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RAG-Chatbot is a retrieval-augmented generative AI system designed to answer questions and provide information by leveraging external data sources. It combines a chatbot interface with a retrieval system, enabling users to interact with a knowledge base in a conversational manner. The chatbot is powered by a large language model and integrates seamlessly with a wiki-based knowledge repository to deliver accurate and up-to-date responses.
• Knowledge Retrieval: Accesses a vast repository of structured and unstructured data to provide relevant answers. • Generative Capabilities: Uses advanced AI to generate human-like responses based on the retrieved information. • Real-Time Interaction: Engages in dynamic conversations, allowing users to ask follow-up questions. • Context Understanding: Maintains context within a conversation for more coherent and relevant responses. • Multi-Language Support: Capable of understanding and responding in multiple languages. • Customizable Integration: Can be integrated with various data sources and systems to suit different use cases.
What does RAG stand for?
RAG stands for Retrieval-Augmented Generation, referring to the chatbot's ability to augment its responses with external data retrieval.
Can RAG-Chatbot access real-time data?
Yes, RAG-Chatbot can be configured to access real-time data sources, ensuring up-to-date and accurate responses.
How can I customize RAG-Chatbot for my organization?
You can customize RAG-Chatbot by integrating it with your organization's specific data sources, such as internal wikis, databases, or documentation repositories.