RAG & Knowledge
infigraph
AST-powered code intelligence engine — graph database plus hybrid semantic search across 62 languages, zero LLM dependency.
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View guide →AI-native database for hybrid search across dense, sparse, and tensor vectors plus full text
infinity is an open-source rag & knowledge project by infiniflow (infiniflow/infinity on GitHub). It has 4,734 GitHub stars and 455 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 23 September 2026.
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.
Pull and run the Infinity Docker image with a persistent data volume.
Install the Python SDK matching the server.
Create a table with text and vector columns, insert rows, and query with dense matching plus filters.
Combine dense embedding, sparse embedding, tensor, and full-text search with filtering in a single query.
RRF, weighted sum, and ColBERT reranking are supported out of the box.
Strings, numerics, vectors, tensors, and structured data in one database.
No external dependencies; deploy via Docker or binary, or embed it in Python as a module.
An intuitive Python API/SDK and an HTTP API for other clients.
Designed for LLM applications including search, recommenders, Q&A, conversational AI, and copilots.
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 infinityStart 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:nightlyInstall the Python SDK, then connect on port 23817 and run a dense-vector match.
pip install infinity-sdk==0.7.3InfiniFlow, the company also known for RAGFlow; Infinity is their dedicated AI-native database project.
Yes, it is open source under the Apache-2.0 license; you run it on your own infrastructure.
No. Infinity is self-hosted software; you generate embeddings with your own models or providers separately.
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.
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.
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Free and open — grab the source, star the repo, and start creating.