation (RAG) to improve its responses, Neo4j to store structured data, and Large Language Models (LLMs) to understand and generate natural language.

We created 2 types of Nodes/Labels, "Bank" and "Owner" and 1 type of relationship between them: "IS_OWNED_BY". The blog below lays out how we created a chatbot to query the relationship between the Node Types mentioned using RAG (Retrieval Augmented Generation) techniques.