Codelab: Building an AI Agent With Couchbase AI Services & Agent Catalog

This CodeLab tutorial guides users in creating a Hotel Search Agent using LangChain, Couchbase AI Services, and Agent Catalog, integrating Arize Phoenix for observability. The process involves setting up Couchbase Capella for data and AI models, utilizing a unified platform that combines operational data, vector search, and AI models to streamline building AI applications. The tutorial emphasizes reducing operational overhead and latency by co-locating data and AI services, and using tools like Agent Catalog to manage agent prompts and tools efficiently. The guide includes steps for setting up an environment in Couchbase Capella, integrating the Agent Catalog for managing agent capabilities, building a LangChain agent with dynamic tool and prompt management, and implementing semantic caching to enhance response efficiency. It concludes with the use of Arize Phoenix for observing and evaluating the agent's performance, offering a scalable method for developing complex, multi-agent systems with robust data and tool management.

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