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It offers a streamlined rag workflow adaptable to enterprises of any scale Retrieval augment generation, in short rag is a mechanism to integrate large language model to a custom data. Whether you're a solo developer, a data scientist, or an enterprise architect, there's a framework built for you
Thus, rag comes up with a super powerful technique that distinguishes it from others.
It offers a ui for experimentation, supports multiple rag configurations, and enables scalable deployment for production environments. Few of the important component to build rag pipeline are Loaders to load external data, large language models integration, vector database, embedding models support, memory, and so on What is retrieval augmented generation
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