向量数据库与嵌入
What are embeddings in the context of AI engineering?
原题How do embedding models convert text to vectors?
原题What is Contrastive Learning, and how is it used to train embedding models?
原题What is the difference between sparse and dense embeddings?
原题Explain cosine similarity, dot product, and Euclidean distance for vector search.
原题What is a vector database, and how does it differ from a traditional database?
原题How does Approximate Nearest Neighbor (ANN) search work?
原题Compare HNSW, IVF, and flat indexes. How do you pick one, and what does recall@k cost in latency?
原题How does an Embedding Cache work?
原题How do you choose the right embedding model for your use case?
原题What is embedding dimensionality, and how does it affect performance and cost?
原题How do you handle embedding drift when the embedding model is updated?
原题What are multi-modal embeddings, and how are they generated?
原题How do you index and query multi-tenant data in a vector database?
原题What is quantization of embeddings, and how does it reduce storage costs?
原题How do you benchmark and evaluate embedding model quality?
原题What is the role of metadata in vector databases?
原题How do you handle large-scale vector search with billions of vectors?
原题What is hybrid search (combining keyword search with vector search)?
原题How do you fine-tune an embedding model for a specific domain?
原题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?
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