GraphRAG

"If you are looking for a fast and powerful way to integrate natural language capabilities on top of a graph database, ArangoDB has a great solution for GraphRAG. The ability to dynamically generate AQL from any natural language query, and then get a very contextually-relevant response fast from ArangoDB, is a game changer."

- Martin Kovar, Head of Product Development

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Rapid Deployment of GraphRAG

GraphRAG is acknowledged as the most complete and advanced type of RAG compared to other approaches, including Vector Store RAG. With ArangoDB GraphRAG, you can deploy RAG natural language interfaces on top of your ArangoDB Knowledge Graphs in just a few days, without the need for complex integration and coding.

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Rich, Contextual Understanding

Improve the contextual understanding of your AI models by integrating them with ArangoDB’s knowledge graphs. This allows your LLM-powered apps to access and utilize vast amounts of structured information, leading to more accurate and contextually relevant responses. ArangoDB’s multi-model capabilities provide a significant advantage over other RAG systems, in turn enriching your applications and value to end users.

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Predictable Cost & Scaling Model

There’s no need to create similarity embeddings (which could take days or many hours to complete) as a mandatory first step when using Vector Store RAG. You can throttle or otherwise cap JSON results generated by AQL. And you can scale horizontally to get desired performance. Therefore, GraphRAG with ArangoDB yields an extremely predictable (and attractive) costing model compared to the unpredictability of Vector RAG as data volumes soar.

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Real-World Applications

Apply GraphRAG to a wide range of real-world scenarios, from advanced search engines to intelligent virtual assistants. ArangoDB’s powerful combination of LLMs and knowledge graphs enables sophisticated AI solutions that can address complex, domain-specific queries. Users want rich, contextual responses to their unique, business-driven Q&A through natural language.

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