Concepts on Docs

Architecture

Mon, 01 Jan 0001 00:00:00 +0000

RedisVL sits between your application and Redis, providing a structured way to define, populate, and query vector search indexes.

The Core Pattern

Every RedisVL application follows a consistent workflow: define → create → load → query.

Extensions

Mon, 01 Jan 0001 00:00:00 +0000

Extensions are opinionated, higher-level abstractions built on RedisVL’s core primitives. Each extension encapsulates a common AI application pattern, managing its own Redis index internally and exposing a clean, purpose-specific API.

You don’t need to understand schemas, indexes, or queries to use extensions—they handle that complexity for you.

Semantic Cache

LLM API calls are expensive and slow. If users ask similar questions, you’re paying to generate similar answers repeatedly. Semantic caching solves this by storing responses and returning cached answers when similar prompts are seen again.

Field Attributes

Mon, 01 Jan 0001 00:00:00 +0000

Field attributes customize how Redis indexes and searches your data. Each field type has specific attributes that control indexing behavior, search capabilities, and storage options.

Common Attributes

These attributes are available on most non-vector field types (text, tag, numeric, geo).

Query Types

Mon, 01 Jan 0001 00:00:00 +0000

RedisVL provides several query types, each optimized for different search patterns. Understanding when to use each helps you build efficient search applications.

Vector Queries

Vector queries find documents by semantic similarity. You provide a vector (typically an embedding of text or images), and Redis returns documents whose vectors are closest to yours.

Search & Indexing

Mon, 01 Jan 0001 00:00:00 +0000

Vector search in Redis works differently from traditional databases. Understanding the underlying model helps you design better schemas and write more effective queries.

How Redis Indexes Work

Redis search indexes are secondary structures that sit alongside your data. When you create an index, you’re telling Redis: "Watch all keys that match this prefix, and build a searchable structure from these specific fields."

Utilities

Mon, 01 Jan 0001 00:00:00 +0000

Utilities are optional components that enhance search workflows. They’re not required—you can bring your own embeddings and skip reranking—but they simplify common tasks.

Vectorizers

A vectorizer converts text into an embedding vector. Embeddings are dense numerical representations that capture semantic meaning: similar texts produce similar vectors, enabling similarity search.