In the world of customer service and technical support, providing rapid and accurate resolutions is paramount for ensuring customer satisfaction and loyalty. Traditional retrieval-augmented generation (RAG) systems have proven valuable for question-answering, but face limitations when dealing with complex, interlinked data sources like customer service ticketing systems.
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The Shortcomings of Basic RAG for Customer Service
While basic RAG can integrate external knowledge sources to ground language model responses, treating customer support tickets as disconnected text passages leads to key challenges:
Ignoring Metadata: Support tickets contain rich metadata like issue severity, context details, and links to related tickets that basic text embedding approaches fail to capture.
Missing Connections: Representing each ticket independently as vector embeddings loses the explicit relationships and structure within each ticket and across the ticket knowledge base.
Suboptimal Retrieval: Without encoding relationships, RAG struggles to accurately retrieve the most relevant evidence for answering questions that require reasoning over linked facts.
Lower Answer Quality: By failing to capture connections, basic RAG provides insufficient context, impairing the language model's ability to generate coherent final answers.
Integrating Knowledge Graphs to Supercharge RAG
To overcome these limitations, researchers from LinkedIn have proposed augmenting RAG with knowledge graphs - data structures that explicitly represent concepts as nodes and their relationships as edges.
In their approach, customer service tickets are first transformed into an expansive knowledge graph, with nodes representing tickets, metadata fields like descriptions and priorities, and edges capturing links between related tickets.
When a new customer question is asked, the system parses out key entities and intents using information extraction techniques. These extracted concepts are mapped to the corresponding knowledge graph nodes and used to construct a graph query.
Running this query against the knowledge base retrieves a relevant subgraph - a subset of richly interconnected nodes capturing not just text snippets but associated metadata and relationships. This context subgraph is then fed alongside the original query into a language model to generate the final answered response.
The knowledge graph augmentation boosts accuracy by providing the language model with a concentrated, logically coherent representation of relevant facts rather than disconnected text passages. Their experiments demonstrated significantly improved retrieval recall and customer resolution time compared to the basic RAG approach.
Curious to delve deeper into this?
Join Professor Mehdi as he delves into integrating KG with RAG for customer service use case, in the video below!👇
Stay tuned as we continue exploring the development of knowledge-augmented AI systems to extract maximum value from unstructured data sources!
🛠️✨ Happy practicing and happy building! 🚀🌟
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📽️ Video about Session 1 of KG integration with RAG:
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