Building an Interactive LLM Chatbot with HeatWave Using Python
AI-powered applications require robust and scalable database solutions to manage and process large amounts of data efficiently. HeatWave is an excellent choice for such applications, providing high-performance OLTP, analytics, machine learning and generative artificial intelligence capabilities.
In this article, we will explore a Python 3 script that connects to an HeatWave instance and enables users to interact with different large language models (LLMs) dynamically. This script demonstrates how to:
By the end of this article, you’ll have a deep understanding of how to integrate HeatWave with Python for AI-driven applications.
HeatWave Chat
HeatWave Chat enables you to engage in human-like conversations with an AI. Within a single session, you can ask multiple queries and receive relevant responses. This conversational agent leverages powerful LLMs to understand your input and generate natural-sounding replies. HeatWave Chat enhances the conversation by utilizing a chat history, allowing you to ask follow-up questions seamlessly. Furthermore, it employs vector search to access and utilize knowledge stored within its built-in vector store. All communication and processing occur securely within the HeatWave service, ensuring fast and reliable responses.
In this article I...