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標題: 向量数据库：AI 时代的“长期记忆体” [打印本頁]

作者: saddammolla 時間: 2026-1-27 11:22
標題: 向量数据库：AI 时代的“长期记忆体”
In 2026, a year of explosive growth in artificial intelligence, if large language models (LLMs) are the brain of AI, then **vector databases** are its "long-term memory." What is vectorization?

Traditional databases (such as MySQL) excel at handling structured data, such as names or amounts. However, AI deals with unstructured data—text, images, and videos. To enable machines to "understand" this content, we need to transform it into a series of numerical arrays, known as vector embeddings .

Why is a dedicated database needed?

High-dimensional retrieval: A word or an image may be transformed into a vector with hundreds or thousands of dimensions. Traditional databases cannot quickly search within such complex dimensions.
Similarity Search: Unlike exact matching (such as "search for the user with ID 101"), vector databases perform fuzzy retrieval . They look for data that is "spatially closest." For example, searching for "sad" will help you find "grief" or "depressed" because they are close in the vector space.
The core of the RAG architecture: The currently popular RAG (Retrieval Augmentation Generative) architecture essentially involves first "flipping through a book" (retrieving relevant background knowledge) in a vector database, and then handing it over to a large model to "answer." This solves the problem of large models spouting nonsense (illusions).

Core technologies and applications

Vector databases typically employ ANN (Approximate Nearest Neighbor) algorithms, such as HNSW or IVF, which sacrifice minimal accuracy while maintaining extremely high speed.

Typical examples include Milvus, Pinecone, Weaviate, and pgvector (PostgreSQL), a traditional large-scale application that integrates vector capabilities.
Application scenarios: recommendation systems ("You May Like"), image search, AI-powered intelligent customer service, and building personal knowledge bases.

In summary, vector databases are not only storage tools, but also a bridge connecting raw data with AI inference capabilities. With the popularization of multimodal AI, they have become an indispensable infrastructure in the modern technology stack.

Do you want to understand how to "vectorize" existing data, or do you want to compare the advantages and disadvantages of different architectures such as Milvus and Pinecone?

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