All Tools
A
Vector DBFreeOpen Source
APACHE DORIS
Real-time analytics and hybrid search database for AI
Apache-2.0
ABOUT
AI agents and applications increasingly need to combine real-time analytics (aggregations, filtering, joins) with vector similarity search and full-text retrieval, but traditional architectures require separate systems for each workload — a vector database for embeddings, an OLAP engine for analytics, and a search engine for text queries — increasing complexity, latency, and operational cost. Apache Doris solves this by integrating vector search, inverted indexes, and high-performance SQL analytics into a single distributed database, enabling AI agents to filter billions of records by metadata, rank by vector similarity, and aggregate results in real-time without data movement.
INTEGRATION GUIDE
1. Build AI agent memory stores that combine semantic search with real-time metadata filtering and aggregation
2. Power recommendation systems with hybrid search blending collaborative filtering, vector similarity, and real-time user signals
3. Replace separate analytics and vector databases with a single query engine for AI-powered dashboards
4. Enable real-time semantic logging and debugging for LLM applications with structured trace analysis
TAGS
databaseolapanalyticsvector-searchsqlreal-timehybrid-search