Developing RAG and Agent Applications with LangChain: A Case Study of an AI Assistant for Activity Components

This article describes the methodology for building Retrieval‑Augmented Generation (RAG) and Agent applications using LangChain, and analyzes an AIGC use case in the activity‑component business.

The AI assistant for activity components was realized in three stages: (1) a rapid prototype on the Dify platform to validate the AI‑business fit, (2) a performance‑optimized second version built with LangChain that adds RAG capabilities, and (3) a third version that incorporates Agent functionality to autonomously plan tasks, invoke tools, and retrieve historical activity and component data.

For the RAG implementation, the author leverages LangChain Expression Language (LCEL) to declaratively compose chains and integrates a cloud‑native vector‑search data warehouse for vector retrieval. The core RAG workflow consists of LLM polishing of user intent, structured data generation, knowledge‑base recall, context merging with the LLM, and finally recommending suitable activity components. Detailed steps include natural‑language‑to‑structured‑data conversion, knowledge‑base classification matching, top‑k retrieval, score‑based re‑ranking, and rendering of business‑specific results.

The Agent implementation follows a custom ReAct‑style design that performs planning, requirement decomposition, reflection, reasoning, and tool execution. Because the built‑in LangChain Agent was too generic for the complex business scenario, a hand‑crafted Agent was built to enable the AI to select and call tools based on user requests, such as querying the most popular recent games or listing activities that used a specific component.

A comparative evaluation of two large language models shows distinct trade‑offs: Model 1 exhibits longer latency (50‑70 s) and lower accuracy in interpreting queries, while Model 2 achieves higher speed (10‑20 s) and better understanding, likely due to tool‑call fine‑tuning.

Finally, the RAG and Agent capabilities are unified behind a front‑door AI routing layer, providing a single entry point for AI services.

The article concludes with a summary of the development approach, highlights the practical benefits of combining RAG and Agent techniques, and invites readers to discuss further ideas.