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Retrieval Augmented Generation (RAG) has transformed how we interact with LLMs over the past few years. The technique enhances LLM performance by incorporating reliable external knowledge sources into what the system already knows. Implementing RAG has significantly reduced the possibility of artificial hallucination and, therefore, improved the quality of LLM outputs as the results are augmented by the most current and relevant information available.
While RAG has equipped LLMs with the ability to generate more contextually aware responses, its limitations are obvious—since the generation model pulls relevant information from different knowledge bases independently, it struggles to effectively integrate the retrieved information into a coherent response, especially if the context is more complex or nuanced. For example, if the external knowledge base contains incorrect or noisy information, or if the query includes homonyms and polysemous terms, there may be a high chance of the RAG system generating biased results based on imagined facts or hallucinations.
To further enhance the accuracy and the quality of the output, a “Marie Kondo” approach that organizes, arranges, and filters unstructured data in the knowledge base becomes essential—that’s where GraphRAG enters the picture.
In short, GraphRAG is an advanced version of the RAG system that, instead of treating the knowledge base as a flat repository, presents information as a network of interconnected entities. So, instead of simply retrieving information from isolated knowledge sources, GraphRAG analyzes the relationships and reads data in different contexts to enable a much more cohesive response.
There are three primary factors that affect the quality of outputs generated by an RAG system:
Retriever: The retriever searches targeted knowledge bases, identifies and retrieves relevant documents and data points based on the user query. In the retrieving process, various techniques such as semantic and keyword-based searches are deployed.
Generator: Once the information is retrieved, the LLM model combines the data and initial user query to create a coherent response. The generator, therefore, integrates all data snippets before providing the final response to the user query.
Knowledge Base: The knowledge base is the repository for information extraction. It contains unstructured or structured data such as facts, figures, and entities.
For a more detailed analysis of best practices when developing and deploying production-grade RAG systems, read our full paper here.
Let’s break them down to see the different approaches executed by traditional RAG and GraphRAG.
What are Knowledge Graphs?
Here comes the more interesting part—what sets GraphRAG apart from the traditional RAG is its ability to leverage knowledge graphs (KGs). So, what exactly is a knowledge graph?
Simply put, a knowledge graph is a structured representation of relational information between entities. In a knowledge graph, entities such as people, locations, concepts, terms, and objects are presented as nodes. The relationships that connect each node are represented by edges, indicating their corresponding relevance.
Here’s an example of a commonly seen knowledge graph:
https://neo4j.com/blog/what-is-knowledge-graph/
Such a graph-based approach provides a way for the system to visualize and understand the connections among data points, making it particularly effective for applications that require nuanced understanding, such as recommendation systems, semantic search, and predictive analysis.
To create a knowledge graph, one needs to conceptually map the graph data model before implementing it in a database. Choosing the right database can simplify the design process and speed up the development process.
Neo4j, for example, is one of the leading graph database providers dedicated to developing an approach that integrates knowledge graphs with traditional RAG to enhance the accuracy and contextual relevance of responses in AI applications. Earlier this year, the company also announced its partnership with Google Cloud to launch new GraphRAG capabilities for generative AI applications.
JanusGraph is another open-source graph database designed for scalability and high performance. For complex queries, the system is capable of processing large-scale graphs with billions of vertices and edges before generating a highly optimized and contextually relevant response.
Choosing the Right Tool: Standard RAG or GraphRAG?
Now that we’ve understood the main differences between RAG and GraphRAG, organizations need to decide for themselves when and how they should be used.
Since GraphRAG is capable of navigating complex relationships and, therefore, producing outputs with much more accurate contextual understanding, it’s particularly well-suited for scenarios where data points are interconnected, such as knowledge management, consumer marketing, and trend analysis.
Consumer Market
GraphRAG can be used to recommend similar products based on user search terms, leveraging the relationship between items to produce personalized recommendations. This technique can be used by online shopping sites. Amazon, for example, uses sophisticated recommendation algorithms that analyze user search terms and purchase history to suggest similar products.
Similarly, GraphRAG can be used to detect fraud in banking by identifying interconnected transaction patterns—an unusual transaction may trigger an alarm based on deviations from established networks. In the meantime, GraphRAG can also streamline insurance claims by automatically connecting different policyholders and service providers, significantly speeding up the claiming process.
Even in the courtroom, attorneys rely heavily on previous court decisions to establish legal principles relevant to the case. As the precedents build up, GraphRAG can be utilized to extract key information and connect relevant cases as well as legal doctrines before establishing compelling arguments.
Deploy and Scale
Despite the advanced capabilities offered by GraphRAG, the reality is that LLMs are expensive and usage is never constant. For organizations looking to scale up and down their models on demand, leveraging solutions that can make the scaling as seamless as possible seems to be the optimal choice.
Shakudo is well-acquainted with the challenges of deploying and scaling RAG and GraphRAG use cases and the velocity constraints that arise from creating scaling solutions for these unique workloads.
To minimize the risks present throughout the RAG and GraphRAG development process, Shakudo integrates a production-ready RAG stack that allows the team to set up RAG-based LLM in minutes. The platform supports end-to-end RAG workflows by integrating power vector databases, such as Qdrant, Neon, and Milvus, in VPC setups that not only ensure immediate access but also protect privacy.
The Kubernetes-native environment allows Shakudo to scale up and down freely when the knowledge graphs grow as demand increases.
Shakudo’s ability to handle both types of deployments without heavy DevOps involvement allows businesses to focus on refining their AI applications rather than the infrastructure, making it a strong choice for companies implementing RAG and Graph-RAG solutions in sectors with strict data privacy requirements.
Retrieval Augmented Generation (RAG) has transformed how we interact with LLMs over the past few years. The technique enhances LLM performance by incorporating reliable external knowledge sources into what the system already knows. Implementing RAG has significantly reduced the possibility of artificial hallucination and, therefore, improved the quality of LLM outputs as the results are augmented by the most current and relevant information available.
While RAG has equipped LLMs with the ability to generate more contextually aware responses, its limitations are obvious—since the generation model pulls relevant information from different knowledge bases independently, it struggles to effectively integrate the retrieved information into a coherent response, especially if the context is more complex or nuanced. For example, if the external knowledge base contains incorrect or noisy information, or if the query includes homonyms and polysemous terms, there may be a high chance of the RAG system generating biased results based on imagined facts or hallucinations.
To further enhance the accuracy and the quality of the output, a “Marie Kondo” approach that organizes, arranges, and filters unstructured data in the knowledge base becomes essential—that’s where GraphRAG enters the picture.
In short, GraphRAG is an advanced version of the RAG system that, instead of treating the knowledge base as a flat repository, presents information as a network of interconnected entities. So, instead of simply retrieving information from isolated knowledge sources, GraphRAG analyzes the relationships and reads data in different contexts to enable a much more cohesive response.
There are three primary factors that affect the quality of outputs generated by an RAG system:
Retriever: The retriever searches targeted knowledge bases, identifies and retrieves relevant documents and data points based on the user query. In the retrieving process, various techniques such as semantic and keyword-based searches are deployed.
Generator: Once the information is retrieved, the LLM model combines the data and initial user query to create a coherent response. The generator, therefore, integrates all data snippets before providing the final response to the user query.
Knowledge Base: The knowledge base is the repository for information extraction. It contains unstructured or structured data such as facts, figures, and entities.
For a more detailed analysis of best practices when developing and deploying production-grade RAG systems, read our full paper here.
Let’s break them down to see the different approaches executed by traditional RAG and GraphRAG.
What are Knowledge Graphs?
Here comes the more interesting part—what sets GraphRAG apart from the traditional RAG is its ability to leverage knowledge graphs (KGs). So, what exactly is a knowledge graph?
Simply put, a knowledge graph is a structured representation of relational information between entities. In a knowledge graph, entities such as people, locations, concepts, terms, and objects are presented as nodes. The relationships that connect each node are represented by edges, indicating their corresponding relevance.
Here’s an example of a commonly seen knowledge graph:
https://neo4j.com/blog/what-is-knowledge-graph/
Such a graph-based approach provides a way for the system to visualize and understand the connections among data points, making it particularly effective for applications that require nuanced understanding, such as recommendation systems, semantic search, and predictive analysis.
To create a knowledge graph, one needs to conceptually map the graph data model before implementing it in a database. Choosing the right database can simplify the design process and speed up the development process.
Neo4j, for example, is one of the leading graph database providers dedicated to developing an approach that integrates knowledge graphs with traditional RAG to enhance the accuracy and contextual relevance of responses in AI applications. Earlier this year, the company also announced its partnership with Google Cloud to launch new GraphRAG capabilities for generative AI applications.
JanusGraph is another open-source graph database designed for scalability and high performance. For complex queries, the system is capable of processing large-scale graphs with billions of vertices and edges before generating a highly optimized and contextually relevant response.
Choosing the Right Tool: Standard RAG or GraphRAG?
Now that we’ve understood the main differences between RAG and GraphRAG, organizations need to decide for themselves when and how they should be used.
Since GraphRAG is capable of navigating complex relationships and, therefore, producing outputs with much more accurate contextual understanding, it’s particularly well-suited for scenarios where data points are interconnected, such as knowledge management, consumer marketing, and trend analysis.
Consumer Market
GraphRAG can be used to recommend similar products based on user search terms, leveraging the relationship between items to produce personalized recommendations. This technique can be used by online shopping sites. Amazon, for example, uses sophisticated recommendation algorithms that analyze user search terms and purchase history to suggest similar products.
Similarly, GraphRAG can be used to detect fraud in banking by identifying interconnected transaction patterns—an unusual transaction may trigger an alarm based on deviations from established networks. In the meantime, GraphRAG can also streamline insurance claims by automatically connecting different policyholders and service providers, significantly speeding up the claiming process.
Even in the courtroom, attorneys rely heavily on previous court decisions to establish legal principles relevant to the case. As the precedents build up, GraphRAG can be utilized to extract key information and connect relevant cases as well as legal doctrines before establishing compelling arguments.
Deploy and Scale
Despite the advanced capabilities offered by GraphRAG, the reality is that LLMs are expensive and usage is never constant. For organizations looking to scale up and down their models on demand, leveraging solutions that can make the scaling as seamless as possible seems to be the optimal choice.
Shakudo is well-acquainted with the challenges of deploying and scaling RAG and GraphRAG use cases and the velocity constraints that arise from creating scaling solutions for these unique workloads.
To minimize the risks present throughout the RAG and GraphRAG development process, Shakudo integrates a production-ready RAG stack that allows the team to set up RAG-based LLM in minutes. The platform supports end-to-end RAG workflows by integrating power vector databases, such as Qdrant, Neon, and Milvus, in VPC setups that not only ensure immediate access but also protect privacy.
The Kubernetes-native environment allows Shakudo to scale up and down freely when the knowledge graphs grow as demand increases.
Shakudo’s ability to handle both types of deployments without heavy DevOps involvement allows businesses to focus on refining their AI applications rather than the infrastructure, making it a strong choice for companies implementing RAG and Graph-RAG solutions in sectors with strict data privacy requirements.
Retrieval Augmented Generation (RAG) has transformed how we interact with LLMs over the past few years. The technique enhances LLM performance by incorporating reliable external knowledge sources into what the system already knows. Implementing RAG has significantly reduced the possibility of artificial hallucination and, therefore, improved the quality of LLM outputs as the results are augmented by the most current and relevant information available.
While RAG has equipped LLMs with the ability to generate more contextually aware responses, its limitations are obvious—since the generation model pulls relevant information from different knowledge bases independently, it struggles to effectively integrate the retrieved information into a coherent response, especially if the context is more complex or nuanced. For example, if the external knowledge base contains incorrect or noisy information, or if the query includes homonyms and polysemous terms, there may be a high chance of the RAG system generating biased results based on imagined facts or hallucinations.
To further enhance the accuracy and the quality of the output, a “Marie Kondo” approach that organizes, arranges, and filters unstructured data in the knowledge base becomes essential—that’s where GraphRAG enters the picture.
In short, GraphRAG is an advanced version of the RAG system that, instead of treating the knowledge base as a flat repository, presents information as a network of interconnected entities. So, instead of simply retrieving information from isolated knowledge sources, GraphRAG analyzes the relationships and reads data in different contexts to enable a much more cohesive response.
There are three primary factors that affect the quality of outputs generated by an RAG system:
Retriever: The retriever searches targeted knowledge bases, identifies and retrieves relevant documents and data points based on the user query. In the retrieving process, various techniques such as semantic and keyword-based searches are deployed.
Generator: Once the information is retrieved, the LLM model combines the data and initial user query to create a coherent response. The generator, therefore, integrates all data snippets before providing the final response to the user query.
Knowledge Base: The knowledge base is the repository for information extraction. It contains unstructured or structured data such as facts, figures, and entities.
For a more detailed analysis of best practices when developing and deploying production-grade RAG systems, read our full paper here.
Let’s break them down to see the different approaches executed by traditional RAG and GraphRAG.
What are Knowledge Graphs?
Here comes the more interesting part—what sets GraphRAG apart from the traditional RAG is its ability to leverage knowledge graphs (KGs). So, what exactly is a knowledge graph?
Simply put, a knowledge graph is a structured representation of relational information between entities. In a knowledge graph, entities such as people, locations, concepts, terms, and objects are presented as nodes. The relationships that connect each node are represented by edges, indicating their corresponding relevance.
Here’s an example of a commonly seen knowledge graph:
https://neo4j.com/blog/what-is-knowledge-graph/
Such a graph-based approach provides a way for the system to visualize and understand the connections among data points, making it particularly effective for applications that require nuanced understanding, such as recommendation systems, semantic search, and predictive analysis.
To create a knowledge graph, one needs to conceptually map the graph data model before implementing it in a database. Choosing the right database can simplify the design process and speed up the development process.
Neo4j, for example, is one of the leading graph database providers dedicated to developing an approach that integrates knowledge graphs with traditional RAG to enhance the accuracy and contextual relevance of responses in AI applications. Earlier this year, the company also announced its partnership with Google Cloud to launch new GraphRAG capabilities for generative AI applications.
JanusGraph is another open-source graph database designed for scalability and high performance. For complex queries, the system is capable of processing large-scale graphs with billions of vertices and edges before generating a highly optimized and contextually relevant response.
Choosing the Right Tool: Standard RAG or GraphRAG?
Now that we’ve understood the main differences between RAG and GraphRAG, organizations need to decide for themselves when and how they should be used.
Since GraphRAG is capable of navigating complex relationships and, therefore, producing outputs with much more accurate contextual understanding, it’s particularly well-suited for scenarios where data points are interconnected, such as knowledge management, consumer marketing, and trend analysis.
Consumer Market
GraphRAG can be used to recommend similar products based on user search terms, leveraging the relationship between items to produce personalized recommendations. This technique can be used by online shopping sites. Amazon, for example, uses sophisticated recommendation algorithms that analyze user search terms and purchase history to suggest similar products.
Similarly, GraphRAG can be used to detect fraud in banking by identifying interconnected transaction patterns—an unusual transaction may trigger an alarm based on deviations from established networks. In the meantime, GraphRAG can also streamline insurance claims by automatically connecting different policyholders and service providers, significantly speeding up the claiming process.
Even in the courtroom, attorneys rely heavily on previous court decisions to establish legal principles relevant to the case. As the precedents build up, GraphRAG can be utilized to extract key information and connect relevant cases as well as legal doctrines before establishing compelling arguments.
Deploy and Scale
Despite the advanced capabilities offered by GraphRAG, the reality is that LLMs are expensive and usage is never constant. For organizations looking to scale up and down their models on demand, leveraging solutions that can make the scaling as seamless as possible seems to be the optimal choice.
Shakudo is well-acquainted with the challenges of deploying and scaling RAG and GraphRAG use cases and the velocity constraints that arise from creating scaling solutions for these unique workloads.
To minimize the risks present throughout the RAG and GraphRAG development process, Shakudo integrates a production-ready RAG stack that allows the team to set up RAG-based LLM in minutes. The platform supports end-to-end RAG workflows by integrating power vector databases, such as Qdrant, Neon, and Milvus, in VPC setups that not only ensure immediate access but also protect privacy.
The Kubernetes-native environment allows Shakudo to scale up and down freely when the knowledge graphs grow as demand increases.
Shakudo’s ability to handle both types of deployments without heavy DevOps involvement allows businesses to focus on refining their AI applications rather than the infrastructure, making it a strong choice for companies implementing RAG and Graph-RAG solutions in sectors with strict data privacy requirements.
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