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RagBucket
Build once. Query anywhere with portable RAG artifacts
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
Hey everyone 👋
I built RagBucket after repeatedly facing the same issue while building RAG systems:
the retrieval memory was always trapped inside vector databases, embedding pipelines, and infrastructure setups.
ML models are portable:
`.pt`
`.onnx`
`.gguf`
But RAG systems usually are not.
So RagBucket introduces portable `.rag` artifacts that package:
• semantic vectors
• FAISS indexes
• chunks
• retrieval configs
• runtime metadata
into a reusable file that can be loaded anywhere.
One use case I’m especially excited about:
building reusable domain-specific retrieval artifacts like:
medical.rag
finance.rag
legal.rag
engineering.rag
and loading them into different applications without rebuilding embeddings/indexes every time.
Would genuinely love feedback from the community 🙌
About RagBucket on Product Hunt
“Build once. Query anywhere with portable RAG artifacts”
RagBucket was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #157 on the daily leaderboard. 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
On the analytics side, RagBucket competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how RagBucket performed against the three products that launched closest to it on the same day.
Who hunted RagBucket?
RagBucket was hunted by Anik Chand. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of RagBucket including community comment highlights and product details, visit the product overview.
Anik Chand