The underlying implementation principles of vector databases can vary depending on the specific database system and indexing method. Different vector databases may use different data structures and algorithms to support efficient vector storage and similarity searches. The following are some common underlying implementation principles and concepts:
1. Vector storage :
- Data Structures: Vector databases typically use data structures to store vector data. These data structures can be flat (e.g., arrays or matrices) or specific vector storage engines (e.g., the Flat L2 Index used by Faiss).
- Compression technology: In order to reduce storage space, some vector databases use compression technology to store vector data, especially on large-scale data sets.
2. Vector index :
- Index structure: Vector databases often build index structures to speed up similarity searches. Common index structures include KD trees, tree structures (such as B-trees, R-trees), and hash tables.
- Distance measure: Databases usually use different distance measures (such as Euclidean distance, cosine similarity, Hamming distance, etc.) to measure the similarity between vectors.
3. Similarity search :
- Query processing: When performing a similarity search, the database compares the query vector to the stored vectors and returns the most similar vector