The mirage of precision : why vector databases fall short?

The mirage of precision: why vector databases fall short ?  

The allure of vector databases lies in their efficiency, offering quick similarity comparisons. But this speed comes at a cost: hallucination and a lack of nuance. Imagine asking your colleague, "What's the impact of our new campaign on brand perception?" A vector database might point you to relevant documents based on keywords, but miss the intricate relationships between campaign elements, brand image, and customer sentiment. This leaves you with incomplete answers and misleading interpretations.

Indeed, the very architecture of vector databases predisposes them to this. By reducing complex ideas and relationships into multi-dimensional points - enabling lightning-fast similarity searches - vector DB strips away the context and subtleties essential for a deep understanding : nuances of language, the context of a situation, and the messy complexity of human emotions… All get compressed into a single vector, potentially flattening out crucial details. Consequently, when faced with nuanced queries that demand comprehension of underlying themes or the emotional resonance of language, vector databases fall noticeably short. 

Thus, Vector DBs have been around for years and have proven effective in solving real-world problems. Their architecture, designed ‘by default’,  is neither compatible nor efficient with the specificities that the RAG entails, which hinders the adoption of the technology by companies that want to take accuracy to the next level. However, there is a solution that allows for a deeper understanding that goes beyond the surface…

The best of both worlds 

To solve this, we believe the future lies in the hybridization of two worlds : by merging the contextual richness of knowledge graphs with the dynamic power of RAG, it is possible to obtain a faster, more accurate and more contextually-aware solution. Vector embeddings provide a fast, efficient pre-filtering, narrowing down the search space. Then, the knowledge graph steps in, offering rich context and relationships that LLMs need to respond more precisely to complex inquiries. The result? Precise, relevant, and insightful answers that capture the true essence of your data.

Enter GraphRAG, by Lettria

Lettria introduces a revolutionary solution: GraphRAG. This innovative technology harnesses the power of knowledge graphs and vectors DB to deliver unprecedented conversational intelligence. Forget keyword matching and irrelevant results. GraphRAG delves into the rich tapestry of data, understanding the connections, context, and meaning behind every piece.

At Lettria, we believe in the power of real conversations with data. Our technology is not just a tool but a catalyst for transformation, empowering companies to navigate the complexity of their data with unparalleled precision and insight.