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Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm, blending the strengths of information retrieval and natural language generation. By leveraging large datasets to retrieve relevant information and generate coherent and contextually appropriate responses, RAG systems have the potential to revolutionize applications ranging from customer support to content creation.
Let us look at how RAG works. In a traditional setup, you will have a user prompt which is sent to the Large Language Model (LLM), and the LLM provides a completion:
But the problem with this setup is that the LLM's knowledge has a cutoff date, and it does not have insights into business-specific data.
RAG helps alleviate all the drawbacks that are listed above by allowing the LLM to access the knowledge base. Since the LLM now has context, the completions are more accurate and can now include business-specific data. The below diagram illustrates the value add RAG provides to content retrieval:
As you can see, by vectorizing business-specific data, which the LLM would not have access to, instead of just sending the prompt to the LLM for retrieval, you send the prompt and context and enable the LLM to provide more effective completions.
However, as powerful as RAG systems are, they face challenges, particularly in maintaining contextual accuracy and efficiently managing vast amounts of data.
Other Challenges include:
Knowledge graphs are sophisticated data structures that represent information in a graph format, where entities are nodes and relationships are edges. This structure plays a crucial role in overcoming the challenges faced by RAG systems, as it allows for a highly interconnected and semantically rich representation of data, enabling more effective organization and retrieval of information.
Below are some key advantages for leveraging knowledge graphs:
By linking information retrieved to specific aspects of the graph, knowledge graphs help increase factual accuracy.
Knowledge graphs fundamentally enhance RAG systems by providing a robust framework for understanding and navigating complex data relationships. They enable the AI not just to retrieve information based on keywords, but to also understand the context and interconnections between different pieces of information. This leads to more accurate, relevant, and contextually aware responses, significantly improving the performance of RAG applications.
Now let us look at the importance of knowledge graphs in enhancing RAG application through a coding example. To showcase the importance, we will take the example of retrieving a player recommendation for an NFL Fantasy Football draft. We will ask the same question to the RAG application with and without knowledge graphs implemented, and we will see the improvement in the output.
Let us look at the following code where we implement a RAG solution in its basic level for retrieving a football player of our choosing, which will be provided via a prompt. You can clearly see the output does not retrieve the accurate player based on our prompt.
We have oversimplified the RAG case for ease of understanding. Below is what the above code does:
Provides an example query, , which retrieves the most relevant player, generates a recommendation, and prints the recommendation message.
As you can see, when we were asked for a versatile player, the recommendation was Patrick Mahomes.
Now let us look at how knowledge graphs can help enhance RAG and give a better recommendation. As you see from the output below, the correct player is recommended based on the prompt.
Incorporating knowledge graphs into RAG applications results in more accurate, relevant, and context-aware recommendations, showcasing their importance in improving AI capabilities.
Here are a few key takeaways:
Below are some of the resources that help with learning knowledge graphs and their impact on RAG solutions.
