The current Large Language Models lies in the limitations of vector databases, which, despite their capabilities, often lead to data 'hallucinations'.
To address this gap and improve basic LLMs accuracy on specific use cases, RAG has been very helpful, but is currently limited by the usage of Vector DB.
Unlocking their full potential demands context, Knowledge Graphs are built for this.
Lettria introduces a revolutionary solution: GraphRAG. By merging the contextual richness of knowledge graphs with the dynamic power of RAG tasks, we provides the context that LLMs need to more accurately answer complex questions.
The result? Precise, relevant, and insightful answers that capture the true essence of your data.