Implementing a chatbot
We’ll implement a chatbot now. We’ll assume you have the environment installed with the necessary libraries and the API keys as per the instructions in Chapter 3, Getting Started with LangChain.
To implement a simple chatbot in LangChain, you can follow this recipe:
- Set up a document loader.
- Store documents in a vector store.
- Set up a chatbot with retrieval from the vector storage.
We’ll generalize this with several formats and make this available through an interface in a web browser through Streamlit. You’ll be able to drop in your document and start asking questions. In production, for a corporate deployment for customer engagement, you can imagine that these documents are already loaded in, and your vector storage can just be static.
Let’s start with the document loader.
Document loader
As mentioned, we want to be able to read different formats:
from typing import Any...
