Why Consider Knowledge Graph to Enhance Your RAG?
Retrieval-Augmented Generation (RAG) has become a popular technique for grounding large language models and preventing them from hallucinating incorrect facts. However, basic RAG systems have some key limitations when dealing with complex questions that require reasoning over multiple pieces of information.
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The Limitations of Basic RAG
In a basic RAG system, external text data is split into chunks or passages which are embedded into dense vector representations. When a user asks a question, the system retrieves the most relevant vector-embedded passages based on semantic similarity to the question. These retrieved passages are then fed as context to a language model to generate a final answer.
While this allows language models to make use of external knowledge sources, there are several drawbacks:
Vectorizing passages into fixed-length representations loses the explicit connections and relationships between the information contained within each passage.
Key relevant details spread across multiple sentences or passages can get lost when embedding them independently into vectors.
Each passage is matched independently to the question, making it difficult to connect and aggregate facts spread across multiple passages.
The relevance ranking of passages relies solely on semantic similarity metrics like cosine distance, lacking true explainability.
There is no explicit encoding of logical relationships, rules, or structures within the external knowledge source.
Augmenting RAG with Knowledge Graphs
To overcome these limitations, augmenting RAG systems with knowledge graphs can be considered as a potential enhancement. A knowledge graph explicitly represents entities/concepts as nodes and the relationships between them as labeled edges in a graph structure.
Unlike RAG's unstructured vectorized representations, knowledge graphs maintain the logical connections between pieces of information.
Curious to delve deeper into this?
Join Professor Mehdi as he delves into enhancing RAG with KG, introducing the concept of knowledge graph and why we should consider using it, in the video below!👇 (Note this will be the Session 1 of this video, we’ll publish the 2nd session in a few days!)
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📽️ Video about basic RAG:
📚 Also if you'd like to learn more about RAG systems, check out our book on the RAG system:
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