Create Knowledge Base

With the rapid development of Natural Language Processing (NLP) technology, generative language models (such as GPT, BART, etc.) have achieved excellent performance in various text generation tasks, especially in terms of language generation and context understanding. However, pure generative models have some inherent limitations when dealing with fact-based tasks. For example, because these models rely on fixed pre-trained data, they may "fabricate" information when answering questions that require the latest or real-time information, resulting in inaccurate or factually unfounded generated results. In addition, when faced with long-tail problems and complex reasoning tasks, generative models often perform poorly due to the lack of support from external knowledge in specific domains, making it difficult to provide sufficient depth and accuracy.

At the same time, retrieval models (Retrievers) can solve the problem of fact query by quickly finding relevant information in massive documents. However, traditional retrieval models (such as BM25) often can only return isolated results when dealing with ambiguous queries or cross-domain problems, and cannot generate coherent natural language answers. Due to the lack of context reasoning ability, the answers generated by retrieval models are usually not coherent and complete.

To address the shortcomings of these two types of models, the Retrieval-Augmented Generation (RAG) model has emerged. By combining the advantages of generative models and retrieval models, RAG obtains relevant information from enterprise knowledge bases in real-time and integrates it into the generation task, ensuring that the generated text is both contextually coherent and contains accurate knowledge. This hybrid architecture performs particularly well in scenarios such as intelligent question answering, information retrieval and reasoning, and domain-specific content generation.

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