The advent of Large Language Models (LLMs) has revolutionized the way machines understand and generate human language. However, their ability to retrieve and utilize external knowledge remains a challenge. Retrieval-Augmented Generation (RAG) addresses this by combining the generative capabilities of LLMs with the retrieval of relevant information from external data sources. There are retrieval challenges in conventional RAG approaches as well. RAG models might struggle with ambiguous queries. The lack of clear context can lead to the retrieval of irrelevant documents, impacting the quality of the generated text. Large Language Models often have limitations on the amount of text they can process at once. Knowledge graphs, which represent information in a structured format, have proven to be particularly effective in enhancing the retrieval process, leading to more accurate and contextually relevant responses.
Graph RAG represents a significant advancement in this domain, offering a global approach to RAG that incorporates knowledge graph generation, retrieval-augmented generation, and query-focused summarization (QFS) to support human sense-making over entire text corpora. This blog aims to dissect the methodologies and applications of RAG using knowledge graphs.
Theoretical Framework
Large Language Models and Knowledge Graphs
LLMs, such as Claude-3, have demonstrated remarkable proficiency in generating human-like text. However, their performance is often limited by the size of their context window and the static nature of their training data. Knowledge graphs offer a dynamic and structured way to supplement LLMs with external information. A knowledge graph is a network of entities, their attributes, and the relationships between them, which can be used to represent domain-specific knowledge in a machine-readable format.
Retrieval-Augmented Generation
RAG is a technique that enhances the generative capabilities of LLMs by first retrieving relevant documents or data snippets and then using this retrieved information to inform the generation process. This approach allows LLMs to produce responses that are not only coherent but also factually accurate and up-to-date, based on the latest available information.
Methodology
Knowledge Graph Construction
The construction of a knowledge graph is a critical step in the RAG process. It involves defining the graph structure, parsing and integrating data from various sources, and creating embeddings for each node to facilitate semantic searching. The knowledge graph must accurately represent the domain-specific knowledge and be comprehensive enough to cover the scope of potential queries over the enterprise knowledge landscape.
Retrieval and Question Answering
Once the knowledge graph is constructed, the RAG system can parse consumer queries to identify named entities and intents. The system then retrieves related sub-graphs from the knowledge graph to generate answers. This process ensures that the responses are not only relevant but also grounded in the structured knowledge represented in the graph.
Step 1: Defining the Knowledge Graph Structure
The first step in implementing an RAG system using knowledge graphs is to define the structure of the graph. This involves identifying the key entities, their attributes, and the types of relationships that exist between them. The structure should be tailored to the specific domain and the types of queries the system is expected to handle.
Step 2: Data Integration and Graph Construction
The next step is to integrate data from various sources into the knowledge graph. This may involve parsing historical records, issue tracking tickets, or any other relevant data. The data must be processed to extract entities, attributes, and relationships, which are then used to construct the graph.
Step 3: Node Embedding Generation
To facilitate semantic searching within the knowledge graph, embeddings for each node must be generated. These embeddings are vector representations that capture the semantic meaning of the nodes and can be used to compute similarity between queries and graph elements.
Step 4: Query Parsing and Entity Recognition
When a query is received, the RAG system must parse it to identify named entities and intents. This step is crucial for understanding the user's request and determining which part of the knowledge graph to retrieve.
Step 5: Retrieval from the Knowledge Graph
Based on the parsed query, the system retrieves relevant sub-graphs from the knowledge graph. This retrieval is guided by the node embeddings and the structure of the graph, ensuring that the most pertinent information is selected.
Step 6: Answer Generation
With the relevant sub-graphs retrieved, the RAG system uses the LLM to generate an answer. The retrieved information is provided to the LLM along with the original query, allowing it to produce a response that is informed by the structured knowledge contained in the graph.
Empirical Results and Evaluation
The integration of RAG with knowledge graphs has been empirically shown to improve retrieval accuracy and answering quality. In the case of customer support queries answering, the Graph RAG method outperformed the conventional RAG techniques by more than 60% in Mean Reciprocal Rank (MRR). These results highlight the potential of RAG systems to significantly enhance efficiency and accuracy.
Conclusion:
The implementation of RAG systems using knowledge graphs represents a significant leap forward in the field of AI and natural language processing. By leveraging the structured information contained within knowledge graphs, these systems can provide more accurate, relevant, and contextually rich responses to user queries.