Create a Q&A Chatbot with LangChain Project

LangChain is an advanced framework for developing language model-powered applications. One famous use case is creating customer support or Q&A chatbots. In this Create a Q&A Chatbot with LangChain Project, discover how to create a chatbot using Python and LangChain for the Introduction to Tableau course by Ned Krastev.

To achieve this goal, you'll employ Retrieval Augmented Generation (RAG)—a technique that has become increasingly popular for building AI-powered chatbots. This LangChain tutorial encompasses three stages: indexing, retrieval, and augmented generation.

First, we index by dividing the Introduction to Tableau course transcript into shorter sections with LangChain's Markdown header and token splitters. Next, we convert these sections into numerical vectors through embedding and store these embeddings in a Chroma vector store.

In the second stage, a retriever extracts the portion of the course that is most valuable for addressing a student's question.

In the final stage, the chat model uses the retrieved text and students' questions to generate an "augmented" response, demonstrating how to make a chatbot that provides accurate and contextual answers.

To implement the chain (representing the Q&A chatbot), we'll employ the LangChain Expression Language (LCEL) protocol. It allows connecting Runnable LangChain components so that the output of one serves as the input to the next component.

The student's question is inputted into a prompt template. Filling in the template creates a complete prompt to feed to a chat model. The response from the chat model is then fed to an output parser, ensuring we return the result to the student as a string.

The Create a Q&A Chatbot with LangChain Project will test your skills in:

• Utilizing LangChain's text splitters
• Managing chat messages and chat prompt templates
• Applying LLMs and constructing LCEL chains for course transcription correction
• Generating embeddings for storage in a vector store
• Constructing RunnableLambdas from regular Python functions and lambda functions
• Creating LCEL chains for a Q&A chatbot

So, ready to learn how to build a chatbot using LangChain and OpenAI? Let's begin this exciting chatbot project!

For this Create a Q&A Chatbot with LangChain Project, you'll need a valid OpenAI API key.

Note: This project necessitates using OpenAI's embedding and language models. Accordingly, monitor your token consumption and the associated charges.

You'll also need Jupyter Notebook up and running.

You're advised to create and work in a new environment specifically designed for this exercise, with a version of Python <4.0, >= 3.9. The following installations are required for this LangChain project:

• langchain v0.3.3

Note: pip install langchain==0.3.3 will also install langchain-core and langchain-text-splitters—both required for the project.

• langchain-chroma v0.1.4
• langchain-community v0.3.2
• langchain-openai v0.2.2
• pypdf v5.0.1
• python-dotenv v1.0.1

You can execute the chatbot project with different package versions. Be mindful, however, of package incompatibilities and differences in the LangChain syntax.

Since LangChain is subject to continuous updates, you might encounter minor syntax differences compared to the Build Chat Applications with OpenAI and LangChain course. If this occurs, consult the LangChain API Reference page for current classes and methods.