Building a RAG‑Powered AI Customer Service for Tongcheng Travel FAQs with FastGPT

Traditional customer service relies on static scripts and limited knowledge bases, making it hard to handle complex or new queries. By leveraging Retrieval‑Augmented Generation (RAG) and FastGPT, the author builds an AI chatbot that can accurately understand user intent and retrieve precise answers from a structured FAQ dataset.

Data preparation : The author extracts question‑answer pairs from the Tongcheng Travel website, organizes them into four files, and formats each entry using XML‑like tags: <L1>会员服务</L1>, <L2>找回密码</L2>, <Q>忘记密码了，如何找回？</Q>, <A>...</A>. Entries are separated by a line of six equal signs ("======").

Knowledge base creation : Two FastGPT knowledge bases are created – one for “Tongcheng Travel – Membership Service” and another for “Tongcheng Travel – Train Tickets”. The formatted files are uploaded, and the system indexes the vectors for similarity search.

Workflow construction : In FastGPT’s workflow editor, the author adds nodes for an opening greeting, a question‑classification node (identifying whether the query belongs to the “Train Ticket” or “Membership Service” category), and two AI dialogue components that reference the corresponding knowledge bases. Prompt templates are defined, e.g., "You are a Tongcheng Travel客服, answer based on the provided knowledge" and rules for handling unmatched queries.

Testing : The chatbot is tested with several real‑world queries, such as "提示手机号已被注册怎么办" and "系统显示已发送验证码，长时间没收到短信怎么办？". Screenshots show that the bot returns the exact answers stored in the knowledge base or replies with "当前问题，正在学习中" when no match is found.

Log analysis : Conversation logs reveal the process: the AI first classifies the intent, then searches the appropriate knowledge base using vector similarity, and finally constructs a prompt that includes the matched <L1>, <L2>, <Q>, and <A> tags before sending it to the large language model.

Search methods overview : The article also explains four search techniques—exact search, fuzzy search, full‑text retrieval, and vector similarity—detailing their principles, examples, technical implementations, and typical use cases.

Problem classification : It highlights the importance of classifying user queries (e.g., e‑commerce pre‑sale routing, post‑sale issue identification, multi‑platform unified management) and shows how the classification node acts as an "intelligent router" that not only improves response speed but also builds a structured demand heatmap for product iteration.

Overall, the guide demonstrates a complete end‑to‑end pipeline: from raw FAQ extraction, through knowledge base construction and RAG‑enabled indexing, to workflow design, prompt engineering, testing, and analysis, providing a practical reference for building AI‑driven customer service solutions.