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RAGPipe does the boring part of RAG: extract → chunk → embed → store → query. 3 functions. 1 package. Works with Ollama, OpenAI, Qdrant, Pinecone, or a JSON file. CLI, YAML pipelines, git hooks, and systemd baked in.
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I built RAGPipe because I was tired of writing 40-line setup scripts every time
I needed to add RAG to a project.
LangChain is powerful but overkill for 90% of use cases. LlamaIndex is cleaner
but still framework-y. What I wanted was the docker-compose of RAG — point it at
data, it handles the rest, and it stays out of your way.
So RAGPipe does one thing well: Sources → Transforms → Sinks. Files, git repos,
or web pages in. Qdrant, Pinecone, or a JSON file out. Everything in between
(chunking, embedding, cleaning) is automatic.
The thing I'm most proud of is the CLI. `ragpipe watch .` auto-reindexes on
file changes. `ragpipe git hook .` auto-indexes on every commit.
`ragpipe serve` spins up a local API server your IDE or any tool can hit.
Indexed the entire LangChain codebase (7,388 chunks) in 0.71s. No tricks.
Would love feedback on:
→ What sources/sinks you'd want next
→ Whether the 3-function API feels right or too simple
→ Any edge cases in your data you've hit with other RAG tools
pip install ragpipe-ai — and drop a ⭐ on GitHub if it saves you some boilerplate.