RAG & Wissen
infigraph
AST-powered code intelligence engine — graph database plus hybrid semantic search across 62 languages, zero LLM dependency.
★ 91 SterneNOASSERTION
Anleitung ansehen →infinity ist ein Open-Source-Projekt aus der Kategorie RAG & Wissen von infiniflow (infiniflow/infinity auf GitHub). Es hat 4.734 Sterne auf GitHub und 455 Forks, wird unter der Lizenz Apache-2.0 veröffentlicht und wurde zuletzt am 23. September 2026 auf GitHub aktualisiert.
AI-native database for hybrid search across dense, sparse, and tensor vectors plus full text
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.
AST-powered code intelligence engine — graph database plus hybrid semantic search across 62 languages, zero LLM dependency.
An OLTP graph database with native vector and full-text search, built in Rust on object storage.
seekdb — the AI-native search database unifying vector, text and structured data for agents.
Meta's library for efficient similarity search and clustering of dense vectors, in C++ and Python
Vespa — the AI search platform: vector, text and tensor search at production scale.
Fast, lightweight Python embeddings from Qdrant — dense, sparse, ColBERT, image and rerankers.
Die anderen Projekte der Kategorie RAG & Wissen mit den meisten Sternen im LibreAI-Katalog — die realistische Auswahl, falls infinity nicht das Richtige ist.
Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or…
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context…
Lightweight OCR toolkit turning PDFs and images into structured, LLM-ready data in 100+ languages
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
Verwandte Projekte der Kategorie RAG & Wissen im Katalog, die infinity bei den Sternen am nächsten stehen — zum Weiterstöbern auf beiden Seiten.
OpenKB compiles your documents into a persistent, interlinked wiki knowledge base — no vector DB
RAGApp: deploy a RAG app over your documents with Docker — configure it like a custom GPT
An agent that finds anything on your computer — federated search over your files, in place.
Ragent (容纳智能体): a production-grade Java Agentic RAG platform with hybrid retrieval and an admin console
RAG for your monorepo: query, understand and edit multi-language codebases with a knowledge graph.
nano-graphrag: a small, hackable GraphRAG implementation in about 1,100 lines — no Microsoft GraphRAG bulk
Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.