RagBucket
Build once. Query anywhere with portable RAG artifacts
RagBucket launched on May 22, 2026 with 3 votes, #3,449 of 20,538 launches that month and more than 50% of that year's launches.
What it does
RagBucket packages semantic vectors, FAISS indexes, chunks, retrieval memory, and runtime metadata into a single portable `.rag` artifact. Most RAG systems today are tightly coupled to vector DBs and retrieval pipelines. RagBucket makes retrieval memory portable and reusable across projects, environments, and providers. Build once. Query anywhere. Supports: • OpenAI • Cohere • Gemini • Voyage AI • Groq • Anthropic • Local SentenceTransformers
Build RAG pipelines as reusable, portable .rag artifacts. Build once. Load anywhere.
Portable executable .rag artifacts for Python. Package your entire RAG pipeline into a single file. Build once. Load anywhere. ML models travel as single files — .pt .onnx .gguf .h5 — saved, shared, deployed anywhere. RAG pipelines don't. They're a web of infrastructure, scattered config, and provider lock-in. A compressed, self-contained archive that packages your entire retrieval system. Vectors, chunks, and config — everything in one file. The only external dependency at inference time is an LLM API key. Two completely decoupled phases: build-time artifact generation and runtime retrieval + generation. The artifact is the bridge. Build your artifact from documents. Load and query it…from ragbucket.vercel.app
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