RAG using LangChain, LlamaIndex, and OpenAI

Retrieval-Augmented Generation (RAG) using LangChain, LlamaIndex and OpenAI

Summary

Large Language Models (LLMs) can produce incorrect yet plausible responses due to “hallucination,” stemming from outdated knowledge. The Retrieval-Augmented Generation (RAG) framework addresses this by integrating an information retrieval system into the LLM pipeline. Instead of relying solely on pre-trained data, RAG enables the model to dynamically fetch accurate, up-to-date information from external sources during response generation. This approach enhances the relevance and accuracy of the information provided without the need for constant retraining or fine-tuning.

Local Setup

• Create a Python virtual environment and activate

  python -m venv <env-name>

  .\<env-name>\Scripts\activate

• Install the packages and dependencies as listed in requirements file

  pip install -r requirements.txt --no-cache-dir --disable-pip-version-check

• Start your development Jupyter Notebook or Jupyter Lab server

  jupyter lab --notebook-dir=.\notebook --no-browser

  jupyter notebook

  jupyter_nbextensions_configurator