LangChain: Conversational Concierge Chatbot

I’ll explain how we built a virtual concierge for one of the largest hotel chains in the world. We set out to create a chatbot with three main goals:

1. Style: Use brand voice.
2. Content: Recommend highly curated niche “finds” to explore in the area.
3. Safety: Prevent the chatbot from talking about sensitive topics.

Tools and Techniques

• Chaining prompts: There are ~20 different prompts and ~15 unique paths through the system.
• Message classification: At the beginning of the chain messages are classified to route them appropriately.
• Self evaluation: Evaluation prompts are run in the chain to evaluate if a reply is good. If it isn’t, we branch to a hallucination prevention prompt.
• Tracing and debugging: LangSmith and Chainlit offer visibility into each chain run’s details, allowing us to identify problem areas and experiment with prompt improvements.
• Example selector via embeddings: The most relevant examples (few shot) from a corpus are selected using embeddings and similarity to the incoming message.
• Pinecone hybrid search: Pinecone hybrid search is used to find the most relevant places/activities to recommend.
• Chat history summarization: Current and previous messages are passed through a prompt to summarize intent that may have been expressed over multiple messages.
• Retrieval augmented generation: A markdown document full of hotel details enables the chatbot to answer specific questions about the property.