// rag · infiniflow/infinity

infinity

infinity é um projeto de RAG e conhecimento de código aberto de infiniflow (infiniflow/infinity no GitHub). Tem 4.734 estrelas no GitHub e 455 forks, é publicado sob a licença Apache-2.0 e foi atualizado pela última vez no GitHub em 23 de setembro de 2026.

AI-native database for hybrid search across dense, sparse, and tensor vectors plus full text

★ 4,7 mil estrelas⑂ 455 forksApache-2.0atualizado set 2026
▦ visual: o README deste projeto não inclui nenhuma captura incorporável,
então aqui vai um marcador. O guia abaixo está completo mesmo assim.
// dados do projeto

Dados do projeto

Estrelas no GitHub
4.734
Forks
455
Licença
Apache-2.0
Última atualização no GitHub
23 de setembro de 2026
Tipo de ficha
Guia de instalação completo
Site oficial
Official site
// como funciona

Como funciona

  1. Plan your deployment

    Run the server in Docker (recommended) or from a binary; an x86_64 CPU with AVX2 and Python 3.11+ for the client are expected.

  2. Start the server

    Pull and run the Infinity Docker image with a persistent data volume.

  3. Install the client SDK

    Install the Python SDK matching the server.

  4. Run your first hybrid search

    Create a table with text and vector columns, insert rows, and query with dense matching plus filters.

// recursos

Por que as pessoas gostam tanto

Hybrid search in one engine

Combine dense embedding, sparse embedding, tensor, and full-text search with filtering in a single query.

Multiple rerankers

RRF, weighted sum, and ColBERT reranking are supported out of the box.

Rich data types

Strings, numerics, vectors, tensors, and structured data in one database.

Single-binary deployment

No external dependencies; deploy via Docker or binary, or embed it in Python as a module.

Python and HTTP APIs

An intuitive Python API/SDK and an HTTP API for other clients.

Built for RAG workloads

Designed for LLM applications including search, recommenders, Q&A, conversational AI, and copilots.

Advertisement
// execute você mesmo

Execute você mesmo

  1. Clone the repository

    Clone if you plan to build from source or browse the examples; Docker users can skip ahead.

    git clone https://github.com/infiniflow/infinity.git
    cd infinity
  2. Run the server with Docker

    Start Infinity with host networking and a persistent data directory, as documented.

    sudo mkdir -p /var/infinity && sudo chown -R $USER /var/infinity
    docker pull infiniflow/infinity:nightly
    docker run -d --name infinity -v /var/infinity/:/var/infinity --ulimit nofile=500000:500000 --network=host infiniflow/infinity:nightly
  3. Install the client and search

    Install the Python SDK, then connect on port 23817 and run a dense-vector match.

    pip install infinity-sdk==0.7.3
// casos de uso

O que você pode criar

Advertisement
// perguntas frequentes

Perguntas frequentes

Who maintains Infinity?

InfiniFlow, the company also known for RAGFlow; Infinity is their dedicated AI-native database project.

Is Infinity free?

Yes, it is open source under the Apache-2.0 license; you run it on your own infrastructure.

Does it need a paid API key?

No. Infinity is self-hosted software; you generate embeddings with your own models or providers separately.

How does it compare to Milvus or Qdrant?

Those are primarily vector databases; Infinity's pitch is a single engine that natively combines dense, sparse, and tensor search with full-text search and structured data, deployed as one dependency-free binary.

How do I get started?

Run the Docker image as shown in the README, `pip install infinity-sdk==0.7.3`, connect to port 23817, create a table, and run your first match_dense query.

// continue explorando

Ferramentas semelhantes

RAG e conhecimento

infigraph

AST-powered code intelligence engine — graph database plus hybrid semantic search across 62 languages, zero LLM dependency.

★ 91 estrelasNOASSERTION
Ver o guia →
RAG e conhecimento

helix-db

An OLTP graph database with native vector and full-text search, built in Rust on object storage.

★ 6,1 mil estrelasApache-2.0
Ver o guia →
RAG e conhecimento

seekdb

seekdb — the AI-native search database unifying vector, text and structured data for agents.

★ 3,1 mil estrelasApache-2.0
Ver o guia →
RAG e conhecimento

faiss

Meta's library for efficient similarity search and clustering of dense vectors, in C++ and Python

★ 41,1 mil estrelasMIT
Ver o guia →
RAG e conhecimento

vespa

Vespa — the AI search platform: vector, text and tensor search at production scale.

★ 7,1 mil estrelasApache-2.0
Ver o guia →
RAG e conhecimento

fastembed

Fast, lightweight Python embeddings from Qdrant — dense, sparse, ColBERT, image and rerankers.

★ 3,2 mil estrelasApache-2.0
Ver o guia →
// alternativas

Alternativas em RAG e conhecimento

Os outros projetos de RAG e conhecimento com mais estrelas no catálogo do LibreAI — a lista realista de alternativas se infinity não for o ideal para você.

// mais em rag

Mais em RAG e conhecimento

Projetos de RAG e conhecimento próximos no catálogo, os mais próximos em estrelas de infinity: continue explorando dos dois lados dele.

Baixe o infinity

Grátis e aberto: pegue o código-fonte, dê uma estrela no repositório e comece a criar.