WeKnora's core technology isRetrieval Augmentation Generation (RAG). This technology improves Q&A accuracy in two key steps: first retrieving contextual snippets relevant to the question from the uploaded document (with support for a mix of keywords, vectors, and knowledge graphs), and then feeding these snippets into a large language model to generate the answer. Answers are closely based on document facts. Its modular design allows users to flexibly combine search strategies (e.g., Elasticsearch+Knowledge Graph), embedding models (e.g., BGE), and generative models (e.g., Qwen) to adapt to different scenarios.
This answer comes from the articleWeKnora: Tencent's out-of-the-box enterprise-level Q&A knowledge baseThe
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