Integrating RAG with a Knowledge Graph: Step-by-Step Guide

Key Takeaways

in this video I will discuss how to integrate knowledge graphs into the RG process the first question we need to consider is why this is the classic RG process given a question we retrieve related chunks from the vector database and then include these chunks in the prompt to call a large language model to illustrate the necessity of knowledge graphs in this process let me provide an example suppose the user asks what are the common side effects of cold medications the user wants to know the side effects of medications used to treat colds we aim to use the vector database to find relevant information imagine the vector database contains a section describing one particular medication such as cold medicine a however this section does not explicitly mention the keyword cold without this keyword the question might fail to match the chunk as the similarity score would not be high enough yet this medication is indeed used to treat colds conversely if we already know that cold medicine is a common treatment for colds we could use this knowledge to locate the relevant chunks so where do these relationships come from the answer is the knowledge graph let's explore how a Knowledge Graph can solve such problems first we Define find a simple graph here each node represents an entity and the arrows indicate relationships for instance medication 1 and medication 2 can treat colds and colds themselves have certain symptoms now let's consider how this graph can integrate with the RG process we revisit the question one problem we can address is question expansion for example when the question mentions common cold medications we can use the knowledge graph to identify specific medications such as medication 1 and medication 2 using this information we can expand the question too what are the side effects of common coal medications such as medication 1 and medication 2 this makes the question more specific once the question is expanded it can proceed through two paths first first it can query the vector database to retrieve relevant chunks second it can query the knowledge graph to extract a sub rough corresponding to the question the expanded question now includes key entities such as cold medication 1 and medication 2 using these entities we can locate their corresponding nodes in the graph and extract a subg grath containing these nodes for example the relation relationships for medication 1 and medication 2 indicate their use for colds these relationships form a subg next we combine the subgraph with the retriev chunks since the subgraph is structured data it cannot be directly merged with the textual data to address this we convert the graph into a textual format the graph consists of multiple triplets subject predicate object such as colds can be treated with medication 1 and colds can be treated with medication 2 we translate these triplets in a text then merge the textual representation of the graph with the chunks retrieved from the vector database the combined result continues through the subsequent steps this approach ensures that the final context includes potential answers it compensates for cases where the vector database alone might miss critical information however this process introduces several challenges the first challenge is constructing the knowledge graph itself creating a graph is often labor intensive traditionally it was done manually nowadays large language models can assist by constructing graphs from text using prompts the Second Challenge is filtering redundant information merge results may contain overlap between between the vector database and the graph post-merge filtering is necessary the third challenge is extracting subgraphs for example the simplest approach is one hop neighbors but more comprehensive methods might include two hop or three hop neighbors the more nodes we include the Richer the information but there is a tradeoff and complexity the fourth challenge is leveraging the subro for further Vector data based queries for instance the sub itself can be used to perform additional retrieval potentially enriching the retrieved information finally integrating structured information into the process remains a challenge graphs inherently have structure unlike the unstructured chunks retrieve from a vector database teaching large language models to understand and utilize the structure effectively is difficult potential approaches include writing structured prompts incorporating graph neural network techniques despite these challenges integrating knowledge graphs into rag can enhance performance by reducing hallucinations and improving context quality if you're interested in these topics feel free to leave a comment and let's discuss further