7 Essential RAG Frameworks: Enhance Your AI with Precision and Reliability

The Ultimate Guide to Implementing Retrieval-Augmented Generation in Your AI Projects

Many people have heard of or practiced RAG. Its most direct application currently is building intelligent question-answering systems.

What is RAG?

RAG is short for Retrieval Augmented Generation.

From the name, we can split RAG into three main parts: retrieval, augmentation, and generation. The basic meaning is:

1. Retrieve various things from a knowledge base

2. Merge the retrieved information into the prompt to augment the input information

3. Finally, the large model generates answers that are more factual

Why is RAG needed?

The issue of "hallucinations" by large models has always existed. RAG is an important way to alleviate their hallucinations, although there are other methods like SFT.

Here are some important advantages:

1. Outside information helps large models reason better and be more accurate. It makes their answers match facts.

2. The knowledge base is easy to change. If something is wrong, it can be fixed quickly. The user does not notice. This is unlike SFT which needs retraining and takes time.

3. Engineering allows giving sources that explain the answers. This makes the answers more believable. It is like smart searches that show where answers came from.

So how many open-source frameworks currently make it easy to do this?

Is it necessary to reinvent the wheel?

That's a good question.

So in summary, it's quite necessary to look at the available open-source RAG frameworks and see if they meet development needs.