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Infinity

The AI-native database built for LLM applications — incredibly fast hybrid search of dense vector, sparse vector, tensor (multi-vector), and full-text for RAG pipelines

★ 4,596 GitHub Apache-2.0 vector-databaseraghybrid-searchembeddingsllm RAG & Knowledge

Overview

Infinity is an AI-native database purpose-built for LLM applications and RAG pipelines. Unlike traditional vector databases, Infinity supports four types of search in a single unified engine: dense vector (ANN), sparse vector, tensor (multi-vector), and full-text search (BM25). This hybrid search capability means you get the best of all worlds — semantic similarity from dense vectors, keyword precision from sparse vectors and BM25, and multi-vector matching for complex queries. Key features include: blazing-fast performance with C++20 modules and HNSW indexing, production-ready reliability with 130K+ Docker pulls, a built-in GUI for management, and seamless integration with Python and TypeScript SDKs. Infinity handles the full RAG pipeline — from embedding generation to hybrid retrieval — in a single database. With 4,500+ GitHub stars and active development, Infinity is ideal for developers building retrieval-augmented generation systems, semantic search applications, AI-powered documentation search, and any LLM application that needs fast, accurate information retrieval across multiple search paradigms.

Requirements

Min vCPU
2
Min RAM
4096 MB
Min Disk
10 GB
Rec vCPU
4
Rec RAM
8192 MB
Rec Disk
20 GB

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Docker Compose

# Generated by Run This Ai — docker-compose.yml
services:
  infinity:
    image: infiniflow/infinity:latest
    restart: unless-stopped
    ports:
      - 8080:8080
    volumes:
      - ./data/infinity:/data

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