7 Q&A chatbots with LangChain and LangSmith
This chapter covers
- Implementing RAG with LangChain
- Q&A across multiple documents
- Tracing RAG chain execution with LangSmith
- Alternative implementation using LangChain Q&A specialized functionality
Now that you understand the RAG design pattern, implementing it with LangChain should be straightforward. In this chapter, I’ll show you how to use the LangChain object model to abstract interaction with source documents, the vector store, and the LLM.
We'll also explore LangSmith’s tracing capabilities for monitoring and troubleshooting the chatbot workflow. Additionally, I’ll demonstrate alternative chatbot implementations using LangChain’s specialized Q&A classes and functions.
By the end of this chapter, you’ll be equipped with the skills to build a search-enabled chatbot that can connect to private data sources—a complete version of which you’ll construct in Chapter 12.
Before we start implementing the chatbot with LangChain, let’s review the LangChain classes that support the Q&A chatbot use case.
7.1 LangChain object model for Q&A chatbots
As discussed earlier, the key benefit of using LangChain for your LLM-based application is its ability to handle communication between components like data loaders, vector stores, and LLMs. Instead of working with each API directly, LangChain abstracts these interactions. This allows you to swap out any component with a different provider without changing the overall design of your application.