Ekiya Embeddings

Ekiya's relies on Embeddings.

Embeddings are vector representations of data that capture semantic relationships. Embeddings are used to do Retrieval Augmented Generation (RAG), one other technique to avoid hallucinations.

Their complexity arises from the need to effectively capture the underlying patterns and nuances in the input data.

Embeddings trained on general corpora do not capture domain-specific nuances effectively. Building embeddings for specific domains or industries requires domain adaptation techniques to ensure relevance. It requires careful consideration of the inherent complexities and characteristics of the data at hand.

Ekiya provides a strong approach to capture the unique characteristics and knowledge embedded in a specific domain.

To build useful embeddings a lot of different aspects have to be taken in account like: -- What are the goals and tasks? What needs to be captured? Semantics, relationships, context, everything? -- Is there a specific jargon, abbreviations, or specialized terminologies that need to be handled? -- Are there domain-specific knowledge sources, such as dictionaries or ontologies that could be incorporated as well? -- Which LLM should we use to produce the embeddings? -- Get the insights of domain experts to ensure that the embeddings capture the nuances of the domain. -- Where to keep your embeddings and how to include them in your AI solution? -- ...

Embeddings are very useful tools to build tailored GenAI solutions. However, designing them requires strong expertise, an engineering method and experience.

From a technical standpoint, the process of generating embeddings involves converting diverse sources of knowledge —such as text, sentences, images, diagrams, and more— into mathematical representations expressed as high-dimensional vectors.