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How To Improve Your Rag System for More Efficient Question-Answering
Data Science   Latest   Machine Learning

How To Improve Your Rag System for More Efficient Question-Answering

Last Updated on March 26, 2024 by Editorial Team

Author(s): Eivind Kjosbakken

Originally published on Towards AI.

Improve your RAG system with tools learned in this article

This article continues my last article on making a RAG system. This article will improve on the RAG system developed in the previous article by splitting the data more intuitively, giving the RAG system more options for retrieval, and using a better LLM.

Improve your RAG system with this article. Image by ChatGPT. “make an image on “improving on a RAG system”” prompt. ChatGPT, 4, OpenAI, 23 Mar. 2024.

My motivation for this article is similar to my last article: to create a RAG system that can search emails for me instead of having to find emails myself with a direct word search. If you have not read my last article, I recommend reading that first, as I will build on my code from there. In this article, I will implement several improvements to the RAG system that make it more viable to use in a real-world setting.

· Motivation· Improving chunking· Adding an option for returning info from a specific email· Using a better LLM· Upgrading the context window· Conclusion

First, you can import all required packages:

# import packagesfrom langchain_community.document_loaders import WebBaseLoaderfrom langchain_text_splitters import RecursiveCharacterTextSplitterfrom langchain_community.embeddings import GPT4AllEmbeddingsfrom langchain_community.vectorstores import Chromafrom langchain_community.llms import LlamaCppfrom langchain_core.output_parsers import StrOutputParserfrom langchain_core.prompts import PromptTemplatefrom langchain.docstore.document import… Read the full blog for free on Medium.

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