chapter six

6 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 will have the knowledge needed to build an enterprise semantic search chatbot, which you will construct in a subsequent chapter.

Before we start implementing the chatbot with LangChain, let’s review the LangChain classes that support the Q&A chatbot use case.

6.1 LangChain object model for Q&A chatbots

As I mentioned in earlier chapters, the main advantage of implementing your LLM-based application in LangChain, rather than using directly the APIs of data loaders, vector stores, and LLMs directly, is its ability to abstract the communication with all these components. This means if you want to replace any component with an alternative provider, the design of your application remains unaffected.

6.1.1 Content Ingestion (Indexing) Stage

6.1.2 Q&A (Retrieval and Generation) Stage

6.2 Vector Store Content Ingestion

6.2.1 Splitting and Storing the Documents

6.2.2 Ingesting Multiple Documents from a Folder

6.3 Q&A Across Stored Documents

6.3.1 Querying the vector store directly

6.3.2 Asking a Question through a LangChain Chain

6.3.3 Completing the RAG Chain Setup

6.4 Chatbot memory of message history

6.4.1 Amending the Prompt

6.4.2 Updating the Chat Message History

6.4.3 Feeding the Chat History to the RAG Chain

6.4.4 Putting Everything Together

6.5 Tracing Execution with LangSmith

6.5.1 Inspecting the LangSmith Traces

6.6 Creating a Q&A Chain with RetrievalQA

6.7 Summary