向量数据库与嵌入
Your vector database for RAG is consuming too much memory. How do you reduce it?
原题Your vector database cannot scale to millions of embeddings. How do you fix the bottleneck?
原题Your new embedding model has different dimensions from the existing vectors in production. How do you handle the mismatch?
原题Your vector search returns irrelevant results despite high similarity scores. How do you fix it?
原题You deployed a new embedding model, and search quality crashed overnight. How do you handle embedding drift?
原题Your semantic search fails for short queries. How do you improve it?
原题
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