[Lecture] 大模型練丹術 - RAG tutorials

發表人 **mtr** 發表於 2025-03-21, 18:32

Retrieval-Augmented Generation (RAG) is a hybrid approach that combines information retrieval with generative models. It enhances the performance of language models by incorporating external knowledge, which improves accuracy and factual correctness.

In a Simple RAG setup, we follow these steps:

Data Ingestion: Load and preprocess the text data.

Chunking: Break the data into smaller chunks to improve retrieval performance.

Embedding Creation: Convert the text chunks into numerical representations using an embedding model.

Semantic Search: Retrieve relevant chunks based on a user query.
Response Generation: Use a language model to generate a response based on retrieved text.

This notebook implements a Simple RAG approach, evaluates the model’s response, and explores various improvements.

https://github.com/FareedKhan-dev/all-r ... me-ov-file