What is RAG and Knowledge Graph?

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🧠 What is RAG and Knowledge Graph?

How RAG Works

Retrieval-Augmented Generation (RAG) combines a user's query with external documents to generate answers. The process involves:

1. Search: Looking through a vector database for relevant information using vector similarity.

2. Select: Choosing the top relevant documents.

3. Extract: Pulling out useful content.

4. Generate: Combining this content with a language model to create an answer.

What is a Knowledge Graph?

A knowledge graph is a structured representation of information that models complex data and highlights connections within a domain. Key components include:

• Entities: Real-world objects or concepts.

• Attributes: Properties or characteristics of entities.

• Relationships: Connections between entities.

• Nodes and Edges: Graphical representation of entities and their relationships.

Issues with Baseline RAG

Baseline RAG has limitations, such as:

• Connecting the Dots: It may not link related advancements spread across different documents.

• Holistic Understanding: It may miss overarching trends or themes, focusing instead on similar phrases without context.

How GraphRAG Works

GraphRAG improves upon baseline RAG by using knowledge graphs instead of vector databases for information retrieval. This approach offers several advantages:

1. Richer Responses: Provides more complete and varied answers.

2. Better Data Connection: Generates responses better connected to the original data, reducing hallucinations.

3. Global Overviews: Offers overviews of the dataset at different levels of granularity.

4. Efficiency: More efficient than summarizing the full text while still generating high-quality responses.

GraphRAG uses an LLM to extract a rich knowledge graph from text documents. This graph captures the semantic structure of the data, identifying communities of densely connected nodes. When a user asks a question, GraphRAG retrieves the most relevant information from the knowledge graph to condition the LLM’s response, improving accuracy and coherence.

Conclusion

GraphRAG offers significant improvements over baseline RAG by leveraging knowledge graphs for more meaningful and connected responses. Future posts will explore implementation details of GraphRAG.

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