2026 AI 資料庫：4 種架構調查

🔑 增強重點摘要

• •The emergence of 'Predictive Databases' like Aito represents a shift toward automated machine learning (AutoML) integrated directly into the database engine, bypassing the traditional ETL and model deployment pipeline.
• •Vector databases have evolved beyond simple similarity search to include hybrid search capabilities, combining dense vector embeddings with traditional keyword-based BM25 filtering for improved retrieval accuracy.
• •The 'ML-in-DB' category is increasingly dominated by extensions to existing relational databases (e.g., PostgreSQL extensions like pgvector or DuckDB integrations), which prioritize data locality to reduce latency in analytical workloads.

🛠️ 技術深入

• •Aito's predictive architecture utilizes Bayesian inference, which calculates conditional probabilities directly from the underlying data distribution at query time.
• •Unlike traditional neural network-based ML, Aito's approach avoids the 'cold start' problem and model drift by treating the database state as the model itself.
• •Vector databases typically employ HNSW (Hierarchical Navigable Small World) or IVF (Inverted File Index) algorithms to manage high-dimensional vector space partitioning.
• •ML-in-DB architectures often leverage User-Defined Functions (UDFs) or specialized SQL extensions to execute model inference within the database's memory space, minimizing data serialization overhead.

🔮 前景展望基於引用來源的 AI 分析

⏳ 時間線