Redis Vector Search Technology for AI Applications: Implementation and Best Practices

This article introduces vector recall technology based on Redis and its applications in AI business. Vectorization converts high-dimensional data (text, images, audio) into low-dimensional numerical representations using deep learning models like Word2Vec, BERT, ResNet, and VGG.

Vector similarity computation enables applications in recommendation systems, question-answering systems, image retrieval, and text retrieval. Redis, as a high-performance in-memory database, provides millisecond-level retrieval speed through the RedisSearch module, supporting vector search capabilities.

RedisSearch supports multiple data types (Hash, JSON), vector precision options (FLOAT32, FLOAT64, BFLOAT16, FLOAT16), and search algorithms including FLAT (Brute Force) and HNSW (Hierarchical Navigable Small World). The article provides detailed explanations of HNSW parameters (M, EF_CONSTRUCTION, EF_RUNTIME, EPSILON) and similarity algorithms (L2/Euclidean Distance, IP/Inner Product, COSINE/Cosine Distance).

Practical implementation covers creating indexes using FT.CREATE command for both Hash and JSON data structures, performing KNN vector retrieval and range queries, and optimization strategies including algorithm selection, parameter tuning, result limiting, field selection, and distributed storage.

The article demonstrates a real-world intelligent customer service application using vector recall for Q&A matching, including Java code examples for index creation and query execution. It also discusses hybrid search combining sparse retrieval (BM25) with dense retrieval (vector-based semantic search), comparing Redis with ElasticSearch in terms of performance, text search capabilities, and extensibility.

Key recommendations include using Redis version 2.10+ for BFLOAT16 support, preferring HNSW over FLAT algorithms, being cautious with special characters in text fields, and selecting appropriate field types (NUMERIC, TEXT, TAG) based on data characteristics.