How ClickHouse Uses ChatGPT

This article details the transformation of ClickHouse's internal data warehouse from a traditional BI-first approach to an AI-first model, significantly enhancing user accessibility to analytics.

The article discusses the importance of LLM observability using ClickStack, OpenTelemetry, and MCP, highlighting how to instrument LibreChat for enhanced insights into AI-driven applications.

The article explores the analysis of Wimbledon tennis data using ClickHouse, detailing the unique scoring system of tennis and how to implement a function to compute points needed to win a game.

The article discusses the integration of ClickHouse with the Model Context Protocol (MCP), highlighting its benefits for connecting third-party services to large language models (LLMs).

This article details the process of building a chatbot named 'HackBot' that utilizes data from Hacker News and Stack Overflow, leveraging ClickHouse and LlamaIndex.

This article discusses the development of a Retrieval-Augmented Generation (RAG) pipeline for Google Analytics using ClickHouse and Amazon Bedrock.

This article explores how to integrate ClickHouse with OpenAI models using User Defined Functions (UDFs) to enhance SQL workloads with AI capabilities.

This article introduces the concept of vector search using ClickHouse, exploring the significance of vectors and embeddings in enhancing search capabilities.

This article discusses how to integrate real-time analytics into a Supabase application using ClickHouse, highlighting the differences between OLTP and OLAP databases.