Infinity

Infinity is a cutting-edge, AI-native database designed specifically to address the performance and complexity challenges faced by modern Large Language Model (LLM) applications. Built for AI developers, Infinity provides incredibly fast, robust support for Retrieval-Augmented Generation (RAG) by offering comprehensive hybrid search capabilities across rich data types, ensuring your LLM applications deliver accurate, relevant, and verifiable results at production scale.

Key Features

Infinity focuses on delivering speed and versatility, allowing you to move beyond basic vector storage and build truly sophisticated RAG pipelines.

⚡️ Ultra-Low Latency Performance

Engineered for speed, Infinity achieves performance benchmarks that significantly accelerate your AI applications. You can expect 0.1 millisecond query latency and support for 15,000+ Queries Per Second (QPS) on million-scale vector datasets. For full-text requirements, the database maintains a remarkable 1 millisecond latency and handles 12,000+ QPS across 33 million documents, ensuring real-time responsiveness even under heavy load.

🔍 Comprehensive Hybrid Search and Retrieval

Go beyond simple vector similarity search. Infinity supports true hybrid search across dense embeddings, sparse embeddings, tensors, and full-text data, all combined with robust filtering capabilities. This versatility is crucial for maximizing relevance, especially when dealing with complex queries. Furthermore, Infinity includes built-in rerankers like RRF, weighted sum, and ColBERT to refine results and boost the quality of information passed to your LLM.

🧩 Native Support for Rich Data Types

Infinity is built to handle the complex, mixed data environments common in RAG applications. It natively supports a wide range of data types, including strings, numerics, structured data, and various vector formats (dense, sparse, tensor). This rich data support enables advanced retrieval techniques, such as multi-vector retrieval and mixed data type queries, optimizing the contextual data available to your foundation models.

🚀 Simplified Deployment and Intuitive API

Designed for the modern AI development workflow, Infinity features a single-binary architecture with zero external dependencies, making deployment quick and predictable. The intuitive Python API allows you to embed Infinity directly into your environment as a simple Python module, ensuring a seamless and developer-friendly experience from prototype to production.

Use Cases

Infinity’s specialized architecture makes it the ideal foundation for building high-performance, reliable LLM applications:

1. High-Volume Question-Answering Systems: When building customer service bots or internal knowledge bases, you need sub-second retrieval from massive datasets. Infinity’s low-latency full-text and vector search ensures that the RAG pipeline quickly fetches the most relevant and precise facts, leading to higher-quality, verifiable LLM responses.

2. Building Advanced Copilots: For engineering or domain-specific copilots, the ability to handle mixed data types and complex queries is essential. Infinity allows the copilot to simultaneously search code embeddings (dense vectors), documentation keywords (full-text), and structured project metadata, dramatically improving the contextual relevance and actionability of the generated suggestions.

3. Real-Time Recommender Systems: By leveraging the hybrid search capabilities, you can build sophisticated recommenders that weigh user behavior (vectors) alongside catalog metadata (full-text/structured data) and tensor representations of media. This results in more personalized and faster recommendations that adapt instantly to user interaction.

Why Choose Infinity?

Infinity stands apart from traditional vector databases and general-purpose systems because it is fundamentally designed as an AI-native database—optimized for the specific demands of RAG.

Unlike basic vector stores that primarily handle similarity search, Infinity offers specialized functional value crucial for production-grade LLM development:

• RAG-First Architecture: Infinity was specifically engineered to address the inherent challenges of RAG, including latency bottlenecks and the need for complex, multi-modal data retrieval.

• Beyond Basic Vector Search: You gain advanced capabilities like superior full-text search, multi-vector retrieval (retrieving information represented by multiple embeddings), and refined data analytics directly within the database.

• Production Reliability: The combination of ultra-low latency benchmarks (e.g., 0.1ms vector query time) and the single-binary, dependency-free architecture ensures your application is fast, reliable, and easy to maintain at massive scale.

• Information Gain: By supporting sophisticated hybrid search and rerankers (RRF, ColBERT), Infinity ensures that the context retrieved for the LLM is maximally relevant, thereby enhancing the accuracy and reducing hallucinations in the final generated output.

Conclusion

For AI developers focused on building accurate, high-performance RAG applications, Infinity delivers the speed, flexibility, and specialized tools required to succeed. By providing ultra-fast hybrid search across all necessary data modalities, Infinity accelerates your development cycle and ensures your LLM applications are ready for production.