Unverified50% confidenceFactExact time
The project adopts a RAG approach, using sentence-transformers to generate vectors and FAISS for vector retrieval, enabling semantic search over the company's data table schemas, KPI definitions, and business rules.
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9/29/2026
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VerifiedRAG（检索增强生成）技术架构将文档切片、向量化、存入向量数据库，查询时先检索相关片段再交给模型生成回答76% similarVerifiedThe core idea of RAG is to combine the language capabilities of a general-purpose large model with the specialized content of an enterprise's private knowledge base, first retrieving relevant document segments and then feeding them as context to the large model to generate answers.75% similarVerifiedThe working principle of RAG is divided into a retrieval phase (converting the question into a vector to recall relevant content from the knowledge base) and a generation phase (inputting the recalled context together with the question into the model to produce an answer).75% similarVerifiedRAG technology enables LLMs to provide accurate answers based on enterprise data by retrieving private enterprise documents and data and injecting relevant information into prompts.74% similarVerifiedThe RAG strategy works by first building a vector index of the project code, then using semantic retrieval to find the most relevant code snippets, and finally assembling them into a prompt to send to the model.74% similar
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