Building an end-to-end RAG flow
In the previous sections, we delved into the various steps in the RAG flow individually with simple data to demonstrate the usage. It would be a good idea to take a step back and use a real-world dataset, albeit a simple one, to complete the whole flow. For this, we will use the GitHub issues dataset (https://huggingface.co/datasets/lewtun/github-issues). We will look at how we can read this data and use it in the RAG flow. This would lay the foundation for the full end-to-end RAG flow implementation in later chapters.
In this example, we will load GitHub comments to be able to answer questions, such as how we can load data offline. We need to follow these steps to load the data and set up the retriever:
- Preparing the data: First, we need to prepare our dataset. We will use the Hugging Face
datasetslibrary:# Load the GitHub issues dataset issues_dataset = load_dataset("lewtun/github-issues", split="train"...
