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RagLeap
No CUDA, no 2GB torch. 23MB reranker. 6 vector DBs.
Open Source
Developer Tools
Artificial Intelligence
GitHub
Visit Website See on Product Hunt Twitter ⧉Github ⧉

Upvotes4

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Comments3

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Hunted by

TC Antony

The open-source core of RagLeap — a self-hosted RAG chat engine (document ingestion, retrieval, citations) powering RagLeap's full AI business manager: Voice AI, WhatsApp/Telegram/Discord bots, database & CRM integrations, workflow automation, and a private executive assistant. Foundation layer — hosted platform at ragleap.com - antonyrag/ragleap-core

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Upvotes4

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Comments3

3 commentsSee comments on PH ⧉

I built this because every RAG library I tried assumed I had a GPU. RagLeap's reranker runs on ONNX Runtime — a 23MB quantized model instead of the 2GB+ torch/CUDA stack most RAG frameworks pull in even for CPU-only use. It supports 6 vector backends (pgvector, FAISS, Pinecone, Weaviate, Qdrant, Milvus), 8 embedding providers, and 12+ generation providers with automatic fallback if one goes down. Real per-call cost tracking with monthly budgets. 238 tests, real CI on every PR. Everything in the docs is either live-verified against real infrastructure or explicitly labeled unverified — no marketing claims, no numbers we haven't actually measured. pip install ragleap-rag Contributions welcome — the roadmap, and what we've deliberately said no to, are both documented in the open: github.com/antonyrag/ragleap-core Would love feedback, especially from anyone who's fought GPU dependency bloat before.

Comment highlights

🚀 2 Hour Update
We just hit:
- 4 GitHub stars
- 1 fork
- 141 commits on ragleap-core
pip install ragleap-rag is live if you want to try the CPU-first RAG engine.
Biggest question I'm getting: "How is this different from LangChain?"
Answer: No abstraction layers. 23MB ONNX reranker instead of 2GB torch. Built-in WhatsApp/Telegram/Discord. You own the DB.
What feature should I build next?
A) More vector DBs
B) Better WhatsApp bot templates
C) One-click deploy script
Comment below 👇 I'll build the top voted one this week.

Quickstart for anyone who wants to try it:
pip install ragleap-rag
from ragleap import RagLeap, ProviderConfig, EmbeddingConfig
rag = RagLeap(
database_url="postgresql://user:pass@localhost/mydb",
embedder=EmbeddingConfig(provider="gemini", api_key="YOUR_KEY"),
primary=ProviderConfig(provider="gemini", api_key="YOUR_KEY"),
)
rag.init_schema()
rag.ingest("doc.pdf", open("doc.pdf","rb").read())
answer = rag.ask("your question")
print(answer["answer"])
Works on CPU with 23MB ONNX. No GPU needed.
I also added WhatsApp/Telegram/Discord examples in /examples folder on GitHub.
Which one should I demo next?

About RagLeap on Product Hunt

“No CUDA, no 2GB torch. 23MB reranker. 6 vector DBs.”

RagLeap was submitted on Product Hunt and earned 4 upvotes and 3 comments, placing #92 on the daily leaderboard. The open-source core of RagLeap — a self-hosted RAG chat engine (document ingestion, retrieval, citations) powering RagLeap's full AI business manager: Voice AI, WhatsApp/Telegram/Discord bots, database & CRM integrations, workflow automation, and a private executive assistant. Foundation layer — hosted platform at ragleap.com - antonyrag/ragleap-core

RagLeap was featured in Open Source (68.7k followers), Developer Tools (517.1k followers), Artificial Intelligence (475.4k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 230.8k products, making this a competitive space to launch in.

Who hunted RagLeap?

RagLeap was hunted by TC Antony. 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.

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