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, building a RAG-based chatbot with LangChain will feel much more approachable. In this chapter, I’ll walk you through how to use the LangChain object model to manage interactions with source documents, the vector store, and the LLM.
We’ll also explore how to use LangSmith’s tracing tools to monitor and troubleshoot the chatbot workflow. On top of that, I’ll demonstrate alternative implementations that leverage LangChain’s specialized Q&A classes and functions.
By the end of this chapter, you’ll have the skills to create a search-enabled chatbot that can seamlessly connect to private data sources.
Before diving into the implementation, let’s take a moment to review the key LangChain classes that support the Q&A chatbot use case.
7.1 LangChain object model for Q&A chatbots
As discussed earlier, one of LangChain’s biggest advantages for LLM-based applications is its ability to orchestrate communication between components such as data loaders, vector stores, and LLMs. Instead of integrating directly with each API, LangChain provides abstractions that let you swap out any component with a different provider—without disrupting the overall design of your application.