The Power of LangChain’s Question Answer Framework

Question Answer Abstraction by LangChain

LangChain introduces three types of question-answer methods. It is imperative to understand how these methods work in order to create and implement our customized and complex question-answer applications. These three methods are, VectorstoreIndexCreator, RetrievalQA, and load_qa_chain, as shown above. We can choose the one that best suits our needs and application.

In this article, I will provide a detailed discussion of each method. You can access the comprehensive Jupyter notebook through the following GitHub repository link. If you wish to follow this article sequentially, you’ll need to generate an OPENAI_API_KEY at https://openai.com/ and include it in your notebook. Additionally, the sample document can be found in the same GitHub link. All the required libraries are as follow:

import os
from langchain.indexes import VectorstoreIndexCreator
from langchain.indexes import VectorstoreIndexCreator
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.chains.question_answering import load_qa_chain
from langchain.chains…