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Dynamixs AI - Retrieval-Augmented-Generation (RAG)
Retrieval Augmented Generation (RAG) is a term from the field of artificial intelligence and machine learning.
- Retrieval - refers to the ability to quickly and efficiently access stored information or retrieve it from a database or other storage system.
- Augmented - refers to the idea of improving or expanding an existing function or capability by adding something new.
- Generation - refers to the creation of new content or results based on available information.
In summary, Retrieval Augmented Generation (RAG) describes a method that uses artificial intelligence or machine learning to quickly and efficiently access stored information and use it to generate new content or results. This method can help improve the accuracy and relevance of generated content by drawing on a broader range of information.
The following section give a general overview how the Retrieval-augmented generation (RAG) is realized in Dynamixs AI.
The Model
The retrieval data in Dynamixs.AI is generated by the LLM nomic-embed-text-v2-moe which is a sentence-transformers model supporting more then 100 languages and trained on over 1.6B pairs. The model is used in Q4 quantitation and a size of 328MiB which provides a good quality for most use cases. The model maps sentences & paragraphs to a 768 dimensional dense vector space. It can be run on a CPU with adequate performance.