Why Infinity is a Good Alternative in RAGFlow

Tailored for RAG/LLM Workloads

Infinity is an AI-native database built from the ground up for hybrid search (dense vectors, sparse vectors, multi-vectors/tensors, full-text, structured data) with fused ranking and reranking. It outperforms Elasticsearch in benchmarks for RAG-specific tasks (e.g., faster query latency ~0.1ms on million-scale vectors, higher QPS, better hybrid recall).

Performance & Efficiency

Claims several times faster than Elasticsearch for multi-recall scenarios, lower resource consumption, and advanced features like real-time search, better pruning for phrase queries, and native support for RAG needs (e.g., tensor-based reranking).

Integration in RAGFlow

Introduced as an option in v0.14 (late 2024), with ongoing improvements. RAGFlow docs and blog state Infinity will become the preferred/default engine once fully mature, as it’s more powerful for their use case. Switching is simple (set DOC_ENGINE=infinity in .env), and it replaces Elasticsearch entirely for storage/retrieval.

Fully Open Source & Vendor-Neutral

Apache 2.0-licensed, no licensing drama, developed openly by InfiniFlow.

Drawbacks

Newer and less battle-tested than Elasticsearch’s ecosystem (e.g., fewer plugins, tools like Kibana). Some early RAGFlow users reported minor integration bugs, but these have been fixed in recent releases.