Kentaro Ishikawa@kentaro_ishikawa489

This product aims to run the RAG architecture locally. The quality of responses from chatbots like ChatGPT varies based on the information stored in the database. To enhance the quality of a chatbot's response on a particular topic, it's essential to register accurate summary texts in the database. With this product, to improve the precision of the user's chatbot, it first generates accurate summary texts using the local environment model and then registers them. Subsequently, users can use the chatbot to obtain answers to their questions. A distinctive feature is that it operates models for text embedding in the RAG architecture and conversations with chat AI in a local environment

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