Detailed Introduction
Infinity is an AI-native database built for LLM applications, offering hybrid search over dense embeddings, sparse embeddings, tensors (multi-vector), full-text and structured fields. It focuses on delivering low-latency, high-throughput retrieval for RAG, search, recommendation, QA and conversational AI, while providing an easy-to-use Python SDK and single-binary deployment options for production integration.
Main Features
- High-performance hybrid search: combine dense/sparse/tensor/full-text retrieval with diverse reranking strategies.
- Rich data types: support vectors, text, numeric and structured fields in a unified schema.
- Developer-friendly client: intuitive Python SDK and single-binary operation for simple deployment.
- Scalable & observable: designed for high QPS workloads with benchmarks and operational tooling.
Use Cases
Suitable for vector search, retrieval-augmented generation (RAG), similarity recommendation, knowledge retrieval, conversational context retrieval and large-scale full-text search. Enterprises can deploy Infinity privately to satisfy compliance requirements and use the Python SDK to quickly integrate retrieval into LLM-driven applications.
Technical Features
- Low-latency, high-throughput: millisecond-level queries and thousands+ QPS for large-scale datasets.
- Hybrid index architecture: unifies vector, sparse and full-text indexes to improve retrieval accuracy.
- Single-binary & Python embedding: run as a standalone binary or embedded in Python processes for flexible deployment.
- Open-source license: Apache-2.0 licensed for community and enterprise adoption.