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Use cases

AI-Powered Chatbots

Enhance your AI-powered chatbots with CrateDB's robust vector store support, and real-time data analysis, enabling intelligent, personalized responses. 

Digital Twins and Generative AI: How TGW Revolutionizes Warehouse Operations with CrateDB's Combination of Time Series, Documents, and Vectors

In this talk, TGW Logistics showcases their use of CrateDB to optimize distribution centers. With up to half a million items handled daily, they focus on automation and data-driven decisions.

How to Build AI-driven knowledge assistants

White Paper: How to Build AI-driven Knowledge Assistants with a Vector Store, LLMs and RAG Pipelines

This white paper explores how CrateDB provides a scalable platform to build Generative AI applications that cover the requirements of modern applications, such as AI-driven knowledge assistants. CrateDB is not just handling vectors, but also provides in a single storage engine a unique combination of all the data types needed for end-to-end applications, including RAG pipelines.

Demo: Building an AI Chatbot with CrateDB and LangChain

This video shows step by step how to build an AI-powered chatbot using LangChain to connect to the different LLMs and CrateDB to store embeddings and run similarity searches against them.

How to Build AI-driven Knowledge Assistants with a Vector Store, LLMs and RAG Pipelines

Other resources on AI-powered chatbots

FAQ

AI chatbots are applications or interfaces that engage in human-like conversations using natural language understanding (NLU), natural language processing (NLP), and machine learning (ML). These chatbots use conversational AI to communicate with users, though not all chatbots employ this technology. Databases such as CrateDB can enhance AI-powered chatbots by providing robust vector store support and real-time data analysis, enabling them to deliver intelligent and personalized responses.

AI chatbot needs a database to store chat information and relevant user metadata. This database is the chatbot's memory, organizing data to enable quick and accurate responses. AI applications benefit from flexible data modeling, so a database that supports various data structures is advantageous. Examples of databases suitable for AI-powered chatbots include CrateDB, MongoDB, MySQL, and Postgres. CrateDB offers support for multiple data formats within a single database and even within a single table, thanks to its dynamic data schema.

AI chatbots gather data from a variety of sources, including publicly available data, databases, websites, APIs, and structured knowledge bases. They also rely on real-time information and updates to provide accurate responses. CrateDB’s dynamic schema supports multiple data formats from various data sources seamlessly, including structured, unstructured, semi-structured, and binary data.

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