Introduction to Knowledge Augmented Generation (KAG)

Knowledge Augmented Generation (KAG) is a framework that enhances Retrieval Augmented Generation (RAG) by utilizing knowledge graphs to maintain entity relationships and enable advanced reasoning capabilities. In this article, we will delve into the architecture of KAG and provide a hands-on guide to setting it up using Docker, making complex AI retrieval more accessible.

What is Knowledge Augmented Generation (KAG)?

KAG is a significant improvement over standard RAG, which has limitations such as the chunking process that loses logical connections between entities. Introduction to KAG at 2 seconds

Limitations of Standard RAG

The biggest limitation of standard RAG is the chunking process, which loses logical connections between entities. This is because each retrieved chunk is treated individually during the generation process. Limitations of Standard RAG at 45 seconds

Architecture of KAG

The architecture of KAG is designed to overcome the limitations of standard RAG. It uses knowledge graphs to maintain entity relationships and enable advanced reasoning capabilities. Architecture of KAG at 150 seconds

Setting Up KAG on Your Local Machine

To set up KAG on your local machine, you can use Docker. This makes complex AI retrieval more accessible and allows you to easily manage knowledge bases and query them. Setting Up KAG at 240 seconds

Querying the Knowledge Base

Once you have set up KAG on your local machine, you can query the knowledge base using Python code. This allows you to easily retrieve and manipulate data from the knowledge base. Querying the Knowledge Base at 360 seconds

Conclusion

In conclusion, Knowledge Augmented Generation (KAG) is a significant improvement over standard Retrieval Augmented Generation (RAG). It uses knowledge graphs to maintain entity relationships and enable advanced reasoning capabilities, making it a powerful tool for complex AI retrieval. By following the steps outlined in this article, you can set up KAG on your local machine using Docker and start querying the knowledge base using Python code. Whether you're interested in retrieval augmented generation or different variants of it, KAG is definitely worth checking out.

The project itself is open-source under Apache 2.0, and the authors are releasing a new version that will have custom schema and visual queries. This will allow you to use different models at different stages, potentially improving performance. So, if you're interested in retrieval augmented generation or agents, make sure to subscribe to the channel and check out the updated KAG repo. Thanks for watching, and as always, see you in the next one!