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My First RAG Chatbot: What I Built and How
- HelloGitHub has been hearing from users that their search function isn’t cutting it for finding open-source projects.
- Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a language model by retrieving relevant information from a knowledge base before generating a response.
- OceanBase’s open-source RAG chatbot is designed to deliver spot-on answers to user's document related queries through natural conversation.
- To build a RAG system from scratch 'OceanBase' is a good choice due to the training tutorial tailored for beginners.
- To boost the question-answering game, the data is optimized deep within where imported tables are made into cleaner and more precise content.
- The RAG chatbot has gone through process optimization using the Tongyi Qianwen text-embedding-v3 model for debugging.
- To optimize RAG, data quality is crucial, with retrieval making sure the relevant content is pulled up quickly and accurately.
- OceanBase’s distributed architecture shines when dealing with massive data, making it ideal for RAG applications that require frequent data updates and synchronization.
- In addition to vector data, RAG databases need to support hybrid searches of relational data, graph search (knowledge graph), and real-time queries with low-latency responses, transaction processing, and high availability.
- The future of OceanBase is promising in RAG technology.
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